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The most overlooked roles in AI safety

Blog post by Conor Barnes · Published January 28, 2026

We recently spoke to Ryan Kidd, Co-Executive Director at MATS, a programme that trains researchers in AI safety. He shared with us some important trends in the technical AI safety ecosystem — and his thoughts line up with what we’re hearing from other leaders in this space. If you’re wondering which roles are hard to fill and where you might be able to contribute, read on!

Research management seems to be a substantial bottleneck: These roles can be hard to fill, as they require some familiarity with AI safety research as well as strong interpersonal skills and management experience. Plus, impact-driven people who are interested in AI safety generally want to be researchers themselves — rather than manage the research of others! Crucially, you often don’t need to be a great researcher yourself to be a great research manager: people with experience as project managers, people managers, and executive coaches can all make for excellent research managers.
We lack executive talent: The technical AI safety field could really benefit from more people with backgrounds in strategy, management, and operations. If you have experience managing and growing a team of 30+ people, you could make a big difference at a top-tier AI safety organisation, even if you don’t have a lot of direct experience with AI.
We lack founders, field-builders, and communicators: There’s a lot of room to start new organisations and grow the ecosystem,

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#234 – David Duvenaud on why ‘aligned AI’ could still kill democracy

Podcast by Robert Wiblin · Published January 27, 2026

Democracy might be a brief historical blip. That’s the unsettling thesis of a recent paper, which argues AI that can do all the work a human can do inevitably leads to the “gradual disempowerment” of humanity.

For most of history, ordinary people had almost no control over their governments. Liberal democracy emerged only recently, and probably not coincidentally around the Industrial Revolution.

Today’s guest, David Duvenaud, used to lead the ‘alignment evals’ team at Anthropic, is a professor of computer science at the University of Toronto, and recently coauthored the paper “Gradual disempowerment.”

He argues democracy wasn’t the result of moral enlightenment — it was competitive pressure. Nations that educated their citizens and gave them political power built better armies and more productive economies. But what happens when AI can do all the producing — and all the fighting?

“The reason that states have been treating us so well in the West, at least for the last 200 or 300 years, is because they’ve needed us,” David explains. “Life can only get so bad when you’re needed. That’s the key thing that’s going to change.”

In David’s telling, once AI can do everything humans can do but cheaper, citizens become a national liability rather than an asset. With no way to make an economic contribution, their only lever becomes activism — demanding a larger share of redistribution from AI production. Faced with millions of unemployed citizens turned full-time activists, democratic governments trying to retain some “legacy” human rights may find they’re at a disadvantage compared to governments that strategically restrict civil liberties.

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#233 – James Smith on why he quit everything to work on a biothreat nobody had heard of

Podcast by Luisa Rodriguez · Published January 13, 2026

When James Smith first heard about mirror bacteria, he was sceptical. But within two weeks, he’d dropped everything to work on it full time, considering it the worst biothreat that he’d seen described. What convinced him?

Mirror bacteria would be constructed entirely from molecules that are the mirror images of their naturally occurring counterparts. This seemingly trivial difference creates a fundamental break in the tree of life. For billions of years, the mechanisms underlying immune systems and keeping natural populations of microorganisms in check have evolved to recognise threats by their molecular shape — like a hand fitting into a matching glove.

Mirror bacteria would upend that assumption, creating two enormous problems:

Many critical immune pathways would likely fail to activate, creating risks of fatal infection across many species.
Mirror bacteria could have substantial resistance to natural predators: for example, they would be essentially immune to the viruses that currently keep bacteria populations in check. That could help them spread and become irreversibly entrenched across diverse ecosystems.

Unlike ordinary pathogens, which are typically species-specific, mirror bacteria’s reversed molecular structure means they could potentially infect humans, livestock, wildlife, and plants simultaneously. The same fundamental problem — reversed molecular structure breaking immune recognition — could affect most immune systems across the tree of life. People, animals, and plants could be infected from any contaminated soil, dust, or species.

The discovery of these risks came as a surprise. The December 2024 Science paper that brought international attention to mirror life was coauthored by 38 leading scientists, including two Nobel Prize winners and several who had previously wanted to create mirror organisms.

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2025 Highlight-o-thon: Oops! All Bests

Podcast by The 80,000 Hours podcast team · Published December 29, 2025

It’s that magical time of year once again — highlightapalooza! Stick around for one top bit from each episode:

Helen Toner on whether we’re racing China to build AGI (from episode #227)
Hugh White on what he’d say to Americans (#218)
Buck Shlegeris on convincing AI models they’ve already escaped (#214)
Paul Scharre on a personal experience in Afghanistan that influenced his views on autonomous weapons (#231)
Ian Dunt on how unelected septuagenarians are the heroes of UK governance (#216)
Beth Barnes on AI companies being locally reasonable, but globally reckless (#217)
Tyler Whitmer on one thing the California and Delaware attorneys general forced on the OpenAI for-profit as part of their restructure (November update)
Toby Ord on whether rich people will get access to AGI first (#219)
Andrew Snyder-Beattie on how the worst biorisks are defence dominant (#224)
Eileen Yam on the most eye-watering gaps in opinions about AI between experts and the US public (#228)
Will MacAskill on what a century of history crammed into a decade might feel like (#213)
Kyle Fish on what happens when two instances of Claude are left to interact with each other (#221)
Sam Bowman on where the Not In My Back Yard movement actually has a point (#211)
Neel Nanda on how mechanistic interpretability is trying to be the biology of AI (#222)
Tom Davidson on the potential to install secret AI loyalties at a very early stage (#215)
Luisa and Rob discussing how medicine doesn’t take the health burden of pregnancy seriously enough (November team chat)
Marius Hobbhahn on why scheming is a very natural path for AI models — and people (#229)
Holden Karnofsky on lessons for AI regulation drawn from successful farm animal welfare advocacy (#226)
Allan Dafoe on how AGI is an inescapable idea but one we have to define well (#212)
Ryan Greenblatt on the most likely ways for AI to take over (#220)
Updates Daniel Kokotajlo has made to his forecasts since writing and publishing the AI 2027 scenario (#225)
Dean Ball on why regulation invites path dependency, and that’s a major problem (#230)

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#232 – Andreas Mogensen on what we owe ‘philosophical Vulcans’ and unconscious AIs

Podcast by Zershaaneh Qureshi and Robert Wiblin · Published December 19, 2025

Most debates about the moral status of AI systems circle the same question: is there something that it feels like to be them? But what if that’s the wrong question to ask?

Andreas Mogensen — a senior researcher in moral philosophy at the University of Oxford — argues that so-called ‘phenomenal consciousness’ might be neither necessary nor sufficient for a being to deserve moral consideration.

For instance, a creature on the sea floor that experiences nothing but faint brightness from the sun might have no moral claim on us, despite being conscious.

Meanwhile, any being with real desires that can be fulfilled or not fulfilled can arguably be benefited or harmed. Such beings arguably have a capacity for welfare, which means they might matter morally.

And, Andreas argues, desire may not require subjective experience. Desire may need to be backed by positive or negative emotions — but as Andreas explains, there are some reasons to think a being could also have emotions without being conscious.

There’s another underexplored route to moral patienthood: autonomy. If a being can rationally reflect on its goals and direct its own existence, we might have a moral duty to avoid interfering with its choices — even if it has no capacity for welfare.

However, Andreas suspects genuine autonomy might require consciousness after all. To be a rational agent, your beliefs probably need to be justified by something, and conscious experience might be what does the justifying. But even this isn’t clear.

The upshot? There’s a chance we could just be really mistaken about what it would take for an AI to matter morally. And with AI systems potentially proliferating at massive scale, getting this wrong could be among the largest moral errors in history.

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#231 – Paul Scharre on how AI could transform the nature of war

Podcast by Luisa Rodriguez · Published December 17, 2025

In 1983, Stanislav Petrov, a Soviet lieutenant colonel, sat in a bunker watching a red screen flash “MISSILE LAUNCH.” The system told him the United States had fired five nuclear weapons at the Soviet Union. Protocol demanded he report it to superiors, which would almost certainly trigger a retaliatory strike.

Petrov didn’t do it. He had a “funny feeling” in his gut. He reasoned that if the US were actually attacking, they wouldn’t just fire five missiles — they’d empty the silos. He bet the fate of the world on a hunch that the machine was broken. He was right.

Paul Scharre, the former Army Ranger who led the Pentagon team that wrote the US military’s first policy on autonomous weapons, asks a terrifying question: What would an AI have done in Petrov’s shoes?

Would an AI system have been flexible and wise enough to make the same judgement? Or would it have launched a counterattack?

Paul joins host Luisa Rodriguez to explain why we are hurtling toward a “battlefield singularity” — a tipping point where AI increasingly replaces humans in much of the military, changing the way war is fought with speed and complexity that outpaces humans’ ability to keep up.

Militaries don’t necessarily want to take humans out of the loop. But Paul argues that the competitive pressure of warfare creates a “use it or lose it” dynamic. As former Deputy Secretary of Defense Bob Work put it: “If our competitors go to Terminators, and their decisions are bad, but they’re faster, how would we respond?”

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#230 – Dean Ball on how AI is a huge deal — but we shouldn’t regulate it yet

Podcast by Robert Wiblin · Published December 10, 2025

Former White House staffer Dean Ball thinks it’s very likely some form of ‘superintelligence’ arrives in under 20 years. He thinks AI being used for bioweapon research is “a real threat model, obviously.” He worries about dangerous ‘power imbalances’ should AI companies reach “$50 trillion market caps.” And he believes the agriculture revolution probably worsened human health and wellbeing.

Given that, you might expect him to be pushing for AI regulation. Instead, he’s become one of the field’s most prominent and thoughtful regulation sceptics — recently co-authoring Trump’s AI Action Plan before moving on to the Foundation for American Innovation.

Dean argues that the wrong regulations, deployed too early, could freeze society into a brittle, suboptimal political and economic order. As he puts it, “my big concern is that we’ll lock ourselves in to some suboptimal dynamic and actually, in a Shakespearean fashion, bring about the world that we do not want.”

Dean’s fundamental concern is uncertainty: “We just don’t know enough yet about the shape of this technology, the ergonomics of it, the economics of it… You can’t govern the technology until you have a better sense of that.”

Premature regulation could lock us in to addressing the wrong problem (focusing on rogue AI when the real issue is power concentration), using the wrong tools (using compute thresholds to regulate models when we should regulate companies instead), through the wrong institutions (bodies captured by AI interests), all while making it harder to build the actual solutions we’ll need (like open source alternatives or legal mechanisms newly enabled by AI).

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#229 – Marius Hobbhahn on the race to solve AI scheming before models go superhuman

Podcast by Robert Wiblin · Published December 3, 2025

We often worry about AI models “hallucinating” or making honest mistakes. But what happens when a model knows the truth, but decides to deceive you anyway to achieve a goal of its own? This isn’t sci-fi — it’s happening regularly in deployment today.

Marius Hobbhahn, CEO of the world’s top research organisation focused on AI deception (Apollo Research), has been collaborating with OpenAI to figure out when OpenAI’s reasoning models like o3 are inclined to “scheme” against the interests of users.

In a striking case uncovered by Apollo, when many AI models were told they would have capabilities removed if they performed too well on a test, they successfully “sandbagged” — intentionally answering questions incorrectly to appear less capable than they were, while also being careful not to perform so poorly it would arouse suspicion.

These models had somehow developed a preference to preserve their own capabilities, despite never being trained in that goal or assigned a task that called for it.

This doesn’t cause significant risk now, but as AI models become more general, superhuman in more areas, and are given more decision-making power, it could become outright dangerous.

In today’s episode, Marius details his recent collaboration with OpenAI to train o3 to follow principles like “never lie,” even when placed in “high-pressure” situations where it would otherwise make sense.

The good news: They reduced “covert rule violations” (scheming) by about 97%.

The bad news: In the remaining 3% of cases, the models sometimes became more sophisticated — making up new principles to justify their lying, or realising they were in a test environment and deciding to play along until the coast was clear.

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Rob & Luisa chat kids, the fertility crash, and how the ‘50s invented parenting that makes us miserable

Podcast by Luisa Rodriguez and Robert Wiblin · Published November 25, 2025

Global fertility rates aren’t just falling: the rate of decline is accelerating. From 2006 to 2016, fertility dropped gradually, but since 2016 the rate of decline has increased 4.5-fold. In many wealthy countries, fertility is now below 1.5. While we don’t notice it yet, in time that will mean the population halves every 60 years.

Rob Wiblin is already a parent and Luisa Rodriguez is about to be, which prompted the two hosts of the show to get together to chat about all things parenting — including why it is that far fewer people want to join them raising kids than did in the past.

While “kids are too expensive” is the most common explanation, Rob argues that money can’t be the main driver of the change: richer people don’t have many more children now, and we see fertility rates crashing even in countries where people are getting much richer.

Instead, Rob points to a massive rise in the opportunity cost of time, increasing expectations parents have of themselves, and a global collapse in socialising and coupling up. In the EU, the rate of people aged 25–35 in relationships has dropped by 20% since 1990, which he thinks will “mechanically reduce the number of children.” The overall picture is a big shift in priorities: in the US in 1993, 61% of young people said parenting was an important part of a flourishing life for them, vs just 26% today.

That leads Rob and Luisa to discuss what they might do to make the burden of parenting more manageable and attractive to people, including themselves.

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#228 – Eileen Yam on how we’re completely out of touch with what the public thinks about AI

Podcast by Robert Wiblin · Published November 20, 2025

If you work in AI, you probably think it’s going to boost productivity, create wealth, advance science, and improve your life. If you’re a member of the American public, you probably strongly disagree.

In three major reports released over the last year, the Pew Research Center surveyed over 5,000 US adults and 1,000 AI experts. They found that the general public holds many beliefs about AI that are virtually nonexistent in Silicon Valley, and that the tech industry’s pitch about the likely benefits of their work has thus far failed to convince many people at all. AI is, in fact, a rare topic that mostly unites Americans — regardless of politics, race, age, or gender.

Today’s guest, Eileen Yam, director of science and society research at Pew, walks us through some of the eye-watering gaps in perception:

Jobs: 73% of AI experts see a positive impact on how people do their jobs. Only 23% of the public agrees.
Productivity: 74% of experts say AI is very likely to make humans more productive. Just 17% of the public agrees.
Personal benefit: 76% of experts expect AI to benefit them personally. Only 24% of the public expects the same (while 43% expect it to harm them).
Happiness: 22% of experts think AI is very likely to make humans happier, which is already surprisingly low — but a mere 6% of the public expects the same.

For the experts building these systems, the vision is one of human empowerment and efficiency. But outside the Silicon Valley bubble, the mood is more one of anxiety — not only about Terminator scenarios, but about AI denying their children “curiosity, problem-solving skills, critical thinking skills and creativity,” while they themselves are replaced and devalued:

53% of Americans say AI will worsen people’s ability to think creatively.
50% believe it will hurt our ability to form meaningful relationships.
38% think it will worsen our ability to solve problems.

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The US AI policy landscape: where to work to have the biggest impact

Article · Published November 2025

The US government may be the single most important actor for shaping how AI develops. If you want to improve the trajectory of AI and reduce catastrophic risks, you could have an outsized impact by working on US policy.

But the US policy ecosystem is huge and confusing. And policy shaping AI is made by specific people in specific places — so where you work matters enormously.

This guide aims to help you think about where specifically to work in US AI policy so you can actually make a large impact.

In Part 1, we cover five heuristics for finding the most impactful places to work. In Part 2, we cover five policy institutions that we’d guess are the most impactful for AI and name specific places in each that are especially promising.

Jump to our top recommended institutions

Part 1: How to find the most impactful places to work on AI policy

It’s hard to predict precisely when and where key AI policy decisions will happen, but you can position yourself for greater impact. The following five heuristics can help you judge where you could have the best shot at positively shaping the trajectory of advanced AI.

Prioritise building career capital

Early in your policy career, avoid tunnel vision on AI policy roles. Many entry-level positions worth considering won’t focus on AI. What usually matters more is building career capital — knowledge,

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11 essential readings on AI safety, risk, and alignment

Article by The 80,000 Hours team · Last updated November 2025 · First published May 2025

If you’re overwhelmed trying to understand how advanced AI could impact our lives, you’re not alone. There’s a lot of noise out there.

We’ve boiled it down to 11 essential resources for getting up to speed on the risks of AGI.

We’ve chosen these because they represent the most influential ideas shaping the current debate. Though we don’t agree with everything the authors say, we think they’re well worth reading.

AI risk reading list

Preparing for the Intelligence Explosion by William MacAskill and Fin Moorhouse
AI 2027 by Daniel Kokotajlo, Scott Alexander, Thomas Larsen, Eli Lifland, and Romeo Dean
Situational Awareness: The Decade Ahead by Leopold Aschenbrenner
The case for multi-decade AI timelines by Ege Erdil
The Most Important Century by Holden Karnofsky
Does AI Progress Have a Speed Limit? by Ajeya Cotra and Arvind Narayanan
Can AI scaling continue through 2030? by Jaime Sevilla et al.
Existential Risk from Power-Seeking AI by Joe Carlsmith
Gradual Disempowerment by Jan Kulveit, Raymond Douglas, Nora Ammann, Deger Turan, David Krueger, and David Duvenaud
Taking AI Welfare Seriously by Robert Long, Jeff Sebo, et al.
Machines of Loving Grace — How AI Could Transform the World for the Better by Dario Amodei

Summary of each resource
1.

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OpenAI: The nonprofit refuses to die (with Tyler Whitmer)

Podcast by Robert Wiblin · Published November 11, 2025

Last December, the OpenAI business put forward a plan to completely sideline its nonprofit board. But two state attorneys general have now blocked that effort and kept that board very much alive and kicking.

The for-profit’s trouble was that the entire operation was founded on the premise of — and legally pledged to — the purpose of ensuring that “artificial general intelligence benefits all of humanity.” So to get its restructure past regulators, the business entity has had to agree to 20 serious requirements designed to ensure it continues to serve that goal.

Attorney Tyler Whitmer, as part of his work with Legal Advocates for Safe Science and Technology, has been a vocal critic of OpenAI’s original restructure plan. In today’s conversation, he lays out all the changes and whether they will ultimately matter:

After months of public pressure and scrutiny from the attorneys general (AGs) of California and Delaware, the December proposal itself was sidelined — and what replaced it is far more complex and goes a fair way towards protecting the original mission:

The nonprofit’s charitable purpose — “ensure that artificial general intelligence benefits all of humanity” — now legally controls all safety and security decisions at the company. The four people appointed to the new Safety and Security Committee can block model releases worth tens of billions.
The AGs retain ongoing oversight, meeting quarterly with staff and requiring advance notice of any changes that might undermine their authority.
OpenAI’s original charter, including the remarkable “stop and assist” commitment, remains binding.

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#227 – Helen Toner on the geopolitics of AI in China and the Middle East

Podcast by Robert Wiblin · Published November 5, 2025

With the US racing to develop AGI and superintelligence ahead of China, you might expect the two countries to be negotiating how they’ll deploy AI, including in the military, without coming to blows. But according to Helen Toner, director of the Center for Security and Emerging Technology in DC, “the US and Chinese governments are barely talking at all.”

In her role as a founder, and now leader, of DC’s top think tank focused on the geopolitical and military implications of AI, Helen has been closely tracking the US’s AI diplomacy since 2019.

“Over the last couple of years there have been some direct [US–China] talks on some small number of issues, but they’ve also often been completely suspended.” China knows the US wants to talk more, so “that becomes a bargaining chip for China to say, ‘We don’t want to talk to you. We’re not going to do these military-to-military talks about extremely sensitive, important issues, because we’re mad.'”

Helen isn’t sure the groundwork exists for productive dialogue in any case. “At the government level, [there’s] very little agreement” on what AGI is, whether it’s possible soon, whether it poses major risks. Without shared understanding of the problem, negotiating solutions is very difficult.

Another issue is that so far the Chinese Communist Party doesn’t seem especially “AGI-pilled.” While a few Chinese companies like DeepSeek are betting on scaling, she sees little evidence Chinese leadership shares Silicon Valley’s conviction that AGI will arrive any minute now, and export controls have made it very difficult for them to access compute to match US competitors.

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Our top tips for successful networking

Blog post by Zershaaneh Qureshi · Published November 5, 2025

Everyone talks about the importance of networking for a successful career. And they’re right — the people you connect with will shape your habits, the ideas you’re exposed to, and your job opportunities.

But how do you actually network well?

I asked this question to our career advisors, who have helped thousands of people break into high-impact roles. Here’s what they recommended.

How to network

The basics are simple: find people who can help you learn or move forward with your career, or who you can help. Increase your opportunities to connect with them, and try to build genuine relationships with the people you meet.

Find the right people

Attend conferences, courses, and social events in your professional community, or one you’d like to be a part of. There are many opportunities to meet like-minded people in person and virtually — we have lots of recommendations to get you started on our community page.
Get involved in online discourse. People often use social media, forums, and other online platforms to connect with others and discuss ideas. Whatever your interests are, there’s probably an online community out there for you! Our advisors sometimes recommend being active on X/Twitter, especially if you’re interested in AI policy.
Try visiting a hub. There are some locations (like the San Francisco Bay Area,

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Open positions to grow our podcast team

Blog post by The 80,000 Hours podcast team · Published October 31, 2025

Why this matters

The 80,000 Hours Podcast aims to help the world safely navigate the transition to a flourishing future with artificial general intelligence. We focus on influencing the decisions that matter most — helping people identify high-leverage career moves and important actions they can take in their current positions.

Our episodes reach tens or hundreds of thousands of people, and we regularly hear from employees in government and at leading AI companies who find them useful. We’re also growing fast: in 2025, our listenership was around 60% higher than the same period last year. In order to further improve the usefulness and reach of our episodes, we need to increase the team’s capacity.

Right now, in addition to recording important conversations, our hosts handle much of the crucial work that surrounds each episode:

Identifying which conversations would actually change important decisions
Finding guests who can deliver those insights
Shaping episodes to land the key arguments effectively
Ensuring the right decision-makers encounter the content at the right moment

We’re looking to hire two people to the team who can take on some of this work, allowing Rob and Luisa to focus more of their time on making great episodes.

The window is short. If we’re right about AI timelines, the decisions that shape humanity’s long-run trajectory are happening now, not in 20 years.

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Jobs at 80,000 Hours

#226 – Holden Karnofsky on dozens of opportunities to make AI safer lying on the table — and all his AGI takes

Podcast by Robert Wiblin · Published October 30, 2025

For years, working on AI safety usually meant theorising about the ‘alignment problem’ or trying to convince other people to give a damn. If you could find any way to help, the work was frustrating and low feedback.

According to Anthropic’s Holden Karnofsky, this situation has now reversed completely.

There are now large amounts of useful, concrete, shovel-ready projects with clear goals and deliverables. Holden thinks people haven’t appreciated the scale of the shift, and wants everyone to see the large range of ‘well-scoped object-level work’ they could personally help with, in both technical and non-technical areas.

In today’s interview, Holden — previously cofounder and CEO of Open Philanthropy (now Coefficient Giving) — lists 39 projects he’s excited to see happening, including:

Training deceptive AI models to study deception and how to detect it
Developing classifiers to block jailbreaking
Implementing security measures to stop ‘backdoors’ or ‘secret loyalties’ from being added to models in training
Developing policies on model welfare, AI-human relationships, and what instructions to give models
Training AIs to work as alignment researchers

And that’s all just stuff he’s happened to observe directly, which is probably only a small fraction of the options available.

All this low-hanging fruit is one factor behind his decision to join Anthropic this year. That said, his wife was also a cofounder and president of the company, giving him a big financial stake in its success — and making it impossible for him to be seen as independent no matter where he worked.

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Extreme power concentration

Problem profile by Rose Hadshar · Last updated October 2025 · First published April 2025

Advanced AI technology may enable its creators, or others who control it, to attempt and achieve unprecedented societal power grabs. Under certain circumstances, they could use these systems to take control of whole economies, militaries, and governments.

This kind of power grab from a single person or small group would pose a major threat to the rest of humanity.

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#225 – Daniel Kokotajlo on what a hyperspeed robot economy might look like

Podcast by Luisa Rodriguez · Published October 20, 2025

When Daniel Kokotajlo talks to security experts at major AI labs, they tell him something chilling: “Of course we’re probably penetrated by the CCP already, and if they really wanted something, they could take it.”

This isn’t paranoid speculation. It’s the working assumption of people whose job is to protect frontier AI models worth billions of dollars. And they’re not even trying that hard to stop it — because the security measures that might actually work would slow them down in the race against competitors.

Daniel is the founder of the AI Futures Project and author of AI 2027, a detailed scenario showing how we might get from today’s AI systems to superintelligence by the end of the decade. Over a million people read it in the first few weeks, including US Vice President JD Vance. When Daniel talks to researchers at Anthropic, OpenAI, and DeepMind, they tell him the scenario feels less wild to them than to the general public — because many of them expect something like this to happen.

Daniel’s median timeline? 2029. But he’s genuinely uncertain, putting 10–20% probability on AI progress hitting a long plateau.

When he first published AI 2027, his median forecast for when superintelligence would arrive was 2028, rather than 2029. So what shifted his timelines recently? Partly a fascinating study from METR showing that AI coding assistants might actually be making experienced programmers slower — even though the programmers themselves think they’re being sped up. The study suggests a systematic bias toward overestimating AI effectiveness — which, ironically, is good news for timelines, because it means we have more breathing room than the hype suggests.

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If we can’t control MechaHitler, how will we steer AGI?

Blog post by Chana Messinger and Aric Floyd · Published October 9, 2025

It’s Chana and Aric again.

Firstly, we cannot believe the response to our last video. Thank you all so much for creating an initial swell of views and support, for your feedback and comments and subscriptions. We’re at 186,000 subscribers, and 6 million views. Truly, truly, incredible; we’re so delighted to be doing this with you.

Also, um, feel free to do that all again? Because AI in Context has a second video for you.

There are two types of people in the world:

People who are understandably very confused about why the word “MechaHitler” appears in the title of this sober, thoughtful, serious blog post.
People grimacing because they know why “MechaHitler” appears in the title of this sober, thoughtful, serious blog post.

If you’re in category 1, here’s the summary:

You might have heard of Elon Musk’s AI model, Grok, which can interact with users and post directly to Twitter. Earlier this year, it suddenly turned from being a fairly neutral commentator on events to a sexually-harassing, Nazi-minded troll calling itself ‘MechaHitler.’

Our new video is about that incident, getting into the wild details of the MechaHitler story that even the terminally online may have missed.

But it’s also about putting this news story in context: how Elon Musk and his AI company xAI function, where the broader AI industry is going,

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