What happens when civilisation faces its greatest tests?
This compilation brings together insights from researchers, defence experts, philosophers, and policymakers on humanity’s ability to survive and recover from catastrophic events. From nuclear winter and electromagnetic pulses to pandemics and climate disasters, we explore both the threats that could bring down modern civilisation and the practical solutions that could help us bounce back.
You’ll hear from:
Zach Weinersmith on how settling space won’t help with threats to civilisation anytime soon (unless AI gets crazy good) (from episode #187)
Luisa Rodriguez on what the world might look like after a global catastrophe, how we might lose critical knowledge, and how fast populations might rebound (#116)
David Denkenberger on disruptions to electricity and communications we should expect in a catastrophe, and his work researching low-cost, low-tech solutions to make sure everyone is fed no matter what (#50 and #117)
Lewis Dartnell on how we could recover without much coal or oil, and changes we could make today to make us more resilient to potential catastrophes (#131)
Andy Weber on how people in US defence circles think about nuclear winter, and the tech that could prevent catastrophic pandemics (#93)
Toby Ord on the many risks to our atmosphere, whether climate change and rogue AI could really threaten civilisation, and whether we could rebuild from a small surviving population (#72 and #219)
Mark Lynas on how likely it is that widespread famine from climate change leads to civilisational collapse (#85)
Kevin Esvelt on the human-caused pandemic scenarios that could bring down civilisation — and how AI could help bad actors succeed (#164)
Joan Rohlfing on why we need to worry about more than just nuclear winter (#125)
Annie Jacobsen on the rings of annihilation and electromagnetic pulses from nuclear blasts (#192)
Christian Ruhl on thoughtful philanthropy that funds “right of boom” interventions to prevent nuclear war from threatening civilisation (80k After Hours)
Athena Aktipis on whether society would go all Mad Max in the apocalypse, and the best ways to prepare for a catastrophe (#144)
Will MacAskill on why potatoes are so cool (#130 and #136)
Content editing: Katy Moore and Milo McGuire Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Music: Ben Cordell Transcriptions and web: Katy Moore
AI 2027, a research-based scenario and report from the AI Futures Project, combines forecasting and storytelling to explore a possible future where AI radically transforms the world by 2027.
The report goes through the creation of AI agents, job loss, the role of AIs improving other AIs (R&D acceleration loops), security crackdowns, misalignment — and then a choice: slow down or race ahead.
Kokotajlo’s predictions from 2021 (pre-ChatGPT) in What 2026 looks like have proved prescient, and co-author Eli Lifland is among the world’s top forecasters. So even if you don’t end up buying all its claims, the report’s grounding in serious forecaster views, research, and dozens of wargames makes it worth taking seriously.
Why watch our AI 2027 video
Containing expert interviews, our analysis, and discussion of what a sane world would be doing, we think the video will be an enjoyable and informative watch whether you’re familiar with the report or not.
The video:
Takes you through one of the most detailed and influential AI forecast to date and brings you into the story
Explains the key upshots — how AI progress could accelerate dramatically via powerful feedback loops,
Ryan Greenblatt — lead author on the explosive paper “Alignment faking in large language models” and chief scientist at Redwood Research — thinks there’s a 25% chance that within four years, AI will be able to do everything needed to run an AI company, from writing code to designing experiments to making strategic and business decisions.
As Ryan lays out, AI models are “marching through the human regime”: systems that could handle five-minute tasks two years ago now tackle 90-minute projects. Double that a few more times and we may be automating full jobs rather than just parts of them.
Will setting AI to improve itself lead to an explosive positive feedback loop? Maybe, but maybe not.
The explosive scenario: Once you’ve automated your AI company, you could have the equivalent of 20,000 top researchers, each working 50 times faster than humans with total focus. “You have your AIs, they do a bunch of algorithmic research, they train a new AI, that new AI is smarter and better and more efficient… that new AI does even faster algorithmic research.” In this world, we could see years of AI progress compressed into months or even weeks.
With AIs now doing all of the work of programming their successors and blowing past the human level, Ryan thinks it would be fairly straightforward for them to take over and disempower humanity, if they thought doing so would better achieve their goals. In the interview he lays out the four most likely approaches for them to take.
The linear progress scenario: You automate your company but progress barely accelerates. Why? Multiple reasons, but the most likely is “it could just be that AI R&D research bottlenecks extremely hard on compute.” You’ve got brilliant AI researchers, but they’re all waiting for experiments to run on the same limited set of chips, so can only make modest progress.
Ryan’s median guess splits the difference: perhaps a 20x acceleration that lasts for a few months or years. Transformative, but less extreme than some in the AI companies imagine.
And his 25th percentile case? Progress “just barely faster” than before. All that automation, and all you’ve been able to do is keep pace.
Unfortunately the data we can observe today is so limited that it leaves us with vast error bars. “We’re extrapolating from a regime that we don’t even understand to a wildly different regime,” Ryan believes, “so no one knows.”
But that huge uncertainty means the explosive growth scenario is a plausible one — and the companies building these systems are spending tens of billions to try to make it happen.
In this extensive interview, Ryan elaborates on the above and the policy and technical response necessary to insure us against the possibility that they succeed — a scenario society has barely begun to prepare for.
This episode was recorded on February 21, 2025.
Video editing: Luke Monsour, Simon Monsour, and Dominic Armstrong Audio engineering: Ben Cordell, Milo McGuire, and Dominic Armstrong Music: Ben Cordell Transcriptions and web: Katy Moore
The interview in a nutshell
Ryan Greenblatt, chief scientist at Redwood Research, contemplates a scenario where:
Full AI R&D automation occurs within 4–8 years, something Ryan places 25–50% probability on.
This triggers explosive recursive improvement (5–6 orders of magnitude in one year) — a crazy scenario we can’t rule out
Multiple plausible takeover approaches exist if models are misaligned
Both technical and governance interventions are urgently needed
1. AI R&D automation could happen within 4–8 years
Ryan estimates a 25% chance of automating AI R&D within 4 years, and 50% within 8 years. This timeline is based on recent rapid progress in AI capabilities, particularly in 2024:
AI systems have progressed from barely completing 5–10 minute tasks to handling 1.5-hour software engineering tasks with 50% success rates — and the length of tasks AIs can complete is doubling every 6 months and shows signs of increasing.
Current internal models at OpenAI reportedly rank in the top 50 individuals on Codeforces, and AIs have reached the level of very competitive 8th graders on competition math.
Training compute is increasing ~4x per year, and reasoning models (o1, o3, R1) show dramatic improvements with relatively modest compute investments.
Algorithmic progress has been increasing 3–5x per year in terms of effective training compute — making less compute go further.
Reinforcement learning on reasoning models is showing dramatic gains, and DeepSeek-R1 reportedly used only ~$1 million for reinforcement learning, suggesting massive potential for scaling.
But there’s also evidence that progress could slow down:
10–20% of global chip production is already earmarked for AI. (Though Ryan is uncertain about the exact number.)
AI companies are running out of high-quality training data.
Scaleups are going to require trillion-dollar investments, and each order of magnitude might yield less improvement.
It’s unclear whether narrow skills improvements will generalise to broad domains.
2. AI R&D automation could trigger explosive recursive self-improvement
When AI can automate AI research itself, that could set off an intelligence explosion as smarter AIs improve algorithms faster before hitting efficiency limits.
At the point when full AI R&D automation starts, Ryan expects:
10–50x faster progress than current rates (median estimate ~20x)
Companies might dedicate 80% of compute to internal use, squeezing out external customers
Advantages that add up to a more than 50x labour speed advantage compared to humans:
5x from generically running faster
3x from AIs working 24/7
2x from better coordination and context sharing between AIs
2x from the ability to swap between different capability levels for efficiency
Ryan’s median estimate is 5–6 orders of magnitude of algorithmic progress within one year after full automation begins.
3. Multiple plausible takeover scenarios exist if models are misaligned
As part of his work at Redwood, Ryan has also explored AI takeover scenarios from these superintelligent models.
Early takeover mechanisms include:
AIs using compute without authorisation within companies
Models escaping containment and coordinating with internal copies
Massive hacking campaigns to destabilise human response
Late takeover mechanisms include:
“Humans give AIs everything”: AIs appear helpful while secretly consolidating control
Waiting for takeover might increase chances of success with more resources and infrastructure available to them — but AIs could attempt earlier takeover due to fear of being replaced by newer models with different preferences, or due to rapid progress in safety research and/or coordination between humans.
4. Both technical and governance interventions are urgently needed
Ryan thinks there are several promising areas where listeners could contribute to reduce the above risks.
Technical research:
Ensuring AIs can’t cause harm even if they’re misaligned through AI control
Creating “model organisms”: testable examples of misaligned models
Showing current AIs’ capabilities to increase awareness and political appetite for action like “pausing at human level”
Interpreting non-human-language reasoning to detect deceptive cognition
Governance work:
Enabling verification of training claims between nations through compute governance
Hardening defences against model theft, unauthorised deployment, bioweapons, and cyberattacks
Facilitating coordination between companies and countries
Podcast by Robert Wiblin · Published June 24th, 2025
The era of making AI smarter by just making it bigger is ending. But that doesn’t mean progress is slowing down — far from it. AI models continue to get much more powerful, just using very different methods. And those underlying technical changes force a big rethink of what coming years will look like.
Toby Ord — Oxford philosopher and bestselling author of The Precipice — has been tracking these shifts and mapping out the implications both for governments and our lives.
As he explains, until recently anyone can access the best AI in the world “for less than the price of a can of Coke.” But unfortunately, that’s over.
What changed? AI companies first made models smarter by throwing a million times as much computing power at them during training, to make them better at predicting the next word. But with high quality data drying up, that approach petered out in 2024.
So they pivoted to something radically different: instead of training smarter models, they’re giving existing models dramatically more time to think — leading to the rise in “reasoning models” that are at the frontier today.
The results are impressive but this extra computing time comes at a cost: OpenAI’s o3 reasoning model achieved stunning results on a famous AI test by writing an Encyclopedia Britannica‘s worth of reasoning to solve individual problems — at a cost of over $1,000 per question.
This isn’t just technical trivia: if this improvement method sticks, it will change much about how the AI revolution plays out — starting with the fact that we can expect the rich and powerful to get access to the best AI models well before the rest of us.
Companies have also begun applying “reinforcement learning” in which models are asked to solve practical problems, and then told to “do more of that” whenever it looks like they’ve gotten the right answer.
This has led to amazing advances in problem-solving ability — but it also explains why AI models have suddenly gotten much more deceptive. Reinforcement learning has always had the weakness that it encourages creative cheating, or tricking people into thinking you got the right answer even when you didn’t.
Toby shares typical recent examples of this “reward hacking” — from models Googling answers while pretending to reason through the problem (a deception hidden in OpenAI’s own release data), to achieving “100x improvements” by hacking their own evaluation systems.
To cap it all off, it’s getting harder and harder to trust publications from AI companies, as marketing and fundraising have become such dominant concerns.
While companies trumpet the impressive results of the latest models, Toby points out that they’ve actually had to spend a million times as much just to cut model errors by half. And his careful inspection of an OpenAI graph supposedly demonstrating that o3 was the new best model in the world revealed that it was actually no more efficient than its predecessor.
But Toby still thinks it’s critical to pay attention, given the stakes:
…there is some snake oil, there is some fad-type behaviour, and there is some possibility that it is nonetheless a really transformative moment in human history. It’s not an either/or. I’m trying to help people see clearly the actual kinds of things that are going on, the structure of this landscape, and to not be confused by some of these charts.
Recorded on May 23, 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
Blog post by Brenton Mayer · Published June 20th, 2025
We’re looking for someone to build and lead 80,000 Hours’ recruiting function from scratch.
We’d like to make 15–25 strong hires each year through a recruiting team. Currently, we make around 10 hires — each of which represents a very large investment in capacity from team leads — which is a major bottleneck on our growth.
Our ideal candidate can solve the problems, hire the team, and build the systems needed to pull this off. So we expect this to be a challenging role which will require substantial relevant experience.
We’re looking for someone who has experience with:
The personnel challenges faced by organisations working in EA or AI — you’ve either worked at an EA organisation, an AI safety organisation, or have otherwise developed a strong understanding of the landscape of hiring challenges these organisations face.
Professional recruiting — this could be as an internally-facing recruiter or as a headhunter. Recruiting for your own team could be enough, especially if you’ve been especially interested in recruitment along the way.
Leading a team — you’ve managed people before and understand what it takes to build and lead a function.
We expect that you’ll initially report to Brenton Mayer (COO) and then transition to reporting to Sashika Coxhead (Head of People Operations) when she returns from maternity leave.
If you meet all three of the criteria mentioned above, we’d really love to hear from you through our EOI form.
80,000 Hours’ aim is to help people find careers that tackle the world’s most pressing problems. To do this, one thing we do is maintain a public list of what we see as the issues where additional people can have the greatest positive impact.
We’ve just made significant updates to our list. Here are the biggest changes:
We’ve broadened our coverage of particularly pressing issues downstream of the possibility that artificial general intelligence (AGI) might be here soon. In particular, we added a profile on AI-enabled power grabs near the top of our list and are adding several writeups of new emerging challenges that advanced AI could create or worsen.
We’ve removed ‘meta’ problems for simplicity and clarity. Our problem profiles list used to feature articles on building effective altruism, broadly improving institutional decision making, and global priorities research — which are all approaches to improving our ability to solve the world’s most pressing problems. Grouping these ‘meta problems’ with object-level problems sometimes causes confusion and makes it hard to compare across cause areas, so we’ve now taken them off the list. But we still think these topics are very important, so the articles are still live on our site, and related articles appear on our list of impactful career paths.
We’ve streamlined the presentation by consolidating related issues and restructuring the page as a more unified ranking rather than separate categories.
AI guide by Benjamin Todd · Published June 16th, 2025
About half of people are worried they’ll lose their job to AI. And they’re right to be concerned: AI can now complete real-world coding tasks on GitHub, generate photorealistic video, drive a taxi more safely than humans, and do accurate medical diagnosis. And over the next five years, it’s set to continue to improve rapidly. Eventually, mass automation and falling wages are a real possibility.
But what’s less appreciated is that while AI drives down the value of skills it can do, it drives up the value of skills it can’t. Wages (on average) will increase before they fall, as automation generates a huge amount of wealth, and the remaining tasks become the bottlenecks to further growth. As I’ll explain, ATMs actually increased employment of bank clerks— until online banking automated the job much more.
Your best strategy is to learn the skills that AI will make more valuable, trying to ride the wave of automation. So what are those skills? Here’s a preview:
Skills most likely to increase in value as AI progresses
These will be especially valuable when combined with knowledge of fields needed for AI including machine learning, cyber & information security, data centre & power plant construction, robotics development and maintenance, and (lesso) fields that could expand a lot given economic growth.
In contrast, the future for these skills seems a lot more uncertain:
Podcast by Robert Wiblin · Published June 12th, 2025
For decades, US allies have slept soundly under the protection of America’s overwhelming military might. Donald Trump — with his threats to ditch NATO, seize Greenland, and abandon Taiwan — seems hell-bent on shattering that comfort.
But according to Hugh White — one of the world’s leading strategic thinkers, emeritus professor at the Australian National University, and author of Hard New World: Our Post American Future — Trump isn’t destroying American hegemony. He’s simply revealing that it’s already gone.
“Trump has very little trouble accepting other great powers as co-equals,” Hugh explains. And that happens to align perfectly with a strategic reality the foreign policy establishment desperately wants to ignore: fundamental shifts in global power have made the costs of maintaining a US-led hegemony prohibitively high.
Even under Biden, when Russia invaded Ukraine, the US sent weapons but explicitly ruled out direct involvement. Ukraine matters far more to Russia than America, and this “asymmetry of resolve” makes Putin’s nuclear threats credible where America’s counterthreats simply aren’t.
Hugh’s gloomy prediction: “Europeans will end up conceding to Russia whatever they can’t convince the Russians they’re willing to fight a nuclear war to deny them.”
The Pacific tells the same story. Despite Obama’s “pivot to Asia” and Biden’s tough talk about “winning the competition for the 21st century,” actual US military capabilities there have barely budged while China’s have soared, along with its economy — which is now bigger than the US’s, as measured in purchasing power. Containing China and defending Taiwan would require America to spend 8% of GDP on defence (versus 3.5% today) — and convince Beijing it’s willing to accept Los Angeles being vaporised. Unlike during the Cold War, no president — Trump or otherwise — can make that case to voters.
So what’s next? Hugh’s prognoses are stark:
Taiwan is in an impossible situation and we’re doing them a disservice pretending otherwise.
South Korea, Japan, and one of the EU or Poland will have to go nuclear to defend themselves.
Trump might actually follow through and annex Panama and Greenland — but probably not Canada.
Australia can defend itself from China but needs an entirely different military to do it.
Our new “multipolar” future, split between American, Chinese, Russian, Indian, and European spheres of influence, is a “darker world” than the golden age of US dominance. But Hugh’s message is blunt: for better or worse, 35 years of American hegemony are over. The challenge now is managing the transition peacefully, and creating a stable multipolar order more like Europe’s relatively peaceful 19th century than the chaotic bloodbath Europe suffered in the 17th — which, if replicated today, would be a nuclear bloodbath.
In today’s conversation, Hugh and Rob explore why even AI supremacy might not restore US dominance (spoiler: China still has nukes), why Japan can defend itself but Taiwan can’t, and why a new president won’t be able to reverse the big picture.
This episode was originally recorded on May 30, 2025.
Video editing: Simon Monsour Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Music: Ben Cordell Transcriptions and web: Katy Moore
Independently carry out complicated tasks for hours, days, or weeks
Interact with the physical world in complex and adaptive ways
Consistently learn from past interactions to improve performance over time
In other words, we don’t yet have AGI — AI systems with general intelligence that can reliably replace humans on a wide range of tasks.
In 2024, OpenAI’s Sam Altman declared, “we are now confident we know how to build AGI.” But how will we get there?
Some people think that we can build AGI by scaling up existing models. But others argue that scaling has only seriously improved AI performance in areas like software engineering, where the tasks are clearly defined and often quickly verifiable.
Blog post by Matt Beard · Published June 4th, 2025
Sometimes, our advising team speaks to people who have enthusiasm for technical AI safety and a related skill set but need concrete ideas for how to enter the field. This list was developed in consultation with our advisors to find the resources they commonly share, including articles, courses, organisations, and fellowships.
While we recommend applying to speak to an advisor for 1-1 tailored guidance, this page gives a practical, non-comprehensive snapshot of how you might move from ‘interested in technical AI safety’ to ‘starting to work on technical AI safety.’
AI models today have a 50% chance of successfully completing a task that would take an expert human one hour. Seven months ago, that number was roughly 30 minutes — and seven months before that, 15 minutes.
These are substantial, multi-step tasks requiring sustained focus: building web applications, conducting machine learning research, or solving complex programming challenges.
Today’s guest, Beth Barnes, is CEO of METR (Model Evaluation & Threat Research) — the leading organisation measuring these capabilities.
Beth’s team has been timing how long it takes skilled humans to complete projects of varying length, then seeing how AI models perform on the same work.
The resulting paper from METR, “Measuring AI ability to complete long tasks,” made waves by revealing that the planning horizon of AI models was doubling roughly every seven months. It’s regarded by many as the most useful AI forecasting work in years.
The companies building these systems aren’t just aware of this trend — they want to harness it as much as possible, and are aggressively pursuing automation of their own research.
And having AI models rapidly build their successors with limited human oversight naturally raises the risk that things will go off the rails if the models at the end of the process lack the goals and constraints we hoped for.
Beth thinks models can already do “meaningful work” on improving themselves, and she wouldn’t be surprised if AI models were able to autonomously self-improve in as little as two years from now — in fact, she says, “It seems hard to rule out even shorter [timelines]. Is there 1% chance of this happening in six, nine months? Yeah, that seems pretty plausible.”
While Silicon Valley is abuzz with these numbers, policymakers remain largely unaware of what’s barrelling toward us — and given the current 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. Inasmuch as there are experts, they are saying that this is concerning. … And to the extent that I am an expert, I am an expert telling you you should freak out. And there’s not especially anyone else who isn’t saying this.
Beth and Rob discuss all that, plus:
How Beth now thinks that open-weight models are a good thing for AI safety, and what changed her mind
How our poor information security means there’s no such thing as a “closed-weight” model anyway
Whether we can see if an AI is scheming in its chain-of-thought reasoning, and the latest research on “alignment faking”
Why just before deployment is the worst time to evaluate model safety
Why Beth thinks AIs could end up being really good at creative and novel research — something humans tend to think is beyond their reach
Why Beth thinks safety-focused people should stay out of the frontier AI companies — and the advantages smaller organisations have
Areas of AI safety research that Beth thinks is overrated and underrated
Whether it’s feasible to have a science that translates AI models’ increasing use of nonhuman language or ‘neuralese’
How AI is both similar to and different from nuclear arms racing and bioweapons
And much more besides!
This episode was originally recorded on February 17, 2025.
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
We’re excited to announce that 80,000 Hours has completed its spin-out from Effective Ventures (EV) and is now operating as an independent organisation. We announced this decision here in December 2023 and we’ve now concluded spinning out from our parent organisation. We’re deeply grateful to the Effective Ventures leadership and team for their support, especially during the complex transition process over the past year.
Our new structure
We’ve established two new UK entities, each with their own board:
80,000 Hours Limited — this is a nonprofit entity that houses our website, podcast, job board, one-on-one service, and our operations.
80,000 Hours Foundation — this is a registered charity that will facilitate donations and own the 80k intellectual property.
Our new boards
Board of Directors (80,000 Hours Limited):
Konstantin Sietzy — Deputy Director of Talent and Operations at UK AISI
Alex Lawsen — Senior Program Associate at Open Philanthropy and former 80,000 Hours Advising Manager
Anna Weldon — COO at the Centre for Effective Altruism (CEA) and former EV board member
What if there’s something it’s like to be a shrimp — or a chatbot?
For centuries, humans have debated the nature of consciousness, often placing ourselves at the very top. But what about the minds of others — both the animals we share this planet with and the artificial intelligences we’re creating?
We’ve pulled together clips from past conversations with researchers and philosophers who’ve spent years trying to make sense of animal consciousness, artificial sentience, and moral consideration under deep uncertainty.
You’ll hear from:
Robert Long on how we might accidentally create artificial sentience (from episode #146)
Jeff Sebo on when we should extend extend moral consideration to digital beings — and what that would even look like (#173)
Jonathan Birch on what we should learn from the cautionary tale of newborn pain, and other “edge cases” of sentience (#196)
Andrés Jiménez Zorrilla on what it’s like to be a shrimp (80k After Hours)
Meghan Barrett on challenging our assumptions about insects’ experiences (#198)
David Chalmers on why artificial consciousness is entirely possible (#67)
Holden Karnofsky on how we’ll see digital people as… people (#109)
Sébastien Moro on the surprising sophistication of fish cognition and behaviour (#205)
Bob Fischer on how to compare the moral weight of a chicken to that of a human (#182)
Cameron Meyer Shorb on the vast scale of potential wild animal suffering (#210)
Lewis Bollard on how animal advocacy has evolved in response to sentience research (#185)
Anil Seth on the neuroscientific theories of consciousness (#206)
Peter Godfrey-Smith on whether we could upload ourselves to machines (#203)
Buck Shlegeris on whether AI control strategies make humans the bad guys (#214)
Stuart Russell on the moral rights of AI systems (#80)
Will MacAskill on how to integrate digital beings into society (#213)
Carl Shulman on collaboratively sharing the world with digital minds (#191)
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Additional content editing: Katy Moore and Milo McGuire Transcriptions and web: Katy Moore
OpenAI’s recent announcement that its nonprofit would “retain control” of its for-profit business sounds reassuring. But this seemingly major concession, celebrated by so many, is in itself largely meaningless.
Litigator Tyler Whitmer is a coauthor of a newly published letter that describes this attempted sleight of hand and directs regulators on how to stop it.
As Tyler explains, the plan both before and after this announcement has been to convert OpenAI into a Delaware public benefit corporation (PBC) — and this alone will dramatically weaken the nonprofit’s ability to direct the business in pursuit of its charitable purpose: ensuring AGI is safe and “benefits all of humanity.”
Right now, the nonprofit directly controls the business. But were OpenAI to become a PBC, the nonprofit, rather than having its “hand on the lever,” would merely contribute to the decision of who does.
Why does this matter? Today, if OpenAI’s commercial arm were about to release an unhinged AI model that might make money but be bad for humanity, the nonprofit could directly intervene to stop it. In the proposed new structure, it likely couldn’t do much at all.
But it’s even worse than that: even if the nonprofit could select the PBC’s directors, those directors would have fundamentally different legal obligations from those of the nonprofit. A PBC director must balance public benefit with the interests of profit-driven shareholders — by default, they cannot legally prioritise public interest over profits, even if they and the controlling shareholder that appointed them want to do so.
As Tyler points out, there isn’t a single reported case of a shareholder successfully suing to enforce a PBC’s public benefit mission in the 10+ years since the Delaware PBC statute was enacted.
This extra step from the nonprofit to the PBC would also mean that the attorneys general of California and Delaware — who today are empowered to ensure the nonprofit pursues its mission — would find themselves powerless to act. These are probably not side effects but rather a Trojan horse for-profit investors are trying to slip past regulators.
Fortunately this can all be addressed — but it requires either the nonprofit board or the attorneys general of California and Delaware to promptly put their foot down and insist on watertight legal agreements that preserve OpenAI’s current governance safeguards and enforcement mechanisms.
As Tyler explains, the same arrangements that currently bind the OpenAI business have to be written into a new PBC’s certificate of incorporation — something that won’t happen by default and that powerful investors have every incentive to resist.
Without these protections, OpenAI’s new suggested structure wouldn’t “fix” anything. They would be a ruse that preserved the appearance of nonprofit control while gutting its substance.
Listen to our conversation with Tyler Whitmer to understand what’s at stake, and what the AGs and board members must do to ensure OpenAI remains committed to developing artificial general intelligence that benefits humanity rather than just investors.
This episode was originally recorded on May 13, 2025.
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 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.
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:
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.
Insisting that a majority of board members are truly independent by prohibiting indirect as well as direct financial stakes in the business.
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.
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
Our site has an overview of what’s happening with AGI, but here’s some more essential reading for understanding the field. We don’t agree with everything the authors say, but we think they’re well worth reading.
These authors argue that an “intelligence explosion” could compress a century of technological progress into a decade, creating numerous grand challenges that humanity must prepare for now. You can listen to Will MacAskill discuss this piece on our podcast.
2. AI 2027 by Daniel Kokotajlo, Scott Alexander, Thomas Larsen, Eli Lifland, and Romeo Dean (April 2025)
An analysis of a concrete scenario in which AGI arrives soon via the automation of AI research. The team also provides its own forecasts of several key outcomes in the accompanying research. This has become one of the most discussed pieces of research in the field.
A former OpenAI employee makes a compelling case — across five in-depth chapters — that AGI is coming much sooner than many expect, and few realise just how much it will change the world. We think this piece might underplay the challenge of aligning AGI with human interests and the need for international coordination on AI risks. However, many of its predictions about the development of agentic reasoning models have proved remarkably accurate.
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.
For instance:
Ministerial appointments in complex areas like health or defence typically go to whoever can best shore up the prime minister’s support within their own party and prevent a leadership challenge, rather than people who have any experience at all with the area.
On average, ministers are removed after just two years, so the few who manage to learn their brief are typically gone just as they’re becoming effective. In the middle of a housing crisis, Britain went through 25 housing ministers in 25 years.
Ministers are expected to make some of their most difficult decisions by reading paper memos out of a ‘red box’ while exhausted, at home, after dinner.
Tradition demands that the country be run from a cramped Georgian townhouse: 10 Downing Street. Few staff fit and teams are split across multiple floors. Meanwhile, the country’s most powerful leaders vie to control the flow of information to and from the prime minister via ‘professionalised loitering’ outside their office.
Civil servants are paid too little to retain those with technical skills, who can earn several times as much in the private sector. For those who do want to stay, the only way to get promoted is to move departments — abandoning any area-specific knowledge they’ve accumulated.
As a result, senior civil servants handling complex policy areas have a median time in role as low as 11 months. Turnover in the Treasury has regularly been 25% annually — comparable to a McDonald’s restaurant.
MPs are chosen by local party members overwhelmingly on the basis of being ‘loyal party people,’ while the question of whether they are good at understanding or scrutinising legislation (their supposed constitutional role) simply never comes up.
The end result is that very few of the most powerful people in British politics have much idea what they’re actually doing. As Ian puts it, the country is at best run by a cadre of “amateur generalists.”
While some of these are unique British failings, many others are recurring features of governments around the world, and similar dynamics can arise in large corporations as well.
But as Ian also lays out, most of these absurdities have natural solutions, and in every case 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.
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.
You’ll hear from:
Michael Webb on using AI as a career advisor and the human skills AI can’t replace (from episode #161)
Holden Karnofsky on kicking ass in whatever you do, and which weird ideas are worth betting on (#109, #110, and #158)
Chris Olah on how intersections of particular skills can be a wildly valuable niche (#108)
Michelle Hutchinson on understanding what truly motivates you (#75)
Benjamin Todd on how to make tough career decisions and deal with rejection (#71 and 80k After Hours)
Jeff Sebo on what improv comedy teaches us about doing good in the world (#173)
Spencer Greenberg on recognising toxic people who could derail your career (#183)
Dean Spears on embracing randomness and serendipity (#186)
Karen Levy on finding yourself through travel (#124)
Leah Garcés on finding common ground with unlikely allies (#99)
Hannah Ritchie on being selective about whose advice you follow (#160)
Pardis Sabeti on prioritising physical health (#104)
Sarah Eustis-Guthrie on knowing when to pivot from your current path (#207)
Danny Hernandez on setting triggers for career decisions (#78)
Varsha Venugopal on embracing uncomfortable situations (#113)
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Content editing: Katy Moore and Milo McGuire Transcriptions and web: Katy Moore
Problem profile by Cody Fenwick · Published April 24th, 2025
Advanced AI technology may enable its creators, or others who control it, to attempt and achieve unprecedented societal power grabs. Under certain circumstances, they could use these systems to take control of whole economies, militaries, and governments.
This kind of power grab from a single person or small group would pose a major threat to the rest of humanity.
