80,000 Hours Podcast

When attorneys general intervene in corporate affairs, it usually means something has gone seriously wrong. In OpenAI’s case, it appears to have forced a dramatic reversal of the company’s plans to sideline its nonprofit foundation, announced in a blog post that made headlines worldwide.

The company’s sudden announcement that its nonprofit will “retain control” credits “constructive dialogue” with the attorneys general of California and Delaware — corporate-speak for what was likely a far more consequential confrontation behind closed doors. A confrontation perhaps driven by public pressure from Nobel Prize winners, past OpenAI staff, and community organisations.

But whether this change will help depends entirely on the details of implementation — details that remain worryingly vague in the company’s announcement.

Return guest Rose Chan Loui, nonprofit law expert at UCLA, sees potential in OpenAI’s new proposal, but emphasises that “control” must be carefully defined and enforced: “The words are great, but what’s going to back that up?” Without explicitly defining the nonprofit’s authority over safety decisions, the shift could be largely cosmetic.

Links to learn more, video, and full transcript: https://80k.info/rcl4

Why have state officials taken such an interest so far? Host Rob Wiblin notes, “OpenAI was proposing that the AGs would no longer have any say over what this super momentous company might end up doing. … It was just crazy how they were suggesting that they would take all of the existing money and then pursue a completely different purpose.”

Now that they’re in the picture, the AGs have leverage to ensure the nonprofit maintains genuine control over issues of public safety as OpenAI develops increasingly powerful AI.

Rob and Rose explain three key areas where the AGs can make a huge difference to whether this plays out in the public’s best interest:

1. Ensuring that the contractual agreements giving the nonprofit control over the new Delaware public benefit corporation are watertight, and don’t accidentally shut the AGs out of the picture.
2. Insisting that a majority of board members are truly independent by prohibiting indirect as well as direct financial stakes in the business.
3. Insisting that the board is empowered with the money, independent staffing, and access to information which they need to do their jobs.

This episode was originally recorded on May 6, 2025.

Chapters:

• Cold open (00:00:00)
• Rose is back! (00:01:06)
• The nonprofit will stay 'in control' (00:01:28)
• Backlash to OpenAI’s original plans (00:08:22)
• The new proposal (00:16:33)
• Giving up the super-profits (00:20:52)
• Can the nonprofit maintain control of the company? (00:24:49)
• Could for profit investors sue if profits aren't prioritised? (00:33:01)
• The 6 governance safeguards at risk with the restructure (00:34:33)
• Will the nonprofit’s giving just be corporate PR for the for-profit? (00:49:12)
• Is this good, or not? (00:51:06)
• Ways this could still go wrong – but reasons for optimism (00:54:19)

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

When you have a system where ministers almost never understand their portfolios, civil servants change jobs every few months, and MPs don't grasp parliamentary procedure even after decades in office — is the problem the people, or the structure they work in?

Today's guest, political journalist Ian Dunt, studies the systemic reasons governments succeed and fail.

And in his book How Westminster Works ...and Why It Doesn't, he argues that Britain's government dysfunction and multi-decade failure to solve its key problems stems primarily from bad incentives and bad processes.

Even brilliant, well-intentioned people are set up to fail by a long list of institutional absurdities that Ian runs through — from the constant churn of ministers and civil servants that means no one understands what they’re working on, to the “pathological national sentimentality” that keeps 10 Downing Street (a 17th century townhouse) as the beating heart of British government.

While some of these are unique British failings, we see similar dynamics in other governments and large corporations around the world.

But Ian also lays out how some countries have found structural solutions that help ensure decisions are made by the right people, with the information they need, and that success is rewarded.

Links to learn more, video, highlights, and full transcript.

Chapters:

• Cold open (00:00:00)
• How Ian got obsessed with Britain's endless failings (00:01:05)
• Should we blame individuals or incentives? (00:03:24)
• The UK left its allies to be murdered in Afghanistan (to save cats and dogs) (00:09:02)
• The UK is governed from a tiny cramped house (00:17:54)
• “It's the stupidest conceivable system for how to run a country” (00:23:30)
• The problems that never get solved in the UK (00:28:14)
• Why UK ministers have no expertise in the areas they govern (00:31:32)
• Why MPs are chosen to have no idea about legislation (00:44:08)
• Is any country doing things better? (00:46:14)
• Is rushing inevitable or artificial? (00:57:20)
• How unelected septuagenarians are the heroes of UK governance (01:01:02)
• How Thatcher unintentionally made one part of parliament work (01:10:48)
• Maybe secrecy is the best disinfectant for incompetence (01:14:17)
• The House of Commons may as well be in a coma (01:22:34)
• Why it's in the PM's interest to ban electronic voting (01:33:13)
• MPs are deliberately kept ignorant of parliamentary procedure (01:35:53)
• “Whole areas of law have fallen almost completely into the vortex” (01:40:37)
• What's the seed of all this going wrong? (01:44:00)
• Why won't the Commons challenge the executive when it can? (01:53:10)
• Better ways to choose MPs (01:58:33)
• Citizens’ juries (02:07:16)
• Do more independent-minded legislatures actually lead to better outcomes? (02:10:42)
• "There’s no time for this bourgeois constitutional reform bulls***" (02:16:50)
• How to keep expert civil servants (02:22:35)
• Improving legislation like you’d improve Netflix dramas (02:34:34)
• MPs waste much of their time helping constituents with random complaints (02:39:59)
• Party culture prevents independent thinking (02:43:52)
• Would a written constitution help or hurt? (02:48:37)
• Can we give the PM room to appoint ministers based on expertise and competence? (02:51:51)
• Would proportional representation help? (02:56:20)
• Proportional representation encourages collaboration but does have weaknesses (02:58:51)
• Alternative electoral systems (03:07:44)

This episode was originally recorded on January 30, 2025.

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

How do you navigate a career path when the future of work is uncertain? How important is mentorship versus immediate impact? Is it better to focus on your strengths or on the world’s most pressing problems? Should you specialise deeply or develop a unique combination of skills?

From embracing failure to finding unlikely allies, we bring you 16 diverse perspectives from past guests who’ve found unconventional paths to impact and helped others do the same.

Links to learn more and full transcript.

Chapters:

• Cold open (00:00:00)
• Luisa's intro (00:01:04)
• Holden Karnofsky on just kicking ass at whatever (00:02:53)
• Jeff Sebo on what improv comedy can teach us about doing good in the world (00:12:23)
• Dean Spears on being open to randomness and serendipity (00:19:26)
• Michael Webb on how to think about career planning given the rapid developments in AI (00:21:17)
• Michelle Hutchinson on finding what motivates you and reaching out to people for help (00:41:10)
• Benjamin Todd on figuring out if a career path is a good fit for you (00:46:03)
• Chris Olah on the value of unusual combinations of skills (00:50:23)
• Holden Karnofsky on deciding which weird ideas are worth betting on (00:58:03)
• Karen Levy on travelling to learn about yourself (01:03:10)
• Leah Garcés on finding common ground with unlikely allies (01:06:53)
• Spencer Greenberg on recognising toxic people who could derail your career and life (01:13:34)
• Holden Karnofsky on the many jobs that can help with AI (01:23:13)
• Danny Hernandez on using world events to trigger you to work on something else (01:30:46)
• Sarah Eustis-Guthrie on exploring and pivoting in careers (01:33:07)
• Benjamin Todd on making tough career decisions (01:38:36)
• Hannah Ritchie on being selective when following others’ advice (01:44:22)
• Alex Lawsen on getting good mentorship (01:47:25)
• Chris Olah on cold emailing that actually works (01:54:49)
• Pardis Sabeti on prioritising physical health to do your best work (01:58:34)
• Chris Olah on developing good taste and technique as a researcher (02:04:39)
• Benjamin Todd on why it’s so important to apply to loads of jobs (02:09:52)
• Varsha Venugopal on embracing uncomfortable situations and celebrating failures (02:14:25)
• Luisa's outro (02:17:43)

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

Throughout history, technological revolutions have fundamentally shifted the balance of power in society. The Industrial Revolution created conditions where democracies could flourish for the first time — as nations needed educated, informed, and empowered citizens to deploy advanced technologies and remain competitive.

Unfortunately there’s every reason to think artificial general intelligence (AGI) will reverse that trend.

Today’s guest — Tom Davidson of the Forethought Centre for AI Strategy — claims in a new paper published today that advanced AI enables power grabs by small groups, by removing the need for widespread human participation.

Links to learn more, video, highlights, and full transcript. https://80k.info/td

Also: come work with us on the 80,000 Hours podcast team! https://80k.info/work

There are a few routes by which small groups might seize power:

• Military coups: Though rare in established democracies due to citizen/soldier resistance, future AI-controlled militaries may lack such constraints. 
• Self-built hard power: History suggests maybe only 10,000 obedient military drones could seize power.
• Autocratisation: Leaders using millions of loyal AI workers, while denying others access, could remove democratic checks and balances.

Tom explains several reasons why AI systems might follow a tyrant’s orders:

• They might be programmed to obey the top of the chain of command, with no checks on that power.
• Systems could contain "secret loyalties" inserted during development.
• Superior cyber capabilities could allow small groups to control AI-operated military infrastructure.

Host Rob Wiblin and Tom discuss all this plus potential countermeasures.

Chapters:
• Cold open (00:00:00)
• How AI enables tiny groups to seize power (00:00:50)
• The 3 different threats (00:02:14)
• Is this common sense or far-fetched? (00:03:24)
• “No person rules alone.” Except now they might. (00:06:27)
• Underpinning all 3 threats: Secret AI loyalties (00:12:31)
• Are secret AI loyalties possible right now? (00:16:59)
• Key risk factors (00:20:30)
• Preventing secret loyalties in a nutshell (00:22:07)
• Are human power grabs more plausible than 'rogue AI'? (00:24:32)
• If you took over the US, could you take over the whole world? (00:33:22)
• Will this make it impossible to escape autocracy? (00:37:31)
• Threat 1: AI-enabled military coups (00:41:34)
• Will we sleepwalk into an AI military coup? (00:51:47)
• Could AIs be more coup-resistant than humans? (00:57:53)
• Threat 2: Autocratisation (01:00:48)
• Will AGI be super-persuasive? (01:11:06)
• Threat 3: Self-built hard power (01:13:31)
• Can you stage a coup with 10,000 drones? (01:21:23)
• That sounds a lot like sci-fi... is it credible? (01:23:33)
• Will we foresee and prevent all this? (01:27:54)
• Are people psychologically willing to do coups? (01:29:22)
• Will a balance of power between AIs prevent this? (01:33:31)
• Will whistleblowers or internal mistrust prevent coups? (01:35:48)
• Will rogue AI preempt a human power grab? (01:44:31)
• The best reasons not to worry (01:47:09)
• How likely is this in the US? (01:49:28)
• Is a small group seizing power really so bad? (01:56:58)
• Countermeasure 1: Block internal misuse (02:00:33)
• Countermeasure 2: Cybersecurity (02:10:27)
• Countermeasure 3: Model spec transparency (02:12:36)
• Countermeasure 4: Sharing AI access broadly (02:21:55)
• Is it more dangerous to concentrate or share AGI? (02:26:45)
• Is it important to have more than one powerful AI country? (02:29:31)
• In defence of open sourcing AI models (02:32:38)
• 2 ways to stop secret AI loyalties (02:40:19)
• Preventing AI-enabled military coups in particular (02:53:18)
• How listeners can help (02:59:06)
• How to help if you work at an AI company (03:03:00)
• The power ML researchers still have, for now (03:07:09)
• How to help if you're an elected leader (03:10:29)

This episode was originally recorded on January 20, 2025.

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 are aiming for a place where we can decouple the scorecard from our worthiness. It’s of course the case that in trying to optimise the good, we will always be falling short. The question is how much, and in what ways are we not there yet? And if we then extrapolate that to how much and in what ways am I not enough, that’s where we run into trouble." —Hannah Boettcher

What happens when your desire to do good starts to undermine your own wellbeing?

Over the years, we’ve heard from therapists, charity directors, researchers, psychologists, and career advisors — all wrestling with how to do good without falling apart. Today’s episode brings together insights from 16 past guests on the emotional and psychological costs of pursuing a high-impact career to improve the world — and how to best navigate the all-too-common guilt, burnout, perfectionism, and imposter syndrome along the way.

Check out the full transcript and links to learn more: https://80k.info/mh

If you’re dealing with your own mental health concerns, here are some resources that might help:

• If you’re feeling at risk, try this for the the UK: How to get help in a crisis, and this for the US: National Suicide Prevention Lifeline.
• The UK’s National Health Service publishes useful, evidence-based advice on treatments for most conditions.
• Mental Health Navigator is a service that simplifies finding and accessing mental health information and resources all over the world — built specifically for the effective altruism community
• We recommend this summary of treatments for depression, this summary of treatments for anxiety, and Mind Ease, an app created by Spencer Greenberg.
• We’d also recommend It’s Not Always Depression by Hilary Hendel.
• Some on our team have found Overcoming Perfectionism and Overcoming Low Self-Esteem very helpful.
• And there’s even more resources listed on these episode pages: Having a successful career with depression, anxiety, and imposter syndrome, Hannah Boettcher on the mental health challenges that come with trying to have a big impact, Tim LeBon on how altruistic perfectionism is self-defeating.

Chapters:

• Cold open (00:00:00)
• Luisa's intro (00:01:32)
• 80,000 Hours’ former CEO Howie on what his anxiety and self-doubt feels like (00:03:47)
• Evolutionary psychiatrist Randy Nesse on what emotions are for (00:07:35)
• Therapist Hannah Boettcher on how striving for impact can affect our self-worth (00:13:45)
• Luisa Rodriguez on grieving the gap between who you are and who you wish you were (00:16:57)
• Charity director Cameron Meyer Shorb on managing work-related guilt and shame (00:24:01)
• Therapist Tim LeBon on aiming for excellence rather than perfection (00:29:18)
• Author Cal Newport on making time to be alone with our thoughts (00:36:03)
• 80,000 Hours career advisors Michelle Hutchinson and Habiba Islam on prioritising mental health over career impact (00:40:28)
• Charity founder Sarah Eustis-Guthrie on the ups and downs of founding an organisation (00:45:52)
• Our World in Data researcher Hannah Ritchie on feeling like an imposter as a generalist (00:51:28)
• Moral philosopher Will MacAskill on being proactive about mental health and preventing burnout (01:00:46)
• Grantmaker Ajeya Cotra on the psychological toll of big open-ended research questions (01:11:00)
• Researcher and grantmaker Christian Ruhl on how having a stutter affects him personally and professionally (01:19:30)
• Mercy For Animals’ CEO Leah Garcés on insisting on self-care when doing difficult work (01:32:39)
• 80,000 Hours’ former CEO Howie on balancing a job and mental illness (01:37:12)
• Therapist Hannah Boettcher on how self-compassion isn’t self-indulgence (01:40:39)
• Journalist Kelsey Piper on communicating about mental health in ways that resonate (01:43:32)
• Luisa's outro (01:46:10)

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

Most AI safety conversations centre on alignment: ensuring AI systems share our values and goals. But despite progress, we’re unlikely to know we’ve solved the problem before the arrival of human-level and superhuman systems in as little as three years.

So some are developing a backup plan to safely deploy models we fear are actively scheming to harm us — so-called “AI control.” While this may sound mad, given the reluctance of AI companies to delay deploying anything they train, not developing such techniques is probably even crazier.

Today’s guest — Buck Shlegeris, CEO of Redwood Research — has spent the last few years developing control mechanisms, and for human-level systems they’re more plausible than you might think. He argues that given companies’ unwillingness to incur large costs for security, accepting the possibility of misalignment and designing robust safeguards might be one of our best remaining options.

Links to learn more, highlights, video, and full transcript.

As Buck puts it: "Five years ago I thought of misalignment risk from AIs as a really hard problem that you’d need some really galaxy-brained fundamental insights to resolve. Whereas now, to me the situation feels a lot more like we just really know a list of 40 things where, if you did them — none of which seem that hard — you’d probably be able to not have very much of your problem."

Of course, even if Buck is right, we still need to do those 40 things — which he points out we’re not on track for. And AI control agendas have their limitations: they aren’t likely to work once AI systems are much more capable than humans, since greatly superhuman AIs can probably work around whatever limitations we impose.

Still, AI control agendas seem to be gaining traction within AI safety. Buck and host Rob Wiblin discuss all of the above, plus:

• Why he’s more worried about AI hacking its own data centre than escaping
• What to do about “chronic harm,” where AI systems subtly underperform or sabotage important work like alignment research
• Why he might want to use a model he thought could be conspiring against him
• Why he would feel safer if he caught an AI attempting to escape
• Why many control techniques would be relatively inexpensive
• How to use an untrusted model to monitor another untrusted model
• What the minimum viable intervention in a “lazy” AI company might look like
• How even small teams of safety-focused staff within AI labs could matter
• The moral considerations around controlling potentially conscious AI systems, and whether it’s justified

Chapters:
• Cold open (00:00:00)
• Who’s Buck Shlegeris? (00:01:25)
• What’s AI control? (00:01:51)
• Why is AI control hot now? (00:05:46)
• Detecting human vs AI spies (00:10:44)
• Acute vs chronic AI betrayal (00:15:41)
• How to catch AIs trying to escape (00:18:10)
• The cheapest AI control techniques (00:33:18)
• Can we get untrusted models to do trusted work? (00:39:33)
• If we catch a model escaping... will we do anything? (00:51:01)
• Getting AI models to think they've already escaped (00:53:39)
• Will they be able to tell it's a setup? (00:59:01)
• Will AI companies do any of this stuff? (01:01:05)
• Can we just give AIs fewer permissions? (01:07:16)
• Can we stop human spies the same way? (01:11:05)
• The pitch to AI companies to do this (01:16:13)
• Will AIs get superhuman so fast that this is all useless? (01:18:29)
• Risks from AI deliberately doing a bad job (01:19:50)
• Is alignment still useful? (01:26:05)
• Current alignment methods don't detect scheming (01:30:39)
• How to tell if AI control will work (01:33:08)
• How can listeners contribute? (01:37:28)
• Is 'controlling' AIs kind of a dick move? (01:38:51)
• Could 10 safety-focused people in an AGI company do anything useful? (01:44:12)
• Benefits of working outside frontier AI companies (01:49:40)
• Why Redwood Research does what it does (01:53:38)
• What other safety-related research looks best to Buck? (02:01:07)
• If an AI escapes, is it likely to be able to beat humanity from there? (02:02:02)
• Will misaligned models have to go rogue ASAP, before they're ready? (02:09:22)
• Is research on human scheming relevant to AI? (02:10:24)

This episode was originally recorded on February 21, 2025.

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

"There’s almost no story of the future going well that doesn’t have a part that’s like '…and no evil person steals the AI weights and goes and does evil stuff.' So it has highlighted the importance of information security: 'You’re training a powerful AI system; you should make it hard for someone to steal' has popped out to me as a thing that just keeps coming up in these stories, keeps being present. It’s hard to tell a story where it’s not a factor. It’s easy to tell a story where it is a factor." — Holden Karnofsky

What happens when a USB cable can secretly control your system? Are we hurtling toward a security nightmare as critical infrastructure connects to the internet? Is it possible to secure AI model weights from sophisticated attackers? And could AI might actually make computer security better rather than worse?

With AI security concerns becoming increasingly urgent, we bring you insights from 15 top experts across information security, AI safety, and governance, examining the challenges of protecting our most powerful AI models and digital infrastructure — including a sneak peek from an episode that hasn’t yet been released with Tom Davidson, where he explains how we should be more worried about “secret loyalties” in AI agents.

You’ll hear:

• Holden Karnofsky on why every good future relies on strong infosec, and how hard it’s been to hire security experts (from episode #158)
• Tantum Collins on why infosec might be the rare issue everyone agrees on (episode #166)
• Nick Joseph on whether AI companies can develop frontier models safely with the current state of information security (episode #197)
• Sella Nevo on why AI model weights are so valuable to steal, the weaknesses of air-gapped networks, and the risks of USBs (episode #195)
• Kevin Esvelt on what cryptographers can teach biosecurity experts (episode #164)
• Lennart Heim on on Rob’s computer security nightmares (episode #155)
• Zvi Mowshowitz on the insane lack of security mindset at some AI companies (episode #184)
• Nova DasSarma on the best current defences against well-funded adversaries, politically motivated cyberattacks, and exciting progress in infosecurity (episode #132)
• Bruce Schneier on whether AI could eliminate software bugs for good, and why it’s bad to hook everything up to the internet (episode #64)
• Nita Farahany on the dystopian risks of hacked neurotech (episode #174)
• Vitalik Buterin on how cybersecurity is the key to defence-dominant futures (episode #194)
• Nathan Labenz on how even internal teams at AI companies may not know what they’re building (episode #176)
• Allan Dafoe on backdooring your own AI to prevent theft (episode #212)
• Tom Davidson on how dangerous “secret loyalties” in AI models could be (episode to be released!)
• Carl Shulman on the challenge of trusting foreign AI models (episode #191, part 2)
• Plus lots of concrete advice on how to get into this field and find your fit

Check out the full transcript on the 80,000 Hours website.

Chapters:

• Cold open (00:00:00)
• Rob's intro (00:00:49)
• Holden Karnofsky on why infosec could be the issue on which the future of humanity pivots (00:03:21)
• Tantum Collins on why infosec is a rare AI issue that unifies everyone (00:12:39)
• Nick Joseph on whether the current state of information security makes it impossible to responsibly train AGI (00:16:23)
• Nova DasSarma on the best available defences against well-funded adversaries (00:22:10)
• Sella Nevo on why AI model weights are so valuable to steal (00:28:56)
• Kevin Esvelt on what cryptographers can teach biosecurity experts (00:32:24)
• Lennart Heim on the possibility of an autonomously replicating AI computer worm (00:34:56)
• Zvi Mowshowitz on the absurd lack of security mindset at some AI companies (00:48:22)
• Sella Nevo on the weaknesses of air-gapped networks and the risks of USB devices (00:49:54)
• Bruce Schneier on why it’s bad to hook everything up to the internet (00:55:54)
• Nita Farahany on the possibility of hacking neural implants (01:04:47)
• Vitalik Buterin on how cybersecurity is the key to defence-dominant futures (01:10:48)
• Nova DasSarma on exciting progress in information security (01:19:28)
• Nathan Labenz on how even internal teams at AI companies may not know what they’re building (01:30:47)
• Allan Dafoe on backdooring your own AI to prevent someone else from stealing it (01:33:51)
• Tom Davidson on how dangerous “secret loyalties” in AI models could get (01:35:57)
• Carl Shulman on whether we should be worried about backdoors as governments adopt AI technology (01:52:45)
• Nova DasSarma on politically motivated cyberattacks (02:03:44)
• Bruce Schneier on the day-to-day benefits of improved security and recognising that there’s never zero risk (02:07:27)
• Holden Karnofsky on why it’s so hard to hire security people despite the massive need (02:13:59)
• Nova DasSarma on practical steps to getting into this field (02:16:37)
• Bruce Schneier on finding your personal fit in a range of security careers (02:24:42)
• Rob's outro (02:34:46)

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

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, 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 the Forethought Centre for AI Strategy — argues we need to prepare for in his new paper “Preparing for the intelligence explosion.” Not in the distant future, but probably in three to seven 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 — creating 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 scientific progress into mere years.

Will compares the resulting situation to a mediaeval king suddenly needing to upgrade from bows and arrows to nuclear weapons to deal with an ideological threat from a country he’s never heard of, while simultaneously grappling with learning that he 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 remarked that if they’d had to make decisions on a much more accelerated timeline — like 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 own capacity to make wise choices. And in “Preparing for the intelligence explosion,” Will lays out 10 “grand challenges” we’ll need to quickly navigate to successfully avoid things going wrong during this period.

Will’s thinking has evolved a lot since his last appearance on the show. While he was previously sceptical about whether we live at a “hinge of history,” he 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.

But Will also sees reasons for optimism. The very AI systems causing this acceleration could be deployed to help us navigate it — if we use them wisely. And while AI safety researchers rightly focus on preventing AI systems from going rogue, Will argues we should equally attend to ensuring the futures we deliberately build are truly worth living in.

In this wide-ranging conversation with host Rob Wiblin, Will maps out the challenges we’d face in this potential “intelligence explosion” future, and what we might do to prepare. They discuss:

• Why leading AI safety researchers now think there’s dramatically less time before AI is transformative than they’d previously thought
• The three different types of intelligence explosions that occur in order
• Will’s list of resulting grand challenges — including destructive technologies, space governance, concentration of power, and digital rights
• How to prevent ourselves from accidentally “locking in” mediocre futures for all eternity
• Ways AI could radically improve human coordination and decision making
• Why we should aim for truly flourishing futures, not just avoiding extinction

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

This episode was originally recorded on February 7, 2025.

Chapters:

• Cold open (00:00:00)
• Who’s Will MacAskill? (00:00:43)
• Why Will now just works on AGI (00:01:03)
• Will was wrong(ish) on AI timelines and hinge of history (00:04:21)
• A century of history crammed into a decade (00:09:19)
• Science goes super fast; our institutions don't keep up (00:16:15)
• Is it good or bad for intellectual progress to 10x? (00:21:44)
• An intelligence explosion is not just plausible but likely (00:23:41)
• Intellectual advances outside technology are similarly important (00:30:04)
• Counterarguments to intelligence explosion (00:32:42)
• The three types of intelligence explosion (software, technological, industrial) (00:39:00)
• The industrial intelligence explosion is the most certain and enduring (00:42:01)
• Is a 100x or 1,000x speedup more likely than 10x? (00:53:44)
• The grand superintelligence challenges (00:57:39)
• Grand challenge #1: Many new destructive technologies (01:01:29)
• Grand challenge #2: Seizure of power by a small group (01:09:10)
• Is global lock-in really plausible? (01:11:06)
• Grand challenge #3: Space governance (01:21:50)
• Is space truly defence-dominant? (01:32:19)
• Grand challenge #4: Morally integrating with digital beings (01:36:04)
• Will we ever know if digital minds are happy? (01:45:01)
• “My worry isn't that we won't know; it's that we won't care” (01:50:39)
• Can we get AGI to ...

When OpenAI announced plans to convert from nonprofit to for-profit control last October, it likely didn’t anticipate the legal labyrinth it now faces. A recent court order in Elon Musk’s lawsuit against the company suggests OpenAI’s restructuring faces serious legal threats, which will complicate its efforts to raise tens of billions in investment.

As nonprofit legal expert Rose Chan Loui explains, the court order set up multiple pathways for OpenAI’s conversion to be challenged. Though Judge Yvonne Gonzalez Rogers denied Musk’s request to block the conversion before a trial, she expedited proceedings to the fall so the case could be heard before it’s likely to go ahead. (See Rob’s brief summary of developments in the case.)

And if Musk’s donations to OpenAI are enough to give him the right to bring a case, Rogers sounded very sympathetic to his objections to the OpenAI foundation selling the company, benefiting the founders who forswore “any intent to use OpenAI as a vehicle to enrich themselves.”

But that’s just one of multiple threats. The attorneys general (AGs) in California and Delaware both have standing to object to the conversion on the grounds that it is contrary to the foundation’s charitable purpose and therefore wrongs the public — which was promised all the charitable assets would be used to develop AI that benefits all of humanity, not to win a commercial race. Some, including Rose, suspect the court order was written as a signal to those AGs to take action.

And, as she explains, if the AGs remain silent, the court itself, seeing that the public interest isn’t being represented, could appoint a “special interest party” to take on the case in their place.

This places the OpenAI foundation board in a bind: proceeding with the restructuring despite this legal cloud could expose them to the risk of being sued for a gross breach of their fiduciary duty to the public. The board is made up of respectable people who didn’t sign up for that.

And of course it would cause chaos for the company if all of OpenAI’s fundraising and governance plans were brought to a screeching halt by a federal court judgment landing at the eleventh hour.

Host Rob Wiblin and Rose Chan Loui discuss all of the above as well as what justification the OpenAI foundation could offer for giving up control of the company despite its charitable purpose, and how the board might adjust their plans to make the for-profit switch more legally palatable.

This episode was originally recorded on March 6, 2025.

Chapters:

• Intro (00:00:11)
• More juicy OpenAI news (00:00:46)
• The court order (00:02:11)
• Elon has two hurdles to jump (00:05:17)
• The judge's sympathy (00:08:00)
• OpenAI's defence (00:11:45)
• Alternative plans for OpenAI (00:13:41)
• Should the foundation give up control? (00:16:38)
• Alternative plaintiffs to Musk (00:21:13)
• The 'special interest party' option (00:25:32)
• How might this play out in the fall? (00:27:52)
• The nonprofit board is in a bit of a bind (00:29:20)
• Is it in the public interest to race? (00:32:23)
• Could the board be personally negligent? (00:34:06)

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

A casino offers you a game. A coin will be tossed. If it comes up heads on the first flip you win $2. If it comes up on the second flip you win $4. If it comes up on the third you win $8, the fourth you win $16, and so on. How much should you be willing to pay to play?

The standard way of analysing gambling problems, ‘expected value’ — in which you multiply probabilities by the value of each outcome and then sum them up — says your expected earnings are infinite. You have a 50% chance of winning $2, for '0.5 * $2 = $1' in expected earnings. A 25% chance of winning $4, for '0.25 * $4 = $1' in expected earnings, and on and on. A never-ending series of $1s added together comes to infinity. And that's despite the fact that you know with certainty you can only ever win a finite amount!

Today's guest — philosopher Alan Hájek of the Australian National University — thinks of much of philosophy as “the demolition of common sense followed by damage control” and is an expert on paradoxes related to probability and decision-making rules like “maximise expected value.”

Rebroadcast: this episode was originally released in October 2022.

Links to learn more, highlights, and full transcript.

The problem described above, known as the St. Petersburg paradox, has been a staple of the field since the 18th century, with many proposed solutions. In the interview, Alan explains how very natural attempts to resolve the paradox — such as factoring in the low likelihood that the casino can pay out very large sums, or the fact that money becomes less and less valuable the more of it you already have — fail to work as hoped.

We might reject the setup as a hypothetical that could never exist in the real world, and therefore of mere intellectual curiosity. But Alan doesn't find that objection persuasive. If expected value fails in extreme cases, that should make us worry that something could be rotten at the heart of the standard procedure we use to make decisions in government, business, and nonprofits.

These issues regularly show up in 80,000 Hours' efforts to try to find the best ways to improve the world, as the best approach will arguably involve long-shot attempts to do very large amounts of good.

Consider which is better: saving one life for sure, or three lives with 50% probability? Expected value says the second, which will probably strike you as reasonable enough. But what if we repeat this process and evaluate the chance to save nine lives with 25% probability, or 27 lives with 12.5% probability, or after 17 more iterations, 3,486,784,401 lives with a 0.00000009% chance. Expected value says this final offer is better than the others — 1,000 times better, in fact.

Ultimately Alan leans towards the view that our best choice is to “bite the bullet” and stick with expected value, even with its sometimes counterintuitive implications. Where we want to do damage control, we're better off looking for ways our probability estimates might be wrong.

In this conversation, originally released in October 2022, Alan and Rob explore these issues and many others:

• Simple rules of thumb for having philosophical insights
• A key flaw that hid in Pascal's wager from the very beginning
• Whether we have to simply ignore infinities because they mess everything up
• What fundamentally is 'probability'?
• Some of the many reasons 'frequentism' doesn't work as an account of probability
• Why the standard account of counterfactuals in philosophy is deeply flawed
• And why counterfactuals present a fatal problem for one sort of consequentialism

Chapters:

• Cold open {00:00:00}
• Rob's intro {00:01:05}
• The interview begins {00:05:28}
• Philosophical methodology {00:06:35}
• Theories of probability {00:40:58}
• Everyday Bayesianism {00:49:42}
• Frequentism {01:08:37}
• Ranges of probabilities {01:20:05}
• Implications for how to live {01:25:05}
• Expected value {01:30:39}
• The St. Petersburg paradox {01:35:21}
• Pascal’s wager {01:53:25}
• Using expected value in everyday life {02:07:34}
• Counterfactuals {02:20:19}
• Most counterfactuals are false {02:56:06}
• Relevance to objective consequentialism {03:13:28}
• Alan’s best conference story {03:37:18}
• Rob's outro {03:40:22}

Producer: Keiran Harris
Audio mastering: Ben Cordell and Ryan Kessler
Transcriptions: Katy Moore