• A thesis on why the ‘Data Economy’ and attracting data projects to a blockchain should be second in priority after Decentralized Finance.

• Today, energy is the backbone of the global economy, intertwining everything we use and produce in our daily lives, from industrially produced commodities and mobility to cooking our food. However, with advancements in solar and battery, the marginal cost of energy production is literally a near-zero cost phenomenon. Consequently, I believe Data(Data Assets) will replace Energy as the backbone of the global economy.

• Returning to the comparison between blockchains and nation-states, access to affordable and reliable energy plays a crucial role in a nation's competitiveness. When energy costs approach near zero, access to data will then become a nation's competitive advantage. Hence, a nation-like blockchain's access to data will be its MOAT.

• This thesis emphasizes why blockchains should prioritize the Data Economy for on-chain activity and can even serve as a foundational research platform for blockchains that are strongly considering tapping into the data economy

Prerequisites: Basic understanding of web3

Table of contents

1. First Principles - What’s Blockchain & Data

2. Why Blockchain + Data?

   1. Access, reliability & traceability

   2. Value creation

   3. With the emergence of IoT & AI, Consumers evolving into prosumers

   4. Data Dilemma

3. Trends to note, and accordingly align strategic priorities

   1. Data Privacy

   2. Expansion of Data-Driven revenue streams: Collection, sharing, & analyses

   3. Data Security

   4. Data Synchronicity

   5. Data Ownership & Portability

   6. Data Governance, DAOs as Data Trusts

4. What interesting projects do we see in the space?

   1. Data Marketplaces - Data DeFi

   2. New business lines with existing data

   3. Data DAO/Unions

   4. Data Tokens

   5. Data Wallets

   6. Marketing & Advertising in a cookieless world

   7. Privacy projects

   8. Composable & Reusable Data

   9. Blockchain-ready sensor technologies

   10. Public-Private Data Partnerships

5. The road to perpetually sustainable crypto economic activity

   1. Architecture

   2. Stack

   3. The data economy will be deployed in(chains)

   4. Priority framework for a blockchain

6. Conclusion

   1. How should Blockchains Strategize

What’s Blockchain & Data?

What is data? - Data is a collection of discrete values that convey information. It describes attributes like quantity, quality, facts, statistics, etc that may be further interpreted. Data is the smallest factual unit of information. When such information is processed, analyzed, and connected with other pieces of information we get data-driven insights or intelligence. Hence,  Data can be regarded as the atom of decision-making. It is used in scientific research, finance, and almost every form of human organization.

What is a blockchain? - A blockchain is a database or ledger that is distributed among the nodes of a p2p computer network. As a database, a blockchain stores information electronically in a digital format. Data stored in blockchains is tamper resistant, has better accuracy, and is peer-reviewed. It facilitates the exchange of data, aka “transactions”.  Blockchain leverage protocols, a set of rules that govern the blockchain network, it is a set of economic incentivization mechanisms that in turn manifest and accrue $Value . They are a set of guidelines that facilitate & incentivize information exchange in a secure, fast, simple, p2p & trustless way.

Why blockchain & data?

It's no secret that Blockchain and Data Science go hand-in-hand. Blockchain technology aims to create a secure and decentralized system for managing data, while ‘Data Science’ focuses on analyzing and understanding data. A great way to illustrate the relationship between blockchains and data is to think of a token (any ERC standard) as a wrapper for various types of data, ranging from a picture to a Netflix show or even a LinkedIn subscription. In the case of NFTs, every data item becomes non-fungible, assigned a unique ID, stored on the blockchain, governed by protocols, and valued through cryptocurrencies.

When modern data management and blockchains are combined, the possibilities are truly endless.

Access, reliability & traceability

Access to reliable data is a powerful tool that can be used to solve complex problems, such as predicting cancer years in advance. Unfortunately, less than 1% of the world's data is currently analyzed, and there are challenges with reliability and accuracy. While we have great AI models, they require vast amounts of reliable data to be more effective.

Presently, data is often collected and stored in isolated servers controlled by the platforms that gather it. This creates issues regarding data reliability and challenges in verifying its origin for data consumers. However, blockchain technology offers solutions to these problems by leveraging traceability, peer-to-peer (p2p) components, consensus mechanisms, incentivization, and tokenization features.

By utilizing a blockchain, real-world data can be recorded more effectively while ensuring its reliability. The consensus mechanism and immutability of the blockchain eliminate the need for trust in a central entity or middleman to validate the authenticity of the data.

Value Creation

Data is often referred to as the new oil, and the combination of blockchains and data allows us to extract the maximum value from this valuable resource. Thought leaders like Shermin Voshmgir envision blockchain networks as data-driven economies. As modern society increasingly spends time in the digital realm, recording more interactions with data on a public blockchain, rather than on existing isolated systems like SaaS platforms and two-sided marketplaces, we can create the internet of value. This serves as the foundational infrastructure for a globalized digital economy, promoting a free market and democratic structure.

Tokenomics, which involves cryptocurrency mechanisms, further enhances the value creation of data. Currently, value creation is primarily seen in two-sided marketplaces. For example, on LinkedIn, we pay for a sales navigator because it provides a data repository of the business ecosystem. Similarly, b2c brands pay for marketing on Instagram because it offers a data repository of user behavior patterns, which brands utilize for targeted advertisements.

However, blockchains and cryptocurrencies have the potential to transform data into its own asset class—a paid resource accessible to everyone and a sellable commodity producible by anyone.

To delve deeper into how startups can establish durable data moats, I recommend reading: 'How Can Startups Establish Durable Data Moats?'"

Emergence of IoT & AI. Consumer evolves into a Prosumer

Key trends to note: The emergence of the Internet of Things (IoT) suggests that the data we capture and transfer through sensors, without human intervention, is poised to scale exponentially. This indicates that the role of data in our daily lives is set to become increasingly significant with each passing day

At the beginning of the 21st century, with the rise of tech platforms, millions of consumers became prosumers, producing their own information goods such as videos on YouTube, news blogs, and ebooks. This revolution disrupted traditional industries like television, news, and publishing, as the cost of information goods plummeted close to zero. With the advent of the Internet of Things (IoT), this trend has the potential to extend from information goods to physical goods(IoT-integrated 3D printing). While the exact manifestation is still uncertain, the trajectory is clear. Consider a smart inhaler equipped with sensors: any new startup that effectively leverages the data collected by its inhaler can drastically reduce its purchase cost, empower consumers to become prosumers, and challenge traditional brands in the manufacturing of such devices.

READ: The Zero Marginal Cost Society by Jeremy Rifkin

https://us.macmillan.com/books/9781137280114/thezeromarginalcostsociety#:~:text=In%20The%20Zero%20Marginal%20Cost,and%20the%20eclipse%20of%20capitalism.

The opportunity to extract actionable insights from data using AI/ML will revolutionize various industries, including transportation, healthcare, finance, manufacturing, retail, and many more. AI training models not only demand vast amounts of data but also require a clear audit trail to ensure data quality.

Data Dilemma

On one hand, during the peak of the Covid-19 pandemic, extensive data capture, sharing, and analysis have proven to accelerate healthcare innovations, improve disease surveillance, and enable more informed public health decisions. On the other hand, instances of data misuse, such as the Cambridge Analytica scandal, highlight privacy breaches and demonstrate how data can be exploited, infringing upon individuals' freedom of choice. The use of data raises numerous questions, sparks contrary opinions, offers promising solutions, and evokes both utopian and dystopian visions. Data privacy, protection, and ownership have gained significant attention from corporations, regulators, governments, and consumers, aiming to prevent data concentration in the hands of a few entities.

While blockchain technology may not provide all the answers or an ideal solution, coupling modern data management with blockchain features—from encryption, smart contracts, and decentralization, to tokenization, secure wallets, and more—can lead us to a crucial milestone that ensures fairness for all stakeholders involved in the data value chain.

Trends to note, and accordingly align strategic priorities

Data Privacy

Blockchains are predominantly public in nature, requiring individuals or businesses to expose their wallet details (public address) when interacting and participating in protocols, such as receiving airdrops. This means that anyone can view the entire financial history and holdings associated with a public address. However, this shouldn't be the case. There is a growing need for innovation to enhance consumer protection, strengthen security, and safeguard people's privacy.

Web2 has conditioned people to trade their data and privacy for free and convenient internet services. Web3, powered with privacy, offers an alternative approach. In this new model, individuals can regain control over their data and selectively disclose personal information at their discretion. Privacy-enhancing solutions like zero-knowledge (ZK) and identity (ID) solutions add a crucial privacy layer to blockchain networks.

Expansion of Data-Driven revenue streams: Collection, sharing, & analyses

Enterprises that capitalized on the platform wave of the last decade have successfully built innovative business models around the data they collected, offering valuable services to users across the value chain. Examples include Google, Amazon, LinkedIn, and others. Enterprises that missed this trend, found themselves lagging behind in comparison.

Fortunately, the upcoming data landscape is wide open for competition and presents an opportunity to reclaim the edge. The emergence of sensors, digital-first customers, and smart devices presents a new opportunity for both established and emerging enterprises to diversify their revenue streams through data and gain a competitive edge. Eg- Ford harnessing and creating value of automobile or driver data

http://mitsloan.mit.edu/ideas-made-to-matter/why-data-driven-customers-are-future-competitive-strategy

Many platforms, such as Bloomberg and Forbes, face challenges in converting freemium users into premium users. By developing a better understanding of ethical data collection and combining it with web3 circular economy monetization techniques (such as marketplace tokens or DAO networked businesses), these platforms can sustain and evolve, enjoying improved revenue benefits.

Data Security

With the advent of 5G technology, traditional platforms become more vulnerable to hacking. Centralized servers and databases pose a significant risk as they represent key single points of failure. In a distributed and decentralized ecosystem, data can be safeguarded more effectively, as it becomes exponentially more difficult to steal or manipulate data across multiple nodes.

Read: [Blockchains for better security]

Another intriguing approach to data security is token-controlled access and private vaults. While specific blockchain features like encryption and hashing are currently utilized, whether the data is attested to a blockchain remains a business secret. It's important to note that this is an example of permissioned blockchain features without crypto economics, at least as of today.

Read: [Visa Payment Tokenization]

Please note that while blockchain addresses temporary cybersecurity challenges, it also introduces a new set of crypto-economic security challenges that are constantly under debate, discussion, and resolution.

Data Synchronicity

Primarily an ERP use case, tailored for app-specific, sovereign blockchains. The lack of real-time data synchronization has resulted in a significant amount of administrative communication. This includes email exchanges, document transfers, and phone calls among various stakeholders. Let's consider the example of the marine insurance value chain, which typically involves the following steps:

1. A ship owner approaches a broker.

2. The broker engages an insurer.

3. The insurer takes on the risk and seeks another broker.

4. The broker finds a re-insurer to share the risk.

5. The re-insurer approaches another broker to find a retrocession insurer.

Insuring a vessel in the marine business often requires around 100 document transactions and involves approximately 50 different stakeholders throughout the value chain. Unfortunately, there is a lack of transparency in this process, leading to information loss. Furthermore, significant value leaks occur due to intermediaries in the value chain, who take a portion of the premium as it traverses through multiple entities. Transactional costs alone can account for up to 40% of the premium. This antiquated and inefficient system has left insurers' customers dissatisfied.

Blockchain technology, with its inherent tamper-proof data synchronization capabilities, has emerged as an early application to address these challenges. However, enterprise-level use cases like this have historically struggled to gain traction. This can be attributed to enterprises' reluctance to relinquish their walled gardens and their resistance to adopting public blockchain solutions. Herein lies the opportunity: by solving such use cases on a public blockchain with crypto-economic incentive mechanisms, an uncharted territory can be explored.

Data Ownership & Portability

When you go online to any application today, you'll find it running on a few different components: code, data (which includes all the information that populates the websites you see, such as user data and application data), and value mechanisms that enable business models. A significant portion of any online application, product, or service is driven by data. Platforms like Wikipedia, YouTube, Facebook, Airbnb, and Google are all built around information sourced from individual users. However, despite their contributions, the vast majority of internet users own less than 1% of the data they generate. This could be higher if not for the overhead costs of cloud infrastructure.

Enabling social networks to operate in a peer-to-peer (p2p) architecture, promoting community building and ownership, will fuel the next wave of value creation. User ownership serves as a powerful motivator for users to contribute to products in meaningful ways, whether through ideas, computing resources, code, or community building. The concept of user ownership lies at the core of the success of Bitcoin and Ethereum, as the first user-owned and operated networks at scale.

With the advent of interoperability, users can now move from one platform to another and take their data with them. For example, I can write this article on Mirror.xyz, store the data on a public blockchain like Arweave (owned by me), and publish it on another platform like Farcaster for distribution.

https://www.coinbase.com/blog/decentralizing-social-media-a-guide-to-the-web3-social-stack

Data Governance - DAOs as Data Trusts

Trusts, which have long existed in property rights law, are legal structures that separate the legal ownership and control of the property from its equitable ownership and benefits. Data trusts apply a similar structure to data assets, appointing an independent body (such as a DAO) as a fiduciary or steward of the data asset on behalf of the data owner. These trusts aggregate data and unlock its value in a fair and transparent manner.

Data governance involves establishing decision rights and an accountability framework to ensure responsible behavior in the valuation, creation, consumption, and control of data and analytics. Traditional data governance frameworks often face challenges, including a lack of incentives and enforceability.

To address these challenges and create a standardized, effective data governance system, there is a need to shift toward incentive models. This is where blockchain technology plays a crucial role. The blockchain serves as a natural data governance platform, offering unique capabilities that can revolutionize existing practices. For example, I can be incentivized to wear a smartwatch and supply standardized health data. When this data is tokenized on the blockchain, it provides an audit trail that allows for traceability and ensures that it is not misused.

By leveraging blockchain's characteristics such as transparency, immutability, and decentralized control, data governance can be significantly improved. Blockchain provides a trustless environment where data integrity is assured, and decision-making processes are more transparent and auditable.

For further insights, I recommend reading about bottom-up data trusts and their role in disrupting the 'one size fits all' approach to data governance.

What are some interesting types of projects we see in the space?

Data Marketplaces - Data DeFi

A data marketplace is like an Amazon but for data sets, utilizing the decentralized finance (DeFi) architecture. It leverages data tokens to create a seamless connection between data providers, services, and consumers with DeFi - allowing the data ecosystem to be integrated into various financial products and services powered DeFi. On one end of the spectrum, there are consumers such as analytics, AI, and ML companies that require data sets to enhance their products, and they can also monetize their existing algorithms, analytics, and dashboards. On the other end, we have data providers, which can range from individual car drivers to average users of Google Chrome or enterprise companies hosting vast amounts of untapped data. These data providers can sell their data in a p2p marketplace and capitalize on this valuable resource.

In the traditional non-Web3 world, notable data marketplace projects include Snowflake Data Marketplace, European Data Union, and Data.gov. Within the Web3 ecosystem, we have innovative platforms like Acentrick, Ocean Protocol, and Xy Labs. Ocean Protocol, in particular, stands out as a progressive project aiming to make AI, algorithms, and data sets more accessible. At its core, Ocean Protocol operates as a two-sided marketplace, connecting data consumers and data providers through the use of the Ocean token. Acentrick, on the other hand, is a web3-native startup focused on enterprises. They assist enterprises in unlocking value by facilitating data sharing and also offer a white-label solution that can be adopted by other enterprises.

Examples in web2 - Snowflake Data Marketplace & web3 - Ocean Protocol, and Acentrick.

Note: Ocean Protocol is a comprehensive project encompassing data tokenization, access controls, Data NFTs, and Data Tokens, offering a wide range of capabilities in the data marketplace domain.

New business lines with existing Data

Data silos exist, but new business opportunities can be created. Banks can offer "identity-as-a-service" using customer data, charging retailers and fintech for verifying customer details. Collaborative projects like ConnectID and partnerships such as Mastercard with the Australian Post showcase the potential. A similar model can be built with web3 Identity solutions like Polygon ID and Kilt. Additionally, Web3 identity and reputation techniques, like Gitcoin Passports, open up endless possibilities.

Data Tokens

Data tokens serve as a bridge between data assets and DeFi tools, enabling a wide range of use cases with access control. Data has the potential to generate income for individuals, but the lack of an effective price signal has hindered its monetization. Data tokens aim to address this challenge by creating an open data economy, where data producers are incentivized to generate data, and buyers can easily access and purchase it. This concept has the potential to unlock new opportunities and promote the widespread production and exchange of valuable data.

Read: Data Tokens

Data Unions

A Data Union is an organization that crowdsources data. Here individuals contribute valuable data through an application. The data is then offered for sale on a marketplace, and members are rewarded with tokens for their contributions. By building a Data Union, you empower users to earn value from their data and incentivize them to use your app. Data Unions are particularly useful for collecting challenging datasets like real-time health data, web user data, pollution monitoring, smart city data, and more. Examples of Data Union platforms include Swash for browser data, dimo for smart cars, and unbanx for banking data.

Data Wallets

Dataswift and Alga are emerging projects in the field of data wallets, which aim to provide individuals with comprehensive control over their data. Similar to how a cryptocurrency wallet allows users to have full access and ownership of their digital assets, a data wallet empowers individuals to have complete control over their personal data. This concept applies the principles of ownership and control that are fundamental to web3 and extends them to the realm of data.

Marketing & Advertising in a cookieless world

Evolving data regulations and growing customer concerns about privacy are driving the phasing out of traditional digital ad targeting methods relying on third-party cookies. The rise of NFTs introduces a new paradigm for gaining customer insights. Prominent web3 marketing approaches include token or NFT airdrops. Eg- where users can participate by filling out forms to have a chance to win exclusive access to products or services.

However, it's important to note that tracking wallet addresses in web3 marketing campaigns may raise privacy concerns. Early movers in this space, such as Cookie3 and Adshares, offer innovative solutions. Cookie3 utilizes AI and ML tools to perform wallet scoring and on-chain activity analysis, enabling web3 projects to identify and engage ideal community members. Adshares, on the other hand, is a protocol built specifically for web3 advertising.

The integration of NFTs into social media platforms opens up a vast array of untapped marketing opportunities that are yet to be fully explored.

Note: refer to articles on Google phasing out third-party cookies and Pepsico's involvement in web3 marketing.

Privacy Projects

Blockchains are mostly public, exposing wallet details and financial history. Innovations are needed to enhance consumer protection, security, and privacy. Fortunately, solutions are fast arising, empowering individuals to control and selectively reveal their data. ZK and ID solutions add privacy to blockchain networks.

• Zero Knowledge Airdrops - Projects often face the challenge of airdropping tokens to community members without requiring their public keys(preserving anonymity) and for off-chain activities. To address this, a system has been developed where prospective recipients provide commitments via public channels. A Merkle tree is then constructed using these commitments, and recipients can later claim their share by providing a zero-knowledge Merkle proof without revealing their specific commitment. This process ensures anonymity by mixing public addresses with other entitled users.

• Programmable Privacy - Meta's partnership with Oasis Labs aims to utilize Oasis Labs' privacy layer and confidential computing capabilities to measure fairness in AI models. This involves computing data with smart contracts in a secure enclave, preventing transaction IDs and account addresses from being traced on analytics platforms and block explorers. Another project, Secret Network, is also working on similar initiatives. However, it is important to note that these projects are still in their early stages and may require several more years before achieving widespread market adoption.

Composable & Reusable Data

Ceramic Network is a project that seeks to expand the capabilities of data composability and reusability beyond web browsers. It aims to enable users to export and import their data across various social media and communication platforms such as Linkedin, Instagram, and Whatsapp. Ceramic simplifies the process of building applications with composable Web3 data by providing a marketplace of data models that can be seamlessly integrated into applications. This allows for storing, updating, and retrieving data from these models. Additionally, when multiple applications use the same data models, their data becomes interoperable. By decentralizing application databases, Ceramic promotes data composability and reusability across all applications.

Blockchain-ready sensor technologies

Integrating sensor technologies with blockchain can indeed enhance the capabilities and applications of existing blockchains. One practical instance for examples sake is the implementation of a decentralized and autonomous water management system in an apartment complex, aiming to improve allocative efficiencies and overcome the tragedy of the commons. (Duh, we’re running out of water and we don’t care :()

A combination of blockchains, protocols, and trusted sensors can be employed. Trusted sensors equipped with crypto chips provide tamper-proof digital identities, making them reliable sources of data. By leveraging this added trust, society can authorize an industrial robot to autonomously manage various tasks, including water consumption regulation.

For further information on this topic, you may explore resources such as RIDDLE&CODE + IOTA and the concept of smart meters. These sources delve deeper into the integration of blockchain and sensor technologies for improved water management and other related applications.

Public-Private data partnerships

Finding a balance between data protection and data-driven innovation is a “catch-22” that needs to be negotiated between governments & corporations. GaiaX is one such project that brings together representatives from various sectors such as policymakers, regulators, Enterprise C suite, and upcoming projects to develop a framework that promotes sovereignty and innovation for Europe’s data infrastructure. GaiaX is further broken down into light house projects that carry forward the GaiaX framework into their respective and specific industries. Center for the fourth industrial revolution is a similar initiative by the world economic forum. It is a global multi-stakeholder platform focused on inclusive technology governance and responsible digital transformation.

The road to perpetually sustainable crypto economic activity

Blockchain's role in the data ecosystem is indeed an evolving process that requires continuous trial and error and iterative improvements. The key objective should be aligning the ecosystem with the market's needs and addressing the challenges and requirements of data management effectively.

To achieve this, a protocol must ensure that most of the on-chain activities related to data, such as computation within smart contracts and transactions between users, occur on the blockchain infrastructure. This approach should be designed in way that it minimizes costs for users while maintaining fair decentralization, minimal trust, and security.

In terms of architecture,

• one conceptualization involves designing an application on top of a protocol that operates on a sovereign chain, serving as a layer 3. This layer provides control, predictable operational costs, and avoids congestion within the network.

• This layer can then communicate with layer 2 solutions like Polygon, Arbitrum, or Optimism, which offer scalability.

• Finally, Ethereum serves as the base layer 1, providing decentralization and security.

By leveraging this multi-layered architecture, the data ecosystem can benefit from the strengths of each layer, ensuring efficient operations, scalability, and security while minimizing costs for users. It allows for flexibility in adapting to changing market needs and evolving technological advancements.

Stack

The data economy will be deployed in

1. Side-chains, sovereign chains, and rollups: These chains offer low transaction costs, high throughput, and leverage the security of the Ethereum network. They are well-suited for data applications that require high-volume computation and frequent trading of data sets.

2. Hybrid chains: Hybrid chains provide a flexible framework for use cases that require a faster time-to-market or additional security measures. They can strike a balance between public and permissioned features, while also offering more predictable operational costs in terms of gas fees.

3. Private chains: Privacy chains can address the privacy requirements of data projects. They allow for secure and private computations without revealing the underlying data.

4. NOTE-Identity frameworks: As identity plays a crucial role in data projects, having a robust Self-Sovereign Identity (SSI) framework is important to meet compliance requirements. Projects like Acentrik emphasize the need for sound identity solutions to ensure privacy, security, and compliance in data-related activities.

By considering these components and designing a stack tailored to specific use cases, the data economy can benefit from optimized performance, scalability, privacy, and compliance. It's important to brainstorm and evaluate the most suitable combination of chains and technologies based on the specific requirements of each data project.

Conclusion

How should blockchains strategize

1. Enterprise Logo acquisition: The focus is on acquiring new enterprise partners and investors to enhance the blockchain's credibility and attract more participants to the ecosystem. (investors love enterprises)

2. Real Value Use Cases: Identifying and implementing use cases that bring together the solutions provided by the protocol, collaboration with ecosystem partners, addressing market problems, and most importantly aligning all the stakeholders with the values and philosophy of Web3 while maintaining pragmatism.

3. Find your Dapp: Every blockchain needs just one killer Dapp that is ready for mass adoption and can onboard the next billion users.

Metrics to consider

• Total DApps: The number of decentralized applications built on the blockchain platform, indicating the level of developer activity and ecosystem growth.

• Total Generators: The number of individuals or organizations generating data, services, or applications within the ecosystem, reflecting user engagement and innovation.

• Total Wallets: The number of digital wallets holding tokens or assets on the blockchain, representing user adoption and participation.

• Monthly active wallets: The number of wallets that engage in transactions or interactions within a given month, measuring user activity and retention.

• Number of transactions: The total volume of transactions occurring on the blockchain, indicating the level of economic activity and usage.

• Monthly NFT volume: The volume of Non-Fungible Tokens (NFT) transactions taking place on the blockchain, reflecting the popularity and demand for unique digital assets.

• Speculative bets: Whether we like it on it, the industry is driven by speculation. If a project has less value but is good for public sentiment. It should be prioritized

• Fundamental use case bets: Evaluating the adoption and success of blockchain applications that provide tangible value and solve real-world problems.

By following this framework and focusing on these priorities, blockchain platforms can tier-1 partnerships, drive real value through meaningful use cases, and achieve sustainable growth in the data-driven economy.

It's not personal (against internet companies) and here is why it shouldn't be - “Those privileged by circumstances will always exploit their opportunity”. If it isn't the silicon six. It is still your unicorn startup. It's simply the name of the game.

I myself have contributed to products that collected data without consent and used it in pretty unpleasant ways against the person who provided the data.

So what's the solution? - NFTs (An Erc721 Token)

Think of NFTs as a wrapper for any data. From enveloping your profile picture to your search strings on Google, and even to the very house, you live in. Or even just the bed if you are a couch potato. (Fractional NFTs need an article of their own.) Owners of Data NFTs can hard code rules that regulate and license access to any external third party(user, advertisers, etc).

For instance, Ocean Protocol’s Technology gives Data NFT owners the ability to create “Datatokens”(ERC20). Advertisers & data engineers buy these data tokens to gain access to the underlying dataset of this NFT.

This allows an economic model - where the money paid; Directly reaches the data owner, in the monetization process. And where a better portion of the power is returned to the data owner.

Here is a better example:

A small production company creates an NFT [ERC721] for their movie(Also a data set). Amazon & Netflix can use Ocean Protocol’s technology to buy the rights to this movie And create sub-licenses for viewers to watch this movie.

On Ocean Protocol, the license to access the movie is represented by ”Datatokens”. Hence giving the owner of the movie direct access to the revenue through data tokens.

Here is where things get super interesting:

Firstly, The price of the data token is regulated by the Ocean Marketplace, Where data investors trade and stake Datatokens for profit.

Secondly, Such marketplaces open up the data economy. Access moves from a handful of companies to data owners; in a free market. Where they understand the fair price of their dataset and are incentivized to trade.

Defined as DataFi, an extension of DeFi. Liquidity pools and tokens can now be backed by real-world datasets.

Data is the new oil or gold. I finally see how it fits into the puzzle of finance and especially decentralized finance(DeFi)

Currency was first backed by gold, and then it became debt/credit bonds and so many other things until governments started printing it out of thin air.

DataFi and DeFi are one of the many applications of web3 that can change this. At large, it truly is the biggest economic innovation we have ever seen.

I hope you enjoyed my article. I hope was able to open a whole other dimension of thinking when it came to NFTs, Data & Finance.

Prerequisites

An understanding of blockchain fundamentals, consensus mechanisms, blocks, transactions, nodes, scaling solutions, Optimistic rollups, and other relevant topics.

Going back to First Principles

Don’t trust, Verify.

Engineers must build upon this principle to ensure the sanctity of blockchain networks as a trustless system is preserved. It assumes the average user to be self-sovereign, where they can verify transactions themselves on their own nodes, Rather than trusting a block scanner. Additionally, they must also ensure whatever solutions or design methods that are proposed, are scalable. When building solutions in the context of scalability and preservation of trust-lessness, we come across a very big obstacle called the Data Availability problem (DA problem)

Scalability is often misunderstood to be a measure of blockchain throughput alone, but I would argue that blockchain scalability can be defined using the following

 Scalability = (Throughput * Block Size) / Operational Cost

Where:

• Throughput - the number of transactions the blockchain network can process per unit of time.

• Block Size - maximum number of transactions that can be included in a single block of a blockchain

• Operational Cost represents the cost associated with operating a node in the blockchain network, (computational resources, storage, and energy consumption).

Let’s first visualize a blockchain computing transactions

Here, we can assume that all factors of a blockchain that affect the operational cost of a node i.e. number of transactions, storage requirements, bandwidth, no of computations per time interval, etc are proportional and can be represented with c. And accordingly, the computation capacity of a node and transaction capacity of a network is proportional and can be represented in the order of magnitude of c i.e. O(c). This implies that if the transaction capacity of a network is greater than O(c) then one individual can run a full node and the blockchain doesn’t work.

Let’s scale the blockchain

Increase Block-size

Now if we want to scale these blockchains to include more transactions, then the first approach that comes to mind would be increasing the block size. But this would come with limitations. In the above example, the throughput is up by 1000 and correspondingly the load on each node is also up by 1000. While this would work, this is not what we would want(going back to first principles). Because the only nodes that can process transactions of such magnitudes would be in data centers. We want the average user to have the ability to run a node on a laptop.

Newer Chains

The second most basic approach would then be to move to a newer, cheaper, less congested chain. Technically this can be considered a scaling strategy if we’re still processing 1000x transactions. Here, the load on the node would still remain the same. But the problem is that each chain would eventually get congested(with more adoption) and moving from one chain to the next isn’t sustainable. We would end up in chains like Theranos Chain with a market cap of say $1 million. This would make 51% very affordable.

Now back to the scalability trilemma, which states that each network can only have two out of the three properties (decentralization, scalability & security). We can have Decentralization + Security with the likes of Bitcoin and Ethereum, Scalability + Security with super big blocks and we can have decentralization and scalability with altcoins. To have all three? Very difficult.

This brings us to the most famous strategy to scale Ethereum - rollups and this is where the Data Availability Problem kicks in.

What are Rollups?

In a rollup, transactions happen off the Ethereum chain(execution) and on the rollup chain such as Arbitrum or Optimism. These transactions are then batched(called calldata) and posted on Ethereum where consensus is reached. Thereby, inheriting the security of Ethereum validators while decentralization and scalability are offered by the roll-up chain. For the sake of simplicity let’s focus only on the DA problem wrt Optimistic rollups.

In order to implement a roll-up transaction on Ethereum, the optimistic rollup must implement a system that replays the exact state transitions that were originally executed in the rollup. Optimistic rollups rely on a fraud-proving scheme to detect cases where transactions are not calculated correctly. After a rollup batch is submitted on Ethereum, there's a time window (called a challenging period) during which anyone can challenge the results of a rollup transaction by computing a fraud-proof.

Example of a rollup batch transaction(click).

Light Clients

The ability to run Ethereum nodes on modest hardware is critical for true decentralization. This is because running a node gives users the ability to verify the information by performing cryptographic checks independently rather than trusting a third party to feed them data. A light node is a node running light client software(eg-lodestar/light client). The light node only processes block headers, only occasionally downloading the actual block contents.

https://twitter.com/VitalikButerin/status/1543730197769371650

In the context of rollups, light clients serve as a bridge between the users and the rollup chain. Instead of requiring users to run a full node and validate all transactions on the rollup chain, light clients provide a lightweight way to access and interact with the rollup. They achieve this by relying on a compact representation of the rollup state, such as transactions bundled in the form of a Merkle tree and submitted as proof (other cryptographic proofs are also possible).

For scalability, ensuring the participation of light nodes in consensus is crucial. But for light nodes to participate in consensus and correspondingly for roll-up techniques to be secure we must overcome the “Data-Availability” problem.

What is the data availability problem?

The data availability problem -

• Is the need to prove to the whole network that the summarized form of some transaction data(call data) that is being added to the blockchain (from the rollup to Ethereum) represents a set of valid transactions,

• But doing so without requiring all nodes to download all data. Because the full transaction data is necessary for independently verifying blocks. (requiring all nodes to download all transaction data is a barrier to scaling.)

• Solutions to the data availability problem aim to provide sufficient assurances that the full transaction data was made available (in some form) for verification and communicated to network participants that do not download and store the data for themselves.

In the context of rollups, it precisely means challenging the submission of an optimistic roll-up via a fraud-proof.

Fraud-Proof

At any point in time, the optimistic rollup’s state (accounts, balances, contract code, etc at a particular time) is organized as a Merkle tree called a “state tree”. The root of this Merkle tree (state root), which references the rollup’s latest state, is hashed and stored in the rollup contract. Every state transition on the chain produces a new rollup state, which an operator(someone who submits blocks to Ethereum) commits to by computing a new state root.

Verifying Blocks

As mentioned earlier, Light clients can be expected to download every single block header(but not the full blockchain state or transaction history). The light client can contain block headers, Merkle tree’s root hashes, a hash of the previous block along with other information. The role of the light client now is to just listen for challenges in the network. It is important to note that the light client does not have any transaction or any portion of the state. However, any such information can be authenticated if the light client is provided with Merkle Branches i.e. the witness data submitted by the challenger.

The possibility of fraud proofs theoretically allows light clients to get a nearly-full-client-equivalent assurance that blocks that the client receives are not fraudulent ("nearly", because the mechanism has the additional requirement that the light client is connected to at least some other honest nodes): if a light client receives a block, then it can ask the network if there are any fraud proofs, and if there are not then after some time the light client can assume that the block is valid.

The incentive structure for fraud proofs is simple:

• If someone sends an invalid fraud-proof, they lose their entire deposit.

• If someone sends a valid fraud-proof, the creator of the invalid block loses their entire deposit, and a small portion goes to the fraud-proof creator as a reward.

The Data Unavailability Attack

Data unavailability attacks. A malicious validator publishes a block. The block header is valid but some or all block data is invalid. Ideally, we would assume Fraud Proof to be the solution. But that’s not possible because not publishing data is a fault or error that cannot be directly assigned or linked to a specific entity i.e. it is not a uniquely attributable fault. Alternatively, we can consider zkproofs in the form of ZkStarks or ZkSnarks (a magic way of proving block validity without revealing transactional data). This would still result in some unpleasant consequences.

Consequences of data unavailability attacks-

• Convince the network to accept invalid blocks

• Prevent nodes from learning about the state - Even if they prove with zk that the new state root is correct (through the state root hash). Nodes can still not learn about the new state i.e. they cannot learn about the balances of some accounts.

• Prevent transactions that interact with these accounts and agree on blocks that touch these specific accounts.

To conclude, What is the data availability problem?

• Incorrectness can be proven, even to a light client

• But data availability cannot. Because it is not possible to link the fraud to a specific entity. (you can’t say it was this validator and penalize them)

Why is it not possible to link the attack to a specific entity?

Let’s go through two possible cases illustrated in the image below.

In Case 1, V1 is the attacker

• At T1, V1 publishes a block with missing data

• At T2, V2 raises an alarm (eg - as a challenger with a fraud-proof)

• At T3, V1 publishes the remaining data after seeing the alarm.

In Case 2,

• At T2, V2 raises a false alarm

At T3,

case 1 and case 2 seem completely indistinguishable from each other. As there is a fully published block along with an alarm. Now any client looking at this situation at time T4 has no idea who is at fault. V1 or V2, (Remember we said that this error cannot be linked to a specific entity). This situation brings us to a situation Vitalik call’s the fisherman's dilemma.

Fisherman's Dilemma

The fisherman here is basically a node, and the dilemma is basically whether should he or should not submit fraud-proof. It basically illustrates the complex trade-offs and uncertainties involved in reporting and challenging the validity of blocks. Recall that not challenging the validity of these blocks would result in some serious consequences.

If Fisherman’s incentive is > 0

Recall that at time T4, both case 1 and case 2 are identical. If there is an incentive (>0 in the figure) for raising an alarm i.e. V2 gets a reward for raising the alarm, we have a money pump vulnerability. Nodes can go on raising false alarms with no penalties

If Fisherman’s incentive is = 0

This is a DOS vulnerability that can be exploited. V2 can raise false alarms and force nodes to download all data in a blockchain and nullify the benefits of light clients, sharding, and scaling solutions.

If the Fisherman’s incentive is < 0

Then raising an alarm is in itself an altruistic act. The attacker now only needs to economically outlast these altruistic fishermen, post which he is free to include blocks with unavailable data.

So what’s the solution?

There are a variety of solutions to how blockchains achieve the data availability problem. While a lot of innovations are still ongoing. At a high level the techniques widely discussed and debated are-

1. Using data availability committees (DAC) within the validator network who can attest to the availability of data

2. Using Data availability sampling (DAS)

3. Using DAS with sharding

4. Or leverage an entirely new layer for Data availability like Celestia.

5. Leveraging Sparse Fraud Protection

Resources used to write this article (recommended for further reading)

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• Ethereum Research

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• Data availability available and unavailable

Before we get into this. Let me give you some context… (If you are short on time or an attention span feel free to jump straight to the curriculum.)

At the beginning of my sabbatical. I met quite a few interesting individuals who unknowingly had a significant contribution to my blockchain learning journey.

Let me take a moment to acknowledge them

1. Harsh, Founder of Gaia Grid — After leaving his high-paying financial consultancy job in New York. He returned to India with a vision to create a self-sustaining off-grid community. He taught himself traditional farming methods (the Adivasi way) to conserve soil and recharge the groundwater. ( He recharged it from 900 ft to 12 ft )

2. Dr. Regi George & Lalitha George, Founders of Tribal Health Initiative — They strive to improve the lives of tribal communities living in Sitillingi Valley. Through healthcare, community work, and organic farming. They also homeschooled their children who are now highly successful in their respective disciplines of study and work.

3. Anu & Krishna, Founders of Thulir School — They created an alternative education system tailor-made to suit the local needs of the same tribal communities in Sittilingi Valley. They work to improve the lives of school-going children and additionally provide useful learning opportunities to teenagers who stopped schooling & enable them to be employable.

4. Divya — My childhood friend for almost 20 years. Who I believe is the most knowledgeable doctor. A topper, national level rank holder, and an avid reader. She chose a testing but a fulfilling job as a doctor for rural communities. Over high paying job with leading hospitals in the city.

5. Sushma & Swaminathan — My sister and brother-in-law respectively. Post their masters, They now reside in the bay area and work in leading enterprises such as Walmart Labs and Salesforce.

What is similar among all these wonderful individuals is their approach to learning.

Divya as a doctor believes learning is never-ending. She is a classic example of child-led education. I believe this is the reason she is so passionate and dedicated to her field.

The other founders are advocates of self-education and apostles of the philosophy — “An individual must address the needs of their mind, body, and soul to lead a fulfilling life.”

Sushma & Swaminathan, are avid self-learners themselves. Gave me insight into how leading universities of the world go about educating their students.

So this is where my journey starts. I decided to self-educate myself on Blockchain Protocols and web3.

(Never have I ever been so driven and passionate about any of my other pursuits.)

After a few weeks into my self-curated program, I realized the potential for a utopian world that can easily be maneuvered into dystopia.

I believe it’s part, in purpose to support the progress of this technology and raise my hand in times of concern. (Like the social dilemma situation or some financial crisis-like situations we’ve seen in recent times. Which is a by-product of the information age and web2)

Disclaimer: This is just a learning journey. I only wish to publish this for inspiration so you can start your own. I do not mean to dismiss any form of conventional education. This is purely a learning experiment.

The curriculum — The way, the method, the style

Let’s face the truth — Capitalism will force us to specialize.

But I feel most of us make this decision too early in our careers before experimenting, or even narrowing our options and realizing our complete skill set. That’s why I guess most people hate their jobs.

Additionally, it’s too early to decide the requirements in this field. For example, How did companies know that they needed digital marketing when Facebook, LinkedIn & Google were at their nascent stage? Or how did companies know — they needed employer branding to address the talent war in the market? The ones that saw it early gained a strategic advantage.

So at this stage in my journey, I first choose to increase the scope of my subjects. It will also act as complementary skill sets (through cross-functional expertise) before I decide on my area of specialization.

Step 1: Deciding areas of interest.

• Engineering

• Entrepreneurship

• Leadership

• Personal Brand & Strategy

• Conceptual knowledge

• Research

• Philosophy

Please note: There is no mention of technical analysis that helps speculate market behavior (the rise and fall of coins). Becoming a crypto millionaire is not a priority but I’m sure it will happen.

Engineering — I remember being very good at it, but I don't recall why my interests deviated from the subject. The reason for pursuing engineering was that I had realized how it handicapped me in my previous GTM roles for early-stage startups.

I tried picking up a language(javascript) right off the bat. Very soon realized that it can lead to broken information.

After speaking to some successful software engineers, I was recommended to invest time in picking up the fundamental concepts of computer science. Once I reached this milestone picking up any new language should be a no-brainer.

Resources: Introduction for CS50, Objected Oriented Programming, Data structures & algorithms

Once I had a fair understanding of software engineer I jumped straight into blockchain. To get some hands-on experience with Solidity.

Resources — Crypto Zombie, Chainlink — Smart Contracts Course

Post completion of all the above resources. I intend to start working on projects for my portfolio.

Entrepreneurship - Just like every other eccentric individual in their mid20s. I wish to venture into an entrepreneurial adventure at some point in life. But only after I’ve gained the skills & expertise. Possibly once I’ve served successfully served in a leadership role with a great company in the crypto space.

I see that young founders fall short in life experiences & wisdom.

For this I found the a16z crypto startup school to be the best place to start. It helps in getting a better overview on how web3 is different from web2. I also, actively brainstorm on the different areas and applications of this technology. Sometimes I study upcoming ventures to learn how they’re disrupting their respective sectors.

Leadership — I like to believe I have an interesting approach to this one. From what I’ve seen in my experience — Leadership is mostly about finding talent, building a team, and inspiring belief.

So I picked up cricket after 10 years. I’ve now built a team that practices every week. I’ve tried to create a culture where people let go of their inhibitions and contribute to the best of their ability. We also coach each other and strive to become better with every practice session. We will soon start competing in friendlies.

I believe this skill would be transferable. No resources for this one yet, but it involved me connecting with 100s of people who I thought might be interested in this.

Personal Brand & Strategy - At this stage, the only product that I have is myself. My objective is to instill credibility in the blockchain community and be the subject matter expert.

It involves networking with the right people, seeking mentorship, mentoring and building strong relationships. I somehow find it difficult to build superficial relationships. So the number of connections is small but strong and growing.

The next steps would be to build a community, collaborate on projects host networking events and expand.

What I consider strategy is basically finding your niche, experimenting, failing & then succeeding. It pretty much involves everything from curating this curriculum to venturing out and participating professionally. I try to always look at the big picture.

Some activities here involve building a social brand, writing content, participating in the ecosystem (governance, buying NFTs, p2p lending & borrowing, etc), and following the right people and companies.

Important to spend a lot of time here consistently. It does seem redundant but then I’m confident the payout will be big in the long run.

Conceptual Knowledge — I consider this to be the most challenging. The intellectual barrier to understanding blockchain is large. Maybe this is what excites me most. I’ve spent days to try and fully understand the Bitcoin whitepaper.

Because I didn’t have a complete grasp of it, I felt miserable. Only to realize very soon that nobody understands it completely.

The tech is also evolving at a rapid pace. It’s almost impossible to catch up with. But this ever-changing landscape does satisfy those that desire constant novelty.

I am currently pursuing the blockchain specialization course from Buffalo University & Defi specialization from Duke University on Coursera.

Somewhere I feel there are better resources, so I highly recommend you do your due diligence on this one.

Research — This is a tough fish to catch as well. I approach this by critically analyzing white papers. It consumes a lot of time and I do feel I lack a lot of technological understanding. I’m hoping I’ll overcome it soon though.

Highly recommend starting with Bitcoin, Ethereum, AAVE, Compound, Maker & Chainlink. Post which you can explore some on tokenization and then pick and choose whatever you like.

Philosophy - This is my favorite. Usually consists of binge-watching podcasts of CEOs from blockchain companies.

Highly recommend Lex Freidman’s interviews with Charles Hopkinson, Vitalik Buterin, Silvio Micalli & my personal favorite Sergey Nazarov. Must check out Balaji as well.

I also try to read books. Currently reading Radical Markets by Eric Posner & Gren Weyl and Token Economy by Shermin Voshmgir.

Step 2: Execution. Self-programming.

I would like a share a few interesting techniques here.

My first challenge was making it a habit. So I don’t have to seek motivation every day. After 3 good months into this process, I believe it’s kind of a habit for me now, thankfully.

The first thing I do after waking up is planning my day. I usually divide it into two 4-hour sessions (breaks only for water). In each session, I try to focus only on two tasks. One for learning and the second for something more hands-on.

When I saw the social dilemma I learned that our brains can be programmed. I thought deeply about the social conditioning that happens through movies, social media, reels, posts, etc.

Did you know even Hitler did this to his people too? Subsidizing the radio so every household could have one, which then gave him a platform to condition his people to support the nazi regime.

I figured I could try and program my own brain. So I decided to only watch tech-oriented shows & movies for entertainment. I recommend machines that changed the world & Billion Dollar Code.

I then reworked my Reddit, twitter & LinkedIn algorithms to only show me blockchain content. (At least tried to do so. It’s still web2 …lol)

Step 3: Faith

When I started this journey, I was burdened with self-doubt. It was unchartered territory. No one in my circle had even tried self-learning for anything. Well, maybe a ukulele or a guitar but definitely not something as complicated as blockchain.

But with time, I was able to answer people’s questions. People looked to me for inspiration and wanted to work/study together. People approach me to join their firms, startups, etc.

It’s great validation. But I’m not taking it tightly. Can’t get complacent. I’m no expert yet. Just an amateur.

My journey is just starting. Super excited to see web3 taking off in India.

Thank you for taking the time to read. I hope I was able to add value to your time.

Stay tuned for my next post — Roles and Functions in a blockchain startup.