Ethereum offers us hardness. We can give Ethereum code and receive a very strong guarantee that it will run any time we call on it. We can give Ethereum data and know with certainty it will still be available in the future, unaltered and uncorrupted.

These simple building blocks give rise to more complex forms of coordination. Money. Contracts. Governance. Identity. Today Ethereum is the hard foundation of a digital economy where millions collaborate across time and space and nation.

Hardness lets us control the future in the small ways necessary to enable large scale coordination. We take strands of possible futures and collapse them into something specific and strong, like a cord of rope. Firmly anchored, we can step into the future confident it will bear our weight.

~

Blockchains like Ethereum are not the only source of hardness. There are two other sources of hardness we rely on all the time: institutions and atoms.

Over the centuries we have built institutions that are reliable enough to serve as a source of hardness. Governments and legal systems are the source of hardness we used to create property rights, contracts, and a modern economy.

Atoms, and other basic properties of our world, also give us the hardness required for some complex social structures. Gold can serve as money because its supply is limited - not by the rules of any institution, but as a result of the physical properties of our earth.

Only recently, after the invention of blockchains, have we begun to realize that these three things share a common underlying property. It is not a coincidence we make money out of them, or that we can use either institutions or blockchains to create contracts.

All of them give us the ability to constrain the future, to make parts of it more certain, fixed points strong enough to coordinate around.

If law, money, and government are the infrastructure of our civilization, then atoms, institutions, and blockchains like Ethereum are the raw materials this infrastructure is built with.

~

This is the problem Ethereum solves. It offers us a new source of hardness, one that can do things and go places that institutional hardness and atom hardness cannot:

• Ethereum is a decentralized source of hardness. It is not under the control of any centralized institution like a company or government. It strives to be accessible to every human being, built in a way that no one can prevent you from accessing or using it.

• Ethereum is digital and global. Anyone with an internet connection can use it. Ethereum and the network of L2s built on top of it will form a seamless, interoperable platform for hardness. But institutional hardness is fragmented by nation state borders, and slow to adapt to the needs of a technological civilization.

• Ethereum is transparent and auditable. We can verify, instead of naively trusting. Anyone can validate the chain for themselves using simple tools. Every year, those tools will become easier, faster, and more lightweight, until almost any device can reach out and touch Ethereum’s hardened surface for itself.

• Ethereum’s hardness does not depend on the political winds. Ethereum’s contracts and property rights don’t stop working if your government does. Ethereum’s data won’t be taken away from you by a change to a corporation’s terms of service. When that matters, it matters a lot.

This helps make sense of why some blockchains are not very interesting: they are just centralized institutions in disguise. That might be a good way to be an institution, but it isn’t a novel contribution to humanity’s sources of hardness.

~

Blockchains like Ethereum will not replace institutions as our only source of hardness. But they will compete with and complement them. Humans now have options: there is a market for hardness. Ethereum-hardness will be used where institutions falter or cannot go, and institution-hardness will fill the gaps where human discretion or intent is necessary to create a system usable by humans.

Just as an architect must carefully plan not only the design of a building, but the materials used to construct that design, so must we carefully consider the materials for our civilization’s infrastructure.

What do we want our civilization to be made of?

More: Atoms, Institutions, Blockchains

Blockchains can be made consistently resistant to manipulation, censorship, or capture. Their design can make them reliably transparent and accessible. Blockchains do not merely have those properties temporarily - we value them because we have good reason to believe this characteristic hardness will persist.

This deceptively simple property has enabled higher-order features. By combining this reliability with a programmable computing environment, blockchains have enabled digital property (like cryptocurrencies, tokens, and NFTs), complex global financial systems, and other use-cases where we care about reliability, like identity systems and social networks.

When we claim that something is reliable, we are making a statement about the future. Whether something - a blockchain, a bridge, a person - actually is reliable will be determined by future events. We do our best to understand the reasons things might turn out one way or the other (is this cryptography secure? Is the engineering sound?), but ultimately we are making an informed prediction.

This fact can make blockchains hard to explain. Other technologies are much easier to demonstrate, because their distinctive qualities appear quickly. In 1992, to prove that email worked all you had to do was try it once. The proof that someone on the other side of the world received and then instantly responded to your message was right there in your inbox.

But demonstrating the value of a blockchain is not so easy. At first, it might be hard to tell the difference between a blockchain application and a centralized one. But over time, they will diverge. A centralized payment processor might censor your transactions, a media network could deplatform you, and a social network could shut off API access or seize your username. A blockchain application, if designed properly, will not.

This means that establishing the value of blockchains relies more on explanation than other technologies. If the value proposition is “developers can build on this API and it will never be turned off”, you can’t simply prove that value with a tech demo because it depends greatly on future outcomes. You must provide reasons, justifications for why such a claim should be trusted. You must argue for it.

But then something magical happens when groups of people are able to form accurate expectations about a blockchain’s future behaviour. That future reliability reaches back through time into the present, influencing and changing behaviour in the now.

When people know they have a solid foundation to build on, they can invest in and improve their digital property, identities, and commons. Developers can build businesses, protocols, and applications knowing that the infrastructure they rely on can’t be pulled out from under them. The difference between centralized and blockchain ecosystems becomes apparent in the present, because the latter is the site of an outpouring of energy, growth, and investment in both private goods and the digital public commons.

The crucial point is that this does not result merely from blockchains being reliable, but from humans being able to form accurate expectations of that reliability.

Introducing Trust Experience (TX)

When we use blockchains, we are always interacting with expectations about future behaviour, whether we realize it or not.

As a user, your decision to store assets on Ethereum is influenced by expectations about whether those funds will still be there in the future. Your decision to invest time and social capital into your ENS, Lens, or Farcaster profile might be influenced by your expectations about whether the protocol is likely to steal that profile away from you in the near future. As a developer, you have expectations about whether Ethereum will suddenly change the rules of the protocol that you rely on as stable infrastructure for your product or business.

Even people who are least actively thinking about these things are influenced by how others think about them. The most casual or low-information user might use a default chosen by someone else who did think about these things (for instance, someone who uses Ethereum simply because that’s where developers chose to build). Or, they might simply interact with the most “popular” ecosystem - a designation selected by the behaviour of crowds and markets which do factor in beliefs about future behaviour.

But how do people form these expectations, whether they are sophisticated entities or the most casual individuals? Are there commonalities between them? What information and experiences shape our decisions to trust something? And what can we say about that process? Can it be good, or bad? Can it be improved, and if so, how?

Let’s call this Trust Experience, or TX.

TX is the set of experiences that shape and inform our expectations about how a blockchain (or other system) will behave in the future. It is the sum of all external inputs which leads us to believe that it will function in a certain way in the future - to trust it, or distrust it.

If User Experience (UX) is about how a person interacts with and experiences a technology, Trust Experience (TX) is about how a person interacts with and experiences forming expectations about the future behaviour of that technology.

The components that influence TX will vary greatly depending on the user and their needs. These inputs could be very diverse, including:

• Reading the code of a smart contract you are going to interact with

• Seeing celebrities buy an NFT

• Noticing an icon in your wallet that shows you’re using Ethereum

• Learning about the security properties of different L2s on L2beat

• Looking at a dashboard showing the concentration of Bitcoin mining

• Verifying a protocol was audited, even if you don’t understand the report

• Thinking that Sam Bankman-Fried is a good kid with impressive credentials

Importantly, TX is not just a list of things we would prefer people use to make these judgements. TX is about what actually shapes trust, not what we wish shaped it.

Everyone who interacts with a blockchain or blockchain application will have some kind of trust experience, though it will vary widely. A casual user might need only a social proof (“my friend Josh, who is a big nerd, uses Ethereum”), whereas a developer building an application will engage more deeply (“I’ve read the docs”) and rely on other social proofs (“An ex-Facebook executive is building on Bitcoin”). Large corporations, governments, and regulators might look to certain kinds of credentials or other social or market signals, relying on experts to validate technical information.

TX is not unique to blockchains. There are many technologies and systems that we rely on in modern life where future performance is essential. Cloud services where we store our backups, protocols of various kinds, concrete structures we live inside, and legal contracts are all cases where a significant part of the value lies in what happens (or doesn't happen) in the future. We have built around each of these technologies the components of TX that allows people to gain enough confidence in them to use and rely on them.

But it is still early days for blockchain TX. Today, our TX is a lot like early software UX.

Early software UX was built by people who understood how a computer worked, and it assumed a lot of pre-existing knowledge from its users. The term “user experience” was only coined in 1993, and the theory and practice of making software usable by ordinary people was still nascent.

Today’s blockchain TX is analogous. It is mostly created by people who understand how a blockchain works, and it often assumes pre-existing knowledge from its users. Just as the software industry had to invent the practice of UX design, we must figure out how to craft a TX that can move beyond power users and scale blockchains to billions of people.

What are the components of TX?

What kinds of things contribute to TX? We can identify some general categories that are common across blockchains and other systems.

1. We do our own research

When deciding to trust something, we often try to gain our own understanding of it. In crypto, this might involve reading white papers, code, documentation, listening to podcasts, consulting various other primary sources, and improving our knowledge of the relevant domains (“how does a hash function work, anyways?”). We try to understand, for ourselves, how these things work and why.

Similarly, if you purchase a house you might want to inspect the foundation yourself or look for mold under the floorboards. And if you are deciding whether to hire a lawyer, you might want to understand their motivations and experience, in order to better model their future behaviour.

Sometimes doing our own research might involve trying to understand the incentives of the actors in a system. Does a cloud provider have a strong incentive to not lose our data, because they would suffer legal penalties if they did? Do the builders of an office tower have liability if it collapses? In a blockchain, are there sufficient incentives to pay stakers or miners to support the protocol in the long-term?

Blockchains have a particular advantage with this component of TX. Blockchains are incredibly transparent, relative to other systems. The internal logic - code, specifications - that shapes their behaviour is open and transparent for anyone to read (for those with the knowledge to understand it).

But there are obvious limits to relying on our own understanding of a system to predict its future behaviour. Many systems, including blockchains, are extremely complex. Even people who have expertise in one component of the system (e.g. consensus protocols) might not have the skills to evaluate other components (e.g. a complex solidity application). Few people can assess primary sources beyond their narrow domain of expertise.

2. We rely on other people: friends, influencers, experts

We often base our decisions to trust a system on the advice or recommendations of other people. Even when we are doing our own research, we are still relying on the people who created the educational resources we use to learn.

Sometimes this might simply be people we know: our family, friends, or coworkers. We look to relative expertise in our social circle, linking into chains of trust (“my friend Josh, who is a big nerd, uses Ethereum. He learned about it from his friend Tim, who is an even bigger nerd, and spends a lot of time on ethresear.ch”).

In other cases, we are looking to someone further away on the social graph but with deeper expertise. In crypto, this takes many forms: smart contract auditors, following researchers on twitter, listening to podcasts, trying to figure out who is credible.

L2Beat is an example of a contributor to TX that uses experts (the L2Beat team members who inspect and evaluate the real properties of L2s) to inform and educate users about L2s at scale.

Relying on experts has limitations as well. It depends on a healthy ecosystem that produces and maintains expert networks. For various reasons there may just not be a sufficient supply of experts (particularly experts who speak your language!). Or there might be misaligned incentives which produce “experts” who are not trustworthy (see: bond ratings agencies in 2007, or popular crypto twitter influencers in 2014, 2017, 2021…).

This is one reason why intellectual honesty is such an important value for blockchain ecosystems. A community that values intellectual honesty is perhaps less likely to be overrun by grifters or faux-experts. Or at least it is more likely to maintain enough authentic experts to compete against the bad ones.

Experts need to gain their expertise from somewhere. This underlines the importance of (1), because making it easy for anyone to do their own research might produce a larger number of credible experts who can contribute to the overall TX of a blockchain ecosystem.

3. Historical behaviour of a system

We often look to the past to understand the future. This is captured by the “Lindy effect”, the idea that the future life expectancy of a technology is proportionate to its current age. It’s no surprise that in the crypto ecosystem, we often talk about things achieving “Lindy” status.

When we don’t have precise historical analogs, we look to similar systems. Has this type of plane crashed before? Has any blockchain ever failed?

This is a component where blockchain TX tends to be weak, given that blockchains are so new and still evolving relative to other sources of hardness.

4. Mass social proofs like crowds & markets

We are social animals, accustomed to using social cues and group behaviour to inform our own beliefs. What do my friends think? Does my tribe like this chain, or that one? What does the market say?

These are very powerful components of TX. In some cases they can result in a powerful default that is difficult to change. This can be a positive thing, because it might indicate a justified confidence in the reliability of a system. But a poorly chosen default - or a default that is not updated when the facts change - can lead people to use unsafe systems.

We’re very familiar in crypto with how these components can create terrible TX for a blockchain ecosystem. Celebrities, famous investors, and many “experts” were invested in Terra before that blockchain fell apart. And markets can sometimes be badly wrong, as they were with Enron and FTX.

How can we improve Ethereum’s TX?

One of the core goals of the Ethereum community should be to improve the TX of the Ethereum ecosystem.

Blockchains are novel, strange, and unintuitive. People, cultures, and societies have reasonable questions and concerns about relying on them as global infrastructure. What are their limits? Will they fail or become corrupted? Are they safe? For blockchains to succeed, those questions need to be answered, not just once but over and over again. And the way we do that is by creating the best possible TX for our ecosystem.

But how should we approach that task? That project is much bigger than this blog post.

But as a starting point, here are three principles for good blockchain TX:

First, TX needs to create accurate expectations.

TX that consists of an echo chamber of simple, clear, and pleasing explanations about a blockchain, but which ultimately creates false expectations, is bad TX.

Good TX must always be striving to create accurate perceptions of reliability and not just sell the most idealized story. Often, that means being honest about limitations, weaknesses, and risks.

This is again why both transparency and intellectual honesty are such crucial properties for blockchain communities. There are many obvious incentives for actors in crypto ecosystems to deceive others or conceal real limitations or risks in protocol design. Good TX means pushing back against those corrosive influences and holding our ecosystem to a higher standard.

Second, TX is a property of an ecosystem, not an individual product.

If you are an application developer, you have control over the four corners of your application. This gives you significant power to shape your user’s TX.

But your user’s perceptions and expectations about the cryptocurrency or NFTs they use in your application, or about Ethereum as a whole, or about the idea of blockchains in general, largely depends on things outside of your control. The broader ecosystem is what supplies the experts, educational resources, memes, or crowd / market behaviour which shapes a large part of your user’s TX. This is true in non-blockchain cases, as well. The TX of your chequing account is influenced by your expectations about the fiat currency stored inside of it, the legal system of your jurisdiction, the behaviour and reputation of the banking industry at large, and many other factors.

The upside is that applications get to inherit good TX from the ecosystem they are part of. It is easier for a protocol like MakerDAO or Farcaster to make credible claims to their users about how it functions when it is built on an underlying network that has both hardness and great TX.

TX is also a lot more credible when it is not sourced from a single party. Experts might be more credible if they are independent from each other, and even disagree to some extent. Maybe a slightly adversarial ecosystem is more likely to find truth, and prove credible over time.

All of this means that when we’re trying to assess TX, we need to look at the whole ecosystem and not one narrow application. It also means that good TX is not the responsibility of any single actor, but something that we can only achieve together as an ecosystem.

Third, TX needs to scale both up and down to meet people where they are.

Ethereum’s TX needs to serve an extraordinary range of users. Individual users, developers, large corporations, government agencies and regulators, from every nation and culture on earth, all need different things to help them come to an informed understanding about Ethereum’s real properties and reliability.

This means that good TX should meet people where they are. Good TX does not mean naively assuming every ordinary user will read the yellow paper or inspect smart contract code. For many users, good TX might just mean that the defaults provided to them (by the market, their tribe, their regulator, their friends, their wallet) are good enough, and the process that chooses that default is informed by a deeper engagement with blockchain TX.

But making TX as widely accessible as possible does not mean underestimating people or only dumbing things down for them. We should not let a desire for familiar Web2 UX trump the interests of accurate and informed TX.

First, because there are many people who require a high standard for their TX - they want to verify before they trust. Blockchain TX must also scale to meet them.

Second, because good TX - just like good UX - will actually teach people things over time and improve users’ ability to understand complex systems.

Modern UX relies on many conventions, symbols, or conceptual metaphors that were invented and introduced over decades of UX innovation. The interface you are using to read this blog post contains components developed and introduced over decades (a keyboard, GUI, a desktop, a mouse cursor, a scrolling document, a touch screen…). We take them for granted now because technologists were successful at teaching them to us. But they were strange and foreign to people once. Good TX must be able to accomplish something similar.

Stray observations, addendum, caveats:

• TX helps make sense of why the crypto community is dominated by debate and argument. We are all trying to reason about what will happen in the future, trying to answer questions that do not have empirical answers: Will Bitcoin’s 21 million supply cap hold? Will Ethereum’s PoS lead to censorship or concentration of power?

• TX helps clarify how and why we talk about “education” so much in crypto communities. We implicitly understand that “education” is about more than educating developers about how to build dapps. There is an implicit idea that everyone should be able to learn how and why Ethereum works, even if they are only a very casual user.

• TX is of course not the only thing we should care about with blockchains. A blockchain with great TX, and which is very reliable, but which cannot scale to meet demand for blockspace will have limited impact on the world. A blockchain with a terrible developer experience & ecosystem will not attract useful applications.

• The elephant in the room is that the “expectation” many blockchain users care about most is the price of some digital asset. There is a speculative mania around crypto, which will likely continue for some time, that can distract people from the underlying value of these platforms. But that underlying value really does exist, and it will still be there when the mania ends. TX is obviously not about future financial performance of some asset, but instead about these more fundamental properties.

• One barrier for blockchain TX is that people’s standards for software TX is very low. People have an expectation that software is ephemeral, that you can always hit “undo”, and that the companies that control that software will rise and fall over time. Most people don’t understand that the TX of the digital world can be a lot better, that software actually can have hardness, and so this is not yet a differentiating factor.

• Our relation to TX is probably modified or proportionate to our time preference. A blockchain that can meet expectations for 1 year before collapsing is an outright failure. But a blockchain that can meet expectations for 30 years is obviously valuable for many use-cases. Many people use “cheap but centralized” blockchains for the occasional transactions, but would never leave funds on them.

• There is an obvious connection here to my article Atoms, Institutions, Blockchains which introduces the concept of “hardness” as the distinctive feature of blockchains which allows them to perform functions otherwise limited to institutions (like law, or money) or nature (like gold). You could think of TX as a framework for how people experience and reason about hardness once it exists. Although you don’t have to read or care about AIB to make use of this post.

• There is a popular (though misguided) idea in crypto that the Right Way to gain blockchain mass adoption is to completely hide or abstract the “blockchain” from the user (let’s call it the “veil of abstraction” thesis). TX is intended as a rebuttal to this idea, and maybe a path forward. By framing TX as analogous and complementary to UX, maybe we can find more practical ways as an ecosystem to make crypto more approachable without hiding the essential distinctive properties that separates blockchains from other software, and helping users make informed choices. Good UX and good TX are not incompatible - this is just a design problem at an early stage of a new technology. All problems are soluble.

• Dan Finlay’s recent post The Protocol Seeking Protocol contains some complementary ideas, in particular relating to how people (and groups of people) discover and then learn to rely on new sources of hardness. I think this fits neatly into the “TX” framing and Dan’s ideas go deeper than this post on how we do this in practice.

• One of the most obvious holes in the blockchain ecosystem’s TX is the lack of any reliable and informed resource that analyzes, measures, and compares key security metrics of different L1 chains. How does the “economic security” of Ethereum compare to Bitcoin, or Tron, or Solana, or Cardano? How should we make those comparisons? What are the metrics we should look to? Who will build the “L2beat” for L1s?

Thanks to Rachel, Tim, Danny, Liam, Josh, Jesse, Dankrad, Justin, Trent, Jason, and ST for their comments on earlier drafts.

Old UX image of Norton Commander from Yuri Samoilov

In the Year in Ethereum 2021, we published a chart comparing the total transaction volume of the Ethereum and Bitcoin blockchains. Last year, Ethereum transferred approximately $11.6 trillion (ETH and ERC-20s), and Bitcoin transferred approximately $4.6 trillion (BTC and USDT).

After publishing, we saw a graph from Galaxy Digital claiming that in 2021 Bitcoin had settled $12.41 trillion USD:

Ark Invest also posted a chart with similar figures, showing Bitcoin settled $13.1 trillion:

What’s going on here? Figuring this out was a fun mystery to work through, and along the way we learned a lot about the nuances of how different sources define and report on-chain metrics.

TL;DR

• Different blockchain data providers sometimes use very similar terminology to describe completely different metrics

• Our lower figure from Coin Metrics excludes all UTXO change-related transactions and some internal transactions (e.g. within exchanges), which we believe is the best figure to use to compare to Ethereum

• The larger figures from Galaxy and Ark exclude only obvious or “naive” UTXO change transactions, and thus still include a significant amount of change & internal transactions

The first thing we did of course was to review and triple-check our work to ensure that we had not made an error or misrepresented any data.

Our graph was created using the following methodology, which is the same approach used for a similar graph in 2020:

• For Ethereum, we are including transaction volume from ETH and all ERC-20 assets which had a volume of greater than $500M.

  • This means we are under-counting Ethereum’s effective volume, since we’re missing the long-tail of ERC-20 activity, as well as other non ERC-20 assets like NFTs.

• For Bitcoin, we are including volume from BTC and also USDT on Omni.

• For all assets, we are using the “Adjusted Transfer Value” metric from Coin Metrics. Coin Metrics defines this as “The USD value of the sum of native units transferred that interval removing noise and certain artifacts”.

  • The reason we chose this metric is because we want to show and compare “real” economic value transfer on each network, removing as much non-meaningful activity as possible. This is especially important since elsewhere in the post we compare these figures to Visa’s total settlement for the year.

The calculation and data sources for this are documented in a google sheet here, which was linked in the original YiE 2021 post.

After reviewing, we were very sure we had not made any errors.

So, we had a mystery. Why are Coin Metrics and Galaxy/ARK’s data diverging so significantly on what should be comparable figures?

We reached out to Galaxy Digital, Coin Metrics, and ARK Invest to see if they could help shed light on what was going on here. We benefited from the help of Kyle Waters from Coin Metrics, Jacob Panek at AllianceDAO (formerly of Coin Metrics), Nishita Jain from ARK Invest, and Rafael Schultze-Kraft from Glassnode. Unfortunately Galaxy Digital never got back to us.

The first thing we wanted to understand was the Coin Metrics data we were relying on.

Coin Metrics has two relevant metrics here:

• “Transferred Value” (~$13T in 2021 for Bitcoin)

• “Adjusted Transfer Value” (~$4T) which we used in our chart

Both of these metrics remove “obvious change”. If you’re only familiar with how Ethereum and other EVM chains work, you might be unfamiliar with change outputs and the Bitcoin UTXO model.

Ethereum uses an “account” model, which is easy to understand intuitively: addresses are like accounts, and they own sums of assets like ETH and ERC-20s. In Bitcoin, addresses own discrete “Unspent Transaction Outputs” (UTXOs). So if you have 5 BTC, what you actually have might be a UTXO with 2 BTC, and a UTXO with 3 BTC, both controlled by your wallet.

Anytime you transfer Bitcoin, you are “using up” one of those UTXOs. So imagine you want to send only 0.5 BTC to someone. Your wallet will “spend” the whole 2 BTC UTXO, sending 0.5 to your recipient (creating a new UTXO of 0.5 BTC), and sending 1.5 BTC back to you in a new UTXO containing 1.5 BTC. Here’s a more in-depth explanation.

So, when measuring total transaction volume on Bitcoin, data sites typically filter out “change outputs” like the 1.5 BTC transfer in the example above, because no money is actually changing hands. It’s just something required by the UTXO model. Only the 0.5 represents “real” economic activity.

Some change transactions are very easy to identify. For instance, when a change output gets sent back to the same address that sent the transaction, this is “obvious change”. However sometimes people use different addresses to receive their change (this is generally a good security practice), meaning not all change will be captured by this heuristic.

So both figures provided by Coin Metrics remove obvious change. The Adjusted Transfer Value (ATV) metric that we used for our graph goes further, by using two additional heuristics to remove non-meaningful transactions:

• ATV removes transactions that are assumed to be change due to their use of round numbers. For example, if a UTXO is split into 2.0000 BTC and 0.195482 BTC, we might assume that the latter is change.

• ATV also removes “early spend” transactions. These are transactions that are sent, and then re-sent shortly afterwards, maybe multiple times. This is a common heuristic that is used to identify transactions that are internal (e.g. moving money around within an exchange), or related to the operation of an application like a mixer.

• ATV also makes several adjustments which apply to ETH and ERC-20s, like removing pass-through accounts, which arise from events like exchange deposits or flash loans.

Around the same time, we heard back from Nishita at ARK Invest. She shared that the $13T figure from the graph came from Glassnode, specifically their “change-adjusted volume” metric. This metric is defined by Glassnode as “The total amount of coins transferred on-chain, adjusted by change volume. Only successful transfers are counted”.

We then reached out to Glassnode to learn more about this metric. Rafael told us that Glassnode uses three different metrics for volume:

• Naive volume: ~$48 trillion for BTC in 2021

• Change-adjusted volume: ~$13 trillion for BTC in 2021

• Entity-adjusted: ~$3.3 trillion for BTC in 2021

At this point, the picture is starting to come together clearly:

• Coin Metrics and Glassnode each report a volume figure that adjusts for obvious change transactions, showing approximately $13 trillion 2021. However they label them differently: Coin Metrics calls this “Transfer Volume” and Glassnode labels this “Change-adjusted Volume”.

• Coin Metrics and Glassnode each report a volume figure that makes further adjustments, though they diverge on what adjustments they are making leading to different figures. Coin Metrics calls this “Adjusted Transfer Volume” and it removes even-amount change and early-spend transactions, which results in a figure of $4T. Glassnode uses proprietary methods to remove various transactions within clusters of accounts which are believed to be controlled by single entities, which results in a figure of $3.3T.

• These different metrics explain the discrepancy between our graph and ARK/Galaxy Digital’s graph. Our graph is showing the second figure, which adjusts all change and a lot of “internal” transactions ($4.6T). ARK/Galaxy are showing a mostly-unadjusted figure, which would still capture significant amounts of change and internal transactions ($13T).

• Both are true, they are just measuring different things.

Phew! Mystery solved.

In conclusion, here’s what we take away from this:

• The terms used across different blockchain data providers are not always consistent. In this case, “adjusted” and “unadjusted” were used to mean different things between Coin Metrics and Glassnode. If you’re using data from multiple sources, be very careful to learn the details of what your metrics are measuring.

• For the purposes of our chart comparing Ethereum and Bitcoin volume, we should continue to use metrics that remove as much change as possible from the Bitcoin data. Because Ethereum does not use a UTXO model, only by removing all change outputs are we making a fair comparison. Frankly, data that includes change outputs is not a very useful indicator of any kind of real economic activity or user adoption.

• That means we should use either Coin Metric’s adjusted transfer volume, or Glassnode’s entity adjusted volume. Unfortunately neither provider offers a metric that only removes as much change as possible, but does not make other adjustments (e.g. for intra-entity transactions). One challenge with this according to Coin Metrics is that there is a lot of overlap between these approaches - for instance, users withdrawing from an exchange, and the exchange receiving change back to their own address.

• Are volume metrics which do not exclude internal transactions useful? This really depends on your goal. Internal transactions obviously do not represent “real” economic activity, since they are just one person or one entity moving money between wallets they control. Sharing these figures without explaining that they include internal transactions can be very misleading, because readers might assume that these figures represent the “common sense” meaning of a transaction, which is between different persons or entities.

  • However, these transfers are still taking place on-chain, with fees being paid for them - they indicate something about the capacity of a network to settle value, and are useful for understanding fee market dynamics.

Further reading:

• Following our conversations, Coin Metrics published a report digging into some of the details of how they do this: https://coinmetrics.io/on-chain-volume/

• NYDIG also published a useful article  about the complexities of calculating transaction volume on Bitcoin’s blockchain and pointing out the differences between Glassnode and Coin Metrics transaction volume, as described above:

What do a book, a radio broadcast, and the human voice all have in common?

Today the answer is easy: they all contain information.

But if you asked someone the same question 100 years ago, they would struggle. They would not have easily identified that these different things all share an abstract property like “information”.

The modern idea of information is a recent invention. It was not until the 1940s that new communications technologies pushed people working on the cutting edge to articulate that there was something universal underlying sound, electromagnetic waves, symbols on paper, and much more.

Although humans have been creating and using information technologies like writing, printing, and telegrams for hundreds or thousands of years, it was only in the last century that we articulated clearly what all of these things have in common, and realized that they can be understood as a category.

In the decades since, the idea of information has spread into mass culture. Today, it is intuitive to most people that speech, images, films, writing, DNA, and software are all just different kinds of information.

I believe that a similar situation exists today with respect to blockchains. A new technology has forced us to reconsider things we thought we understood. But instead of books, telephones, and voices, this time it is money, law, and government. We can sense the outline of a category that unites these seemingly disparate things.

Perhaps there is an analog to information hidden in the foundations of our civilization. An abstract property that once revealed, might help remake our understanding of the world, and help us answer plainly what problem blockchains are supposed to solve.

1.

Call this property hardness.

Human civilization depends in part on our ability to make the future more certain in specific ways.

Fixed, hard points across time that let us make the world more predictable.

We need these hard points because it is impossible to coordinate at scale without them. Money doesn’t work unless there is a degree of certainty it will still be valuable in the future. Trade is very risky if there isn’t confidence that parties will follow their commitments.

The bonds of social and family ties can only reach so far through space and time, and so we have found other means of creating certainty and stability in relationships stretching far across the social graph. Throughout history we have found ways to make the future more certain, creating constants that are stable enough to rely upon.

One source of hardness has been physical stuff - atoms - in the natural world around us. We found objects and systems that had some convenient properties which, we learned through experience, were quite hard to change. We picked up shells, rocks, and metals from our environment, possessed and defended them, and used them as a basis for commerce.

Over time we learned to create our own hardness, and not just borrow it from nature. We built institutions - groups of humans who work together, who behave in predictable ways over long periods of time. We learned to design these institutions to become very reliable - so that we could give an instruction to an institution, and be sure that those instructions would be followed - even years, decades, or centuries later.

Recently, we’ve invented a new way to create hardness: blockchains. Using an elegant combination of cryptography, networked software, and commoditized human incentives, we are able to create software and digital records that have a degree of permanence.

If law, money, and government are the infrastructure of our civilization, then Atoms, Institutions, and Blockchains are some of the raw materials this infrastructure is built with.

Just as an architect must carefully plan not only the design of a building, but the materials used to construct that design, so must we carefully consider the materials for our civilization’s infrastructure.

But the civilization we are trying to build is stretching the limits of what those materials can do. It is becoming increasingly obvious that Atoms and Institutions alone cannot support the global digital civilization we strive towards.

This is the problem that blockchains solve. They are a new source of hardness, with new strengths and weaknesses, which make them a suitable complement to address the limitations of Atoms and Institutions.

2.

Speaking clearly about hardness is challenging because we do not have pre-existing terminology.

We are very accustomed to talking about certain sources of hardness, like institution-hardness. But the language we use there - terms like trustworthy or enforceable or promise - are too intertwined with ideas about relying on people, and the types of relationships that people can have with one another.

If we are going to speak precisely, we need terminology that sheds these meanings, and refers only to a more narrow concept that unites atom, institutional, and blockchains.

“Hardness” is defined as the capacity of a system to make something very likely to be true in the future. Hardness is most useful where it is customizable or programmable - where humans can choose something specific we want to be true in the future.

When speaking about hardness, there are three things we need to consider:

1. What is the hardness about? What is the “something” which is being made very likely to be true in the future?

2. What is the source of that hardness? What is the reason it is very likely to be true?

3. How hard is it? How can we measure the degree of hardness?

First, what is the hardness about? What is its informational content?

I will call this the “cast”. The cast is the “thing that is hard”, and it always takes the form of a statement or claim about the future. I’m coining a term here because I do not think there is a suitable existing word for the concept. I use the word “cast” for its dual connotations in English of a thing we throw ahead of us and a thing which hardens to protect or shape.

For example, one important “cast” about gold is that its supply will remain predictable in the future. We could express this precisely as “between X and Y kilograms of gold will enter the market each year for the next 20 years”.

Or, consider a loan agreement. The cast here might be something like “If Alice does not pay Bob back $100 before July 1, then the legal institutions of my jurisdiction will use increasingly severe threats and actions to force her to pay Bob”.

In the case of a digital asset on Ethereum, one cast might be “this asset can only be transferred if a transaction is signed using the private key that corresponds to the public key X”.

Casts are descriptions of some future state of the world. A cast is hard if that future state of the world is very likely to turn out to be true. These casts might be claims about something not changing (“the object in my security deposit box will still be there in 10 years”), or something changing at a certain rate (“The supply of Bitcoin will inflate predictably for the next 100 years”), or something that is conditional on other actions or events (“If we get divorced, we will divide our assets in the following way”).

In practice, we are usually interacting with bundles of interrelated casts, which woven together, create stability and predictability in our affairs. For instance, if you own gold but store it in a bank, there are many interlocking casts that matter to you: casts about gold’s supply in the future, the integrity of the institution holding your gold for you, the physical properties of the vault where it is stored, the strength of the legal agreement between you and that institution, the reliability of the legal system in the jurisdiction where you live, and many others.

Second, there is the source of hardness. This is the reason that the cast can be hard in the first place. It is what the cast is made out of: atoms, institutions, or blockchains.

Sometimes the properties of our physical universe are the source of hardness. There just is a certain amount of gold contained within the earth, and only a portion of it is accessible to us with our current technology. That simple physical truth is the reason casts about the supply of gold are hard.

In other cases, the source of hardness might be an institution. A contract is only hard if there is an institution that makes it so. Groups of people - lawyers, judges, police officers - who share a common understanding of how to work together, and who have proven over decades that they are likely to behave in a predictable fashion.

Or, the source of hardness might be a blockchain. The reason a smart contract will operate the way it was programmed is that blockchains provide very high assurances that this will happen, by creating incentives for people to maintain the network and making it extraordinarily expensive to censor or stop its function.

With these first two components identified, we can ask the third question: how hard is a particular cast? Hardness is always measurable in theory, even if it is difficult to do so in practice.

For example, take the example of gold. How hard is the cast “between X and Y kilograms of gold will enter the market each year for the next 20 years”.

This is something we (as a species) know a lot about. People spend a lot of time and money trying to predict how much gold will be mined every year, based on what we know about the properties of our planet, the technology available, and the industry built to extract gold from the earth.

One way to measure the hardness of a cast is to estimate probabilities. If you had all the relevant data, you might come to a conclusion that there is an 80% likelihood that “between X and Y kilograms of gold will enter the market each year for the next 20 years”.

Another way to measure hardness is to estimate how much it would cost to create a world where the cast turns out to be false. Is there a price that someone could pay to create a world where less than X, or more than Y kilograms of gold enter the market? What does it cost to break the cast?

There is a price of course, though it is probably very high. Someone with the resources of a nation state could disrupt global gold production over an extended period to bring it below X. And it is also conceivable that in the next 20 years, someone could obtain a near-earth asteroid that contains a large amount of gold, causing the production to exceed Y.

In the example of a legal contract, we can also measure the hardness in terms of probabilities and costs. If Alice does not pay Bob back, and Bob sues her for breach of contract, Bob’s lawyer might advise him that there is a certain chance of success. Because the final outcome is at the discretion of a person (like a judge), these probabilities can be very hard to estimate.

As with the gold example, we might also express this in terms of cost. what would Alice have to pay to change the outcome? She could flee the jurisdiction, or she could spend a large sum on expensive lawyers that diminish Bob’s chances of success.

It’s important to keep in mind here that the content of the contract is not the content of the cast. A legal contract is a tool used within a broader institutional context, but it does not contain or express all of our implicit expectations about the future state of the world we are trying to create using this tool. The cast is about how the whole system will behave in the future, and the words of a contract are just one part of that system. The contract itself cannot tell you if you can afford a lawyer to enforce it, or whether the legal system will behave in a corrupt fashion.

Having explained the basics of what “hardness” is, we should offer a few clarifications and nuances:

• Hardness is not the only way we create durable relationships and coordinate with each other. Family or social relationships, culture, religious or political beliefs, and many other more diffuse or “informal institutions” influence human behaviour and help us coordinate. These are important, but they are distinct from the kind of durability provided by AIB.

• There is a very wide range of “how hard” something needs to be, depending on our use-case and the social context. In many cases we are able to get by with a minimum level of certainty about the future for certain kinds of relationships, like a handshake business deal.

• Talking about hardness is also not a claim that something, once made hard, should never be changed or adapted. Hardness is just a tool, not an end in itself. But there is no contradiction in believing both that hardness is an essential characteristic of some systems, and that sometimes it is overridden by other factors. To make an analogy: apartment buildings must be able to stand firmly in place for decades for them to be useful, but that does not mean we shouldn’t sometimes tear them down to build something better.

• Hardness is also not a claim that the systems and mechanisms we use to create hardness should themselves be static or unchanging. The thing that is being made hard is the cast, the claim about the future. In many cases the only way to make that cast hard is for the thing that makes it so - like an institution or a blockchain - to be adaptive and flexible. Democracies, for instance, are often a better institutional source of hardness for political casts, even though they are constantly changing and re-inventing themselves.

• Hardness is related to legitimacy, but they are distinct ideas. Legitimacy is about how human beings react to or perceive some state of affairs. But hardness is just a dry, abstract property of some system to make something more likely to be true in the future. This property might contribute to legitimacy - a legal system is perhaps more likely to be legitimate if it is predictable - but it is not the only or even most important factor in many cases.

3.

Atoms, Institutions, and Blockchains are all different from each other. As a species we have been very opportunistic, and creative, in how to find or create hardness that enables human coordination. Over time, the balance between sources of hardness has shifted, often without our realizing this has happened. This has created structural weaknesses in the infrastructure of our civilization.

Atoms

“Atom” hardness refers to hardness that is sourced from the physical properties of our universe. This includes more than literal atoms (i.e. matter, physical stuff), but all other properties of nature like physical laws and constants. “Atom” is just a convenient shorthand.

The easiest example is early money. Humans found objects in our environment - shells, gems, rocks, metals - that had a collection of useful features.

One feature was that they were scarce. This meant that there was a better chance that these objects would retain their value over time. The cast “shells of this type appear on the beach at a consistent rate of X” was hard, because of a predictable natural process in the local ecosystem of a society.

Another feature is that these objects were small enough to be easily possessed, transferred, and defended. By exchanging these objects, and placing them in the possession of different people, we could in effect record information about a social relationship relating to money, value, or social status. The ability to possess and defend those objects conferred hardness to those relationships. The universe stores information, and we can manipulate that information by manipulating our environment.

We have so far relied on examples of casts about the supply of objects - gold, shells, etc - but atom-hardness includes all of the ways that we use physical reality to create hardness about casts that are socially important to us. We create walls, barriers, homes, and other secure structures to increase the hardness of casts about ownership of physical things and other social relationships.

Atom-hardness has advantages and disadvantages. One great virtue of atom-hardness is that it is automatic. To state the obvious, a wall just is. You do not need to depend on some other party to enforce the wall, or enforce the supply of gold within the earth. Another virtue is that atom-hardness is not subject to human discretion. There is no person you can bribe or corrupt to subvert physics.

But atom-hardness is limited in frustrating ways, making it increasingly unreliable as a source of hardness for human civilization.

First, we are limited to what nature provides: we can only find properties in nature, and use them where those properties happen, by accident, to fit some social need. We cannot say to the universe “we’d like another type of shiny rock, with a supply curve a bit different than gold, spread more evenly across the following nation states”.

Second, atom-hardness doesn’t allow for very expressive or customizable hardness. Over time, humans have developed a need for complex casts, like “if Alice and Bob divorce, the rule for splitting their assets shall be…”. We can’t give nature specific instructions about complex and subtle human relationships.

Third, atom-hardness relies on implicit limitations of human capabilities. But as human technology has improved, we have overcome many of these limits, undermining the hardness of some atom-casts.

Using shells for money works for a while, until your society expands into an ecosystem where those shells are plentiful. Once a civilization expands outside of a certain ecosystem, the assumptions they made about what is hard might change. Humanity might someday face that problem with gold, whose supply is limited on earth, but abundant throughout our solar system.

New technology sometimes undermines atom-hardness completely, dissolving casts that we took for granted. Often, we then use institution-hardness as a substitute.

For instance, limitations on the speed of travel and communications were once relied on as a source of hardness for casts that shaped our political systems.

In the early 1800s in North America, there was a hard cast with the content “the fastest information can travel is roughly at the speed of a pigeon”. This cast was hard because the physical properties of our universe seemed to prevent, as far as we knew at the time, communication traveling any faster than this.

One way this cast was socially useful related to elections. The results of voting on the east coast of North America could not be reported fast enough to influence voting on the west coast, ensuring that later voting periods were not influenced by this information.

But eventually, we had technology that undermined this hardness and broke the cast. But we wanted to keep the cast, so we just remade it by substituting institution-hardness where atom-hardness had failed us. In Canada, there are now laws that prevent reporting election results or exit polls before a certain time. In the United States, there is a long-standing convention about the same among media organizations (not law, but a different form of institutional hardness with similar effects).

This has been a common pattern over time. As our control over nature increases, things we once believed were hard have crumbled away, and we have patched the holes using institution-hardness.

Institutions

Over time, humans developed a need for a different kind of hardness. Hardness that could be used for casts that were specific, complex, and conditional. To do this, we used institutions:  groups of human beings who together act in sufficiently predictable ways to be a source of hardness themselves.

“Institution” here is an extremely broad category, including:

• Legal systems

• Legislatures

• Police forces

• Governments

• Central banks and other monetary authorities

• Private banks

• Corporations

• Startups

• Religious institutions

The details of every case are different, and different institutions within this very broad category vary in how exactly they create hardness. But all of them share the basic property of using organized human behaviour as a foundation for sufficiently hard casts that enable social, political, and economic activity. Groups of humans who are motivated and organized to behave in a certain way, such that the institution itself outlives the participation of any particular individual person.

One key advantage of institutional hardness is that it is extremely customizable. The design space of possible casts built with atom hardness is very limited. But the design space of possible casts built with institutional hardness is limited only by what rules you can convince people to enforce. When we needed hardness that could create more complex casts, we created what we needed out of what we had: people.

Thus we built institutions, and they provided sufficiently customizable casts to enable grander and more complex forms of human activity and coordination. We created casts that gave order to our societies, punishing antisocial behaviour (“if you harm others, retribution will be taken”). We created flexible all-purpose systems that let anybody create casts related to commerce (“if you write down your promises in a certain format, we will force your counterparty to abide by these terms”). We created casts that spawned new assets, and provided sources of credit to growing economies (“this piece of paper is backed by the full force of the United States”). Using institutions, we built the hard foundations on which human civilization has grown and expanded.

This category extends to institutions who we might not always think of as rule-making bodies. Today, many private American corporations act as institutional sources of hardness that govern massive amounts of human activity. Facebook (now Meta) is an institution that controls and defines casts that apply to anyone using its platform, and is capable of enforcing those casts. This is also true of Twitter, Google, Apple, and other companies.

But institutional hardness has its limitations. Some of these have become more apparent, as the scale of human civilization begins to test them:

• Most institutions, and the things that they can make hard, are bounded by nation-state borders. There is one set of rules in one place, and another set of rules in another place. This introduces complexity and cost for a civilization and economy that spans many international borders. Relying only on institutions for hardness means access to that hardness is demarcated along nation-state boundaries.

• Many institutions depend on a central state of some kind. In some cases this means that the institution’s function as a source of hardness can be undermined by the state that controls it. A judiciary that serves only as a function of political interests loses its hardness, because it can no longer reliably or predictably enforce consistent rules. Or worse, people believe these systems remain hard, and the state can exploit individuals’ reliance and expectations when it chooses. And if the state falls, it might bring down all of the institutional sources of hardness that enable other parts of society to function, like commerce.

• Institutions are often very opaque. This makes it hard to tell how hard a cast actually is. It is very difficult for any person to predict precisely how a legal rule will be applied, and also very hard to predict how expensive it is to break an institution-made cast. We have built entire professions (lawyers) whose function is, in part, simply to guess at these things, and they are often wrong.

• Institutions are made of people and people are fallible. They are cruel, evil, stupid, and prejudiced. In most places, in most times, consistently throughout our history, institutions have only been reliable sources of hardness for some people, and not for others. Using institutions as a source of hardness for critical social infrastructure often turns that infrastructure into a tool of oppression wielded by those who control the institution.

• Institutions are extremely expensive and difficult to create. The institutions that we rely most on for institutional hardness have today evolved over centuries and millennia. We cannot easily experiment, invent, or test new institutions, which means the rate of innovation proceeds slowly, over decades.

Over time, a greater and greater share of the systems that are socially important to us have become grounded in institution-hardness, rather than atom-hardness.

Consider the privacy of your personal communications. For most of human history, people had a strong expectation that their private communications could not be easily monitored (cast: “If I criticize the government while speaking to my family, the state will not learn about it”).

Until very recently, casts about your personal privacy had a strong foundation in atom-hardness. A private conversation in a home could not be easily monitored, assuming a listener was not physically present in your home. As technology enabled new kinds of communication, more and more of our private communications passed through a wire that was controlled by an institution.

But even then, the simple physics of it made it extremely expensive to do this at scale. J. Edgar Hoover did not spy on every American in the 1950s not because he didn’t want to, or because he wouldn’t have been able to get away with it, but because it was simply impossible to do. There were not enough FBI agents in America to spy on the private communications of every American. The cost of breaking the cast was too high.

But new technologies, and the architectures of control we built on top of those technologies, have radically reduced this cost. Today, the material costs of mass surveillance are cheap. The US government, and others, already conduct surveillance on a mass scale. Today, casts about your personal privacy are made only of institutional hardness, and they are broken constantly.

The rapid development of internet and software technologies have increased our dependence on institutional hardness - and made us more exposed to its failures. The problem with software eating the world, is that behind most software is an institution. We are hollowing out the infrastructure of civilization, and replacing it with forms of hardness that may not be up to the task.

The internet has let us build the early stages of a global, digital civilization. But today it is built on weak foundations. The internet we have reflects the shortcomings of institutional hardness. It is increasingly balkanized, carved up along nation-state boundaries. It is a fragile and unstable foundation, as the individual companies that control it rise and fall. And most of it is owned by a handful of companies, controlled by a small group of people, who live in one country.

Blockchains

Satoshi’s invention was a new source of hardness. It was the first new source of hardness that humans have invented in thousands of years.

Satoshi will not be remembered primarily as the inventor of Bitcoin. What Satoshi gave us was a kernel of something grander. It is the insight that through clever design, we can create systems bounded by cryptography, and fueled by simple human incentives, which together provide a source of natively digital hardness. Satoshi used this new source of hardness to create the first blockchain application: Bitcoin.

The same design that makes Bitcoin possible was extended and expanded into a broader design space, giving rise to the first and most widely adopted programmable blockchain, Ethereum. Just as institutions let us create hard casts with any arbitrary content that can be interpreted and executed by humans, Ethereum lets us create hard casts with arbitrary content that can be interpreted and executed by a computer.

Like Atoms and Institutions, Blockchains have strengths and weaknesses that make them appropriate for certain use-cases but not others:

• Blockchains are natively digital. They create hardness about software, data, and programs. Anyone with an internet connection can access, and interact with, a blockchain. This is also a limitation, because blockchain hardness depends on a sufficiently technological civilization that can maintain the internet and other infrastructure.

• Blockchains do not depend on any institutions, even if they do require human maintenance. Blockchain hardened casts will remain hard if a government fails, a judiciary succumbs to corruption, or a police force refuses to enforce the law. Blockchains do depend, to some degree, on the behaviour of groups of humans - for instance, to maintain client software that facilitates the protocol, or wallet software that lets humans interact with it. But these services can be performed by anyone, anywhere, anonymously - a loose, decentralized community of engineers. And it is not an exclusive right - anyone can create and maintain an Ethereum client.

• Measuring Blockchain hardness is extremely transparent. The specific cryptographic tools used by blockchains are public and available for study, and generally their properties are very well understood. The cost of attacking the system to subvert it - i.e. through a so-called “51% attack” - is known, and we can estimate it accurately.

• Blockchain casts are made using software. Software can be poorly written, include errors, and most people cannot read software or write it themselves. Without the convenience of human discretion (as exists in institutions), small mistakes that differ from our intent can be severe. On the other hand, the opportunity to learn to write and read software is available to anyone on earth. Anyone can upload a contract to Ethereum, virtually no one can write a law.

• Blockchains let us create new casts that, if they were grounded in institutional hardness, would require creating entirely new institutions. Anyone can create an asset, and define the parameters of that asset, in a way that would be impossible through any other means.

Armed with the idea of hardness, it is easy to explain what blockchains are for and why they matter. Blockchains, like institutions, are a source of hardness. We need hardness because it is what makes it possible for us to build complex global coordination tools like law, governance, and money.

Blockchains have a different set of tradeoffs than institutions, making them suitable for some purposes but not others. They can go places and do things that institutions cannot, and they are more resistant than institutions to certain kinds of failure. Maybe they can even enable new types of coordination mechanisms that were never possible until now.

It is also worth noting that this third type of hardness I am referring to as “blockchains” will very likely grow to include things that are not, technically, chains of blocks. Recent and emerging innovations in cryptography like secure multi-party computation and homomorphic encryption may have equally significant contributions to humanity’s toolkit for creating hardness. But Satoshi’s invention marks a zero-to-one moment in our understanding of how cryptography and economic incentives can be used to create hardness, so I use the term blockchain for now.

Understanding blockchains as a source of hardness also helps us make sense of the dizzying array of narratives and buzzwords that have been used to make sense of them over time:

• Blockchains can be credibly neutral because their rules are transparent and verifiable, and casts about those rules are made hard by the blockchain itself.

• Ethereum enables composability between applications because applications built on Ethereum can be very hard, with high assurances that they will not change. Applications that are deployed on mainnet will stay there, letting developers build on top of what has already built, with high confidence that those foundations can never crumble beneath them.

• Ethereum enables hyperstructures - protocols that can run forever - because blockchains provide a native source of hardness that makes the structures built on top of them extremely likely to persist into the future.

• Ethereum enables decentralized finance because it lets us create programmable hardness sufficient to recreate the complex intersecting web of relationships that characterize traditional finance. But whereas traditional finance is built out of institutional hardness (e.g. legal contracts), defi is built out of blockchain hardness.

• Blockchains can be trustless in the sense that they create hardness without any reliance on the behaviour of individual people or institutions. People also like to say that blockchains are “trust machines” or that they create trust. The mystery of why we use both these seemingly contradictory explanations is unraveled once we are armed with the concept of hardness: blockchains are trustless in the sense that their hardness does not depend on people, but they are trustworthy in that they are still capable of producing hardness. In the former the speaker is using the word “trust” as a stand-in for institutional-hardness, whereas in the latter “trust” means just “hardness”.

• Ethereum enables web3*:* an ecosystem of web services, protocols, applications, and communities whose foundations are built primarily on blockchain hardness, rather than institutional hardness. A digital environment where people’s identities, their possessions, their means of commerce, the way they govern themselves collectively, and the way they publish information to the world, all of it grounded primarily in blockchain hardness, rather than institutions.

• Hardness also helps us articulate what is special about some blockchains and not others. Blockchains cease being a novel contribution to humanity’s hardness infrastructure when they collapse into just another institution. In other words, when they become centralized, dependant on specific groups of people. Digital institutions might still be useful, but they bring with them the same advantages and disadvantages of analog institutions.

Humans have a natural need for hardness in the systems we use to coordinate, the systems we rely on to bring predictability to our civilization. We feel it’s absence when it is missing. One underlying reason for the increasing backlash against “big tech” is that it has become clear that the institutions that control the internet today are not suitable sources of hardness, and the casts we try to make with them keep breaking. And across the political spectrum, all over the world, many people are increasingly skeptical of the institutions that, through our reliance on them as a source of hardness, wield great influence over our lives.

We want a strong foundation for the global civilization we are trying to stitch together. One that does not change under our feet with each election, or the rise and fall of individual American companies.

4.

Think about Atoms, Institutions, and Blockchains like a system of checks and balances. Using them together to build our civilization’s critical infrastructure makes it more resilient, and less exposed to the limitations of any one of them. They are a set of building materials that, used together in aggregate, make a stronger whole.

But that system of checks and balances is changing beneath our feet. Most of the time, we don’t even realize it. The last protections of atom-hardness are crumbling as new technology lowers the cost of rights infringements that were once impossible at scale. The growth of a global digital civilization mediated via the internet has accelerated our reliance on institutions, stretching and testing them past their breaking point.

Blockchains will not replace institutions as our only source of hardness. But they will compete with and complement them. Humans now have options: for the first time, there is a market for hardness that can be used to produce the complex casts necessary for modern civilization. Blockchain-hardness will be used where institutions falter or cannot go, and institution-hardness will fill the gaps where human discretion or intent is necessary to create a system usable by humans. A new dimension of our politics has opened up. We will debate not only which systems to use - what forms of governance, what kinds of markets - but also what sources of hardness should be used to construct those systems.

What do we want our civilization to be made out of?

(Thank you to Danny, Luke, Sina, Saffron, Trent, gubsheep, the Farcaster community, and many others who gave feedback on earlier drafts).

Ethereum is the foundation for a digital civilization.

It is hardened, secure, and reliable. It is the bedrock necessary to support the digital cities being built on top of it.

Those cities are growing fast. Because Ethereum is open to everyone, many different users have found reasons to build on it:

• Markets use it as financial infrastructure

• Artists use it to give permanence to their work

• Assets use it as a settlement layer

• Communities use it to govern shared resources

This year Ethereum applications exploded into public consciousness. The old term “web3” became fashionable again as the world began to understand the vision of a more decentralized internet built on Ethereum.

Just like in 2018, 2019, and 2020 our goal is to zoom out and show you the bigger picture.

In our view, the most important developments this year in Ethereum were:

1. Layer 2 arrives - after years of development, L2 protocols launch on mainnet and expand Ethereum’s capacity

2. Creator economy goes mainstream - NFTs are everywhere and artists use Ethereum to earn billions

3. Core protocol upgrades - the Ethereum R&D community ships multiple upgrades, preparing for the transition to Proof of Stake

4. DAOs pass the tipping point - DAOs become a viable tool for communities to self-govern, accumulating billions of assets and drawing in new users

Before covering the themes above, let’s stop to take stock of Ethereum’s growth. In past years, we’ve tracked different core metrics that try to place Ethereum in context. This year, some of those metrics crossed important milestones.

A. What blockchains do people pay to use?

For the second year in a row, Ethereum is the world’s most in-demand blockchain.

This graph shows the total transaction fees for selected L1 blockchains. This is the sum of all fees paid to use each L1 - to send transactions, or interact with a smart contract. In total, they represent the value of that blockchain’s total “block space” - the total transaction capacity of that blockchain in a given year.

Total transaction fees are just one metric, and viewed alone they are an imperfect representation of the value of a blockchain or its utility to users. However, it does show us the comparative value of each blockchain to its users. In a functioning market, things are worth what people are willing to pay for them.

If we broaden our comparison to include applications, L2 networks, and other L1s, we see the following:

Each of the pink entries is an application built on Ethereum, like Uniswap or ENS. For these applications, the fee totals here are not blockchain transaction fees, but other types of fees that the user pays to use the application (like fees paid to liquidity providers on an exchange).

Incredibly, in 2021 the value paid to use applications built on Ethereum exceeds the value paid to use all other L1 blockchains combined.

As the demand for Ethereum’s blockspace continues to outpace every other blockchain, we can look elsewhere for meaningful comparisons. This year, we can compare the fees paid to use Ethereum L1 to the revenues of payment networks like Visa and Stripe:

Total transaction fees and corporate revenue are not entirely comparable (Visa and Stripe earn revenue from sources other than fees paid by direct users, and Ethereum can do many things Stripe cannot). However, they give an indication of Ethereum’s scale and value to the market.

B. How much value did Ethereum move?

One of the simplest use-cases of a blockchain is to transfer assets. How much value was transferred on Ethereum this year?

Since surpassing Bitcoin in mid-2020, Ethereum continues to be the blockchain that settles the greatest flow of assets around the world.

This year Ethereum moved approximately $11.6 trillion USD. That is more than Visa, and more than double Bitcoin.

C. Value locked in DeFI

Finally, we can track the total value of all assets locked in DeFi protocols. Having assets “locked into DeFi” means that a user has deposited some funds into a protocol, usually to earn a return in exchange for letting the protocol use their assets - for example, as liquidity.

Ethereum’s DeFi sector continues to have the greatest amount of locked assets, by a large margin.

Once again, we must look outside of the blockchain ecosystem to find comparable figures. In 2021, the total value locked in DeFi on Ethereum ($153 billion) exceeded the assets under management of Robinhood ($80 billion) and Bridgewater Associates ($140 billion).

1. Layer 2 Arrives

After years of R&D, the technologies that will scale Ethereum went live this year.

The transaction capacity of Ethereum is no longer simply the capacity of Ethereum’s Layer 1.

Instead, it is the capacity of Ethereum’s L1 plus the capacity of all “Layer 2” protocols that inherit Ethereum’s security.

This graph shows us the total cumulative transactions on Ethereum in 2021, including both L1 and L2.

In 2021, Ethereum’s L1 did about 1.2 million transactions per day (or about 15 transactions per second). As Layer 2 protocols came online, the effective capacity of Ethereum began to increase, and the combined transaction rate (the top line of the chart) started to curve upward.

All L2 protocols are still early in their deployment, and some have not yet removed all temporary trust assumptions (see L2Beat for more info). There are also notable differences between the technologies grouped here. Some of the StarkEx chains for instance are technically Validiums, which means their proofs are stored off-chain, not on-chain.

As L2 protocols continue to mature and gain marketshare, the L2 portion of this graph will grow until it far exceeds the transaction capacity of L1. “Ethereum” no longer refers to a single protocol, but to a community of protocols that share a common L1.

You may have read that Ethereum is expensive or slow, but that’s only because people are making the wrong comparisons. Ethereum is the first blockchain to mature to the point of having multiple production-ready L2s built on it. Although gas fees remain high for some use-cases on L1, 2021 marks the inflection point towards a future where most users interact with Ethereum exclusively through L2.

The largest share of L2 transactions are currently being done on application-specific L2 protocols called “ZK rollups”. These are L2s specialized for certain kinds of applications, like trading or simple token transfers.

The ZK rollup ecosystem built on Ethereum made progress in 2021:

• Loopring launched a zkRollup decentralized exchange back in 2020. They completed the launch of their v2 in early 2021, and added support for NFT minting and trading in August 2021.

• Matter Labs launched a payments rollup (zkSync) in June 2020, which is integrated into wallets like Argent and applications like Gitcoin (and continue work on “zkSync 2.0” which will feature EVM compatibility)

• Aztec launched a private payments rollup (“zk.money”) in March 2021, adding support for the DAI stablecoin in April.

• Multiple projects launched using Starkware’s StarkEx platform, including:

  • DeversiFi (a decentralized exchange) launched in June 2020 (validium)

  • ImmutableX (an NFT exchange) launched in April 2021 (validium)

  • dYdX (a defi trading platform) launched in April 2021 (rollup)

  • Sorare (a fantasy football NFT project) launched in July 2021 (validium)

This year saw the launch of two new significant entrants to the rollup ecosystem: Arbitrum and Optimism.

Arbitrum and Optimism are notable as the first generalized rollups to reach production. This means that each rollup operates like a natural extension of Ethereum - they are “EVM compatible”. Users can easily migrate Ethereum-based assets to them, and developers can deploy Solidity contracts and applications to the rollups themselves where users can interact with them.

Arbitrum launched on mainnet May 14th, and removed their whitelist on August 31st. Optimism followed a few months later, launching on mainnet on August 19th and removing their whitelist on December 16th.

As the L2 ecosystem has grown, users are depositing more and more funds into them. As of this writing, about $6 billion USD sits in L2 protocols.

Rollups in context

The crypto community has been working to scale blockchains with “Layer 2” technologies for a decade.

Payment channels on Bitcoin were discussed as early as 2012. While channels would eventually enter production on Ethereum as a relatively niche scaling solution, they were not capable of scaling smart contracts.

In 2017, Vitalik and Joseph Poon proposed a new solution called Plasma. The basic idea was to scale Ethereum by creating separate blockchains which would be anchored to Ethereum, inheriting security through clever code and economic mechanisms.

This line of research led to a new technique called a “rollup”. Building on Plasma’s ideas, Rollups scale Ethereum by creating a distinct L2 blockchain that can be cheaper and faster to use, while still inheriting the security of L1.

Applications exist on the rollup chain, and users interact with the rollup chain directly. In the background, the protocol bundles (“rolls up”) everyone’s transactions and stores a record of them on Ethereum L1. These records ensure that these transactions benefit from Ethereum’s strong security.

But there are two different ways to do this, leading to two different types of rollup design: Optimistic rollups and Zero-Knowledge rollups.

In a Zero-Knowledge rollup, cryptography is used to prove that the transaction was valid and store that proof on Ethereum. Most of the data can be thrown away (meaning you don’t need to store it on-chain), and only a tiny piece of data remains. But it is enough to mathematically prove that the transaction was valid.

Today there are many application specific rollups live, which we covered above. But generalized ZK rollups are still a work in progress, being built by teams like Matter Labs, Starkware (alpha launched in November), Polygon Hermez, Scroll Tech, and the Privacy & Scaling Explorations group.

To simplify a complex subject: ZK rollups work by converting code that runs on the rollup into a special mathematical equation. This equation is what gives us the concise proofs that are stored on mainnet.

It is a lot easier to define such an equation when the possible inputs are constrained. For instance, if we are only going to do simple token transfers. These application specific equations are easier to design.

It is much harder, in comparison, to define the equation that can take any possible code input and create a proof from it. That is the challenge of creating a ZK rollup that can be used for general arbitrary code.

This kind of “general” computation (also known as “EVM equivalence”) is much easier to achieve with Optimistic rollups.

With Optimistic Rollups, the L2 rollup chain leaves bundled-up records of transactions on L1.

However, the “proof” that would guarantee these results are valid is not actually run in most cases. The protocol is optimistic - it assumes that each block is valid, but we preserve the right to always prove it if necessary.

Because Optimistic Rollups need to preserve data so that someone can run the proof later, they require a larger amount of transaction data to be posted on-chain (instead of a tiny proof, already verified using zero-knowledge tech). But because no new cryptographic innovations are required, fully generalized Optimistic Rollups (like Arbitrum and Optimism) already exist today in production.

In either case, this means that you have a very high assurance that your transaction on a rollup is final. If someone sends you ETH on a rollup, the proof of that transfer lives on Ethereum, and you will always be able to withdraw that ETH to L1 if needed.

The long wait for Layer 2 is over. It’s here now - just very unevenly distributed. It will take months for applications, exchanges, wallets, to support L2 and help users make the transition.

2. Ethereum’s Creator Economy Goes Mainstream

In last year’s blog post, we noted that Ethereum’s “creator economy” was showing signs of growth.

Cryptoart volume had risen steeply in December, and there were signs that more and more artists were experimenting with the tools that Ethereum gives them to capture the value of their work.

The year that followed exceeded expectations.

In 2021, artists, musicians, writers, and other creators used Ethereum to earn a combined $3.5 billion. This sum places Ethereum as one of the largest global platforms for creators.

In 2021, Ethereum earned artists and musicians more money than OnlyFans or Patreon, and nearly as much as the largest creator platforms in the world.

What is the creator economy?

Ethereum’s “creator economy” is the set of tools, services, and markets that enable creative people around the world to earn money from their work using Ethereum.

Until now, the internet’s “creator economy” has been dominated by large centralized platforms. Most of these use a similar model, where a platform like YouTube or Spotify earns revenue from ads or subscriptions, and then passes on a small fraction of that revenue to creators.

This business model turned Spotify into a $40 billion company. But it has always been unsatisfying to the majority of artists, who earn a tiny fraction of each stream. In 2020, only 13,400 artists made more than $50K from Spotify (from a pool of 1.2 million artists on the platform).

An even greater number of artists work for free by posting their work to Instagram or Twitter. The artist gets paid in “exposure”, while the platforms capture the value.

Ethereum gave creators new tools to monetize their work. One tool stands out in particular: the NFT. A digital certificate representing ownership of any digital file, ranging from art, music, photography, videos, or game items. During 2021, platforms like OpenSea, Rarible, Foundation, Zora, and Mirror enabled artists to create, sell, and trade NFTs.

The NFT ecosystem is in very early stages. Remember that a year ago, the market for NFTs barely existed. Today the vast majority of volume and users are concentrated on one platform (OpenSea). However, there are many projects working on launching competitors, including decentralized exchanges. As we learned from the history of decentralized exchanges (a “dex”) and Uniswap’s incredible growth, decentralized projects that give ownership to their users can meaningfully compete with centralized incumbents.

The rapid expansion of the NFT market drew mainstream interest. Steph Curry, Eminem, and Shaquille O’Neal bought Bored Apes. Adidas did too, and they also bought digital land and released their own NFT collection. Budweiser, Paris Hilton, Trey Songz, Drake Bell, and the CEO of Shopify all registered ENS names and put them in their twitter handles.

Looking beyond the soaring NFT valuations and celebrity interest, the emergence of Ethereum’s creator economy marks a quiet revolution for our community.

For most of the crypto industry’s history, the majority of the people who could make a living using these technologies belonged to only a few categories: investors, developers, or people who work for crypto companies.

But in 2021, that changed.

This year, a much greater number of people from new and very different professional backgrounds began using Ethereum to support themselves financially. In 2021, people can earn money using Ethereum without any pre-existing capital, without technical skills, without a background in finance or investing, and without a job at a crypto startup.

These creators are not just casual users, but core members of the Ethereum community who depend on it and have a meaningful stake in the ecosystem. This change has led to an influx of new people, ideas, communities, talent, viewpoints and concerns, changing Ethereum’s ecosystem and influencing its future.

Most notably, many new users of Ethereum are rightly concerned by the energy consumed by Proof of Work. Fortunately, they are joining a community who shares those concerns. The Ethereum community has known since 2014 that Proof of Work would grow to consume too much energy, and has been working ever since to end it.

3. Core Protocol Upgrades

In 2021, the Ethereum community made steady progress towards the transition to Proof of Stake, and shipped two major mainnet upgrades, bringing changes like a new fee market and many minor optimizations.

The research, development, coordination, and implementation of these upgrades is being performed by independent teams all over the world, through open research and collaboration. As always, Ethereum is a bazaar, not a cathedral.

A significant amount of this work is done by the teams that build and maintain individual clients. Ethereum is notable as the only blockchain that has multiple independent teams actively building production client software.

Client diversity has always been a priority for the Ethereum community, even as it remains challenging in practice. Client diversity helps protect against bugs in a specific client which might destroy its monetary value, but also as a check against governance being captured by a clique of core developers. Ethereum is a “specification first” blockchain, meaning that the rules of the protocol are independent from any particular piece of software or set of individuals maintaining that software. Anyone can implement the specification, and build their own client to compete with the existing ones.

This year, client teams and the larger R&D community shipped two substantive upgrades to Ethereum mainnet: “Berlin” in April, and “London” in August. These upgrades included multiple changes. Most notable was EIP-1559, which reformed the Ethereum fee market (discussed in depth below), but also included critical changes like EIP-2929 which improved Ethereum’s defense against DOS attacks.

Teams also shipped the “Altair” upgrade to the “Beacon chain” - the Proof of Stake chain that has been running in parallel alongside Ethereum mainnet since Dec 1, 2020. The Altair upgrade both enabled light clients for consensus as well as updating slashing and liveness incentives.

In the background of all this work is the constant progress towards “the Merge” - the moment when Ethereum’s Proof of Work system will be turned off forever, in favour of Proof of Stake (discussed in depth below).

Even during this period of change, Ethereum mainnet continues uninterrupted, processing and securing billions of dollars.

EIP-1559 - Fee Market Reform

On August 5, EIP-1559 went live on Ethereum mainnet. This upgrade introduced several reforms to Ethereum’s “fee market”, the set of rules that define the marketplace in which users pay to have their transactions recorded in the Ethereum blockchain.

EIP-1559 had multiple goals:

• Make it less likely that a user would overpay for their transaction

• Reduce the rate of stuck transactions

• Enhance protocol security by making re-orgs less likely, and making DOS attacks more expensive

• Burn a portion of fees, which can accrue value to ETH and raise Ethereum’s economic security

As with most Ethereum protocol upgrades, this was the result of many years of research, development, testing, and debate by the Ethereum community. A 2014 blog post by Vitalik shared early ideas, followed by an ethresear.ch post in July 2018, and the draft EIP (“Ethereum Improvement Proposal”) in 2019. This was followed by two years of analysis and discussion, including a study by Tim Roughgarden.

EIP-1559’s adoption is not yet complete. Some wallets, exchanges, and other applications need to upgrade their own software to fully take advantage of the new fee market.

This graph shows the breakdown of transactions on Ethereum “Type 2” (tx that utilize EIP-1559) and “Legacy” transactions. By the end of 2021, about 35% of transactions on Ethereum were still using “legacy” transactions. However, even legacy transactions benefit from EIP-1559, because it improves the way gas estimates are produced.

Many wallets and exchanges already enabled EIP-1559 transactions, including MetaMask, Rainbow, MyCrypto, Frame, Trezor, Brave, Coinbase (and Coinbase wallet), Blockfi, FTX, and several others.

Four months after the change took effect, EIP-1559 is accomplishing its goals in improving the user experience on Ethereum:

In this graph, the blue line shows the median gas price for legacy transactions, and the orange line shows the gas price for type 2 (EIP-1559) transactions. After EIP-1559 was implemented, type 2 transactions have been on average lower than legacy transactions.

This graph shows the benefits in more detail. The blue line shows the difference in gas prices between the two types of transactions since EIP-1559 went live (the higher the line, the greater the savings). Legacy transactions are consistently 10-20 gwei more expensive.

EIP-1559 also seems to have reduced the likelihood of stuck transactions, without requiring users to pre-emptively over pay for inclusion (which many users, accustomed to stuck transactions, had been doing). If you’re interested in learning more, see this post by pintail, this post from Coinbase, and this post by blocknative.

FEEL THE BURN

EIP-1559 also introduced a change whereby a portion of the fee that every user pays to use the protocol is destroyed (“burned”). This means that with every transaction, an amount of ETH is being removed from the total supply.

This creates a mechanism which accrues value to ETH - an important piece of the broader vision to make Ethereum the world’s most secure blockchain.

Ethereum’s consensus mechanism depends, in part, on the value of ETH. Under PoW that is because miners are paid in ETH for their work to secure the network, and under PoS it is true because stakers are both paid in ETH to secure the network, and because they are required to stake ETH to provide that security.

Burning a portion of the fees creates a relationship between the use of the protocol (tx fees) and the value of ETH itself (by reducing supply). Since August, 1.32 million ETH were burned in 2021.

The yellow bars represent days and weeks of negative issuance, where Ethereum was used heavily enough - and enough fees were paid - that the burn from EIP-1559 exceeded the new issuance that occurs with every block. Keep in mind that these are the results under the current PoW system, and the shift to PoS will enable even lower issuance.

THE MERGE

The migration from Proof of Work to Proof of Stake has been the vision of the Ethereum community since its earliest days.

While Proof of Work may have been necessary to launch the first experiments in blockchain design, it has become clear in the intervening 12 years that better designs are possible. Designs that are far more secure, and do not consume extraordinary amounts of energy in order to produce strong economic security guarantees.

Ethereum’s Proof of Stake system is the culmination of 7+ years of research and development. Why has this taken so long? Since the beginning of this process, the Ethereum community has been unwilling to compromise on decentralization.

While there are different Proof of Stake systems on other chains today, most of them make significant concessions on decentralization. They rely on some form of delegation, meaning that the actual role of block validation is centralized in the hands of a small number of stakers.

While some communities settled, Ethereum innovated. New technology and techniques were advanced to keep Ethereum maximally decentralized and secure. Advancements in BLS signatures used in Ethereum’s PoS enable many thousands of nodes to participate in consensus, meaning that there is no delegation required in Ethereum’s PoS. Thousands of individual validators participate, instead of a handful of professional staking orgs.

At the same time, Ethereum’s design keeps the technical requirements for staking low. Low enough that anyone with a consumer laptop can stake from home, and be on a similar footing to professional staking services.

Ethereum’s Proof of Stake system is already live - it’s just not being used to secure any user activity yet. The Beacon Chain - Ethereum’s Proof of Stake mechanism - has been live since December 1 2020, running without any major issues.

Over the last year, users have staked more than 8 million ETH into the Beacon chain, worth approximately $26 billion USD:

The remaining work to be done is to merge the existing Ethereum L1 into its new Proof of Stake infrastructure, replacing Proof of Work.

With the beacon chain humming along, the Ethereum community passed multiple milestones towards the merge:

• In April, the Rayonism project saw developers hack together testnets that simulated the merge, as well as some early sharding designs.

• In October, client teams gathered in Greece for the Amphora retreat that produced a short-lived multi-client testnet.

• In November, the Kintsugi testnet continued that work with a long-lived multi-client testnet based on the latest specification.

Once specifications are finalized and new testnets have been used extensively by end users and application developers, existing testnets will be used to conduct trial runs of the Merge.

Assuming these trial runs go smoothly, the focus will then shift to the final step: carrying out the Merge on mainnet and ending Proof of Work forever.

Client diversity

Ethereum has many client implementations, each of which has a different share of the total validators who participate in Proof of Stake.

Today, client diversity is far from optimal:

In an ideal world, no client should have more than 33% of the network. If each individual client is below this percentage, it reduces the risk that a bug found in a client would impact the network.

Ideal client diversity is a theoretically achievable goal: Ethereum has 4 excellent staking clients (Lighthouse, Nimbus, Prysm, Teku), with a fifth candidate (Lodestar) emerging recently.

Before the merge, it would be beneficial if no client had more than 50% of the network. Without going into too much detail, in the case of an (unlikely) chain fork, clients staying below 50% would provide a buffer of safety for client devs to fix the issue without a mass slashing event.

It’s in everyone’s interest to move towards better client diversity - even the developers of a majority client. To their credit, Prysmatic Labs recognizes this fact and is working to reduce their majority.

The staking ecosystem expands

Ethereum’s Proof of Stake allows everyone, from hobbysts to institutions, to stake ETH and participate in securing the network. As the community of stakers has grown, an ecosystem of services, tools, infrastructure, and applications has developed:

• Rocketpool, the greatly anticipated decentralized staking protocol, launched in November 2021. Rocketpool is a protocol that lets users deposit ETH and share in staking rewards, without having to manage any staking infrastructure on their own. In short, users of the protocol securely deposit their ETH to a network of individual node operators, coordinated by the Rocketpool protocol. Decentralized staking protocols are an essential piece of infrastructure for fostering a healthy staking ecosystem. As of this writing, 74K ETH have been staked into Rocketpool.

• Lido is also a staking protocol, but one with additional trust assumptions in its current implementation. By January 2021 (2 months after the beacon chain launch), 100K ETH were staked with Lido. By the end of this year, 1.6 million ETH worth $13 billion were staked in Lido, by 33K unique depositors. Recently, Lido has made good progress towards client diversity among their network of operators who serve the protocol.

• Along with this, there was growth in the ecosystem of staking tools

  • The StakeHouse community released eth-wizard (a CLI staking installation tool for hobbysts) and Wagyu (a one-click Eth2 staking installer)

  • Existing node hardware providers Dappnode and Avado expanded their offerings for Ethereum staking

  • Stereum launched an open-source node setup tool

Want to stake your ETH? The best place to start is ethereum.org.

4. DAOs Pass The Tipping Point

One of the earliest dreams for Ethereum was to enable “Decentralized Autonomous Organizations” (DAOs). Because Ethereum enables anyone to write arbitrary rules in code, we can use those rules to design organizational or governance systems that allow groups of people to jointly make decisions through voting or other mechanisms.

The DAO ecosystem had a breakout moment his year. A simple but workable tech stack came together to enable existing DAOs to get things done, and new entrants pushed the boundaries of what a DAO can be and who can be a part of one.

The number of unique monthly voters who participate in DAOs on Snapshot grew throughout the year, and exploded in November and December:

Today, there are hundreds of DAOs that see daily activity, pay members, build products, and vote on the use of shared funds. Collectively DAOs on Ethereum govern assets worth more than $16 billion.

But first: what is a DAO, again?

“DAO” is used to describe a large range of structures. Generally, a DAO refers to any group of people who use a blockchain like Ethereum to collectively govern some on-chain thing. Sometimes that thing is money (like ETH or tokens in a DAO treasury fund), but could also include NFTs, the parameters of an on-chain protocol like MakerDAO, or all of the above.

While DAOs have existed on Ethereum since 2016, these early experiments were more science project than real product.

But in the last few years, DAOs became a practical necessity. The first major driver were DeFi protocols who wanted to credibly decentralize themselves - handing control over their protocol to the crowd of users, who vote using tokens (“governance tokens”).

The basic structure pioneered by protocols like Compound in 2020 have become the de-facto standard for many DAOs launched since, like Uniswap in September 2020.

In 2021, DAO growth was notable because it was happening outside of the DeFi protocols who were the first major entrants. These DAOs began to show the breadth of the design space possible for on-chain organizations.

• Ethereum Naming Service (ENS). First launched in 2017, ENS is a decentralized naming protocol that lets users create simple usernames (yourname.eth) which can be used as Ethereum accounts, to receive non-ETH crypto (including Bitcoin), or as a web URL. In 2021, ENS launched a DAO which governs certain parameters of the ENS protocol, and a community fund to support the ENS ecosystem.

• ConstitutionDAO. In the span of a few short days in November, strangers from across the internet pooled a collective $44 million dollars in order to buy a copy of the US constitution. In total, 17,521 distinct accounts donated to the effort. The project gained mainstream coverage, and resulted in a wild livestream of bemused auctioneers mediating a bidding competition between a representative of the new internet of organizations, and the old world of finance.

• PleasrDAO began with a tweet, and soon grew into an investment club. PleasrDAO is exploring a different part of the DAO design space - rather than a mass organization with tens of thousands of members, it’s a relatively small investment club where members pool capital to make joint purchases. Notably, PleasrDAO has purchased several high profile cultural assets, like the Doge 1/1, or the unreleased WuTang album.

• Friends with Benefits (FWB) is an online social community where membership takes the form of ownership of the $FWB token. The token is used to control access to various online and offline spaces: chat groups, meetups, dinners, after parties, etc.

• MakerDAO began as a DAO, but then established some traditional org structures in 2018. MakerDAO is the creator of the DAI stablecoin, the first decentralized stablecoin on Ethereum and one of the most successful DeFi projects to date. This year, MakerDAO completed the promised return to decentralization by winding down its foundation and handing control of the Maker protocol over to a DAO governed by its token holders.

DAOs are an incredibly general toolkit, enabling a large design space. With private keys and smart contracts, we can design nearly any arbitrary system through which humans can jointly govern on-chain assets and protocols.

As a result, we see this huge range of utility for DAOs. Soon it may become difficult to talk about them as a useful category. DAOs are a means for protocols to meaningfully decentralize themselves away from founder control, tools for online communities to quickly and seamlessly pool capital to achieve shared goals, a new type of social network, and a new way to collectively fund public goods.

The growth in DAOs this year was enabled by a set of software tools that makes it easy to get started.

Many DAOs use a simple combination of tools. For the on-chain smart contracts, most use a fork of Compound’s system. Voting itself is handled by projects like Snapshot, and discussion and debate of individual proposals happens on forums like a discourse.

In 2021, the range of tools available to create and manage DAOs expanded:

• Mirror launched a suite of tools that help people create “Media DAOs”: joint publishing and ownership of content created on Mirror.

• Coordinape spun out of the Yearn community, and offers a framework for distributed teams to collaboratively decide on compensation for work done by DAO members.

• Rabbithole launched tools to onboard new users to DAOs and build the skills and credentials to work for DAOs.

• Forward-thinking governments have developed novel legal structures to streamline DAO formation and reporting, like in Wyoming.

There’s no shortage of problems and challenges for DAOs to overcome as they scale to suit the needs of the millions of people around the world who want to use them. A few key challenges that we hope to see progress on in 2022:

• Today most DAOs use some form of coin voting. This means that decisions are made in proportion to ownership of some “governance token”. There are many known downsides of coin-voting systems. Experiments in sybil-resistance like Proof of Humanity and BrightID offer one way to build governance systems on the basis of individual people, and not tokens.

• Finding management structures and work systems that enable distributed groups to meaningfully build products and coordinate actions.

• Legal systems can be very slow to adapt to new forms of productive human activity, and DAOs are no exception. The lack of clear legal frameworks can introduce risk for DAO members. Work continues on this in jurisdictions around the world - for recent work in the US context, see this paper by David Kerr and Miles Jennings.

New organizations are emerging to address these, and other challenges. The DAO Research Collective is one such organization, with the goal of making it easier for DAO founders to find answers and information about these questions.

5. Onwards!

As with every year, there’s too much happening in the Ethereum ecosystem to sum up in one blog post. But Ethereum is much bigger than the 4 themes above - a few other developments from 2021:

• Identity: The Ethereum Name Service (ENS) saw breakout growth, growing to hundreds of thousands of users, hundreds of integrations, and a new DAO community treasury ($1.9B) to fund continued development of the ecosystem. Proof of humanity and BrightID gained use as an anti-sybil mechanism, the Sign in with Ethereum standard gained traction, and it became fashionable to have an Ethereum identity.

• Gaming: Axie Infinity saw incredible growth to 2.9 million users, becoming the most successful game built on Ethereum. New kinds of crypto organizations like Yield Guild spurred interest in the play-to-earn gaming ecosystem. Skyweaver launched on Polygon, and Dark Forest shipped v0.6 and continued to build a cult following around its crypto-native on-chain strategy game.

• Public goods funding: CLRFund (a protocol that lets anyone run their own CLR) completed their trusted setup ceremony and facilitated multiple rounds. Gitcoin continued their successful CLR product, funding more than $24 million across the year. Optimism ran an experiment in retroactive funding for public goods, and the Ethereum Foundation launched a client incentive program to sustainably fund client development and maintenance. A new public goods organization - 0xPARC - launched to focus on application-level innovation.

2021 was a bullish year for the Ethereum community. The ecosystem passed major milestones and the end of Proof of Work is in sight.

It’s easy to feel like a winner when the market is in your favour. It’s even easier to get distracted.

But this city won’t build itself - see you on the other side of Proof of Work.

-- Josh Stark & Evan Van Ness

🙏 Special thanks to Bruno Lulinski, Miguel Pereira, and Nazim Rizvic, who assisted with research, drafting, and data analysis for this year’s post!

Thank you also to Superphiz, Lakshman Sankar, Liam Horne, Danny Ryan, Tim Beiko, Dankrad Feist, Trent Van Epps, Barry Whitehat, and Fabien from Snapshot.

All data cited in this blog post can found here.

Ethereum is the digital frontier.

Because it is open and programmable, Ethereum offers a vast uncharted expanse filled with opportunity.

Over the last few years, this frontier has attracted its first major settlements. The freewheeling bazaar of decentralized finance (“DeFi”) has grown into a sector that is beginning to compete with Silicon Valley. This year, a second ecosystem began to put down roots: the creator economy of cryptoart, music, and media.

Meanwhile, the Ethereum community is poised to adopt the most significant changes to the core protocol in its short history. The frontier is finally paving its busy roads.

Like in 2018 and 2019, our goal is to zoom out and show you the bigger picture. This blog post is not comprehensive — writing a comprehensive roundup about Ethereum is nearly impossible. Instead, we focus on a handful of trends we believe were most significant.

In our view, these were the biggest developments of 2020:

1. Bigger than Bitcoin. Ethereum eclipsed Bitcoin as the blockchain with the most valuable blockspace, and the blockchain used to transfer the most value.

2. Ethereum’s Creator Economy. Artists, musicians, and creators of all types exit to Ethereum, where they control the value of their work.

3. DeFi: Revenge of the Dex …and more! Decentralized exchanges changed the way the crypto industry traded, reaching a high of 16% of centralized trading volume.

4. Ethereum’s scaling upgrades are underway. The first of the Eth2 upgrades — the beacon chain — went live on December 1, kicking off a multi-year process that will see Ethereum grow into the world’s settlement layer.

In 2020, Ethereum reached a critical mass. From a high enough vantage point, it’s clear that the weight in the industry has shifted. This year will be remembered as the year that the entire crypto industry began orbiting Ethereum.

1. Bigger than Bitcoin

What is the world’s most popular blockchain?

For the first time in history, the answer is not “Bitcoin”.

The answer is: “well… according to which metrics?”

In 2020, Ethereum passed Bitcoin on two of the most important metrics we have to compare blockchains. First, Ethereum was used to transfer the most value:

Ethereum transferred ~$1.6 trillion USD worth of assets, 60% more than Bitcoin’s ~$1 trillion.

Ethereum’s most successful application — the stablecoin — is the primary driver of Ethereum’s growing transaction volume. Stablecoins are cryptocurrencies that hold a fixed fiat value (usually dollars), and so are more useful as a store of value or medium of exchange than a volatile asset like BTC or ETH.

Ethereum’s volume grew throughout the year, and since July, has consistently been settling more value than Bitcoin.

Second, another key metric used to compare and measure the real use of cryptocurrencies are total transaction fees. Every time someone uses Ethereum or Bitcoin (to send money, or use an application), they pay a small fee. Fees are a useful metric because they show real demand from paying users.

In 2019, total fees paid to use Bitcoin was 4x the fees paid to use Ethereum.

In 2020, that flipped. Fees paid to use Ethereum were nearly double Bitcoin’s.

Another way to frame this is in terms of the value of blockspace. Every blockchain has a limited capacity. Over the whole year, there’s only so much “block space” that can be used for transactions. In 2020, Ethereum’s blockspace was worth about twice as much as Bitcoin’s blockspace.

If we look at cumulative fees across 2020, there is a clear shift in August when demand for Ethereum increased and fees began to rise.

Remarkably, even applications built on Ethereum have — momentarily — had more paying use than the entire Bitcoin blockchain. On August 10, fees paid to use Uniswap were tied with fees paid to use Bitcoin, and since then have occasionally exceeded 24h BTC fees.

Of course, Bitcoin still leads by other metrics.The marketcap of Bitcoin is still higher than the marketcap of all Ethereum-based assets. And, Bitcoin has greater name recognition with people outside of crypto.

But when you look at real use of these platforms, Ethereum has taken a clear lead over Bitcoin, and no other blockchain is within several orders of magnitude.

2. Ethereum’s Creator Economy

Beeple is a digital artist. His real name is Mike Winkelmann.

His work ranges from concert visuals (where he’s worked with One Direction, Eminem, Justin Bieber, and many others), to 3D / Motion Design work for clients like Apple, Nike, Activision, and others.

For 13 years, Beeple has created and published a piece of 3D art every single day as a way to hone his skills and build a large following on instagram.

He first heard about Ethereum in 2017, but didn’t look very closely at it. Then this summer, he noticed some artists who work in his industry start to sell art on Ethereum.

These artists were selling unique digital artwork in the form of “non-fungible tokens” (NFTs). And they were making good money doing it. In July, Trevor Jones sold a piece of digital art for $55,555, at the time a record amount. Beeple decided to try it out for himself.

Up until this point, Beeple’s “everyday” pieces didn’t pay well, or at all. Instead, Beeple’s art made money for Instagram and Twitter, who sell ads to his millions of followers. You could say that on these platforms, Beeple — and every other artist — just gets paid in exposure.

Ethereum gave Beeple an exit from that system. On Ethereum, 13 years of art stopped being an asset just for Instagram & Twitter, and became an asset for Mike. He was able to put his work on an open market, and find out what it was worth.

In December, Beeple sold a set of his everydays for a combined 3.5 million, setting a new record for cryptoart sales. During this sale, he sold three editions of artwork for a total of $582,000 in five minutes.

2020 was the year Ethereum’s creator economy reached a critical mass. If ICOs were Ethereum’s first breakout application, and DeFi is the second, then the creator economy is quickly becoming the third.

The same tools that helped Beeple take ownership of his art are being used by other artists, musicians, fashion designers, writers, and more. But it was cryptoart where the growth was the most obvious. Over the last year, monthly volume of people buying and selling crypto art exploded:

In total, cryptoart volume was about $23 million in 2020. While this is small in comparison to the broader crypto markets, it’s not insignificant compared to other platforms for creators. To give a sense of scale, this early cryptoart volume on Ethereum is about 5% of the $500 million Patreon paid out to creators in 2019.

What makes the creator economy notable isn’t the absolute volume, but the incredible relative value offered to artists. For Beeple, cryptoart wasn’t just marginally better than the way he worked before. It was a zero-to-$3.5 million moment.

That’s why most of the artists who are exiting to Ethereum are not long-time crypto adherents. They’re established artists with pre-existing followings, who are drawn to what Ethereum offers: a better way to monetize their work, and an unexplored canvass for experimentation.

To name a few: Murat Pak, who might best be known as the artist behind the Archillect twitter account, has recently started creating & selling NFTs — and explaining them in detail to their followers (Pak’s Niftygateway). Pak’s NFTs have brought in more than $1 million USD in 2020. Mario Klingemann, who uses AI & machine learning to create bizarre and surprising generative art, has also gotten in to the NFT game (Mario’s Superrare). Or Joanie Lemercier, whose work ranges from large light protection installations, to climate activism, to silkscreen prints (Joanie’s Nifty gateway).

The same toolkit that makes Ethereum a frontier for art also offers a better model to musicians. In the last 6 months alone, musicians sold more than $1M of NFTs and social tokens.

One of the most outspoken musicians to embrace Ethereum is RAC, aka André Anjos. In October, André released a fan token called $RAC. The token was distributed to his existing fanbase to retroactively reward them. People received tokens if they had previously bought merch, or subscribed to RAC on various platforms. $RAC gives its holders access to exclusive perks, access to new work, and more. Other artists, like 3LAU, Tokimonsta, and Portugal. The Man are experimenting with similar ideas.

Behind the scenes, the creator economy’s growth is being enabled by a new generation of platforms, tools, and markets.

Over the last few years, a large number of creator-economy marketplaces have sprung up. Some act as platforms for any type of NFT (Rarible, Niftygateway, Opensea), whereas others specialize in a certain type (for cryptoart: AsyncArt, KnownOrigin, MakersPlace, Foundation, and Superrare) or a certain aesthetic, acting almost like a brand or label (Zora). Recently launched Mirror aims to be a user-owned publishing platform, and Cent is trying to tokenize and sell tweets. Audius, a crypto based music-streaming platform, grew to have more than 1 million monthly users in October.

For most of its history, the crypto space has been synonymous with money. But Ethereum’s frontier is much larger than finance. At the same time, the creative web is at a turning point, as artists, musicians, writers, and creators of every type are increasingly dissatisfied with the platforms that exclude them, censor them, control their relationships with their audience, and extract fees for that privilege.

Ethereum’s digital frontier is an exit from those systems.

3. DeFi: Revenge of the Dex

In 2018, many had concluded Decentralized Exchanges (Dexes) were a failed experiment.

In our review of that year, we included a short note about Uniswap, a new dex which had launched in November. In 2019, Uniswap grew substantially — daily volume went up by 6,000%.

In 2020, Uniswap was used to trade more than $58 billion worth of assets. In September, Uniswap was briefly doing more volume than Coinbase. This growth isn’t limited to Uniswap — dex volume is increasing across the board:

In October, combined dex volume reached a high of 16% of all centralized exchange (CEX) volumes.

It was another year of incredible growth in DeFi, Ethereum’s financial system. DeFi is a network of connected financial services & protocols being built on the internet, expanding access to basic financial services to everyone on earth. Today, you can use DeFi applications to save money, take out a loan, make payments around the world, earn interest on your assets, or speculate on crypto markets.

In February, DeFi hit the milestone of $1 billion in total value locked within DeFi protocols. By the end of December, that had grown to $15 billion.

And in November, the number of unique addresses that have ever used DeFi protocols surpassed 1 million.

It’s impossible to sum up everything that happened in DeFi this year, but four developments are worth calling out in particular:

1. In January, Aave introduced flash loans. Flash loans let anyone borrow an asset, do something with that asset, and then repay the original loan all in the space of a single Ethereum transaction. In effect, this makes it possible to offer unsecured loans. The lender’s risk is protected by the fact that the loan and the repayment happen, in effect, at the same time — if the borrower can’t repay the loan, then the loan was never made in the first place.

This is the decentralization of arbitrage. On Ethereum, if you find a market inefficiency and have some technical savvy, then you can profit from it. You don’t need to be an institution with many millions of dollars to take advantage of that arbitrage.

2. This summer, Compound introduced liquidity mining — kicking off a protocol arms race. Compound introduced a governance token, $COMP, which users could earn (“mine”) in exchange for adding liquidity to Compound, or borrowing from it. This tactic was quickly copied by other protocols, especially very new ones who had no depositors, and needed to quickly gain marketshare by offering high rates to “liquidity miners.”

Lost in the noise was this: protocols learning how to use the tools that Ethereum offers them to bootstrap themselves into existence, competing against established protocols in a brutal free market.

3. Uniswap kicked off a trend of rewarding past-users with tokens. When Uniswap introduced a token ($UNI) and its own “liquidity mining” incentives, it also distributed 15% of all $UNI to early users of the protocol. This included users who previously provided liquidity, and anyone that had ever used Uniswap — even once.

Distinct from liquidity mining, this is something new. Imagine if AirBnB or Uber had given 15% of their equity to early hosts, drivers, and users. Crypto protocols like Uniswap are experimenting with new models, that blur the lines between user and owner. What would the world be like, if our most valuable marketplaces were owned by the people who use them?

4. Bitcoin gets a new life as a wrapped token on Ethereum. A Bitcoin is “wrapped” when it is locked somewhere (into a protocol like TBTC, or by a custodian like BitGo), and then an IOU token is issued on Ethereum, which can be redeemed back on the Bitcoin blockchain.

Why are Bitcoin holders moving their Bitcoin onto Ethereum? Because on Ethereum, you can do things with your Bitcoin besides hold it. You can use your Bitcoin as collateral for a loan, or lend it out and earn interest on it. At the beginning of the year there were about 1,000 wrapped Bitcoin on Ethereum, and by November there were ~154,000, nearly 1% of the total Bitcoin supply, being put to work on Ethereum.

4. Ethereum’s Scaling Upgrades

After years of research, planning, development, and testing, the Ethereum community laid the foundation for the multi-year, multi-step Eth2 upgrades.

This marks the beginning of a new phase. Over the next few years, the Ethereum community plans to make multiple inter-dependent upgrades to the core protocol, radically expanding the capacity and security of the network.

You’ve probably heard this project referred to as “Eth2”, but this has always been a bad name. “Eth1” and “Eth2” are not sequential versions of a thing, but rather two components of a whole — Ethereum — that will be merged together. The broader project is really a set of separate, but related, upgrades. The first of these upgrades — the Beacon chain — launched on December 1, 2020.

The beacon chain goes live

The beacon chain is the backbone of Ethereum’s new Proof of Stake consensus mechanism. Today, the beacon chain isn’t being used by the Ethereum protocol — it’s just running smoothly, while the community fine-tunes and hardens it, in preparation for supporting a hundred-billion dollar network.

Ethereum’s Proof of Stake consensus system is unique. No other blockchain community has prioritized decentralization — the ability for anyone, even with consumer hardware, to run a node — to the same degree as Ethereum.

What happens next? There are two major tasks in the near future. One is to merge the Eth1 chain with Eth2, swapping out Proof of Work for the beacon chain’s Proof of Stake. The other, is to add data shards, which will open a new route to immediate scalability through rollups.

A rollup-centric roadmap

In October, Vitalik proposed an adjustment to Ethereum’s roadmap. This plan would prioritize expanding use of rollups to give the Ethereum ecosystem massive scalability gains in the near future.

Rollups are a layer 2 scaling technology. The idea is that we can keep data on-chain, while moving execution “off-chain”, to an environment that can handle faster performance, while still preserving the characteristic safety guarantees of “on-chain” Ethereum transactions. For a detailed explanation, check out Vitalik’s guide.

The key to the rollup-centric roadmap is that rollups become even more powerful when combined with a specific Eth2 upgrade: data shards. Data shards allow anyone to pay transaction fees to post data to several different sharded chains. Today (without data shards) rollups give Ethereum a 3000 transaction per second throughput capacity, but with data shards that throughput reaches more than 100,000 transactions per second.

We have rollups live on mainnet today (though they don’t yet use Eth2 data shards). Loopring, Fuel and zkysnc already have ETH and token transfers enabled. All are capable of doing thousands of transactions per second with similar security assumptions as transacting on the Ethereum mainnet.

2020 also saw optimistic rollups launch testnets with Solidity/EVM support. In 2021, we will see developers begin to port their applications to optimistic rollups (in fact, this blog post is late enough that we’ve already begun to see it). As a result, transaction fees for end users will be lower, even as demand for Ethereum blockspace continues to grow.

If you want to read more about how Rollups fit into Ethereum’s roadmap, we recommend this recent post by Ben Edgington.

The hobbyist staker community grows

As the beacon chain launch grew closer, a community of hobbyist stakers grew up around it. This is a direct result of the Ethereum community’s decision to prioritize a PoS consensus mechanism that made it viable to participate with consumer hardware.

Communities like this used to exist in the early days of PoW mining, before ASICs centralized mining in the hands of large companies. Readers who were around in 2014–2016 will remember that being able to contribute to the network’s consensus on an individual basis was an important part of the crypto ethos, which has since been mostly lost. Now, Ethereum is bringing it back.

It’s another example of what makes the Ethereum community great. There was a need for education, resources, and tech support to help people learn how to set up their clients and stake successfully. The largest of these communities is Ethstaker, a community of about 6,000 people on Discord. If you’re interested in staking, this is the right place to start.

What did it all mean?

Ethereum is growing up.

Ethereum’s digital frontier is no longer an experiment, no longer a blank canvass. It’s an economy, a growing city-state. Still rough around the edges, sure. But that’s always been part of the charm.

Since 2015, Ethereum has always been the underdog. But 5 years later, it’s the protocol with the largest volume and the most valuable blockspace. There are still other metrics to conquer — but you can feel the Ethereum community’s goals expanding beyond simple comparisons to digital gold.

Being the most-used cryptocurrency isn’t worth much if crypto itself fails, if it doesn’t solve real problems, or if it can’t scale. Ethereum’s next challenges will be bigger, because they will be the whole crypto market’s challenges.

So, what are you waiting for? Winter is over, and we’ve got work to do. The frontier doesn’t build itself.

— Josh Stark & Evan Van Ness

Thank you to George Trotter, Jacob Horne, Mike Winkelmann, Superphiz, Richard Chen, and Danny Ryan.

Ethereum began as a blank canvas.

Ethereum is an open, permissionless blockchain that developers can use to create any kind of application they want.

This year, the early strokes laid down on that canvas began to form into a coherent picture.

You don’t have to squint so much to see the direction Ethereum is going. Protocols and applications built on Ethereum are beginning to find product-market fit, quickly building communities of thousands of paying users.

The empty spaces on the canvas are no longer sources of uncertainty or doubt. Instead, they look more like opportunities: what can we build over there?

2019 was the year Ethereum grew more confident. The technical roadmap gained clarity as difficult engineering problems were solved, and the largest developer community in crypto is building applications that people actually pay to use.

This last year, it felt like every big challenge facing the crypto industry as a whole was being addressed primarily by the Ethereum community:

• It’s on Ethereum where decentralized exchanges are solving the problem of trusted third parties, by letting users trade without ever giving up their private keys

• It’s on Ethereum where smart wallets are solving intractable UX challenges, without requiring users give up control over their funds

• It’s on Ethereum where stablecoins like Dai are solving the volatility problems that have made other cryptocurrencies poor payments technology. On Ethereum, peer to peer digital cash isn’t a dream, it’s real — go use it!

Like last year, our goal is to zoom out and show you the bigger picture. This is a summary of the most important developments and trends in Ethereum — the things that we’ll say mattered when we look back in ten years.

In our view, the biggest developments of 2019 were:

1. The Ethereum Economy continued to grow. DeFi remains the largest sector within Ethereum, and we saw early signs of growth in gaming & decentralized autonomous organizations (DAOs).

2. Ethereum nudged into the mainstream. Major corporations, financial institutions, consumer brands, and even celebrities began actually using Ethereum.

3. Ethereum 1.0 improved. A number of projects led to major performance improvements, and a direction was chosen for long-term sustainability.

4. Eth2 made progress as an engineering project, and laid the foundation for launching in early 2020.

5. Layer 2 made steady progress, with new tech capturing the community’s attention. Rollups and Zero Knowledge technology made the largest strides this year, while other technologies like state channels made steady progress.

1. The Ethereum Economy Grows

In this year’s review, we want to put Ethereum’s adoption into context of the rest of the cryptocurrency industry.

One obviously important metric to use is simply: are people actually paying to use cryptocurrencies and decentralized applications?

Any time someone uses a blockchain like Bitcoin or Ethereum, they pay a small fee which is collected by miners. Here are the total fees paid in 2019:

This graph shows the top 16 blockchains, ranked by fees paid (for obvious reasons, it does not include blockchains where there are no fees). You can see all of the underlying data here.

The reality is that there are only two blockchains with significant use: Bitcoin and Ethereum. As we’ll see in later sections, many individual applications on Ethereum have more paying use than most blockchains with market caps in the billions of dollars.

💰 The Year of Decentralized Finance (Again)

In 2019, Decentralized Finance (DeFi) remained the most significant ecosystem within the Ethereum economy.

The DeFi vision is simple: an alternative worldwide financial system. The internet has made information cheap and universally accessible, and Satoshi made information into money. Every person on earth should be able to access a financial system — payments, savings, lending, investing — through the internet.

Although this system is early, it exists today. If you live in a country with a weak financial system or hyperinflation, DeFi may already be a better alternative than your domestic banks and financial institutions.

DeFi gives us cryptocurrencies that hold a stable value (“stablecoins”), a necessary innovation for any realistic payment use-case. DeFi has enabled thousands of users to earn interest, by lending out their ETH to borrowers. DeFi has enabled the easy creation of assets that automatically execute trading strategies & grow your wealth.

All of this is available today, through a financial system that never closes, is openly auditable and transparent, and where your account can’t be shut down if you work in the wrong industry or have the wrong political views.

This year, one of Ethereum’s under-appreciated virtues came into clearer focus: applications built on Ethereum are interoperable and composable. If you are creating or issuing a new asset onto Ethereum, you can easily “plug in” to a protocol that facilitates asset trades. As David Hoffman puts it, Ethereum is “money legos” that make it easy to create more complex systems. This allows DeFi products to quickly become more useful to users, taking advantage of the extensive infrastructure that has already been built.

While DeFi is still relatively early and risky, the convenient thing about being a financial system is that it comes equipped with tools to help manage risks. The interest rates paid to users who lend their ETH in DeFi have been relatively high, in part because using these platforms introduces risk.

Insurance protocols like Nexus Mutual have sprung up to give users a way to hedge the unavoidable risks of working at the frontier of a new industry. Nexus Mutual launched in July, and in the next 5 months users paid about $11,000 in premiums. That’s greater than the total transaction fees paid to use the privacy cryptocurrency Grin over the same period ($7,600).

📈 How much did DeFi grow in 2019?

The simplest metric used to track DeFi’s growth is the total value of assets “locked” (TVL) in Ethereum’s DeFi ecosystem. In other words, how much money is currently stored in the smart-contracts that serve as infrastructure for this financial system?

In 2018, this value more than tripled from ~$70 million to $300 million.

In 2019, it more than doubled to $667 million on December 31, 2019:

Another way of viewing the growth of the DeFi ecosystem is to visualize not only the total value locked, but the number of accounts using them and how many interactions those accounts have with the application.

At the start of 2019, MakerDAO was the only DeFi protocol with significant funds, a total of 1.86 million ETH (at the time, valued at ~$260.4 million). In the graph below, the size of the circle corresponds to the amount of ETH locked in the protocol:

But by the end of 2019, the field was much more diverse:

Compound grew its TVL by 1,000%, from 35,000 ETH to 350,000 ETH, and rapidly grew its number of users. MakerDAO is captured in a moment of transition between the original “single collateral” version of DAI (green circle) to “Multi-collateral” DAI (yellow circle). Both types of DAI have a combined TVL of 2.15 million ETH, an increase of 16%.

🌐 Decentralized Exchanges grow in variety and volume

Decentralized Exchanges (DEXs) are an alternative to centralized cryptocurrency exchanges. DEXs let you trade your assets over the internet without requiring a centralized intermediary like Coinbase, Binance, or Quadriga.

One of the most exciting developments in 2019 was the rise of Uniswap.

Uniswap is a new kind of decentralized exchange. In a “normal” DEX, traders interact with each other — they offer to sell at a price, find a buyer, and then trade directly.

With Uniswap, you trade directly with the Uniswap protocol. The Uniswap protocol itself is the market maker, who sets prices and offers to trade. This is accomplished 100% on-chain, with no off-chain dependencies.

Because Uniswap is built on Ethereum, anyone can use it by calling its contracts. This lets other DeFi protocols utilize Uniswap as a way to bootstrap liquidity for a market, quickly enabling anyone to trade a new asset. Uniswap is one particularly useful “money lego” that has been integrated into many other products and services.

Because Uniswap is built on Ethereum, it is uncensorable. You might be able to take down one particular front-end for Uniswap, but the underlying contracts — the real product itself — remains accessible to anyone with an internet connection.

In 2019, Uniswap went from an average daily volume of $25K to $1.5 million (an increase of +6,000%), and grew its available liquidity from $500K to $25 million (+5,000%).

Over the course of 2019, Uniswap liquidity providers (people who “lock” their assets into the protocol, in order to earn a return for the service of providing liquidity) earned more than $1.2 million in fees.

As a useful comparison, that is greater than the total combined transaction fees paid in 2019 to use Ethereum Classic ($587K), Litecoin ($413K), and Ripple ($179K).

Those three blockchains have a combined marketcap of $13 billion. Uniswap is a team of 4 that raised a $1.8 million seed round in mid-2019.

Also in 2019:

• Beyond Uniswap, DEXs on Ethereum had an impressive year. Total trading on DEX’s built on Ethereum passed $2.3 billion.

• Kyber, one of the largest decentralized exchanges, grew its volume 443% from $70 million to $380 million.

• 0x ended the year with $254 million in volume, and the number of trades increased 27% — a year of focusing on liquidity and R&D.

• We saw the rise of DEX aggregators like Totle, dex.ag and 1inch.exchange. These are services which allow users to find the best price across DEXs.

🏦 Lending Services

One of the most significant developments in Ethereum’s DeFi ecosystem was the rapid growth of lending services.

These applications let users lock their cryptocurrency into smart contracts, where other users can borrow it, paying you interest. In 2019, more than $600 million in loans were originated on Ethereum. That is more than double the total value of all payday loans made in Washington state in 2017 ($248 million).

For instance, right now you can take any DAI you have, and start earning 5.9% interest annually by lending it through the Compound lending protocol. In 2019, Compound grew its TVL from $20 million to $91 million.

Or, you could lock some of your ETH into the MakerDAO protocol, as collateral to take out a loan of DAI (which you could then lend on Compound, if you wanted).

In 2019, users paid $5.5 million in fees to the MakerDAO protocol, a total that is greater than the sum of all transaction fees paid on every blockchain other than Bitcoin and Ethereum.

💵 Stablecoins

Stablecoins fulfill the original vision of cryptocurrency as “peer to peer digital cash” — a useful means of making cheap, fast payments over the internet. A digital dollar you can use to make payments, receive your salary, and that won’t lose its value overnight in the volatile cryptocurrency markets.

Virtually all stablecoins are built on Ethereum. This year, Ethereum gained even greater dominance as the default platform for stablecoins, as Tether — the oldest and largest stablecoin — moved a majority of its outstanding supply over to Ethereum, transitioning off the (Bitcoin based) Omni protocol.

In 2019, Tether issued approximately $ 2.3 billion onto Ethereum, with more than $1 billion of that total directly coming at the expense of Bitcoin’s Omni. This was the largest migration of assets from one blockchain to another in history.

MakerDAO, the protocol which supports the Dai stablecoin (covered extensively in Year in Ethereum 2018), went through a major upgrade. While originally DAI could only use ETH as the “single collateral” available to issue new DAI, in November MakerDAO successfully launched “Multi collateral DAI” which makes it possible to use many assets on Ethereum in the system.

💥 A Cambrian explosion of Ethereum-based assets

One major new trend in DeFi is the expansion of products and protocols that facilitate the use of synthetic assets. By “synthetic asset” we simply mean an asset that is designed to have specific characteristics, often imitating the profile of other assets.

The programmability and interoperability of assets on Ethereum makes this easy. Using Ethereum smart-contracts, developers have created assets that track the value of other cryptocurrencies, automatically implement specific trading strategies, or create custom derivatives.

Set protocol launched TokenSets in April, a suite of products that allow anyone to purchase an ERC-20 that then implements a specific trading strategy. For instance, you might buy $1,000 worth of the “ETH 20 Day MA Crossover” set, which rebalances between ETH and the USDC stablecoin, depending on a specific market indicator. The goal is that when ETH is rising, this TokenSet is mostly ETH, and when ETH is falling, it swaps into a stablecoin to avoid losses.

Universal Market Access (UMA) introduced a service that lets anyone create synthetic tokens that track the price of real-world assets like stocks, or really anything at all that you have a price feed for. At the ETHBoston hackathon, hackers used UMA to create a synthetic asset that tracks whether DAI deviates from its dollar peg, resulting in a massive payoff if the peg breaks. At ETHWaterloo, the UMA team created a synthetic asset that tracked the amount of human fesces sightings in San Francisco, which they promptly integrated into a fork of Uniswap so people could trade it on testnet.

Synthetix launched their “synths” product, which are ERC-20s that track the value of major currencies, commodities, or indices. Notably, Synthetix began trying to implement their protocol on EOS, decided it was a mistake, and have doubled down on building on Ethereum.

In 2019, users paid approximately $3.5 million in fees to use Synthetix, a total that is greater than the sum of all transaction fees paid on every blockchain other than Bitcoin and Ethereum.

Also in 2019:

• Efforts to make Bitcoin useable on Ethereum gained momentum. Kyber, Bitgo, and Republic launched wBTC, an ERC-20 backed by real Bitcoin, stored with trusted custodians. In August, Keep & Summa announced their plans for “trustless BTC” (tBTC), which required no central counterparty to custody the BTC. Launched is planned for 2020.

• RealT tokenized the legal ownership of several houses in Detroit — starting with 9932 Marlowe. Owners of these tokens have access to liquidity through Uniswap, where they can always find a counterparty to buy out their stake in the home.

• Compound — already mentioned above in the lending section — launched cDai, a token that accrues interest. Products like cDai are collapsing the distinction between a “lending service you use” and “an asset you buy”.

• Many synthetic assets depend on some kind of Oracle to report information. “Trustless” oracles are unsolved, and remain one of the hard problems in the cryptocurrency space.

👾 Ethereum’s gaming sector grows

In 2019, Ethereum’s gaming ecosystem showed early signs of growth.

One of the standout examples is Gods Unchained: a turn-based, collectible card game similar to Blizzard’s Hearthstone. The difference is that Gods Unchained cards are assets on Ethereum: they can be bought, sold, traded over DEXs, or even someday used as collateral.

In 2018, they sold $4 million worth of cards to the market, and this year they began “activating” those cards to be used in the game. This led to the largest number of transfers of “non-fungible” tokens (ERC-721s) in history:

In November, GU enabled open trading of the cards. Within a few days, more than $220K worth of cards had been traded on OpenSea, a popular marketplace for NFTs.

The other notable gaming launch was PoolTogether.

PoolTogether is a no-loss lottery system, made possible by Ethereum. It’s simple: players deposit DAI into a program running on Ethereum. That pool of money earns interest over time, by automatically lending it out on Compound. Then, at the end of the period, one player wins the earned interest (as of this writing: $688 every week). It is “no loss” because if you don’t win, you haven’t lost the price of your “ticket” since you can just withdraw your funds.

PoolTogether launched in June, and in the next six months its users won a total of $3,594. Over the same six month period, Tezos validators earned transaction fees totalling $3,745.

PoolTogether is especially notable as an indicator of a larger trend: financial applications that look like games, and games that look like finance. There has always been a space where these two realms collide — WoW, EVE Online, casinos, the stock market — but Ethereum lets us collapse the distinction entirely. Expect to see more entrants to this new category in 2020.

Also in 2019:

• Skyweaver, an Ethereum-based card game launched their Beta in June and raised $3.75 million from investors.

• Decentraland, a virtual world where property exists as an asset on Ethereum, readies for its public launch in early 2020.

• Cryptokitties — the first breakout game on Ethereum — maintained its dominance, as Cryptokitties were still the most-transferred NFT on Ethereum.

👹 Decentralized Autonomous Organizations

Since the early days of Ethereum, people have dreamed of creating Decentralized Autonomous Organizations, or DAOs.

The idea is simple: Ethereum lets us write un-censorable code that executes on a distributed, decentralized platform. We could create a program that holds funds, and defines a governance process used to determine how to manage those funds. For instance, by defining a voting process.

Since the painful “The DAO” fork in 2016, the idea has been haunted by a mixed legacy. But in 2019, new projects revived interest in DAOs.

While 2019 did not become the “Year of the DAOs”, it did have at least a few “Months of Moloch”. MolochDAO, which launched in February 2019, is aimed at funding important public goods in the Ethereum ecosystem. Using money donated from several sources (like the Ethereum Foundation, Vitalik, Joe Lubin, and ConsenSys), the members of the MolochDAO vote on which projects to fund.

This simple model ended up being the DAO project that most captured the Ethereum community’s attention, and was echoed by several other efforts like MetaCartel and MarketingDAO. Ross Campbell began an effort to launch “LAOs”, or “Legal DAOs” that interoperate with traditional legal systems, influenced by the MolochDAO framework.

In another notable example of the value of Ethereum’s composable nature, Saint Fame — a “decentralized fashion house” DAO — launched their first product in December.

Saint fame issues tokens on Uniswap, which can later be redeemed for a specific product: a designer t-shirt. Trading the tokens openly on Uniswap lets the price of the t-shirt rise and fall in response to demand. Members of the DAO will vote on how funds are used to create new designs, which can then be sold through the same mechanism.

Saint Fame is built using Aragon — a comprehensive framework and toolkit for DAOs. Aragon itself saw early signs of adoption, and by the end of 2019 more than 900 DAOs had been created by the framework.

2. Ethereum nudges into the mainstream

In September, NBA player Spencer Dinwiddie announced plans to “tokenize” his contract. He would sell 90 Ethereum-based tokens, which would give the holder a portion of Spencer’s future contract value, plus interest. In exchange, he would receive $13.5 million out of his $34 million contract up-front.

The NBA initially opposed the move, claiming it breached Spencer’s contract with the league. But over the next few months, the plan was back on, set to launch in January 2020.

The most astonishing thing about this story is that in 2019, it didn’t seem out of the ordinary. If you had speculated in 2015 that within a few years an NBA player would be actually using Ethereum as a platform for financial innovation, no one would have taken you seriously.

But this year it happened — along with a lot of other small moves towards the mainstream:

• The Sacramento Kings launched a rewards-token built on Ethereum.

• Star Trek announced a series of collectible iconic ships, which will be issued as NFTs on Ethereum.

• Opera, the 6th largest browser in the world, added native support for Ethereum at the end of 2018, and in 2019 launched a desktop browser complete with a “Dapp store”.

• Samsung released a developer platform built for Ethereum, and announced a new phone with a built-in Ethereum wallet.

• Ethereum’s DeFi ecosystem was covered in Bloomberg

• Gridplus — an energy company built on Ethereum — went live in February, and now serves electricity customers in Texas

Meanwhile in the enterprise world, the lines started to blur between “Enterprise Blockchain” and “Mainnet Ethereum”.

Mainstream enterprise corporations have realized that spinning up a private or consortium chain isn’t that different from a siloed, centralized database. But they’ve started to pursue an interest in mainnet Ethereum, which offers an open platform backed with billions of dollars of economic security.

One of the strongest proponents of mainnet for enterprise has been EY, who continued their work on “Nightfall”. Nightfall is a project that aims to enable enterprise to use Ethereum mainnet, while ensuring that the transactions are private and scalable — addressing two of the major concerns that limit enterprise use of Ethereum today. In December, EY launched v3 of Nightfall, bringing the transaction cost down to pennies instead of dollars.

Meanwhile, Microsoft continued its deep involvement with the Ethereum ecosystem. In May, Microsoft released the Azure Blockchain Development Kit, specifically to support Ethereum development. Visual Studio, Microsoft’s popular IDE, now supports Ethereum through an extension, fully integrated with Truffle. In June, they announced VeriSol, a formal verification tool for Ethereum.

In October, Microsoft joined with the Enterprise Ethereum Alliance to work on a tokenized incentive system for use within enterprise consortiums. And in November, Microsoft launched Azure Blockchain Tokens, a service that lets enterprises issue their own tokens onto Ethereum.

Many of these developments — in both consumer and enterprise contexts — might seem small. But together they paint a clear picture: even as general interest in cryptocurrency has slowed and flattened, Ethereum continues to reach outside the cryptocurrency industry, and is seeing early signs of adoption beyond the core community.

Also in 2019:

• HyperLedger Besu becomes first public-chain client in Hyperledger.

• Cloudflare launched an Ethereum Gateway.

• JP Morgan announced plans for their JPM Coin bank-backed cryptocurrency. In May, they added to Quorum an implementation of the Zether confidential payments protocol.

• Banco Santander settled a $20mm bond on mainnet Ethereum (you can see the transactions here).

• Many, many other examples — too many to list here.

3. Ethereum 1.0 performance & sustainability

Every application we covered above — from the hundreds of millions of dollars in DeFi protocols, to trading cards, to enterprise applications — runs on today’s Ethereum protocol and today’s Ethereum clients.

This year, “Ethereum 1.0” and the clients that support it received some of the most significant upgrades since Ethereum’s homestead release in 2016. These changes began to address state growth, client sync times, client disk IO, transaction throughput, and issuance. In 2019, more EIPs were deployed than in any other year.

🏆 Geth

Geth, Ethereum’s dominant client, received major upgrades this year. In July the Geth team released v1.9.0, which included major performance improvements and many new features. This year, the Geth team reduced the time to fast-sync a full node by half to ~ 4 hours, and implemented a 10x reduction in disk IO.

The Geth team deserves extraordinary credit for continuously improving the majority client software powering Ethereum. While this work may not always receive the attention of a new breakout application or area of theoretical research, it is the work that makes Ethereum possible.

Have you thanked your Geth maintainers today?

⚙️ ETH 1.X

At Devcon4 in November 2018, a group of core developers began to talk informally about how to make Ethereum 1.0 more performant in state size, sync times, and disk IO. While the long-term goal is a migration to Eth2, Ethereum must remain sustainable until that time.

In the months after Devcon, this initiative became known as “ETH 1.X”. While many took this as an opportunity to push for a wide variety of ideas on how to change the EVM, fundamentally the core goal has always been sustainability through ideas like state rent, stateless clients, or repricing gas costs.

The results have been major improvements to Ethereum 1.0 across the board.

Ethereum’s maximum throughput increased from ~25 transactions per second, to ~38. This was achieved by increasing the block limit to 10mm gas, while block times were reduced to 13 seconds after the Istanbul hard-fork. EIP-2028 contributed to this improvement as well, by reducing the gas cost of a single byte in a tx input from 68 gas to 16 gas.

Client optimizations to Parity suggested by Alexey Akhunov made it possible to increase the block limit without a corresponding increase in the uncle rate. As a result, uncle rates plummeted in 2019.

Ethereum’s issuance of new ETH fell in 2019. The Constantinople hard-fork reduced issuance per block from 3 ETH to 2 ETH. Further, the decline in uncle rates above also reduced issuance, since uncle blocks win partial rewards.

As a result, Bitcoin and Ethereum now have similar issuance rates, with ETH’s planned issuance rate to drop again when the network is entirely proof of stake:

To address long-term sustainability, 2019 saw major advances in research towards a “stateless” model for Eth1. Other approaches that were considered at the beginning of the year, like state rent, have been de-prioritized.

The goal of the stateless model is to reduce the amount of state-data that must be stored by each node. Using simple techniques like Merkel trees, we can provide a “block witness” to prove that a specific piece of data is in a specific block, without requiring the client to hold that block’s state data. Many nodes will still hold full state, but using these techniques we can allow some nodes to store less data.

One related project is Beam Sync, which uses block witnesses to cut sync time to a matter of minutes, while completing a “full” sync in the background. Beyond that, other types of “semi-stateless” clients will be introduced as work progresses.

4. Eth2 is (almost) here.

Ethereum’s vision has always been a scalable, proof-of-stake blockchain. It has been clear since the early days of cryptocurrency that despite being a technological leap forward, proof of work is deeply flawed.

Even optimistic estimates of Bitcoin’s energy use put it as roughly comparable to the total energy used by countries like Portugal or New Zealand. Migrating to proof of stake will eliminate this wasteful energy use, and allow Ethereum to grow over decades without radically increasing the world’s energy consumption.

While the broad outlines of Ethereum’s migration to proof of stake were clear even in 2015, filling in the details has taken careful, difficult work.

Ethereum’s migration to proof of stake — known as Eth2 — has taken longer than many expected. After multiple years of R&D, 2018 saw the project transition from a research topic to an engineering challenge. In 2019, multiple independent teams worked together to build the software necessary to launch the first phase of Eth2. There is now little doubt that Eth2 will enter production in 2020.

Like any good open-source project, Eth2 is being built in the open. This process can seem messy to those who aren’t familiar with the “bazaar” model of open-source software development, or who are more familiar with cryptocurrencies controlled by a single client team.

Eth2 is a large project that will be rolled out in phases over multiple years. The first phase — Phase 0 — is expected to launch in Q2 2020. This involves launching the Beacon Chain, which serves as the “backbone” of Eth2. Phase 1 then introduces the Shard chains, which are secured by the underlying Beacon chain. In Phase 2, the system comes together into a functional whole. Shard chains become useable for transactions and smart contracts, and all of the core features familiar to users of Ethereum today.

At the beginning of the year, 9 independent teams were inspired to begin work on implementing Phase 0’s beacon chain. By the end of the year, Eth2 is beginning to see mature testnets.

These began in the summer as short-lived private testnets from a single team, and by September multiple clients were able to interact on a shared testnet. Key to this progress was Joe Delong’s push for a weeklong networking lock-in in Skeleton Lake, Ontario, where Eth2 implementer teams achieved interoperability between their clients and established networking standards. By December, this progress had enabled longer-lived public testnets between multiple teams. You can even view these testnets on public block explorers like Etherscan.

As Eth2 came into clearer view, the broader developer community was able to offer feedback — and criticism — that led to adjustments. Specifically, shortly after Devcon, Vitalik published several notes proposing changes to Phase 1 that would reduce the complexity of interaction between shards.

Discussion remains active on the best approaches to implement Phase 1 and Phase 2, as well as how best to migrate the existing Ethereum blockchain into Eth 2. Currently there are proposals being debated that would have Eth1 be the first shard of Eth2, which would take place between Phases 1 & 2. There is also active debate around finalizing the current Eth1 proof-of-work chain with Eth2´s beacon chain, which would enable a much earlier reduction of ETH issuance.

Meanwhile, Phase 1’s data availability will allow Layer 2 solutions like rollups and state channels to blossom. For example, the potential throughput of rollups (which can already facilitate 2,000–3,000 tps on Eth1) will increase by about 100x.

Eth2 is coming — get ready to stake in 2020.

5. Layer 2 and off-chain tech

The idea behind all Layer 2 technology is that we can perform expensive computation “off chain”, while still retaining Ethereum’s characteristic security guarantees. This “second layer” can process transactions or computation much faster than Ethereum mainchain, leading to a more scalable network overall.

In late 2018, Barry Whitehat, proposed ZK rollup. The basic idea is that we perform many transactions *off-chain, *and “bundle” them together. This “rolled up” group of transactions are then verified using succinct Zero-Knowledge Proofs (SNARKs), which confirms that each transaction is correctly signed by owners, preventing any invalid or manipulated transactions.

In early 2019, multiple streams of work adapted this idea in a new direction. In June, John Adler & Mikerah published their work on “Minimal Viable Merged Consensus”, and in parallel Plasma Group published their work on the “Optimistic Virtual Machine” in July.

Eventually, the research community settled on “Optimistic rollups” to describe this category of technique, which bore similarities to an idea Vitalik initially called “shadowchains” in a 2014 blog post. Vitalik helpfully summed up this area of research in an August blogpost.

Optimistic rollups use a similar technique of “bundling” transactions as ZK rollups, but use a different mechanism to “prove” them. Instead of using SNARKs, Optimistic Rollups use a cryptoeconomic mechanism that lets the system “Optimistically” assume there are no invalid transactions, while still catching, preventing, and punishing (slashing a deposit) those who try.

In October, Plasma Group shipped a demo that used an Optimistic Rollup to deliver a lightning-fast version of Uniswap at Devcon called Unipig.

ZK Rollup and Optimistic Rollup have different advantages and disadvantages. Optimistic rollups are easier to implement in the near-term, and flexible enough to be used with different applications. ZK Rollup, however, has more potential in the long term, but is more specialized due to its use of zero knowledge proofs, and will require more R&D before it is useful for a wide variety of applications.

Meanwhile, State Channels entered into a far less sexy, but no less important, phase of their development. With no outstanding research problems, several teams worked towards implementing a viable framework that applications could use to support channelized apps.

In July, the primary state channels teams met at ETHNewYork to discuss unifying standards and ensuring interoperability. This led to the announcement of a single, unified specification, simply called State Channels. Counterfactual and Magmo merged their engineering teams, replacing their own brands with the new joint project, and continued to make engineering progress.

State channels projects launched onto mainnet this year. In March, Connext — a micropayments platform — launched the Dai Card, a simple browser-based payment system running built on channels. In September, Connext launched v2.0 of their platform onto mainnet, built on top of the now-unified State Channels codebase.

In July, Celer launched their mainnet alpha. By October, CelerX mobile app was supporting 60k monthly active users.

Adex, with a custom implementation of their own payment channels framework, quietly built a significant payment channels network, settling more than 9 million transactions in a 2 month period over the summer.

Starkdex — a project between Starkware and 0x — launched a PoC in June. In October, they released OpenZKP, an open source rust implementation of ZKPs. In October, Starkware announced their plans to launch the first STARK powered Dex in collaboration with Deversifi, in early 2020.

What did it all mean?

This post isn’t comprehensive. How could it be? There’s too much happening on Ethereum to keep track of, even when you write a weekly newsletter about it.

A few other items that deserve note:

• Smart wallets launched and gained traction. Smart wallets use smart contracts to implement access and control logic, making the wallet more useful. For instance, this enables multi-factor authentication, “batching” of transactions for ease of use, and better recovery methods in case of lost keys. Three notable entrants in this category are Argent, Dharma, and Gnosis Safe.

• Big ethereum wallets continued to get bigger. Metamask exceeded 1 million installs on the Chrome store, and launched a mobile version. Brave, an Ethereum-based browser and wallet, surpassed 10 million monthly active users, and reached more than 350,000 “publishers” for its micropayment platform as of December.

• Prediction markets made steady progress. Augur doubled its open interest from $1.3 million to $2.7 million, Gnosis’ Sight prediction market launched into Alpha, and Numerai’s Erasure protocol went live on mainnet.

• Ethereum Name Service (ENS) launched the new permanent registrar, made ENS names NFT compatible, and saw users pay $350K in fees across its two registrar contracts. That’s roughly equivalent to the total fees paid to use Dash, Monero, NEO, Bitcoin Cash, and Bitcoin SV combined in 2019.

• The Ethereum community expanded its ability to fund public goods. MolochDAO launched, Gitcoin Grants captured the community’s attention, and the Ethereum Foundation’s grant program was reborn as Ecosystem Support.

• Ethereum.org got a reboot, and the Ethereum community has translated it into 20 languages, while the site has more than 100 contributors

• Oh, and ETH is money.

If you only pay attention to generic crypto news, you might think 2019 was a disappointing year. The market drifted sideways, new basechain protocol launches uniformly disappointed, and the hyped-up blockchain headlines disappeared from the news. It might seem like crypto is dying.

But if you’ve read this far, it should be easy to see why the Ethereum community hasn’t shared this sentiment.

In 2019, it’s clear that there’s enough real activity at the application layer to give us some confidence that Ethereum is growing in the right directions. The work being done at the protocol level means that Eth1 can be sustainable in the medium-term, and steady tangible progress on Eth 2 builds confidence that Ethereum could someday scale to billions of users. It’s been a harsh winter, but Ethereum kept on building straight through it.

Crypto is dead? Long live Ethereum.

— Josh Stark (ETHGlobal, L4, Ethereum Foundation) & Evan Van Ness (Venture Partner at ConsenSys Labs, Week in Ethereum News)

Thank you to Jinglan Wang, Danny Ryan, Hayden Adams, Alex Xu, Hugh Karp, David Hoffman, James Hancock, Alexey Akhunov, and many others for their contribution to this piece.

Two partners at Union Square Ventures recently published separate blogs critical of Ethereum: “Is Ethereum the AOL of Crypto?” by Albert Wenger, and “Some Thoughts on Crypto” by Fred Wilson.

I’m fond of saying that “Ethereum needs better critics”. Hopefully Albert and Fred continue being critics, and through debate with the Ethereum community, become better ones over time. A positive trait in the Ethereum community is our interest in deliberately seeking out flaws, exposing them, and fixing them.

The explicit goal of this short post is to bait Albert and Fred into being better critics. Expand on your arguments, tell us (beyond generalities) what you think Ethereum is doing wrong, and what others are doing better.

Ethereum isn’t perfect. If it was perfect, what would there be to do? Ethereum has many flaws, but it has incredible potential. This means there are many interesting problems to solve, and that solving those problems matters.

“Is Ethereum the AOL of Crypto?” by Albert Wenger

The gist of Albert’s post is that one area of activity on Ethereum, Decentralized Finance (or “Open Finance” if you prefer) may be circularly dependent on a small segment of the Ethereum community for both its funding and its users. There are two arguments there, which I’ll do my best to steelman:

1. Is DeFi dependent on Ethereum for funding?

Albert’s first argument goes like this: DeFi has been a success for Ethereum so far. However, if it is the case that most DeFi companies are being financed “by Ethereum”, then it may create a circular dependency that could be undone by some adverse event, similar to how the dot-com bust undid AOL. For instance, if most DeFi companies are being funded by large ETH holders selling ETH, and those companies were to fail, this could lower the price of ETH. This in turn would mean there is less funding available for new DeFi companies, meaning less success, meaning a lower ETH price, and on and on.

The obvious question is simply: is it true that a lot of DeFi is “financed via Ethereum”?

Albert gives two examples of what he means by this:

• When a DeFi project has its own token. I assume the point here is that a project with its own token is financially dependent on the price of that token, which we assume might be correlated with the price of ETH. Such a project might also simply hold a lot of ETH.

• When a DeFi project is financially supported by organizations or people with large holdings of ETH (e.g. ConsenSys, which he calls out specifically).

Surveying the DeFi industry, Albert’s claim seems mostly false. Most of the top DeFi projects (1) do not have their own token, and (2) have raised (and continue to raise) outside VC capital.

To use one metric, out of the 10 top DeFi projects listed on DeFi Pulse:

• 4 have their own token (MakerDAO, Synthetix, Bancor, Kyber)

• 8 have raised VC money (MakerDAO, Compound, Synthetix, dYdX, Uniswap, Set, Dharma, wBTC (via Kyber), Bancor, and Kyber)

• 1 raised money from ConsenSys (Nuo)

This doesn’t include other stablecoins like USDT, USDC, Gemini, and others — all funded by VC and large exchanges.

It doesn’t seem true that DeFi is being propped up by large ETH holders. What companies or projects is Albert thinking of when he writes this critique, if not the ones listed above?

2. Is DeFi dependent on a small circular economy of users?

Separate from the AOL analogy, Albert is getting at a deeper question: is it dangerous that so much of DeFi is “self-referential” within Ethereum?

Traders who want leverage are the primary consumers of lending products, borrowing from ETH holders. Financial services and products (derivatives, trading services, etc) let users interact with or have exposure to a variety of ETH based assets, and some of those assets have little use outside of speculation.

Whether you view the above description as a strike against DeFi probably depends on your frame of reference. On the one hand, it’s true that the lofty vision of a global alternative financial system that banks the unbanked and provides accessible financial services to the world is far from reality.

On the other hand, what should we expect? Ethereum launched 5 years ago, Dai has existed for under 2 years, and DeFi as a proper noun is about 1 year old. Shouldn’t we expect an emergent financial system to begin as a very local phenomenon, while basic infrastructure and services are laid down, before expanding its scope? Even then, isn’t some amount of self-reference a good thing, since it creates demand for ETH?

It’s not even really true that DeFi is entirely walled off within Ethereum: see for instance efforts to integrate with Bitcoin like wBTC and tBTC, or the many companies working to bridge Ethereum to the legacy financial system (here are three examples just from the last month). DeFi’s integration with other ecosystems is just as true at the social level: Bitfinex, a company whose culture is very Bitcoin-centric, used Ethereum for their token sale.

“Some Thoughts on Crypto” by Fred Wilson

Fred’s piece is a bit lighter, and has only a few remarks about Ethereum.

Fred’s claims are simple: Ethereum is hard to build on, it’s hitting scaling challenges, and “many developers are looking elsewhere.”

Yes, Ethereum’s developer experience still needs improvement. This has been, and remains, a large priority for the community. But while development on Ethereum is frustrating relative to established areas of software development, it is dominant over every other smart contract platform. Don’t take my word for it — ask projects who have tried to build on other chains or incubators who target blockchain startups.

Yes, some developers are looking at other platforms. They should! It would be irresponsible for any entrepreneur or developer not to kick the tires and figure out the best stack for their needs. Especially when many (not yet launched) chains are making incredible claims about their (theoretical) performance.

But so far, when developers are voting with their money and time, the vast majority of them are still choosing Ethereum:

In terms of distribution, Ethereum remains the IBM of our ecosystem. As more competing chains come online and begin spending their warchests, the Ethereum ecosystem might bleed a few developers. The difference between Ethereum and its competitors is that Ethereum actually has developers at risk of leaving.

The important question isn’t whether some developers build on other platforms, but whether any other chain can displace Ethereum as the de-facto smart contract platform used by the majority of the industry. Does Fred think this is likely? If so, why specifically?

As for scaling challenges — Ethereum is absolutely running up against these — an unfortunate side effect of having developers and users on your platform! Your view on whether those challenges are insurmountable will depend on your view of the adoption of layer 2 scaling tech and progress on ETH 2.0, topics too large to cover here.

The reason Fred’s piece touched a nerve isn’t these points by themselves, which are relatively obvious and well-understood concerns. What set people off was the strange contrast between his disappointment in Ethereum and his other comments.

For instance, his very next point in the blog post is that “Stablecoins […] are a bright spot”. On twitter, I pointed out that virtually every stablecoin with any use is an ERC-20, which seems at odds with his dim view of Ethereum. Fred clarified that his concern is that they might “leave” Ethereum.

Many stablecoins probably will launch alt-versions of themselves on other chains eventually, but it’s hard to imagine why a project would abandon all of the capital, users, infrastructure, services, and liquidity available to their ERC-20s. Until there is any evidence that developers or users are abandoning Ethereum, isn’t the success of stablecoins — virtually all built on Ethereum — a point in its favour?

I am curious what specific weaknesses of Ethereum and *specific *strengths of its imitators motivated Fred’s post. But it’s hard to know what to make of his conclusions right now.

If Ethereum is doing everything wrong, why is the entire crypto industry building on it? Between Fred publishing his post, and my sitting down to draft this one, Binance announced plans to launch a USD stablecoin, and Paxos announced a gold-backed token.

Both will be built on Ethereum.

Note: I am (obviously) biased towards Ethereum. I co-founded and operate two Ethereum based companies: L4 (open source state channels) and ETHGlobal (largest dev relations & on-boarding org in the industry), and I work with the Ethereum Foundation on a few special projects.

Thanks to Eric Conner, Ryan Sean Adams, Evan Van Ness, and others for their feedback on this post.

Ethereum began as a bold experiment. Can we build a universal platform for digital money and assets, un-censorable applications, and decentralized organizations?

We started with a slightly smaller experiment: was it possible to launch a blockchain that could execute arbitrary programs? Over time, the Ethereum community began new experiments. Would developers find it interesting? What applications are actually useful? The community learned from its successes and failures, and iterated on their work. New people joined the community and started running their own experiments.

In 2018, the Ethereum community ran more experiments than ever before. What did we learn? This summary of the year in Ethereum is our attempt to identify the most important developments — the things that we’ll say mattered, 10 years out.

These developments took place at every level of the Ethereum “stack”. This includes the core protocol and the clients that implement it, commonly referred to as “Layer 1”. It includes the supporting developer tools & infrastructure, that make engineering on Ethereum possible. It includes “off chain” technologies, that let developers build fast and performant applications. And it includes the products and businesses built on Ethereum.

Keeping track of everything in this ecosystem is becoming more and more difficult every week. There are competing implementations of many infrastructure technologies — including two clients with ~50% (Geth) and ~40% (Parity) of the network. There are multiple competing off-chain technology stacks being built, multiple ETH 2.0 clients under development, and most markets have multiple competing businesses. It feels messy and chaotic — the Ethereum ecosystem is a bazaar, not a cathedral. While this can make it hard to follow, it’s a credit to the community: we’re too big to measure with simple tools.

Our goal is to help you sort through that noise and see the bigger picture. In our view, here are the most important developments in 2018:

• 1. More people started using Ethereum, for more things — but we’re still far from mass adoption.

• 2. Decentralized Finance (DeFi) and stablecoins — a new class of applications that saw many product launches and some breakout successes.

• 3. The year of #BUIDL — it became radically easier to build applications on Ethereum. Our development and security tooling improved significantly, we got better at sharing best practices, and hackathons became a trend.

• 4. Layer 2 scaling — multiple “layer 2” applications launched, and we made critical progress towards making these scaling solutions easy to use for developers.

• 5. Zero knowledge technology — this year it felt like every technical conversation in Ethereum was “We can do it this way right now, but of course once we have good zkSTARKs…”

• 6. ETH 2.0 / Serenity — the roadmap solidified, and it moved from a research project to an engineering effort.

1. Did adoption of Ethereum grow in 2018?

From 2015–2017, it was an open question whether there would ever be any demand for Ethereum. Today, that answer feels settled: since late 2017, the Ethereum blockchain has continued to be used near maximum capacity:

This graph shows the utilization of the Ethereum blockchain at different points in time. Specifically, it is a measure of the total gas used divided by the gas limit. When the line approaches 1, it means the Ethereum blockchain is being used at nearly 100% capacity.

Yes, the graph above is encouraging — people are paying to use the Ethereum blockchain. But it means we started asking harder questions: we’re near 100% capacity even with such a small user base? What would happen to fees if we on-boarded millions of users, when the total capacity of the network is so low? How many people actually use Ethereum? What are they using it for? And what are the right metrics to measure growth?

Measuring “use” of Ethereum isn’t easy. For instance, we can get a basic picture using raw on-chain statistics, like the number of transactions on the network:

But this doesn’t tell the whole story. If Ethereum was being used near full capacity, how can the number of transactions be declining? The composition of transactions on Ethereum shifted from a large number of simple ones, to a smaller number of complex ones. For instance, token transfers (~50K gas) or opening up a MakerDAO CDP (up to 900K gas) both “take up” more of the network’s capacity than a simple ETH transfer (21K gas).

As Ethereum’s app layer continues to grow, we should expect the transaction count to decline while the chain operates at full capacity. And as more activity moves into side chains, state channels, or plasma chains (see Layer 2, below), measuring on-chain transactions increasingly only tell part of the story.

What are people using the network for?

Many significant and long-awaited applications went live in 2018, and appear to be attracting users.

Dai, the stablecoin launched by MakerDAO, has been live since late 2017, and the total supply of Dai grew to 69m as of December 31st, 2018.

Users of MakerDAO have locked more than 1.7% of all ether into smart-contracts that serve as collateral for the Dai stablecoin. As of December 31st, the value of that ether was over $275 million. We discuss MakerDAO, and other “decentralized finance” applications, in detail in the next section.

Augur, a decentralized prediction market under development since 2015, launched in July 2018 and has seen open interest (the amount of value currently “bet” on the system) grow to a high over over $2.96m in November 2018. However, the total number of users has remained low.

Spankchain, a payment channels service for the adult entertainment industry, launched in April 2018 and processed $70,000 in payments to performers between April and December 2018.

Many more applications launched in 2018, including many in the “decentralized finance” category. Many others were games like Gods Unchained (beta, November 2018), or gambling services like FunFair (mainnet, September 2018). There are hundreds of other applications that use the Ethereum blockchain, which you can find listed here or here.

Overall, consumer use of applications on Ethereum remains low. When we use measurements like daily active users who interact with a dapp’s smart-contract on the blockchain, we see an average of between 10,000–15,000 users on any given day in 2018.

However, note that this is a measurement of on-chain transactions and does not include, for example, someone opening an app and browsing their collectibles, or opening Veil (alpha, September 2018, mainnet January 15 2019) to view their open predictions on Augur.

User adoption of new technology moves in phases. Users come to applications, creating demand for better infrastructure, and applications then build on that infrastructure to meet the expectations of users. We can analogize from web development that there is a reinforcing loop of app and infrastructure development.

By the end of 2017, we had learned that there was demand from people who wanted to build applications on Ethereum. In 2018, the community shipped apps that were useful without scaling upgrades, and built infrastructure that will make it possible for the next wave of applications to work at a larger scale.

What should we be measuring, anyways?

Is “daily active on-chain transactions” the right measurement to quantify user adoption? This year, people started thinking (and tweeting) about what metrics we should use to gauge Ethereum’s success.

The answer, surely, is that it depends on what counts as success. Some businesses require large numbers of users (like consumer apps or games), while others seek high volumes of value (like some financial services).

As layer 2 scaling technologies are adopted, more user activity will move “off-chain” where it is more difficult to measure. This already influences the data used to measure Ethereum’s adoption. For instance, DappRadar does not currently include statistics on games that use Loom’s Dappchains, nor does it list activity in Spankchain’s payment channels.

But this isn’t a bug, it’s a feature. We want to build web 3 — an internet that, among other things, respects user’s privacy instead of spying on them. That means giving users the option to keep their business to themselves, off-chain, where it isn’t easy to measure and incorporate into statistics.

2. The Year of DeFi

This year we talked about a new narrative within the larger set of Ethereum applications. Many projects launched which were explicitly financial — applications or protocols that give users new tools with which to manage and use Ethereum-based money or assets. As a group, these became known as “Decentralized Finance” or “DeFi”.

There are now companies building out a stack of financial primitives — the basic building blocks of a financial system. While these tools are in very early stages, it is possible today to use Ethereum-based protocols to take out loans, to lend money and earn a return, to buy bundles of assets, to hedge your risk, to trade assets trustlessly, and to make payments for zero fees. Because these systems are largely open and interoperable, it is becoming possible to combine them in useful ways, building applications that can borrow, lend, and invest simply by calling an API.

Within this category there are stablecoins (Dai, mainnet December 2017), lending tools (Dharma, mainnet May 2018, Marble, mainnet beta July 2018), margin trading and derivatives products (Daxia, mainnet January 2018, dYdX, mainnet October 2018, bZx, mainnet September 2018, Market Protocol, testnet November 2018, UMA, under development), bundled investment products (Set Protocol, mainnet June 2018), money market protocols (Compound, mainnet September 2018), credit default swaps (CDx, under development), token swap services (Kyber Network, mainnet February 2018), subscription payment services (8x, testnet October 2018), payment channel hubs (Connext, mainnet September 2018), and prediction markets (Augur, mainnet July 2018, Gnosis PM, alpha December 2017).

Over the last year, the share of ETH “locked up” (e.g. used as collateral) in the smart-contracts of some DeFi applications has grown:

The above graph hides MakerDAO by default, in order to let us see other applications. If we include MakerDAO, the graph looks like this:

The most successful DeFi protocol — and the most successful application on Ethereum in 2018 — is MakerDAO.

Over the last year Dai survived a 94% fall in the value of its underlying collateral. The system was battle-tested in the first few months of its launch, and appears to have functioned as intended. It has quickly became a core piece of infrastructure for many Ethereum applications.

Within the community of people that actually use Ethereum apps on a regular basis, it’s hard to overstate the impact of having an easy to use, decentralized stablecoin. If you work in this ecosystem, you will remember that 12 months ago you probably sometimes paid, and were paid, in ETH. Today, everyone uses Dai — for contract payments, for event sponsorships, for petty cash.

MakerDAO is not just Dai, though that is the most outward-facing product. It also includes a system of “Collateralized Debt Positions” (CDPs), which allows anyone to lock up ETH as collateral and receive a “loan” in DAI. This system facilitates the collateral backing that makes Dai possible, while also being a lending product in its own right that can be used for, among other things, leveraged trading.

Dai is not the only stablecoin built on Ethereum — though it is the only one of significant size that is “decentralized” in the sense that it is backed by digital assets in an automated collateral system, rather than off-chain assets like dollars held in fiat bank accounts. Other Ethereum-based stablecoins include TrueUSD (mainnet March 2018), Paxos (mainnet October 2018), Gemini Dollar (mainnet October 2018), USD Coin (mainnet October 2018), and sUSD (mainnet June 2018).

Collectively, all stablecoins built on Ethereum had a marketcap of ~770 million at the end of the year, large enough that as a group they would have ranked as the 14th largest cryptocurrency on Dec 31.

While significant and growing, this volume is still dwarfed by trading volume in Tether, which during the same period saw a daily average of around $5 billion USD.

Within the broader category of DeFi, decentralized exchanges (DEXs) are the next most significant. In 2018, this ecosystem grew and matured. There are not just multiple competing DEXs, but multiple types of DEXs now live in production. However, volume remains low compared to any centralized exchange.

There are now several DEXs that use the 0x protocol. Radar Relay launched their beta in August 2017, raised a series A in July 2018, and released v2 of their product a few months ago in September. Paradex launched October 2017, and was acquired by Coinbase in May 2018. DDEX launched their open beta on January 9 2018, became the leading 0x relayer by volume, and recently announced that they are forking the 0x protocol to create a competing protocol called Hydro.

This year saw an expansion of the type of DEXs built on Ethereum. Kyber, which launched in March 2018, does away with the order book and simply lets users receive a quote, and instantly swap one asset for another. Airswap, which launched in April 2018, similarly provides a simple “token swap” service. Uniswap, which implements a novel automated market-making feature inspired by a reddit post from a few years back, launched in November 2018. It operates entirely on-chain, and makes markets itself using a deterministic algorithm. Gnosis’ DutchX protocol went live on mainnet in October 2018, with a user-interface to the protocol called slow.trade that went live in December. StarkWare started working on zero-knowledge technology that will help scale DEXs and is expecting to launch on testnet in Q1 2019.

Why did DeFi take off in 2018? One reason is that many of these applications are useful today, even before critical scaling technologies are in place. Basic financial use-cases like lending and borrowing do not require high transaction throughput — they simply require a secure programmable base layer blockchain. Ethereum’s simplest use-case is the creation, exchange, and use of digital assets like ETH. One way of looking at DeFi is that it is simply building the basic financial infrastructure to use those digital assets.

3. The year of BUIDL — better tools, better frameworks, more hacking

2018 was the year of #BUIDLing. This was the year that it became radically easier to get started building applications on Ethereum.

Developer tools improved, new security tools were released, key frameworks were released, and hackathons became a fixture in the community. In 2018, the vision of average developers being able to build something useful on Ethereum became a reality, and the tooling necessary to use smart contracts in production improved.

We even gained a new meme to go along with the technical progress: BUIDL. While the term — an inversion of the bitcoin meme “HODL” — had been used by different people over the years, it never gained any real traction until ETHDenver in February 2018. In the weeks following, it became an unofficial mantra for the Ethereum community — a reaction against the unhealthy focus on price and speculation common across the crypto industry.

Security tooling

At the end of 2017, security tooling & best practices were on everyone’s mind. Multiple high profile hacks and security failures forced the Ethereum community to improve best practices, and invest more resources in security audits and tooling.

In 2018, the Ethereum security community improved. New security tools became available that made it easier to build secure applications. Trail of Bits released several tools in March 2018 (who also maintain a useful list of resources here), including static analysis tools, fuzzing tools, and more. Securify, an automated security scanner for Ethereum smart contracts, was released in July 2018. Mythril, a security analysis tool initially released in 2017, became a platform and re-branded to MythX.

The Ethereum security community made progress towards “best practices”, though the community doesn’t always agree on all of them. Valuable resources like the Smart Contract Weakness registry helped the industry share best practices and common anti-patterns. Notable “traditional” security researchers began working in the Ethereum space, including Trail of Bits and Sigma Prime, adding to the stable of high-quality auditing firms already working in the space.

Despite this progress, there’s still significant work required. In particular better formal verification frameworks and tooling, which is a consistent complaint from developers building on Ethereum.

Infrastructure

Ethereum’s primary clients — Geth and Parity — continued to be improved and refined, thanks to essential work by their development teams. New clients were released, like Pantheon in Java and Nethermind in .NET Core.

There has long been an understanding that Ethereum needs to diversify the node infrastructure available to application developers. This market has long been dominated by Infura, but in 2018 many teams began working on alternatives.

Dappnode, a project to make it cheap & easy to run a personal Ethereum node, launched in July (you can even buy a pre-configured one). VIP node, a service that allows users to “subscribe” to node access thereby creating an incentive for more full nodes, went live this year. Denode, a project that similarly seeks to provide market incentives for more decentralized node infrastructure, received a grant from the Ethereum Foundation in September. Other projects — like LightJS by Parity, released in November — make it easier for developers to build dapps that don’t need to rely on full nodes.

Decentralized storage solutions like IPFS and Swarm continued to make progress. Swarm POC3 was released in June, and now includes a messaging layer. The Ethereum Name Service (ENS), a decentralized service that lets people use human-readable names (like alice.eth) in place of Ethereum addresses, launched a mainnet integration with the .xyz domain registry (September 2018), and announced a planned integration with .luxe.

Developer collaboration across the ecosystem improved

The global community of Ethereum researchers & developers got better at working with each other in 2018. The primary forum for cryptoeconomic research on Ethereum — ethresear.ch — only launched in August 2017, and was not widely used until early 2018. Today it is the de-facto R&D hub for Ethereum, and an essential technical resource for everything from plasma to sharding.

The very first Plasma researcher’s call didn’t happen until January 2018, and the first state channels researcher calls weren’t until August 2018. There are now many public calls related to Ethereum development, ranging from core development of the protocol, to layer 2 tech, to individual areas like curation markets or product management.

The ETHSecurity community formed in mid 2018 to try and share best practices and shared learnings. The fellowship of Ethereum magicians — a community of Ethereum developers aimed at producing better EIPs and improving Ethereum’s technical maintenance — launched in early 2018.

Gitcoin, a project that facilitates bounties for open-source development, launched their pilot in November 2017. In 2018, their platform was used to pay out ~$500,000 in bounties and grants to more than 700 developers.

Hackathons became a big deal

In October 2017 ETHWaterloo set a record as the largest Ethereum hackathon ever, which was quickly beaten by ETHDenver in February 2018. Over the rest of the year, there were 6 more ETHGlobal hackathons that served more than 5,800 developers, as well as other events like ETHMemphis and two hackathons held by Status.

In 2018 it was finally practical to run an Ethereum hackathon — there were enough developers who wanted to learn how to build on this tech, the ecosystem was varied enough that they had many interesting projects to work on, and the tools were mature enough that it was actually feasible to build a working demo in 36 hours. Many of the individual projects mentioned above — including the Goerli testnet, Set protocol, Denode, and Cryptokitties were conceived or launched at ETHGlobal events.

4. Layer 2: research, development, and live on mainnet

One of the early narratives about 2018 is that it would be the year of Ethereum’s layer 2 scalability solutions.

The idea behind layer 2 scalability is to off-load computation from Ethereum onto “off chain” systems, while still retaining a blockchain’s characteristic security guarantees. These off-chain systems can process transactions faster and more efficiently than the Ethereum main-chain, leading to more scalable payments or smart-contracts.

In 2017, there were no significant state channel or plasma chain projects live on mainnet, and few people understood the technology or its potential. What happened in 2018?

State & Payment Channels

State channels are the most basic layer 2 technique. At the start of 2018, there were several custom-built channels applications still under development. Today, many of those projects have launched to main-chain, and critical infrastructure has been built that will soon radically shorten the development cycle for channelized solutions.

Spankchain (micropayments through payment channels) launched their beta in April, and have been live in production ever since. Funfair (casino games running in state channels) launched to mainnet in September. Connext (payment channel hubs for micropayments) launched their first non-custodial hub on mainnet in September in collaboration with Spankchain. Celer Network (a state channel network & liquidity solution) launched their testnet & demo apps in October. Raiden, the long-anticipated ERC20 payment channel network, launched their alpha release running live on mainnet in December.

The number of live projects using channels will only increase as the technology becomes easier for developers to use. Counterfactual (a framework that makes it easier to build a channelized application) published their work on generalized state channels in June, open-sourced all their code in November, and is launching a full demo environment in January 2019. Magmo, a framework for a specific subset of channelizable applications (“force move games”) using state channels, released a demo application at DevconIV.

Plasma

Plasma is a scaling technique where operations are moved off-chain into a secondary blockchain, where they can be performed faster and at lower cost.

The idea is based on “sidechains”, originally a proposal to scale bitcoin dating back to 2014. Plasma introduced a novel improvement: unlike on a sidechain, a user of a Plasma chain would always have a guarantee that they can withdraw their assets to main-chain, even if the operator of that Plasma chain tried to censor or steal from them.

Plasma research has made large strides since the paper release in August 2017, though the technology remains further from production than state channels. The year began with only a few teams actively working on Plasma, as the research community began to explore various tradeoff and design choices within a family of related techniques derived from the original paper.

The majority of these designs have focused on the simplest use case: payments. These designs include Plasma MVP (introduced by Vitalik in January 2018) and Plasma Cash (introduced by Vitalik & Karl in March, and the subject of continuing formal work). More recently, researchers have begun exploring zero-knowledge-proof based Plasma-like designs like the “Rollup” (introduced by Barry Whitehat in September).

Each of these can be augmented to mitigate their shortcomings, while minimizing tradeoffs. This unfortunately resulted in a “naming meme”, where each new tweak on an existing design was given a unique name, leading to significant confusion for anyone not deeply involved in the research community. Useful taxonomies that divide up the design space are a work in progress.

At the same time, research continued into expanding Plasma beyond payments. While this work continues, current consensus among researchers is that an optimized “full EVM” plasma (which could run any smart contract) is a complex challenge.

This broad exploration of a large design space has been productive for researchers, but practical implementations are still mostly theoretical or in early stages. One exception is the Plasma Cash implementation built by Loom, released in June 2018.

5. Zero Knowledge is coming

Over the past year the Ethereum developer community began to appreciate that new zero-knowledge technology will have a significant impact on blockchain technologies. Over the past 12 months, it has felt like every technical conversation in the Ethereum community takes the form of “well, we can do it this way for now, but of course once we have good zkSTARKs, it will be like this…”.

Most people in the crypto industry will have heard of zero-knowledge tech, most famously used in the privacy cryptocurrency Zcash. But zero-knowledge technology won’t just be used for privacy. It has important implications for many scalability techniques as well. Recent research and development into this technology (specifically, a class of zero-knowledge tech called zkSTARKs) may dramatically lower the computational cost required to use them in production, opening up new opportunities to integrate them with programmable blockchains like Ethereum.

In short, zero-knowledge proofs let us prove that some operation happened, without having to share the underlying data. If the verification of that proof can be done cheaply enough, then it could let Ethereum smart-contracts verify that an operation took place off-chain. This means that we could, for instance, conduct large numbers of operations off-chain, and then cheaply verify that they happened. Or, we could conduct intensive computation off-chain, and still have it verified on-chain.

2018 was the year that the full potential of zero-knowledge tech started to sink in. In January, Eli Ben-Sasson & his co-authors published their long-awaited paper on zkSTARKs. The Ethereum community began to work on how this technology can be used for scaling and in conjunction with other technologies, like Plasma. On the layer 1 side, developers made plans to ensure that ETH 2.0 has the requisite support for zkSTARKs, like STARK-friendly hash functions.

New zkSNARK libraries were released, like iden3’s snarkjs and circom, adding to existing libraries like Zokrates. In December 2018 a team at ETHSingapore built a zkSNARK “rollup” scaling proof of concept, later released on testnet as the (not technically Plasma) Plasma Ignis. BarryWhiteHat contributed critical work on using zkSNARKs on Ethereum. And Ben-Sasson & others launched Starkware, a company aimed at commercial applications of zkSTARKs, and received a $4mm grant from the Ethereum Foundation.

6. The Road to ETH 2.0

ETH 2.0 is the name for the long-term research and development of the Ethereum platform, incorporating fundamental base-layer upgrades like Proof of Stake and Sharding.

The history of ETH 2.0, aka Serenity, has featured false starts, dead ends and more false starts. But in 2018, the long-term Ethereum roadmap began to solidify.

In January 2018, the FFG testnet launched, though it suffered from networking issues that made it difficult to use. A few months later however, the research direction moved away from FFG, and towards a plan that would see Casper and Sharding implemented together. In Q2, consensus began to form around what is now the current plan.

Explaining ETH 2.0 is beyond the scope of this post. If you want to get caught up, we recommend EthHub’s summary here, Vitalik’s talk from DevconIV, or James Prestwich’s recent guide.

Once the research vision was clear, it was possible to create a specification for what became known as “ETH 2.0”. This allowed many different engineering teams to begin implementing that specification into client software. At the end of 2018, there were at least 8 teams building clients for ETH 2.0. Recently, Ben Edgington also began a weekly newsletter which closely tracks the research and implementation of ETH 2.0.

While all roadmaps are subject to change and projections are uncertain, the beacon chain is expected to go live in 2019, with a beacon chain testnet scheduled to happen in the next few months. The beacon chain will allow ETH holders to choose to transfer their ETH to the beacon chain in order to earn rewards as a validator. However, that ETH cannot be transferred back to the “ETH 1” chain.

The next phase will include shards, which will be managed by the beacon chain. It’s also possible that the beacon chain will be used to finalize the current proof of work chain, somewhat similar to the way that FFG was once planned a year ago to be used for finalization.

While the roadmap has considerably firmed up, there are still unsolved problems in blockchain sharding. While the first few phases are relatively clear and there are no significant unsolved theoretical problems left, plenty of interesting research and implementation problems remain for future phases so that we get to a truly scalable layer 1 of Ethereum.

What did it all mean?

We warned you this post would be long. But it’s still not comprehensive. A lot more happened in the Ethereum ecosystem this year, including a few developments worth noting quickly here:

• Ethereum core developers came to rough consensus on a set of short-term upgrades to the current Ethereum protocol (“Ethereum 1.X”), while ETH 2.0 is under development

• Regulators across the world started paying attention to cryptocurrency, including securities regulators. Many jurisdictions are now in the process of deciding how digital assets, like the kind that can be created on Ethereum, are treated under law.

• Non-plasma sidechain technology, like POA network and Parity-bridge, launched into production

• UX made progress, like Universal Logins and meta transactions

• The Ethereum Foundation launched a grants program to fund critical work from across the community.

Should we view this year as a success, or a setback?

A strange thing about this moment in Ethereum’s history is that you will receive wildly different answers depending on someone’s frame of reference.

If your baseline is 2015–2016, you remember when Ethereum was still firmly an experiment, with virtually no users, developer tools, or even applications. The contrast with 2018 is startling. There are real applications now, live on mainnet, that provide real utility to their users — even if those user bases remain small. The thing we believed could happen, which may have once seemed impossible, is starting to happen, in bits and pieces.

But if your frame of reference is the hyped up narrative sold to you by ICO whitepapers and glossy keynote conferences, then it must be disappointing. Mass adoption not only has not yet arrived, but remains over the horizon. There are hard problems yet to be solved, and the technical progress zigs and zags, instead of following the comforting straight line of a tidy infographic roadmap.

Welcome to the real. There are experiments to be run, lessons to be learned, and hard problems to solve. Grab a shovel, and see you next year.

— Josh Stark (L4), Evan Van Ness (Week in Ethereum, ConsenSys), and Daniel Zakrisson (hardfork.se)

Thanks to Georgios Konstantopoulos, Jeff Coleman, Spencer Noon, Alex Wade, Xuanji Li, Danny Ryan, Heather Davidson, Gregor Zavcer, Mike McDonald, Corey Petty, Ameen Soleimani, Jens Frid, and others who took time to answer questions and contribute to the creation of this article.

A concept I often use to explain the potential of blockchain technology is “the space of possible economic relationships”.

What do I mean by economic relationships? I mean any situation where two or more persons enter into a relationship concerned with value:

• A transfer of cash

• An informal agreement to borrow money from a friend

• A legal contract to buy a house

• The relationship between all the shareholders of a corporation

• The relationship between all persons who use US dollars

This is meant to be very broad, including informal agreements, legal contracts, and very diffuse relationships that we don’t often think about, like sharing a national currency.

Now, let’s imagine a space that contains all possible economic relationships, and put some points on a graph.

This space includes not just all economic relationships that we’re familiar with or which are even used, but every possible economic relationship that could exist.

In reality, only some of this graph is available to us. Let’s draw a line to indicate which economic relationships are actually possible today.

Every economic relationship currently used by human beings (not just the ones we have highlighted as examples) is in this section. Every kind of purchase, corporate structure, debt instrument, store of value, and medium of exchange is somewhere inside that shape.

This shape used to look different, and was once much smaller. Until we invented money, we might have only had access to a very small area near the bottom right. Currency zones only became possible with stable political systems. Binding contracts required the slow evolution of complex legal systems. Public companies with shareholders required centuries of common law and commercial experience, and creating new specializations in professions like finance and law. All of these required technology, too: metalworking for money, writing for law, and even information technology for modern stock exchanges (ever heard of the paper crisis?).

We only have access to these economic relationships because these are the only ones for which we’ve built out the necessary institutions and technology. There are probably many other kinds of economic relationships out there, in the blank space, that we haven’t been able to access.

Recently, the invention of blockchain technology has expanded the frontier of this space. Now it probably looks a lot weirder — maybe something like this:

Blockchains let us do things that weren’t possible before:

• A small group of cryptography enthusiasts can successfully bootstrap a global currency into existence, without relying on the infrastructure of a nation state.

• We can now send money anywhere in the world very quickly and relatively cheaply, again without relying on traditional institutions.

• We can use very simple computer code to organize and collect millions of dollars from thousands of contributors all over the world.

Types of economic relationships that used to have big, expensive barriers to entry are suddenly a lot easier, and we’ve only really just begun experimenting with the technology.

Blockchains have expanded the space of possible economic relationships. But it’s been so sudden, we’re not really sure what to do with them yet. Usually these new capabilities develop slowly, because they require legal and political systems that are naturally gradual and expensive to change.

But blockchains let us do some things without those institutions, which means new capabilities become available much faster than usual. The limiting factor isn’t going to be whether we have the capability to do these things, but our ability to recognize that the space of possible economic relationships has expanded, and identify new useful things to build in those spaces.

The internet is changing again.

Over the last decade internet-based services have trended towards centralization. Today, a handful of companies control the platforms we use to search for information, store our personal data, manage our online identities, and communicate publicly and privately.

At the same time, a group of seemingly unrelated technologies are being developed on the fringes of the tech industry, ranging from encrypted messaging to digital money. Within that loose community, “web 3” has become a catchall term for a vision of a new, better internet. An internet where payments and money are natively digital, where “decentralized” applications compete with centralized ones, and where users have more control over their identity and data.

However, we often struggle to articulate what this all means. How might “web 3” be different from previous eras of the internet? What is “decentralization” and why does it matter? How can these new technologies actually be used? We’re spending years building infrastructure to make blockchains more scalable — but who will actually use that infrastructure, for what, and why should anyone care?

This article attempts to explain the web 3 vision in clear, simple terms. We discuss the core animating idea behind the many projects that form web 3, and survey three key trends.

This article isn’t a prediction about the future. The future isn’t fixed: we have to make the right choices to get the world we want. The point of this article is to describe a future that might be, a future compelling enough to be worth building, and a future clear enough that we know which direction to travel first.

The web 3 reformation

The internet has gone through major generational shifts before. These expanded the performance, features, and scale of the internet. We went from plaintext websites to streaming video. We went from static web pages to full-featured applications served remotely through the browser. We went from listservs to global social networks that drive modern politics and culture.

As the web matured, we grew to rely more and more on a handful of large companies. Google built the fastest and most convenient search engine, and have been rewarded with control over 74% of all search traffic. Facebook built the most popular social network, and was rewarded with control over the online identities of 2.2 billion people.

Web 3 is different from previous generational shifts. At its core, web 3 isn’t about speed, performance, or convenience. In fact, many web 3 applications are, at least today, slower and less convenient than existing products.

Instead, web 3 is about power. It’s about who has control over the technologies and applications that we use every day. It’s about breaking the dynamic that has shaped the last decade of the web: the tradeoff between convenience and control. We’ve become so accustomed to this dynamic that it seems inevitable: of course using the internet means being surveilled, and of course having a social media account means having my personal data sold to advertisers or worse. How could it be any other way?

Web 3 rejects the premise. We can have the benefits of the internet without handing the majority of power to a minority of companies. The dynamic described above isn’t an iron law of the universe, it’s just a product of the technology available at the time and the choices we made along the way.

“Web 3” is a movement to build different technologies and make better choices. We aren’t trying to replace the web, but rather keep what we like while changing its underlying structure — a reformation, not a revolution.

The projects within it can seem disparate, but they all share a common theme. Web 3 is a group of technologies that restructure control over the internet. These range from financial projects (cryptocurrencies), to basic communications technology (end to end encrypted messaging), to mass consumer use-cases (open social networks and p2p markets), to critical internet infrastructure (decentralized DNS).

Web 3 includes more than just cryptocurrencies, blockchains, and other products of cryptoeconomic design. It encompasses any technology that helps reform the centralized internet and lets users take back control over their digital lives. However, we believe that these technologies are the most significant contributors to the web 3 movement today, and for that reason focus on them in this article.

Three trends for web 3

In this article we survey three trends, and discuss how they might develop over time:

• First, money will become a native feature of the internet.

• Second, “decentralized” applications will offer users new capabilities.

• Third, users will have more control over their digital identities and data.

It’s important to keep in mind that each of these are speculative. Inevitably, web 3 will include technologies and applications we can’t anticipate, and the ones we discuss below will look different than we can imagine today.

1. Money

In web 3, money is a native feature of the internet.

In the past, the internet was simply a portal to the offline, traditional financial system. But cryptocurrencies are fundamentally digital — sending a transaction does not require interacting with some offline system, it only requires sending a message over the internet. We will soon live in a world where “money” is just something the internet does:

• Sending or receiving payments is now something that any piece of software can do, and by extension, something that can be done by any person with an internet connection and a phone.

• Digital payments will unlock new business models that were previously impractical. They will radically lower the costs of some transactions (e.g. cross-border remittances), enable new use-cases (e.g. machine payments), and be available to massive new markets (e.g. people who could not previously access the traditional financial system).

• An ecosystem of basic financial primitives — lending, derivatives, exchanges — provide the building blocks of more complex financial applications that can be used by anyone.

• We will create new kinds of money, as technologists explore the cryptoeconomic design space. We have only started to explore these possibilities, like protocol tokens and non-fungible digital assets.

Cryptocurrency is only possible because Satoshi invented a way to support a currency and payment network without handing control to any centralized company. Bitcoin is decentralized in that it is “controlled” by a variety of actors, ranging from large mining companies, to individual node operators, to core protocol developers. All of them exert influence in different ways and to different degrees, but no individual actor has exclusive power over the network.

“Decentralization” has become a central concept for web 3. However, it’s often used more as a slogan than a precise technical description. Decentralization can refer to a huge range of possibilities. Is a platform “decentralized” if it is controlled by 20 entities? 100? 10,000? What kind of decentralization are we even considering?

Decentralization isn’t a binary state — it’s a direction along a spectrum of possibilities. Saying a system ought to be “decentralized” is a little like telling an engineer that a bridge ought to be “big”. No doubt, but it’s not very useful information on its own — we also need to know what river you want to span and the load you need to bear.

The point of web 3 isn’t that all systems must be decentralized as much as possible, but rather that we are newly able to explore more points along the spectrum. The degree of decentralization that is useful or necessary will depend on the application. Bitcoin is an example of this: it is decentralized to a sufficient degree that payments are very difficult to censor and it would be very difficult to change the basic parameters of the currency, i.e. a deflationary monetary policy where the total amount of bitcoin is permanently capped. Others will attempt to create similar global currencies with other features that take advantage of decentralization, e.g. stablecoins like Dai that attempt to solve the problem of volatility.

Because bitcoin was the first, it’s often taken to be the only possible design for a cryptocurrency. As a result, “cryptocurrency” has become synonymous with particular political views about money, such as the virtue of deflationary currencies.

It’s important to understand that there are many possible designs for a digital currency, which might serve different purposes. Bitcoin enthusiasts believe the purpose of cryptocurrency is to allow people to opt into the enthusiast’s preferred monetary policy, the superiority of which is self-evident. A more humble interpretation is that the innovation of cryptocurrencies is that people can opt into whatever monetary policy they choose, whatever that happens to be. All they need is an internet connection and a phone.

2. Decentralized applications and services

The products and services that make up the internet today tend to be produced and controlled by individual corporations. If you use an internet-based application, there is very likely a legal entity somewhere that controls it. That company employs people to work on it, decides what features get prioritized, controls the servers that host the application’s data, and ultimately determines what code gets pushed to the product.

The promise of web 3 is that there might be other options. Maybe we can build products and services that are not controlled by any single company, but still have comparable usefulness to their centralized equivalents. Just as with Bitcoin, these products would be “decentralized”, though the rationale for decentralization, and the benefits it offers, may be very different.

Imagine, for instance, what a “decentralized publishing platform” might look like. This would be a social platform like Twitter or Medium that lets users share content, comment on it, and “vote” for content that they like.

The platform comes with built-in mechanisms to encourage users to contribute. Instead of just “upvoting” posts, users can send each other micropayments, or set up recurring payments for content creators whose work they want to support. The best Twitter accounts or Medium authors get paid directly in the platform, instead of having to figure out some secondary way to monetize their large followings. Even if each upvote was only a few cents, it would still be an improvement over centralized platforms like YouTube, where creators earn only a few thousand dollars per million views. On our decentralized platform, there is no central entity to take a large cut of the money that users pay each other. The platform could even use inflation funding to create a pool of rewards, which are distributed to the top posts each day.

The core rules that govern our decentralized service are defined in an open-source protocol. Users interact with that protocol using client-software of their choice. In other words, there would be a variety of apps, all made by different developers, but which all connected to the same social network. These clients may offer different features from one another, but all conform to the same shared protocol, analogous to the way that email clients all use the same standard for sending and receiving emails.

Users could use any client they choose, and clients can implement different features or offer third party services. Because we’re building on a decentralized protocol, client developers don’t have to ask permission from any central company, and they can build their product without fear that someday their API access could be revoked. Users don’t have to wait for twitter to add new anti-spam or anti-harassment features, they just use a client that offers them. An ecosystem of services grows on top of the open protocol, letting users do everything they can do with twitter today and more.

This illustrates an under-appreciated benefit of decentralized platforms: sustainable ecosystems of third party services. App developers can build useful products on top of a decentralized protocol without fear that someday their API access will be turned off, because there is no one company who can turn it off. The platform can remain neutral, meaning that a larger network of developers will invest their time and money into building businesses on top of it. Chris Dixon’s Why Decentralization Matters explores this argument in depth.

Of course, we will still use products and services created by centralized companies. But it will be possible for these companies to restrict the type of control they have over their products, placing more of that control in the hands of users.

This already exists in encrypted messaging apps, with products like Signal (made by Open Whisper Systems) that never see or hold any customer communications. By designing their application with end-to-end encryption, they have intentionally limited the control they have over their users.

Where early internet startups committed to an ideal of “don’t be evil”, web 3 companies aim higher to try and ensure that they can’t be evil — at least with respect to specific kinds of evil. Web 3 includes technologies that let developers limit their control over customers, by refusing to have certain kinds of power in the first place. That doesn’t mean we won’t have to trust centralized companies to some degree, but it does mean we can trust them less.

3. User control over identity and data

In web 3, users will have more control over their identity and their data.

Today, most of our online identities belong to someone else, like a Gmail address or Facebook account. Web 3 is laying the groundwork for personal control of online identities.

In part, this is a result of the infrastructure being built out for cryptocurrencies. Holding cryptocurrency requires holding private keys, and millions of people use apps that let them do just this. But the same technology can let people manage any kind of blockchain-based data, including a personal identity. The fact that we call these apps “wallets” is no coincidence — in the future they will hold not just your money, but also your ID.

At the same time, web 3 will make it possible for users to retain control of their data. First, because users will have the option of using their own identity, instead of one provided by a third party, limiting the opportunities for identity providers like Facebook to capture user data. Second, because the emergence of decentralized services will mean that in some cases, there won’t be a central company that is in a position to collect, store, and sell your personal data when you are using social media, renting an apartment, or catching a ride home. In general, a greater share of our use of the internet will be through platforms and systems that collect no data about us.

Finally, the new capabilities offered by web 3 technologies — control over our identities and data, and the availability of a worldwide payment network — will make it easier for individual users to capture the value that have turned social media companies into billion dollar businesses.

These companies collect your data because it’s valuable. In web 3, users capture that value instead. If you want to sell data about your personal browsing habits, you will be able to do so directly — but you get paid, not Facebook. And as we experiment with novel mechanisms for ownership over digital assets, individual users will have new ways to actually own a piece of the technology that they use every day — an opportunity currently available only to entrepreneurs, venture capitalists, and accredited investors.

Conclusion

Web 3 is not inevitable. The descriptions above of a potential future face many barriers, some of which may never be overcome. Some of the “open problems” in web 3 include:

• Are entrepreneurs actually incentivized to build decentralized applications? Who will fund them? Today, the route to profitability and venture-scale returns for these applications is unclear, whereas the traditional “centralized” business model is more reliable.

• Will decentralized applications be worse than centralized ones? A product controlled by a single company might have a more coherent product vision, and is able to rapidly iterate on new features. Centralized products may always have better UX and ease of setup.

• Will the cryptographic components of decentralized applications be too challenging for most users? How can users manage private keys in a way that can be safely recoverable? Is that even possible without returning to some form of centralized service?

• Will decentralized applications be more expensive to use? Low level decentralized systems (e.g. file storage, computation, oracles) have many redundancies built in to make them function. Will these layers make decentralized applications too costly?

• Will decentralized applications built with “smart contracts” ever make sense? Is it possible to write “immutable” code that fulfills an application’s requirements forever? If we need to upgrade smart contracts, then who gets to decide those upgrades, and is it any different from a centralized application?

• How will decentralized systems be “governed”? Decision making by centralized companies with total control is easier than building consensus among a group of actors with different interests and priorities. How will governance of the base layers work across different political ideologies and cultures?

• Do enough users actually care about privacy, controlling their own identity, or access to open financial services? Or will the goals of web 3 always be a niche concern?

• How will governments and regulators respond to web 3? There is inevitable tension created by technologies that give people new capabilities. Web 3 includes technologies that avoid censorship and surveillance, and could be used to sidestep financial regulation and law enforcement.

• Can the base-layer blockchains used by web 3 apps ever scale to serve millions or billions of users?

Even with this uncertainty, web 3 is a worthwhile vision for the future. Cryptocurrency and blockchain enthusiasts are rightly criticized for focusing myopically on technology, rather than the problems that technology is supposed to solve. Cryptocurrencies and blockchains aren’t ends in themselves: they are only valuable in so far as they solve a problem. The web 3 vision is helpful because it reorients us towards that problem: the internet has grown too centralized, and it needs to be opened up.

Focusing on the web 3 vision instead of any specific technology also helps us step outside the partisan politics that defines much of the cryptocurrency space. Web 3 is agnostic about whose platform ends up being used — the focus is on what the technology can actually do and what problems it solves for people.

Ethereum or Bitcoin could fail, but if they did the web 3 vision would not die. We would just build new, better versions of those systems using the same applied body of knowledge that we’ve developed over the last 8 years — cryptoeconomics — and keep on building.

Web 3 won’t be a utopia, and we shouldn’t delude ourselves into thinking it will be. If anything, the last 20 years should teach us that technology is not a magic bullet, and that the same human problems that exist everywhere exist on the internet, too. Politics, power, and control didn’t disappear with the invention of the web, they just took different forms. The promise of web 3 is that at least this time, constraints on power and control are design requirements, not afterthoughts. We have a second chance to build the internet as it was supposed to be, and we might not get a third — let’s make the most of it.

For ethereum 2018 is the year of infrastructure. This is the year when early adoption will test the limits of the network, renewing focus on technologies built to scale ethereum.

Ethereum is still in its infancy. Today, it isn’t safe or scalable. This is well understood by anyone who works closely with the technology. But over the last year, the ICO-driven hype has begun to far exaggerate the current capabilities of the network. The promise of ethereum and web3 — a safe, easy to use decentralized internet, bound by a common set of economic protocols, and used by billions of people — is still on the horizon, and will not be realized until critical infrastructure is built.

The projects working to build this infrastructure and expand the capabilities of ethereum are commonly referred to as scaling solutions. These take many different forms, and are often compatible or complimentary with each other.

In this long post I want to dive deep into one category of scaling solution: “off-chain” or “layer 2” solutions.

• First, we’ll discuss the scaling challenges of ethereum (and all public blockchains) in general.

• Second, we’ll cover the different approaches to solving the scaling challenge, distinguishing between “layer 1” and “layer 2” solutions.

• Third, we’ll delve into layer 2 solutions and explain how they work — specifically, we’ll talk about state channels, Plasma, and Truebit

This article focuses on giving the reader a thorough and detailed conceptual understanding of how layer 2 solutions work. But we won’t dig into code or specific implementations. Rather, we focus on understanding the economic mechanisms used to build these systems, and the common insights that are shared between all layer 2 technologies.

1. The scaling challenges of public blockchains

First, it’s important to understand that “scaling” isn’t a single, specific problem. It refers to a collection of challenges that must be overcome to make ethereum useful to a global user base of billions of people.

The most commonly discussed scaling challenge is transaction throughput. Currently, ethereum can process roughly 15 transactions per second, while in comparison Visa processes approximately 45,000/tps. In the last year, some applications — like Cryptokitties, or the occasional ICO — have been popular enough to “slow down” the network and raise gas prices.

The core limitation is that public blockchains like ethereum require every transaction to be processed by every single node in the network. Every operation that takes place on the ethereum blockchain — a payment, the birth of a Cryptokitty, deployment of a new ERC20 contract — must be performed by every single node in the network in parallel. This is by design — it’s part of what makes public blockchains authoritative. Nodes don’t have to rely on someone else to tell them what the current state of the blockchain is — they figure it out for themselves.

This puts a fundamental limit on ethereum’s transaction throughput: it cannot be higher than what we are willing to require from an individual node.

We could ask every individual node to do more work. If we doubled the block size (i.e., the block gas limit), it would mean that each node is doing roughly double the amount of work processing each block. But this comes at the cost of decentralization: requiring more work from nodes means that less powerful computers (like consumer devices) may drop out of the network, and mining becomes more centralized in powerful node operators.

Instead, we need a way for blockchains to do more useful stuff without increasing the workload on individual nodes.

Conceptually, there are two ways we might go about solving this problem:

I. What if each node didn’t have to process every operation in parallel?

The first approach rejects our premise — what if we could build a blockchain where every node didn’t have to process every operation? What if, instead, the network was divided into two sections, which could operate semi-independently?

Section A could process one batch of transactions, while Section B processed another batch. This would effectively double the transaction throughput of a blockchain, since our limit is now what can be processed by two nodes at the same time. If we can split a blockchain into many different sections, then we can increase the throughput of a blockchain by many multiples.

This is the insight behind “sharding”, a scaling solution being pursued by Vitalik’s Ethereum Research group and others. A blockchain is split into different sections called shards, each of which can independently process transactions. Sharding is often referred to as a Layer 1 scaling solution because it is implemented at the base-level protocol of ethereum itself. If you want to learn more about sharding, I recommend this extensive FAQ and this blog post.

II. What if we could squeeze more useful operations out of ethereum’s existing capacity?

The second option goes in the opposite direction: rather than increase the capacity of the ethereum blockchain itself, what if we could do more things **with the capacity we already have? **The throughput of the base-layer ethereum blockchain would be the same, but in practice we would be able to do many more operations that are useful to people and applications — like transactions, state updates in a game, or simple computations.

This is the insight behind “off-chain” technologies like state channels, Plasma, and Truebit. While each of these is solving a different problem, they all function by performing operations “off chain” instead of on the ethereum blockchain, while still guaranteeing a sufficient level of security and finality.

These are also known as Layer 2 solutions because they are built “on top of” the ethereum main-chain. They do not require changes to the base level protocol — rather, they exist simply as smart contracts on ethereum that interact with off-chain software.

2. Layer 2 solutions are cryptoeconomic solutions

Before diving into specific layer 2 solutions, it’s important to understand the underlying insight that makes them possible.

The basic power of a public blockchain is in cryptoeconomic consensus. By carefully aligning incentives and securing them with software & cryptography, we can create networks of computers that reliably come to agreement about the internal state of a system. This is the key insight of Satoshi’s whitepaper, which has now been applied in the design of many different public blockchains including bitcoin and ethereum.

Cryptoeconomic consensus gives us a core hard kernel of certainty — unless something extreme like a 51% attack happens, we know that on-chain operations — like payments, or smart-contracts — will execute as written.

The insight behind layer 2 solutions is that we can use this core kernel of certainty as an anchor — a fixed point to which we attach additional economic mechanisms. This second layer of economic mechanisms can extend the utility of public blockchains outwards, letting us have interactions off of the blockchain that can still reliably refer back to that core kernel if necessary.

These layers built “on top” of ethereum won’t always have the same guarantee as on-chain operations. But they can still be sufficiently final and secure to be very useful — especially when that slight decrease in finality lets us perform operations much faster or with lower overhead costs.

Cryptoeconomics didn’t begin and end with Satoshi’s whitepaper — it’s a body of techniques that we are only learning to apply. Not just in the design of core protocols, but in the design of second layer systems that extend the functionality of the underlying blockchain.

I. State channels

State channels are a technique for performing transactions and other state updates “off chain”. However, things that happen “inside” of a state channel still retain a very high degree of security and finality: if anything goes wrong, we still have the option of referring back to the “hard kernel” of certainty found in on-chain transactions.

Most readers will be familiar with the idea of a payment channel, which has been around for several years, and recently implemented on bitcoin through the lightning network. State channels are the more general form of payment channels — they can be used not only for payments, but for any arbitrary “state update” on a blockchain — like changes inside a smart contract. State channels were first described in detail by Jeff Coleman in 2015.

The best way to explain how a state channel works is by looking at an example. Keep in mind that this is a conceptual explanation, meaning that we won’t get into the technical details of a specific implementation.

Imagine that Alice and Bob want to play a game of tic tac toe, where the winner receives 1 eth. The naive way to do this would be to create a smart contract on ethereum that implements the rules of tic tac toe and keeps track of each players’ moves. Every time a player wants to make a move, they send a transaction to the contract. When one player wins, as determined by the rules, the contract pays out the 1 eth to the winner.

This works, but is inefficient and slow. Alice and Bob are having the entire ethereum network process their game, which might be overkill for what they need. They have to pay gas costs every time a player wants to make a move, and they have to wait for blocks to be mined before making the next move.

Instead, we can design a system that lets Alice and Bob play tic-tac-toe with as few on-chain operations as possible. Alice and Bob will be able to update the state of the game off-chain, while still having full confidence that they can revert back to the ethereum main-chain if necessary. We call this system a “state channel”.

First, we create a smart contract “Judge” on the ethereum main-chain that understands the rules of tic-tac-toe, and can identify Alice and Bob as the two players in our game. This contract holds the 1 eth prize.

Then, Alice and Bob begin playing the game. Alice creates and signs a transaction describing her first move, and sends it to Bob, who also signs it, sends back the signed version, and keeps a copy for himself. Then Bob creates and signs a transaction describing his first move, and sends it to Alice, who also signs it, sends it back, and keeps a copy. Each time, they are updating the current state of the game between them. Each transaction contains a “nonce”, which simply means that we can always tell later in what order the moves happened.

So far, none of this is happening on-chain. Alice and Bob are simply sending transactions to each other over the internet, but nothing is hitting the blockchain yet. However, all of the transactions could be sent to the Judge contract — in other words, they are valid ethereum transactions. You can picture this as two people writing a series of blockchain-certified cheques back and forth to each other. No money has actually been deposited or withdrawn from a bank, but each has a stack of cheques that they could deposit whenever they want.

When Alice and Bob are done playing the game — perhaps because Alice has won — they can close the channel by submitting the final state (e.g. a list of transactions) to the Judge contract, paying only a single transaction fee. The Judge ensures that this “final state” is signed by both parties, and waits a period of time to ensure that no one can legitimately challenge the result, and then pays out the 1 eth award to Alice.

Why do we need this “challenge period” where the Judge contract waits?

Imagine that instead of sending the real final state to the Judge, Bob sent an old version of the state — one where he was winning ahead of Alice. The Judge is just a dumb contract — on its own, it has no way of knowing whether this is the most recent state or not.

The challenge period gives Alice a chance to prove to the Judge contract that Bob has lied about the final state of the game. If there is a more recent state, then she will have a copy of the signed transactions, and she can submit those to the Judge. The Judge can tell that Alice’s version is more recent by checking the nonce, and Bob’s attempt to steal the win is rejected.

Features and limitations

State channels are useful in many applications, where they are a strict improvement over doing operations on-chain. However, it’s important to keep in mind the particular tradeoffs that have been made when deciding whether an application is suitable for being channelized:

• State channels rely on availability. If Alice lost her internet connection during a challenge (maybe because Bob, desperate to claim the prize, sabotaged her home’s internet connection) she might not be able to respond before the challenge period ends. However, Alice can pay someone else to keep a copy of her state and maintain availability on her behalf.

• They’re particularly useful where participants are going to be exchanging many state updates over a long period of time. This is because there is an initial cost to creating a channel in deploying the Judge contract. But once it is deployed, the cost per state update inside that channel is extremely low.

• State channels are best used for applications with a defined set of participants. This is because the Judge contract must always know the entities (i.e. addresses) that are part of a given channel. We can add and remove people, but it requires a change to the contract each time.

• State channels have strong privacy properties, because everything is happening “inside” a channel between participants, rather than broadcast publicly and recorded on-chain. Only the opening and closing transactions must be public.

• State channels have instant finality, meaning that as soon as both parties sign a state update, it can be considered final. Both parties have a very high guarantee that, if necessary, they can “enforce” that state on-chain.

At L4, we’re building Counterfactual: a framework for generalized state channels on ethereum. Our general purpose, modular implementation will let developers use state channels in their application without needing to be state channel experts themselves. You can read more about the project here. We’ll be releasing a paper describing our technique in Q1 2018.

The other notable state channels project for ethereum is Raiden, which is currently focused on building a network of payment channels, using a similar paradigm as the lightning network. This means that rather than have to open up a channel with the specific person(s) you want to transact with, you can open up a single channel with an entity connected to a much larger network of channels, enabling you to make payments to anyone else connected to the same network without additional fees.

In addition to Counterfactual and Raiden, there are several application-specific channel implementations on ethereum. For instance, Funfair has built state channels (which they call “Fate channels”) for their decentralized gambling platform, Spankchain has built one-way payment channels for adult performers (they also used a state channel for their ICO), and Horizon Games is using state channels in their first ethereum-based game.

II. Plasma

On August 11 2017, Vitalik Buterin and Joseph Poon released a paper titled Plasma: Autonomous Smart Contracts. The paper introduced a novel technique that could enable ethereum to reach many more transactions per second than currently possible.

Like state channels, Plasma is a technique for conducting off-chain transactions while relying on the underlying ethereum blockchain to ground its security. But Plasma takes the idea in a new direction, by allowing for the creation of “child” blockchains attached to the “main” ethereum blockchain. These child-chains can, in turn, spawn their own child-chains, who can spawn their own child-chains, and so on.

The result is that we can perform many complex operations at the child-chain level, running entire applications with many thousands of users, with only minimal interaction with the ethereum main-chain. A Plasma child-chain can move faster, and charge lower transaction fees, because operations on it do not need to be replicated across the entire ethereum blockchain.

In order to understand how Plasma works, let’s walk through an example of how it could be used.

Let’s imagine that you’re creating a trading-card game on ethereum. The cards will be ERC 721 non-fungible tokens (like Cryptokitties), but have certain features and attributes that lets users play against each other — like in Hearthstone, or Magic the Gathering. These kinds of complex operations are expensive to do on-chain, so you decide to use Plasma instead for your application.

First, we create a set of smart-contracts on ethereum main-chain that serve as the “Root” of our Plasma child-chain. The Plasma root contains the basic “state-transition rules” of our child chain (things like “transactions cannot spend assets that have already been spent”), records hashes of the child-chain’s state, and serves as a kind of “bridge” that lets users move assets between the ethereum main-chain and the child-chain.

Then, we create our child-chain. The child-chain can have its own consensus algorithm — in this example, let’s say that it uses Proof of Authority (PoA), a simple consensus mechanism that relies on trusted block producers (i.e. validators). Block producers are analogous to miners in a “Proof of Work” system — they are the nodes that receive transactions, form blocks, and collect transaction fees. Let’s keep our example simple, and say that you (the company that created the game) are the only entity that is creating blocks — i.e. your company runs a few nodes that are the block producers for our child-chain.

Once the child-chain is created and active, the block producers make periodic commitments to the root contract. This means they are effectively saying “I commit that the most recent block in the child-chain is X”. These commitments are recorded on-chain in the Plasma root as a proof of what has happened in the child-chain.

Now that the child-chain is ready, we can create the basic components of our trading card game. The cards themselves are ERC721’s, initially created on the ethereum main-chain, and then moved onto the child-chain through the plasma root. This introduces a crucial point: Plasma lets us scale interactions with blockchain-based digital assets, but those assets should be created first on the ethereum-main chain. Then, we deploy the actual game application smart-contracts on the child-chain, which contains all of the game logic and rules.

When a user wants to play our game, they are only interacting with the child chain. They can hold assets (the ERC721 cards), buy and trade them for ether, play rounds of the game against other users — whatever our game lets them do — without ever interacting directly with the main-chain. Because only a much smaller number of nodes (i.e. block producers) have to process transactions, fees can be much lower and operations can be faster.

But how can this be safe?

By moving more operations off the main-chain and onto a child-chain, it’s clear we can perform more operations. But how secure is it? Are transactions that happen on the child-chain actually considered final? After all, we’ve just described a system where a single entity controls the block production for our child chain. Isn’t that centralized? Can’t the company steal your funds or take your collectible cards whenever it wants?

The short answer is that even in a scenario where a single entity controls 100% of block production on a child chain, Plasma gives you a basic guarantee that you can always withdraw your funds and assets back onto the main chain. If a block producer starts acting maliciously, the worst that can happen is they force you to leave the child-chain.

Let’s walk through a few different ways block producers could behave badly, and see how Plasma deals with those scenarios.

First, imagine that a block producer tries to cheat you by lying — by creating a fake new block where suddenly your funds are controlled by them. They are the only block producer, so they’re free to introduce a new block that doesn’t actually follow the rules of our blockchain. Just like other blocks, they will have to publish a commitment to the Plasma root contract containing evidence of this block.

As mentioned above, the user always has an ultimate guarantee that they can withdraw their assets back to main-chain. In this scenario, the user (or rather an application acting on their behalf) would detect the attempted theft, and withdraw before the block producer can try and use the assets they’ve “stolen”.

Plasma also creates a mechanism to prevent fraud short of withdrawing to main-chain. Plasma includes a mechanism whereby anyone — including you — can publish a fraud proof to the root contract, to try and show that the block producer has cheated. This fraud proof would contain information about the previous block, and allows us to show that according to the state-transition rules of the child-chain, the false block doesn’t properly follow from the previous state. If fraud is proven, the child-chain is “rolled back” to the previous block. Even better, we construct a system where any block producer who signed off on the false block is penalized by losing an on-chain deposit.

plasma.io/plasma.pdf

But submitting a fraud proof requires having access to the underlying data — i.e. the actual history of blocks that are used to prove the fraud. What if the block producers are also not sharing information about previous blocks, to prevent Alice from being able to submit a fraud proof to the root contract?

In this case, the solution is for Alice to withdraw her funds and leave the child-chain. Essentially Alice submits a “Proof of Funds” to the root contract. After a delay period during which anyone can challenge her proof (e.g. to show she actually spent those funds in a later valid block), Alice’s funds are moved back to the ethereum main-chain.

Lastly, block producers can censor users of the child-chain. If they wanted, block producers could simply never include certain transactions in their blocks, effectively preventing a user from performing any operations on the child-chain. Once again, the solution is simply to withdraw all of our assets back onto the ethereum main-chain as above.

Withdrawals themselves pose risks, however. One concern is what would happen if everyone using a child-chain tried to withdraw at the same time. In the case of a mass withdrawal, there might not be enough capacity on the ethereum main-chain to process everyone’s transactions within the challenge period, meaning users could lose funds. Although there are many possible techniques for preventing this, e.g. by extending the challenge period in a way that is responsive to demand for withdrawals.

It’s worth noting that it doesn’t need to be the case that all block producers are controlled by one entity — this is simply the extreme case in our example. We can create child-chains whose block production is spread among many different entities — i.e. actually decentralized in a way that is more similar to public blockchains. In those cases, there is less risk that block producers would interfere in the way described above, and so less risk that a user would have to move their assets back to the ethereum main-chain.

Now that we’ve covered both state channels and Plasma, it’s worth noting a few points of comparison.

One difference is that state channels can perform instant withdrawals when all of the parties in the channel consent to the withdrawal. If Alice and Bob agree to close out a channel and withdraw their funds, so long as they both agree to the final state they can get their assets out of the channel immediately. This is not possible on Plasma, where users must always go through a withdrawal process that involves a challenge period, as described above.

State channels should also be less expensive per transaction than Plasma, and be faster. This means that we will likely build state channels on Plasma child-chains. For example, in an application where two users are exchanging a series of small transactions. Building a state channel at the child-chain level should be cheaper and faster than performing each of those transactions on the child-chain directly.

Finally, it’s worth noting that this is only a partial description that leaves out many details. Plasma itself is in very early stages. If you’re interested in learning more about current work on Plasma, check out Vitalik’s recent proposal for a “Minimal Viable plasma” (i.e. a stripped-down plasma implementation). There’s work being done by a group based in Taiwan, which you can find in this repo. OmiseGo is working on an implementation for their decentralized exchange — they posted a recent update about their progress here.

III. Truebit

Truebit is a technology to help ethereum conduct heavy or complex computation off-chain. This makes it different from state channels and Plasma, which are more useful for increasing the total transaction throughput of the ethereum blockchain. As we discussed in the opening section, scaling is a multi-faceted challenge that requires more than high transaction throughput. Truebit won’t let us do more transactions, but it will let ethereum based applications do more complex things in a way that can still be verified by the main-chain.

This will let us do operations useful to ethereum applications that are too computationally expensive to do on chain. For instance, validating Simple Payment Verification (SPV) proofs from other blockchains, which could let ethereum smart-contracts “check” whether a transaction has happened on another chain (like bitcoin or dogecoin).

Let’s walk through an example. Imagine that you have some expensive computation — like an SPV proof — that needs to be performed as part of an ethereum application. You can’t simply do it as part of a smart contract on ethereum main-chain, because SPV proofs are far too computationally expensive. Remember, it’s very costly to perform any computation on ethereum because every node must perform that operation in parallel. Blocks in ethereum have a maximum gas limit that sets a cap on how much computation can be done by all the transactions combined in that block. But an SPV proof is so computationally expensive that it would require many multiples of the entire gas limit for an individual block, even if it were the only transaction inside.

Instead, you pay someone else a small fee to do the computation off chain. The person you paid to do this is called a solver.

First, the solver pays a deposit held in a smart contract. Then, you give the solver a description of the computation they need to execute for you. They run the computation, and return the result. If the result is correct (more on that in a second), their deposit is returned. If it turns out that solver did not properly perform the computation — i.e. they cheated or made a mistake — they lose their deposit.

But how can we tell whether the result was correct, or false? Truebit uses an economic mechanism called the “verification game”. Essentially, we create an incentive for other parties called challengers to check the solvers’ work. If a challenger is able to prove through the verification game that a solver submitted a false result, then they collect a reward, while the solver loses their deposit.

Because the verification game is performed on-chain, it cannot simply compute the result (which would defeat the entire purpose of the system — if we could do the computation on-chain, we wouldn’t need Truebit). Rather, we force both the solver and challenger to identify the specific operation that they disagree about. In effect, we are backing both parties into a corner — finding the actual line of code where they disagree about the outcome.

Once that specific operation is identified, it’s small enough to actually be executed by the ethereum main-chain. We then execute that operation through a smart-contract on ethereum, which settles once and for all which party was telling the truth and which was lying or mistaken.

If you want to learn more about Truebit, you can read the paper here, or this blog post by Simon de la Rouviere.

Conclusion

Layer 2 solutions share a common insight: once we have the hard kernel of certainty provided by a public blockchain, we can use it as an anchor for cryptoeconomic systems that extend the usefulness of blockchain applications.

Now that we’ve surveyed some examples, we can be more specific about how layer 2 solutions apply this insight. The economic mechanisms used by layer 2 solutions tend to be interactive games: they work by creating incentives for different parties to compete against or “check” one another. A blockchain application can assume that a given claim is likely true, because we’ve created a strong incentive for another party to provide information showing it to be false.

In state channels, this is how we confirm the final state of the channel — by giving parties a chance to “rebut” each other. In Plasma, it’s how we manage fraud-proofs and withdrawals. In Truebit, it’s how we ensure that solvers’ tell the truth — by giving an incentive to verifiers to prove the solver wrong.

These systems will help address some of the challenges involved in scaling ethereum to a massive global user base. Some, like state channels and Plasma, will increase the transaction throughput of the platform. Others, like Truebit, will make it possible to conduct more difficult computation as part of a smart contract, opening up new use cases.

These three examples represent only a small portion of the possible design space for cryptoeconomic scaling solutions. We’ve not even covered the work being done on “inter-blockchain protocols” like Cosmos or Polkadot (although whether these are “layer 2” solutions or something else altogether is a topic for another post). We should also expect to invent new and unexpected layer 2 systems that improve on existing models or offer new tradeoffs between speed, finality, and overhead.

More important than any particular layer 2 solution is further development of the underlying techniques and mechanisms that make them possible in the first place: cryptoeconomic design.

These layer 2 scaling solutions are a powerful argument for the long-term value of programmable blockchains like ethereum. Building the economic mechanisms underlying layer 2 solutions is only possible when a blockchain is programmable: you need a scripting language to write the programs that enforce the interactive games. This is much more difficult (or in some cases, like Plasma, probably impossible) on blockchains like bitcoin, which offer only limited scripting possibilities.

Ethereum lets us build layer 2 solutions to access new points on the tradeoff matrix between speed, finality, and overhead cost. This makes the underlying blockchain more useful for a larger variety of applications, since different types of applications with different threat models will have natural preferences towards different tradeoffs. For high value transactions where we want protection against even nation-states, we use the main chain. For trading digital collectibles where speed is more important, we can use Plasma. Layer 2 lets us make these tradeoffs without compromising the underlying blockchain, preserving decentralization and finality.

Further, it’s very hard to predict in advance what scripting capabilities will be needed for a given scaling solution. When ethereum was being designed, Plasma and Truebit had not yet been invented. But because ethereum is fully programmable, it is capable of implementing virtually any economic mechanism we can invent.

The only way to take full advantage of the value of blockchain technology — that core kernel of certainty created by cryptoeconomic consensus — is with a programmable blockchain like ethereum.

Thanks to Vitalik Buterin, Jon Choi, Matt Condon, Chris Dixon, Hudson Jameson, Denis Nazarov, and Jesse Walden for their comments on an earlier draft of this article.

Cover photo: Construction of the Tunkhannock Viaduct railway bridge in Pennsylvania (cc). Roman engineering principles being extended to new uses.

A few months ago Parker Thompson, a well known Silicon Valley VC, tweeted that “the concept of crypto-economics is stupid. It’s economics. Inventing your own word is just an excuse to ignore well-understood concepts.”

The term “cryptoeconomics” causes a lot of confusion. People are often unclear on what it is supposed to mean. The word itself can be misleading, as it suggests that there is a parallel “crypto” version of the whole of economics. This is wrong, and Parker is right to mock such a generalization.

In simple terms, cryptoeconomics is the use of incentives and cryptography to design new kinds of systems, applications, and networks. Cryptoeconomics is specifically about building things, and has most in common with mechanism design — an area of mathematics and economic theory.

Cryptoeconomics is not a subfield of economics, but rather an area of applied cryptography that takes economic incentives and economic theory into account. Bitcoin, Ethereum, zcash and all other public blockchains are products of cryptoeconomics.

Cryptoeconomics is what makes blockchains interesting, what makes them different from other technologies. As a result of Satoshi’s white paper, we have learned that through the clever combination of cryptography, networking theory, computer science and economic incentives we can build new kinds of technologies. These new cryptoeconomic systems can accomplish things that these disciplines could not achieve on their own. Blockchains are just one product of this new practical science.

This article aims to explain cryptoeconomics in clear, simple terms. First, we examine Bitcoin as an example of cryptoeconomic design. Second, we consider how cryptoeconomics relates to economic theory in general. Third, we look at three different areas of cryptoeconomic design and research that are active today.

1. What is cryptoeconomics? Bitcoin as a case study

Bitcoin is a product of cryptoeconomics.

Bitcoin’s innovation is that it allows many entities who do not know one another to reliably reach consensus about the state of the bitcoin blockchain. This is achieved using a combination of economic incentives and basic cryptographic tools.

Bitcoin’s design relies on economic incentives and penalties. Economic rewards are used to enlist miners to support the network. Miners contribute their hardware and electricity because if they produce new blocks, they are rewarded with amounts of bitcoin.

Second, economic costs or penalties are part of bitcoin’s security model. The most obvious way to attack the bitcoin blockchain would be to gain control of a majority of the network’s hashing power — a so-called 51 percent attack — which would let an attacker reliably censor transactions and even change the past state of the blockchain.

But gaining control of hashing power costs money, in the form of hardware and electricity. Bitcoin’s protocol intentionally makes mining difficult, meaning that gaining control of a majority of the network is extremely expensive — enough that it would be hard to profit from the attack. As of August 16, 2017, the cost of a 51 percent attack on bitcoin would be around $1.88 billion in hardware and $3.4 million in electricity every day.

Without these carefully calibrated economic incentives, bitcoin wouldn’t work. If mining did not come with a high cost, it would be easy to launch a 51 percent attack. If there were no mining reward, there would be no industry of people who buy hardware and pay for electricity to contribute to the network.

Bitcoin also relies on cryptographic protocols. Public-private key cryptography is used to give individuals safe, exclusive control of their bitcoin. Hash functions are used to “link” each block in the bitcoin blockchain, proving an order of events and the integrity of past data.

Cryptographic protocols like these give us the basic tools necessary to build reliable, secure systems like Bitcoin. Without something like public-private key infrastructure, we could not guarantee to a user that they have exclusive control over their bitcoin. Without something like hashing functions, nodes would not be able to guarantee the integrity of the history of bitcoin transactions contained in Bitcoin’s blockchain.

Without the hardness of cryptographic protocols like hashing functions or public-private key cryptography, we would have no secure unit of account with which to reward miners — no confidence that our record of past accounts was authentic and exclusively controlled by a rightful owner. Without a carefully calibrated set of incentives to reward an industry of miners, that unit of account could have no market value because there would be no confidence that the system could persist into the future.

In this way, bitcoin’s design requires an understanding of both cryptography and how incentives affect the security properties and functionality of systems built with cryptography. Cryptoeconomics is strange and counterintuitive. Most of us are not used to thinking of money as a design or engineering problem, nor are we used to economic incentive design being an essential component of a new technology. Cryptoeconomics requires us to think about information security problems in economic terms.

One of the most common mistakes in this industry is made by those who view blockchains only through a lens of computer science or applied cryptography. We have a strong tendency to prioritize the things we are most comfortable with, and see things outside of our domain of expertise as less important.

In blockchain technology, this leads many people to assume or abstract away the crucial role of economic incentives. This is one reason we see meaningless phrases like “blockchains are trustless”, “bitcoin is backed only by math” or “blockchains are immutable.” These are all wrong in their own way, but all have the effect of obfuscating the essential role of a large network of people whose necessary participation in the network is maintained through economic incentives.

Cryptoeconomic systems like bitcoin feel like magic to someone who views them only as a product of computer science, because bitcoin can do things that computer-science alone could never accomplish. Cryptoeconomics isn’t magic — it’s just interdisciplinary.

2. How does it relate to economics more generally?

The term cryptoeconomics can be misleading because it suggests a comparison to economics as a whole. This is part of what leads people like Parker to dismiss the term. Economics is the study of choice: how people and groups of people respond to incentives. The invention of cryptocurrency and blockchain technology does not require a new theory of human choice — the humans haven’t changed. Cryptoeconomics is not the application of macroeconomic and microeconomic theory to cryptocurrency or token markets.

Cryptoeconomics has most in common with mechanism design, a field related to game theory. In game theory, we look at a given strategic interaction (a “game”) and then try to understand the best strategies for each player, and the likely outcome if both players follow those strategies. For instance, we might use game theory to look at a negotiation between two firms, relations between countries or even evolutionary biology.

Mechanism design is often referred to as reverse game theory because we start with a desired outcome and then work backwards to design a game that, if players pursue their own self interest, will produce the outcome we want. For instance, imagine we are responsible for designing the rules of an auction. We have an objective that we want bidders to actually bid the real value they place on an item. To achieve this, we apply economic theory to design the auction as a game where the dominant strategy for any player is to always bid their true value. One solution to this problem is called a Vickrey auction, where bids are secret and the winner of the auction (defined as the player with the highest bid) only pays the second highest amount that was bid.

Cryptoeconomics, like mechanism design, focuses on designing and creating systems. Like in our auction example, we use economic theory to design “rules” or mechanisms that produce a certain equilibrium outcome. But in cryptoeconomics, the mechanisms used to create economic incentives are built using cryptography and software and the systems we are designing are almost always distributed or decentralized.

Bitcoin is a product of this approach. Satoshi wanted bitcoin to have certain properties — for instance, that it be able to reach consensus about its internal state and that it be censorship-resistant. Then, Satoshi set out to design a system that would achieve those properties, assuming people responded in rational ways to economic incentives.

Most often, cryptoeconomics is used to provide a security guarantee about a distributed system. For instance, we have a cryptoeconomic security guarantee that the bitcoin blockchain is secure against a 51 percent attack unless someone is willing to spend a few billion dollars. Or, in a state channel — a topic we discuss later — we can have a cryptoeconomic security guarantee that an off-chain process is nearly as secure and final as an on-chain transaction.

It is worth noting that mechanism design is not a panacea. There is a limit to how much we can rely on incentives to predictably shape future behaviour. As Nick Szabo rightly points out, ultimately we are speculating about people’s future mental states and making assumptions about how they react to certain incentives. A cryptoeconomic system’s security guarantee depends in part on the strength of its assumptions about how people react to economic incentives.

3. Three examples of cryptoeconomics

There are at least three different kinds of systems being designed today that could be called “cryptoeconomic”.

Example 1: Consensus protocols

Blockchains are able to reach reliable consensus without having to rely on a central trusted party — a product of cryptoeconomic design. Bitcoin’s solution, which we surveyed above, is called “proof-of-work” consensus because miners must commit work — in the form of hardware and electricity — in order to participate in the network and receive mining rewards.

Improving proof-of-work systems and designing alternatives to them is one active area of cryptoeconomic research and design. Ethereum’s current proof-of-work consensus mechanism includes many variations and improvements on the original design, enabling faster block times and being more resistant to the mining centralization that can result from ASICs.

In the near future, Ethereum plans to migrate to a “proof-of-stake” consensus protocol called Casper. This is an alternative to proof-of-work that does not require “mining” in the usual sense: there is no need for specialized mining hardware or huge expenditures of electricity.

Remember that the whole point of requiring miners to buy hardware and spend electricity is to impose a cost on miners, as a way of raising the cumulative cost of attempting a 51 percent attack sufficiently high that it becomes too expensive. The idea behind proof-of-stake systems is to use deposits of cryptocurrency to create the same disincentive, rather than real-world investments like hardware and electricity.

In order to mine in a proof-of-stake system, you must commit a certain amount of ether into a smart contract “bond.” Just like in proof-of-work, this raises the cost of a 51 percent attack — an attacker would have to commit a very large amount of ether to successfully attack the network, which they would then lose forever.

Casper is being designed by Vlad Zamfir, Vitalik Buterin, and others at the Ethereum Foundation. You can read more about the history of Casper’s design in this series of posts by Zamfir or hear him talk about it on a recent podcast. Buterin wrote a long post about Casper’s design philosophy here, and there is a useful FAQ on the Ethereum GitHub wiki here.

Example 2: Cryptoeconomic application design

Once we have solved the fundamental problem of blockchain consensus, we are able to build applications that sit “on top” of a blockchain like Ethereum. The underlying blockchain gives us (1) a unit of value that can be used to create incentives and penalties, and (2) a toolkit with which we can design conditional logic in the form of “smart contract code.” The applications we build with these tools can also be a product of cryptoeconomic design.

For instance, the prediction market Augur requires cryptoeconomic mechanisms in order to function. Using its native token REP, Augur creates a system of incentives that rewards users for reporting the “truth” to the application, which is then used to settle bets in the prediction market. This is the innovation that makes a decentralized prediction market possible. Another prediction market, Gnosis, uses a similar method, though also lets users specify other mechanisms for determining true outcomes (commonly called “oracles”).

Cryptoeconomics is also applied to design token sales or ICOs. Gnosis, for instance, used a “Dutch auction” as a model for its token auction, on the theory that this would result in a more fair distribution (an experiment that had mixed results). We mentioned earlier that one area where mechanism design has been applied is in the design of auctions, and token sales gives us a new opportunity to apply some of that theory.

These are a different kind of problem than building the underlying consensus protocols, but they share enough similarities that both can be fairly seen as cryptoeconomic. Building these applications requires an understanding of how incentives shape users’ behaviour and careful design of economic mechanisms that can reliably produce a certain result. They also require an understanding of the capabilities and limitations of the underlying blockchain on which the application is built.

Many blockchain applications are not products of cryptoeconomics; for instance, applications like Status and MetaMask — wallets or platforms that let users interact with the Ethereum blockchain. These do not involve any additional cryptoeconomic mechanisms beyond those that are already part of the underlying blockchain.

Example 3: State channels

Cryptoeconomics also includes the practice of designing much smaller sets of interactions between individuals. The most notable of these are state channels. State channels are not an application but a valuable technique that can be used by most blockchain applications to become more efficient.

A fundamental limitation of blockchain applications is that blockchains are expensive. Sending transactions requires fees, and using ethereum to run smart-contract code is comparatively costly to other kinds of computation. The idea behind state channels is that we can make blockchains more efficient by moving many processes off-chain, while still retaining a blockchain’s characteristic trustworthiness, through the use of cryptoeconomic design.

Imagine Alice and Bob want to exchange a large number of small payments of cryptocurrency. The normal way for them to do this would be to send transactions to the blockchain. This is inefficient — it requires paying transaction fees and waiting for the confirmation of new blocks.

Instead, imagine that Alice and Bob sign transactions that could be submitted to the blockchain, but are not. They pass these back and forth between one another, as fast as they want — there are no fees at this point, because nothing is actually hitting the blockchain yet. Each update “trumps” the last one, updating the balance between the parties.

When Alice and Bob have finished exchanging small payments, they “close out” the channel by submitting the final state (i.e. the most recent signed transaction) to the blockchain, paying only a single transaction fee for an unlimited number of transactions between themselves. They can trust this process because both Alice and Bob know that each update passed between them could be sent to the blockchain. If the channel is properly designed, there is no way to cheat — say, by trying to submit a previous update as though it were the most recent — since recourse to the blockchain is always available.

For illustrative purposes, you can think of this as similar to how we interact with other trusted sources, like a legal system. When two parties sign a contract, most of the time they never need to take that contract to court and ask a judge to interpret and enforce it. If the contract is properly designed, both parties simply do what they promised to do, and never interact with the courts at all. The fact that either party could go to the court and have the contract enforced is enough to make the contract useful.

This technique is not just useful for payments, but for any update to the state of an Ethereum program — hence the more general term “state channel” rather than the narrow “payment channel.” Instead of sending payments back and forth, we can send updates to a smart contract back and forth. We can even send entire Ethereum smart contracts that, if needed, will be sent to the blockchain and executed. These programs never have to be executed to be useful. All that is needed is a sufficiently high guarantee that they could be executed if necessary.

In the future, most blockchain applications will use state channels in some form. It is almost always a strict improvement to require less on-chain operation, and many things done on-chain today can be moved into state channels while still preserving a sufficiently high guarantee to be useful.

The description above skips over many important details and nuances of how state channels work. For a more detailed description, Ledger Labs built a toy implementation last summer that demonstrates the basic concept.

Conclusion

Thinking about the blockchain space through the lens of cryptoeconomics is helpful. Once you understand the idea, it helps to clarify many of the controversies and debates in our industry.

For instance, “permissioned” blockchains that are centrally managed and do not use proof-of-work have been a source of constant controversy since they were first proposed. This area of work is often referred to as “distributed ledger technology” and is focused on financial and enterprise use cases. Many partisans of blockchain technology dislike them — they may be blockchains in the literal sense, but there is something about them that feels wrong. They seem to reject the thing that many people see as the whole point of blockchain technology: being able to produce consensus without relying on a central party or traditional financial systems.

A cleaner way to make this distinction is between blockchains that are products of cryptoeconomics and blockchains that are not. Blockchains that are simply distributed ledgers and do not rely on cryptoeconomic design to produce consensus or align incentives might be useful for some applications. But they are distinct from blockchains whose whole purpose is to use cryptography and economic incentives to produce consensus that could not exist before, like Bitcoin and Ethereum. These are two different technologies, and the clearest way of distinguishing between them is whether or not they are products of cryptoeconomics.

Secondly, we should expect that there will be cryptoeconomic consensus protocols that do not rely on a literal chain of blocks. Obviously, such a technology would have something in common with blockchain technology as we call it today, but labelling them blockchains would be inaccurate. Again, the relevant organizing concept is whether such a protocol is the product of cryptoeconomics, not whether it is a blockchain.

The ICO craze has also focused attention on this distinction, though few have articulated it clearly. Many people independently identified that one of the strongest signs of a token’s value is whether it forms a necessary component of the application to which it is connected. To put this in clearer terms, the question should be: is the token part of a necessary cryptoeconomic mechanism in the application? Understanding the mechanism design of a project holding an ICO is an essential tool in determining that token’s utility and likely value.

In the past years, we’ve moved from thinking about this new field solely through the lens of one application (Bitcoin), to thinking about it in terms of one underlying technology (blockchains). What needs to happen now is to step back once again and view this industry in terms of a unifying approach to solving problems: cryptoeconomics.

Thanks to Jeff Coleman, Ethan Wilding, and Vlad Zamfir for their comments on an earlier draft of this article.

While thinking about the lessons of the DAO hard fork I’ve been reaching for terminology necessary to make a useful distinction, and thought it would be useful to set it out here.

The DAO hard fork was controversial. Some believe that forking the network to return stolen funds was a betrayal of the basic principles that animate the ethereum project. Others viewed it as a pragmatic solution that, though it shouldn’t be used often, was necessary in this case.

Hard forks will often be contentious, even when they are changing the protocol rather than “undoing” the history of transactions. Because a hard fork will, by definition, break backwards-compatibility, the entire community must agree on a change in order to continue together on the same blockchain. If unanimity is not achieved, the minority can reject the change by continuing as a distinct blockchain.

When a hard fork is proposed, we debate the merits of the change that it would introduce. Will rescuing the DAO funds set a bad precedent? Will increasing the block size centralize the bitcoin network? Typically, then, when people say that a hard fork is “good” or “bad” they are commenting on whether the changes introduced by the hard fork ought to be accepted or rejected.

But as we’ve seen in this case, sometimes a minority cannot be persuaded and they decide to continue with their own blockchain. The hard-fork results in two blockchains, diverging from a common ancestor. Let’s call this a contested fork.

The possibility of a contested fork requires us to ask a second-order question: when is it good that the network diverges after a hard fork? When is it bad? What are the benefits and costs of either?

In some cases, diverging into two blockchains might benefit both chains. First, it’s possible that a blockchain could want to grow in two different directions that are, for some reason, mutually exclusive. For example, imagine a blockchain wants to add a new feature “A” that, while adding some utility, would reduce the ability of that blockchain to perform a different function “B”. Those who place more importance on feature A will support the fork, and those who place more importance on feature B will advocate rejecting the fork. In that case, splitting into two blockchains might be for the best if it allows each project to pursue a distinct feature set.

A contentious fork could also be a good thing where there is a sincere ideological divide within that blockchain’s community. If the community is divided into two populations that have a different set of beliefs about what values and principles ought to guide decision-making about that blockchain, then it might be that each would be able to better govern their blockchains separately, rather than have to argue and compete within the confines of a single platform, resulting in gridlock or ineffective decision making. In other words, it may be the case that the governance priorities of the blockchain’s community cannot be reconciled.

But splitting a blockchain into two also has costs. First, each chain will individually have less hashing power than their shared ancestor, lowering the cost of mounting a 51% attack against either continuing chain. Second, blockchains benefit from network effects: the whole platform has more value when there are more people using it and building applications on it. Splitting the community of developers and companies between two blockchains could, the argument goes, slow development of the overall ecosystem.

Clarifying the benefits and costs of a contested fork gives us a simple framework for assessing them. When the benefits of a contested fork outweigh the costs, let’s call that an additive fork. The resulting separate chains are, on the whole, more valuable than when they were a single chain.

When the opposite is true — when the loss of network effects and cooperation outweighs the benefits of continuing as separate chains — lets call that a subtractive fork.

Whenever a hard fork is proposed, we need to have two discussions: first, whether the change introduced by the hard fork should be accepted. When the community does not reach a unanimous view on the first question, we need to ask the second: in light of that disagreement, is it worth splitting the network? Would this divergence be additive or subtractive to the system as a whole?

Of course, making this distinction doesn’t help us answer the question itself. A lot of the factors that determine whether a fork is additive or subtractive are speculative or novel. A fork that appears additive may still end with the minority chain withering away, even if it had great potential in the beginning.

Moreover, the factors that determine whether a fork is additive or subtractive will likely change over time. When there is a much larger developer community, it may be less costly to split them between two distinct blockchains. When the ethereum platform is more feature-complete and the value of ether is tied more to present capabilities than future expectations, reducing active development on one chain or another might be less important to us. And when it is easier for different chains to interoperate, the costs of splitting them might be far less than today.

The term “smart contract” has no clear and settled definition. The idea has long been hyped to the public as a central component of next-generation blockchain platforms, and as a key capability for any practical enterprise application.

They are defined variously as “autonomous machines”, “contracts between parties stored on a blockchain” or “any computation that takes place on a blockchain”. Many debates about the nature of smart contracts are really just contests between competing terminology.

The different definitions usually fall into one of two categories. Sometimes the term is used to identify a specific technology — code that is stored, verified and executed on a blockchain. Let’s call this type of definition “smart contract code”.

Other times, the term is used to refer to a specific application of that technology: as a complement, or substitute, for legal contracts. Let’s name these “smart legal contracts”.

Using the same term to refer to distinct concepts makes answering even simple questions impossible. For instance, one question I’m often asked is simply: what are the capabilities of a smart contract?

If we are talking about smart contract code, then the answer depends on the capabilities of the language used to express the contract and the technical features of the blockchain on which it operates.

But if we are asking about using that technology to create a binding legal agreement, or an effective substitute for a binding legal agreement, the answer depends on far more than the technology. This answer depends on existing legal doctrine and how our legal, political and commercial institutions decide to treat the technology. If businesspeople don’t trust it, the legislature doesn’t recognize it and the courts can’t interpret it, then it won’t be a very practically useful “contract”.

It would be futile to try and change the way people use the term already. Practically speaking, we are probably stuck using — or at least reading — the term “smart contract” for now. This makes it essential for anyone interested in this space to understand the different ways the term is used and be able to distinguish clearly between them.

Smart contracts as smart contract code

Blockchains can run code. While the first blockchains were designed to perform a small set of simple operations — mainly, transactions of a currency-like token — techniques have been developed to allow blockchains to perform more complex operations, defined in full-fledged programming languages.

Because these programs are run on a blockchain, they have unique characteristics compared to other types of software. First, the program itself is recorded on the blockchain, which gives it a blockchain’s characteristic permanence and censorship resistance. Second, the program can itself control blockchain assets — i.e., it can store and transfer amounts of cryptocurrency. Third, the program is executed by the blockchain, meaning it will always execute as written and no one can interfere with its operation.

To developers and others working directly with blockchain technology, the term “smart contracts” is most often used to refer to this blockchain code. You’ll see this use of the term in the Ethereum documentation, on stackexchange and in technically minded articles. The term has been particularly associated with the Ethereum project, whose primary purpose is to be a platform for smart contract code. But today, the term is used generically across the community to refer to any complex program that is stored and executed on a blockchain.

Calling these programs contracts is helpful in that this code is governing something important or valuable. We only go to the trouble of creating a binding contract when it’s important that we be able to enforce the terms. Similarly, we only use smart contract code when the code controls something important, like money or identity.

That said, smart contract code need not resemble anything we would ordinarily think of as a “contract”. While the code could articulate a conditional financial transaction (“send 1 BTC from Alice to Bob on July 1, 2016”), it could also be a governance application that controls account permissions (“if Alice has voted yes, remove Bob’s voting rights over Application X and notify the following accounts…”).

In many cases, smart contract code is not used in isolation but as a small piece in a larger application. Every DApp, DAO, or other blockchain-based application is built using smart contract code to perform operations on their chosen blockchain. Any Ethereum application that you’ve read about — like Augur, Slock.it, or Boardroom — is made out of smart contract code.

Imperfect, misleading, and someday outdated

The term receives a lot of valid criticism. Relying on the metaphor of a “contract” is misleading because it emphasizes a single narrow use case. The term fails to capture one of the key capabilities of blockchain programs: that they have a kind of independent agency.

Smart contract programs can themselves hold balances of cryptocurrency, or even control other smart contract programs. Once they are created, they can act autonomously when called to perform an action. For this reason, many prefer the term “smart agent”, analogous to the more general concept of a software agent.

Eventually, this use of the term may simply fade from use as blockchain technology matures.

Developers will be more likely to refer to a specific language (“Let’s look at your Solidity code”) or platform (“Our application runs on Eris.db”) that they are working with, as opposed to a generic term that could describe any complex operation on a blockchain.

The capabilities and purpose of smart contract code as distinct from other code may simply become clear from context, without requiring the use of a clumsy analogy like “contract”. It might end up being more similar to how we speak of HTML and JavaScript today, without having to think about how the former is a “markup” language, playing a distinct role from JavaScript in the overall web application.

Smart contracts as smart legal contracts

Among those who work in finance or law, the term “smart contract” is often read quite differently than the definition above.

“Smart contract” here refers to a specific use case of smart-contract code — a way of using blockchain technology to complement, or replace, existing legal contracts. This is the definition of the term I considered in my last piece: the use of code to articulate, verify, and enforce an agreement between parties. A smart legal contract.

These smart legal contracts would most likely be a combination of smart contract code and more traditional legal language. For instance, imagine a supplier of goods enters into a smart legal contract with a retailer. The payment terms could be defined in code and executed automatically when delivery is made. But the retailer would likely insist the contract include an indemnity clause, whereby the supplier agrees to indemnify the retailer against claims flowing from a defective product. There would be no point representing this clause in code, since it is not something that can self-execute — it exists to be interpreted and enforced by a court in the case of litigation.

Commercial agreements are full of boilerplate clauses that protect parties from various edge-case liabilities, and these are not always suitable for representation and execution through code, meaning that smart legal contracts will require (at least for the foreseeable future) a blend between code and natural language.

This is the basic idea behind Eris Industries’ dual integration system, Primavera de Fillipi’s proposed Legal Framework for Crypto-Ledger Transactions, and R3’s Corda smart contracts system.

Could smart legal contracts ever be considered legally enforceable? Probably. Despite what many think, the conditions under which an agreement becomes a legally enforceable contract are flexible and attuned to the underlying relationship between the parties, rather than dependent on the form the contract takes. Anything from a verbal agreement to an email conversation can become a contract at law, if the basic elements of a contract can be found.

Many contracts, many use cases

The category of smart legal contracts is complicated by the fact that there are many different types of contracts in the world, only some of which are obvious candidates for use as “smart contracts”. A legal contract could be anything from a verbal agreement for someone to paint your house to a derivative traded electronically in financial markets.

Since early 2015, the use cases attracting the most attention are smart legal contracts as smart financial instruments like shares, bonds, or derivatives contracts. Articulating these contracts in code could allow financial markets to become more automated and simplify many process-intensive systems related to trading and servicing of financial instruments.

These “smart financial instruments” do not exist at scale today, although many people are working to build them. R3’s recently announced Corda platform is designed to facilitate this type of smart-contract. Digital Asset Holdings recently acqui-hired Elevance, a Swiss firm that has developed a way to model financial agreements in code. In April, Barclays’ revealed details of a scheme, in cooperation with R3, to represent ISDA agreements in smart contract code.

Financial instruments are just one type of contract that could benefit from blockchain code. As the technology matures, other assets — e.g. real estate, or intellectual property — may be stored and traded over blockchain systems. As new asset types go “on-chain”, the agreements used to govern those assets in the world today (like a mortgage or licensing agreement) may benefit from blockchain-based analogs.

Alternatives to traditional legal agreements

Many advocates for blockchain technology see larger possibilities. Rather than merely imitate or complement the legal contracts we use today, perhaps smart contract code could be used to facilitate new types of commercial arrangements.

We might even call this a third definition of the term: using smart contract code to create novel, alternative forms of agreements that are nonetheless commercially useful. Let’s call these “smart alternative contracts”.

This approach takes a broader view of the real-world problem solved by contracts. Commerce depends on individuals being able to form stable, predictable agreements with one another. Contracts, along with a strong legal system, are the primary mechanisms we use to shape each party’s incentives to the point where they have sufficient confidence in their relationship to engage in the risky business of trade.

But perhaps legal agreements are not the only solution to this general problem. Smart contract code offers a new set of tools to articulate and enforce terms, and they can be used to create systems of incentives that may be sufficient to make commercial relationships possible.

The most widely discussed opportunity of this type is machine-to-machine commerce. The growing ecosystem of smart devices — particularly those that are in some fashion autonomous — will eventually need a way to engage in basic commercial interactions with one another. For instance, a washer that buys its own detergent or a car that can pay to recharge itself.

These transactions still require a minimum level of trust to be commercially viable, but are ill-suited for legal contracts, which are comparatively expensive and require the involvement of legal persons like a corporation or human. Smart alternative contracts might enable an entirely new type of commerce carried out between our computers, cars, phones, and appliances.

There probably are — or will be — other types of commercial interaction that aren’t well suited to traditional legal contracts. New markets, suddenly made possible by technology, but which are underserved by legal tools that are slow to innovate and adapt.

Smart alternative contracts might let us stretch the web of trust out a little further, a little faster, beyond the reach of the legal system, where they can enable new forms of commerce not possible today.

Conclusion

The lack of clear terminology in this field is an unfortunate reality. Those of us who work in the blockchain space should be mindful of how the term is being used in different communities, and be prepared to ask a series of annoying, though necessary, clarifying questions when asked about the nature and potential of “smart contracts”.

The different uses of the term illustrate a broader challenge in our industry. The interdisciplinary nature of blockchain technology, and “smart contracts” in particular, lead people to see the technology as primarily belonging to their own discipline, at the expense of the others.Lawyers often look at smart contracts and see marginally improved legal agreements, without appreciating the fuller potential of blockchain-code to extend beyond law’s reach.

Developers, on the other hand, consider smart contracts and see the limitless possibilities of software, without appreciating the subtleties and commercial realities reflected in traditional legal agreements.

As with any interdisciplinary field, both must learn from the other.

Over the last year, the concept of a “smart contract” has received renewed attention in both the technology industry and in legal and business circles. Recent advancements in a field known as “blockchain technology” have led some to believe that smart contracts could soon offer alternatives to traditional commercial and financial agreements, with dire results for the legal and financial sectors. While this enthusiasm may be premature, lawyers nonetheless remain mostly unaware of this important emerging technology and the long-term implications for their profession.

In this context, “smart contract” refers specifically to the use of computer code to articulate, verify, and execute an agreement between parties. Whereas a typical contract is drafted using natural language, the terms of smart contracts are expressed in code, similar to a programming language like javascript or HTML. The contract is then “executed” by a computer — given the conditions of the agreement, and a set of defined inputs, the smart contract enforces its own terms.

Readers familiar with blockchain technology will know that the term “smart contract” is often used in a more general sense to refer to any script or program that operates on a blockchain. However for the purposes of this article, I focus on the narrower meaning described above: using code in place of traditional contractual agreements between parties.

The term “smart contract” was first popularized by computer scientist Nick Szabo in his 1997 paper The Idea of Smart Contracts. The vending machine, he described, is the simplest form of a “smart contract” — a mechanical device designed to transfer ownership of a good (a candy bar) when provided with a certain defined input ($1.50). Because the machine itself “controls” the property — by being physically sealed — it is able to enforce the terms of the “contract”.

Extending the concept, Szabo suggested that computer code could be used in place of mechanical devices to facilitate far more complex transactions of digital property. Rather than transfer ownership of a candy bar, a smart contract could transfer ownership of real-estate, or shares, or intellectual property. The program would define what “inputs” were necessary for the contract to execute — things like payment, or votes of board members, or any other condition that can be expressed by code.

Consider a basic options contract. A call options contract entitles the holder to buy a given security at a defined price. In our example, Alice buys our “smart options contract” from Bob. The contract entitles Alice to purchase 100 shares of Acme Inc. from Bob at a defined price of $50 per share. The contract has an expiry date, after which Alice is no longer entitled to buy the share at the defined “strike price”.

Expressed in pseudo-code, a simple “smart options contract” might look like this:

contract Option {   strikePrice = $50
   holder = Alice
   seller = Bob
   asset = 100 shares of Acme Inc.
   expiryDate = June 1st, 2016   function exercise ( ) {

             If Message Sender = holder, and
      If Current Date < expiryDate, then
         holder send($5,000) to seller, and
         seller send(asset) to holder
   }

}

In the first section, the smart options contract defines the relevant terms — the underlying asset, the strike price, the identities of each party, and the expiry date. Then, a function we’ve named “exercise” enables the holder to trigger the purchase of shares at the strike price at any moment before the expiry date. The function first checks to see if the entity triggering it (the “Message Sender”) is the holder, and then checks to see that the contract is still within the expiry date. If both are true, then the contract immediately executes by transferring cash from the holder to the seller, and the assets from the seller to the holder, according to the contract’s terms.

Until recently, smart contracts were little more than theory. In general, there were two fundamental challenges that needed to be addressed before smart contracts could be used in any real-world setting.

(1) How would a smart contract actually control real assets so that it could enforce an agreement? A vending machine, to return to Szabo’s example, controls property by physically securing it inside of itself. But how could code do the same? In our options contract above, the “exercise” function transfers money and assets between the two parties. But how can a computer program control real-world assets like cash and shares?

(2) What computer would be trusted to “execute” those terms in a way that both parties could rely upon? Parties must not only agree on the code of their contract, but also the computer which interprets and executes that code. A shared standard, at the minimum, would have to exist, and be used in a way that was verifiable by each party — ideally, without requiring the parties to physically inspect the computer in question.

In the last few years, solutions to both of these problems have come into sight. Emerging research and development surrounding what is called “blockchain technology” may provide a basis to make smart contracts a reality in the near future.

The first use of blockchain technology was the digital currency bitcoin, made famous by its mysterious creator and sudden price increase in late 2013. In the last few years, the underlying “blockchain” technology has been intensely studied and adapted to expand its use beyond simple digital currencies. Startups, open-source communities, and large financial institutions alike are improving and expanding the technology with the aim of one day using it to facilitate exchange of fully digital assets.

A blockchain is an authoritative database. It is a database that, by virtue of the way it is maintained and updated, has very high trust properties. Blockchains are not controlled by a single party. There is no single company, organization, or person that has ultimate control over a blockchain. Rather, a blockchain is maintained, updated, and secured by a network of participating computers.

Each computer keeps a full copy of the blockchain database, and each copy is kept in synchronization with the others by a system of cryptographically-enforced rules called a consensus algorithm. Crucially, blockchains are append-only databases, meaning that once information is validly added, it can never be removed. Each update to the blockchain is secured by a cryptographic process known as a hash function, which allows the network to immediately detect and reject any attempt to distribute an edited copy of the database.

In this way, blockchains form the foundation for the recording and transfer of fully digital assets. Because the blockchain is always kept in synchronization, there is only ever one true record of ownership — essential to prevent anyone trying to double-spend their assets by sending it to multiple parties at the same time, a problem that plagued previous attempts to create digital assets. Because it is impossible to edit a blockchain once it has been properly updated, parties have mathematically-enforced confidence that the record of their ownership will persist into the future.

While the technology is still in early stages, many now believe that if blockchains can create a secure platform for the trade of digital assets, they may also solve the two fundamental challenges facing smart contracts.

First, recall that smart contracts require a way for computer code to control real assets. By enabling fully digitized assets, blockchains make it possible for code to exercise control over property. On a blockchain, control over an asset means controlling a cryptographic key that corresponds to the asset in question, rather than any physical object. Thus in our example above, the options contract could itself have control of the underlying assets, rather than an escrow agent. When the “exercise” function is called, the operation of the code would transfer the assets without requiring any human assistance.

Second, smart contracts need a “trusted computer” that would execute the terms of the contract. This is the blockchain itself. The blockchains that are being developed today are not only databases, but distributed computers that can execute code as well as record ownership of assets. Our “smart option” example would itself be uploaded and stored on a blockchain, and would be executed by the blockchain when instructed to do so. The same properties that make blockchains ideal to record ownership of assets also make them ideal for executing smart contracts. Once the code of the contract is uploaded and recorded onto the blockchain, the parties can have confidence that the contract cannot be altered, and that it will always perform as expected.

Blockchain smart contracts may not be as far away as we expect. Banks, exchanges, and other financial institutions are actively developing blockchain technologies that will enable them to store and trade real assets over blockchain systems. Nasdaq, in partnership with blockchain startup Chain, has developed and begun testing a private-market equity trading platform. A next-generation open-source blockchain called Ethereum, launched in July 2015, aims to be the foundation for a new industry of non-traditional decentralized commerce. A consortium of 42 banks, working with blockchain firm R3, have begun work on a shared industry platform based on blockchain technology specifically designed to facilitate financial agreements. Within a few years, financial markets may be trading fully-digital assets across blockchain networks, with the terms of those trades enforced by code.

The impact will not be limited to financial contracts, although these are the most obvious use cases. As techniques are developed that enable other types of property to be recorded and transacted on a blockchain the possible applications for smart contracts will multiply.

If they ever become widely used, smart contracts could alter the nature of corporate & commercial transactions. The advantages of software that have revolutionized so many industries — automation, predictability, and speed — could finally be brought to bear on segments of the legal industry.

Representing contractual terms in code, rather than natural language, could bring clarity and predictability to agreements. A smart contract could be tested against any set of inputs — in other words, against any set of material facts which it takes as inputs — allowing lawyers on either side of a deal to know precisely how the contract would execute in every computationally-possible outcome.

In our simple Smart Options example above, each of Alice and Bob could “dry run” the contract in a simulated environment, where every possible input is tested. While this is unnecessary in such a simple example, imagine a contract with thousands of inputs, and hundreds of nested if-then statements — as is common in many complex financial agreements. These, too, could be tested against every possible input defined in the code. Analogous to how software developers “debug” their own code by testing it in every possible circumstance, lawyers could test contracts, giving each side of a deal a clearer understanding of their risk — and perhaps requiring fewer billable hours.

Of course, smart contracts will never fully replace natural-language law. Many types of agreements can never be fully expressed in code or executed by a computer — for instance, those that involve human performance rather than just the exchange of dematerialized assets. Even fully self-executing contracts will ultimately need to make reference to legal terms and concepts that will define each party’s rights if their relationship leads to litigation. Rather, the emergence of smart contracts will lead to a re-evaluation of common practice, as lawyers and clients alike discover which types of agreements and terms are best suited to code, which should be left to natural language, and how to combine each to achieve the best of both worlds.

For now, smart contracts are still speculative fiction. But for the first time we have a technology that could be used to bring them into commercial use. While that day may still be years away, law firms would be wise to consider how these innovations could impact their business. By the time smart contracts become viable, the legal industry should hope that they have lawyers to match.

Imagine thousands of computers all over the world connected by the internet. Some are home PCs, some are laptops, some are servers sitting deep underground. But each of them runs the same computer program, which connects all of these computers into a network of equals.

This program sets out rules for how the computers should work together. How to talk to each other. How to store data. Specifically, this program lets them behave as though they were all together a single, world-spanning computer. Every one of the thousands of devices that makes up the whole does the same thing, in the same order, in lock-step. They’re all recording the same information and running the same programs. In a real sense, it is one computer. This computer is Ethereum.

The Ethereum world-computer is slow. It has to be. Every one of the thousands of component computers — which we will call “nodes” — is doing doing the same things at the same time. This also makes it expensive, compared to a regular computer. Every operation must be performed in parallel, all over the world, requiring hardware and electricity.

But the point of Ethereum isn’t to be fast or cheap. The point is to be trustworthy. Any program that runs on Ethereum is guaranteed to run the same way everywhere, on every node. Data stored on Ethereum is available everywhere, and it is permanent. Ethereum is a place to run programs and store data that are simple, but for which trust is paramount.

Imagine you need to record something important. Like ownership of a house. It’s a simple piece of information, but it must have permanence. You need to be sure, first, that no one else can delete or edit this information. You also need to be sure it will always be there— so that even years later, you can refer to it as proof of ownership. And you need other people to be able to see it, and to be able to verify for themselves that it is authentic, and has not been edited. Ethereum lets you do this.

Or, imagine that you need to run a simple piece of code. The code is an agreement between two parties. It’s a bet — Alice receives money if the price of oil goes down, Bob receives money if the price goes up. Both parties must have confidence that the program will operate in the way they intend, and that it cannot be deleted or edited without their permission. Alice wins. Ethereum lets them do this, too.

It turns out there are many situations where we do not need fast or powerful computers, just trustworthy ones. People are already building applications on Ethereum that take advantage of this. Devices like locks or cars that can respond to payments of cryptocurrency, or make payments themselves. Voting and governance systems that are automatically enforced. Prediction markets where people can bet on future events. Triple-entry accounting systems. Platforms for musicians to sell their music, without anyone else getting a cut. These applications are only the first — the network is not yet even a year old.

Our world is full of systems that depend on trust. Any activity that involves money, value, property, or control requires a way to create trust, to convince people that they can rely on it. No one engages in trade or casts their vote unless they have confidence that the system will work as it is supposed to work. Today, we rely on trusted intermediaries for this purpose. Banks, financial institutions, legal systems, and governments all act as middlemen in different contexts.

Now we have a new way to create trust, a new technology unlike anything that has existed before. It doesn’t rely on any person or company or government. It doesn’t exist in any one place and can’t be permanently destroyed. Anyone can use it to create trust, and to create applications or services that are inherently trustworthy.

The traditional middleman won’t disappear. But they face a radically new form of competition.

You’re up early for work this morning, in the kitchen making coffee. In the backyard, out the window above the sink, is your landpad & charging station, decorated with the bright bold outlines of its QR address.

On the landpad sits the delivery drone, charging quietly. As its batteries fill, small amounts of money are paid into your house’s account every few kilowatts. It could charge lots of places — there are cheaper sources of power available of course —but in the end the drone cares about location. Your backyard is close to several distribution centres and a dense neighbourhood that makes a lot of purchases that are in the right weight category for this model. It’s a nice bit of cash every month. Some neighbourhoods tried to ban renting backyard space. It worked in some places, but others weren’t happy having to pay a higher average delivery fee for drones having to charge farther away.

This drone is one of several that have often used your landpad over the last few weeks. When it landed for the first time you thumbed through the ownership structure. Unlike the last few that took up residency in your backyard, this one owns itself. When it was built it was owned 50% by the manufacturer, and the remainder split between a variety of private investors, pension funds, and a bundle of smaller shareholders. But last year, a charity collective purchased a majority of the shares and set it on a path to self-ownership, voting the drone to take its profits and invest in purchasing its own shares. Last April, the drone purchased the final shares and became sovereign.

So the drone owns itself. You had heard of these before but hadn’t contracted with one. A bit novel, but in the end it was a delivery drone like any other. The collective that did this has some ideological motivation for setting devices on a path to self-sovereignty, but it doesn’t seem to make a lot of business sense to you. Drones can’t make strategic decisions. Traditionally owned drones have shareholders who can instruct it to upgrade itself with new processors, or even scrap the drone for parts when it makes economic sense to do so. But drones themselves aren’t capable of doing much more than deliver packages and recharge themselves.

But new business models and services for delivery drones have appeared to mitigate this problem. This drone outsources strategic decisions to a streaming management service — a distributed organization of enthusiasts, experts, predictive algorithms, and other drones that provides strategic direction services for a fee. The management service comes up with broad outlines of a strategy for its collective of subscribing drones, and distributes instructions out to them. Beyond making sure the drones remain competitive with upgrades, the service performs high-level management functions like trying to predict demand. Drones are instructed to take up landing leases in neighbourhoods where demand for a new product might spike, or where a new distribution point is about to open. Drones report their outcomes back to the management service, which then refines its strategy accordingly. The drones cooperate with each other where appropriate to avoid competing directly, and there’s some form of equalization payment constantly flowing among the collective to ensure individual drones don’t suffer too much from unforeseen market conditions — a safety net.

The drones pay a small streaming fee to the collective for access to its decision making structure. Not just self-owning drones — many drones owned by corporate interests also subscribe to streaming management services to run their drones in an efficient and competitive manner. Everything, of course, is instant and automated. Tiny payment channels opening and closing between hundreds of thousands of drones and the ecosystem of services that support them, trading value and other information between each other seamlessly. Vast arrays of smart contracts flicker into form to guide every interaction, a web of rights and obligations connecting drones to their owners, their insurers, their customers, and their chosen management service. Contracts being altered, closed, wound up, abandoned as market realities change or participants form new strategic alliances, to better take advantage of the shifting demand from consumers who rely on these fleets of robots for the movement of physical goods.

You thumb through the drone’s identity page and notice a field you hadn’t seen filled in before. It might be a cute joke — or maybe they really believe this stuff — but the collective that set this drone on a path towards self-ownership also gave it a name. This drone is Russell.

Russell’s battery is fully charged and it will waste no time waiting on the ground. While it was charging it was listening to the constant stream of contract requests being sent out by every distribution point within the relevant radius, tracking them as they were announced, bid on, and secured by other drones. Once the battery was charged, Russell secured the first contract it could that met its margin requirements and headed towards the distribution point, a local coffee roaster. The drone lifts off. Within a few minutes, another drone will have landed to take its place to charge before returning to the skies.

The coffee roaster is a few blocks away. The drone identifies the QR-addressed landpad on the roof of the shop where a small bright yellow box sits. The drone picks up the box and sends a message to the smart contract created when it accepted the offer from the coffee shop. The coffee shop receives a notification that the box has been picked up, which it passes onto the customer’s phone. A payment channel opens up between the roastery and Russell, and small amounts of currency begin to be transferred at the agreed-upon time/distance rate as the drone begins moving towards its destination.

The roastery needs to know that the drone is taking a direct route. Not that the drone would ever behave in any other way — why would it risk it? But the loose, decentralized network of commerce & code that facilitates our daily activities depends on provable trust.

Part of the contract between the Russell & the roastery was an agreement to use a decentralized geolocation service called Beholder. As Russel flies towards its destination Beholder instructs the drone to use its cameras to pick out obscure but easily identifiable features of the landscape around it, and then answer a question about that feature. Is the garage door of this house open? Is the streetlight at the intersection of 4th and Broadway currently green or red? Beholder contracts out to other drones (and other non-drone sensors, like cameras in cars or those installed on homes) to generate the challenges & verify Russell’s answers. Where possible, sensors are also used to identify Russell visually and confirm its location, and report back to the Beholder network. All of the sensors involved are paid small amounts of value for their role in Beholder’s service. It isn’t unbreakable — that’s not the point. But the cost of breaking it is high enough to reach a minimum threshold of trust in the drone’s location. Distributed, autonomous crypto-economics in action.

The drone arrives at its location and deposits the box in the backyard of a two story brownstone. The delivery pad scans a code on the underside of the box and signs a message to the contract signalling delivery once the box’s weight is fully resting on the pad and the drone has released the cargo. The drone scans a code displayed by the delivery pad and signs a message back to the contract as well, to prove that it has delivered the box.

Several things happen at once. The payment from the recipient for the small bag of coffee ($42.76, or 0.06127 ΞTH), held in escrow by the contract, is released to the coffee shop. The payment channel between the coffee shop and the drone is closed, its task complete, and the drone lifts off again having already secured its next delivery at the delicatessen a block over. A woman’s phone buzzes in bed to tell her that the coffee she ordered (Santa Barbara Blue, 30% Arabica, espresso grind) has arrived.

Cover photo by Skye Jones, CC 2.0

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In the third post, we explored how blockchains can serve as a platform for what I called “value technologies” — things that act like currencies, shares, or rewards points.

In this post, we take this idea further. Many people now believe that blockchains will let us build things more complex and more interesting than the simple value technologies discussed above. We can write software that can itself control currencies, shares, or rewards points. This “software agent” would be more like an independent economic entity. Living on the blockchain, it would be able to send, receive, or hold money — whatever it was programmed to do. Maybe it even provides a useful service to people, or to other software, for which it is paid.

The question people have begun to ask, in other words, is: can we make companies out of code?

Ok, let’s back up. What would it mean for there to be software that “lives” on a blockchain?

You understand that bitcoin is a currency-like blockchain application. It was built to let people send, receive, and hold bitcoin. Bitcoin’s blockchain is structured like a ledger — a long list of transactions that records who has control over every amount of bitcoin that exists. It was built to do this one thing.

Another way to think about this is that the Bitcoin blockchain is a big computer that runs only one program. The program lets you move bitcoin from one address to another, and that’s about it. You can’t write new programs, or install entirely new software, or do much of anything beyond simple transactions.

But what if you could write new programs, install new software? What if there was a blockchain that had the same fundamental benefits as the bitcoin blockchain, but gave anyone the ability to write code that would run on it — not just transactions of a currency-like token, but things with additional rules & complexity… like shares, rewards points, or corporations?

This is the idea behind another blockchain called Ethereum. It comes with a “built in” set of programming languages that let anyone write code that “runs” on the Ethereum blockchain in a way similar to how a bitcoin transaction “runs” on the bitcoin blockchain. Instead of simply tracking balances of bitcoin, the Ethereum blockchain tracks the state of all of the programs that are stored on it.

A software agent can “live” on the Ethereum blockchain in the sense that the code that constitutes it is stored on the blockchain. Because there is always consensus on the state of the blockchain — just like there is always consensus on the state of who owns what bitcoin — the program will always behave as functioned. It cannot be altered or deleted unless it was programmed to allow it to be altered or deleted. Once it’s on the blockchain, it can be autonomous — it doesn’t depend on any individual computer to survive. It exists on thousands of computers all over the world, and every copy is kept in perfect sync by the blockchain.

Crucially, for our purposes, these software agents can natively control money. In the case of Ethereum, they can control balances of Ether, the native token of that blockchain. They can also control other smart agents, or any other program on the blockchain. In short, they can be programmed to perform any operation that people can perform within the Ethereum system.

In the first post, we discussed how blockchain technology might cut out middlemen. Firms like Western Union sit in the middle of a simple transaction — moving money from A to B — and extract a fee. Bitcoin, by letting anyone anywhere send money for free, can compete with the service provided by Western Union.

The promise of blockchain “smart agents” is that they allow blockchain technology to cut out other kinds of middlemen, too. Even ones that provide complex services, and sit in the middle of complex transactions. At the top of this post I said the question was: can we make companies out of code? A better formulation might be: can we write code that can compete with services today only offered by companies?

One of the most promising areas for this to happen is in peer to peer marketplaces, where a company sits in the middle of complex transactions facilitated by technology. For instance, transactions where a rider pays another person a small fee in exchange for a ride.

Uber runs a service that connects riders to drivers. When a user requests a ride, Uber’s software matches them to a nearby driver. When the trip is verified as complete, the software calculates the fee based on the phone’s GPS, and facilitates the transfer of money from the consumer to the driver. Uber takes a 20% cut.

Maybe a blockchain service could offer something similar. The core business logic — how to match drivers and riders, how to verify trips are complete, how to calculate fees — would be set in place & guaranteed by a blockchain. Payment is, of course, handled over the same platform. There is no legal entity, because there doesn’t have to be — this is all just software that a community of people have created for public use. Beyond the small fees paid as part of transactions on the blockchain, there is no entity to take a cut — which means either lower prices for riders, larger profits for drivers, or both in lesser amounts.

This blockchain “ride sharing” platform wouldn’t so much be a company as a protocol. Think of it like email. No one owns email: it is just a widely accepted protocol — a system of agreed upon rules — for exchanging information. But there are many email clients made by companies that let people use the email protocol.

A blockchain ridesharing protocol would simply be a set of rules — enforced by the blockchain’s lock-step consensus — that define how to enter into a certain type of transaction: exchanging money for a ride. There would be many clients — apps on your phone — that would let you interface with it. But the underlying service — matching, and transfer of funds — would be facilitated by the blockchain.

Of course, Uber does more than match riders and drivers. They provide many other services that improve on their core offering in some way. They make software that makes it easy & convenient to call a ride. They provide an identity and reputation system for riders & drivers. They are a brand, and they’ve spent a lot of money advertising their services and creating new markets for their product.

One mistake many blockchain evangelists make is discounting the importance of these other functions. A good piece of technology is necessary but not sufficient for success as a product — especially when the product is something new or unfamiliar. I believe that an open source community could build an excellent ride sharing protocol, but I do not believe they could run an effective nationwide sales organization.

But maybe we don’t have to, in some cases. Uber has already created the market for technology-driven ridesharing. Maybe a “no name” version could compete. It wouldn’t come with all the bells and whistles at first, but it would be cheaper.

We might enter a world where there are innovator marketplace services and generic marketplace services. Uber, AirBnB and others had the idea, proved the technology, and changed consumer habits to build the market. But once the market exists — once renting out your apartment is simply a thing you do — there might be room for a generic version, run entirely by code, to compete with lower prices and by returning nearly the whole share of revenue to labour.

You’re a little early for work that day and decide to walk to the far Starbucks instead of going to the close Starbucks. The close Starbucks is one of those ones they cram into a rough corner of an office building, halfway between the fire exit and a fern. The far Starbucks is glass and wood and gleaming like a Starbucks should. You arrive and wait in line to buy a coffee.

Your previous purchases have filled your wallet with Starbucks points. You’re dimly aware of this. There are notifications from your phone occasionally, when you reach certain milestones (100,000th point collected! Grande Achievement unlocked!). You remember a few recent promotions that you sought out specifically for the high point rewards (first day of pumpkin spice lattes was 4.5x last fall). Some of the points are bound to the Close Starbucks, in the sense that they offer you 1.3x when used at that location instead of the base 1x at any other Starbucks — the property management company subsidizes them, in a vain effort to entice consumers into their monument to brutalism.

Some offices give employees points that are worth more at Starbucks near to their offices — a way to get them back into the office sooner — or in some other way valuable. (The ad agency up the street incentives their employees this way to visit coffee shops, bars, and restaurants that are deemed culturally relevant and therefore important for their employees to have visited, consumed, absorbed, reflected upon).

Some points are redeemable not only at Starbucks but at a variety of cooperating retailers. Others won’t mature for another six months, and offer increasing returns of 1% every month after the maturity date — a way for Starbucks Inc. to hedge against demand uncertainty into the next fiscal year. Other Starbucks points offer, when spent, a chance to win a vacation in beautiful Maui. (When these points are used, your phone lights up with special graphics. You saw someone win once, the gleaming golden ticket appearing briefly on the wall menus and then residing proudly on a highlighted tab on a woman’s iPhone.)

You don’t know most of this, but your wallet does. It lazily keeps track of the multipliers and values and important dates of your various Starbucks proprietary currency and turns it into actionable advice. A map that shows the effective price of your preferred products at nearby locations, built from the bundle of value in your wallet. A system setting that prevents you from spending points below a certain redeemable value as compared to the maximum likely market value of the points within a six month horizon.

Another set of algorithms identifies ways to exchange surplus points into points for other businesses on the burgeoning secondary market, and that automatically carries out the trades. Did you visit a chain restaurant once on a trip that you will probably never visit again? Your wallet knows that, and it will trade those points away for something you will use. There is a billion dollar marketplace, consisting of trades often worth less than 50 cents, where rewards points from virtually every consumer-facing business in the world are traded by our wallets instantly, conveniently, invisibly. Worrying about, and managing, the secondary market value of points has become a new concern for the modern CEO. A thousand little central banks. Some people say that, in 10 years, we’ll use currencies issued by corporations and non-state organizations as often as we use fiat.

All of this can be abstracted away depending on your preferences — you know some people who go deep into it, but you’ve paid for a fairly good wallet and trust it to manage everything well. You often collect points, and spend them, without knowing where they came from or what they were used for. From your perspective, Friday’s after-works beers were cheaper than usual and that’s the end of it.

This is a lot more controversial than it sounds. Some merchants have taken to distributing points only to users whose wallets enable some form of brand awareness. Distributing points to people who don’t know the points came from Swiss Chalet, from the brand’s perspective, undermines part of the value of the points system for the merchant (and they have data to prove it). But many consumers feel they deserve the points without being forced to see an ad dozens of times a day. Why shouldn’t they be able to control what they have to see on their phone? Even some in the industry believe it’s an unnecessary source of friction that undermines this aspect of the New Economy. Blocking apps try to trick retail POS terminals and beacons into believing that the brand has been viewed, and points should be received. Merchants build software to detect the most common ones, and have appealed publicly to Apple/Xiaomi/Android to block the blockers from their ecosystems.

On the whole, it’s been a good thing. Directly creating monetary incentives for consumers that are easily translated into actionable advice through mediating wallet software is usually more effective and reliable than paying for advertising. Secured identities through wallets has solved a lot of the fake-view problem that plagued online ads. Some products still need ads to move units — lifestyle items, class signifiers, luxury services. But most goods are mundane, interchangeable, and most people are price sensitive.

Where rewards programs were once the exclusive domain of national brands, now they’re easy and cheap enough to implement that small businesses use them. Setting up your points system costs about as much as setting up a website. It costs less than printing and shipping an order of punch cards.

The deceptive or manipulative rewards programs / loyalty points that used to succeed by exploiting common human cognitive biases have a harder time succeeding. Your wallet is now doing the math for you, and it is very good at math. These still exist to some extent, in a different form. There’s a whole grey industry of trying to identify bugs in wallets’ pricing mechanisms or blind spots in their market data, which are then exploited by unscrupulous retailers. When a major brand gets caught doing this, it’s a scandal.

You collect far more points and coupons than your mother ever did. But she had to carefully cut them out of newspapers, and track their endless permutations in her own head, on her own calendar. She would carry a binder full of them, and embarrass you by awkwardly messing through it at the grocery shop. You collect more points than you did in your twenties, even though you had signed up for the credit cards and rewards cards that offered them. You lost your aeroplan card shortly after ordering it and could barely keep track of which purchases offered which rewards with which cards. Thick leather wallets. Forgotten passwords. Account fatigue.

Now your wallet does it all, and to you, it is just money.

You purchase your coffee (**^**34 sbux.Pts @ 1.0x + 4 sbux.Pts @ 2.0x + 0.000084 btc + 0.000009 eth, v15 sbux.Pts [lock: 24hrs] + 10 amzn.Pts @ 2.56 ++0.1 @ 60 days) and head to work.

Cover Photo by Thibault Poriel licensed under CC BY-SA 2.0

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