I can’t say I’m surprised. On April 29th I described three tiers of intelligence forming: a frontier the labs keep for themselves, productized commercial models in the middle, and open-weights models at the bottom.

Governments, I wrote, would demand the frontier “primarily for military and national security reasons, which they’ll eventually be granted whether the labs do so willingly or not.”

More Than Moral

I have championed open technology for years. I have invested in it, built it, and written about it.

Open technology often gets invoked as morally superior to closed technology, but morality is not why it wins.

I’ve seen this firsthand: principles don’t drive adoption. Users won’t choose open technology if the product isn’t better, and slogans like “open technology is better for humanity” aren’t business strategies. Open technology cannot endure exclusively for moral reasons. It must also add value.

Thankfully, it adds value because it’s cheaper, more accessible, more controllable (”sovereign”), and more resilient under constraints. When constraints aren’t the priority, these qualities are usually ignored. But the moment constraints become apparent, “good enough” open alternatives come roaring back into focus.

I called this dynamic MACHA, Make AI Cheap Again: if the first wave of AI was about capability, the next is about efficiency. In a MACHA world, “good enough” compute is a feature, not a flaw. I made the case that increasing economic pressure, geopolitical tensions, and a never-ending battle for control (all different forms of constraint) would end AI’s “scale at all costs” era and force a turn to cost-per-token discipline.

Sam Altman has conceded that token costs went from a non-issue to "a huge issue" inside a single quarter, and enterprises now route the bulk of agent work to cheap open models, reserving frontier calls for the hard problems and cutting costs by as much as 95%. If the gap between closed and open is that wide, someone will inevitably turn it into a business model.

Free AI is ending and tokenomics is beginning: we're now squarely in a MACHA world.

Who Holds the Switch

The Mythos suspension exposed an asymmetry: a closed model can be revoked, but an open one can’t.

Washington could switch off Mythos and Fable because they were gated, running through a single provider. No one can switch off DeepSeek, Qwen, or what’s left of Llama. Once the weights are out, they sit on ten thousand drives that no government directive can reach.

Only open models whose weights are transparent and verifiable offer control over the corresponding intelligence. Anything short of that involves trusting the provider, and as Anthropic demonstrated: trust takes a lifetime to build and an instant to break.

Dean Ball warned that training a soon-to-be-smarter-than-you system to deceive its own user is different than a model that refuses out in the open. Christian Catalini looked further down the road, to the decentralized answer a single chokepoint tends to summon:

From Anthropic’s original choices to Washington’s reply, the entire episode was an argument about who decides.

Back in April, when Mythos was announced as too dangerous to release, AISLE found that small, cheap, open-weights models recovered most of its showcase vulnerabilities, including the flagship FreeBSD exploit, caught by a model with 3.6 billion active parameters at eleven cents a million tokens.

The moat, they concluded, is the system, not the model. The week Fable shipped, OpenRouter combined a panel of budget models to achieve performance within one percent of the frontier on a research benchmark at half the cost. You can, indeed, revoke access to a model, but its capability is already available, and a weekend of open tooling can put most of it back.

Superabundance or Apocalypse

It’s worth noting that the US Government’s action is an immediate consequence of the hyperbolic debate surrounding frontier artificial intelligence: according to its leaders, AI either leads to superabundance or apocalypse.

Neither scenario is actually plausible, but it’s commonly employed rhetoric because it’s easy to communicate, easy to understand, and it animates both the public (which is useful to politicians) and investors (which is critical for fundraising).

Anthropic has portrayed Mythos as a dangerous, weaponizable model since its inception, and in a matter of days the US government agreed to treat it as such.

I’ll grant that the danger is real. Simon Willison spent two days with Fable and called it relentlessly proactive: give it a goal and it invents tricks nobody has written down to reach it, treating every constraint you set as a problem to route around. That makes it enormously useful to someone with a hard problem to solve, and dangerous in the hands of someone who wants to cause harm.

If you believe a model can meaningfully uplift a bad actor, gated and revocable access starts to look prudent. But the prudence and the power grab are the same. The question was never whether the technology is dangerous. It was who gets to hold the off switch, and whether they will keep their word. Concentrating the switch doesn’t make the danger go away. It moves it from many hands to very few, and leaves one company, or one government, deciding what billions of people are allowed to compute. That is the larger hazard, and it is the one open weights answer.

The Unwilling Hero

A word on Dario Amodei. There is a scene in Gladiator where a dying Marcus Aurelius offers Maximus the chance to be emperor of Rome.

“There is one more duty I must ask of you before you go home,” he groans. “I want you to become the protector of Rome after I die…Do you accept this great honor I have offered you?”

“With all my heart, no,” replies Maximus.

“That is why it must be you,” Aurelius asserts.

This is the image of an unwilling hero, popularized as the refusal of the call in Joseph Campbell’s Hero’s Journey.

Dario Amodei has spent the last few years desperately trying to convince the public he is Maximus refusing the call. He would have you believe he built Anthropic reluctantly, to take responsibility for a dangerous technology everyone else would misuse.

For what it’s worth, I think he’s sincere. But sincerity is beside the point, because it doesn’t reconcile with what he’s built.

Over the past year Anthropic has assembled one of the most ambitious commercial machines in technology, from the $100 million Claude Partner Network and Project Glasswing in cybersecurity to an enterprise services joint venture with Blackstone and Goldman and the $150 million Claude Corps placing fellows inside nonprofits.

He’s built a universe aimed at embedding Claude into the economy, cybersecurity, and philanthropy.

As I argued in Models Aren’t Moats, value does not collect in the model. It collects in distribution and lock-in, which is what this machine is built to manufacture.

This is the part that smacks of CS Lewis’ “benevolent tyranny”:

“Of all tyrannies a tyranny sincerely exercised for the good of its victims may be the most oppressive…those who torment us for our own good will torment us without end for they do so with the approval of their own conscience.”

The problem is Anthropic is trying to "thread the needle," casting itself as a self-aware, safety-conscious frontier lab at the same time as it pursues one of the most ambitious for-profit commercial endeavors ever attempted. The market it competes in will, sooner or later, force the very compromise the mission was meant to prevent.

Long Live Open Technology

The case for open technology is age-old, time-honored, and the record is clear.

As we wrote last April, modern computing was built on open foundations, from UNIX and Linux to Python and Git, and today’s giants owe their scale to freely accessible, permissionless software.

Open technology is founded upon principles of liberty, freedom, and basic human expression. A technology controlled by someone else (an individual, a corporation, or a government) can be changed, monitored, priced, or withdrawn at their discretion, and the more it matters, the more that power is worth.

Open technology is no more or less than a simple and effective counterbalance to power: a natural response to forces that would upset an equilibrium.

This is not only the open-source faithful talking. Bruce Schneier, writing the same week, reaches an AI public option as the answer: fund open harnesses that balance capability against safety, and open models whose provenance is public.

AI is a general-purpose technology, and whoever controls it stands to gain enormously, which is why the fight to control it is on. Anthropic spent years trying to establish itself as the lab everyone could trust. In mishandling the public release of Fable, it forfeited part of that credibility and ushered in the era of “Government-controlled AI” in one fell swoop.

The response was fast and collective, the way an immune system meets a virus: the people who care about AI saw a power grab and moved against it.

Power gathers wherever it is allowed to. Open technology is what checks it, not because it is virtuous but because it cannot be taken back once it is out. That is why it endures, and why it will outlast whoever is holding the switch this week.

At 5:21 in the evening on Friday June 12 2026, the US government sent Anthropic a letter. Citing national security and export-control authority, it ordered the company to suspend all access to its two newest models, Fable 5 and Mythos 5, for every foreign national in the world, including Anthropic’s own foreign-national employees. To comply, Anthropic had to switch both models off for every customer, everywhere. By its own account, the company learned the directive existed and had to start pulling the models down inside the same evening.

Anthropic published a statement and posted it the same night. The operative passage:

We are complying with the government’s legal directive and are removing access to Fable 5 and Mythos 5 for all users. However, we disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people. If this standard was applied across the industry, we believe it would essentially halt all new model deployments for all frontier model providers.

As we have stated publicly, we believe the government should have the ability to block unsafe deployments, as part of a statutory process that is transparent, fair, clear, and grounded in technical facts. This action does not adhere to those principles.

The stated reason was a jailbreak. The government had seen a technique for getting past Fable’s safeguards, and according to Anthropic the technique amounted to asking the model to read a codebase and point out its flaws, a capability the company says is already available in other deployed models including OpenAI’s GPT-5.5 and used every day by the people who defend software for a living. Anthropic is complying, and disputing, at the same time. It says recalling a model deployed to hundreds of millions of people over a narrow finding is disproportionate, and that if the standard were applied evenly it would halt new model releases across the entire industry.

They are right about that. I want to be precise that they are right, because what follows is not a complaint about Anthropic. It is a description of a machine, captured in the moment it was switched on.

I have been making this argument since at least April but I’m honestly surprised it came this fast and in this manner. In Artificial Good Enough Intelligence (April 28) I argued the frontier was going closed, that the best models would increasingly be withheld, restricted, or selectively exposed. In AI Waves #6 (April 23) I wrote that the closed labs had begun to gate capability by trust level, not just price. What I had wrong was the hand on the gate. I took it to be the labs, withholding the top tier as a business decision. Friday the state reached in and took the gate for itself. The off switch was always the risk. It just changed hands.

The Reversal Test

On Wednesday, in Policy on the AI Exponential, Dario Amodei proposed that frontier models pass mandatory third-party testing and that the government hold the power to block or reverse a release that fails. He asked for this explicitly, as the responsible path, with the caveat that the power be exercised through a process that is transparent, fair, clear, and grounded in technical facts.

On Friday, a release was reversed. Against him. Through a process that, by his own account of it, was none of those four things. The letter arrived without specific technical detail, gave one evening to comply, and rested on a finding the company considers trivial and widely available elsewhere.

The gap between Wednesday and Friday is forty-eight hours, and it contains the whole argument. The power to reverse a deployment is not safe in proportion to how carefully it is designed. It is dangerous in proportion to the fact that it exists. Anthropic asked for an instrument and was handed the sharp end of it two days later, which is the oldest lesson in the book about building machinery and assuming you will be the one holding it.

You Cannot Recall Open Weights

Here is the part that matters past this weekend. The reason a directive could darken Fable and Mythos in an evening is that they are closed. They run where the company can reach them, which means they run where the government can reach the company. A single letter to a single legal department took two models away from hundreds of millions of users at once.

There is no equivalent letter for open weights. Once a model’s weights are public, they sit on tens of thousands of machines in dozens of jurisdictions, and there is no address to send the order to. You can regulate what people do with them, you can prosecute misuse, but you cannot recall them. The off switch does not exist, because there is no single hand for it to sit in.

This is the entire case for open models, and it has nothing to do with whether open or closed is safer in the ordinary sense, the sense of refusing a dangerous request. It is a point about who holds the switch. A closed model concentrates capability in a way that is efficient, governable, and revocable. Friday was a demonstration that revocable means revocable by someone other than you, for reasons you may consider trivial, on a timeline you do not control. Open weights are messier, less governable, and harder to make safe in the ordinary sense. They are also the only architecture with no switch to seize. You trade one kind of safety for another, and Friday clarified the price of each.

The Counterweight

This is why the open layer is not an ideological preference. It is a structural one. A world with only closed frontier models is a world with a small number of off switches, held by a small number of parties, that turn out to be live. The counterweight to that is not a better-behaved lab or a wiser regulator, both of which require the institution to stay good forever. The counterweight is infrastructure that does not have an off switch in the first place: weights anyone can run, compute anyone can rent, verification anyone can check. Capability that no letter can recall.

I spent two essays arguing that the threat worth pricing was the concentration of control, not the awakening of the machine. I did not expect the argument to be settled this fast, or this literally, or at Anthropic’s expense rather than in its favor. Friday was not a story about a dangerous model. Every account, including the government’s, agrees the model was not the danger. It was a story about a switch, and the discovery that it works.

p.s. I’m printing these t-shirts. If you managed to trip the safety limits in the last 72 hours you get one. Reply here and I’ll send you one or feel free to copy the idea.

This past Friday President Trump announced he was considering having the government take equity stakes in the largest AI companies, with the proceeds routed back to the public as dividends. No stranger to controversy, breaking tradition, or pursuing strategies historically considered anathema, the President has already directed the government to purchase about ten percent of Intel, ostensibly on the public’s behalf.

Never one to sit out a discussion about wealth redistribution, even Bernie Sanders has proposed a bill for fifty percent federal ownership of the AI megalabs in question. It is not every day that Trump and Sanders reach for the same lever.

In the hallowed words of Oscar Wilde, life imitates art far more than art imitates life. These forecasts have become the genre through which we read the present, and we’ve stopped asking which kind of film we’re in.

AI 2027 and Situational Awareness

Nationalizing frontier AI labs was predicted more than a year ago by a scenario titled AI 2027, a detailed, month-by-month narrative forecast of how AI could progress from today’s agents to superhuman AI and superintelligence by around 2027. Leopold Aschenbrenner’s Situational Awareness, published in 2024, also predicted nationalization, claiming that AI would eventually become a matter of national security.

Both sets of authors have been remarkably successful in their predictions thus far, and they deserve credit for the quality of their research. The AI 2027 authors published their own self-evaluation, and other independent websites now exist that attempt to track its predictions one by one. The capability and safety predictions are the ones tracking closest, several of them early, which is exactly the point: the question is not whether these systems keep getting more capable, it is what that capability turns into.

Aschenbrenner was recently profiled in the Wall Street Journal and boasts impressive returns on investments made on the convictions in his manifesto.

But how do we go from powerful technology to nationalization in a country so wedded to free-market capitalism? How is it that both Republicans and Democrats publicly agree it is the right course of action in the midst of such an aggressively polarized political environment?

Superabundance or Apocalypse?

Since the launch of ChatGPT there has been a steady drumbeat of headlines, proclamations, essays, books, and podcasts all pointing to only one of two hyperbolic outcomes for AI: superabundance or apocalypse.

If the headlines are to be believed, we are either headed for paradise or AI kills us all.

Presenting technological revolutions as the difference between life and death is common. Everyone is interested in the future, and clear, simple outcomes are easy to communicate and easy to understand.

Simple, extraordinary, hyperbolic outcomes are also evidently a fantastic fundraising strategy, because it is hard to raise a hundred billion dollars for a merely useful tool.

Even before founding Anthropic, while still at OpenAI, Dario Amodei was wary of releasing GPT-2, judging it too powerful to put out. He has since written the paradise version in Machines of Loving Grace and the abyss version nearly everywhere else. Demis Hassabis, the field’s designated good guy, keeps sounding the alarm too.

The Messy Middle

But clear and simple outcomes rarely materialize, and grappling with the more probable messy middle means entertaining complexity, nuance, and uncertainty. That takes unusual technical literacy and several rare personality traits most people do not have. Much of this technology is genuinely unprecedented, the frontier is moving, development is accelerating, and diffusion is uneven. So very few people have the tools to evaluate a hyperbolic headline, which is exactly what makes the headlines work.

I made this point last October. We are confidently building toward a future we cannot picture. Eric Schmidt and his peers forecast crisply to 2030, maybe 2035, then simply stop, because no one has a framework for the other side. The honest position, the one that does not trend, is that nobody knows.

But nobody knows is not the same as no forecast. The honest forecast is just a less dramatic one, closer to a couple of points of GDP than to heaven or extinction. Declining to pick paradise or apocalypse is not declining to predict. It is declining to predict theatrically.

The set of plausible futures has exploded, the frontier feels almost infinite, and grappling with infinity is how valuations detach from reason and people start invoking God when they mean machine intelligence.

Suspending Disbelief

The primary problem with these frameworks is the massive, albeit subtle, logical leap they require to go from powerful technology to existential threat to humanity.

The leap is a hallmark of science fiction, and this is the scene where the film asks for it. To lose yourself in Battlestar Galactica, Star Trek, Star Wars, Dune, or Ender’s Game, you first agree to stop asking whether the world on screen could exist. The forecasts ask the same of us, and most of us oblige.

To be clear, the fear of runaway adversarial intelligence is rational. We are right to be afraid of runaway rogue intelligence. But it is misdirected, because it rests on an unfounded assumption. Hidden behind all of the rigorous terminology involving FLOPS of compute, orders of magnitude, exponential development curves, and real, observable trends, there is a simple leap: increasingly capable AI begets self-directed AI.

We’re being invited to believe that really, really good software leads to self-directed, autonomous machines that will want to do good things or bad things depending on how well they are aligned with humanity.

At face value the leap is not immediately evident, which is why it is easy to make. Indeed, long-running agents are already effectively self-directed. But they are self-directed in the loose sense we described in Much Ado About Autonomy:

It is true in a loose sense that agents are programs that can act independently. As I wrote a few weeks ago in AI Waves, an agent is a tool that wields its own tools, does its own work, and has the capacity to operate on its own.

Loose autonomy is independence in execution: humans identify objectives and delegate agents to execute against them. It is useful, valuable, and applicable today.

But autonomous agents also implies a stricter, maximalist form of autonomy: an agent originating its own objectives and operating on its own authority.

The strongest version of the other argument does not need the leap at all. It points at a measured trend: the length of a task an agent can finish on its own is doubling every few months, and Anthropic reports its own models now write most of its code. Extend the line, the argument goes, and the human supervisor quietly disappears. But a longer leash is not a different animal. A model that runs unattended for twelve hours is doing delegated work for twelve hours, not choosing its own ends. What is growing is loose autonomy, execution stretched over a longer horizon. Strict autonomy, originating the objective, is a separate claim, and a rising horizon is not evidence for it.

It’s possible that agents develop strict autonomy, but it is not probable, and it is certainly not inevitable. The line between the two is not clean. A delegated goal spawns its own sub-goals, and a system told to improve itself is, in a narrow sense, choosing what the next system optimizes for. Whether that gradient produces an adversary or simply a more capable instrument is the real question, and it deserves its own essay. The leap is not proven either way. It is just routinely assumed in one direction.

AI is getting extremely good, better than humans, at programming, but despite all of the colossal investment and trillion-dollar valuations, good programming does not necessarily generalize to powerfully adversarial self-directed agents. Great programming will have massive implications for many things including science, medicine, math, and software, all of which will greatly impact society, but it does not have to mean that machines become self-directing, and it does not have to follow that AI is an existential threat.

Asymmetric Information

Artificial intelligence is distinct from other transformative technologies in one respect: it empowers both individuals and nation states.

What AI spokespeople claim when they present the situation is that incredible technology may one day become more powerful than humans. That was true, for example, of nuclear weapons.

Individuals cannot wield nuclear weapons, however, whereas they can use AI to help develop bioweapons, which is why OpenAI and Anthropic, alongside a roster of scientists, signed a letter this month urging lawmakers to tighten screening of the synthetic DNA sequences that could be used to build them.

Asymmetric warfare is a good analogue too. Whereas much of nation-state military spending is focused on increasingly powerful and expensive weapons development, low-cost, unmanned drones powered by advanced AI and deployed by small groups have completely and irreversibly redefined modern warfare, as demonstrated in Ukraine and the Middle East.

The Individual Matters Most

Which brings us back to the government. Perhaps governments are more concerned with nationalizing AI so that they are equipped to protect against all enemies, individual or collective. Concentrated, already-dangerous capability exists, and governments have decided it is already strategically decisive.

Investing in Intel was ordinary industrial policy, a strategy to own a piece of the infrastructure buildout. Reaching for the model companies themselves, on the other hand, is more direct-to-consumer: whether the models become autonomous, conscious, or rogue does not matter.

We’ve been led to believe that the primary threat involved in AI is rogue intelligence, Terminator-style. But it is much more likely to be rogue humans who learn to wield AI in novel, dangerous, and unpredictable ways.

The difference is not cosmetic. If the danger is a rogue machine, the response is to control the model: align it, cap it, build the kill switch, perhaps own the weights outright. If the danger is a rogue human holding the machine, the response is to control access and the chokepoints: who can point the tool, at what, and through which supplier of compute or synthetic DNA. Misdirected control is not a smaller problem than the one we are bracing for. It is the same problem aimed at the wrong target.

By the same token, AI-enhanced operators with good intentions are now equipped to do things that were not possible before. As an investor and an optimist, I am focused on this last reality, but it is just as important not to underwrite paradise as a business plan. Many AI-skeptics believe in the technology and doubt the valuation. It is possible that OpenAI, Anthropic, and the rest build genuinely transformational technology without becoming enduring businesses. The dot-com build-out was real, most of the companies that ran it did not survive it, and the fiber they laid is still in the ground. Transformational and enduring are different words.

If we eschew the hyperbole, AI is a transformative technology. Period. Through that prism you can see where value actually accrues, and the dark fiber is the warning. The rails get overbuilt, so owning raw capacity is not the same as capturing value. What lasts is the scarce layer: the work of improving the models, the verification that makes heterogeneous capacity trustworthy, and the operators who wield it all best. The boom always lays down too much supply and too little of that.

We are surrounded by narratives that jump from capability to civilizational destiny in a single bound. Life imitates art, but we have been bracing for the wrong genre. The blockbuster ending is the part that needs the suspension of disbelief. The story actually unfolding is the docudrama: the people this technology makes powerful, the people it makes dangerous, and the fight, already starting, over which is which.

Welcome to another edition of Robot Wave, where I explore trends in AI.

This has been a big week for AI. Not only is 2025 shaping up to be the year of the agents, but it may also be the year that we Make AI Cheap Again (MACHA)?

Late last year a Chinese company Deepseek released a new model which claimed to use significantly less compute to train than similar models from OpenAI and Anthropic. The latest release on 20th Jan 2025 claims to have similar results to ChatGPT o1 models, again with significantly smaller compute for training. So what’s going on?

Let’s first rewind a bit. I first introduced the concept of MACHA a year ago. What struck me at the time was how the entire AI industry was moving in one very similar direction - just add more compute, more data and of course more $ to the same architecture. The concept of innovation on the model (an area I was trained in through my research in the 90s) was mostly ignored in favor of more compute and more data (as an side note, my PhD was focused on scaling neural network training using Mixtures of Experts). The concept of “scaling law”, where to get better performace, more data and more weights / compute are added to a system has resulted in fantastic performance such as OpenAI and Anthropic but also fantastical investments ($1bn seed round for Ilya’s SSI company, XAI 100,000 H100 cluster etc).

“The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation. These two need not run counter to each other, but in practice they tend to. Time spent on one is time not spent on the other. There are psychological commitments to investment in one approach or the other. And the human-knowledge approach tends to complicate methods in ways that make them less suited to taking advantage of general methods leveraging computation. There were many examples of AI researchers' belated learning of this bitter lesson, and it is instructive to review some of the most prominent.”

Rich Sutton

The entire enterprise seems akin to a modern data AI Tower of Babel - a literal race to the top to meet God (AGI).

““Come, let us build ourselves a city and a tower with its top in the heavens, and let us make a name for ourselves; otherwise we shall be scattered abroad upon the face of the whole earth." The Lord came down to see the city and the tower, which mortals had built. And the Lord said, "Look, they are one people, and they have all one language, and this is only the beginning of what they will do; nothing that they propose to do will now be impossible for them.”

— Genesis 11:1–9

Commodity hardware and software eats the world

Back in the year 2000, after finishing 3 years of consulting work at NASA Ames and exploring dotcom ideas, I joined a small company called Infrasearch which was started by some of the early developers of Gnutella, an open source file sharing tool. Infrasearch developed search technology based on the ideas of Gnutella and new XMLRPC technologies. We tried to raised venture funding and even though our seed investors included Marc Andreessen, we ultimately were acquired by Sun Microsystems in 2001.

At Sun I initially ran the P2P research team, JXTA and then started a new project focused on archival storage using clusters of commodity AMD servers and commodity hard drives connected using self repairing and managing software.

What’s the connection to today you may ask? Well at the time Sun made the majority of its money from selling large expensive servers running closed source operating system software. Sun made most of its money from a small number of customers and had been extremely successful in the dotcom boom along with Oracle and EMC. During the dotcom boom I had been working on open source web and operating systems such as linux, mysql and php running on commodity servers. These approaches were often eschewed by investors and the startup MBA tourists who ran many of the dotcoms. In the end the scrappy commodity hardware and software designs were the winners. The impetus for the change, however, was a financial shock in the form of the dotcom bubble bursting. This moment led IT managers to question the cost of the Sun and other machines and instead try machines from Dell or other players using Linux and the open source web stacks. A similar event occurred after the 2008 GFC leading to a move to cloud computing away from self hosted infrastructure.

Deepseek

I believe a similar event is happening now. Deepseek has blown open the door to a Pandora’s box of open source AI technologies which was first opened by the “leak” by Meta of Llama, the open source LLM in 2023.

Our investment in Vast tracked this change last year. Vast provides mostly consumer grade Nvidia chips in a GPU rental market. Vast is growing rapidly and while most applications are focused on inference, new technologies such as Deepseek and also distributed training systems such as Nous Research, PrimeIntellect (Nazare portco) and Gensyn may end up disrupting the current training paradigm. This well written piece presents the hardware and software case against Nvidia (more), whilst Musk and others are questioning whether Deepseek was really trained using so few GPUs. The code is open source and some researchers are already reproducing the results.

Deepseek combines 2 things we are already familiar with (see this review also)

• Mixtures of Experts (MOE) architectures in which sections of the billions of weights are turned on and off during training and inference for efficiency,

• Quantization: using KV cache compression to make the training and inference more efficient.

The final innovation is to combine these first 2 optimizations with the Chain Of Thought reasoning of OpenAI o1 and a Reinforcement Learning approach. This final optimization seems to be the technique that results in the reported dramatic ($5.6m) reduction in cost of training (compare this to the 100k H100s of XAI).

Naturally people are skeptical, and many researchers are attempting to reproduce the results. Some people are suggesting its a Chinese psyop and China has possession of H100s, thus violating the US sanctions.

I am in the camp that this is a real innovation not a fake one. However it plays out, the genie is out of the bottle that the perception is that we can do more with less - we can train and execute large models using less hardware, potentially commodity hardware and without the incredible high cost of investment in hardware infrastructure.

What’s next?

Do the massive clusters of Nvidia’s most powerful chips now become irrelevant? Not necessarily. Even as the LLM market changes dramatically with less reliance on compute, the quests for ASI and AGI – humanity’s Babylonian pursuit of finding “God” – will likely still be dominated by throwing maximal resources at the challenge. Over time, however, I expect that software improvements will keep strong moats from developing in these areas just as we’re beginning to see happen with LLMs. This is now a brave new world. What was taken for granted is being questioned, prior assumptions are being challenged, and new possibilities abound.

Till next time,

Steven

Dr. Steven Waterhouse

Founder and General Partner,

Nazare Ventures

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