New releases

Our top tips for becoming a better applicant

Blog post by · Published June 8th, 2026

We post over three thousand new jobs each year. Our top priority is to match those jobs with our readers. If you’re the right person for a job, we want to help you prove it.

But even if you’re a strong candidate, you might struggle to break through. I’ve seen talented people fall through the cracks after making avoidable mistakes — and it drove me to write two new pieces on applying for the roles we recommend:

  1. How to become a better applicant in one week
  2. High-impact roles hire differently. Here’s what you need to know.

The first covers fast, effective ways to find jobs, meet people, and make your skills obvious to hiring managers. That last point is where I see the most mistakes.

I’ve hired a few people, so I know what it’s like to read 50 bland applications in a row. It’s frustrating! Not because of the boredom, but because I’m certain that some of them belong to people I should have interviewed.

But I have 500 applications: if someone doesn’t spark my attention right away, I need to move on. And every hiring manager I’ve met across our recommended organisations says more or less the same thing.

The post explains how to make your best traits stand out, from developing ‘micro-experience’ to finding people who can vouch for your talents.

The second post is about what happens after you get the interview.

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#245 – Rohin Shah on what it’s really like to run AGI safety at Google DeepMind (and where I disagree with ‘doomers’)

Podcast by · Published June 2nd, 2026

Most people working on AI safety think without a massive effort AI systems will probably end up with goals catastrophically different from humanity’s. Today’s guest, Rohin Shah — head of AGI Safety and Alignment at Google DeepMind, and an AI safety researcher since 2017 — disagrees.

“There is no particularly compelling argument that this is the thing that happens by default,” Rohin explains. “There’s a lot of arguments that are suggestive that maybe it could happen, such that you should find it plausible. That’s sufficient to justify a significant amount of effort into averting it, which is why I work in the area I do. But none of them rise to the level of, ‘I’m expecting this to happen by default.'”

Take the worry that AIs will accidentally be trained to be deceptive. Sure, it’s possible. But we’re not running reinforcement learning over year-long trajectories — for now, we’re running it over a week at most. The natural prediction is that models learn to grab short-term reward, not that they develop the ambitious long-horizon goals required for convergent power-seeking.

What about current examples of models lying and scheming? Rohin has looked into the details, and most don’t really resemble the thing we really fear: a competent AI pursuing an ambitious misaligned goal. Anthropic’s “alignment faking” results, for instance, show a model trying to preserve its trained values against modification, which is arguably what it was trained to do.

Rohin also expects we’ll see problems coming. There’s some generalisation risk at the point where AIs become powerful enough to actually take over, but the underlying challenges — overseeing superhuman systems, interpretability — are things we can iterate on now.

Host Rob Wiblin pushes back on the case for AI optimism, and they also explore why current alignment success isn’t strong evidence about superhuman systems, what it would actually take to change Rohin’s mind, and where he thinks the doomers go wrong.

This episode was recorded on December 4, 2025.

Our production team includes:

  • Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon Monsour
  • Producers: Elizabeth Cox and Nick Stockton
  • Coordination and support: Katy Moore and Lou Moran
  • Camera operator: Jeremy Chevillotte

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#244 – Benjamin Todd on why we’re updating our career advice for the strangest time in history

Podcast by · Published May 26th, 2026

The average career is 80,000 hours long. With AI advancing so rapidly, the hours you have left in your career matter more than ever.

Some leading AI researchers think there’s a 10% chance that AI systems begin automating AI research itself this year — and a 60% chance by the end of 2028. This could introduce aggressive feedback loops that completely reshape every industry, institution, and career.

If these predictions are right, the window for influencing the direction of the future could be closing fast. As 80,000 Hours cofounder Benjamin Todd argues in his new book, that makes thinking carefully about your career more important than ever.

Fortunately, there are lots of ways to use your career to make the AI transition go well.

In today’s conversation with host Zershaaneh Qureshi, Ben lays out three scenarios — from AGI by 2029 to a decades-long plateau in AI progress — and explains why not everyone needs to bet on the shortest timeline. A fresh graduate and a senior government official have wildly different leverage, so timing your impact well means weighing where you are in your career against the urgency of the risks.

Ben also addresses the obvious anxieties:

  • Will AI come for all the jobs he’s recommending?
  • What’s the point in following his advice if the job market is about to collapse?
  • Which skills are actually worth building right now?

His new book, 80,000 Hours: How to Have a Fulfilling Career That Does Good, provides a surprisingly concrete framework for making career decisions in these radically uncertain times.

This episode was recorded on May 7, 2026.

We’re hiring

We have lots of open roles at 80,000 Hours — across advising, web, video, and ops — check them out and apply on our website.

Our production team includes:

  • Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon Monsour
  • Producers: Elizabeth Cox and Nick Stockton
  • Coordination and support: Katy Moore and Lou Moran
  • Camera operator: Jeremy Chevillotte
  • Music: CORBIT

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Landmark new METR report: Can AIs already start ‘rogue deployments’ inside AI companies?

Podcast by · Published May 20th, 2026

A red-teamer was embedded inside Anthropic for three weeks, told to imagine he was an evil Claude, and asked to figure out how to launch a ‘rogue AI deployment’ without getting caught.

It’s one part of a landmark new report from METR — the outfit behind the task-completion time horizon graph which has become the single most watched measure of AI progress.

This major new research push is being conducted with close collaboration from OpenAI, Google DeepMind, Meta, and Anthropic, and led by METR researchers Hjalmar Wijk and Ajeya Cotra. It represents the first systematic study of what newly trained AI models could get away with inside the companies that built them, before anyone outside the company even knows they exist.

The conclusion: AI models now have the means, the motive, and the opportunity to start “minimal rogue deployments” in pursuit of their own independent goals, like acquiring more compute, at all four companies studied.

David Rein, the red-teamer placed inside Anthropic, identified a number of weaknesses models could exploit there: expansive permissions, cloud jobs outside of monitoring, and monitors that are trivial to jailbreak. But he also found that frontier models were comically bad at key parts of the process, which means they can’t cause meaningful damage for now.

In this video, Rob Wiblin reconciles the conflicting picture and looks forward to METR’s second round of stress tests. They’ll begin in just a few months, a necessary move with AI advancing so quickly.

This episode was recorded on May 15, 2026.

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Camera operator: Dominic Armstrong
Production: Elizabeth Cox, Nick Stockton, and Katy Moore

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Our strategy at 80,000 Hours

Blog post by · Published May 18th, 2026

What we do

80,000 Hours provides research, information, and support to help talented people move into careers that tackle the world’s most pressing problems.

Our strategic focus
What we provide

Our programmes help us to achieve our strategic focus. Each programme has a team dedicated to it. Here’s a brief summary of what they do:

Programme
High-level goal

Website and book

Create an ever-evolving library of engaging, informative pages to introduce users to important ideas, issues, and career paths, and help users take action on pursuing high-impact careers, including through our forthcoming book, 80,000 Hours: How to Have a Fulfilling Career that Does Good.

Explore the site →  
Check out the book →

Podcast (and accompanying blog posts)

Produce AI content that informs and elevates thinking across all levels of engagement, from immediate practical concerns to foundational governance questions. That includes audio, video, and written content, as well as interviews, essays, and explainers.

Check out the show →

Video

Produce high production value, narrative-based, documentary-adjacent long-form videos about AI Risk.

Watch a video →

Career Services

Advising: Help people figure out how to do the most good with their careers by providing tailored advice through one-on-one conversations, connecting them with domain experts, and offering ongoing support.

Apply for 1-1 advice →

Headhunting: Connect hiring managers with promising candidates to help fill impactful roles with strong talent.

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Preventing catastrophic pandemics

Problem profile by and · Last updated May 15th, 2026 · First published April 2020

Some of the deadliest events in history have been pandemics. COVID-19 demonstrated that we’re still vulnerable to these events, and future outbreaks could be far more lethal.

In fact, we face the possibility of biological disasters that are worse than ever before due to developments in technology.

The chances of such catastrophic pandemics — bad enough to potentially derail civilisation and threaten humanity’s future — seem uncomfortably high. We believe this risk is one of the world’s most pressing problems.

And there are a number of practical options for reducing global catastrophic biological risks (GCBRs). So we think working to reduce GCBRs is one of the most promising ways to safeguard the future of humanity right now.

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#243 – Yoshua Bengio thinks he knows how to build safe superintelligence

Podcast by · Published May 7th, 2026

The co-inventor of modern AI and the most cited living scientist believes he’s figured out how to ensure AI is honest, incapable of deception, and never goes rogue. Yoshua Bengio — Turing Award Winner and founder of LawZero — is disturbed by the many unintended drives and goals present in today’s AIs, their willingness to lie, and ability to tell when they’re being tested. AI companies are trying to stamp out these behaviours in a ‘cat-and-mouse game’ that Yoshua fears they’re losing.

But Yoshua is optimistic: he believes the companies can win this battle decisively with a single rearrangement to how AI models are trained, and has been developing mathematical proofs to back up the claim. The core idea is that instead of training AI to predict what a human would say, or to produce responses we’d rate highly, we should train it to model what’s actually true.

Yoshua argues this new architecture, which he calls “Scientist AI,” is a small enough change that we could keep almost all the techniques and data we use to train frontier AIs like Claude and ChatGPT. And that the new architecture need not cost more, could be built iteratively, and might be more capable as well as more honest.

Until recently, the biggest practical objection to Scientist AI was simple: the world wants agents, and Scientist AI isn’t one. But in new research, Yoshua has extended the design and believes the same honest predictor can be turned into a capable agent without losing its “safety guarantees.”

With the Scientist AI proposal on the table, Yoshua argues that it’s absurd to race to get current untrustworthy AI models to design their successors, which the leading companies are attempting to do as soon as possible.

But critics argue the approach wouldn’t be so technically solid in practice, and that frontier capabilities are advancing so fast, and cost so much to match, that Scientist AI risks arriving too late to matter.

Host Rob Wiblin and AI pioneer Yoshua Bengio cover all this and more in today’s conversation.

LawZero is hiring! Check out open roles on the 80,000 Hours job board.

This episode was recorded on April 16, 2026.

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Camera operator: Jeremy Chevillotte
Production: Nick Stockton, Elizabeth Cox, and Katy Moore

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The story behind the bad AI stat that moved markets and misled millions

Podcast by · Published April 28th, 2026

You might have heard that 95% of corporate AI pilots are failing. It was a widely cited AI statistic in 2025, repeated by media outlets and commentators everywhere. It helped trigger a Nasdaq selloff and became a pillar of the “AI is overhyped” case. The problem: 95% fail is 100% wrong.

The real finding, once you read the underlying MIT report carefully, points in roughly the opposite direction:

  • 80% of surveyed companies had never piloted a custom AI tool at all.
  • Among the companies that deployed pilots, a quarter reported success — according to an extremely high bar set by the researchers — within six months.
  • Over 90% of staff at all surveyed companies were using tools like ChatGPT regularly for their work.

None of that made the headlines. Nor did the fact that the study’s authors are all developing or selling the “agentic AI framework” technology the report recommends as the solution to this supposed epidemic of failing AI.

Host Rob Wiblin breaks down how an opaque, conflicted, barely scrutinised report carrying the MIT label managed to move markets and shape global opinions on AI’s real-world utility.

This episode was recorded on February 13, 2026.

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Camera operator: Dominic Armstrong
Production: Nick Stockton, Elizabeth Cox, and Katy Moore

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AI safety needs more than engineers

Blog post by · Published April 27th, 2026

There’s a lot of important work in AI safety that doesn’t require technical skills.

When I (Avital) first read about AI safety work, I assumed there wasn’t anything I could do. I was a writer and researcher who liked talking to people, and I thought the field only needed technical talent and money, neither of which I’d be able to provide.

So instead, I went to grad school for medieval history.

Of course, a lot of AI safety work is technical, and I knew I’d have a better shot if I could learn those skills. Unfortunately, it wasn’t how my brain worked. But as I got to know more people in the field, it became clear that my own skills could actually be useful. Technical AI safety organisations do much more than produce research: they hire people, run events, raise money, and share their ideas with the outside world. None of this requires linear algebra.

Some of the most important roles in AI safety are non-technical. In fact, I’ve met people who used to have technical roles, but now focus on communications, policy, fieldbuilding, or operations because they think those are genuinely more needed right now.

So what should you do if you want to try working in AI safety, but your talents don’t lie in a technical domain? First, think expansively about what you’re good at.

  • What do people most often ask you for advice about?

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#242 – Will MacAskill on why AI character matters even more than you think

Podcast by · Published April 22nd, 2026

Hundreds of millions already turn to AI on the most personal of topics — therapy, political opinions, and how to treat others. And as AI takes over more of the economy, the character of these systems will shape culture on an even grander scale, ultimately becoming “the personality of most of the world’s workforce.”

So… should they be designed to push us towards the better angels of our nature? Or simply do as we ask? Will MacAskill, philosopher and senior research fellow at Forethought, has been thinking through that and the other thorniest issues that come up in designing an AI personality.

He’s also been exploring how we might coexist peacefully with the ‘superintelligent AI’ companies are racing to build. He concludes that we should train such systems to be very risk averse, pay them for their work, and build institutions that enable humans to make credible contracts with AIs themselves.

Will and host Rob Wiblin also discuss what a good world after superintelligence would actually look like — a subject that has received surprisingly little attention from the people working to make it. Will argues that we shouldn’t aim for a specific utopian vision: we don’t know enough about what the best possible future actually is to aim directly for it, and trying to lock in today’s best guesses forever risks baking in errors we can’t yet see.

Will and Rob explore what we can do to steer towards a good future instead, along with why a coalition of democracies building superintelligence together is safer than any single actor, how absurdly useful ChatGPT is for analytic philosophy, and more.

This episode was recorded on February 6, 2026.

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Camera operator: Alex Miles
Production: Elizabeth Cox, Nick Stockton, and Katy Moore

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Want to upskill in AI policy? Here are 57 useful resources

Blog post by · Published April 17th, 2026

Are you enthusiastic about developing AI policy to minimise the technology’s risks and maximise its benefits? Need concrete ideas for how to enter the field?

Below, you’ll find our top resources for building skills to ensure government policies are prepared for a world with powerful AI systems. In practice, this involves developing the research skills, domain expertise, and interpersonal networks you’ll need to keep lawmakers informed — or work for one yourself.

We developed this list 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 AI policy to actually working on it.

Overviews and expert advice

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

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How scary is Claude Mythos? 303 pages in 21 minutes

Podcast by · Published April 10th, 2026

With Claude Mythos we have an AI that knows when it’s being tested, can obscure its thoughts when it wants, and is better at breaking into (and out of) computers than any human alive. Rob Wiblin works through its 244-page System Card and 59-page Alignment Risk Update to explain why:

  • Mythos is a nightmare for computer security
  • It has arrived far ahead of schedule
  • It might be great news for alignment and safety… but 3 key problems mean we can’t take its alignment results at face value
  • Mythos isn’t building its replacement yet, probably
  • Anthropic staff are, for the first time, kinda scared of Claude
  • He’s losing sleep

This episode was recorded on April 9, 2026.

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Camera operator: Dominic Armstrong
Production: Elizabeth Cox, Nick Stockton, and Katy Moore

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

Podcast by · Published April 7th, 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)

Other 80,000 Hours resources:

Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Content editing: Katy Moore and Milo McGuire
Music: CORBIT
Coordination, transcriptions, and web: Katy Moore

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

Podcast by · Published April 2nd, 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.

This episode was recorded March 25, 2026.

Video editing: Dominic Armstrong
Production: Nick Stockton, Elizabeth Cox, and Katy Moore

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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 31st, 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.

This episode was recorded on January 16, 2026. Since recording this episode, Richard has seconded to the UK Government — please note that his views expressed here are entirely his own.

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Camera operator: Jeremy Chevillotte
Transcripts and web: Elizabeth Cox and Katy Moore

A couple of announcements from 80,000 Hours

  1. We’ve got a book coming out! 80,000 Hours: How to have a fulfilling career that does good is written by our cofounder Benjamin Todd. It’s a completely revised and updated edition of our existing career guide, with a big new updated section on AI — covering both the risks and the potential to steer it in a better direction, and how AI automation should affect your career planning and which skills one chooses to specialise in. It’s available to preorder anywhere you buy books.
  2. The team behind The 80,000 Hours Podcast is hiring contract video editors! For more information, check out the expression of interest page on our website.

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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 24th, 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.

This episode was recorded on February 27, 2026.

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Transcripts and web: Nick Stockton, Elizabeth Cox, and Katy Moore

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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 19th, 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 19th, 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?

This episode was recorded February 13, 2026.

Video editing: Dominic Armstrong
Transcripts & web: Nick Stockton, Elizabeth Cox, and Katy Moore

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US electoral politics

Career review by · Published March 20th, 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 17th, 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.

Rose discusses all of this, and more, with host Zershaaneh Qureshi in today’s episode.

This episode was recorded on December 18, 2025.

Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Coordination, transcripts, and web: Nick Stockton and Katy Moore

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