The 80,000 Hours Podcast on Artificial Intelligence

Imagine you’re living 15,000 years ago. Your people are hunter-gatherers and you sleep under the stars. If someone told you humans would one day build cities with millions of people, fly through the air, or carry all human knowledge in their pockets, you couldn’t even begin to picture what they meant... Yet here we are.

How did our lives change so far beyond recognition? The story is complex, but there’s a rough pattern. A few times in history, some radical breakthrough in technology — like the development of the plough and the steam engine — has led to a wave of productivity, innovation, and social change that ultimately reshaped the world.

Now we’re on the cusp of a huge new breakthrough: artificial intelligence that can meet or exceed human capabilities across a wide range of tasks.

This could bring another era of transformation. There could be an explosion of intelligence and innovation, and a whole new population of digital beings. And with this, civilisation could see changes at least as profound as those brought about by industrialisation or the rise of agriculture — but instead of taking hundreds or thousands of years to unfold, this time around the world could become unrecognisable over the span of decades or less.

This transformation could bring enormous benefits, helping us solve currently intractable global problems. But it could also pose severe risks, some of which could be existential — meaning they could cause human extinction, or an equally permanent and severe disempowerment of humanity.

There aren’t nearly enough people trying to address these challenges, and we think that’s a serious problem.

This article is narrated by the author, Zershaaneh Qureshi. It explores how advanced AI could be so transformative, and why working on its risks may be your best opportunity to have a positive impact on the world.

You can see the original article on the 80,000 Hours website: https://80000hours.org/problem-profiles/artificial-intelligence/ 

Chapters:

Audio editing: Dominic Armstrong
Production: Zershaaneh Qureshi, Elizabeth Cox, Katy Moore, and Lou Moran

What might it be like to live through the creation of AI that surpasses human abilities? That future may be closer than you think. 

In this series, one expert interview at a time, we'll walk you through what's at stake — and what you could do to help.

The 20th century saw unprecedented change: nuclear weapons, satellites, the rise and fall of communism, third-wave feminism, the internet, postmodernism, game theory, genetic engineering, the Big Bang theory, quantum mechanics, widespread birth control, and more. Now imagine all of it compressed into just 10 years.

That’s the future Will MacAskill — philosopher, founding figure of effective altruism, and now researcher at Forethought Research — argues we need to prepare for in his paper “Preparing for the intelligence explosion.” Not in the distant future, but probably in 3–7 years.

The reason: AI systems are rapidly approaching human-level capability in scientific research and intellectual tasks. Once AI exceeds human abilities in AI research itself, we’ll enter a recursive self-improvement cycle, with AI acting autonomously to create wildly more capable systems.

Soon after, by improving algorithms and manufacturing chips, we’ll deploy millions, then billions, then trillions of superhuman AI scientists working 24/7 without human limitations. These systems will collaborate across disciplines, build on each discovery instantly, and conduct experiments at unprecedented scale and speed — compressing a century of progress into years.

Will compares this to a mediaeval king suddenly needing to upgrade from bows and arrows to nuclear weapons to deal with an ideological threat from a kingdom he’s never heard of, while simultaneously learning he’s descended from monkeys and his god doesn’t exist.

What makes this acceleration perilous is that while technology can speed up almost arbitrarily, human institutions and decision making are much more fixed.

Consider the case of nuclear weapons: in this compressed timeline, there would have been just a three-month gap between the Manhattan Project’s start and the Hiroshima bombing, and the Cuban Missile Crisis would have lasted just over a day.

Robert Kennedy Sr, who helped navigate the actual Cuban Missile Crisis, once said that if they’d had to make decisions faster — like in 24 hours rather than 13 days — they would likely have taken much more aggressive, much riskier actions.

So there’s reason to worry about our capacity to make wise choices quickly. And in his paper, Will lays out 10 “grand challenges” we’ll need to navigate to avoid things going wrong.

Will now believes we’re entering one of the most critical periods for humanity ever — with decisions made in the next few years potentially determining outcomes millions of years into the future.

In this wide-ranging conversation, Will and host Rob Wiblin discuss:

Learn more and read the full transcript on the 80,000 Hours website.

This episode was originally released in March 2025.

Chapters:

Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Camera operator: Jeremy Chevillotte
Transcriptions and web: Katy Moore

We don’t yet have a reliable way to tell whether an AI model is genuinely trying to help us — or faking it.

A model might sincerely want to do exactly what you ask. Or it could be happy to secretly cheat, as long as its answer gets positive reinforcement during training. It might even follow the rules just to gain our trust, all while concealing goals of its own.

The problem is: each of these three motivations scores the same during testing.

Ajeya Cotra — previously a senior research analyst at Coefficient Giving, now working at METR (Model Evaluation & Threat Research) — explains how dangerous this dynamic could become as we train very general and very capable AI models.

She likens humanity’s future trust in AI systems to an orphaned child who inherits a $1 trillion company. This child has to hire someone to run the company, guide his life, and manage his wealth — but he can only choose this person based on a work trial or interview that he designs, with no resumes or reference checks.

And, because he’s so rich, all sorts of people apply — for all sorts of reasons. Some applicants will truly want to help. But the role will attract others who only pretend to care while they’re being monitored, but intend to exploit the child as soon as they can get away with it.

Like a child trying to judge adults, at some point humans will need to judge the trustworthiness and reliability of machine learning models that are as goal-oriented as people, and greatly outclass us in knowledge, experience, breadth, and speed.

And we can’t rely on models’ performance during training tasks to guide us, as current reinforcement learning would give the same grades to three vastly different motivations:

Worse still, training might actively encourage deception. 

Imagine training a model to run a business, and measuring its success by the balance in its bank account. A highly capable model might experiment with dishonest strategies. Maybe it steals some money and covers it up. (This isn’t a hypothetical worry; models often come up with creative — sometimes undesirable — approaches during training that their developers didn’t anticipate.)

A model that cheats and covers its tracks would look like a star performer — and get reinforced for exactly that behaviour. If cheating is only caught some of the time, the model still might not learn to stop deceptive behaviour. Instead, it might learn that deceiving without being caught gives it a competitive advantage.

In this conversation, Ajeya and host Rob Wiblin discuss the above, as well as:

Learn more and read the full transcript on the 80,000 Hours website.

This episode was originally released in May 2023.

Chapters:

Producer: Keiran Harris

Audio mastering: Ryan Kessler and Ben Cordell

Transcriptions: Katy Moore

The human brain runs on just 20 watts — a fraction of a cent worth of electricity per hour. What would happen if AI could do the same?

Plenty of people have toyed with this question. But perhaps nobody has followed through and considered all the implications as much as Carl Shulman, whose behind-the-scenes work has greatly influenced how leaders in artificial general intelligence (AGI) picture the world they’re creating.

Carl simply follows the logic to its natural conclusions, leading to a world where:

As the economy grows, each person could afford the equivalent of a team of hundreds of machine ‘people’ to help them with every aspect of their lives.

But with growth rates this high, it doesn’t take long to reach Earth’s physical limits — the toughest to engineer around being the planet’s ability to release waste heat. If this machine economy and its insatiable demand for power generates more heat than the Earth radiates into space, the planet will rapidly heat up and become uninhabitable for biological organisms.

This eventually creates pressure to move economic activity off-planet. There’s little need for computer chips to be on Earth, and solar energy and minerals are more abundant in space. So you could develop populations of billions of digital scientific researchers orbiting in space, sending the results of their work — like drug designs — back to Earth.

These are just some of the wild implications if AGI could merely match what evolution has already managed.

In this interview with host Rob Wiblin, Carl explains the above, and Rob pushes back on whether that’s realistic or just a cool story:

In the last section of the episode, Carl addresses the moral status of machine minds themselves. Would they be conscious or otherwise have a claim to moral rights? And how might humans and machines coexist with neither side dominating or exploiting the other?
Learn more and read the full transcript on the 80,000 Hours website.

This episode is the first part of Rob’s marathon interview with Carl Shulman in 2024. The second episode is ongovernment and society after AGI, and you can listen to them in either order.

Chapters:

Producer and editor: Keiran Harris
Audio engineering lead: Ben Cordell
Technical editing: Simon Monsour, Milo McGuire, and Dominic Armstrong
Transcriptions: Katy Moore

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.

We tend to imagine the death of democracy as a dramatic event: a coup, tanks in the streets, a strongman tearing up the constitution. But Rose Hadshar, researcher at Forethought Research, believes AI-enabled power concentration could be far quieter — and far harder to stop.

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

Almost nobody wants this to happen, but we may find ourselves unable to prevent it:

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.

In this conversation, Rose and host Zershaaneh Qureshi discuss all of this, and more:

Learn more and read the full transcript on the 80,000 Hours website.

This episode was originally released in March 2026.

Chapters:

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

When OpenAI announced a deal to build massive data centres in the UAE, it celebrated that it was “rooted in democratic values” — a "clear alternative to authoritarian versions of AI." The UAE scores 18 out of 100 on Freedom House’s democracy index. Political parties are banned, elections are fake, and dissidents are persecuted. Saudi Arabia has received a similar deal.

This is what AI geopolitics looks like in practice: messy, contradictory, and enormously consequential.

The two superpowers competing to build superintelligence — the United States and China — are “barely talking at all.” You might expect two rivals developing potentially the most powerful and militarily significant technology in history to be in constant negotiation about how to deploy it without coming to blows.

Instead, the little dialogue that exists keeps collapsing. That’s the assessment of Helen Toner, director of the Center for Security and Emerging Technology — DC’s top think tank focused on the geopolitical and military implications of AI — who has been closely tracking the US’s AI diplomacy since 2019. 

Helen isn't sure productive talks are even possible yet. At the government level, there's almost no shared understanding between the US and China of what artificial general intelligence (AGI) is, whether it could arrive soon, or whether it poses serious risks. And without agreement on the problem, negotiating solutions is nearly impossible.

And while the US struggles to engage its rival, it’s empowering its autocratic allies.

If AI capability really does determine future national power, the US has just approved massive data centres with "hundreds of thousands of next-generation Nvidia chips," handing world-class supercomputers to Gulf autocracies — countries that also conduct joint military exercises with China and whose rulers maintain tight personal and commercial relationships with Chinese leaders.

The justification? "If we don't sell it, China will." 

But that claim is transparently false: severe production constraints and US export controls mean that China can’t come close to matching what these deals provided.

In this conversation recorded in Washington, DC, host Rob Wiblin and Helen discuss the above, plus:

Learn more and read the full transcript on the 80,000 Hours website.

This episode was originally released in November 2025.

Chapters:

Video editing: Luke Monsour and Simon Monsour
Audio engineering: Milo McGuire, Simon Monsour, and Dominic Armstrong
Music:CORBIT
Coordination, transcriptions, and web: Katy Moore

In 2024, AI models could complete tasks that take a human expert roughly one hour. Seven months before that, they were limited to 30-minute tasks — and seven months before that, 15 minutes.

Every seven months, the length of tasks AI models can manage doubles. (And this trend has continued since this episode was recorded in 2025.)

And these aren’t trivial tasks. We’re talking about substantial, multi-step tasks requiring sustained focus: building web applications, conducting AI research, and solving complex programming challenges.

Beth Barnes is CEO of METR (Model Evaluation & Threat Research) — the leading organisation measuring these capabilities. METR’s paper, “Measuring AI ability to complete long tasks,” is regarded by many as the most useful AI forecasting work in years for revealing this seven-month-doubling trend.

But the companies building these systems aren’t just aware of the trend: they want to harness it as much as possible, and are aggressively pursuing automation of their own research.

This is both exciting and troubling, as it could radically speed up advances in AI capabilities — accomplishing what would have taken years or decades in just months, as we covered in the first episode of this series.

And having AI models rapidly build their successors with limited human oversight naturally raises the risk that things could go wrong, if their resulting creations lack the goals and constraints we hoped for.

Beth thinks models can already do “meaningful work” on improving themselves, and wouldn’t be surprised if AI models were able to autonomously self-improve within two years.

While Silicon Valley is abuzz with these numbers, policymakers remain largely unaware of what’s barrelling towards us — and given the lack of regulation of AI companies, they’re not even able to access the critical information that would help them decide whether to intervene. 

Beth adds: “The sense I really want to dispel is, ‘But the experts must be on top of this. The experts would be telling us if it really was time to freak out.’ The experts are not on top of this… I am an expert telling you you should freak out. And there’s not especially anyone else who isn’t saying this.”

Beth and host Rob Wiblin discuss all that, plus:

Learn more and read the full transcript on the 80,000 Hours website.

This episode was originally released in June 2025.

Chapters:

Video editing: Luke Monsour and Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Transcriptions and web: Katy Moore

For years, one thing stood between us and a world where almost anyone could build a biological weapon: it was really, really hard.

Working with dangerous pathogens required rare, hands-on lab skills — the kind you can't just Google. Experts called this 'tacit knowledge,' and it was our best line of defence against bad actors weaponising biology.

That defence is now crumbling.

The Virology Capabilities Test measures exactly these kinds of skills, and finds that modern AI models crushed top human virologists — even in their area of greatest specialisation and expertise — with AI averaging 45% on the test, and human experts scoring only 22%.

And that’s just one data point. But as Dr Richard Moulange, one of the world’s top experts on AI biosecurity, explains: it’s just one of many that show how AI is dissolving the barriers that have historically kept biological weapons out of reach.

In September 2025, 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 never having existed before, some even outperformed existing viruses from that family.

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

In this conversation, Richard and host Rob Wiblin discuss:

Learn more and read the full transcript on the 80,000 Hours website.

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

Chapters:

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

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 perhaps already is. In this 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: 

Robert argues the right path 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.

In this interview, Robert and Luisa talk through the above, and much more.

Learn more and read the full transcript on the 80,000 Hours website.

This episode was originally released in March 2026.

Chapters:

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

Nobody knows how AIs think, or why they do what they do.

Or at least, we don’t know much. Not the companies building them, the researchers studying them, or the governments beginning to rely on them.

This 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 founders of mechanistic interpretability — the field of trying to give us insight into what’s happening inside AI models.

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.

But Neel now has a warning for us: the most ambitious vision of mechanistic interpretability is probably dead. He doesn’t see a path to deeply and reliably understanding what AIs are thinking. The technical and practical barriers are too great to get us there before competitive pressures push us to deploy human-level or superhuman AIs. 

Neel argues no single approach will guarantee safe alignment, and our only choice is the 'Swiss cheese' model of protection: layering multiple safeguards on top of one another.

That doesn’t mean mechanistic interpretability has failed. It won’t be a silver bullet for AI safety, but it will be one of the best tools in our arsenal.

For example, by inspecting the neural activations in the middle of an AI’s thoughts, we can see many of the concepts the model is thinking about — from refusing to answer a question, to the option of deceiving the user. 

We can’t track every thought a model is having at every moment, but catching 90% of the concepts it uses 90% of the time should help us muddle through — as long as mechanistic interpretability is paired with other techniques to fill the gaps.

In this episode, Neel takes us on a tour of the race to understand what AIs are really thinking. He and host Rob Wiblin cover:

Learn more and read the full transcript on the 80,000 Hours website.

This episode was originally released in September 2025.

Chapters:

Video editing: Simon Monsour, Luke Monsour, Dominic Armstrong, and Milo McGuire
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Camera operator: Jeremy Chevillotte
Coordination, transcriptions, and web: Katy Moore

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

That situation has now reversed completely.

According to Holden Karnofsky — cofounder and former CEO of Open Philanthropy (now Coefficient Giving), now working at Anthropic — there's an overwhelming amount of concrete, useful safety work that needs doing in both technical and nontechnical areas, and nowhere near enough people to do it.

In this conversation alone, Holden lists 39 projects he’s excited about, including:

And that’s just what he’s observed directly, likely a small fraction of what’s available.

All this low-hanging fruit is part of why he joined Anthropic this year. (Though his wife is 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 works.)

Holden argues that for many people, working at a frontier AI company is the highest-impact way to steer artificial general intelligence (AGI) — by developing cheap safety tools other companies might actually adopt, prototyping policies that regulators could mandate, and generating hard data about what advanced AI can really do. But he's clear that external groups have distinct advantages and can be equally valuable.

Critics worry that Anthropic’s efforts to stay at that frontier encourage competitive racing towards AGI, significantly or entirely offsetting any useful research they do. 

But Holden thinks this seriously misunderstands the current strategic situation. He believes the problem isn't that everyone wants to slow down but can't coordinate. Many major players simply don't believe the risks are real (or don't care about them, even if they do), don't want to slow down, and would be thrilled if a competitor dropped out — because they’d have a better chance at ‘winning.’

Host Rob Wiblin and Holden discuss all of this and much more, including:

Learn more and read the full transcript on the 80,000 Hours website.

This episode was originally released in October 2025.

Chapters:

Video editing: Simon Monsour, Luke Monsour, Dominic Armstrong, and Milo McGuire
Audio engineering: Milo McGuire, Simon Monsour, and Dominic Armstrong
Music:CORBIT
Coordination, transcriptions, and web: Katy Moore

Hundreds of prominent AI scientists and other notable figures signed a statement in 2023 saying that mitigating the risk of extinction from AI should be a global priority. At 80,000 Hours, we’ve considered risks from AI to be the world’s most pressing problem since 2016. 

But what led us to this conclusion? Could AI really cause human extinction? We’re not certain, but we think the risk is worth taking very seriously. 

In particular, as companies create increasingly powerful AI systems, there’s a concerning chance that:

This article is written by Cody Fenwick and Zershaaneh Qureshi, and narrated by Zershaaneh Qureshi. It discusses why future AI systems could disempower humanity, what current AI research reveals about behaviours like power-seeking and deception, and how you can help mitigate the dangers.

You can see the original article — packed with graphs, images, footnotes, and further resources — on the 80,000 Hours website: 

https://80000hours.org/problem-profiles/risks-from-power-seeking-ai/ 

Chapters:

Audio editing: Dominic Armstrong
Production: Zershaaneh Qureshi, Elizabeth Cox, and Katy Moore