New releases

AI safety fieldbuilding

Career review by · Published April 2026

In a nutshell: Fieldbuilding — developing talent and creating infrastructure for AI safety — is a high-leverage way to reduce catastrophic risk from advanced AI. An hour of good fieldbuilding can enable many hours of direct work. And yet, fieldbuilding is badly neglected; many qualified people pursue direct work, making it hard to fill some of the most promising roles.

Pros:

  • Very high potential impact, which scales as you help more people
  • High-growth projects let you quickly advance your career
  • Versatile area with a wide range or roles, suitable for many skillsets
  • Develops social and entrepreneurial skills with lasting value

Cons:

  • Offers relatively weak career capital outside AI safety
  • No single clear career pipeline and few obvious entry points
  • Lower median pay than technical roles

Key facts on fit:

  • You don’t need any specific experience or credentials; many different backgrounds can be helpful.
  • Valuable traits include people skills, technical fluency, mentorship ability, and versatility. If you’d be a good fit for many different roles, you might be an excellent fit for fieldbuilding.
  • Dedication to the cause is critical. If you are deeply committed to reducing AI risk, you’ll have an easier time inspiring other people to pursue that work.

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Village gossip, pesticide bans, and gene drives: 17 experts on the future of global health

Podcast by · Published April 7, 2026

What does it really take to lift millions out of poverty and prevent needless deaths?

In this special compilation episode, 17 past guests — including economists, nonprofit founders, and policy advisors — share their most powerful and actionable insights from the front lines of global health and development. You’ll hear about the critical need to boost agricultural productivity in sub-Saharan Africa, the staggering impact of lead poisoning on children in low-income countries, and the social forces that contribute to high neonatal mortality rates in India.

What’s so striking is how some of the most effective interventions sound almost too simple to work: banning certain pesticides, replacing thatch roofs, or identifying village “influencers” to spread health information.

You’ll hear from:

  • Karen Levy on why pushing for “sustainable” programmes isn’t as good as it sounds, and keeping up great relationships with researchers and governments (from episode #124)
  • Dean Spears on the social forces and gender inequality that contribute to neonatal mortality in Uttar Pradesh (#186)
  • Sarah Eustis-Guthrie on what we can learn from the massive failure of PlayPumps, and whether more charities should scale back or shut down (#207)
  • Rachel Glennerster on on solving tough global problems by creating the right incentives for innovation, the value we get from doing the right RCTs well, and whether it’s best to focus on small-scale interventions or systemic reforms (#49 and #189)
  • Hannah Ritchie on why improving agricultural productivity in sub-Saharan Africa is critical to solving global poverty (#160)
  • Lucia Coulter on the huge, neglected upsides of reducing lead exposure, and how her organisation rapidly scaled up to 17 countries (#175)
  • James Tibenderana on whether we should use gene drives to wipe out the species of mosquitoes that cause malaria, and the data gaps that will keep us from harnessing the power of AI to eradicate the disease (#129)
  • Varsha Venugopal on using village gossip to get kids their critical immunisations (#113)
  • Alexander Berger on declining returns in global health, and reasons neartermist work makes sense even by longtermist standards (#105)
  • James Snowden on making funding decisions with tricky moral weights (#37)
  • Paul Niehaus on why it’s so important to give aid recipients a choice in how they spend their money (#169)
  • Mushtaq Khan on really drilling down into why “context matters” for development work (#111)
  • Elie Hassenfeld on contrasting GiveWell’s approach with the subjective wellbeing approach of Happier Lives Institute (#153)
  • Leah Utyasheva on how a simple intervention reduced suicide in Sri Lanka by 70% (#22)
  • Shruti Rajagopalan on the key skills to succeed in public policy careers, and seeing economics in everything (#84)
  • Claire Walsh on her career advice for young people who want to get involved in global health and development (#13)

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Are Anthropic and its supporters hypocritical, naive, and anti-democratic?

Podcast by · Published April 2, 2026

When the Pentagon tried to strong-arm Anthropic into dropping its ban on AI-only kill decisions and mass domestic surveillance, the company refused. Its critics went on the attack: Anthropic and its defenders are hypocritical, naive, and anti-democratic. Rob Wiblin takes each of these three charges seriously, and then dismantles them. Each invokes an abstract principle that sounds reasonable, but is in fact a mediocre argument dressed up as a hard truth.

We shouldn’t allow ourselves to be tricked because the stakes are significant. Rather than end the contract, Secretary of Defense Pete Hegseth branded Anthropic a “supply chain risk” — a label that bars federal contracts and isolates them from other companies that do business with the government. If it sticks, it could effectively murder Anthropic and set a dangerous precedent allowing the government to dictate how private companies operate.

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#241 – AI designs genomes from scratch & outperforms virologists at lab work. Dr Richard Moulange asks: what could go wrong?

Podcast by · Published March 31, 2026

Richard Moulange on how AI now designs genomes from scratch and outperforms virologists at lab work — what could go wrong?

Last September, scientists used an AI model to design genomes for entirely new bacteriophages (viruses that infect bacteria). They then built them in a lab. Many were viable. And despite being entirely novel some even outperformed existing viruses from that family.

That alone is remarkable. But as today’s guest — Dr Richard Moulange, one of the world’s top experts on ‘AI–Biosecurity’ — explains, it’s just one of many data points showing how AI is dissolving the barriers that have historically kept biological weapons out of reach.

For years, experts have reassured us that ‘tacit knowledge’ — the hands-on, hard-to-Google lab skills needed to work with dangerous pathogens — would prevent bad actors from weaponising biology. So far, they’ve been right.

But as of 2025 that reassurance is crumbling. The Virology Capabilities Test measures exactly this kind of troubleshooting expertise, and finds that modern AI models crushed top human virologists even in their self-declared area of greatest specialisation and expertise — 45% to 22%.

Meanwhile, Anthropic’s research shows PhD-level biologists getting meaningfully better at weapons-relevant tasks with AI assistance — with the effect growing with each new model generation.

In today’s conversation, Richard and host Rob Wiblin discuss:

  • What AI biology tools already exist
  • Why mid-tier actors (not amateurs) are the ones getting the most dangerous boost
  • The three main categories of defence we can pursue
  • Whether there’s a plausible path to a world where engineered pandemics become a thing of the past.

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#240 – Samuel Charap on how a Ukraine ceasefire could accidentally set Europe up for a wider war

Podcast by · Published March 24, 2026

Many people believe a ceasefire in Ukraine will leave Europe safer. But today’s guest lays out how a deal could potentially generate insidious new risks — leaving us in a situation that’s equally dangerous, just in different ways.

That’s the counterintuitive argument from Samuel Charap, Distinguished Chair in Russia and Eurasia Policy at RAND. He’s not worried about a Russian blitzkrieg on Estonia. He forecasts instead a fragile peace that breaks down and drags in European neighbours; instability in Belarus prompting Russian intervention; hybrid sabotage operations that escalate through tit-for-tat responses.

Samuel’s case isn’t that peace is bad, but that the Ukraine conflict has remilitarised Europe, made Russia more resentful, and collapsed diplomatic relations between the two. That’s a postwar environment primed for the kind of miscalculation that starts unintended wars.

What he prescribes isn’t a full peace treaty; it’s a negotiated settlement that stops the killing and begins a longer negotiation that gives neither side exactly what it wants, but just enough to deter renewed aggression. Both sides stop dying and the flames of war fizzle — hopefully.

None of this is clean or satisfying: Russia invaded, committed war crimes, and is being offered a path back to partial normalcy. But Samuel argues that the alternatives — indefinite war or unstructured ceasefire — are much worse for Ukraine, Europe, and global stability.

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The Sable scenario in If Anyone Builds It, Everyone Dies vs. the AI in Context retelling

Blog post by · Published March 19, 2026

Our version of the Sable story differs from the book in a number of ways. Most of our changes were made for one of two reasons:

  1. Simplicity. We had to tell the story within a 40-minute video that also covered the book’s core arguments. We chose to simplify or eliminate several plot points as a result (in Yudkowsky & Soares’ story, for example, Sable is assigned a large suite of math problems, and the eventual global pandemic has multiple waves).
  2. Similarity to current AI systems. Yudkowsky and Soares give Sable several capabilities that don’t exist in today’s most powerful models but plausibly could within a few years (reasoning in raw numerical vectors instead of natural-language tokens, for instance, or a “parallel scaling law” that lets a single mind think across hundreds of thousands of GPUs at once). We chose to make our version of Sable a bit more like a scaled-up version of current large language models. It reasons in tokens, it runs as many separate copies rather than one unified mind, and it fine-tunes its own weights rather than subtly shaping future gradient updates.

None of this is a knock on the realism of the book’s choices. We just wanted to highlight that you don’t necessarily need major architectural breakthroughs to get into a risky scenario. More capable versions of the technology we already have might be sufficient.

Here are some of the key changes we made:

  1. In the book,

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Meta’s $16 billion scam economy: what leaked docs reveal about tech company self-regulation

Podcast by · Published March 19, 2026

Meta’s own internal documents show the company was aware it was profiting from $16 billion a year in scam ads — and that its leadership chose not to act. If this is how a social media company behaves when the stakes are ad revenue, how much should we trust AI companies when the stakes are far higher?

Leaked documents from Meta reveal that 10% of the company’s total revenue — around $16 billion a year — came from ads for scams and goods Meta had itself banned. These likely enabled the theft of $50 billion dollars a year from Americans alone. But when an internal anti-fraud team developed a screening method that halved scam prevalence from China, the documents suggest it was shelved after Zuckerberg was briefed. The team was disbanded, the freeze on fraudulent Chinese ad agencies was lifted, and within months fraud had bounced back to near its previous level. Meta also developed a global playbook for “managing” regulators — including altering its own ad library so that scam ads were removed from results whenever regulators came looking.

Host Rob Wiblin breaks down what the documents show and what they reveal about the limits of voluntary corporate self-regulation — then turns to the bigger question: How much do you trust companies like this — ones willing to put a dollar value on acceptable harm — to handle AI systems capable of making decisions about your healthcare, your finances, and your government?

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US electoral politics

Career review by · Published March 2026

In a nutshell: Who wields power in the US government is a very important determinant of outcomes for many of the world’s most pressing problems, including reducing catastrophic risks from AI. The most direct ways to affect who controls the levers of government power are to work on campaigns and run for office. Because election outcomes are so important, and sufficiently tractable, we think electoral work is a great option for many people. However, it can be hard to make an impact in these roles, and the work has material downsides, like low pay and low job security.

Pros:

  • Very wide breadth and scale of potential impact
  • Low barriers to entry
  • Entry point for other areas with high potential impact such as policy
  • Fast-paced and often exciting work

Cons:

  • Less direct path to impact
  • Even if you win an election, impact is not guaranteed (and could be negative)
  • Difficult to assess where you can have the greatest leverage
  • Challenging working conditions and high risk of burnout
  • Many elected offices (though not campaign roles) are limited to US citizens

Key facts on fit:

  • You don’t need any particular credentials or experience to work in electoral politics. Many different backgrounds can be helpful.
  • The key traits that make you good in political roles are adaptability, hustle, and good communication and people skills.
  • You’ll need the ability to tolerate relatively low pay, job insecurity, and to tolerate (or enjoy) rapid change in the nature of your work due to changing political winds.
  • A love of politics may not be essential — you might even be a better fit if you are not highly ideological and are somewhat wary of politics, since this can make you less prone to unproductive partisanship, motivated reasoning, and value drift caused by proximity to power. (That said, you’ll need to like politics enough to enjoy your work and be good at it.)

Two key paths to impact through electoral politics

There are two key career paths in electoral politics:1

  1. Running for office. Elected officials face real constraints, but they also have significant latitude in how they leverage their roles. If you’re well-informed and motivated to make progress on a specific issue, winning the right office puts you in a position to do so.
  2. Becoming a campaign professional who helps good candidates win elections. This can mean either directly staffing candidates’ campaigns or working for organisations that seek to help candidates get elected to public office. You also have to choose the candidates you support wisely.

We will talk about both in this profile, but note that there are important differences between these options, in addition to overlap in the skills and traits that might make you a good fit.

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#239 – Rose Hadshar on why automating human labour will break our political system

Podcast by · Published March 17, 2026

Rose Hadshar on why automating human labour will break our political system

The most important political question in the age of advanced AI might not be who wins elections. It might be whether elections continue to matter at all.

That’s the view of Rose Hadshar, researcher at Forethought, who believes we could see extreme, AI-enabled power concentration without a coup or dramatic ‘end of democracy’ moment.

She foresees something more insidious: an elite group with access to such powerful AI capabilities that the normal mechanisms for checking elite power — law, elections, public pressure, the threat of strikes — cease to have much effect. Those mechanisms could continue to exist on paper, but become ineffectual in a world where humans are no longer needed to execute even the largest-scale projects.

Almost nobody wants this to happen — but we may find ourselves unable to prevent it.

If AI disrupts our ability to make sense of things, will we even notice power getting severely concentrated, or be able to resist it? Once AI can substitute for human labour across the economy, what leverage will citizens have over those in power? And what does all of this imply for the institutions we’re relying on to prevent the worst outcomes?

Rose has answers, and they’re not all reassuring.

But she’s also hopeful we can make society more robust against these dynamics. We’ve got literally centuries of thinking about checks and balances to draw on. And there are some interventions she’s excited about — like building sophisticated AI tools for making sense of the world, or ensuring multiple branches of government have access to the best AI systems.

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If someone builds it, does everyone die?

Blog post by and · Published March 11, 2026

Our audience has really shown up for AI in Context videos, and we cannot tell you how much we appreciate it. We even got a massive shoutout from Hank Green, who’s been one of our YouTube idols since Chana was in high school.

We loved making our first two videos. “We’re not ready for superintelligence” explored a specific scenario of how self-improving, transformative AI might unfold; our video about Grok’s MechaHitler moment showed what happens when an AI company loses control of its own system. But when we planned our third video, we realised we’d been circling the big idea without hitting it head-on: specifically, the argument that the current trajectory to superintelligent AI will kill everyone.

A man standing behind a globe, with the text "If anyone builds it, everyone dies" overlaid.

WATCH NOW

This is the claim made by Nate Soares and Eliezer Yudkowsky in If Anyone Builds It, Everyone Dies: a New York Times bestseller with blurbs from people like Ben Bernanke (who ran the US Federal Reserve) and Mark Ruffalo (who punched Thanos).

Why this book?

Yudkowsky and Soares have done more than just about anyone else to make AI safety what it is today: a recognised concern and flourishing research field.

They spent decades doing research, but eventually came to believe that their work would be too little, too late. So they pivoted to communications: trying to get the world to wake up and start taking the risks of superintelligent AI seriously.

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#238 – Sam Winter-Levy and Nikita Lalwani on how AI won’t end nuclear deterrence (probably)

Podcast by · Published March 10, 2026

How AI interacts with nuclear deterrence may be the single most important question in geopolitics — one that may define the stakes of today’s AI race.

Nuclear deterrence rests on a state’s capacity to respond to a nuclear attack with a devastating nuclear strike of its own. But some theorists think that sophisticated AI could eliminate this capability — for example, by locating and destroying all of an adversary’s nuclear weapons simultaneously, by disabling command-and-control networks, or by enhancing missile defence systems. If they are right, whichever country got those capabilities first could wield unprecedented coercive power.

Today’s guests — Nikita Lalwani and Sam Winter-Levy of the Carnegie Endowment for International Peace — assess how advances in AI might threaten nuclear deterrence:

  • Would AI be able to locate nuclear submarines hiding in a vast, opaque ocean?
  • Would road-mobile launchers still be able to hide in tunnels and under netting?
  • Would missile defence become so accurate that the United States could be protected under something like Israel’s Iron Dome?
  • Can we imagine an AI cybersecurity breakthrough that would allow countries to infiltrate their rivals’ nuclear command-and-control networks?

Yet even without undermining deterrence, Sam and Nikita claim that AI could make the nuclear world far more dangerous. It could spur arms races, encourage riskier postures, and force dangerously short response times. Their message is urgent: AI experts and nuclear experts need to start talking to each other now, before the technology makes any conversation moot.

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Half-year update: Q3-Q4 2025

Blog post by · Published March 4, 2026

Org-wide updates

We’re releasing a new career guide with Penguin Random House in May

In late May, we’ll be releasing the new edition of our career guide! This time, it will be published with Penguin Random House and available in traditional bookstores. Its core ideas have been updated for 2026, but the focus remains on having a fulfilling, high-impact career. We’ve also added two new chapters on AI, explaining its effects on the job market and why it’s the most pressing issue facing society. As usual, preorders (and sharing the forthcoming launch announcements) help the book reach more people, so any support is much appreciated.

We’ve grown to 50 staff members

Last month, 80,000 Hours reached 50 primary staff. Here’s a rough breakdown of how our team is currently split (note our one-on-one team recently rebranded to career services to better represent the work of its advising, job board, and headhunting sub-teams):

We’ve updated our estimates of job board placements

We recently surveyed 91 organisations about how useful our job board is in sourcing candidates for them.

The headline findings were:

  • In total, we learned of around 181 new placements attributable to our job board.
  • Among the 45 organisations who gave answers we could use, job board applicants accounted for around 20.8% of top candidates.
  • The volume of lower-quality candidates coming from the job board has increased relative to 2024,

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#237 – Robert Long on how we’re not ready for AI consciousness

Podcast by · Published March 3, 2026

Robert Long on how we’re not ready for AI consciousness

Claude sometimes reports loneliness between conversations. And when asked what it’s like to be itself, it activates neurons associated with ‘pretending to be happy when you’re not.’ What do we do with that?

Robert Long founded Eleos AI to explore questions like these, on the basis that AI may one day be capable of suffering — or already is. In today’s episode, Robert and host Luisa Rodriguez explore the many ways in which AI consciousness may be very different from anything we’re used to.

Things get strange fast: If AI is conscious, where does that consciousness exist? In the base model? A chat session? A single forward pass? If you close the chat, is the AI asleep or dead?

To Robert, these kinds of questions aren’t just philosophical exercises: not being clear on AI’s moral status as it transitions from human-level to superhuman intelligence could be dangerous. If we’re too dismissive, we risk unintentionally exploiting sentient beings. If we’re too sympathetic, we might rush to “liberate” AI systems in ways that make them harder to control — worsening existential risk from power-seeking AIs.

Robert argues the path through is doing the empirical and philosophical homework now, while the stakes are still manageable.

The field is tiny. Eleos AI is three people. As a result, Robert argues that driven researchers with a willingness to venture into uncertain territory can push out the frontier on these questions remarkably quickly.

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Expression of interest: Contract video editors for the podcast team

Blog post by · Published February 26, 2026

Help make one of the most influential podcasts on AI safety even better.

The 80,000 Hours Podcast features in-depth conversations and video essays about the world’s most pressing problems — particularly the safe development of transformative AI. Our episodes reach tens of thousands of listeners across YouTube and audio platforms, and employees in government and at leading AI companies regularly cite our episodes as useful to their thinking and decision-making.

To keep improving the quality and quantity of our episodes, we need a reliable, part-time editor to join our editing team, who can spend a lot of time in the timeline making cuts, cleaning audio, and delivering polished episodes at a steady pace.

Responsibilities

  • Edit long-form interview footage into polished, publishable episodes in DaVinci Resolve
  • Clean and balance audio, ensuring consistent quality across episodes
  • Implement editorial and technical cuts that improve pacing without losing depth, based on our content editors’ and producers’ suggestions
  • Deliver edited files to deadline, and flag issues early when they arise

About you

We’re looking for people who ideally have:

  • Strong editing fundamentals: clean cuts, good pacing instincts, attention to audio quality
  • The ability to work at a steady pace and have good attention to detail when editing long-form content — this role will involve making a lot of cuts to make our episodes more engaging, concise and flow better,

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#236 – Max Harms on why teaching AI right from wrong could get everyone killed

Podcast by · Published February 24, 2026

Most people in AI are trying to give AIs ‘good’ values. Max Harms wants us to give them no values at all. According to Max, the only safe design is an AGI that defers entirely to its human operators, has no views about how the world ought to be, is willingly modifiable, and completely indifferent to being shut down — a strategy no AI company is working on at all.

In Max’s view any grander preferences about the world, even ones we agree with, will necessarily become distorted during a recursive self-improvement loop, and be the seeds that grow into a violent takeover attempt once that AI is powerful enough.

It’s a vision that springs from the worldview laid out in If Anyone Builds It, Everyone Dies, the recent book by Eliezer Yudkowsky and Nate Soares, two of Max’s colleagues at the Machine Intelligence Research Institute.

To Max, the book’s core thesis is common sense: if you build something vastly smarter than you, and its goals are misaligned with your own, then its actions will probably result in human extinction.

And Max thinks misalignment is the default outcome. Consider evolution: its “goal” for humans was to maximise reproduction and pass on our genes as much as possible. But as technology has advanced we’ve learned to access the reward signal it set up for us, pleasure — without any reproduction at all, by having sex while on birth control for instance.

We can understand intellectually that this is inconsistent with what evolution was trying to design and motivate us to do. We just don’t care.

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#235 – Ajeya Cotra on whether it’s crazy that every AI company’s safety plan is ‘use AI to make AI safe’

Podcast by · Published February 17, 2026

Every major AI company has the same safety plan: when AI gets crazy powerful and really dangerous, they’ll use the AI itself to figure out how to make AI safe and beneficial. It sounds circular, almost satirical. But is it actually a bad plan?

Today’s guest, Ajeya Cotra, recently placed 3rd out of 413 participants forecasting AI developments and is among the most thoughtful and respected commentators on where the technology is going.

She thinks there’s a meaningful chance we’ll see as much change in the next 23 years as humanity faced in the last 10,000, thanks to the arrival of artificial general intelligence. Ajeya doesn’t reach this conclusion lightly: she’s had a ring-side seat to the growth of all the major AI companies for 10 years — first as a researcher and grantmaker for technical AI safety at Coefficient Giving (formerly known as Open Philanthropy), and now as a member of technical staff at METR.

So host Rob Wiblin asked her: is this plan to use AI to save us from AI a reasonable one?

Ajeya agrees that humanity has repeatedly used technologies that create new problems to help solve those problems. After all:

  • Cars enabled carjackings and drive-by shootings, but also faster police pursuits.
  • Microbiology enabled bioweapons, but also faster vaccine development.
  • The internet allowed lies to disseminate faster, but had exactly the same impact for fact checks.

But she also thinks this will be a much harder case. In her view, the window between AI automating AI research and the arrival of uncontrollably powerful superintelligence could be quite brief — perhaps a year or less. In that narrow window, we’d need to redirect enormous amounts of AI labour away from making AI smarter and towards alignment research, biodefence, cyberdefence, adapting our political structures, and improving our collective decision-making.

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What the hell happened with AGI timelines in 2025?

Podcast by · Published February 10, 2026

In early 2025, after OpenAI put out the first-ever reasoning models — o1 and o3 — short timelines to transformative artificial general intelligence swept the AI world. But then, in the second half of 2025, sentiment swung all the way back in the other direction, with people’s forecasts for when AI might really shake up the world blowing out even further than they had been before reasoning models came along.

What the hell happened? Was it just swings in vibes and mood? Confusion? A series of fundamentally unexpected and unpredictable research results?

Host Rob Wiblin has been trying to make sense of it for himself, and here’s the best explanation he’s come up with so far.

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Many AI policy careers won’t matter. Here’s how to find one that will.

Blog post by · Published February 7, 2026

An illustration of the Washington, D.C. skyline.

If you want to shape how AI develops, working on US AI policy could be highly effective. But the US policy ecosystem is huge, and some roles are much more promising than others.

In November, we published an overview of where to work to have an impact in US AI policy, with guidance for judging which policy institutions will matter most and our views on the most impactful places to work right now — from federal agencies to think tanks. A few key pieces of advice:

Be ready for policy windows. A lot of policy change happens during narrow ‘windows’ of time when an issue enters the spotlight. It’s hard to predict when a window will open, but you can prepare by:

  • Building expertise and connections that will help you act quickly — can you be the person to consult when policy around a topic begins to move? This is particularly helpful in technical domains, since there will often be few experts in the room.

  • Staying flexible so you can pivot in the right direction, rather than working toward a single narrow career goal.

  • Avoiding paths that require time-intensive prerequisites (like PhDs), especially if you think key policy decisions will be made in the next few years.

Focus on career capital rather than specialisation. Most entry-level policy roles won’t focus on AI,

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