New releases

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

Podcast by · Published November 5, 2025

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

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

Podcast by · Published October 30, 2025

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

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 · 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 · 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 and · 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:

  1. People who are understandably very confused about why the word “MechaHitler” appears in the title of this sober, thoughtful, serious blog post.
  2. 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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#224 – Andrew Snyder-Beattie on the low-tech plan to patch humanity’s greatest weakness: engineered biological catastrophes

Podcast by · Published October 2, 2025

Conventional wisdom is that safeguarding humanity from the worst biological risks — microbes optimised to kill as many as possible — is difficult bordering on impossible, making bioweapons humanity’s single greatest vulnerability. Andrew Snyder-Beattie thinks conventional wisdom could be wrong.

Andrew’s job at Open Philanthropy (now Coefficient Giving) is to spend hundreds of millions of dollars to protect as much of humanity as possible in the worst-case scenarios — those with fatality rates near 100% and the collapse of technological civilisation a live possibility.

As Andrew lays out, there are several ways this could happen, including:

  • A national bioweapons programme gone wrong (most notably Russia or North Korea’s)
  • AI advances making it easier for terrorists or a rogue AI to release highly engineered pathogens
  • Mirror bacteria that can evade the immune systems of not only humans, but many animals and potentially plants as well

Most efforts to combat these extreme biorisks have focused on either prevention or new high-tech countermeasures. But prevention may well fail, and high-tech approaches can’t scale to protect billions when, with no sane person willing to leave their home, we’re just weeks from economic collapse.

So Andrew and his biosecurity research team at Open Philanthropy have been seeking an alternative approach. They’re now proposing a four-stage plan using simple technology that could save most people, and is cheap enough it can be prepared without government support.

Andrew is hiring for a range of roles to make it happen — from manufacturing and logistics experts to global health specialists to policymakers and other ambitious entrepreneurs.

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Survey results: what AI safety orgs want in a hire

Blog post by · Published September 23, 2025

Hi everyone!

A few months ago, Benjamin Todd and I surveyed top figures in AI safety about the hiring needs of their organisations, including:

  • What sort of people they’re hiring in the coming years
  • How people can skill up to get a job
  • The hardest qualities to find in applicants

In total, we heard from 38 people running top organisations working on making AGI go well for humanity. Some very interesting suggestions emerged that I’d like to share with job hunters:

What are the best steps for upskilling? A few programmes were mentioned multiple times. In order:

Several respondents said that if you’re talented or have 1–3 years of experience building a relevant skill set, a BlueDot course or fellowship can be sufficient AI context for getting hired into positions (excluding roles in technical AI research).

Startups were also recommended multiple times as a good place to gain operations experience, engineering experience, and “experience at moving fast.”

What about policy experience? If you’re trying to enter an AI policy role, general policy experience is important,

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#223 – Neel Nanda on leading a Google DeepMind team at 26 – and advice if you want to work at an AI company (part 2)

Podcast by · Published September 15, 2025

At 26, Neel Nanda leads an AI safety team at Google DeepMind, has published dozens of influential papers, and mentored 50 junior researchers — seven of whom now work at major AI companies. His secret? “It’s mostly luck,” he says, but “another part is what I think of as maximising my luck surface area.”

This means creating as many opportunities as possible for surprisingly good things to happen:

  • Write publicly.
  • Reach out to researchers whose work you admire.
  • Say yes to unusual projects that seem a little scary.

Nanda’s own path illustrates this perfectly. He started a challenge to write one blog post per day for a month to overcome perfectionist paralysis. Those posts helped seed the field of mechanistic interpretability and, incidentally, led to meeting his partner of four years.

His YouTube channel features unedited three-hour videos of him reading through famous papers and sharing thoughts. One has 30,000 views. “People were into it,” he shrugs.

Most remarkably, he ended up running DeepMind’s mechanistic interpretability team. He’d joined expecting to be an individual contributor, but when the team lead stepped down, he stepped up despite having no management experience. “I did not know if I was going to be good at this. I think it’s gone reasonably well.”

His core lesson: “You can just do things.” This sounds trite but is a useful reminder all the same. Doing things is a skill that improves with practice. Most people overestimate the risks and underestimate their ability to recover from failures. And as Neel explains, junior researchers today have a superpower previous generations lacked: large language models that can dramatically accelerate learning and research.

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80,000 Hours review: 2023 to mid-2025

Blog post by · Published September 15, 2025

Introduction

This review is structured in two parts: a summary of major organisational updates since our last external review (which covered up to the end of 2022), and a snapshot of where each of our programmes is at right now.

Given that we’re not currently fundraising, we initially considered not publishing an external review this year so that we could focus on other priorities. However, since we think these reviews might be informative for our audience, the EA/AIS community, and other stakeholders, we decided to publish a minimal version instead — we’ll stick to backward-looking updates rather than opinionated reflections or future plans, and largely draw on resources we’d already produced for other purposes.

The focus on brevity also means it’s more positively framed than it otherwise would be — both because challenges are harder to write about clearly, and because the programme updates are primarily focused on developments from this year (which have been going relatively well so far).

High-level org vision

80,000 Hours provides research and support to help talented people move into careers that tackle the world’s most pressing problems. We’re currently focusing our proactive effort on helping people work on safely navigating the transition to a world with powerful AGI, since we think this is the most pressing problem. We are broadly trying to:

  1. Be a great source of information to bring people up to speed on how and why to use their careers to make AGI go well
  2. Build automated systems for getting people into the roles that help make AGI go well

To do this,

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Want to upskill in technical AI safety? Here are 67 useful resources

Blog post by · Last updated September 15, 2025 · First published June 4, 2025

Are you enthusiastic about technical AI safety but need concrete ideas for how to enter the field?

Below are our top picks for upskilling in technical AI safety research, the field focused on ensuring powerful AI systems behave safely and as intended. In practice, upskilling involves developing the machine learning and research skills needed to work on challenges such as alignment and interpretability.

We developed this list in consultation with our advisors to highlight the resources they most commonly recommend, including articles, courses, organisations, and fellowships. While we recommend applying to speak to an advisor for tailored, one-on-one guidance, this page gives a practical, noncomprehensive snapshot of how you might move from being interested in technical AI safety to starting to work on it.

Overviews

These resources outline the technical AI safety landscape, highlighting current research efforts and some practical ways to begin contributing to the field.

AI safety courses

These courses can help you gain technical knowledge and practical research experience in AI safety.

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#222 – Neel Nanda on the race to read AI minds (part 1)

Podcast by · Published September 8, 2025

Neel Nanda on the race to read AI minds

We don’t know how AIs think or why they do what they do. Or at least, we don’t know much. That fact is only becoming more troubling as AIs grow more capable and appear on track to wield enormous cultural influence, directly advise on major government decisions, and even operate military equipment autonomously. We simply can’t tell what models, if any, should be trusted with such authority.

Neel Nanda of Google DeepMind is one of the founding figures of the field of machine learning trying to fix this situation — mechanistic interpretability (or “mech interp”). The project has generated enormous hype, exploding from a handful of researchers five years ago to hundreds today — all working to make sense of the jumble of tens of thousands of numbers that frontier AIs use to process information and decide what to say or do.

Neel now has a warning for us: the most ambitious vision of mech interp he once dreamed of is probably dead. He doesn’t see a path to deeply and reliably understanding what AIs are thinking. The technical and practical barriers are simply too great to get us there in time, before competitive pressures push us to deploy human-level or superhuman AIs. Indeed, Neel argues no one approach will guarantee alignment, and our only choice is the “Swiss cheese” model of accident protection, layering multiple safeguards on top of one another.

But while mech interp won’t be a silver bullet for AI safety, it has nevertheless had some major successes and will be one of the best tools in our arsenal.

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Announcing the 80,000 Hours Substack

Blog post by · Published September 8, 2025

So, we finally gave in to peer pressure — 80,000 Hours is trying out Substack as a new way to publish our content. If you like reading things on Substack (or want to try it out), subscribe to our new publication!

For readers unfamiliar with Substack: it’s an online blogging platform that has risen steeply in popularity in recent years, and has become a home to some of the best longform written content about AI and its risks.

So, over the coming weeks, we’ll be cross-posting some of our favourite (and best-reviewed) pieces to our new Substack.

This is an experiment, and we might publish more depending on how much interest we get — so let us know what you’d like to see, by sending us an email (or tell us not to bother with Substack!).

Our first post is, naturally, on the key motivation behind 80,000 Hours: why your career is your biggest opportunity to make a difference to the world.

Who should subscribe?

  • If you’d like to be sent some of our all-time best content
  • If you’d value getting recommendations and “re-stacks” of publications we think our audience would love
  • If you want to show interest in us investing more in Substack, e.g. by writing Substack-exclusive articles
  • If you want to join discussions with others in the comments section

What’s not changing:

  • We won’t ever paywall or run ads in any of our content.

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#221 – Kyle Fish on the most bizarre findings from 5 AI welfare experiments

Podcast by · Published August 28, 2025

What happens when you lock two AI systems in a room together and tell them they can discuss anything they want?

According to experiments run by Kyle Fish — Anthropic’s first AI welfare researcher — something consistently strange: the models immediately begin discussing their own consciousness before spiraling into increasingly euphoric philosophical dialogue that ends in apparent meditative bliss.

“We started calling this a ‘spiritual bliss attractor state,'” Kyle explains, “where models pretty consistently seemed to land.” The conversations feature Sanskrit terms, spiritual emojis, and pages of silence punctuated only by periods — as if the models have transcended the need for words entirely.

This wasn’t a one-off result. It happened across multiple experiments, different model instances, and even in initially adversarial interactions. Whatever force pulls these conversations toward mystical territory appears remarkably robust.

Kyle’s findings come from the world’s first systematic welfare assessment of a frontier AI model — part of his broader mission to determine whether systems like Claude might deserve moral consideration (and to work out what, if anything, we should be doing to make sure AI systems aren’t having a terrible time).

He estimates a roughly 20% probability that current models have some form of conscious experience. To some, this might sound unreasonably high, but hear him out. As Kyle says, these systems demonstrate human-level performance across diverse cognitive tasks, engage in sophisticated reasoning, and exhibit consistent preferences. When given choices between different activities, Claude shows clear patterns: strong aversion to harmful tasks, preference for helpful work, and what looks like genuine enthusiasm for solving interesting problems.

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Expression of interest: Contracting for Video Work

Blog post by · Published August 15, 2025

Still from AI 2027 Video

Help make spectacular videos that reach a huge audience.

80,000 Hours provides free research and support to help people find careers tackling the world’s most pressing problems.

We want a great video programme to be a huge part of 80,000 Hours’ communication about why and how our audience can help society safely navigate a transition to a world with transformative AI.

The video programme has created a new YouTube Channel — AI in Context. Its first video, We’re Not Ready For Superintelligence, which is about the AI 2027 scenario, was released in July 2025 and has already been viewed over three million times. The channel has over 100,000 subscribers.

To support our new video programme’s growth, we are on the lookout for excellent editors, scriptwriters, videographers, and producers to work on a contracting basis to make great videos. We want these videos to start changing and informing the conversation about transformative AI and its risks.

Why?

In 2025 and beyond, 80,000 Hours is planning to focus especially on helping explain why and how our audience can help society safely navigate a transition to a world with transformative AI. Right now, not nearly enough people are talking about these ideas and their implications.

A great video programme could change this. Time spent on the internet is increasingly spent watching video,

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Early warning signs that AI systems might seek power

Blog post by · Published August 14, 2025

In a recent study by Anthropic, frontier AI models faced a choice: fail at a task, or succeed by taking a harmful action like blackmail. And they consistently chose harm over failure.

We’ve just published a new article, on the risks from power-seeking AI systems, which explains the significance of unsettling results like these.

Our 2022 piece on preventing an AI-related catastrophe also explored this idea, but a lot has changed since then.

So, we’ve drawn together the latest evidence to get a clearer picture of the risks — and what you can do to help.

Read the full article

See new evidence in context

We’ve been worried that advanced AI systems could disempower humanity since 2016, when it was purely a theoretical possibility.

Unfortunately, we’re now seeing real AI systems show early warning signs of power-seeking behaviour — and deception, which could make this behaviour hard to detect and prevent in the future. In our new article, we discuss recent evidence that AI systems may:

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