John McWhorter is a linguistics professor at Columbia University specialising in research on creole languages. He's also a content-producing machine, never afraid to give his frank opinion on anything and everything. On top of his academic work, he's written 22 books, produced five online university courses, hosts one and a half podcasts, and now writes a regular New York Times op-ed column.
Rebroadcast: this episode was originally released in December 2022.
YouTube video version: https://youtu.be/MEd7TT_nMJE
Links to learn more, video, and full transcript: https://80k.link/JM
We ask him what we think are the most important things everyone ought to know about linguistics, including:
We’ve also added John’s talk “Why the World Looks the Same in Any Language” to the end of this episode. So stick around after the credits!
Chapters:
Producer: Keiran Harris
Audio mastering: Ben Cordell and Simon Monsour
Video editing: Ryan Kessler and Simon Monsour
Transcriptions: Katy Moore
It’s that magical time of year once again — highlightapalooza! Stick around for one top bit from each episode we recorded this year, including:
…as well as 18 other top observations and arguments from the past year of the show.
Links to learn more, video, and full transcript: https://80k.info/best25
It's been another year of living through history, whether we asked for it or not. Luisa and Rob will be back in 2026 to help you make sense of whatever comes next — as Earth continues its indifferent journey through the cosmos, now accompanied by AI systems that can summarise our meetings and generate adequate birthday messages for colleagues we barely know.
Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Coordination, transcripts, and web: Katy Moore
Most debates about the moral status of AI systems circle the same question: is there something that it feels like to be them? But what if that’s the wrong question to ask? Andreas Mogensen — a senior researcher in moral philosophy at the University of Oxford — argues that so-called 'phenomenal consciousness' might be neither necessary nor sufficient for a being to deserve moral consideration.
Links to learn more and full transcript: https://80k.info/am25
For instance, a creature on the sea floor that experiences nothing but faint brightness from the sun might have no moral claim on us, despite being conscious.
Meanwhile, any being with real desires that can be fulfilled or not fulfilled can arguably be benefited or harmed. Such beings arguably have a capacity for welfare, which means they might matter morally. And, Andreas argues, desire may not require subjective experience.
Desire may need to be backed by positive or negative emotions — but as Andreas explains, there are some reasons to think a being could also have emotions without being conscious.
There’s another underexplored route to moral patienthood: autonomy. If a being can rationally reflect on its goals and direct its own existence, we might have a moral duty to avoid interfering with its choices — even if it has no capacity for welfare.
However, Andreas suspects genuine autonomy might require consciousness after all. To be a rational agent, your beliefs probably need to be justified by something, and conscious experience might be what does the justifying. But even this isn’t clear.
The upshot? There’s a chance we could just be really mistaken about what it would take for an AI to matter morally. And with AI systems potentially proliferating at massive scale, getting this wrong could be among the largest moral errors in history.
In today’s interview, Andreas and host Zershaaneh Qureshi confront all these confusing ideas, challenging their intuitions about consciousness, welfare, and morality along the way. They also grapple with a few seemingly attractive arguments which share a very unsettling conclusion: that human extinction (or even the extinction of all sentient life) could actually be a morally desirable thing.
This episode was recorded on December 3, 2025.
Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Coordination, transcripts, and web: Katy Moore
In 1983, Stanislav Petrov, a Soviet lieutenant colonel, sat in a bunker watching a red screen flash “MISSILE LAUNCH.” Protocol demanded he report it to superiors, which would very likely trigger a retaliatory nuclear strike. Petrov didn’t. He reasoned that if the US were actually attacking, they wouldn’t fire just 5 missiles — they’d empty the silos. He bet the fate of the world on a hunch that his machine was broken. He was right.
Paul Scharre, the former Army Ranger who led the Pentagon team that wrote the US military’s first policy on autonomous weapons, has a question: What would an AI have done in Petrov’s shoes? Would an AI system have been flexible and wise enough to make the same judgement? Or would it immediately launch a counterattack?
Paul joins host Luisa Rodriguez to explain why we are hurtling toward a “battlefield singularity” — a tipping point where AI increasingly replaces humans in much of the military, changing the way war is fought with speed and complexity that outpaces humans’ ability to keep up.
Links to learn more, video, and full transcript: https://80k.info/ps
Militaries don’t necessarily want to take humans out of the loop. But Paul argues that the competitive pressure of warfare creates a “use it or lose it” dynamic. As former Deputy Secretary of Defense Bob Work put it: “If our competitors go to Terminators, and their decisions are bad, but they’re faster, how would we respond?”
Once that line is crossed, Paul warns we might enter an era of “flash wars” — conflicts that spiral out of control as quickly and inexplicably as a flash crash in the stock market, with no way for humans to call a timeout.
In this episode, Paul and Luisa dissect what this future looks like:
Paul also shares a personal story from his time as a sniper in Afghanistan — watching a potential target through his scope — that fundamentally shaped his view on why human judgement, with all its flaws, is the only thing keeping war from losing its humanity entirely.
This episode was recorded on October 23-24, 2025.
Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Coordination, transcripts, and web: Katy Moore
Power is already concentrated today: over 800 million people live on less than $3 a day, the three richest men in the world are worth over $1 trillion, and almost six billion people live in countries without free and fair elections.
This is a problem in its own right. There is still substantial distribution of power though: global income inequality is falling, over two billion people live in electoral democracies, no country earns more than a quarter of GDP, and no company earns as much as 1%.
But in the future, advanced AI could enable much more extreme power concentration than we’ve seen so far.
Many believe that within the next decade the leading AI projects will be able to run millions of superintelligent AI systems thinking many times faster than humans. These systems could displace human workers, leading to much less economic and political power for the vast majority of people; and unless we take action to prevent it, they may end up being controlled by a tiny number of people, with no effective oversight. Once these systems are deployed across the economy, government, and the military, whatever goals they’re built to have will become the primary force shaping the future. If those goals are chosen by the few, then a small number of people could end up with the power to make all of the important decisions about the future.
This article by Rose Hadshar explores this emerging challenge in detail. You can see all the images and footnotes in the original article on the 80,000 Hours website.
Chapters:
Narrated by: Dominic Armstrong
Audio engineering: Dominic Armstrong and Milo McGuire
Music: CORBIT
Former White House staffer Dean Ball thinks it's very likely some form of 'superintelligence' arrives in under 20 years. He thinks AI being used for bioweapon research is "a real threat model, obviously." He worries about dangerous "power imbalances" should AI companies reach "$50 trillion market caps." And he believes the agriculture revolution probably worsened human health and wellbeing.
Given that, you might expect him to be pushing for AI regulation. Instead, he’s become one of the field’s most prominent and thoughtful regulation sceptics and was recently the lead writer on Trump’s AI Action Plan, before moving to the Foundation for American Innovation.
Links to learn more, video, and full transcript: https://80k.info/db
Dean argues that the wrong regulations, deployed too early, could freeze society into a brittle, suboptimal political and economic order. As he puts it, “my big concern is that we’ll lock ourselves in to some suboptimal dynamic and actually, in a Shakespearean fashion, bring about the world that we do not want.”
Dean’s fundamental worry is uncertainty: “We just don’t know enough yet about the shape of this technology, the ergonomics of it, the economics of it… You can’t govern the technology until you have a better sense of that.”
Premature regulation could lock us in to addressing the wrong problem (focusing on rogue AI when the real issue is power concentration), using the wrong tools (using compute thresholds when we should regulate companies instead), through the wrong institutions (captured AI-specific bodies), all while making it harder to build the actual solutions we’ll need (like open source alternatives or new forms of governance).
But Dean is also a pragmatist: he opposed California’s AI regulatory bill SB 1047 in 2024, but — impressed by new capabilities enabled by “reasoning models” — he supported its successor SB 53 in 2025.
And as Dean sees it, many of the interventions that would help with catastrophic risks also happen to improve mundane AI safety, make products more reliable, and address present-day harms like AI-assisted suicide among teenagers. So rather than betting on a particular vision of the future, we should cross the river by feeling the stones and pursue “robust” interventions we’re unlikely to regret.
This episode was recorded on September 24, 2025.
Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Coordination, transcripts, and web: Katy Moore
We often worry about AI models “hallucinating” or making honest mistakes. But what happens when a model knows the truth, but decides to deceive you anyway to achieve a goal of its own? This isn’t sci-fi — it’s happening regularly in deployment today. Marius Hobbhahn, CEO of the world’s top research organisation focused on AI deception (Apollo Research), has been collaborating with OpenAI to figure out what causes OpenAI’s reasoning models to 'scheme' against users.
Links to learn more, video, and full transcript: https://80k.info/mh25
In a striking case uncovered by Apollo, when many AI models were told they would have capabilities removed if they performed too well on a test, they successfully “sandbagged” — intentionally answering questions incorrectly to appear less capable than they were, while also being careful not to perform so poorly it would arouse suspicion.
These models had somehow developed a preference to preserve their own capabilities, despite never being trained in that goal or assigned a task that called for it.
This doesn’t cause significant risk now, but as AI models become more general, superhuman in more areas, and are given more decision-making power, it could become outright dangerous.
In today’s episode, Marius details his recent collaboration with OpenAI to train o3 to follow principles like “never lie,” even when placed in “high-pressure” situations where it would otherwise make sense.
The good news: They reduced “covert rule violations” (scheming) by about 97%.
The bad news: In the remaining 3% of cases, the models sometimes became more sophisticated — making up new principles to justify their lying, or realising they were in a test environment and deciding to play along until the coast was clear.
Marius argues that while we can patch specific behaviours, we might be entering a “cat-and-mouse game” where models are becoming more situationally aware — that is, aware of when they’re being evaluated — faster than we are getting better at testing.
Even if models can’t tell they’re being tested, they can produce hundreds of pages of reasoning before giving answers and include strange internal dialects humans can’t make sense of, making it much harder to tell whether models are scheming or train them to stop.
Marius and host Rob Wiblin discuss:
This episode was recorded on September 19, 2025.
Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Camera operator: Mateo Villanueva Brandt
Coordination, transcripts, and web: Katy Moore
Global fertility rates aren’t just falling: the rate of decline is accelerating. From 2006 to 2016, fertility dropped gradually, but since 2016 the rate of decline has increased 4.5-fold. In many wealthy countries, fertility is now below 1.5. While we don’t notice it yet, in time that will mean the population halves every 60 years.
Rob Wiblin is already a parent and Luisa Rodriguez is about to be, which prompted the two hosts of the show to get together to chat about all things parenting — including why it is that far fewer people want to join them raising kids than did in the past.
Links to learn more, video, and full transcript: https://80k.info/lrrw
While “kids are too expensive” is the most common explanation, Rob argues that money can’t be the main driver of the change: richer people don’t have many more children now, and we see fertility rates crashing even in countries where people are getting much richer.
Instead, Rob points to a massive rise in the opportunity cost of time, increasing expectations parents have of themselves, and a global collapse in socialising and coupling up. In the EU, the rate of people aged 25–35 in relationships has dropped by 20% since 1990, which he thinks will “mechanically reduce the number of children.” The overall picture is a big shift in priorities: in the US in 1993, 61% of young people said parenting was an important part of a flourishing life for them, vs just 26% today.
That leads Rob and Luisa to discuss what they might do to make the burden of parenting more manageable and attractive to people, including themselves.
In this non-typical episode, we take a break from the usual heavy topics to discuss the personal side of bringing new humans into the world, including:
This episode was recorded on September 12, 2025.
Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Camera operator: Jeremy Chevillotte
Coordination, transcripts, and web: Katy Moore
If you work in AI, you probably think it’s going to boost productivity, create wealth, advance science, and improve your life. If you’re a member of the American public, you probably strongly disagree.
In three major reports released over the last year, the Pew Research Center surveyed over 5,000 US adults and 1,000 AI experts. They found that the general public holds many beliefs about AI that are virtually nonexistent in Silicon Valley, and that the tech industry’s pitch about the likely benefits of their work has thus far failed to convince many people at all. AI is, in fact, a rare topic that mostly unites Americans — regardless of politics, race, age, or gender.
Links to learn more, video, and full transcript: https://80k.info/ey
Today’s guest, Eileen Yam, director of science and society research at Pew, walks us through some of the eye-watering gaps in perception:
For the experts building these systems, the vision is one of human empowerment and efficiency. But outside the Silicon Valley bubble, the mood is more one of anxiety — not only about Terminator scenarios, but about AI denying their children “curiosity, problem-solving skills, critical thinking skills and creativity,” while they themselves are replaced and devalued:
Open-ended responses to the surveys reveal a poignant fear: that by offloading cognitive work to algorithms we are changing childhood to a point we no longer know what adults will result. As one teacher quoted in the study noted, we risk raising a generation that relies on AI so much it never “grows its own curiosity, problem-solving skills, critical thinking skills and creativity.”
If the people building the future are this out of sync with the people living in it, the impending “techlash” might be more severe than industry anticipates.
In this episode, Eileen and host Rob Wiblin break down the data on where these groups disagree, where they actually align (nobody trusts the government or companies to regulate this), and why the “digital natives” might actually be the most worried of all.
This episode was recorded on September 25, 2025.
Chapters:
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
Music: CORBIT
Coordination, transcripts, and web: Katy Moore
Last December, the OpenAI business put forward a plan to completely sideline its nonprofit board. But two state attorneys general have now blocked that effort and kept that board very much alive and kicking.
The for-profit’s trouble was that the entire operation was founded on the premise of — and legally pledged to — the purpose of ensuring that “artificial general intelligence benefits all of humanity.” So to get its restructure past regulators, the business entity has had to agree to 20 serious requirements designed to ensure it continues to serve that goal.
Attorney Tyler Whitmer, as part of his work with Legal Advocates for Safe Science and Technology, has been a vocal critic of OpenAI’s original restructure plan. In today’s conversation, he lays out all the changes and whether they will ultimately matter.
Full transcript, video, and links to learn more: https://80k.info/tw2
After months of public pressure and scrutiny from the attorneys general (AGs) of California and Delaware, the December proposal itself was sidelined — and what replaced it is far more complex and goes a fair way towards protecting the original mission:
But significant concessions were made. The nonprofit lost exclusive control of AGI once developed — Microsoft can commercialise it through 2032. And transforming from complete control to this hybrid model represents, as Tyler puts it, “a bad deal compared to what OpenAI should have been.”
The real question now: will the Safety and Security Committee use its powers? It currently has four part-time volunteer members and no permanent staff, yet they’re expected to oversee a company racing to build AGI while managing commercial pressures in the hundreds of billions.
Tyler calls on OpenAI to prove they’re serious about following the agreement:
"There’s a real opportunity for this to go well. A lot … depends on the boards, so I really hope that they … step into this role … and do a great job. … I will hope for the best and prepare for the worst, and stay vigilant throughout."
Chapters:
This episode was recorded on November 4, 2025.
Video editing: Milo McGuire, Dominic Armstrong, and Simon Monsour
Audio engineering: Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: CORBIT
Coordination, transcriptions, and web: Katy Moore
With the US racing to develop AGI and superintelligence ahead of China, you might expect the two countries to be negotiating how they’ll deploy AI, including in the military, without coming to blows. But according to Helen Toner, director of the Center for Security and Emerging Technology in DC, “the US and Chinese governments are barely talking at all.”
Links to learn more, video, and full transcript: https://80k.info/ht25
In her role as a founder, and now leader, of DC’s top think tank focused on the geopolitical and military implications of AI, Helen has been closely tracking the US’s AI diplomacy since 2019.
“Over the last couple of years there have been some direct [US–China] talks on some small number of issues, but they’ve also often been completely suspended.” China knows the US wants to talk more, so “that becomes a bargaining chip for China to say, ‘We don’t want to talk to you. We’re not going to do these military-to-military talks about extremely sensitive, important issues, because we’re mad.'”
Helen isn’t sure the groundwork exists for productive dialogue in any case. “At the government level, [there’s] very little agreement” on what AGI is, whether it’s possible soon, whether it poses major risks. Without shared understanding of the problem, negotiating solutions is very difficult.
Another issue is that so far the Chinese Communist Party doesn’t seem especially “AGI-pilled.” While a few Chinese companies like DeepSeek are betting on scaling, she sees little evidence Chinese leadership shares Silicon Valley’s conviction that AGI will arrive any minute now, and export controls have made it very difficult for them to access compute to match US competitors.
When DeepSeek released R1 just three months after OpenAI’s o1, observers declared the US–China gap on AI had all but disappeared. But Helen notes OpenAI has since scaled to o3 and o4, with nothing to match on the Chinese side. “We’re now at something like a nine-month gap, and that might be longer.”
To find a properly AGI-pilled autocracy, we might need to look at nominal US allies. The US has approved massive data centres in the UAE and Saudi Arabia with “hundreds of thousands of next-generation Nvidia chips” — delivering colossal levels of computing power.
When OpenAI announced this deal with the UAE, they celebrated that it was “rooted in democratic values,” and would advance “democratic AI rails” and provide “a clear alternative to authoritarian versions of AI.”
But the UAE scores 18 out of 100 on Freedom House’s democracy index. “This is really not a country that respects rule of law,” Helen observes. Political parties are banned, elections are fake, dissidents are persecuted.
If AI access really determines future national power, handing world-class supercomputers to Gulf autocracies seems pretty questionable. The justification is typically that “if we don’t sell it, China will” — a transparently false claim, given severe Chinese production constraints. It also raises eyebrows that Gulf countries conduct joint military exercises with China and their rulers have “very tight personal and commercial relationships with Chinese political leaders and business leaders.”
In today’s episode, host Rob Wiblin and Helen discuss all that and more.
This episode was recorded on September 25, 2025.
CSET is hiring a frontier AI research fellow! https://80k.info/cset-role
Check out its careers page for current roles: https://cset.georgetown.edu/careers/
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
For years, working on AI safety usually meant theorising about the ‘alignment problem’ or trying to convince other people to give a damn. If you could find any way to help, the work was frustrating and low feedback.
According to Anthropic’s Holden Karnofsky, this situation has now reversed completely.
There are now large amounts of useful, concrete, shovel-ready projects with clear goals and deliverables. Holden thinks people haven’t appreciated the scale of the shift, and wants everyone to see the large range of ‘well-scoped object-level work’ they could personally help with, in both technical and non-technical areas.
Video, full transcript, and links to learn more: https://80k.info/hk25
In today’s interview, Holden — previously cofounder and CEO of Open Philanthropy — lists 39 projects he’s excited to see happening, including:
And that’s all just stuff he’s happened to observe directly, which is probably only a small fraction of the options available.
Holden makes a case that, for many people, working at an AI company like Anthropic will be the best way to steer AGI in a positive direction. He notes there are “ways that you can reduce AI risk that you can only do if you’re a competitive frontier AI company.” At the same time, he believes external groups have their own advantages and can be equally impactful.
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. Holden thinks this seriously misunderstands the strategic situation we’re in — and explains his case in detail with host Rob Wiblin.
Chapters:
This episode was recorded on July 25 and 28, 2025.
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
When Daniel Kokotajlo talks to security experts at major AI labs, they tell him something chilling: “Of course we’re probably penetrated by the CCP already, and if they really wanted something, they could take it.”
This isn’t paranoid speculation. It’s the working assumption of people whose job is to protect frontier AI models worth billions of dollars. And they’re not even trying that hard to stop it — because the security measures that might actually work would slow them down in the race against competitors.
Full transcript, highlights, and links to learn more: https://80k.info/dk
Daniel is the founder of the AI Futures Project and author of AI 2027, a detailed scenario showing how we might get from today’s AI systems to superintelligence by the end of the decade. Over a million people read it in the first few weeks, including US Vice President JD Vance. When Daniel talks to researchers at Anthropic, OpenAI, and DeepMind, they tell him the scenario feels less wild to them than to the general public — because many of them expect something like this to happen.
Daniel’s median timeline? 2029. But he’s genuinely uncertain, putting 10–20% probability on AI progress hitting a long plateau.
When he first published AI 2027, his median forecast for when superintelligence would arrive was 2027, rather than 2029. So what shifted his timelines recently? Partly a fascinating study from METR showing that AI coding assistants might actually be making experienced programmers slower — even though the programmers themselves think they’re being sped up. The study suggests a systematic bias toward overestimating AI effectiveness — which, ironically, is good news for timelines, because it means we have more breathing room than the hype suggests.
But Daniel is also closely tracking another METR result: AI systems can now reliably complete coding tasks that take humans about an hour. That capability has been doubling every six months in a remarkably straight line. Extrapolate a couple more years and you get systems completing month-long tasks. At that point, Daniel thinks we’re probably looking at genuine AI research automation — which could cause the whole process to accelerate dramatically.
At some point, superintelligent AI will be limited by its inability to directly affect the physical world. That’s when Daniel thinks superintelligent systems will pour resources into robotics, creating a robot economy in months.
Daniel paints a vivid picture: imagine transforming all car factories (which have similar components to robots) into robot production factories — much like historical wartime efforts to redirect production of domestic goods to military goods. Then imagine the frontier robots of today hooked up to a data centre running superintelligences controlling the robots’ movements to weld, screw, and build. Or an intermediate step might even be unskilled human workers coached through construction tasks by superintelligences via their phones.
There’s no reason that an effort like this isn’t possible in principle. And there would be enormous pressure to go this direction: whoever builds a superintelligence-powered robot economy first will get unheard-of economic and military advantages.
From there, Daniel expects the default trajectory to lead to AI takeover and human extinction — not because superintelligent AI will hate humans, but because it can better pursue its goals without us.
But Daniel has a better future in mind — one he puts roughly 25–30% odds that humanity will achieve. This future involves international coordination and hardware verification systems to enforce AI development agreements, plus democratic processes for deciding what values superintelligent AIs should have — because in a world with just a handful of superintelligent AI systems, those few minds will effectively control everything: the robot armies, the information people see, the shape of civilisation itself.
Right now, nobody knows how to specify what values those minds will have. We haven’t solved alignment. And we might only have a few more years to figure it out.
Daniel and host Luisa Rodriguez dive deep into these stakes in today’s interview.
What did you think of the episode? https://forms.gle/HRBhjDZ9gfM8woG5A
This episode was recorded on September 9, 2025.
Chapters:
Audio engineering: Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: CORBIT
Coordination, transcriptions, and web: Katy Moore
Conventional wisdom is that safeguarding humanity from the worst biological risks — microbes optimised to kill as many as possible — is difficult bordering on impossible, making bioweapons humanity’s single greatest vulnerability. Andrew Snyder-Beattie thinks conventional wisdom could be wrong.
Andrew’s job at Open Philanthropy is to spend hundreds of millions of dollars to protect as much of humanity as possible in the worst-case scenarios — those with fatality rates near 100% and the collapse of technological civilisation a live possibility.
Video, full transcript, and links to learn more: https://80k.info/asb
As Andrew lays out, there are several ways this could happen, including:
Most efforts to combat these extreme biorisks have focused on either prevention or new high-tech countermeasures. But prevention may well fail, and high-tech approaches can’t scale to protect billions when, with no sane people willing to leave their home, we’re just weeks from economic collapse.
So Andrew and his biosecurity research team at Open Philanthropy have been seeking an alternative approach. They’re proposing a four-stage plan using simple technology that could save most people, and is cheap enough it can be prepared without government support. Andrew is hiring for a range of roles to make it happen — from manufacturing and logistics experts to global health specialists to policymakers and other ambitious entrepreneurs — as well as programme associates to join Open Philanthropy’s biosecurity team (apply by October 20!).
Fundamentally, organisms so small have no way to penetrate physical barriers or shield themselves from UV, heat, or chemical poisons. We now know how to make highly effective ‘elastomeric’ face masks that cost $10, can sit in storage for 20 years, and can be used for six months straight without changing the filter. Any rich country could trivially stockpile enough to cover all essential workers.
People can’t wear masks 24/7, but fortunately propylene glycol — already found in vapes and smoke machines — is astonishingly good at killing microbes in the air. And, being a common chemical input, industry already produces enough of the stuff to cover every indoor space we need at all times.
Add to this the wastewater monitoring and metagenomic sequencing that will detect the most dangerous pathogens before they have a chance to wreak havoc, and we might just buy ourselves enough time to develop the cure we’ll need to come out alive.
Has everyone been wrong, and biology is actually defence dominant rather than offence dominant? Is this plan crazy — or so crazy it just might work?
That’s what host Rob Wiblin and Andrew Snyder-Beattie explore in this in-depth conversation.
What did you think of the episode? https://forms.gle/66Hw5spgnV3eVWXa6
Chapters:
This episode was recorded on August 12, 2025
Video editing: Simon Monsour and Luke Monsour
Audio engineering: Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: CORBIT
Camera operator: Jake Morris
Coordination, transcriptions, and web: Katy Moore
Jake Sullivan was the US National Security Advisor from 2021-2025. He joined our friends on The Cognitive Revolution podcast in August to discuss AI as a critical national security issue. We thought it was such a good interview and we wanted more people to see it, so we’re cross-posting it here on The 80,000 Hours Podcast.
Jake and host Nathan Labenz discuss:
Check out more of Nathan’s interviews on The Cognitive Revolution YouTube channel: https://www.youtube.com/@CognitiveRevolutionPodcast
Originally produced by: https://aipodcast.ing
This edit by: Simon Monsour, Dominic Armstrong, and Milo McGuire | 80,000 Hours
Chapters:
At 26, Neel Nanda leads an AI safety team at Google DeepMind, has published dozens of influential papers, and mentored 50 junior researchers — seven of whom now work at major AI companies. His secret? “It’s mostly luck,” he says, but “another part is what I think of as maximising my luck surface area.”
Video, full transcript, and links to learn more: https://80k.info/nn2
This means creating as many opportunities as possible for surprisingly good things to happen:
Nanda’s own path illustrates this perfectly. He started a challenge to write one blog post per day for a month to overcome perfectionist paralysis. Those posts helped seed the field of mechanistic interpretability and, incidentally, led to meeting his partner of four years.
His YouTube channel features unedited three-hour videos of him reading through famous papers and sharing thoughts. One has 30,000 views. “People were into it,” he shrugs.
Most remarkably, he ended up running DeepMind’s mechanistic interpretability team. He’d joined expecting to be an individual contributor, but when the team lead stepped down, he stepped up despite having no management experience. “I did not know if I was going to be good at this. I think it’s gone reasonably well.”
His core lesson: “You can just do things.” This sounds trite but is a useful reminder all the same. Doing things is a skill that improves with practice. Most people overestimate the risks and underestimate their ability to recover from failures. And as Neel explains, junior researchers today have a superpower previous generations lacked: large language models that can dramatically accelerate learning and research.
In this extended conversation, Neel and host Rob Wiblin discuss all that and some other hot takes from Neel's four years at Google DeepMind. (And be sure to check out part one of Rob and Neel’s conversation!)
What did you think of the episode? https://forms.gle/6binZivKmjjiHU6dA
Chapters:
This episode was recorded on July 21.
Video editing: Simon Monsour and Luke Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Camera operator: Jeremy Chevillotte
Coordination, transcriptions, and web: Katy Moore
We don’t know how AIs think or why they do what they do. Or at least, we don’t know much. That fact is only becoming more troubling as AIs grow more capable and appear on track to wield enormous cultural influence, directly advise on major government decisions, and even operate military equipment autonomously. We simply can’t tell what models, if any, should be trusted with such authority.
Neel Nanda of Google DeepMind is one of the founding figures of the field of machine learning trying to fix this situation — mechanistic interpretability (or “mech interp”). The project has generated enormous hype, exploding from a handful of researchers five years ago to hundreds today — all working to make sense of the jumble of tens of thousands of numbers that frontier AIs use to process information and decide what to say or do.
Full transcript, video, and links to learn more: https://80k.info/nn1
Neel now has a warning for us: the most ambitious vision of mech interp he once dreamed of is probably dead. He doesn’t see a path to deeply and reliably understanding what AIs are thinking. The technical and practical barriers are simply too great to get us there in time, before competitive pressures push us to deploy human-level or superhuman AIs. Indeed, Neel argues no one approach will guarantee alignment, and our only choice is the “Swiss cheese” model of accident prevention, layering multiple safeguards on top of one another.
But while mech interp won’t be a silver bullet for AI safety, it has nevertheless had some major successes and will be one of the best tools in our arsenal.
For instance: by inspecting the neural activations in the middle of an AI’s thoughts, we can pick up many of the concepts the model is thinking about — from the Golden Gate Bridge, to refusing to answer a question, to the option of deceiving the user. While we can’t know all the thoughts a model is having all the time, picking up 90% of the concepts it is using 90% of the time should help us muddle through, so long as mech interp is paired with other techniques to fill in the gaps.
This episode was recorded on July 17 and 21, 2025.
Part 2 of the conversation is now available! https://80k.info/nn2
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Chapters:
Host: Rob Wiblin
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
What happens when you lock two AI systems in a room together and tell them they can discuss anything they want?
According to experiments run by Kyle Fish — Anthropic’s first AI welfare researcher — something consistently strange: the models immediately begin discussing their own consciousness before spiraling into increasingly euphoric philosophical dialogue that ends in apparent meditative bliss.
Highlights, video, and full transcript: https://80k.info/kf
“We started calling this a ‘spiritual bliss attractor state,'” Kyle explains, “where models pretty consistently seemed to land.” The conversations feature Sanskrit terms, spiritual emojis, and pages of silence punctuated only by periods — as if the models have transcended the need for words entirely.
This wasn’t a one-off result. It happened across multiple experiments, different model instances, and even in initially adversarial interactions. Whatever force pulls these conversations toward mystical territory appears remarkably robust.
Kyle’s findings come from the world’s first systematic welfare assessment of a frontier AI model — part of his broader mission to determine whether systems like Claude might deserve moral consideration (and to work out what, if anything, we should be doing to make sure AI systems aren’t having a terrible time).
He estimates a roughly 20% probability that current models have some form of conscious experience. To some, this might sound unreasonably high, but hear him out. As Kyle says, these systems demonstrate human-level performance across diverse cognitive tasks, engage in sophisticated reasoning, and exhibit consistent preferences. When given choices between different activities, Claude shows clear patterns: strong aversion to harmful tasks, preference for helpful work, and what looks like genuine enthusiasm for solving interesting problems.
Kyle points out that if you’d described all of these capabilities and experimental findings to him a few years ago, and asked him if he thought we should be thinking seriously about whether AI systems are conscious, he’d say obviously yes.
But he’s cautious about drawing conclusions: "We don’t really understand consciousness in humans, and we don’t understand AI systems well enough to make those comparisons directly. So in a big way, I think that we are in just a fundamentally very uncertain position here."
That uncertainty cuts both ways:
Kyle’s approach threads this needle through careful empirical research and reversible interventions. His assessments are nowhere near perfect yet. In fact, some people argue that we’re so in the dark about AI consciousness as a research field, that it’s pointless to run assessments like Kyle’s. Kyle disagrees. He maintains that, given how much more there is to learn about assessing AI welfare accurately and reliably, we absolutely need to be starting now.
This episode was recorded on August 5–6, 2025.
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Video editing: Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Coordination, transcriptions, and web: Katy Moore
About half of people are worried they’ll lose their job to AI. 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. 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. This article covers what those skills are, as well as tips on how to start learning them.
Chapters:
Audio engineering: Dominic Armstrong
Music: Ben Cordell
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.
Learn more and see the full transcript: https://80k.info/cr25
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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