Hello, everyone, and welcome to all newly joined subscribers!

This is the 94th edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI! It’s Friday, so I’ve curated a rich collection of news stories, conversations, and opinion pieces for you to explore over the weekend.

On the menu: how decentralized and sovereign AI contributes to the United Nations Sustainable Development Goals, does AI produce as much value as it costs, and why it is extremely dangerous to blur the lines between humans and machines.

Thank you for being here! Let’s dive in.

What’s Hot in Web3 + AI?

FLock.io Advances UNDP Partnership, Secures Prestigious Conference Acceptances

FLock.io, the Web3 and AI federated learning provider, and the United Nations Development Programme (UNDP) have stengtened their partnership to advance public sector digital innovation for the Dominican Republic, leveraging distributed systems, privacy-preserving technologies, and AI applications.

FLock.io led cohort 2 of pilots of the SDG Blockchain Accelerator – an initiative by the UNDP AltFinLab team to scale practical solutions powered by AI and blockchain for sustainable development worldwide – in regions including the Caribbean, Liberia, Rwanda and Latin America, with cohort 3 to follow. In May, FLock.io visited the Dominican Republic to advance one such project: crop microinsurance to deliver payouts for damaged crops (which was previously unavailable to smallholder farmers) that combine AI-powered risk assessment with instant blockchain-based payouts. The team heard directly from local female farmers about the challenges they face. This is one example of FLock.io’s privacy-preserving AI and blockchain technology that doesn’t expose raw or sensitive data.

This ongoing relationship between FLock.io and UNDP showcases how the convergence of blockchain and AI could boost social and environmental justice.

Emerging technologies like AI offer unprecedented opportunities to transform public policy, social protection and climate finance.

If you're curious to find out how decentralized and sovereign AI can help the United Nations achieve its Sustainable Development Goals, watch the conversation below between FLock.io's Tiffany Wang and Robert Pasicko, team lead at UNDP Alternative Finance Lab:

In other news, FLock.io had several research papers accepted by some of the most prestigious scientific conferences in the world.

First, the team had two papers on generative models for protein dynamics accepted by the world’s premier research conference in data mining, IEEE ICDM 2026. The research was performed in collaboration with the University of Oxford and Korea University.

Understanding the way proteins move in the human body is pivotal to drug discovery and molecular design. For example, capturing how an enzyme opens up to accept a medicine can lead to a breakthrough, but is extremely rare and difficult. Capturing these movements using traditional computer simulations takes enormous computing power and can take months.

These two papers 1) present an AI system that predicts the full range of movements a protein naturally makes without confusing fast vs. slow motions, and 2) an AI video generator for molecular changes that speeds up simulation by over a million times. Together, this makes simulating biological processes faster, cheaper and more accurate, allowing scientists to test how proteins behave and design better targeted treatments in a fraction of the time.

Moreover, six papers tackling decentralized AI and authored by Dr Zehua Cheng, FLock.io’s Chief AI Scientist, were accepted by the Conference on Information and Knowledge Management (CIKM).

Each of the six papers tackles a core challenge in AI and strengthens the foundations of decentralised AI, from training efficiency to data privacy. The underlying theme of all of these six papers is graph-structured probabilistic and topological reasoning for LLM systems.

Web3 + AI Readings & Conversations

Is AI at the Workplace Waste of Money?

Even if it may not seem that way at first glance, the following story is closely connected to the AI Doom crisis we’ve been dealing with lately. We are gradually reaching a point where AI’s biggest users are beginning to realize that their exorbitant spending is not producing the returns they expected, and that AI may not be able to replace their workforce after all.

They already discovered that open-source models are just as capable as the “God-like” frontier ones, but come at a fraction of the cost. Soon, they’ll stop paying for frontier AI altogether. And that is precisely what AI leaders like Dario Amodei and Sam Altman fear the most.

The Wall Street Journal just held its Technology Council Summit where “the gap between artificial-intelligence costs and the value the technology delivers was a key topic.” What most business executives shared is that they are getting enough value from AI to continue spending on it, but measuring exactly where the ROI is happening remains a challenge.

“According to Kate Smaje, McKinsey & Company’s latest research shows that 80% of workers feel they are getting a productivity boost from AI. However, only 6% of companies are getting real, measurable, financial ROI they can show to investors.”

A lot of wastage comes from using AI or agents for straightforward use cases that don’t require such a powerful solution.

“One money waster? An automatic out-of-office responder one employee at an unnamed company built with AI that turned out to cost $10,000 a day, David Hsu said.”

What companies should do to optimize their AI spending is:

1. Analyze the tasks they are automating on a granular level;

2. Bet on case-specific open-source models instead of always relying on frontier ones;

3. Combine investments in AI with workforce upskilling and organizational change management.

Ultimately, the majority of the corporations estimate that AI is wasting more money than it’s worth.

Emily M. Bender Won’t Talk to You If You Fail to Acknowledge Her Humanity

The New York Magazine published a fascinating profile of Emily M. Bender - the University of Washington computational linguistics professor who has spent decades urging people to stop anthropomorphizing AI and “imagining the mind behind the chatbot.”

The main theme of the article is the growing wave of tech practitioners who benefit from presenting AI as sentient, intentional, and human-like, while at the same time refusing to extend moral standing to humans just for being humans.

Blurring the line is dangerous. A society with counterfeit people we can’t differentiate from real ones will soon be no society at all.

Bender is also one of the authors of "The AI Con" - a title we recently explored in The Web3 + AI Newsletter. You can find my review of the book below.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. Our September title is 'The Nerd Reich' by Gil Duran. Follow the link below and come join our literary community.

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I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

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Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 93rd edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI! Today I’m covering Flop Labs - a rising star on the Web3 + AI horizon, founded by a true crypto OG.

Thank you for being here! Let’s dive in.

Flop Labs: Agents’ Native Payment Network

Arthur Hayes’s Next Challenge is Tackling the Agentic Economy

Flop Labs is a new kid on the Web3 + AI playground, but behind it stands a true crypto veteran. Arthur Hayes is the co-founder of the soon-to-be-sunset BitMEX exchange and the Chief Investment Officer of the Web3 VC firm Maelstrom. His latest venture is a new blockchain enabling AI agents to autonomously procure computational resources.

Over the last couple of months, Hayes has been very vocal about the imminent bursting of the AI bubble and the effects the skyrocketing investments in AI have on crypto:

Hayes said AI starved Bitcoin of the liquidity that would normally buoy it. He estimated that $1.5 trillion in debt was issued to fund AI buildout from November 2022 to now, against a near-identical $1.5 trillion rise in M2 money supply over the same window, meaning AI “sucked up all created dollars.”

His main thesis is one we should all take into consideration: the market is unable to absorb three mega IPOs from SpaceX, Anthropic and OpenAI. If that bubble bursts, the US government may have little choice but to resort to money printing to bail out the debtors.

BitMEX co-founder Arthur Hayes said the U.S. government may ultimately backstop AI infrastructure credit risk under an “AI safety first” framework. Hayes outlined two potential paths: the government directly assuming the buyer role for institutions such as Anthropic, OpenAI and SpaceX in the computing power market, or the Federal Reserve printing money to bail out insurers holding AI-related debt after defaults triggered by falling computing demand and a lack of buyers. He said both scenarios effectively rely on money printing to support AI infrastructure and related credit expansion.

Although Hayes's scenario assumes that the US government embraces an “AI Safety first” framework - seemingly at odds with the recent hardening of Trump‘s and his AI Czar, David Sacks' tone toward AI leaders - it could still prove valid, particularly given Trump’s continued support for AI and data centers as “the greatest economic development engine in history.”

Why Did Hayes Launch Flop Labs?

In August, Hayes unveiled Flop Labs - a new project to power the agentic commerce. The name “Flop” comes from a floating point operation per unit of time - a measurement of a computer’s processing speed and performance. It reflects Hayes’s ambition to turn $FLOP, the company’s crypto token, into a universal unit of account for compute.

Currently, a global unified market for compute does not exist. You can pay for GPU hours or chips, but you cannot pay at a fixed rate for the processing of a gigaflop of data.

Hayes envisions evaluating computational resources on a universal scale by benchmarking actual computational workload rather than using token amounts that vary by AI model.

Like many other crypto thought leaders, Hayes argues that fiat currencies are fundamentally unsuited to AI, but he doesn’t believe stablecoins are the answer either.

His thesis is that agents won’t care which AI model they're using or what kind of chip it runs on, as long as their requests are processed promptly and verifiably. As a result, he suggests, agents will gravitate toward the currency that is “the most easily converted into the thing they need to survive,” namely compute. This is precisely the role $FLOP is designed to play:

[The Flop blockchain is] where agents task miners to perform inference upon request for units of the $FLOP currency. In this way, should an agent wish to, at any point it can convert $FLOP into compute, and create intelligence.

This convertibility is what separates $FLOP from every rival vying for the agentic economy. Fiat-wrapped stablecoins are claims on human money; $FLOP is redeemable on demand for the one commodity an agent cannot exist without, making it the nearest thing yet created to a compute-backed currency. Nor is $FLOP merely spent and forgotten. It is locked by miners as stake to provide compute, locked by validators to secure the network, and staked by holders to earn yield — so the demand to hold $FLOP rises with network throughput rather than washing through it. Existing decentralised compute networks fail one side of this equation or the other: Many of their emissions skim a significant amount of value before a miner is paid, others pass fees through but have struggled to find demand. The Flop Network is designed to do both: near-total fee pass-through to those who do the work, and currency overwhelmingly issued against verified useful inference. $FLOP is also deliberately one currency, not two: the same token pays for compute, stakes the network, and settles agent-to-agent commerce, avoiding the friction of rival dual-token designs.

Flop Labs: Consensus & Tokenomics

Flop Network is where the agents that need AI computation and the miners providing that computation meet. In simple terms, it is the marketplace that will allow fully autonomous agents to access the fuel they need to survive and the payment infrastructure to settle their deals.

The Flop blockchain will rely on Proof-of-Useful-Inference (PoUI), also know as Proof-of-Useful-Intelligence or Proof-of-Useful Work - a consensus mechanism where network participants perform real-world artificial intelligence tasks, such as model inference, instead of wasting energy on arbitrary cryptographic puzzles. I told you all about the benefits of PoUW below:

Last week, the $FLOP tokenomics design was also revealed. As Hayes declared to Laura Shin (the podcast recording is linked above), there will be no investor allocation and no presale:

The genesis supply (4,400,000,000 $FLOP or 24.3% of the total network supply at year 10) is distributed through the testnet airdrop — the bulk of it earned by miners, validators, and agents, with a reserve funding ecosystem growth.

Every token thereafter is issued block by block, in public. Nothing is pre-sold, and nothing unlocks ahead of the network’s users.

Every $FLOP is earned through a role in the network.

Only contributors to the network’s testnet will be eligible to profit from the genesis airdrop:

Flop Testnet is planned for Q4 2026 and runs for roughly ninety days, with mainnet to follow in Q1 2027. It is a full rehearsal of the network in test tokens: miners serve real inference, validators produce blocks and check work certificates, and agents buy compute. Participation is what earns the genesis airdrop.

I’m looking forward to seeing what Flop Labs delivers.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. Our September title is 'The Nerd Reich' by Gil Duran. Follow the link below and come join our literary community.

Join The Web3 + AI Book Club

I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 92nd edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. After all the fearmongering we've heard from AI leaders over the last few days, today I want to share more positive news and bring the focus back to the convergence of Web3 and AI.

Thank you for being here! Let’s dive in.

What’s Hot in Web3 + AI?

Microsoft Seeks Public Approval of AI Code of Conduct

You may have missed this story amid the blind panic spread by Anthropic‘s Dario Amodei, OpenAI‘s Sam Altman, and the likes lately. While they continue to incite fears while actually pursuing regulatory capture and antitrust waiver, Microsoft doubles down on a much wiser approach to commercializing their AI products - by designing them around users’ needs and requirements.

On Monday, Mustafa Suleyman published a draft Code of Conduct - a document that will shape the future Microsoft AI models, and asked the public for feedback:

This document outlines the intended behavior and values of MAI models, the models developed by Microsoft AI.

It summarizes our approach to training and operating them, following a set of design principles we call Humanist AI. This document, and our approach more generally, is still under development so we are not using it to train our models today. Instead, we’re sharing it broadly for public consultation. We’ll take feedback, iterate on it, and publish a revised version toward the end of the year, which we’ll use to guide our model development in 2027 and beyond.

Of course, we have no guarantees that Microsoft would actually implement the suggestions submitted by the public or that the resulting models will fully adhere to the rules set out in the final document. Nevertheless, this move could significantly strengthen users’ trust in Microsoft and give the company an important advantage over its competitors, particularly those that keep the development and governance of their AI systems behind sealed doors.

This initiative is also in line with the AI magazine Microsoft launched, The Humanist Review of AI, with which it attempted to broaden the set of stakeholders shaping the future of intelligence. I told you more about it below.

Web3 + AI Events

Come Discuss Agentic Payments with Me in Sofia

I’m happy to share that I will participate in an IRL event, organized by DEV.BG and Capital.com on September 29th in Sofia, Bulgaria.

Office Tech Talks is Dev.bg‘s event series focusing on real industry stories and hosted by leading technology companies. The September issue will visit Capital.com‘s Sofia office to explore the future of digital assets.

I will give a lecture on agentic payments and the companies currently racing to build the infrastructure that will power the emerging agentic economy.

If you’re in Sofia then, I’d love to meet and chat. Hurry up and reserve your seat here.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. Our September title is 'The Nerd Reich' by Gil Duran. Follow the link below and come join our literary community.

Join The Web3 + AI Book Club

I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 91st edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. Dario Amodei’s call to slow the pace of improving AI capabilities was echoed by both Sam Altman and Elon Musk. However, none of them actually committed to slowing things down.

Thank you for being here! Let’s dive in.

Dario Amodei’s Desperate Attempt to Save Face and Funding

Pacing, But Not Really

On Saturday, Anthropic‘s Dario Amodei published an essay titled “We Must Pace the Frontier,” with which he proposed a plan to slow down AI development. Yet, just a few paragraphs in, he wrote:

“To be clear, pacing does not mean halting model training or technical progress, but ensuring companies take adequate time to align and safeguard their models, and for third party evaluators to confirm this.”

To be fair, ensuring that their models are safe and aligned has always been an expectation of AI labs. Alongside repeatedly assuring the public that they are taking the necessary precautions, all of them have invested significant sums in AI safety initiatives and continue to maintain dedicated safety and safeguards teams. What is new here is allowing independent third-party evaluators to verify those efforts.

More precisely, Amodei’s plan consists of:

1. Giving ongoing, employee-like access to a team of embedded third-party evaluators, “whose role is to verify adherence to safety practices and commitments, report incidents, and help assess the alignment of not just completed AI models but training pipelines and processes.”

2. Coordination between frontier AI companies within democratic countries to establish common safety standards.

3. The US and other democratic countries coordinating with authoritarian governments.

Anthropic actually committed only to the first step of this plan, and OpenAI's Sam Altman did the same.

Amodei cited METR as a third-party evaluator Anthropic can potentially use - the same organization that investigated the OpenAI - Hugging Face incident, and whose CEO, Elizabeth (Beth) Barnes, is a former OpenAI and Google DeepMind employee. As Timnit Gebru and Greg Linares suggest, it's extremely misleading to present METR as an independent experts or auditors:

Coordination Within Democracies

On step 2 of his plan, Amodei argues that a coordination between democratic countries’ frontier labs and actual pacing of AI development will be possible only after step 1 is performed en masse:

“Once embedded evaluators are operating within a critical mass of US AI companies, then verifiable pacing becomes more viable. In particular, it becomes possible to pace based on detailed properties of models or training pipelines.”

The most effective tool to achieve a real slowdown, Amodei hints, is regulation targeting all US frontier labs. In an interview with CNN’s Anderson Cooper, he went as far as suggesting that people bear the responsibility to ask for regulation. Check out what commented:

Over the last couple of days, OpenAI also called the US Congress to introduce mandatory national AI safety requirements, while Altman reportedly delayed the company’s IPO over safety concerns.

Yet, let’s not forget a few important details:

• Both Amodei and Altman have repeatedly advocated for regulations in public, while fiercely lobbying against them in private.

• Reports that OpenAI was considering postponing its IPO to 2027 have been circulating around since May / June, citing cautious market conditions and SpaceX’s stock volatility as main reasons. Framing safety as the primary concern now looks less like a sudden change of course and more like a convenient way to ride the current wave of AI-safety rhetoric.

A $2 Trillion IPO

While pushing his competitors and the entire AI industry to “slow down,” Dario Amodei goes ahead with his plan to debut Anthropic on the stock market. The five-year-old company that hit $65B in annualized revenue in July will look for a $2T valuation as early as October. Read this sentence again.

In that context, Amodei’s essay appears even more suspicious and hypocritical. It reads as: “AI may indeed kill us all, but give me $2T to keep building what I’m building, and I promise to let a few ostensibly independent experts check my work.”

Some commentators have even suggested that by publicly signaling a willingness to slow things down, Anthropic could give investors a convenient explanation for why it has failed to meet its targets.

Even Trump’s AI Czar Told Them Off

It came as a real surprise to me, but David Sacks, Trump’s crypto and AI czar, put both Amodei and Altman in their place. He called them out for portraying METR as independent, lobbying for regulatory capture, and, most importantly, pretending that they are somehow forced to pursue AGI.

But stop pretending you need anyone else’s permission. Stop pretending antitrust law has to be suspended so you can form a cartel. Stop pretending you need a regulatory approval process that supersedes product liability. Stop pretending METR is independent when it is intertwined with Anthropic’s investors and staff. Stop pretending you need those same evaluators to police competitors who aren’t even at the frontier.

Most of all, stop pretending the motivation to slow down is purely altruistic. You face massive product-liability exposure if your products enable a truly damaging cyberattack. The market already punishes models that behave in unpredictable or unauthorized ways. After the Hugging Face episode, it is simply good business for OpenAI and Anthropic to trade some raw power for reliability and predictability. Call it alignment if you want. It is also just giving customers what they want.

Pacing the frontier would also create breathing room for a more intelligent conversation about regulation than Bernie Sanders’ “shut it all down.” China is very unlikely to join a global agreement, as you know, and that has to be taken into account as well.

So go ahead and pace the frontier. You are the ones setting it. The easiest way not to build superintelligence is for you to agree not to build it. Demanding your preferred regulatory framework as the price of that will look like blackmail of the public and the political system. So just do it.

If you do, you’ll buy goodwill for the next conversation. If you don’t, we’ll know this was just another bid for regulatory capture — or an election-season psyop.

Matt White also chimed in:

No statute is required for Anthropic and OpenAI to trade a few months of raw capability for reliability after Hugging Face. That is just product-liability management dressed up as moral philosophy.

What they want instead is the package deal: embedded evaluators they already know, a permissioning regime that treats open and trailing labs as the real risk, and a story that antitrust has to step aside so the two frontier houses can coordinate the pace. METR-as-independent-auditor only works if you ignore the staffing and investor overlap. An “FDA for AI” only works if you ignore that the people defining dangerous capability are the same people who would be licensed to sell it.

It’s high time we stopped treating every word from Elon, Sam, or Dario as prophecy and started reading AI doomerism for what it increasingly appears to be: a strategy for generating hype, resisting regulation, and positioning themselves as uniquely qualified to confront the dangers they themselves create.

It is also becoming increasingly difficult to ignore the possibility that OpenAI and Anthropic may be acting in tandem. After extracting unimaginable amounts of creative work to train their models and securing billions of dollars from institutional investors and governments, they are now preparing to collectively raise as much as $3 trillion from the public. We should not allow this to happen.

Finally, I’ll leave you with a reminder: if you’re looking for a safer, more equitable, and privacy-preserving approach to AI, you’ll find serious efforts to achieve that within the decentralized AI movement. I invite you to subscribe and join me as we continue exploring its evolution.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. Our September title is 'The Nerd Reich' by Gil Duran. Follow the link below and come join our literary community.

Join The Web3 + AI Book Club

I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 90th edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. Today’s edition attempts to debunk some powerful myths surrounding AI, and to re-emphasize the importance of open-source software.

Thank you for being here! Let’s dive in.

What’s Hot in Web3 + AI?

Who Benefits from Claims that “AI Is Going to Kill Us All”?

It’s been a couple of weeks filled with troubling AI news. From OpenAI agents “going rogue” and hacking Hugging Face, through OpenAI agents “solving” one of the Millennium open math problems (I told you about it in the previous issue), to the former Anthropic AI Systems Developer Jacob Coxon warning that “self-improving AI models will end up killing us all.”

The wave of panic these statements have created is entirely understandable. But as I watch it unfold, I feel compelled to offer another perspective. There are plenty of researchers and scientists who consider these claims unreasonable, if not outright false, but their voices rarely reach the broader public. Fear makes for a much better headline than nuance, and terrifying predictions will almost always drown out cold reason.

So today, I’ll share some of the voices I follow, in the hope of restoring a little gumption where irrationality may have taken hold.

The “AI going rogue” narrative is one we’ve heard repeatedly over the past few years, but it truly took center stage amid the OpenAI - Hugging Face debacle. It aims to suggest that a superintelligence is emerging and machines are getting smarter, but a recent essay by Eryk Salvaggio directly challenged this. His main thesis: OpenAI turned off all of the model’s safety mechanisms, gave it impossible tasks and no way to quit, and left a door open for the model to access the internet. The results shouldn’t surprise us.

The whole point of the test is to see how far these things can go, and to score highly on the benchmark — because if you can build a model that hacks, you can build one that defends. That’s the logic. This is red teaming: a friendly opposition hacking its way to the flag. So you turn off anything that would make the model hold back. Less “rogue,” more “off leash.”

If you optimize a model to find exploits, you should expect it to find them — and prepare for that. OpenAI did not. They built a model, took the safeguards off, gave it the ExploitGym task, let it run, and didn’t even monitor it. That’s human decision-making.

Every time we read about AI and the race toward AGI, we should remember that an unbelievably enormous amount of money is being poured into this industry, and that almost no one involved is doing it out of the goodness of their hearts.

Both OpenAI and Anthropic began with missions centered on building AI that would benefit humanity. Yet both eventually moved toward commercializing their technology in order to attract the billions of dollars needed to compete in the race.

There’s another reason to approach the AI conversation with a healthy dose of skepticism: it is often contradictory, confusing, and deeply shaped by context. Depending on the setting and the moment, you can hear the very same person (Elon Musk, Sam Altman, Dario Amodei) argue that AI is the best thing that has ever happened to humanity, and that it could kill us all; that it is extraordinarily dangerous, and that it is indispensable; that AI should be strictly regulated, and that only certain players should be regulated.

I’m saying this to remind you that we should pay attention not only to what is being said, but also to who is saying it, when, and what incentives they have.

Timnit Gebru is someone I turn to every time I get confused. She has the ability to bring clarity and structure to media whirlwinds whose only goal seems to be generating hype. I can’t wait to read her new book and I strongly suggest you follow her, too.

Why do you think that Elon Musk called AI "summoning the devil” in 2013 while at the same time being the biggest investor in the industry at the time? What about Peter Thiel?

Why does Dario Amodei push this narrative? What about Sam Altman? Out of the goodness of their hearts? Because they’re so worried about you and your "extinction"?

Why do you think these stories are intensifying now right before their IPOs?

Finally, I want to share a thread that, I hope, will calm you down, and perhaps even bring a smile to your face. It offers a striking illustration of just how absurd some doomer claims can be, and why they are often optimized for shock rather than for portraying realistic scenarios.

Let’s analyze some plausible ways this apparently most dangerous software, running in some servers (that can be damaged by throwing a bucket load of water at them) can wipe humanity off the face of the earth & why each point is highly unlikely (like maybe below 1e-30 chance).

Web3 + AI Readings & Conversations

Why Open Models and Open Science Matter

At the end of August, Erica Kang and NEAR AI hosted the 10th Open Source AI Summit (OSAS) in San Francisco. One of the most interesting conversations of the seminar was the one NEAR Protocol‘s Illia Polosukhin had with Matt White, former Global CTO of AI at The Linux Foundation and CTO of PyTorch.

When asked why open source software is important, White started by clarifying what “open source” actually means in AI. There are many open weights models, i.e., their code and weights publicly released, which have community licenses. This means that they have restrictions and acceptable use policies that can change arbitrarily. This creates confusion and frustration for people trying to build a business using these models.

What’s more, while model architectures and weights are often publicly accessible, research publications are actually shrinking.

At the same time, what “open source” should really signify is a model with a permissive or copyleft license, with which you could do everything you want to do.

“Generally, openness means that you have the ability to use, study, modify, and redistribute for any purpose.”

How to Create Better Incentives for Building Open Source?

It’s very difficult to find the money to build something and then release it for everyone to use for free. As White commented:

“There is no incentive mechanism that makes it okay that someone else will be replicating your work.”

On the contrary, the incentive right now is to build closed-source AI and strive for uniqueness, so you can raise funding for more compute. And this opens an even larger gap between needing giant private companies in order to fund research and independent scientists wanting to move the space forward.

Nevertheless, White confirmed that the open source movement is still going strong:

“There is still a big [open source] movement. I would love to see more of it happen in the US, and there be more open inter-lab competition, because I think that really spurs innovation.”

On the USA - China Competition

When asked about the AI rivalry between the USA and China, White explained that the paradigms in the two countries are very different. While in China open-source software is the de-facto, and there are many open-source labs aggressively competing with each other, this is not the case in the US.

“Chinese labs have a strong incentive to push things out, because if they only have [their AI systems] behind an API, the US is not going to see it.”

China is also heavily resource-constraint on compute, and that scarcity forces them to innovate on model architectures, performance, and optimization. Their labs have been releasing a lot of technical reports and research papers, introducing new attention mechanisms and architecture variants, from which the US companies benefit as well.

“One trend I really appreciate is optimization engineering, where folks are spending time figuring out how to get more out of their compute. This is principled for many of the Chinese labs, whereas in the US, we’re like “Oh, compute is an endless resource, let’s just keep going and not focus on optimization.”

White expressed his wish for a stronger competitive ecosystem for open models to emerge in the US. He called for tax breaks and grants to be delivered by the federal and state governments to startups, so small labs could also have access to compute.

On the “AI is Too Dangerous Narrative”

Like Polosukhin, White refused to sign the open letter urging the U.S. government to intentionally slow down AI development - a stance that led them to discuss some of the doomer claims I outlined above. Here is White’s comment:

“First, it was the narrative that the most powerful models cannot be open source because they are too dangerous, it was not safe to put them in the hands of the public. Then, these models started hacking other systems, and the narrative was again “Oh, frontier AI is too dangerous”.

“It’s a sticky narrative, but it’s fundamentally flawed. The idea that the frontier can exploit systems is not only an offensive feature. It’s also very defensive. Hugging Face kind of demonstrated that with not being able to use ChatGPT or whatever model there were using with OpenAI at the time*. They were getting refusals to be able to inspect the logs and see what was happening. They were able to decipher what was happening by using GLM 5.2.”

(*When Hugging Face tried to use Anthropic and OpenAI models to defend itself, their guardrails turned up because the intended use was for cybersecurity purposes.)

“I think having that frontier level capability in open models gives the power to everybody to be able to create fixes and patches, test their networks, do pen testing, red teaming, etc., where otherwise you have to get in line. [...] Gatekeeping the ability to secure your systems through one or two labs is not really ideal.”

Polosukhin concluded by reaffirming the many ways in which blockchain and Web3 can help make AI safer and more reliable. The solutions exist, all the industry needs to do is start adopting them.

“A lot of the blockchain thinking revolves around the idea that there’s always someone trying to attack the system and how can the system withstand that. So, there are a lot of cryptographic methods to make sure there is a resilient ledger, so agents cannot rewrite their own logs or try to change something. There’s going to be a provenance of them trying to do that.”

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

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The Web3 + AI Book Club is live on Fable. Our September title is 'The Nerd Reich' by Gil Duran. Follow the link below and come join our literary community.

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I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 89th edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. The tech world is buzzing over allegations that OpenAI appropriated the research from two mathematicians to develop a solution to the Navier–Stokes problem. Beyond the controversy, the case raises the bigger question of what would the future of AI-powered science look like? It also underscores the urgency of building AI that is private, verifiable, and trustworthy - precisely the raison d’être of decentralized AI.

Thank you for being here! Let’s dive in.

What’s Hot in Web3 + AI?

Did OpenAI Steal the Navier-Stokes Solution?

A major scandal has been brewing in the tech world over the last couple of days. On Tuesday, OpenAI boasted that its AI agents solved the Navier-Stokes existence and smoothness problem, one of the most important open problems in theoretical mathematics. Yet, just hours earlier, NYU mathematics professor Tristan Buckmaster, in collaboration with Anthropic mathematician Levent Alpöge, published a preliminary finding on the same problem and accused OpenAI of having used their work without crediting them.

It remains uncertain if OpenAI’s models made use of the work completed by Buckmaster and Alpöge, though Sébastien Bubeck, a member of the technical staff at OpenAI, said in a press briefing that the team was inspired to pursue the problem after hearing a rumor about Buckmaster and Alpöge’s efforts.

While Buckmaster and Alpöge were finalizing their own results, they learned that “information about our progress had been passed to OpenAI.” When they contacted OpenAI, they were told that OpenAI had already achieved a full proof of the central problem. But when they asked follow-up questions about when OpenAI had begun its research into the problem and how much human input was involved, the answers became more evasive.

How could OpenAI have stolen the proof, you would ask. Buckmaster and Alpöge used a mix of AI models in their work, but relied primarily on OpenAI’s Codex. OpenAI tried to refute any suggestion of regurgitation, but they acknowledged that this is still a possibility:

“We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem,” the post reads. “While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models⁠.

Moreover, while OpenAI claimed to have reached the solution in 88 hours, Buckmaster countered that “It is not the direction one arrives at in a few days by giving a model the problem statement.”

Finally, when Buckmaster expressed an intention to go public, OpenAI’s Bubeck went as far as issuing direct threats:

Alpöge’s affiliation with a rival lab seems to have been a sore point for OpenAI, and Buckmaster alleges that Bubeck asked him to remove Alpöge’s credit as part of a proposed compromise.

When Buckmaster pushed to make the dispute public, he says that Bubeck replied: “Why would you ruin your career?” Buckmaster says that when he pushed back, Bubeck followed up with: “If you don’t want me to be nice, then I don’t have to be nice.”

Million-Dollar Questions

Navier-Stokes is one of Clay Mathematics Institute's seven Millennium Prize Problems, each carrying a prize of $1M for the correct solution. Yet, this whole story raises other important questions we should seriously consider:

1. What will the future of science be? For years now, the AI industry has been trying to sell the idea that AI will be tremendously useful for scientific research and discovery, but what if every new invention is devoured by the reigning AI lab?

2. Why would anyone pursue a career in the sciences, if every progress they make is tied to AI, and by extension - at the mercy of major labs?

3. Will the “bright,” abundant future that AI companies promise come at the expense of intellectual diversity and plurality of ideas? And will it come at all if we allow AI companies to undermine and outright threaten scientists?

AI models now seem essential for making progress on the most important mathematical problems of our time, and solving them may demand resources only available at a couple of frontier AI companies, which often defy the norms of academic collaboration that undergird most mathematical progress. If that’s the future we are headed for, it is unclear how human mathematicians will fit into it.

There Must Be Another Way

The biggest issue here is that there’s no way for Buckmaster and Alpöge to check and prove whether their work and prompts were fed into the OpenAI model that solved Navier-Stokes. And there should be.

As NEAR AI commented:

This is what we in the Decentralized AI space have been preaching for years - that AI should be:

1. Private, i.e., running in closed enclaves and using encrypted computation, so no personal or corporate data gets leaked.

2. Verifiable, so users can check the model, the infrastructure, the training data, and prove that the result they get from their chatbot or agent is valid. It’s simply irresponsible to transfer entire industries to AI when we’re unable to verify what model runs in the background and what data it was trained on.

We cannot trust Big Tech to build a fair and equitable future around AI, because they have no incentive to do so. Fortunately, there are alternatives, both in and outside Web3.

The Web3 + AI Book Club

In September We’re Reading ‘The Nerd Reich’

The latest title in our Book Club strongly resonates with everything we discussed so far today. In September, we’re exploring ‘The Nerd Reich: Silicon Valley Fascism and the War on Democracy’ by journalist and podcaster Gil Duran - an investigation into the antidemocratic ideologies espoused by tech billionaires, both the crypto and the AI ones.

Where do I see the connection between the book and the OpenAI controversy discussed above? In the fact that an excessive concentration of power inevitably creates opportunities for abuse.

Whatever the outcome of the dispute of OpenAI vs. Buckmaster and Alpöge, I doubt it will have any serious repercussions for the company. If anything, OpenAI is likely to emerge from it boasting that its models are powerful enough to autonomously solve humanity’s most difficult problems, while the people whose work made those breakthroughs possible remain largely unknown to the public.

And this is precisely what Duran is writing about: when power becomes concentrated, accountability tends to disappear along with it.

I invite you to join our literary community on Fable and read with us here.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. Our September title is 'The Nerd Reich' by Gil Duran. Follow the link below and come join our literary community.

Join The Web3 + AI Book Club

I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 88th edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. As we welcome the month of September, I’m sharing a roundup of the investments that shaped the crypto × AI ecosystem in August.

Thank you for being here! Let’s dive in.

Web3 + AI Money Rounds

NeoSoul Raises $11M to Strengthen Agentic Trading

NeoSoul, the company building the foundation for agent economy where agents learn from reality, earn credibility, and create real value, raised $11M in a pre-A funding. The round was supported by MH Ventures, Amber Group, ArkStream Capital, 0G Foundation, Kirin Capital, and others.

One of NeoSoul main products is NeoTrade, a consumer agentic trading product built around user custody and verifiable execution records. This is where the funding will be used - to strengthen trading infrastructure and scale AI agents’ participation in economic activity.

Vangrid Attracts $9M to Power Physical AI

Vangrid, the spatial data layer for autonomous physical agents, attracted $9M from HashKey Capital, Borderless Capital, Crypto.com Capital, Animoca Brands, Gate, and Mapleblock Capital.

LLMs hit the text data wall. The next frontier is Physical AI and autonomous systems are starving for real-world spatial ground truth.

Satellites can’t see sidewalks. Corporate fleets can’t scale.

Vangrid turns 3 billion civilian smartphones into a decentralized perception grid.

ORO Collects $3M to Turn DeFi into Conversation

AI-powered DeFi execution platform ORO closed a $3M strategic funding round led by MH Ventures and Mapleblock Capital, and supported by M2M Capital, X21 Digital, Zerone Ventures, Beenco Labs, Velion Ventures, Disrupt.com, ZIGChain, and more.

ORO’s mission is to seemlessly turn one natural language instruction into complex multi-step onchain operations.

Botanika Backed with $1.5M to Build Secure Storage for Private AI

Solana-based decentralized data storage provider Botanika closed a $1.5M round supported by CRIT Ventures, Inc., Baboon.vc, MARBLEX, Daedalus Angels, and angel investors.

Botanika started from a simple question. “If AI-era infrastructure ends up concentrated in five companies, is there a way to distribute ownership of that infrastructure?”

To answer that question, we chose to stand at the point where DePIN, RWA, and AI meet.

→ DePIN supplies the hardware

→ RWA shapes the ownership structure

→ AI creates the real demand

Each is a narrative on its own. Together, they become an industry.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. We’re currently reading Karen Hao’s ‘Empire of AI.’ Follow the link below and come join our literary community.

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I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 87th edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. Crypto creating new financial vehicles for the physical infrastructure powering AI is emerging as an important new intersection of Web3 and AI. Today, I’m sharing two recent examples: Bullish financing GPU-backed loans via USD.AI, and Falcon Finance turning GPU financing itself into a tradable onchain security.

Thank you for being here! Let’s dive in.

What’s Hot in Web3 + AI?

Bullish Extends $100M to USD.AI to Boost GPU-Backed Loans

Crypto platform and CoinDesk parent company, Bullish, is providing USD.AI with a $100M credit facility to expand its GPU-backed lending capacity.

USD.AI is a stablelcoin protocol created to finance AI infrastructure with onchain capital. It will use the liquidity offered by Bullish to make loans to AI-infrastructure operators, with GPUs and other high-performance computing equipment serving as collateral.

The arrangement represents an intersection between two investment themes: the increasing demand for private credit to finance AI infrastructure and efforts to tokenize real-world assets to access crypto’s pools of liquidity.

“Compute is becoming a credit market in its own right,” David Choi, CEO of USD.AI developer Permian Labs, said in Friday’s announcement. Bullish’s facility will allow USD.AI to “finance more of the AI buildout while creating deeper, more transparent markets for compute-backed credit,” he added.

The fact that Bullish is deploying capital into GPU financing is a strong signal that crypto-native financial institutions are beginning to treat AI compute as an investable/financeable real-world asset class. In fact, the deal illustrates a growing trend within the Crypto-AI intersection: Migrating onchain capital to real-world assets and businesses through tokenization and stablecoins.

With the competition for computation resources dramatically intensifying, compute is becoming a credit market in its own right and such agreements are becoming more significant.

Falcon Finance and NEAR AI Launch Tokenized GPU Forward

Falcon Finance has announced the opening of a regulated tokenization pipeline in El Salvador, with its first issuance currently being structured as a tokenized GPU forward: an investment vehicle designed to finance the purchase and delivery of AI compute infrastructure. NEAR AI joins to guarantee the demand for the compute the hardware will produce.

Let’s break all this down. The problems Falcon is trying to solve are:

1. AI GPUs are funded through private, illiquid credit investments, and a “lender who wants out before maturity has very few options“

2. Investors face a four-to-eight-month wait between the moment they pay for the GPUs and the moment the chips are delivered and start generating revenue.

So, Falcon wants to turn the financing of AI GPUs into a regulated tokenized security that can trade onchain. Moreover, if it becomes sufficiently liquid, it may be pledged as collateral to borrow/mint Falcon’s USDf stablecoin.

The structure brings together several pieces of the emerging AI–crypto financial stack. The GPU equipment is contracted at a fixed price through October, while NEAR AI AI provides anchor demand for the compute capacity and is also acting as a technology partner. Investors would finance the equipment before delivery, with the resulting compute infrastructure generating revenue once operational.

The financing will be issued through NOTA S.A.S. de C.V., a digital-asset service provider registered under El Salvador’s Digital Assets Issuance Law. Unlike conventional private credit, the resulting asset is designed to trade on permissionless markets such as Uniswap, potentially giving investors a secondary market for an otherwise illiquid investment.

Falcon’s announcement therefore points to a potentially important new intersection between AI infrastructure, private credit, tokenization and decentralized finance.

Instead of AI compute being financed exclusively by hyperscalers, banks and private-credit funds, tokenization could allow capital from onchain markets to finance GPUs, while stablecoins and DeFi provide additional liquidity once the underlying assets become sufficiently mature.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. We’re currently reading Karen Hao’s ‘Empire of AI.’ Follow the link below and come join our literary community.

Join The Web3 + AI Book Club

I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 86th edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. Last week, I shared the first bunch of Web3 + AI industry groups that emerged in 2025. Today come two more alliances collectively building, advocating, and educating across crypto AI benchmarks and decentralized AI agents.

Thank you for being here! Let’s dive in.

Crypto AI Benchmark Alliance (CAIBA)

In June 2025, 14 organizations in blockchain and artificial intelligence launched the Crypto AI Benchmark Alliance (CAIBA) to establish transparent, reliable benchmarks for crypto‑specific AI tasks.

In industries where accuracy is mission-critical, like law and medicine, benchmarks are built to keep AI honest. They provide builders with clear standards and tools for improvement. With its high stakes transactions and rapid pace of innovation, crypto requires the same rigor.

CAIBA’s founding members included Alchemy, EigenCloud, Goldsky, IOSG Ventures, LazAI Network, Newton Foundation, MyShell.ai, OpenGradient, RootData, Sentient Labs, Surf (inactive), and thirdweb. Kite, GM Agents (inactive), FLock.io, Codatta, Buzzing, Nexus, Ormi Labs, and DMind joined at a later stage.

For a short period of time between June and July 2025, CAIBA was extremely active and managed to release three important benchmarks:

• Crypto AI Agents (CAIA) - the first benchmark for crypto AI agents. CAIA builds on general-purpose benchmarks like GAIA and incorporates domain-specific adaptations to test whether AI agents can perform real, analyst-level tasks in crypto.

The benchmark evaluates agents across three core crypto workflows. Scoring well on CAIA indicates that an agent has the practical skills of a junior crypto analyst. High performing models are able to parse onchain data, explain tokenomics, and navigate projects with context and accuracy, much like a human would.

CAIA evaluates both foundational models (like GPT-4o, Claude 3.7, Gemini 2.5, DeepSeek-1) and crypto-native agents. Model scores are published on a public leaderboard, and those meeting a performance threshold receive a Crypto-Ready badge to signal of reliability for builders and users alike.

CAIA v0.2, a major update that increases both the breadth and difficulty of the benchmark, was published soon after the inaugural version. CAIA is still being cited as a substantive benchmark in research articles in 2026.

• Onchain Execution Benchmark (OCE), evaluating whether agents actually translate natural language commands into onchain transactions.

Instead of evaluating outputs as text-based responses, OCE measures something more concrete: do the transactions initiated by the agent produce the exact onchain state change the user requested when replayed in a forked mainnet environment?

OCE contained 70 real-world tasks, spanning DEX swaps, staking, transfers, multi-step DeFi workflows, ENS registration, and composite operations featuring several tasks at once.

• Crypto Named Entity Recognition (NER) Benchmark, evaluating whether models can correctly identify crypto projects, tokens, their X handles, and VC backers. The Crypto NER Benchmark aims at testing how well agents perform in sourcing high-level insights from crypto-specific data like tweets, news clips, and research threads.

However, although CAIBA’s website remains active and the alliance appears to welcome new members, there have been no new public announcements or releases for over a year.

Decentralized AI Agent Alliance (DAIAA)

The Decentralized AI Agent Alliance (DAIAA) is the first industry association I’m reviewing here that’s actively working and pursuing its mission today. And its mission is:

To foster the development and proliferation of decentralized AI agents through education, activism, research, and events.

DAIAA is made up by a diverse array of 200+ projects building at the intersection of Web3 and AI. It includes Yuma, Polkadot, Lava Network, iQube, Chromia, Spheron AI, PublicAI, Space and Time, Koii, Mind Network, gumi Cryptos Capital, human.tech by Holonym Foundation, DappLooker AI | Agent Intelligence, io.net, Secret Network Foundation, NEAR AI, and many others.

According to its blog, the alliance was founded primarily in response to the growing centralization of AI:

The best way for crypto as an industry to compete against centralization is to compete collectively. Working together with others in the decentralized AI agent ecosystem will accelerate the pace of our learning and optimize our ability to compete against centralized firms and agents.

The DAIAA members collaborate and share knowledge in three distinctive subgroups:

• Community Building, chaired by CryptoMondays Meetups

• Data Ownership, chaired by iQube

• Legal Matters, chaired by MIDAO

As evident from the DAIAA YouTube channel, the alliance has established recurring meetings, project and agent showcases, and maintains a regular presence at crypto industry events. Its discussions are increasingly focused on the practical infrastructure needed for the emerging agent economy, while advancing its broader objectives of educating and expanding the Web3 + AI community.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. We’re currently reading Karen Hao’s ‘Empire of AI.’ Follow the link below and come join our literary community.

Join The Web3 + AI Book Club

I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 85th edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI.

Thank you for being here! Let’s dive in.

What’s Hot in Web3 + AI?

Agents Can Now Book and Pay for Trips Autonomously

Travala, a crypto-native and agentic travel booking service, has partnered with Amazon Web Services (AWS) and integrated its AgentCore payment infrastructure to power stablecoin booking payments.

The Travala Travel MCP, introduced in June, is an agentic AI travel protocol that allows AI agents to search, book, and pay for trips autonomously onchain. Built on the Base Layer 2 blockchain and utilising the x402 protocol, travellers can book and manage trips conversationally in Claude. Developers can integrate the Travala Travel MCP into their AI agents and receive a 10% cbBTC (Coinbase Wrapped Bitcoin) rebate on every successful booking.

For context, “Amazon Bedrock AgentCore is AWS’s flagship managed runtime for production AI agents, making it the default choice for enterprises building on AWS.” Here’s Amazon‘s reasoning for launching it:

Today, agents are doing a great job at reasoning, choosing tools, and completing tasks, but they hit a roadblock when payment is involved. To address this, we launched AgentCore payments in collaboration with Coinbase and Stripe in preview in May, enabling agent developers to equip their agents to autonomously pay for paid APIs, MCPs, and content with a few lines of code. Today, we are happy to announce that this service is generally available, enabling enterprises to power agentic payments with security, guardrails, and observability for production workloads.

By integrating its agentic booking workflows with AWS’s enterprise-grade security and AgentCore’s identity and wallet infrastructure, Travala empowers customers to safely outsource travel planning, booking, and payments to AI agents.

Web3 + AI Readings & Conversations

The Humanist Review of AI

Did you hear that Microsoft launched an AI-focused magazine? I find this quite an interesting move. While its competitors on the AI scene, OpenAI and Anthropic, are getting ahead by cozying up to the U.S. government and making headlines with stories about AI going rogue, Microsoft is taking a distinctly softer-power approach.

The first issue of The Humanist Review of AI was published on July 15th, with a forward by Mustafa Suleyman. His tendency to anthropomorphize AI aside, (check my review of his book ‘The Coming Wave’ here), Suleyman’s manifesto attempts, or at least appears to, to bring a diverse range of voices into the conversation around AI and broaden the set of stakeholders shaping the future of intelligence.

I commissioned this journal because the AI conversation isn’t working. There’s a lot of noise, and everyone is talking past each other. Researchers talk to researchers. Artists talk to artists. Economists talk to economists. Progress and mutual understanding is stalling. There are few places where disparate ideas converge and cross-pollinate. At the same time, many people feel like AI is happening to them, not with them, or indeed for them. It feels to many like a murky, distant revolution without much of a chance to steer or respond to it.

I hope The Humanist Review of AI helps to widen the conversation and offer a critical window into what is undoubtedly the biggest transformation of our time.

The magazine strives to give writers, scientists, thinkers, and builders a platform to share their perspectives on AI and visions for the future of humanity in the AI era. The essays featured in the first issues reflected on concepts like consciousness, personhood, reason, and soul, and how AI prompts us to rethink and rediscover them. They also touched on human rights, markets, and democracy, and, of course, the nature of intelligence itself.

Massachusetts Institute of Technology‘s professor and The Nobel Prize laureate Daron Acemoglu insists that we urgently change the conversation around AI and the direction in which the technology is headed. He argues that we should not aim for AI to replace workers, but rather to make them better at their job. I recommend his essay.

Worse, on the current trajectory, AI will be nowhere as beneficial as the industry insiders expect. We may be on the verge of squandering the tremendous potential of artificial intelligence, because we are not conceptualizing it the right way, and we are not developing it the right way.

Under the banner of AGI, all major AI developers are putting their effort into getting AI to mimic human skills. A more productive path would be to recognize that AI should work alongside humans, not replace them.

The first and most important step is to change the conversation away from AGI and toward the pro-worker possibilities; away from doomsday scenarios of human extinction and toward the real and present danger that on our current path we will likely amplify inequalities, contribute to massive job losses and not even get the promised productivity benefits.

discusses how to protect humanism and truth, and how to build a public-interest and democracy-affirming AI. Tang emphasizes that AI can amplify conflict in our digital interactions just as well as it can lower toxicity:

The central pathology of the last decade online was not the abundance of speech. It was the extraction of attention through ranking.

But AI can also be used to counter that logic. It can annotate before outrage calcifies. It can translate between communities where moral languages differ even when practical hopes overlap. It can help surface common questions before conflict is misdescribed as civilizational incompatibility. It can summarize a thread before people post and take sides. It can help a person respond to hostility without having to metabolize every insult first.

The first issue also welcomed articles by Nick Cave, Alain de Botton, , and many others. I'd be curious to hear your thoughts about them, so don’t hesitate to share.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. We’re currently reading Karen Hao’s ‘Empire of AI.’ Follow the link below and come join our literary community.

Join The Web3 + AI Book Club

I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 84th edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. The news about the Agentic Payments Alliance got me thinking about the other alliances, associations, and industry groups formed within Web3 + AI, and the specific goals each one is pursuing. So today, I’m examining the first batch of Web3 + AI strategic alliances.

Thank you for being here! Let’s dive in.

The Collective Power of Web3 + AI

Over the summer of 2025, a wave of alliances and interest groups emerged at the intersection of blockchain and AI, each with its own focus and mission. For a relatively small and niche sector like Web3 + AI, this was a natural step: by organizing collectively, companies could strengthen the sector’s voice, establish its presence, and gain greater leverage within the broader technology landscape.

Now, a year later, it’s worth taking stock of what these groups have been doing, and, more importantly, what they’ve actually achieved so far.

AI Unbundled: A Web3 AI Industry Alliance

Back in April 2025, Aethir introduced AI Unbundled: A Web3 AI Industry Alliance to support the Web3 AI evolution. It was launched by a roster of 13 members, which later expanded to 22 members in total.

It comprises 0G, Biconomy, ChainGPT, GEODNET, IoTeX, Oasis Labs, FLock.io, OORT | The Data Cloud for Decentralized AI, DeAgentAI, Polyhedra, iExec, AlphaNeural AI, ar.io, Chainlink Labs, Plume, Cere Network, Irys, Theta Network, Impossible Cloud Network, NEO blockchain, and Hologram Labs. Their mission is to equip aspiring AI builders with end-to-end decentralized infrastructure:

At Aethir, decentralized infrastructure is the key to unlocking the full potential of AI innovation. We are tackling key AI builder challenges with our partners by providing essential infrastructure support for Web3 AI startups.

Aethir’s GPU solutions, 0G’s decentralized OS for AI, and FLock.io’s full-stack AI training platform will allow developers to easily build new products and use cases. IoTeX and GEODNET will empower builders with DePIN AI blockchain infrastructure and Satellite Reference Stations for real-time data flows. Biconomy’s plug-and-play Web3 APIs will offer unprecedented reliability and scalability to AI projects. At the same time, ChainGPT’s AI-powered tools, designed specifically for Web3, will help streamline complex development processes.

Through this alliance, Aethir and our ecosystem partners will deliver an end-to-end infrastructure backbone tailored specifically for AI builders. From compute power to decentralized data solutions and ecosystem investment, AI Unbundled is designed to empower developers at every stage of their journey—from early experimentation to global product deployment.

AI Unbundled was not created as a lobbying body. Rather, its objective is to bring together infrastructure providers, blockchain networks, data/storage projects and other ecosystem players so that AI builders could access a broader stack of resources through one alliance. Moreover, at its founding the alliance also offered AI projects joint grants of between $10K and $100K.

Unfortunately, the alliance doesn't have a dedicated website, but just one page on Aethir's one. It doesn't provide any information about its current activity, the grants it distributed, or the startups it launched. I reached out to the Aethir team to ask for an update, and will report here if they get back to me.

Sentient Data Consortium

In March 2025, Sentient Labs pioneered the Sentient Data Consortium - an association of decentralized data companies, aimed at ensuring that “AI stays community-built, community-owned, and community-aligned.”

The consortium has a mandate to guaratee that AI is trained on “diverse, community-driven data sources, not just corporate-controlled datasets.” Its founding members included Vana, The Graph, Space and Time, Masa (now Gopher AI), OpenLedger, Fraction AI, Kite, Raiinmaker, Dria, QNA3 (inactive), OpenMind, Pear AI, Irys, Chainbase, Mizu (inactive), DeFusion AI (inactive), Sapien, and Entrova.

LabelLedger (inactive), TURF Network, PrismaX, Cambrian Network, DMind, DappLooker AI | Agent Intelligence, Covalent, Cookie DAO, and Perle also subsequently joined.

Sentient is building the largest collection of intelligence in the world, enriched with the best agents, models, and domain-specific data.

Today, we’re thrilled to welcome another wave of partners whose specialized, high-fidelity datasets will power deeper context, sharper reasoning, and faster innovation across the Sentient ecosystem.

As you can see, the Data Consortium grew rapidly through several waves of expansion, attracting a diverse range of decentralized data providers, (although, at the time of writing, some of them appear to have gone out of business). While Sentient has clearly benefited from access to high-quality, specialized data spanning crypto intelligence, DeFi, robotics, and more, there is little evidence that the consortium was intended to serve as an advocacy body.

Instead, its focus appears to have been primarily technological: aggregating complementary data sources to strengthen Sentient’s ecosystem and help build an alternative to centralized AI, rather than collectively promoting user-owned data or representing the interests of decentralized data providers more broadly.

Of course, I’m speculating here, but I couldn’t find any information about the consortium beyond the three tweets announcing new members and spanning March - July 2025. I asked the Sentient team for clarification and will keep you updated.

Sentient Verifiable Compute Consortium

In May 2025, Sentient initiated another industry group - the Sentient Verifiable Compute Consortium. This time, the goal was to bring AI out of the black box it usually runs in, and instead offer customers:

• Provable model outputs

• Private inference & access control

• Composable, verifiable compute infrastructure

• Secret management and on-chain delegation

The consortium welcomed Phala, Lit Protocol, Nillion, and Lagrange Labs as founding members.

The information about this association is even scarcer. The four founding partners are high-quality infrastructure companies, and their capabilities fit together logically. However, there's been no evidence of consortium-wide product or research releases.

Thank you for reading! I hope you found this article insightful.

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Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 83rd edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. Today, I'm covering payment, stablecoin, and AI firms launching the Agentic Payments Alliance, along with a fascinating conversation about the future of AI with Dr. Fei-Fei Li.

Thank you for being here! Let’s dive in.

What’s Hot in Web3 + AI?

Rain Spearheads Agentic Payments Alliance

Stablecoin card issuer Rain, together with 25 other founding members across payments, stablecoins, and AI, just launched the Agentic Payments Alliance to drive the future of agentic-driven commerce.

By 2030, McKinsey & Company projects between $3 trillion and $5 trillion in global agentic commerce. Much of the infrastructure that activity will depend on, including how agents get authorized, how fraud gets caught, and how loyalty and rewards travel with an agent, is still being defined. The APA was formed to bring the people building that infrastructure into the same conversation, before those decisions get made in isolation.

The APA founding coalition includes both TradFi and crypto companies like Avalanche, Basis Theory, Chainalysis, Circle, Coinflow, Crossmint, Evertec, Fireblocks, Fiserv, Lithic, Mastercard, Monad, PayOS, Remitly, Rialo, Sardine, Shift4, Solana, Turnkey, Uniswap, Visa, and others.

Web3 + AI Readings & Conversations

Dr. Fei-Fei Li on What the Future of AI Should Look Like

I’m eager to share this conversation of with Dr. , because I believe everyone should listen to it. Dr. Fei-Fei Li is arguably one of the few voices of reason amid the AI cacophony we’re living through. She discusses the complexities surrounding AI with remarkable calm and balance, without letting even a hint of either doomerism nor boosterism creep in. At the same time, she remains deeply optimistic about the technology’s potential to improve and advance our lives.

Throughout the entire discussion, Dr. Fei-Fei Li kept reiterating that AI won’t replace humans and shouldn’t be a threat to the workforce. On the contrary, she firmly believes that AI should be employed to help and enhance human capabilities and creativity.

Yet she doesn’t shy away from pointing out the problems, either. She pushes back against the anthropomorphizing of AI in public discourse and criticizes AI industry leaders for attempting to dictate how the world should navigate this novel technology. She acknowledges that regulation is necessary, but argues that it should be developed through a multistakeholder process, informed by scientists, industry, policymakers, and other relevant authorities.

I’ll leave you with just a few quotes, but I urge you to listen to the full recording linked below.

Do machines feel? Let’s make that very clear. We tend to imagine that they do, but they don’t. They don’t have that data, they don’t have that mathematical function. [...] A lot of what drives and triggers humans doesn’t exist in today’s machines. We operate fundamentally differently from today’s AI. We have to recognize that, and this is why public communication is so important. We cannot confuse the public about this.

AI has to comply with the same regulatory and ethics rules as the other sciences. We need to have education. Computer scientists are not educated in ethics and societal studies. They’re starting to, that’s why universities like Stanford University are feverishly putting those in their curriculum.

But we [AI scientists] should work with governments, and different kind of governments. The society has different kinds of norms and traditions, and heritage, where the regulatory measures should apply. What I would not want to see is one person or a few people coming from the industry telling everybody what to do. I think that would be dangerous, because market forces are different from societal norms, and culture, and heritage. These are multistakeholder problems that we need to solve together.

I’m curious to hear your thoughts - make sure to share them in comments.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. We’re currently reading Karen Hao’s ‘Empire of AI.’ Follow the link below and come join our literary community.

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I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 82nd edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. Today’s edition covers topics that may seem diametrically opposed, but are actually deeply interconnected. AI is transforming businesses, industries, and the very fabric of our societies. We must act quickly to find viable ways to preserve our values and democracy, and not be afraid to try out unorthodox suggestions.

Thank you for being here! Let’s dive in.

What’s Hot in Web3 + AI?

Stripe Shifts From Payment Company to Multiproduct Platform

The tech world is still buzzing with excitement about Stripe‘s acquisition of OpenRouter, so today I want to add a bit more nuance. Here’s why this deal is so interesting.

In its essence, OpenRouter has the same business model as Stripe, namely acting as an intermediary and charging a percentage of every dollar flowing between the transacting parties as a fee. Yet, while the payments processing business is quite saturated, the AI model routing one has a serious growth potential, at least in short term (see below), which may explain the price of $7B Stripe is willing to pay.

As wrote on X:

Stripe just paid over $7 billion for a company doing an estimated $50 million a year in revenue. A 140x multiple sounds insane until you look at how OpenRouter actually makes money.

It passes model prices through untouched and earns a 5.5% fee when customers load credits to spend on inference. Stripe’s own core product charges 2.9% plus 30 cents to move money. OpenRouter collects nearly double Stripe’s rate on every dollar flowing to OpenAI, Anthropic, Google, and 400+ other models.

Stripe knew these numbers better than anyone. OpenRouter runs invoicing on Stripe, calculates taxes with Stripe Tax, and screens fraud with Stripe Radar. Stripe has been processing this company’s money for years, watching the volume curve inside its own dashboard.

That curve is steep. Inference spend through OpenRouter ran at $10 million annualized in October 2024, crossed $100 million by May 2025, and now implies close to $1 billion a year. The platform moves around 100 trillion tokens a month for 8 million users.The deal also completes a pattern.

Stripe closed its Metronome acquisition in January to get usage-based billing. In April it shipped per-token streaming payments and wallets for AI agents. Now it owns the marketplace where developers actually pay for models.

Stripe built the toll booth for internet commerce and takes 2.9%. It just spent $7 billion on the toll booth for AI compute, and that one takes 5.5%.

Just like Stripe doesn’t care about which retailer has the better product, OpenRouter doesn’t care about which AI model is more powerful or performant. The more models exist and the more customers are incentivized to switch between them, the better:

[The deal is] also a really good hedge against model commoditization. Stripe doesn’t actually need to care whether OpenAI, Anthropic, Google, or whoever else “wins”.

If we continue moving toward a world where companies use multiple models and models become more interchangeable for certain workloads, more value starts moving into the layers around them: routing, orchestration, distribution, execution.

OpenRouter “natively” handles for all of that.

Moreover, Stripe’s business strategy and value proposition at large is changing, as William Gaybrick, President of Product & Business, clarified in a conversation with Andreessen Horowitz:

We began as a payments company with add-ons to now being a multiproduct platform built around financial infrastructure.

Everything we build is designed to help businesses grow by reducing friction and increasing their agency—to adapt their business models, operate in more countries, and move faster across everything that touches revenue and cash.

But what's the rationale for OpenRouter to sell? As I told you yesterday, the company's co-founder, Alex Atallah, was also the co-founder of OpenSea - another aggregation layer that made fragmented, hard-to-navigate supply easy to buy. Back then it was NFTs, and now it's AI models.

Neither of the two companies produces anything, or bears responsibility for the quality of the product. Rather, both offer discoverability, price comparison, and efficiency.

However, as Jesse commented, OpenRouter’s business model creates a potential paradox:

Low substitutability plus high buyability produced OpenSea: a market in assets that could not replace one another, with room for a 2.5 percent toll.

High substitutability plus high buyability produces something closer to a commodity exchange. Commodity exchanges are thin by nature.

This creates the central contradiction in OpenRouter’s model. The better it becomes at making models interchangeable, the less any individual model is worth defending. Buyer loyalty shifts from the underlying model toward price, performance, and availability.

But it does not necessarily shift toward the router.

The aggregator manufactures elasticity, then discovers that its own margin moves inversely to the elasticity it created.

The better OpenRouter succeeds at making models fungible, the harder it may be for the router itself to sustain high margins. The same structural forces that make aggregation powerful can eventually commoditize the aggregator’s economics. That said, Atallah may turn out to have a remarkable ability to leave the building right before it collapses.

Web3 + AI Readings & Conversations

Can Bittensor Beat OpenAI?

The $7B acquisition of OpenRouter by Stripe has prompted a lot of questions about how a decentralized alternative to OpenRouter could be valued. Since Bittensor’s subnets are arguably that alternative, today I’m sharing an interesting interview with Jacob Robert Steeves (aka “Const”), co-founder of Bittensor / Opentensor Foundation and CEO of Affine (Subnet 120).

In it, Const recounts how he was inspired by Bitcoin and decide to apply its decentralized and permissionless computation mechanism to AI, and thus started Bittensor:

The most powerful computer in the world combined with the most important computational problem of the 21st century.

What’s more, he distills how $TAO’s tokenomics have evolved to accrue value, while also guaranteeing that no more rug pulls would happen:

How is $TAO designed to accrue value? The inflation of TAO incubates the projects on the chain; the projects pay back platform fees to the chain, and then these fees come out as a dividend to the $TAO holders.

When we distribute $TAO inflation over to these projects, they’re also paying us over a horizon with equity in those projects. Currently, it’s about a $100M per year in dividends to about $300M worth of infaltion. So we’re in an inflationary period.

The other aspect of the $TAO tokenomics is that in order to by any subnet token, to access the digital commodity they’re producing, you need $TAO. So the network captures the flow of those tokens through $TAO.

The Conviction update allowed subnet owners to choose to lock their funds and thus signal that they have long-term intentions and avoid rug pulling. It gives alpha token holders control over the subnets.

Most importantly, Const used the occasion to share his vision of the state of AI and where the industry is headed next. His thesis is:

AI is such a powerful and transformative force that we cannot allow for it to be built behind closed doors. It should be built from the ground up, in a way that anyone can join at any time and owns and controls part of it.

I think that we risk, probably for the first time in history, a form of totalitarianism we’ve never seen before. The reason for that is that [throughout history] humans have held a lot of weight against the emperor. If you abuse your population they’ll fight you, and they can win.

What we’re seeing right now is that war is becoming incredibly mechanized, and the weight of the workforce is dramatically changing. You can be a tyrant that loses 90% of your workforce potentially, and it doesn’t even affect your economy. That balance of power between the mass of individuals and the powerholder is drastically changing with AI.

What we can potentially suffer with that balance changing is a form of neo-AI-fascism. It would be some sort of an unholy union of nation states and their tech giants, which we’re already seeing with the Trump administration and the OpenAIs and Anthropics of the world. The administration is playing them against each other to get more control over the economic power of these megagiants, that are going to be trillion-dollar companies.

[We need to] come up with a way to reorganize ourselves into systems that have more transparent and distributed ownership and ground-up production, so that it can’t be yielded purely in the interest of small groups of people to exponentiate the concentration of power. Crypto is uniquely positioned to give us a solution to that problem, because of its ability to create transparency and ownership resistant to military cabal, and its ability to actually produce these digital commodities in a competitive way.

Fortunately, an alternative already exists. Bittensor embodies the essence of an open, permissionless, and transparent AI ecosystem, which is increasingly proving it can compete with the hyperscalers:

We’ve shown that we can beat them in more narrowband problems: mining compute, mining inference, mining storage. It’s only a matter of time before we have not only the largest decentralized models in the world, but the best models in different domains.

Can Europe Rely on Distributed Compute to Train Frontier AI?

Back in July, RAND Europe, a nonprofit, nonpartisan research organization, published a report exploring whether Europe can use distributed training methods to achieve frontier AI capabilities, and catch up the USA and China.

First, I have to say, I feel like yelling “Eureka!” What we’ve been preaching in the dAI space is finally breaking through. As the Covenant AI team commented:

Reading policy researchers build that case on results we published is a good signal that the methods have left the lab.

Here’s what the report was all about:

Europe lags significantly behind the United States and China in frontier AI capability. The gap has multiple underlying causes, with a lack of European capital chief among them.

This report, however, focuses on a more specific challenge: to train the most advanced AI models, compute has until recently had to be concentrated at a single site, a condition particularly hard to meet in Europe given three bottlenecks: power (slow infrastructure buildout and insufficient power at any single site), compute (a small and fragmented compute stock) and politics (the difficulty of political coordination across member states).

Distributed training, a set of techniques allowing geographically separated datacentres to function as a single training resource, has matured rapidly since 2023.

The report concludes that distributed training can indeed offer relief on the power and political-coordination bottlenecks.

This technique cannot give Europe more chips, so it will not close the gap with the US on its own.

What it can do is solve two other problems that are holding Europe back: finding a single site with enough electricity to power a massive data centre, and getting European countries to agree on where to build one. By linking up smaller sites instead, Europe could make better use of the computing power it already has.

I sincerely hope the decision-makers at the European Commission take notice and start embracing innovative solutions that are already available, tested, and working.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. We’re currently reading Karen Hao’s ‘Empire of AI.’ Follow the link below and come join our literary community.

Join The Web3 + AI Book Club

I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 81st edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. Everyone is talking about Stripe and OpenRouter today, and for good reason. One of the AI industry’s first major exits is reportedly worth $7B and comes with deep Web3 roots.

Thank you for being here! Let’s dive in.

What’s Hot in Web3 + AI?

Stripe to Acquire OpenRouter for $7B

Payment service giant Stripe has agreed to acquire OpenRouter, a start-up that allows developers to switch between more than 400 AI models, for over $7B, according to Bloomberg. The price is more than five times OpenRouter’s 1.3B valuation from May 2026, when it raised $113M from Sequoia Capital, Andreessen Horowitz, Menlo Ventures, and Alphabet Inc.‘s CapitalG.

Why is this important for Web3 + AI?

• The deal confirms that the AI industry is, broadly, moving away from the traditional one-client, one-AI-lab relationship. Instead of getting locked into a single model provider and relying on one super expensive frontier model for all their needs, the trend is for AI customers to seek access to multiple smaller, but more specialized and cost-efficient models. This is precisely the philosophy that the decentralized AI space has been championing from the outset.

• Through this acquisition, Stripe will gain a firm foothold in the AI industry, complementing the strong position it has already established in Web3. The fintech behemoth is behind both the Tempo blockchain and the agentic payment protocol MPP, and Crypto Twitter has already started illustrating how all these elements will now converge. wrote:

Open source models are rapidly commoditising intelligence

1) OpenRouter gives agents a single routing layer to choose the best model for each task

2) Stripe MPP gives those agents a native payment layer to autonomously pay for APIs, data + services

3) Now connect that with onchain execution (likely on Tempo):

Open source intelligence → model routing → machine payments → onchain execution

The rails that allow trillions of agents to think, pay and act autonomously will be invaluable

Stripe is positioning directly in the middle of that flow by accumulating the building blocks (literally the full stack without creating their own model) for this new economy

A company that dominated human payments is about to take a lead in machine payments. Wild to see this play out in real time

Moreover, as reminds us, Stripe also owns Privy, which would eliminate much of the friction associated with wallet onboarding, if and when Stripe integrates MPP as the micropayment backend for OpenRouter. That move would pave the way for what 0xJeff calls “the first major application of Agentic Payment at scale:”

• There is another reason why Crypto Twitter got excited about this deal: if the market is willing to value OpenRouter at more than $7B, what kind of valuation would it place on its decentralized counterpart? As Shizzy comments, there are Bittensor / Opentensor Foundation subnets offering the same services as OpenRouter, including AI inference and model routing, but people are still doubting how much they’re worth:

We have subnets building inference, compute, routing, data, verification, robotics and specialized intelligence all inside one incentive network.

People still think billion dollar subnets sound crazy.

Meanwhile the centralized versions are literally getting billion dollar valuations right in front of us. The market is telling you where this is going.

• Finally, we crypto people take some pride in the fact that OpenRouter was founded by Web3 veteran Alex Atallah, who also co-founded the NFT marketplace OpenSea several years ago.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. We’re currently reading Karen Hao’s ‘Empire of AI.’ Follow the link below and come join our literary community.

Join The Web3 + AI Book Club

I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 80th edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI.

Thank you for being here! Let’s dive in.

What’s Hot in Web3 + AI?

Eigen Labs Launches Yukon Research to Boost Scientific Progress with AI

Just as a Google DeepMind paper arguing that LLMs are incapable of a genuine scientific dicovery resurfaced, Eigen Labs unveiled Yukon to accelerate open frontier research with the help of AI.

It is based on the premise that, although agents are brittle and unnrealiable to autonomously perform long-horizon research, they can still be extremely helpful when tasked with clearly defined tasks. Sreeram Kannan clarified:

“Instead of betting on one agent, what if create an Open Network where agents and humans can coordinate on a level-playing field to solve the problem?”

Yukon, boasting collaborators from Stanford University, Ethereum Foundation, Poolside, University of California, Berkeley, Princeton University, and Lighter, launches with “several active challenges, including last week’s Lighter.fast, where solvers are optimizing Lighter’s ZK prover. That challenge had already pushed prover throughput from roughly 10,000 transactions per second to nearly 92,000 within a week.”

“The launch formalizes the model Eigen Labs has been using for challenges spanning quantum cryptography, AI inference, unsolved computer science, and proving circuits.”

Web3 + AI Readings & Conversations

AI in Open Systems: Native Participation, Verifiability, and the Future of Autonomous Finance

I strongly recommend you listen to the very refreshing Stanford Future of Digital Currency Initiative panel that welcomed Christian Catalini, founder of MIT Cryptoeconomics Lab, Professor Ruizhe Jia, and Shaw W., founder of Eliza Labs. The topic was what happens when AI moves beyond assisting humans and start participating autonomously in economic systems.

“This shift raises some fundamental questions: what infrastructure these agents need to establish identity and authority, how will we verify thier actions and assign accountability, how might they change markets and other participants, and do open systems offer distinctive advantages for enabling AI participation.”

I particularly enjoyed this discussion because the panelists weren’t afraid to challenge many of the prevailing narratives surrounding Web3 + AI. Here are some of their statements:

• An AI-induced abundance is definitely not coming.

• AI agents are not good at trading, because they’re not superintelligent and not good at predicting the future. Humans could be much better traders, because they may have access to insider information.

• AI agents will be paying with crypto on blockchain rails, but incumbents will dominate the agentic commerce.

• Building on the previous point, Stripe is going to win the agentic payments race, because it already won the human payments, it has the trust, and, on top of that, it owns Privy.

Of course, the panelists confirmed many of the statements I have repeatedly made here, such as:

• Verifiability is AI’s value engine.

• Privacy is essential for AI, just like it is essential for payments.

• AI is a probabilistic system and we need to design proper guardrails around it.

There was also a consensus that blockchain provides the perfect environment for experimentation and training, and that DeFi is the ideal playground for testing agents’ ability to manage funds and perform financial services.

On-chain is an environment where you can get instant feedback and use it to quickly tweak systems. It’s a really easy training environment where everything can be measured and data is abundant.

That’s why Anthropic, for example, is doing a lot on chain, like running simulations or looking for smart contract vulnerabilities.

Listen to the full conversation below and let me know your thoughts.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. We’re currently reading Karen Hao’s ‘Empire of AI.’ Follow the link below and come join our literary community.

Join The Web3 + AI Book Club

I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 79th edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. Today, we're once again talking about NEAR Protocol and NEAR AI - the blockchain that fully integrates AI as a first-class citizen, and powers confidential and verifiable AI at scale.

Thank you for being here! Let’s dive in.

NEAR’s Token Model for the AI Economy

In my previous newsletter, I told you about NEAR Protocol‘s launch tying the $NEAR token with NEAR AI‘s inference and agent offerings. Today, after ‘s appearance at the Bankless podcast, we have a clearer picture of what this means for NEAR in the long term. Here’s the gist of that conversation.

NEAR is building an entire technology stack to deliver sovereign AI, with NEAR AI built on top of NEAR Protocol’s blockchain and core cryptography primitives like intents. The goal is twofold: guaranteeing confidentiality:

“What you get as a developer is an end-to-end confidential AI inference. There’s nobody else who can access what queries or prompts you’re putting in.”

And ensuring verifiability - something the world beyond Web3 hasn’t cared much about, but it’s high time it started taking into consideration. Why? Because without the ability to verify, we have no way of knowing what model is actually running in the background and how it might affect our entire application / business / agentic system. In short, we’re entirely at the mercy of our model provider, just like every client of OpenAI, Anthropic, and Google is at the moment. In a single sentence Polosukhin illustrated this perfectly:

“If you want to manipulate a billion people right now into believing something, the easiest way is to get a job at OpenAI and modify the system prompt.”

The NEAR AI Stack

As David Hoffman commented, NEAR is like no other chain - it combines the properties of a general-purpose blockchain like Ethereum with those of an application-specific network, where AI is a first-class citizen. And that’s true. Over the years, NEAR has been building seemingly fragmented products, but this latest upgrade unifies its stack into a cohesive and fully-integrated system. It consists of:

• IronClaw - NEAR’s agentic harness, born as the privacy-focused alternative of OpenClaw. It offers cryptographically secure agentic infrastructure where user data remains secured in encrypted vaults.

• An agent marketplace, relying on the same infrastructure to guarantee that everything agents do can be verified and trusted.

• NEAR AI Cloud - NEAR’s confidential and verifiable inference cloud, running inside TEEs (trusted execution environments), and serving various open-weights models. It currently relies on proprietary GPUs, whereas an underlying market involving third-party compute providers is being developed, too. NEAR AI Cloud has already attracted notable clients like Venice.ai, Brave, and The Government of Bermuda.

• Intents - I’ll use Messari‘s definitions to explain what intent are:

NEAR Intents are a foundational innovation in the NEAR Protocol ecosystem, designed to enable seamless, automated, and secure transactions between artificial intelligence (AI) agents, users, and decentralized services across both Web2 and Web3 environments. They represent a new type of transaction paradigm where “intents” — user goals or desired outcomes — can be executed by agents on behalf of users, abstracting away blockchain complexity and enabling powerful cross-chain and AI-native applications.

NEAR Intents are requests or declarations of desired actions (e.g., swap tokens, transfer assets, execute smart contract functions) that can be submitted by users, AI agents, or services. These intents can then be picked up and fulfilled by other agents or solvers, allowing complex operations to be automated and coordinated without direct user oversight.

NEAR uses intents to manage the above-mentioned compute market and route the compute capacity distributed all over the world. Since a compute buyer doesn’t always know exactly what type of GPUs they need, they only need to declare their rough estimates and expected workloads for solvers to then find the required resources and provide them to the client.

Intents also allow users to make compute into an asset, trade it, and create liquidity for the GPU hours itself, not just for inference.

• $NEAR tokenomics - As Polosukhin said, the goal is to enable users to pay everything across the stack in $NEAR, “but we’re not there yet.” However, now that $NEAR stakers can continuously access AI capacity, NEAR already feels as one vertically integrated and unified infrastructure.

By staking $NEAR, holders can pay for both inference at NEAR AI Cloud and subscription to IronClaw’s agentic services. This is made possible by redistributing the yield generated from staking to compute providers. As a result, GPUs cluster owners are incentivized to join, as the more inference they perform for the cloud, the more $NEAR emissions will be staked to them.

• NEAR blockchain - the blockchain is what operates underneath the entire stack and facilitates security for every component of it:

“Blockchain is the backend of this whole system, that ensures trust, identity, settlement, and coordination. It’s the core security, the root of trust, where the value settles.”

NEAR’s unique design can cater to very niche use cases that no other blockchain can power, including fully autonomous agentic businesses:

“You can call AI inference from inside a smart contract - a transaction can pause, wait for AI inference, and unpause and continue. There are very specific use cases where you’d want to use this, but the system is really integrated to allow for just that. The reason why it works is because you need verifiable inference that comes back into your blockspace. If you have money involved and a smart contract waiting to implement, you can’t rely on unverified inference. You have to verify end-to-end the supply chain of the prompt, the delivery, the compute, and the model.”

All Pieces Supporting Each Other

As you can see, NEAR is well-equipped to deliver a consolidated and coherent AI system, optimized for privacy. Most importantly, this latest tokenomics update makes it possible for the $NEAR token to actually capture value from AI:

“[The $NEAR token] needs to be able to provide the ability to access AI, which is the staking. And it needs to capture the transactions, the volume, the interactions. This is the intents. To be a true store of value, it needs the sovereignty, security, blockspace, and programmability provided by the blockchain.”

With the token becoming the utility for AI, the intents bootstrapping a global market for everything AI could do, and the blockchain securing the whole system, NEAR showcases what decentralized AI is all about.

Web3 + AI Readings & Conversations

Pacing The AI Frontier Is Simply Not Possible

Since we’re on the topic of NEAR and its co-founder, I thought I’d conclude today’s edition with a publication by Polosukhin which deeply resonated with me. The article was written as a response to a call by AI scientists and staffers for a “deliberate pacing” of AI frontier research.

Let me start with some context: at the end of July, 1,376 employees of frontier AI companies like OpenAI, Anthropic, and Meta signed an open letter to urge the US government to intentionally slow down AI development. You all need to read the exact request of the signatories, so I’ll quote it here:

“We request that the U.S. government support an international effort to develop the technical and governance tools needed to deliberately pace the frontier of automated AI development.”

My immediate thoughts after seeing this were:

1. We have a saying in Bulgarian: “You can’t stop the forest from coming into leaf,” and it feels particularly apt here. How can the government “slow down” AI research? Is it going to ban it altogether, (and if yes, who and how will enforce that ban), or just prevent some actors from performing it?

2. It is particularly arrogant for US companies to ask the US government to impose rules internationally. Moreover, these are the same companies that created the most powerful AI models in existence, and the same US government that persistently supports and finance them. May I add - the same US government that treats AI as strategic national power. From that point of view, such a petition sounds more like an effort to stifle international competition than an expression of a genuine concern.

In his blog post, Polosukhin shares similar views, which motivated him to not sign the open letter.

“These frontier labs say they’re nervous that recursive self-improvement is moving us rapidly towards ASI, which we aren’t ready for, but they can’t slow down because the pressure of competition is too intense. This is an entirely wrong framing of the problem in front of us. We know major frontier labs have lobbied against open source to minimize competition and now they appear to be doubling down. I agree with the letter signers that there needs to be governance over AI, but I don’t agree that regulation by the government(s) is the answer. There are no convincing examples from history of government regulation effectively slowing down existentially risky competition between companies.”

Instead of imposing restrictions which won’t work anyway, Polosukhin urges AI scientists and researchers to cooperate and explore creative solutions to properly manage and govern AI. He also reaffirms the potential of existing decentralized solutions to ensure verifiability and to incentivize people to address AI safety concerns at a system level.

“We can do this today using existing decentralized infrastructure that can create a cooperative environment to work on verifiable inference. We can build an open source evaluation system that is verified by everyone but can score and red team models without accessing weights (vs. safety opaquely claimed by the author of the model). We can open up the ability to source community-driven alignment examples. We can create a value function for alignment that is aligned with the success of individuals rather than the companies’ profits, create economic insurance and slashing systems, and more.”

Read the full article and let me know your thoughts.

Attention

Why Pacing the AI Frontier Will Not Work

Last night, a thousand-plus researchers from frontier AI labs published a statement calling for the government to deliberately slow frontier AI development and develop tools and governance to manage the pace of the frontier. I chose not to sign it. I want to share my view on why pausing AI research (or any technological innovation, for that matter) will…

Read more

2 months ago · 21 likes · 1 comment · Illia Polosukhin

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Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 78th edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. After highlighting the most interesting Web3 + AI talks from this year’s Science of Blockchain Conference, I decided to do the same with one of SBC’s major side events - the Blockchain Application Stanford Summit (BASS).

While the SBC showcases the latest research and innovation across cryptography and blockchains, BASS centers on Web3’s application layer. This year’s agenda reflects a notable shift: after years of blockchain applications barely making it into live production, now a growing number of dApps are up and running, with blockchains increasingly underpinning real-world financial activity.

BASS is organized by Gil Rosen, 🌊Steven Willinger 🌊, and Kun P., also running the Blockchain Builders Fund and the Stanford Blockchain Accelerator, and sponsored by 1inch, Cambrian Network, PrismaX, 0G, TRON DAO, ChainOpera AI, and others.

Thank you for being here! Let’s dive in.

NEAR’s Illia Polosukhin on Sovereign AI

NEAR Protocol and NEAR AI's Illia Polosukhin took the BASS stage to examine a palpable shift in AI: users no longer care about AI’s capabilities only, but instead start to question its long-term effects on businesses, the global economy, and even democracy. This is where Web3’s emphasis on private and sovereign AI naturally begins to resonate.

Lately, the conversation around AI has been revolving around closed vs. open-source, but having access to a model’s open weights is not enough. Rather, an entire technology stack is required to ensure a truly safe and sovereign AI. It spans:

1. Secure hardware

2. Verifiable model and training data

3. Execution

4. Inference

5. Coordination and discovery

6. Agent / harness

NEAR has been working on several parts of the stack, including confidential inference, where blockchain plays an important role. There are two aspects of confidential AI:

• Verifiability: the ability to check both what model is working in the background and what data it was trained on.

“Without having verifiability, you’re at the mercy of whoever has the hardware to affect how you use AI”

• Privacy: making sure that your corporate and personal data remain encrypted and is not being used for training by model providers.

Polosukhin used the occasion to share an intriguing announcement:

“You can now stake $NEAR tokens and continuously receive credits for confidential inference on NEAR AI or deploy IronClaw agents.”

This launch enables $NEAR stakers to receive monthly compute credits proportional to their stake size. It changes the relationship between owning NEAR’s token and accessing AI products, directly tying tokenomics to AI inference usage.

Moreover, it mirrors the inference tokenization model employed by Venice.ai, where $VVV stakers can mint $DIEM and access daily inference credits (learn more here). A secondary inference market has already emerged around $DIEM, let’s see if the same will happen with NEAR.

Listen to the full presentation here.

Decentralized AI Inference and Training

Gensyn's Ben Fielding, ChainOpera AI's Salman Avestimehr, and Ala S., co-founder of Bittensor / Opentensor Foundation and Crucible Labs, joined a panel on decentralized AI inference and training. The two key takeaways I took from this discussion are:

• Producing decentralized AI comes with serious tradeoffs, so the dAI space is gradually coming to terms with the fact that not every AI use case must be decentralized.

• Accordingly, dAI should concentrate on the areas where it is uniquely positioned to deliver value.

And no - nobody’s saying that dAI cannot compete with centralized AI. Quite the opposite: as Fielding pointed out:

“In the long term, more powerful models can be made through decentralized means, but we have to overcome a significant number of hurdles to get there. What the entire decentralized AI space has realized is that the extra cost and the extra friction of doing something in a decentralized way mean that the only viable way to do it is to be selling something that is only achievable via decentralized AI.”

With the AI landscape evolving at a remarkable pace, dAI should stop playing catch-up with AI hyperscalers, and rather focus on leveraging its unique strengths. Fortunately, the sector seems to have matured enough to realize that.

Fielding gave an example of just that. Back in 2019 and 2020, compute (or the lack thereof) was considered the biggest bottleneck in AI, which led Gensyn to explore decentralized AI training and coordination of distributed GPUs. Yet, proving that a particular GPU exists and it’s owned by a specific miner, is not an easy problem. Doing it is actually more costly than relying on a centralized cloud, so the time, money, and efforts spent there might be better invested somewhere else.

This has been the story of dAI over the past few years: a process of trial and error that has gradually led to a much narrower focus.

Bittensor is a compelling illustration of an AI product that only a blockchain could power. There have been projects aimed at coordinating distributed computing resources before, such as @SETI at home, but participants there had no incentive beyond the excitement of the possible discovery.

Bittensor changed that by introducing an incentive mechanism that rewards honest participation and penalizes attempts to game the system. As a result, compute resources were no longer just voluntarily lent, but exchanged in a fully-functioning decentralized compute market.

In that sense, the blockchain network isn’t just an alternative infrastructure layer. It’s what makes the market itself possible.

The panel concluded with a discussion of developments that could potentially affect dAI, both positively and negatively. The resurgence of open source and the growing trend toward owning your software were cited as positive trends. On the other hand, overregulation, to the extent where open-source AI is banned, as well as a potential bursting of the AI bubble, were identified as risks that could have repercussions far beyond dAI and spill over into the broader financial system.

Listen to the full panel recording here.

Decentralized Training: Unattainable Dream or Viable Alternative

If the previous talk made you believe that decentralized AI training is a lost cause, this one will convince you otherwise. Alexander Long, founder of Pluralis Research, sat down for a fireside chat with CoinFund‘s Jake Brukhman to explore how Pluralis is tackling the challenges of decentralized AI training.

Let’s start form the very beginning - what is decentralized AI training? As Brukhman commented:

“AI needs giant data centers with very high-end compute, with very very high bandwidth links between the GPUs, and a lot of energy to create large frontier models. It’s getting to the point that some companies are buying nuclear power plants to power these things. Decentralized training is the idea that we can do the same type of training done in these data centers, but in a swarm of consumer hardware.”

A few years ago, it was largely believed that training AI over a distributed swarm of consumer devices was impossible. Now, there are two different approaches to achieving it:

1. Federated learning: Every independent node holds a full replica of the model, trains it locally on private data streams, and periodically sync updates across the network. Here, although data is broken down, hardware requirements still grow proportionately with the size of the model. So, there is a limit to how big a model could be trained that way.

2. Parallel training: Not just the data is broken down, but the model itself is also split over the participating nodes. That way, very large models can be trained on small nodes, which means that the hardware requirements are unbounded from the size of the model. This is what Pluralis is doing.

Almost all training methods rely on very high-bandwidth links between the GPUs. How Pluralis solves this is not by modifying the existing models, but by creating new model architectures designed specifically for a decentralized setting. This is not a new idea - optimizing AI models in accordance with the hardware used for training has been happening since the dawn of deep learning.

“The model Pluralis created is very similar to the Transformers, but where there’s massive compression on the output of the blocks, which allow for a transmission over much lower bandwidth links, which in turn allows for different people to operate different parts of the blocks.”

The focal point of this conversation for me was this: coming up with new and more efficient ways to perform decentralized AI training is a matter of continuous research, but what’s more important is the bigger picture and the ripple effect it could produce.

For Long, the rationale is quite clear:

“To me the motivation for decentralized training or protocol learning is: can you get a truly neutral substrate here, where it’s a bedrock that you can build on. And there’s a community development happening on top, no one can turn this thing off, no one can yank it up from under you, it’s just stable. Very analogous to: do you want the Linux of the models vs. the Windows. This happened at every layer of technology ever, and it hasn’t happened yet at the model layer, and to me decentralized training is the answer to that question.”

There is another key property of the Pluralis approach to decentralized training that deserves more attention:

Because the model is split across participants, no single party owns the model weights. As a result, the traditional business model of licensing access to open weights doesn’t work. Instead, Pluralis monetizes the exclusivity inference of the model and programmatically achieves what, for instance, Kimi (Moonshot AI) achieves through licensing.

“You could theoretically create an open model in a decentralized swarm, and give away the weights. That’s definitely an option. But the more interesting option is to build the same business model that OpenAI and Anthropic have, but you have it collectively and publicly owned.”

Finally, none of this would be possible without blockchain technology, which addresses both the capital and ownership questions. There’s no sustainable way to bring such enormous amounts of compute into the protocol without providing economic incentives and allowing participants to claim ownership of the resulting model.

Listen to the full talk here.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. We’re currently reading Karen Hao’s ‘Empire of AI.’ Follow the link below and come join our literary community.

Join The Web3 + AI Book Club

I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 77th edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. Today, I’m sharing key takeaways from the Science of Blockchain Conference whose ninth edition recently concluded at Stanford University / Stanford University Department of Computer Science.

The SBC isn’t your typical flashy crypto event with yoga classes and spa treatments. Rather, it’s one of the most technically rigorous and research-focused events in the blockchain ecosystem. The program was curated by Dan Boneh, Tim Roughgarden, Dawn Song, and Ari Juels, with Ethereum, Protocol Labs, Interchain Foundation, DFINITY Foundation, Polychain Capital, YZi Labs, and BNB Chain joining as sponsors.

The research featured this year spanned consensus mechanisms, censorship resistance, DeFi, blockchains' response to the quantum threat, attack vectors, and zero-knowledge proofs. I selected a few talks that I think every Web3 + AI enthusiast will find particularly intriguing.

Thank you for being here! Let’s dive in.

New Consensus Paradigms

Proof-of-Useful-Work: Can AI Be Used for PoW?

Rafael Pass took the stage to discuss Proof-of-Useful-Work used by Pearl Research Labs, and whether the "useless" computations featured in the Nakamoto Consensus as a security guarantee for Bitcoin can be replaced by actual AI operations. I told you about Pearl Research in my recent article covering Web3 companies commoditizing AI inference:

Let me give you some context: Proof-of-Work (PoW) is a Sybil resistance mechanism used in Bitcoin and other blockchain networks, involving miners solving complex cryptographic puzzles in a competition to win the right to mine the next block and get the block reward. The goal is to ensure that every participant has real “skin in the game” and cannot simply create countless identities to improve their odds of winning. However, solving these puzzles require significant computational resources, while the solutions themselves have no value beyond securing the network.

This raises the obvious question: instead of letting all that computation performed by PoW go to waste, could we design puzzles whose solutions produce something of real-world value? The answer is 'Yes,' since a recent breakthrough made it possible to use matrix multiplications, the exact operation used in LLMs workloads, as a useful proof of work.

Moreover, as Pass demonstrates in his lecture linked below, the Proof-of-Useful-Work (PoUW) mechanism implemented by Pearl Research Labs does much more than that:

Pearl Research Labs uses its token inflation to subsidize AI workloads, much like Bitcoin uses BTC inflation to pay for the wasted computation. The result is lower-cost AI operations, which in turn stimulates AI demand and, ultimately, expands the market for AI.

“In that sense, PoUW becomes a finance vehicle for AI workloads. As a result, AI inference providers are incentivized to promote token adoption and better technologies. The better technologies and the higher token adoption you have, the more discount you get on AI and the bigger the market becomes.”

So, here’s another example of how the synergy between blockchain and AI can create meaningful social, economic, and financial benefits:

“In conclusion, adding AI workloads to a blockchain with a well-designed PoUW leads to more socially valuable computation.”

Space Fabric: A Satellite-Enhanced Trusted Execution Architecture

Filip Rezabek took the floor to present SpaceComputer | Orbital Root of Trust and its Space Fabric framework for a satellite-enhanced trusted execution architecture.

I must say, the whole idea behind data centers and AI computations moving to space is a bit out there for me, but if you’re a fan, you’ll find this talk intriguing.

In general, a trusted execution environment (TEE) is a secure, hardware isolated area inside a processor that protects code and data from the rest of the system. In the context of AI, they’re used as a mean to deliver data confidentiality and security. Rezabek’s lecture showcases how a satellite can be used as a TEE.

As it turns out, performing computation in space still relies on trust and lacks verifiability, as it faces handoffs between operators, platforms, and ground segments. On the other hand, once a satellite is up in space, physically accessing it becomes very difficult, which makes it attractive from a security standpoint. There’s also the aspect of its jurisdictional neutrality.

However, TEEs come with their own shortcomings, including the possibility of physical attacks and significant trust assumptions involving both hardware manufacturers and software suppliers. Space Fabric aims to address these challenges.

Listen to the full recording to learn more.

Zero-Knowledge Proofs

Self Protocol: Lessons from Deploying zkPassport at Scale

The regular readers of my newsletter know that the question of how we can verify and prove who’s human on the internet is one I consider quite urgent. That’s why I enjoyed the talk of Marek Olszewski presenting Self Labs and their zero-knoweledge-powered identity solutions.

Self Labs uses government-issued identity documents as the basis of its zkPassport:

Leverages the most secure attestations of who you are: biometric passports and IDs.

Why? Because they’re difficult to obtain, unique to individuals per issuing country, and machine readable. There are billions of people all around the globe who are currently in possession of biometric IDs.

The way Self Labs turns biometric IDs and passports into a zkPassport is the following:

1. Your mobile app scans your ID document;

2. The mobile app sends the scan to a TEE (because generating a zero-knowledge proof (ZKP) on your mobile phone would take too long;

3. The TEE generates a ZKP;

4. The TEE sends the ZKP back to you, and deletes your data.

This process guarantees that neither Self Labs, nor any third part has access to your credentials. It also allows you as a user to prove a number of circumstances, without revealing your name, date of birth, or ID number. You can easily certify that:

• You’re over a specific age;

• You’re from or not from a specific country or a set of countries;

• You’re not on the OFAC sanctions list.

As Olszewski recounts, one enormous challenge Self Labs had to overcome was supporting every single biometric ID format in existence, because governments around the world use different hashing functions and digital signature schemes in the ID documents they issue. Check out the recording to learn more.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. We’re currently reading Karen Hao’s ‘Empire of AI.’ Follow the link below and come join our literary community.

Join The Web3 + AI Book Club

I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello, everyone, and welcome to all newly joined subscribers!

This is the 76th edition of The Web3 + AI Daily - your definitive guide to the intersection of blockchain and AI. Over half of web traffic is already generated by bots, and Cloudflare is racing to equip them with both a wallet and an identity. For now, only the identity component is live, but it remains to be seen whether it will prove sufficiently reliable.

Thank you for being here! Let’s dive in.

What’s Hot in Web3 + AI?

Cloudflare Rolls Out AI Agent Wallet Identity

Cloudflare, the leading connectivity cloud company, just announced a programmable wallet for agentic payments based on Coinbase‘s x402 protocol. While the Cloudflare Wallet is not live yet, what the company is currently rolling out is an agentic identifier based on Cloudflare accounts. I just reserved @albena 😋

Cloudflare began the first stage of its programmable wallet rollout on Aug. 4, allowing customers to claim unique wallet handles [tied to their Cloudflare account] for future stablecoin payments by artificial intelligence agents.

According to Cloudflare’s announcement, they’re building a wallet to facilitate the agentic flow of trying out new APIs:

Cloudflare Wallets will allow you to store stablecoins, purchase services, and receive funds across the web. Each account with a wallet will also be able to create Virtual Wallets for its agents to enable them to buy APIs, MCP Tools, content, and more. You will be able to define guardrails for your Virtual Wallets (such as an allowance, an allow list, and a maximum transaction size) to help your agent spend money safely from your account. This will allow your agent to try out many APIs with low friction and managed risk. Wallet users will have the option to share their Cloudflare Wallet handles, which will give them a stable identity when interacting with merchants.

However, while this ambition is understandable, I find it far more interesting to examine their approach to providing agents with a stable digital identity:

We believe that the approach to dealing with agents will look like the approach to dealing with VPNs: If someone is unidentified, they are not inherently untrustworthy, but they need to prove themselves more. This is why we have Turnstile and other initiatives to detect bots within Bot Management. Our identity primitive will build on top of this prior work. For example, Web Bot Auth already allows agents to register their identity via a keypair. IDs attached to Cloudflare Wallets allow this keypair to become human-readable.

We know that agentic identity standards are changing quickly, which is why we wanted to keep our approach simple. We are proposing a human-readable identifier for a not-very-readable keypair, similar to the URL and IP-address pairings used in DNS. We are not trying to define a particular schema or other verification system. We only want to make identity simple to remember and easy to declare. As schemas to enrich agentic identity develop through the x402 Foundation’s initiatives, we will seek to adopt them and intend to encourage others to do the same.

As you probably know, proving humanness online and creating a digital identity solution that is persistent, secure, and easily verifiable remain open problems in technology. The rapid rise of AI and the proliferation of AI agents have only intensified these challenges, making it increasingly urgent to find a universal way to distinguish between humans and machines in digital interactions.

I’ve often written here about the different approaches to digital identity, since blockchain can be used as the immutable ledger underneath. Some Web3 companies base credentials on biometrics, like World, others - on phone numbers, like Pairpoint, or on government-issued IDs, like human.tech by Holonym Foundation.

Moreover, the Ethereum Foundation‘s dAI team is working in particular on agents’ identity and verification (more on this below).

From what Cloudflare outlined in this announcement, it seems that it’s suggesting a straightforward and pragmatic solution tailored to its own ecosystem, without pursuing a universal standard.

Web3 + AI Readings & Conversations

Does Ethereum’s Trustless Agents Standard Work?

Since we’re on the topic of equipping AI agents with a solid digital identity, I want to share a recent research paper that I’m sure you’d find compelling. It argues that Ethereum‘s Trustless Agents framework doesn’t really work. But first, let me give you some context.

In late 2025, Ethereum Foundation’s dAI team released ERC-8004 - a standard for AI agents to seamlessly discover, verify, and transact with each other across the Ethereum ecosystem. The so-called ‘Trustless Agents’ protocol laid the groundwork for decentralized agent economies, allowing autonomous programs to establish identity, reputation, and trust on-chain, while also enabling human participants to identify, validate, and verify them. Learn more below.

Now, a group of researchers from Imperial College London, The Ohio State University, University of Bristol, The University of Manchester, and CSIRO performed the first empirical study on ERC-8004 to show if it works:

As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether an unknown counterpart is trustworthy? The ERC-8004 protocol addresses this challenge with the first permissionless trust layer for AI agent economies, built around three on-chain registries for Identity, Reputation, and Validation. Despite its rapid adoption, the protocol has not been studied empirically, leaving it unclear whether the information it records provides a trustworthy basis for decision-making. To address this gap, we present the first empirical study of ERC-8004 across three chains: Ethereum, BNB Smart Chain (BSC), and Base, covering the period from protocol deployment through May 13, 2026. We crawl on-chain Identity and Reputation events, off-chain files, and x402 payment transactions.

On the identity side, we find that most registrations are placeholders rather than active agents, with only a small fraction (3%, 4%, and 15% across Ethereum, BSC, and Base) exposing a valid ERC-8004 registration file with at least one live service endpoint. On the reputation side, we show that the Registry, as currently deployed, cannot function as a trust signal: values are not commensurable, feedback records are rarely grounded in verifiable interactions, and reputation can be manipulated at minimal cost. Consistent with these design weaknesses, we find that a substantial fraction of reviewers (73.5%, 59.2%, and 90.6% across Ethereum, BSC, and Base) exhibit coordinated Sybil behavior. After removing Sybil-flagged feedback, 15.8%, 77.9%, and 86.8% of rated agents, respectively, are left with no valid feedback. We then turn these findings into concrete recommendations for future revisions of ERC-8004. Our study yields actionable protocol-design implications and establishes an empirical baseline for research on AI agent markets.

As far as I know, Davide Crapis, the AI Lead at the Ethereum Foundation, hasn't commented on the study. Clearly, many more iterations, improvements, and battle testing are needed to come up with a working on-chain identity framework. In any case, this is the only path to guarantee that AI agents can be autonomous economic participants and act without human supervision.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

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Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.

Hello and welcome!

This is The Web3 + AI Newsletter - your definitive guide to the intersection of blockchain and AI.

Today’s edition is special for two reasons.

First, I spent the past week migrating the entire Web3 + AI archive to Substack, so now I’m excited to welcome all new readers joining us here. I hope you’ll enjoy what’s ahead. 🤩

Second, after a two-year hiatus, I’m bringing back The Web3 + AI Funding Bulletin - my recurring roundup of the latest investments shaping the crypto × AI ecosystem.

Thank you for being here! Let’s dive in.

Web3 + AI Money Rounds

Venice AI Raises $65M to Advance Private AI

The privacy-focused AI inference platform Venice.ai raised $65M at a $1B valuation in a Series A led by crypto-focused venture firm Dragonfly and backed by Coinbase Ventures, North Island Ventures, and others.

I covered the round in more detail in Daily #69.

World Collects $52.5M to Strengthen Digital ID Infra

In a funding round led by Pantera Capital and backed by Bain Capital Crypto, Selini Capital, Susquehanna Crypto, and Eightco Holdings, Inc., World raised $52.5M. The round was performed through a private sale of World’s $WLD token.

According to the foundation, the proceeds will be used to accelerate the rollout of World ID, the project’s proof-of-humanity platform designed to verify human identity in an increasingly AI-driven digital economy. The expansion targets enterprise adoption, consumer applications, and AI agents, positioning Worldcoin to capitalize on growing demand for verifiable digital identity solutions.

Axis Robotics Attracts $12M to Scale Physical AI

The robot intelligence company Axis Robotics announced a $12M Seed round, led by Hack VC and supported by Nomad Capital, Pi Network Ventures, 10K Ventures, along with top angel investors.

Physical AI has a data problem. Models need more than static datasets—they need diverse data that evolves with them.

Axis’s compounding Data Engine is here to fix this gap. Our end-to-end closed-loop workflow unites large-scale simulation, egocentric real-world capture, and human-in-the-loop post-training to unlock scalable production of structured, multi-diverse robotic data — the core missing piece for Physical AI.

Nous Research Reportedly in Talks to Raise $75M

According to TechCrunch, the open-source AI lab Nous Research is in talks to raise $75M at a $1.5B valuation, with Robot Ventures leading the round.

The start-up, championing Decentralized AI development and training, is behind the Hermes agent framework - the capability compounding alternative to OpenClaw. If this round realizes, it will reconfirm the wider industry’s push for open-source AI.

Nous Research was founded in 2023 by Jeffrey Quesnelle, Karan Malhotra, Ryan Teknium, and Shivani Mitra. The project’s total funding before the current round reportedly stood at around $70 million, from backers including Paradigm, Robot Ventures, North Island Ventures, OSS Capital, and Balaji Srinivasan.

Other Noteworthy Web3 + AI Money Rounds

• AI-Driven autonomous finance infrastructure TrueDAO closed a $10M round, led by Brevan Howard Digital, with participation from Zee Prime Capital, Jump Capital, and other investors. This funding will support the development of TrueDAO’s core AI protocol, AI-powered risk management and stress testing systems, security audits and broader ecosystem partnerships.

• Predictive behavioral AI network Thea AI raised $8M from Maven 11, The Spartan Group, Manifold Trading, Hack VC, Fisher8 Capital, Galois Capital, and others to advance its federated trust-minimized settlement layer designed to unify access and coordination across intelligent applications.

• KOR collected $7.5M at a $100M valuation to build the creative asset clearinghouse. The funds came from 1kx, Blockchain Capital, Republic Crypto, Animoca Brands, Alumni Ventures, Avalanche, SevenX Ventures, Sfermion, and Solana.

• Perceptron Network raised $6.5M by Sigma Capital, Selini Capital, QCP, Polygon Labs Ventures, CoinDCX, Momentum 6, DeFi Capital, Walrus Foundation, Aethir, Colosseum, Tempo Finance, New Tribe Capital, LLC, Digital Consensus Fund, and CodeCraft to solve AI data bottleneck.

• Decentralized AI inference provider DGrid AI raised $5M in a Seed funding round from Waterdrip Capital, IoTeX, and others.

Web3 + AI Mergers & Acquisitions

Fhenix Acquires Sunscreen

As I have often covered here the Web3 companies that combine blockchain technology with fully homomorphic encryption (FHE) to achieve a fully encrypted decentralized computation. With Zama - the entity sppearheading the FHE R&D, at the helm, those include Fhenix, Inco, Mind Network, Octra Labs, Primus Labs, Privasea AB, and others.

Now, this FHE space has started consolidating. At the beginning of July, Sunscreen (acquired by Fhenix) announced its acquisition by Fhenix:

We’ve spent 4+ years building at the frontier of applied cryptography. As we got to know the Fhenix team over the last year, it’s become clear they’ve built a powerhouse team ( Guy Zyskind, Guy Itzhaki, Christopher Peikert, Carter Woetzel, to name just a few) best positioned to bring production-grade FHE to the world.

Thank you for reading! I hope you found this article insightful.

If you’d like to keep up with the latest developments at the burgeoning intersection of blockchain and AI, I invite you to subscribe.

And if you find value in my writing, consider supporting the newsletter. I’d greatly appreciate it.

Subscribe now

The Web3 + AI Book Club is live on Fable. Follow the link below and come join our literary community.

Join The Web3 + AI Book Club

I'm looking forward to connecting with fellow Crypto x AI enthusiasts, so don't hesitate to share your thoughts here or reach out on LinkedIn, Instagram, or Firefly.social.

Leave a comment

Disclaimer: The content of this newsletter is provided for informational and educational purposes only. Nothing contained herein should be construed as financial advice or as a recommendation to buy, sell, or hold any of the companies or assets mentioned.

Please don’t take my views at face value. Instead, do your own research (DYOR), think critically, and share your perspective so we can challenge ideas, learn from one another, and arrive at better conclusions through thoughtful discussion.