Today, centralized AI poses a threat to humanity and the truth.

Hayek’s thought on distributed systems is the ‘Ghost In The Machine’ and should be re-explored when thinking about how we can make permissionless, unbiased, and truthful AI.

The distributed ethos is ubiquitous on the Bittensor Subnets - where decentralized intelligence is the prize.

“Not your AI, not your intelligence” - Keith Singery, Bittensor Validator, 2024

“Crypto is decentralizing, AI is centralizing. Or, if you want to frame it more ideologically, crypto is libertarian, and AI is communist." - Peter Thiel, 2018

“We all want open-source AI, and a wide proliferation of models custom fit for various uses, but the tailwinds behind the economies of scale for AI models are very strong. Data monopolies and compute monopolies restrict open AI model development” - Casey Curaso, ETHDenver, 2024

Bittensor's decentralized AI Marketplace offers a solution to the threat of centralized intelligence.

Building upon Friedrich Hayek's principles of decentralized knowledge and market efficiency,

and Bitcoin’s properties as a commodity that can’t be diluted by centralized powers…

Its subnets offer the chance for highly specialized models to outcompete general models,

while being incentivized to provide the most useful outputs for the individuals or enterprises that use them.

Was Hayek The OG Decentralizoor?

Friedrich Hayek was an Austrian-British economist and philosopher whose work spanned economics, political philosophy, psychology, and history.

Born in 1899, in Austria, Hayek lived through both World Wars and witnessed the transformations in economic and political landscapes across Europe and the United States.

He is best known for his criticisms of Keynesian welfare policies and totalitarian socialism.

Sorry stimmy cheque enjoyers, Hayek wasn’t a fan.

But he would have your back if you wanted to keep your local business open.

Despite sometimes being labelled as a conservative, Hayek identified with classical liberalism, emphasizing individual freedom and limited government intervention​​​​.

One of his most renowned works, "The Road to Serfdom," published in 1944, articulated his fears about the dangers of governmental control over economic decisions, arguing that such control could lead to totalitarianism.

Does that sound relevant Anon?

This book, along with others like "The Constitution of Liberty" and "The Pure Theory of Capital," established Hayek as a leading figure in classical liberalism and a pivotal member of the Austrian School of Economics​​​​.

His article "The Use of Knowledge in Society," published in 1945, is particularly noted for its argument on the price system as a means of communicating and utilizing dispersed knowledge within a society.

I.e: ‘The market decides.’

The core concept that dictated the price of a good or service was ‘Tacit Knowledge’, which is all of the specific knowledge on an issue that an individual or firm may have when they price their product.

Supply, demand, inputs, and output are all considered into a monetary value deemed acceptable by both parties in a transaction.

The total variety of all this specific knowledge, dispersed throughout society, is far too complex for a centralized source to grasp,

thus leading to inefficiencies in centralized power structures that attempt to do so, only to be solved by further decentralization.

Think of the soviet collectivization of farming in the USSR: How did that play out?

Hayek's life and work were set against major global upheavals, including the rise and fall of empires, the Great Depression, and the Cold War.

If you’ve ever read ‘Principles’ by Ray Dalio, you may be aware of the cyclical theory of such crises.

And if you’ve read the news, you might see similar crises forming today.

We may well be in ‘The Fourth Turning’ (Howe & Strauss) - a period of social crises that arise as past generations age and societal memory is lost.

This is theorized as an 80-100 year cycle.

AI-driven solutions have the potential to both mitigate and exacerbate societal challenges during a Fourth Turning.

On the one hand, AI can address complex global issues such as climate change, healthcare, and resource management, offering solutions that are beyond human capabilities in terms of speed and efficiency.

For example, AI can optimize energy consumption, accelerate medical research, and improve disaster response, thereby alleviating some of the crises characteristic of a Fourth Turning.

On the other hand, the rapid advancement and deployment of AI could lead to significant societal upheaval.

Que the return of the Luddites.

Job displacement due to automation, ethical concerns around surveillance and privacy, and the potential for AI to be used in warfare are just a few examples of how AI could exacerbate tensions.

The key to whether AI will be a mitigating or exacerbating factor lies in how it is governed, the ethical frameworks guiding its development, and the inclusivity of its benefits.

To guarantee this inclusivity, Intelligence needs to be commoditized and unbiased.

The existential risk arising from OpenAI, Gemini, and co:

As we become more aware of the risk of bias in the training of models, we can see that the path ahead is fraught with danger.

Take the wrong path and we could easily live in the dystopian world of Black Mirror.

But left to human judgment, would a board of directors even be able to choose the correct ethical path?

Or should there even be a board of directors at all?

Take Google Gemini’s ‘historical accuracy issues’ for example…

In February 2024, Gemini’s depictions of ‘historically accurate’ families from 17th century Scotland, Vikings, and the seeming exclusion of White people from (for example) what a scientist looks like, is causing a stir.

Rightfully so.

Is the AI biased against White people?

It certainly seemed that way, at first glance.

And as far as we know, we don’t have AGI that could have originated this bias.

So by deploying some simple deduction, you could conclude that the trainers of Gemini may carry some implicit bias against White/ European/ American people.

And that may be a reflection of the requirements of Diversity Policy or caused by a manipulation of your prompt.

On the other hand, it could be just an attempt to balance the ethnic makeup of responses to appeal to a wider audience… that compensated too far.

Now, Google apologized for this, but if it is true that the model inherited the bias of the organization that created it, we have a problem.

If we had no other options, this trend could continue as power is accumulated by centralized models like Google’s.

We would have a very dark future indeed.

But are we in danger of applying the heuristics of whatever side of the woke/ anti-woke culture war we favor here?

It could be that way too.

Or this could be a symptom of a far more dangerous.

Yishan (former CEO of Reddit) wrote an X thread that aims to transcend the culture war arguments and shine a light on perhaps the critical existential threat of this event:

“This event is significant because it is a major demonstration of someone giving an LLM a set of instructions and the results being totally not at all what they predicted.

It is demonstrating very clearly, that one of the major AI players tried to ask a LLM to do something, and the LLM went ahead and did that, and the results were BONKERS.

Do you remember those old Asimov robot stories where the robots would do something really quite bizarre and sometimes scary, and the user would be like WTF, the robot is trying to kill me, I knew they were evil!

And then Susan Calvin would come in, and she’d ask a couple questions, and explain,

“No, the robot is doing exactly what you told it, only you didn’t realize that asking it to X would also mean it would do X2 and X3,

these seemingly bizarre things.

And the lesson was that even if we had the Three Laws of Robotics, supposedly very comprehensive, that robots were still going to do crazy things, sometimes harmful things, because we couldn’t anticipate how they’d follow our instructions?

In fact, in the later novels, we even see how (spoiler) the robots develop a “Zeroth Law” where they conclude that it’s a good idea to irradiate the entire planet so that people are driven off of it to colonize the galaxy.

And that’s the scenario where it plays out WELL…. in the end.

There’s a few short stories in between where people are realizing the planet is radioactive and it’s not very pleasant.

Are you getting it?

Woke drawings of black Nazis is just today’s culture-war-fad.

The important thing is how one of the largest and most capable AI organizations in the world tried to instruct its LLM to do something, and got a totally bonkers result they couldn’t anticipate.

What this means is that @ESYudkowsky has a very very strong point.

It represents a very strong existence proof for the “instrumental convergence” argument and the “paperclip maximizer” argument in practice.

If this had been a truly existential situation where “we only get one chance to get it right,” we’d be dead.

Because I’m sure Google tested it internally before releasing it and it was fine per their original intentions. They probably didn’t think to ask for Vikings or Nazis.

It demonstrates quite conclusively that with all our current alignment work, that even at the level of our current LLMs, we are absolutely terrible at predicting how it’s going to execute an intended set of instructions.

When you see these kinds of things happen, you should not laugh.

Every single comedic large-scale error by AI is evidence that when it is even more powerful and complex, the things it’ll do wrong will be utterly unpredictable and some of them will be very consequential.

I work in climate change, I’m very pro-tech, and even I think the biggest danger would be someone saying to AI, “solve climate change.”

Because there are already people who say “humans are the problem; we should have fewer humans” so it will be VERY plausible for an AI to simply conclude that it should proceed with the most expedient way to delete ~95% of humans.

That requires no malice, only logic.

Again, I will say this: any time you see a comedic large-scale error by AI, it is evidence that we do not know how to align and control it, that we are not even close.

Because alignment is not just about “moral alignment” or “human values,” it is just about whether a regular user can give an AI an instruction and have it do exactly that, with no unintended results.

You shouldn’t need to be Susan Calvin.”

Both points of view can be true here

Gemini be displaying bias against White, American, or European civilization to fit the ideals of Google's Diversity Policies

And

There can exist an existential threat to the whole population of Humanity from unintended intelligence output.

Either way Anon, there is plenty to be concerned about here.

In Defence of Yishan’s argument:

The "paperclip maximizer" argument is a thought experiment introduced by philosopher Nick Bostrom.

It illustrates the potential risks of an AI system with even a seemingly harmless final goal, if not properly aligned with human values.

In this scenario, an AI designed to maximize the production of paperclips could, if given sufficient power and autonomy, consume all resources and even harm humanity in its single-minded pursuit of creating as many paperclips as possible.

The paperclip maximizer becomes a symbol for the broader concern that an AI's objectives, if not fully aligned with human values and interests, could lead to unintended and potentially catastrophic consequences, even from tasks that seem benign.

So - The Paperclip Maximiser argument, applied here, implies that this is an accident and no intention from Google to minimize the prominence of White people.

It was merely to rebalance the outputs of its training data to accommodate a global audience.

And implies that this is a logical result of the input, which unfortunately had an unacceptable method.

This could be close to the truth.

Google’s Senior VP came out to apologise

Read the statement for yourself here

“What happened

The Gemini conversational app is a specific product that is separate from Search, our underlying AI models, and our other products. Its image generation feature was built on top of an AI model called Imagen 2.

When we built this feature in Gemini, we tuned it to ensure it doesn’t fall into some of the traps we’ve seen in the past with image generation technology — such as creating violent or sexually explicit images, or depictions of real people. And because our users come from all over the world, we want it to work well for everyone. If you ask for a picture of football players, or someone walking a dog, you may want to receive a range of people. You probably don’t just want to only receive images of people of just one type of ethnicity (or any other characteristic).

However, if you prompt Gemini for images of a specific type of person — such as “a Black teacher in a classroom,” or “a white veterinarian with a dog” — or people in particular cultural or historical contexts, you should absolutely get a response that accurately reflects what you ask for.

So what went wrong? In short, two things. First, our tuning to ensure that Gemini showed a range of people failed to account for cases that should clearly not show a range. And second, over time, the model became way more cautious than we intended and refused to answer certain prompts entirely — wrongly interpreting some very anodyne prompts as sensitive.

These two things led the model to overcompensate in some cases, and be over-conservative in others, leading to images that were embarrassing and wrong.”

So Yes it seems like Google’s Gemini was overcorrecting

It is true that in the past, AI models have generated images that are mostly White people and others that were found to be offensive to non-whites.

This is due to the training data consisting of mostly white people, leading to issues with accuracy when generating non-whites.

So it seems we have flipped from one extreme to the other extreme, as AI trainers looked for balance, but got it wrong.

There’s nothing inherently wrong with AI ‘using what it’s got.’

But from Google’s perspective, they understandably need to apply a customer-centric approach to creating products for their global audience.

The issue with this is that the subjective human input to correct this is wrong and will likely continue to be a source of sticky problems going forward.

They are unlikely to be able to make everyone happy when these subjective criteria come from one centralised authority.

So, if subjective criteria is the main route of the problem, diversifying and decentralising the authority to apply these criteria, could give us a lot more flexibility to create a product that satisfies a diverse range of users.

If Google’s model was achieving its logical endpoint according to the Paperclip maximiser argument, the logic of the AI trainers resulted in the minimisation of European and American civilisation.

Even though the most simple explanation is that the model overcorrected, we can’t avoid critiquing the result.

After all, Google has immense power, data and influence over the world.

Their products literally can influence society’s perception of reality.

The problematic result of Gemini points to a deeper logic, which you can see occur in Social Justice and Intersectional Race Theories.

These theories advocate a hierarchy of oppressive ‘structures’ that are ubiquitous throughout society and ultimately benefit White, Straight Males, at the expense of everybody else.

The solution, according to these theories, is to ‘deconstruct’ these oppressive hierarchies, to achieve an ideal, equal, and equitable society for all.

The prominence of White people in the training data, led the tuners to conclude that they needed as few White people as possible to make a good product.

Since the training data was overwhelmingly white people, they needed to find the balance where there is as few white people as possible, to give fair representation to other ethnicities or phenotypes, without removing white people altogether.

So, this is the ideal endpoint in Diversity theories.

But these theories are riddled with assumptions about reality and would always require interventionism and ultimately coercion to achieve.

There are few guarantees that widespread oppression would not be used to achieve the seemingly noble, idealistic goal and certainly no guarantees that there would be beneficial result of such actions, for the greater good.

Yishan could be pointing to the ‘The Paper Clip Maximiser’ argument yet ignoring the consequences of the Diversity theories at play.

Expunging Google’s responsibility for its own structural bias.

And highlighting a logic that there is a need to reduce the prominence of white people, to achieve a fair output not just for LLM’s but for society at large.

This logic is automatically added in addition to your prompt when you ask for something specific.

Without your permission or intent.

Now in the real-life case of Gemini:

The ‘seemingly harmless final goal’ could have been to balance the ethnic diversity of images to appeal to a global audience, given Google’s training data may include a majority of White people for image generation.

But, whichever way you look at this, this indicates a need to manipulate the output of your request, based on the desired outcome of ‘the central power’ and not yours.

So whatever your desired outcome, a centralized source is prescribing a pre-formulated template that they deem socially correct.

And crucially, this occurs at the expense of the truth.

This surely opens a Pandora's box of unintended consequences.

What happens when they scale this up?

What if they don’t fix it?

What is the ‘fair’ balance?

What if this heuristic is applied in Healthcare, Resource Allocation, Education - and whole demographics are de-prioritized based on a view of the world that may not be true?

The only answer is, not to remove white people but to diversify the criteria of what is an optimally useful output for all users of commoditized AI.

Let users ask for the specific output that they want.

And let the market decide in a decentralized manner.

After all, there would be nothing stopping a user from asking for the specific ethnic output that they want or adding it to their customized instructions.

And to push back a bit harder here, Yishan Seems to have made some logical errors and deflections

Even if Gemini was overcorrecting, the result is biased.

It is quite unjust to deride clear bias as ‘unimportant’ and just a culture war trope, given there are examples where Gemini flat out refuses to generate images in the style of iconic American artist Norman Rockwell, as it ‘idealized’ American life.

Gemini Response:

It is a symptom of a restrictive ideology to be unable to appreciate the expressions of a much-loved artist and to force its ethical justification through the ‘Social Justice’ logic, which views everything through Race, gender, and class and claims that there is something harmful here.

Rockwell’s ‘Four Freedoms’ pieces advocated for:

1. Freedom of Speech

2. Freedom of Worship

3. Freedom from Want

4. Freedom from Fear

The paintings were based upon Roosevelt’s ‘four universal freedoms’ speech, delivered at a time when the Nazis occupied Western Europe, imposing fascism across their new empire.

Rockwell’s pieces contributed to raising War Bonds from the American people to push back against Fascism.

And produced an absolute banger of a meme, which all of Crypto Twitter enjoys to this day:

Yishan himself acknowledges that the solution to the existential threat posed by unintended AI outputs is not just about “moral alignment” or “human values” it is about receiving outputs as expected by the user which are beneficial.

The issue is that Gemini can’t even accept a White American artist in its ethical considerations, despite the fact there is no evidence that Rockwell produced anything harmful to any race, gender, or class.

The themes in his art were for Universal Human Rights and directly contributed to defeating Fascism globally.

The man simply painted his observations of American life as he saw it at the time.

And as an artist, that’s all he had to do.

Yishan acknowledged that Google just intended to produce diverse outputs due to its global user base, which is reasonable.

You wouldn't want Gemini to only produce White people either.

But the very fact that Google had applied the criteria of generating diverse ethnic representations in images has another implicit issue, if true:

For Google to fulfil its diversity policy, it has to intervene with bias against white people and Western Civilisation.

It has to manipulate output to fit a socially correct template, an ideal vision.

Dear reader, I hope we can agree that we can see a clear need for access to intelligence that is not subject to the heuristics and ideology of the founders who built them.

Alongside a mechanism that can effectively align the execution of AI with the well-being of us as humans and beneficiaries.

Is it possible Hayek foresaw the centralizing forces we see threaten us today?

Or is there timeless economic wisdom permeated in his thinking?

I would argue Hayek saw a constant truth of human nature in the desire for centralized powers to control sources of knowledge, pricing incentives, and other commodities.

To create a model that sees the world as they do.

That fits their ideology arbitrarily.

But now this instinct for humans to control narratives, knowledge, and history has crept into the production of Intelligence.

And all of us now have a responsibility to be wise to the ethics of censoring certain outputs of intelligence vs what the creator’s bias would insist on.

Community-lead innovations like Bittensor are commoditizing Intelligence for humankind's benefit and freedom:

Bitcoin provided a commodity that allowed people to protect themselves against the dilution of their currency from central banks.

Bittensor now provides a commodity that allows people to protect themselves against the co-option of their intelligence from centralized intelligence models.

If a model's output is not useful for those who need it, Darwinian competition will ensure that the model will lose its place on the subnet where it resides.

The market will decide which intelligence output is best.

Democratising Intelligence:

At its core, Bittensor aims to democratize the development of machine intelligence by creating a peer-to-peer marketplace for AI models,

Where models are incentivized to create the best responses by emissions of the native cryptocurrency: $TAO.

Hayek's argument for the superiority of decentralized decision-making systems over centralized ones permeates Bittensor's approach.

He posited that individual knowledge of specific circumstances, which is vast and dispersed, is crucial for efficient resource allocation and cannot be centralized effectively.

This concept is called ‘Tacit Knowledge’, and Hayek uses the example of how a shortage of tin would affect its price.

He explained that if for some reason tin became scarce, perhaps due to a reduction in supply, the price of tin would rise.

This price increase would signal to users of tin (such as manufacturers of tin products) that they should economize on tin, perhaps by using less of it or finding substitutes. Importantly, these users do not need to know why tin has become scarce; the price alone communicates the need to adjust their behavior.

This is an example of how tacit knowledge works: the knowledge about the scarcity of tin is dispersed among many individuals and is not explicitly stated, yet it is effectively communicated through the price system without a central authority.

‘Tacit Knowledge’ is accounted for with Bittensor's decentralized mixture of experts (MoE) model, which leverages the collective intelligence of multiple AI models, each specializing in different aspects of data, to generate predictions and insights that surpass the capabilities of any single monolithic model.

Yuma Consensus Enables Decentralisation of Intelligence Across Sub Nets

Bittensor's unique ‘Yuma’ consensus mechanism, which integrates deep learning with blockchain consensus algorithms, ensures trust, security, and decentralization within its AI ecosystem.

The mechanism promotes a meritocratic system where the quality and utility of contributions are directly linked to economic rewards, encouraging participants to provide accurate and valuable data and models.

The Bittensor API is designed to enable subnet owners to write their incentive mechanisms.

These incentive mechanisms allow a diverse set of subnet validators to express their subjective preferences about what the network should learn.

Subnet validators can validate permissionlessly.

Meaning anyone, with any ideology, theory or objective can contribute their subjective criteria to scoring intelligence outputs.

Such an approach:

1. Facilitates the economic market in which producers (subnet miners) are constantly driven to make their knowledge output more useful in terms of speed, intelligence, and diversity.

2. And also decentralizes Bittensor's governance across multiple diverse stakeholders, ensuring that no single group has full control over what is learned.

The Role of Validators in Yuma Consensus

A subnet validator in a subnet expresses their perspective about how performant subnet miners in the subnet are, through a set of weights wi.

Such weights wi are aggregated across all the subnet validators in the subnet, to produce a weight matrix W.

Subnet validators learn their row in W by running the validator module and continuously verifying the responses produced by the subnet miners in terms of speed, intelligence and diversity.

How Yuma Weights the Incentives for Subnets

The Yuma Consensus algorithm translates the weight matrix W into incentives for the subnet miners and dividends for the subnet validators.

However, radical divergence from consensus viewpoints is dangerous, especially if bad actor validators manipulate incentives in ways that benefit themselves, for example, lying about the value produced by miners.

To avoid this scenario Bittensor uses a mechanism called Yuma Consensus.

The Yuma Consensus rewards subnet validators with dividends for producing miner-value evaluations that are in agreement with the subjective evaluations produced by other subnet validators, weighted by stake.

The Intelligence fly-wheel

The platform's economic model and tokenization not only facilitate transactions and reward contributors but also create a virtuous cycle of growth and improvement.

As the network becomes more valuable, with an increasing array of data and services, the value of the tokens and the incentive to contribute high-quality resources also increase.

This model promotes continuous motivation for participants to maintain and improve the quality of their contributions, aligning individual interests with the health and growth of the entire network.

Decentralised AI vs Centralised AI

Bittensor's potential to outcompete centralized intelligence models from companies like OpenAI, Microsoft, Anthropic, and Google Gemini lies in its decentralized, permissionless, and incentivized intelligence marketplace.

By breaking down barriers created by centralized platforms and fostering innovation and inclusivity in AI development, Bittensor could democratize AI, making it accessible to a wider range of entities.

This approach not only accelerates AI development by leveraging collective intelligence but also ensures sustainability by maintaining a continuous motivation for quality contributions.

Why not diversify the scoring criteria for intelligence?

In conclusion, Bittensor, guided by Hayek's principles, presents a compelling case for the advantages of a decentralized AI marketplace.

By incentivizing contributions, ensuring efficient resource allocation through a decentralized model, and fostering a collaborative environment, Bittensor could significantly impact the development of artificial intelligence, making it more inclusive, innovative, and efficient.

Crucially, the application of ‘Tacit Knowledge’ across a diverse array of validator criteria, should help us to come to a more organic and beneficial consensus on what is an appropriate output from a model.

As hard as they try, Google’s efforts to find the right balance in output will always be held back by the limitations of being a centralised source.

There will always be a negative, unintended consequence of sticking to one heuristic, that is universally applied.

We need to distribute the decision-making process and let the market come to a conclusion made from a wider spread of knowledge than one source alone.

Will the future be defined by a battle between Centralizors and Decentralizors?

Corcel, a Bittensor subnet, still interacts with OpenAI via API, and teams like OpenAI have committed to open-sourcing Intelligence developments.

So there will always be interactions and ways differing models can plug into the marketplace.

But the path that OpenAI, Microsoft, and Google take will ultimately be dictated to by the whims of their shareholders and the ideologies present in their corporate structure.

And over time they are incentivized to create moats, monopolize industries and impose themselves on the market.

We would really be hoping for the best, if we didn’t have a decentralised alternative.

Written by @tomfastlane

The Validator's Dilemma

Validators are the backbone of the Polygon network, responsible for validating transactions and creating new blocks.

However, the role comes with its set of challenges:

1. Infrastructure Costs

Running a validator node isn't cheap. From server costs to security measures, the financial burden can be significant. This is especially true for smaller validators who don't have the backing of institutional investment funds.

2. Network Security

Validators are constant targets for adversarial attacks. Whether it's DDoS attacks or attempts to exploit vulnerabilities, maintaining a secure environment is a full-time job.

3. Governance Participation

Being a validator isn't just about transaction validation; it's about active participation in network governance. Validators are expected to vote on protocol upgrades, which requires a deep understanding of the ecosystem.

4. MEV Challenges

Maximal Extractable Value (MEV) is a complex task that requires a deep understanding of the network and smart contract interactions.

Failing to optimize for MEV can result in significant financial losses.

5. Transaction Spam

In February of 2022 a serious threat to the Polygon blockchain arose.

The network’s stability was in jeopardy due to nodes being flooded with spam transactions sent by arbitrageurs, liquidators, and gamers looking for Maximal Extractable Value.

These actors, known collectively as ‘searchers,’ look to profit from inefficiencies in the ordering of transactions.

The ultimate goal of the searchers is to extract profit by using bots to ‘search’ for opportunity-creating transactions which can be exploited by quickly placing their own transactions directly after–and sometimes before–said transaction.

In the process of competing over opportunities, searchers can create spam transactions which lead to suboptimal user experiences such as delayed execution, slippage, and network congestion.

These spam transactions can cause instability in validator nodes, as observed during the outages in Q1 of 2022.

The Delegator's Perspective

From the delegator's point of view, especially institutional ones, the focus is on long-term growth and stability.

They are not interested in validators who offer high returns but can't guarantee security or have a history of poor governance participation.

If a validator isn’t careful to foolproof their node from threats such as arbitrageur spam, this can lead to node instability and potential downtime.

From a delegator’s view, this is a risk that should be avoided and could result in the Validator losing stake.

The One Simple Thing: Patching Your Sentries

So, how can validators address these challenges and retain their delegators?

The answer is simpler than you might think: patching your sentries.

With this code:

https://fastlane-labs.gitbook.io/polygon-fastlane/getting-started-as-a-validator/patching-your-sentry-nodes-with-the-fastlane-patch

By applying a single line of code to your sentry nodes, you can drastically improve the Polygon network user experience and optimize for MEV.

This process is roughly a quick 2-minute task but will allow you to remove transaction spam from arbitrageurs, increase node stability, collect additional revenue and improve the Polygon User experience.

This simple patch shows delegators that you are committed to upholding a secure and healthy network, thereby increasing their likelihood of sticking with you in the long run.

Benefits of Patching Your Sentries With FastLane

1. Enhanced Security: A patched sentry is less susceptible to spam, ensuring that your validator node remains stable.

2. MEV Optimization: Connect to the FastLane Node, which acts as an ‘auctioneer’ - where searchers bid for the winning opportunity transaction. After which there is a smart contract which collects the promised MATIC bids to send to the Validator’s personal Vault.

3. Quick Onboarding: It takes less than 5 minutes to apply the patch, making it a quick and efficient way to improve your service.

4. Governance Boost: By showing that you're proactive about network improvements, you're more likely to gain the support of your delegators in governance proposals.

5. Enhance Polygon User Experience: By disincentivising front running and sandwich attacks

6. Faster Transaction Propagation: By using the FastLane Node as an ‘Auctioneer’

7. An additional, complimentary sentry node: Maintained by the FastLane team to help you with Uptime

8. Reduced load on validator node: Spam is removed by standardising transaction propagation.

The challenge as a validator

Being a Polygon validator is challenging but rewarding.

While the hurdles are many, patching your sentries can go a long way in improving delegator retention.

It not only enhances node stability and user experience but also optimizes MEV, offering a win-win situation for both validators and delegators.

Are you a Polygon Validator ready to improve your revenue and node stability?

We are currently helping 26 Polygon Validators - who produce 30.75% of all blocks - to protect the Polygon network while earning more MATIC from MEV auctions.

By just patching your sentries with a free one-line code you can collect MATIC while keeping the network clean and safe from harmful activities like frontrunning and sandwich attacks.

On average it takes less than 10 minutes to onboard before you can start making a positive influence on the Polygon user experience and collect MATIC that would otherwise just be collected by Searchers.

We are helping validators make MEV more transparent on Polygon and increasing trust with their delegators by building decentralised infrastructure.

If you would like to know more, please email me at tom@fastlane.finance

In the rapidly evolving blockchain landscape, validators play a pivotal role.

Their ability to attract delegators can make or break their success.

While many validators excel in their technical capabilities, some overlook simple fixes that can significantly boost their appeal to potential delegators.

In this article, we'll highlight three easy fixes that validators can implement to attract more delegators.

1. Boost Transparency and Enhance Communication

Delegators seek assurance. They want to know that their assets are managed efficiently and securely. A straightforward way to provide this assurance is through transparent operations and clear communication.

Issue: Some validators fall short in regularly updating their delegators, leading to mistrust or uncertainty.

Easy Fix: Establish consistent communication channels. Whether it's through social media, email newsletters, or community forums, regular updates can foster trust. Share insights about performance, fee adjustments, and any potential risks. Remember, an informed delegator is a confident delegator.

2. Strengthen Infrastructure and Prioritize Security

The blockchain world is no stranger to security breaches. Delegators are acutely aware of these risks and prioritize validators who demonstrate a commitment to security.

Issue: Inadequate infrastructure or outdated security measures can deter potential delegators, fearing their assets might be vulnerable.

Easy Fix: Regularly update and fortify your infrastructure. Engage with cybersecurity experts to conduct periodic audits and address vulnerabilities. Highlighting your proactive approach to security can serve as a significant attraction point for delegators.

3. Optimize Performance and Reevaluate Fees

At the end of the day, delegators are driven by returns on their investments. They seek validators who offer optimal performance without exorbitant fees.

Issue: Validators who don't consistently deliver or who charge high fees without clear justification can struggle to attract and retain delegators.

Easy Fix: Continuously monitor and refine your operations to ensure you're delivering the best possible performance. If you're charging a premium fee, ensure you're offering value-added services that justify the cost. A competitive fee structure combined with top-tier performance can be a winning combination.

Seize the Oppurtunity

The Polygon Network offers immense opportunities for validators.

By implementing these easy fixes, validators can enhance their appeal, attract more delegators, and solidify their position in the ecosystem.

It's all about building trust, ensuring security, and delivering value.

Are you a Polygon Validator ready to make the most of these trends?

We are currently assisting 26 Polygon Validators - who produce 30.75% of all blocks - to earn more MATIC through MEV.

By simply patching your sentries (with a complimentary one-line code), you can amplify your earnings while preserving the network's integrity, shielding it from malicious activities.

Typically, the onboarding process takes less than 10 minutes.

Then you can accrue MATIC that would otherwise be snapped up by Searchers.

To request more details, email me at: tom@fastlane.finance

1. Inadequate Infrastructure and Downtime:

One of the primary responsibilities of a validator is to consistently produce and validate blocks.

Any downtime, whether due to technical glitches, inadequate infrastructure, or poor maintenance, can result in missed blocks.

This not only reduces the rewards for the validator but also impacts the stakers who have delegated their MATIC tokens to them.

Deep Dive: A technically skilled coder understands the nuances of maintaining a robust infrastructure.

However, cutting corners by relying on subpar hardware or not having redundancy measures in place can lead to unexpected downtimes.

In the world of staking, where every second counts, such lapses can be costly and can quickly erode trust.

2. Lack of Transparency and Communication:

In the decentralized space, transparency is paramount. Validators are expected to keep their stakers informed about their operations, performance metrics, and any potential issues. A lack of open communication or withholding of critical information can raise red flags.

Deep Dive: Consider a scenario where a validator is aware of a potential security vulnerability but chooses not to disclose it to their stakers. If this vulnerability is later exploited, the validator's reputation can take a significant hit. Regular updates, open channels of communication, and a proactive approach to addressing concerns can go a long way in building and maintaining trust.

3. Passive Network Participation:

Being a validator on Polygon is not just about validating transactions. Validators are also expected to actively participate in network governance, contribute to protocol upgrades, and be a part of the broader community discussions. A passive approach, where a validator merely performs the bare minimum, can be perceived as a lack of commitment to the network's growth and well-being.

Deep Dive: The Polygon ecosystem is dynamic, with regular protocol upgrades, governance proposals, and community initiatives. Validators who actively participate, voice their opinions, and contribute to the network's betterment are seen as valuable assets. On the other hand, those who remain passive risk being perceived as opportunistic, only in it for the rewards.

For a technically skilled coder running a validator, these pitfalls might seem basic. However, in the daily grind of maintaining nodes, ensuring security, and optimizing for rewards, it's easy to overlook these foundational aspects. The key lies in striking a balance – ensuring technical excellence while also prioritizing transparency, active participation, and stakeholder communication.

In the age of institutional staking, where professional entities with stringent due diligence processes are entering the fray, the margin for error is minimal. Validators who recognize these pitfalls and actively work to avoid them position themselves as trustworthy pillars of the Polygon ecosystem.

Are you a Polygon Validator ready to improve your trustworthiness?

We are currently helping 26 Polygon Validators - who produce 30.75% of all blocks - to protect the Polygon network while earning more MATIC from MEV auctions.

By just patching your sentries with a free one-line code you can collect MATIC while keeping the network clean and safe from harmful activities like frontrunning and sandwich attacks.

On average it takes less than 10 minutes to onboard before you can start making a positive influence on the Polygon user experience and collect MATIC that would otherwise just be collected by Searchers.

We are helping validators make MEV more transparent on Polygon and increasing trust with their delegators by building decentralised infrastructure.

If you would like to know more, please email me at tom@fastlane.finance

Historically, individual delegators prioritized short-term gains, often hopping between validators to chase higher rewards.

The game was simple: find the validator offering the best returns and delegate.

However, as institutional players enter the staking arena, the rules of the game are being rewritten.

Institutional Staking: A Different Beast

Institutional staking is not just about returns; it's about security, reliability, and long-term growth.

Institutional investment funds, with vast amounts of capital at their disposal, are not looking for quick wins.

They seek stability and predictability.

For them, the risk associated with validator downtime, slashing events, or poor network participation can be a deal-breaker.

This shift in delegator profile means validators need to up their game.

It's no longer sufficient to offer competitive staking rewards.

Validators must demonstrate a track record of reliability, invest in top-tier infrastructure, and actively participate in network governance.

Technical Excellence is Paramount

For the technically skilled coder running a validator, this evolution presents both challenges and opportunities.

The modern validator must be adept not just in maintaining node uptime but in navigating the complex interplay of network politics, protocol upgrades, and the ever-present threat of adversarial attacks.

Validators are now expected to be at the forefront of protocol development, actively contributing to the improvement of the Polygon network.

This proactive approach is not just about staying relevant; it's about shaping the future of the network in which they have a vested interest.

Understanding the New Delegator

In the age of institutional staking, understanding the needs and expectations of the new-age delegator is crucial. These are not individuals looking to make a quick buck. They are professional entities, with stringent due diligence processes and a long-term vision.

1. Security First: Institutional delegators prioritize the security of their funds above all else. Validators must invest in state-of-the-art infrastructure, multi-sig wallets, and robust key management solutions.

2. Transparency is Key: Regular communication about validator performance, upcoming protocol changes, and network governance decisions is essential. Institutional delegators appreciate validators who maintain open channels of communication and provide regular updates.

3. Active Network Participation: It's not just about validating transactions. Validators are expected to be active participants in network governance, contributing to protocol upgrades and shaping the future direction of the Polygon network.

4. Sustainability Matters: With the growing emphasis on ESG (Environmental, Social, and Governance) criteria in the investment world, validators who adopt sustainable practices and prioritize energy-efficient infrastructure will have an edge.

Adapting to the New Normal

For validators, the message is clear: adapt or risk obsolescence. The age of institutional staking demands a higher standard of operation, a proactive approach to network participation, and an unwavering commitment to security and transparency.

Validators who recognize these shifts and position themselves accordingly will not only attract institutional delegators but will play a pivotal role in the next phase of Polygon's growth. It's an exciting time to be a part of the Polygon network, and for those validators willing to rise to the challenge, the rewards—both financial and in terms of influence within the network—are significant.

Are you a Polygon Validator ready to make the most of these trends?

Let’s Talk.

We are currently helping 26 Polygon Validators - who produce 30.75% of all blocks - to earn more MATIC through MEV.

By just patching your sentries, (with a free, one-line code) you can boost your earnings while keeping the network clean and safe from harmful activities.

On average it takes less than 10 minutes to onboard before you can start making a positive influence for the Polygon user experience and collect MATIC that would otherwise just be collected by Searchers.

For more details - message me at tom@fastlane.finance

1. Boost Transparency and Enhance Communication

Delegators seek assurance. They want to know that their assets are managed efficiently and securely. A straightforward way to provide this assurance is through transparent operations and clear communication.

Issue: Some validators fall short in regularly updating their delegators, leading to mistrust or uncertainty.

Easy Fix: Establish consistent communication channels. Whether it's through social media, email newsletters, or community forums, regular updates can foster trust. Share insights about performance, fee adjustments, and any potential risks. Remember, an informed delegator is a confident delegator.

2. Strengthen Infrastructure and Prioritize Security

The blockchain world is no stranger to security breaches. Delegators are acutely aware of these risks and prioritize validators who demonstrate a commitment to security.

Issue: Inadequate infrastructure or outdated security measures can deter potential delegators, fearing their assets might be vulnerable.

Easy Fix: Regularly update and fortify your infrastructure. Engage with cybersecurity experts to conduct periodic audits and address vulnerabilities. Highlighting your proactive approach to security can serve as a significant attraction point for delegators.

3. Optimize Performance and Reevaluate Fees

At the end of the day, delegators are driven by returns on their investments. They seek validators who offer optimal performance without exorbitant fees.

Issue: Validators who don't consistently deliver or who charge high fees without clear justification can struggle to attract and retain delegators.

Easy Fix: Continuously monitor and refine your operations to ensure you're delivering the best possible performance. If you're charging a premium fee, ensure you're offering value-added services that justify the cost. A competitive fee structure combined with top-tier performance can be a winning combination.

The Opportunity

The world of blockchain offers immense opportunities for validators.

By implementing these easy fixes, validators can enhance their appeal, attract more delegators, and solidify their position in the ecosystem.

It's all about building trust, ensuring security, and delivering value.

Are you a Polygon Validator ready to make the most of these trends?

We are currently assisting 26 Polygon Validators - who produce 30.75% of all blocks - to earn more MATIC through MEV.

By simply patching your sentries (with a complimentary one-line code), you can amplify your earnings while preserving the network's integrity, shielding it from malicious activities.

Typically, the connection process takes less than 10 minutes, post which you can begin positively influencing the Polygon user experience and accrue MATIC that would otherwise be snapped up by Searchers.

To request more details, email me at: tom@fastlane.finance

1. Inadequate Understanding of zkEVM and Polygon 2.0

Mistake: Not staying abreast with the nuances of zkEVM and Polygon 2.0, leading to operational inefficiencies.

Why It Matters: The zkEVM is a cornerstone of Polygon 2.0, enhancing transaction throughput and reducing costs. A validator's lack of understanding can result in suboptimal performance, making their node less appealing to potential stakers. This can directly impact the rewards and trust associated with the validator.

Solution: Continuous learning is key. Validators should immerse themselves in the documentation, participate in community discussions, and collaborate with peers to ensure they're leveraging the full potential of zkEVM and Polygon 2.0.

2. Overlooking Infrastructure and Security Essentials

Mistake: Failing to upgrade infrastructure or not tailoring it to the specific requirements of Polygon 2.0, leading to missed blocks or attestations.

Why It Matters: Infrastructure is the backbone of a validator's operations. Any lapse can compromise the security and efficiency of the network. Institutional investment funds, with their vast resources, are particularly discerning and will avoid validators with subpar infrastructure, leading to lost delegation opportunities.

Solution: Regular infrastructure audits and updates are crucial. Validators should ensure their setups are in line with the latest protocols and best practices. Engaging with the developer community can provide insights into optimization strategies tailored for Polygon 2.0.

3. Misalignment with Institutional Staker Expectations

Mistake: Not aligning operations with the needs and strategies of institutional stakers, leading to potential divestment.

Why It Matters: Institutional stakers bring in substantial delegations, and their trust is paramount. A misalignment can not only result in lost opportunities but can also tarnish a validator's reputation in the broader ecosystem. Building and maintaining trust with these entities is crucial for long-term success.

Solution: Direct engagement is essential. Validators should proactively reach out to institutional stakers, understand their needs, and tailor their operations accordingly. Transparency, efficiency, and clear communication channels can go a long way in building trust with these institutional entities.

Your Responsibility

Being a validator in the Polygon ecosystem is a role filled with responsibility and potential. By understanding and addressing the significance of the pitfalls outlined above, validators can position themselves as reliable nodes, attracting more delegators and solidifying their stature in the network. In the dynamic world of Web 3.0, understanding the "why" behind every action and implementing the right solutions is the key to sustained success.

Are you a Polygon Validator ready to make the most of these trends?

We are currently assisting 26 Polygon Validators - who produce 30.75% of all blocks - to earn more MATIC through MEV.

By simply patching your sentries (with a complimentary one-line code), you can amplify your earnings while preserving the network's integrity, shielding it from malicious activities.

Typically, the onboarding process takes less than 10 minutes, post which you can begin positively influencing the Polygon user experience and accrue MATIC that would otherwise be snapped up by Searchers.

**1. zkEVM System: The Next Step in Scalability and Efficiency
**
The launch of the Polygon zkEVM and upgrade of the PoS network to a Validium in Polygon 2.0 is a big deal.

The zkEVM, which combines the best parts of Polygon's projects like Hermez and the insights from Polygon Zero, offers a more efficient Ethereum Virtual Machine experience.

For validators, This means a platform that's not just scalable but also more secure.

The zk-rollup ensures that transactions are grouped together and sent as one proof to the main blockchain.

This speeds up transactions and boosts security, making it a top choice for institutional stakers and investment funds.

Over time, continued advancements to Polygon will bring in more delegators for staking and revenue for validators.

2. Institutional Staking: Big Money Meets Blockchain

More and more big-money players are getting into blockchain.

These institutional investors are looking for platforms that are solid and offer good returns.

Polygon, with its advanced zkEVM system and focus on security, is quickly becoming one of their preferred choices.

Plus, the partnerships that Polygon is forming with big names like Starbucks, DraftKings and Lufthansa, further solidify its reputation as a top platform for serious staking operations.

3. Making the Most of MEV (Maximal Extractable Value)

MEV has opened up new ways for validators to earn revenue.

With the rise of complex DeFi transactions, there are more chances to earn through smart order routing and transaction reordering.

Polygon native protocols like FastLane Labs focus on helping validators tap into MEV, while also stopping harmful activities like sandwich attacks and frontrunning, to protect users.

For validators, this means an extra source of income that's both consistent and significant.

By maximizing MEV while keeping the network safe, FastLane is giving Validators a new route to revenue growth.

To summarise:

The combination of the zkEVM system, institutional staking, and MEV optimization is setting the stage for a bright future for Polygon Validators.

As the blockchain world keeps changing, validators who keep up with these trends and adapt will see their revenues grow and play a big part in the future of decentralized finance on Polygon.

Are you a Polygon Validator ready to make the most of these trends?

Let’s Talk.

We are currently helping 26 Polygon Validators - who produce 30.75% of all blocks - to earn more MATIC through MEV.

By just patching your sentries, (with a free, one-line code) you can boost your earnings while keeping the network clean and safe from harmful activities.

On average it takes less than 10 minutes to onboard before you can start making a positive influence for the Polygon user experience and collect MATIC that would otherwise just be collected by Searchers.

To request more details, email me at: tom@fastlane.finance