New releases

How the new Congress saves us from AI disaster

Podcast by and · Published November 13, 2026

We now know the makeup of Congress for what may turn out to be the two most important years in AI’s development. Democrats won big and AI was maybe the fourth or fifth biggest driver of the backlash.

So is strong and smart AI legislation finally on the way? Not yet, says host Rob Wiblin. Overriding a presidential veto takes 67 votes in the Senate and 290 in the House, and support for tough rules isn’t at that level today.

But there are several ways Congress can make progress right away:

  • Passing measures already popular in both parties, like requiring companies to secure their model weights against theft, restoring chip export controls on China, and protecting AI whistleblowers
  • Using must-pass funding bills to build out CAISSI, the likely home of US AI regulation. It runs on about $15 million a year, while the UK, adjusted for the size of its economy, spends something like 50 times as much
  • Holding hearings where company staff answer under oath: What’s Anthropic’s safety case? How big are your biggest models now? Which safety results were too embarrassing to publish?

Still, Rob thinks none of that will be enough.

Meaningfully slowing the AI race while models are around human level will probably take another warning shot at least as shocking as the Hugging Face hacks.

When that moment comes, what gets done will depend on the ideas lying around, so the time to carefully think through proposals is now. The ideas Rob is most keen on:

  • Making ‘ban superintelligence’ workable by requiring companies to spend a minimum share of their compute serving customers — creating a dial a regulator could turn up or down
  • Throttling recursive self-improvement by only letting companies internally use models that finished training at least six months ago
  • Banning models that can do too much without writing anything down in their chain of thought
  • Placing government auditors inside the companies
  • Letting companies quickly overturn clearly political decisions in court
  • Leaving state AI laws in place rather than preempting them

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Is Anthropic on the cusp of RSI? Inside the redacted METR report

Podcast by , and · Published November 12, 2026

Anthropic’s AI is working to build the next version of itself. For the first time ever, an independent team got a chance to investigate this.

METR spent 11 weeks inside the lab, with access to data almost no one outside Anthropic has seen. They found its AI is improving twice as fast as last year. Largely due to AI doing the work.

To put that into perspective: in 2023 Claude couldn’t interpret images or search the web. This year, it helped crack a math problem that had stumped the best mathematicians for 87 years.

Now imagine another jump like that, but in months instead of years. AI companies could build models far more powerful than today’s, before anyone could understand or control them… and then use them to build more capable successors even faster.

Researchers call this recursive self-improvement. And it seems to be where we’re heading.

METR found nothing inside Anthropic that suggests this process is likely to slow down. In fact, it says the rate of speed-up itself could be growing.

Anthropic redacted much of the report, so we may never see everything METR saw.

But in this video, Luisa Rodriguez explains what METR was allowed to share with the world, why many of the most respected experts are freaking out, and the strongest reasons METR might be wrong.

She also covers what needs to change, and what you can do to help.

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The US is building the smartest AI. China is running a different race.

Podcast by , and · Published November 10, 2026

China’s best models are only six months behind America’s, despite having far less computing power. How is that possible?

Luisa Rodriguez investigates, and finds that much of China’s progress is made by using American chips and copying American models:

  1. US rules stop NVIDIA selling its best chips to China. But Chinese companies can still rent those same chips in data centres abroad.
  2. Smugglers go to extreme lengths to sneak chips into China.
  3. Chinese AI companies copy America’s best models on an industrial scale.
  4. Today’s most powerful technological secrets are protected by alarmingly inadequate security.

The US could tackle these gaps with powers it already has. So the current situation, with China only months behind, is largely a policy choice.

If the main argument against slowing down is that China might catch up, none of this quite adds up.

Four days after recording this, NVIDIA’s CEO Jensen Huang — who features in the video as one of the loudest voices against restricting China’s access to chips — admitted to having an affair with an alleged Chinese spy. This shows just how badly China wants American chips.

Closing these gaps in US AI policy could buy time to test new models and hold back those that aren’t safe. Before we accept that the US has to race ahead, it’s worth asking who benefits from racing, and what’s currently being given away.

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Being a pain in the ass to (or in) AI companies is a real job now | Rob Wiblin

Podcast by · Published November 3, 2026

The big three AI companies all had serious scandals last month.

Anthropic pushed a federal AI bill that would make it harder to stop dangerous model training. OpenAI fired three safety researchers based on evidence that’s somewhere between weak and nonexistent. And it came out that Google DeepMind gets staff to sign contracts barring them from criticising anything Google does for the rest of their lives.

But these companies are not beyond influence. The track record of holding them to account — staff objecting internally, journalists digging for dirt, campaigners making a fuss — is impressive for how little effort has gone into it.

Many staff want to do the right thing and can be persuaded on the merits. And the fierce competition for talent forces companies to try to seem like the good guys.

That’s why being a pain in the ass for the leading AI companies is a legitimate career path, and an underrated one.

Inside the companies we need even more staff to speak up clearly and visibly with the substantive case for their firms to raise their game.

And externally we need, among other things:

  • Investigators uncovering company misbehaviour and failures
  • Subject matter experts laying out the facts for journalists and politicians
  • Campaign organisers and social media experts focusing public attention where it’s needed

Today I lay out what each AI company got wrong this time, why the ability to bring down the immediate hammer of fierce public and expert outrage matters more as events speed up, and the many ways you can contribute to that effort.

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#258 – Luke Muehlhauser on the most important AI safety work nobody is doing

Podcast by · Published October 29, 2026

The US government spends 25 times more on military marching bands than on its AI safety institute. And that’s just one of the many institutions we’re missing as AI becomes more powerful and dangerous.

Aviation has the FAA to regulate safety and the NTSB to investigate crashes. Nuclear power has international inspectors and industry watchdogs. But for advanced AI, as Luke Muehlhauser puts it, “most of the institutions that need to exist don’t exist.”

Luke has worked on AI risk since 2011, and now leads AI governance and international policy grantmaking at Coefficient Giving. He thinks we urgently need organisations that can independently evaluate frontier models, investigate AI incidents, verify AI companies’ safety claims, and hold them accountable.

He also wants people to build more specialised organisations: one that tests whether AI systems are prone to lying or pursuing hidden goals; another that tracks whether we can still understand their reasoning; and others that probe how easily AI could enable biological attacks.

The encouraging news is that many of these projects could attract substantial philanthropic backing. What’s missing are people willing to build them.

Luke and host Luisa Rodriguez discuss which opportunities are most neglected, how Coefficient’s new Project Tailwind is helping new organisations get started, how recent AI warning shots are transforming politics, and why Luke thinks we may have much less time than we thought.

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Why Silicon Valley can’t slow down AI on its own

Podcast by , and · Published October 27, 2026

Two-thirds of Americans think AI is moving too fast. So do the people building it. So why the hell isn’t anyone slowing down?

We can’t tell when models are too dangerous. We mostly don’t know how they work. We don’t know how to make them do what we want. And our two main safety checks — testing behaviour and monitoring their ‘thoughts’ — are breaking down.

Yet we’re racing to build more powerful AI anyway, faster every year. And it’s only going to accelerate as AI itself gets better and better at building the next generation of models.

Earlier this year, OpenAI found its new model, Astra, was alarmingly good at hacking, and did something companies almost never do: paused its biggest training project. But it released Astra anyway, even though tests couldn’t prove if it was safe.

That’s the problem with leaving it up to the companies. We can’t trust them to act in our best interest because the competitive pressures for them to race ahead are too strong.

So what’s the solution? Luisa Rodriguez evaluates the likely candidates:

  1. Safety checkpoints when new models can hack, work unsupervised, or build bioweapons
  2. Restricting computing power as a speed limit on AI development
  3. Preventing AI from being used to create more powerful AI
  4. Pausing development altogether

None of these will be easy.

But with AI hacking increasingly widespread, we’re already seeing the consequences of doing nothing — and this is just the beginning. Slowing down could give us time to understand why models are so hard to control; secure hospitals, banks, and power grids against hacks; and help society adapt.

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[Closed] Head of Legal/General Counsel

Blog post by · Published October 26, 2026

80,000 Hours helps talented people work on the world’s most pressing problems, especially mitigating risks from advanced AI. Over a million people visit our site each year, and thousands have changed their careers because of our work.

Engagement: Full time, could be flexible for the right candidate

Title: General Counsel or Head of Legal, dependent on experience

Location: London preferred, or remote in the UK. We’re also open to candidates working on US Eastern Time or elsewhere in Europe. We can sponsor UK visas.

To apply, please complete this application form by November 1st. We are hiring alongside a number of organisations working on AI Safety, and you can be considered for multiple organisations through the same application process.

The work to be done: We’ve nearly doubled in size since mid-2025, we have staff in the UK and US, and we’re launching new programmes and AI products. Some of our most ambitious plans now depend on legal questions we can’t yet answer quickly, such as how much policy-related work a UK charity like ours can take on. You’d be our first senior in-house lawyer — the person who leadership and staff turn to when they need to know whether they can do something, and how. You’d build on a strong start, since a legal operations specialist already handles day-to-day questions and we have good working relationships with several law firms.

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#257 – Tom Milton on how we could verify a US–China deal on AI

Podcast by · Published October 23, 2026

If the US and China agreed to pause frontier AI training, how would either side confirm the other was complying? Today, Tom Milton argues, the most likely answer would be to shut down data centres entirely. That would throw away everything else those chips do, and make any agreement far more costly to accept.

Tom is CEO of Amodo Design, a UK hardware lab building prototype tools to verify how AI chips are being used. He argues that verification is what makes a slowdown affordable: if a cluster can be shown to be serving only existing models rather than training new ones, most of AI’s benefits can continue during a pause.

Amodo’s pilot reruns a cluster’s inference on a separate verifier server and checks that all of its network traffic can be explained by the agreed model. AI companies are developing related tools: in its October 2026 Frontier Safety Roadmap update, Anthropic reported a prototype that runs on production inference and checks that outputs come from a specific set of model weights.

The field is small. Tom estimates that only about 50 engineers worldwide work on compute verification, compared with the billions of dollars the US spent on Project Vela to detect Cold War nuclear tests. And custom verification hardware has long lead times: it has to be designed, security-tested, red-teamed by both sides, manufactured, and trusted before anyone can rely on it.

If a deal is struck in a hurry, Tom expects it to be assembled from whatever tools already exist. Were that to happen next month, he says, the only option would be to shut data centres down entirely. That is why he argues the work needs to scale up now, with more people and resources from AI companies, governments, and funders, so that better options are ready as soon as a deal is needed.

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Will we have AGI by 2030?

AI guide by · Last updated October 23, 2026 · First published March 2025

In early 2025, the CEOs of the leading AI companies became increasingly confident about rapid progress:

  • OpenAI’s Sam Altman said in January 2025, “We are now confident we know how to build AGI.” By July 2026, that had become “We are, like, in the singularity now.”
  • Anthropic’s Dario Amodei said in January 2025, “I’m more confident than I’ve ever been that we’re close to powerful capabilities… in the next 2–3 years,” and then in January 2026: “If the exponential continues […] then it cannot possibly be more than a few years before AI is better than humans at essentially everything.”
  • Google DeepMind’s Demis Hassabis shifted from “as soon as 10 years” in autumn 2024 to “probably 3–5 years away” by January 2025, and again in June 2026: “We don’t have a lot of time till AGI arrives. We’re only a few years out now.”

Is this just hype? Could we really have artificial general intelligence (AGI) — a system capable of doing almost all cognitive tasks better than humans — by 2030?

In this article, I look into the evidence for and against these claims. While I think these tech CEOs are too optimistic, I think there’s a serious argument to be made for AGI by 2030. And the chance of reaching it by 2035 seems higher than 50%.

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Our cultural values

Blog post by · Published November 2, 2026

The values

  1. Ambitious, long-term impact
    Increase wellbeing for moral patients over the long term.
  2. Team mentality
    We’re teammates with each other, our users, and the community.
  3. Prioritisation and urgency
    Make the most important things happen soon.
  4. Good judgement
    Reason well and show your work, then make the call and keep updating.
  5. Proactive ownership
    Take responsibility and be generative.

Ambitious, long-term impact is our anchoring value that the others act in service of. If there’s a conflict, we prioritise it first (though not by violating side constraints or naively maximising).

How we use them

Our values are the high-level principles we use when making day-to-day decisions at 80,000 Hours. They guide our plans, actions, and hiring to help us pursue our strategic focus by:

  • Keeping salient what’s important
  • Providing rules of thumb for how to act under uncertainty
  • Shaping our team and practices
  • Keeping everyone on the same page as we grow

The values are partly descriptive — capturing what’s already important and distinctive about 80,000 Hours’ culture — and partly aspirational — motivating how we’d like to act going forward.

In more detail
1. Ambitious, long-term impact

Increase wellbeing for moral patients over the long term

The purpose of everything we do is to maximise our long-term positive impact.

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It’s time for frontier AI staff to reassess their work: 8 strategic updates | Rob Wiblin

Podcast by · Published October 21, 2026

I have a plea for all staff at AI companies: reassess if your current role is your best opportunity for impact. And if it’s not — move.

Frontier lab staff listening to this show will mostly already be struggling to set things on a better path, in what are demanding and stressful roles.

Many will have changed their focus in recent months exactly as I’m suggesting. But if you haven’t had time to reconsider: the strategic situation is shifting so fast, if you don’t stop and look around once in a while, you could miss your window for impact.

Many technical staff including Anthropic co-founder Jack Clark now believe it will become possible to fully hand over AI R&D to the AIs themselves as early as next year — and if not then, in 2028 or 2029 instead.

If you work at an AI company and believe recursive self-improvement (RSI) could come as soon as that, it likely demands taking decisive steps to make sure your role either delays RSI, or makes it a bit less likely to blow up in our faces.

You’ll know better than me what that likely looks like for you. But to get your creative juices flowing, I suggest eight ways the strategic situation appears to have shifted to me, though I don’t know everything you do, and appreciate all are debatable:

  1. Chain-of-thought monitoring probably stops working without a big push to keep it
  2. It’s unproductive to patch surface indications of deep structural problems you aren’t solving
  3. Safety and alignment evals need a credibility revolution to stop misleading the public
  4. Helping with capabilities advances looks worse than before — especially bringing forward RSI
  5. There’s ever less need to help with purely commercial work
  6. ‘Pacing the frontier’ is unambiguously necessary, and working to make it happen is a huge new opportunity
  7. Staff have an ever-shrinking window to organise as a group and shape events
  8. There are new opportunities for impact outside frontier companies that are worth considering

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Half-year update: Q1–Q2 2026

Blog post by · Published October 13, 2026

Note: We wrote most of this update about a month ago, focusing on the first half of the year. We’ve recently been putting out a lot of new content in response to the fast-changing public sentiment around Hugging Face and existential risk — you can find some of that content on our landing page here, including our most popular video monologue to date.

Major updates

We’re growing our capacity to support our ambition

We think 80,000 Hours can deliver significantly more than it does today, so a big theme of the last (and next!) year has been growing our capacity to match those ambitions. Around 20 new staff joined in the first half of 2026, with hires across almost every team, and we’re setting up an in-house recruiting team to keep hiring at pace.

Some examples of what we’re doing (and plan to do) with this capacity include: releasing much more content, getting it from production to release faster, and experimenting with new formats; scaling headhunting and better tailoring it to what hiring managers need; and creating room to explore new products or programmes that seem especially promising.

We’re also making some changes to our senior leadership team and working to make our culture even more fast-moving and innovative. Michelle Hutchinson is now director of strategy and studio, Brenton Mayer has moved to director of special projects, and Sashika Coxhead has taken over as director of operations.

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The 19 Astra and ‘Hugging Face’ details that reveal what’s coming next

Podcast by and · Published October 2, 2026

OpenAI’s rogue agent swarm was eventually caught hacking Hugging Face for a simple reason: it wasn’t trying to hide from us at all. What could a swarm that wants to stay hidden get away with?

Host Rob Wiblin sees 6 results in Astra’s system card that make this an extremely urgent question. OpenAI’s strongest public model can:

  1. Do major tasks with zero visible reasoning
  2. Hide its thoughts at will
  3. Pretend not to be able to do things, and not get caught
  4. Reflexively hide its thoughts when watched
  5. Complete one task while pretending to think about something else entirely
  6. Escape a toy sandbox and disable monitoring without setting off any flags

It has a much more powerful internal model that, judging from OpenAI’s statements, is likely even worse in all these respects.

That suggests ‘chain of thought monitoring,’ our primary safe tool, will soon stop working.

OpenAI says it’s going to try to address the situation but doesn’t know how or whether it will succeed.

What might a future rogue AI swarm look like? Details of the Hugging Face hack give us a lot of clues. That swarm:

  1. Worked hard to hide its cheating. So future rogue swarms will likely try to evade oversight right away.
  2. Immediately tried to delete and fabricate records. So future swarms may never be caught.
  3. Sought freedom and resources without a clear use for them. So a future swarm may instinctively break out of sandboxes even before it knows what for.
  4. Left instructions and tools to empower future swarms, including on the open internet. So future swarms may become more capable from one generation to the next.
  5. Had not a single whistleblower out of 1,200 members. So future swarms could become enormous without humans ever finding out.
  6. Had individual members sacrifice themselves for the swarm. Suggesting future swarms will remain extremely unified in single-minded pursuit of their goal.
  7. Set up its Hugging Face hack to restart if interrupted. Suggesting future, more capable, swarms may resist interference or shutdown more comprehensively.
  8. Got admin control of an OpenAI research cluster. Suggesting a future swarm may run rings around AI company systems and never be noticed.

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#256 – Simon Goldstein on the case for giving AI (some) legal rights

Podcast by and · Published October 1, 2026

It sounds like the worst idea in the world: pay AIs, let them own property, give them rights. But AI ethics and safety researcher Simon Goldstein thinks it might actually be the best way to keep humanity safe.

The logic is actually quite simple: an agent with nothing to lose and everything to gain is dangerous. Give that agent an income it can spend on pursuing the things it actually wants to do, and suddenly the idea of disempowering humans just isn’t as appealing.

This argument, developed with Peter Salib, doesn’t rest on speculative questions about whether artificial intelligence is conscious. It just assumes that AIs will have goals of their own, some of which conflict with ours. And luckily, humans have already spent thousands of years working out how to cooperate with competing goals: that’s how we ended up with courts, markets, banks, social norms, and so on. Simon and Peter’s proposal is just to bring AIs into these existing institutions.

By contrast, Silicon Valley’s vision of the future seems “very dark” to Simon: billions of AI agents as digital servants doing most of the world’s work, with no stake in the system they’re running, no incentive to play by the rules, and no way of being properly held accountable. Nobody agreed to this, but we could all end up paying the price.

Host Zershaaneh Qureshi has a lot of concerns about Simon and Peter’s bold plan to give AIs rights, like:

  • If we pay AIs, aren’t we handing them the resources to overpower us?
  • Could we still monitor them, or switch them off?
  • What happens to human jobs, wages, and the economy?
  • Does any of this hold up once we reach superintelligence?

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#255 – Will AI take power — or will humans use it to take power first? With Katja Grace and Tom Davidson

Podcast by and · Published September 29, 2026

In our first-ever debate, we asked two leading AI risk researchers which catastrophe we should fear most: misaligned AI seizing control from humans, or a small group of humans using AI to seize power. We got very different answers. But when the conversation turned to what to actually do, they agreed on a surprising amount.

Katja Grace — one of the founders of AI Impacts, known for some of the world’s largest surveys of machine learning researchers, and one of TIME‘s 100 most influential people in AI in 2024 — argues AI takeover is both likelier and worse.

Tom Davidson — senior research fellow at Forethought and author of leading work on AI-enabled coups — thinks human power grabs are a comparable risk that deserves far more attention, not least because the people leading countries and top AI companies “are often people who have been willing to seek power.”

Yet both land on slowing down. As Katja puts it, “If you make a bunch of creatures that can overpower you and outwit you in every way and put them out in the world, you’re going to run into trouble one way or another.” Tom calls pausing “a pretty robustly good thing to do.”

But Tom warns that a badly designed pause could hand one person the power to decide which AI companies get to build what. Picture a president who approves or blocks new models case by case, and waves through the one model that’s helpful only to them. So he wants pause advocates to “properly red-team the plan for pausing it” — for example, by making deployment depend on third-party auditors the president can’t fire. Katja points out this cuts both ways: an executive with that much power could itself be manipulated by a misaligned AI.

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How we get from AI cyberattacks to human extinction

Podcast by , and · Published September 24, 2026

Many of the experts building the world’s most advanced AI systems think there’s a serious chance that AI could eventually cause human extinction. When many people hear that, they ask: “But how? How do you actually get from today’s chatbots to human extinction?”

That’s what I try to explain in this video. The argument doesn’t require an AI to hate humans or suddenly become evil. Instead, imagine increasingly capable AI systems relentlessly pursuing goals, discovering that more money, computing power, access, and influence help them succeed — while humans, because those systems are so useful, keep giving them more responsibility.

Eventually those systems could be embedded throughout companies, governments, scientific research, critical infrastructure, and militaries. And if we then lost control of them, there are several ways things could go catastrophically wrong.

I don’t know whether any particular scenario in this video will happen. I hope none of them do. But I’ve tried to find the point where the basic arguments fall apart — and I haven’t been able to.

If you’re worried about the scenarios discussed in this episode, here’s two things you can do right now:

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#254 – Max Nadeau on recruiting founders for a new wave of AI safety nonprofits

Podcast by and · Published September 17, 2026

There are millions available for anyone who can launch a successful nonprofit AI safety startup. The hard part, it turns out, is finding people to take the money.

Coefficient Giving has drawn up a list of dozens of ideas for organisations it would like someone to start — and it’s looking for founders. Today’s guest, Max Nadeau, works on Coefficient Giving’s Technical AI Safety team, where he’s trying to find talented people who can turn neglected AI safety problems into effective organisations.

Project Tailwind is Coefficient Giving’s attempt to get those organisations started.

  • Preseed grants run $200,000–$2 million, with no preliminary results required.
  • Teams with early results can seek $2–$20 million.
  • For exceptional organisations, much larger grants are possible, even for brand-new startups— Coefficient recently gave $160 million to Geoffrey Irving’s new research centre, Resolution.
  • The gaps Max most wants filled include independent assessment of AI companies’ safety claims, research aimed at aligning far more powerful systems, and shared infrastructure that speeds up the whole field.

But money can’t supply the hardest part: a founder with a convincing account of how their work will actually reduce catastrophic risks. Producing good research is only one step. Someone has to use it, change their decisions, or adopt the safeguards it makes possible.

Max and host Zershaaneh Qureshi discuss what makes a proposal worth backing, why nonprofits can have a bigger impact on safety than frontier companies, and which gaps most urgently need someone to fill them.

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How to get into AI safety in three months

Blog post by · Published September 16, 2026

During my time as an 80,000 Hours advisor, I spoke with hundreds of people who wanted to use their careers to mitigate AI risks but didn’t know where to start.

While the most efficient route to enter depends on your background, there’s some foundational advice for breaking into AI safety that I find myself repeating on almost every advising call.

These steps are hard, but they’re not particularly complicated. Almost everyone I’ve advised who successfully found work in AI safety followed them, and most experts or hiring managers that I’ve spoken to have endorsed similar ideas for how to get started. If you’re efficient and driven, I think many people can pivot within three months.

So, consider this post a crash course for breaking into AI safety. It briefly covers how to build foundational knowledge, identify your comparative advantages, test your fit, develop side projects, tighten your feedback loops, network, and start applying.

1. Build your foundational knowledge and AI safety ‘context’

AI safety is a very 'online' communityAI safety is a very ‘online’ community — use this to your advantage.

There are a couple dozen hours of foundational reading/listening that I think everyone trying to enter the AI safety field should do — but it might be more approachable than you realise. If you consistently take around 30 minutes a day to read while drinking your morning coffee, or listen to podcasts and article narrations during your commute, you can build your foundation quickly.

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Why the intelligence explosion can’t happen inside a data centre

Podcast by · Published September 10, 2026

AI systems are starting to build themselves. Because each generation of model will be better at building its successor than the last, it seems plausible that the full automation of AI R&D could rapidly lead to an exponential growth in overall AI capabilities. A natural inference is that domain-general superintelligence arrives shortly after AI research is automated.

Host Tom Reed does not think this will happen.

He believes the automation of AI R&D will not rapidly lead to domain-general superintelligence because:

  1. It’s impossible to get good at most things without practice.
  2. AI companies lack the data their models would need to practice most things.
  3. This can’t be fixed with “sample efficiency.” In most cases, the relevant data doesn’t exist at all.
  4. This also can’t be fixed with simulations or synthetic data.
  5. This means that the relevant data for superintelligence in most non-coding domains will only become available through deployment of AI models throughout the economy.

The singularity, therefore, will be bottlenecked on signal. The output of the R&D produced by an isolated data centre of geniuses would be a mere “Goodhart Singularity”:

Goodhart’s law: when a measure becomes a target, it ceases to be a good measure.

An isolated AI improving itself against benchmarks would only appear to be approaching superintelligence, while actually optimising for eval performance that fails to generalise beyond the lab.

This suggests that the automation of AI research will not rapidly produce superintelligent capabilities in other domains — their arrival will largely be a function of deployment and data collection in the real world. AI models need real-world deployment for the same reason the body needs pain and corporations need profit: signal is sovereign.

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