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:

• Cold open (00:00:00)
• Introducing Zershaaneh (00:00:55)
• The puzzle of moral patienthood (00:03:20)
• Is subjective experience necessary? (00:05:52)
• What is it to desire? (00:10:42)
• Desiring without experiencing (00:17:56)
• What would make AIs moral patients? (00:28:17)
• Another route entirely: deserving autonomy (00:45:12)
• Maybe there's no objective truth about any of this (01:12:06)
• Practical implications (01:29:21)
• Why not just let superintelligence figure this out for us? (01:38:07)
• How could human extinction be a good thing? (01:47:30)
• Lexical threshold negative utilitarianism (02:12:30)
• So... should we still try to prevent extinction? (02:25:22)
• What are the most important questions for people to address here? (02:32:16)
• Is God GDPR compliant? (02:35:32)

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:

• Swarming warfare: Why the future isn’t just better drones, but thousands of cheap, autonomous agents coordinating like a hive mind to overwhelm defences.
• The Gatling gun cautionary tale: The inventor of the Gatling gun thought automating fire would reduce the number of soldiers needed, saving lives. Instead, it made war significantly deadlier. Paul argues AI automation could do the same, increasing lethality rather than creating “bloodless” robot wars.
• The cyber frontier: While robots have physical limits, Paul argues cyberwarfare is already at the point where AI can act faster than human defenders, leading to intelligent malware that evolves and adapts like a biological virus.
• The US-China “adoption race”: Paul rejects the idea that the US and China are in a spending arms race (AI is barely 1% of the DoD budget). Instead, it’s a race of organisational adoption — one where the US has massive advantages in talent and chips, but struggles with bureaucratic inertia that might not be a problem for an autocratic country.

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:

• Cold open (00:00:00)
• Who’s Paul Scharre? (00:00:46)
• How will AI and automation transform the nature of war? (00:01:17)
• Why would militaries take humans out of the loop? (00:12:22)
• AI in nuclear command, control, and communications (00:18:50)
• Nuclear stability and deterrence (00:36:10)
• What to expect over the next few decades (00:46:21)
• Financial and human costs of future “hyperwar” scenarios (00:50:42)
• AI warfare and the balance of power (01:06:37)
• Barriers to getting to automated war (01:11:08)
• Failure modes of autonomous weapons systems (01:16:28)
• Could autonomous weapons systems actually make us safer? (01:29:36)
• Is Paul overall optimistic or pessimistic about increasing automation in the military? (01:35:23)
• Paul’s takes on AGI’s transformative potential and whether natsec people buy it (01:37:42)
• Cyberwarfare (01:46:55)
• US-China balance of power and surveillance with AI (02:02:49)
• Policy and governance that could make us safer (02:29:11)
• How Paul’s experience in the Army informed his feelings on military automation (02:41:09)

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:

• Introduction (00:00)
• Summary (02:15)
• Section 1: Why might AI-enabled power concentration be a pressing problem? (07:02)
• Section 2: What are the top arguments against working on this problem? (45:02)
• Section 3: What can you do to help? (56:36)

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:

• Cold open (00:00:00)
• Who’s Dean Ball? (00:01:22)
• How likely are we to get superintelligence soon, and how bad could it be? (00:01:54)
• The military may not adopt AI that fast (00:10:54)
• Dean’s “two wolves” of AI scepticism and optimism (00:17:48)
• Will AI self-improvement be a game changer? (00:28:20)
• The case for regulating at the last possible moment (00:33:05)
• AI could destroy our fragile democratic equilibria. Why not freak out? (00:52:30)
• The case AI will soon be way overregulated (01:02:51)
• How to handle the threats without collateral damage (01:14:56)
• Easy wins against AI misuse (01:26:54)
• Maybe open source can be handled gracefully (01:41:13)
• Would a company be sued for trillions if their AI caused a pandemic? (01:47:58)
• Dean dislikes compute thresholds. Here's what he'd do instead. (01:57:16)
• Could AI advances lead to violent conflict between the US and China? (02:02:52)
• Will we see a MAGA-Yudkowskyite alliance? Doomers and the Right (02:12:29)
• The tactical case for focusing on present-day harms (02:26:51)
• Is there any way to get the US government to use AI sensibly? (02:45:05)
• Having a kid in a time of AI turmoil (02:52:38)

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:

• Why models pretending to be dumb is a rational survival strategy
• The Replit AI agent that deleted a production database and then lied about it
• Why rewarding AIs for achieving outcomes might lead to them becoming better liars
• The weird new language models are using in their internal chain-of-thought

This episode was recorded on September 19, 2025.

Chapters:

• Cold open (00:00:00)
• Who’s Marius Hobbhahn? (00:01:20)
• Top three examples of scheming and deception (00:02:11)
• Scheming is a natural path for AI models (and people) (00:15:56)
• How enthusiastic to lie are the models? (00:28:18)
• Does eliminating deception fix our fears about rogue AI? (00:35:04)
• Apollo’s collaboration with OpenAI to stop o3 lying (00:38:24)
• They reduced lying a lot, but the problem is mostly unsolved (00:52:07)
• Detecting situational awareness with thought injections (01:02:18)
• Chains of thought becoming less human understandable (01:16:09)
• Why can’t we use LLMs to make realistic test environments? (01:28:06)
• Is the window to address scheming closing? (01:33:58)
• Would anything still work with superintelligent systems? (01:45:48)
• Companies’ incentives and most promising regulation options (01:54:56)
• 'Internal deployment' is a core risk we mostly ignore (02:09:19)
• Catastrophe through chaos (02:28:10)
• Careers in AI scheming research (02:43:21)
• Marius's key takeaways for listeners (03:01:48)

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:

• Rob’s updated list of suggested purchases for new parents
• How parents could try to feel comfortable doing less
• How beliefs about childhood play have changed so radically
• What matters and doesn’t in childhood safety
• Why the decline in fertility might be impractical to reverse
• Whether we should care about a population crash in a world of AI automation

This episode was recorded on September 12, 2025.

Chapters:

• Cold open (00:00:00)
• We're hiring (00:01:26)
• Why did Luisa decide to have kids? (00:02:10)
• Ups and downs of pregnancy (00:04:15)
• Rob’s experience for the first couple years of parenthood (00:09:39)
• Fertility rates are massively declining (00:21:25)
• Why do fewer people want children? (00:29:20)
• Is parenting way harder now than it used to be? (00:38:56)
• Feeling guilty for not playing enough with our kids (00:48:07)
• Options for increasing fertility rates globally (01:00:03)
• Rob’s transition back to work after parental leave (01:12:07)
• AI and parenting (01:29:22)
• Screen time (01:42:49)
• Ways to screw up your kids (01:47:40)
• Highs and lows of parenting (01:49:55)
• Recommendations for babies or young kids (01:51:37)

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:

• Jobs: 73% of AI experts see a positive impact on how people do their jobs. Only 23% of the public agrees.
• Productivity: 74% of experts say AI is very likely to make humans more productive. Just 17% of the public agrees.
• Personal benefit: 76% of experts expect AI to benefit them personally. Only 24% of the public expects the same (while 43% expect it to harm them).
• Happiness: 22% of experts think AI is very likely to make humans happier, which is already surprisingly low — but a mere 6% of the public expects the same.

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:

• 53% of Americans say AI will worsen people’s ability to think creatively.
• 50% believe it will hurt our ability to form meaningful relationships.
• 38% think it will worsen our ability to solve problems.

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:

• Cold open (00:00:00)
• Who’s Eileen Yam? (00:01:30)
• Is it premature to care what the public says about AI? (00:02:26)
• The top few feelings the US public has about AI (00:06:34)
• The public and AI insiders disagree enormously on some things (00:16:25)
• Fear #1: Erosion of human abilities and connections (00:20:03)
• Fear #2: Loss of control of AI (00:28:50)
• Americans don't want AI in their personal lives (00:33:13)
• AI at work and job loss (00:40:56)
• Does the public always feel this way about new things? (00:44:52)
• The public doesn't think AI is overhyped (00:51:49)
• The AI industry seems on a collision course with the public (00:58:16)
• Is the survey methodology good? (01:05:26)
• Where people are positive about AI: saving time, policing, and science (01:12:51)
• Biggest gaps between experts and the general public, and where they agree (01:18:44)
• Demographic groups agree to a surprising degree (01:28:58)
• Eileen’s favourite bits of the survey and what Pew will ask next (01:37:29)

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:

• The nonprofit’s charitable purpose — “ensure that artificial general intelligence benefits all of humanity” — now legally controls all safety and security decisions at the company. The four people appointed to the new Safety and Security Committee can block model releases worth tens of billions.
• The AGs retain ongoing oversight, meeting quarterly with staff and requiring advance notice of any changes that might undermine their authority.
• OpenAI’s original charter, including the remarkable “stop and assist” commitment, remains binding.

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:

• Hire management for the SSC.
• Add more independent directors with AI safety expertise.
• Maximise transparency about mission compliance.

"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:

• We’re hiring (00:00:00)
• Cold open (00:00:40)
• Tyler Whitmer is back to explain the latest OpenAI developments (00:01:46)
• The original radical plan (00:02:39)
• What the AGs forced on the for-profit (00:05:47)
• Scrappy resistance probably worked (00:37:24)
• The Safety and Security Committee has teeth — will it use them? (00:41:48)
• Overall, is this a good deal or a bad deal? (00:52:06)
• The nonprofit and PBC boards are almost the same. Is that good or bad or what? (01:13:29)
• Board members’ “independence” (01:19:40)
• Could the deal still be challenged? (01:25:32)
• Will the deal satisfy OpenAI investors? (01:31:41)
• The SSC and philanthropy need serious staff (01:33:13)
• Outside advocacy on this issue, and the impact of LASST (01:38:09)
• What to track to tell if it's working out (01:44:28)

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:

• Cold open (00:00:00)
• Who’s Helen Toner? (00:01:02)
• Helen’s role on the OpenAI board, and what happened with Sam Altman (00:01:31)
• The Center for Security and Emerging Technology (CSET) (00:07:35)
• CSET’s role in export controls against China (00:10:43)
• Does it matter if the world uses US AI models? (00:21:24)
• Is China actually racing to build AGI? (00:27:10)
• Could China easily steal AI model weights from US companies? (00:38:14)
• The next big thing is probably robotics (00:46:42)
• Why is the Trump administration sabotaging the US high-tech sector? (00:48:17)
• Are data centres in the UAE “good for democracy”? (00:51:31)
• Will AI inevitably concentrate power? (01:06:20)
• “Adaptation buffers” vs non-proliferation (01:28:16)
• Will the military use AI for decision-making? (01:36:09)
• “Alignment” is (usually) a terrible term (01:42:51)
• Is Congress starting to take superintelligence seriously? (01:45:19)
• AI progress isn't actually slowing down (01:47:44)
• What's legit vs not about OpenAI’s restructure (01:55:28)
• Is Helen unusually “normal”? (01:58:57)
• How to keep up with rapid changes in AI and geopolitics (02:02:42)
• What CSET can uniquely add to the DC policy world (02:05:51)
• Talent bottlenecks in DC (02:13:26)
• What evidence, if any, could settle how worried we should be about AI risk? (02:16:28)
• Is CSET hiring? (02:18:22)

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 (now Coefficient Giving) — lists 39 projects he’s excited to see happening, including:

• Training deceptive AI models to study deception and how to detect it
• Developing classifiers to block jailbreaking
• Implementing security measures to stop ‘backdoors’ or ‘secret loyalties’ from being added to models in training
• Developing policies on model welfare, AI-human relationships, and what instructions to give models
• Training AIs to work as alignment researchers

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:

• Cold open (00:00:00)
• Holden is back! (00:02:26)
• An AI Chernobyl we never notice (00:02:56)
• Is rogue AI takeover easy or hard? (00:07:32)
• The AGI race isn't a coordination failure (00:17:48)
• What Holden now does at Anthropic (00:28:04)
• The case for working at Anthropic (00:30:08)
• Is Anthropic doing enough? (00:40:45)
• Can we trust Anthropic, or any AI company? (00:43:40)
• How can Anthropic compete while paying the “safety tax”? (00:49:14)
• What, if anything, could prompt Anthropic to halt development of AGI? (00:56:11)
• Holden's retrospective on responsible scaling policies (00:59:01)
• Overrated work (01:14:27)
• Concrete shovel-ready projects Holden is excited about (01:16:37)
• Great things to do in technical AI safety (01:20:48)
• Great things to do on AI welfare and AI relationships (01:28:18)
• Great things to do in biosecurity and pandemic preparedness (01:35:11)
• How to choose where to work (01:35:57)
• Overrated AI risk: Cyberattacks (01:41:56)
• Overrated AI risk: Persuasion (01:51:37)
• Why AI R&D is the main thing to worry about (01:55:36)
• The case that AI-enabled R&D wouldn't speed things up much (02:07:15)
• AI-enabled human power grabs (02:11:10)
• Main benefits of getting AGI right (02:23:07)
• The world is handling AGI about as badly as possible (02:29:07)
• Learning from targeting companies for public criticism in farm animal welfare (02:31:39)
• Will Anthropic actually make any difference? (02:40:51)
• “Misaligned” vs “misaligned and power-seeking” (02:55:12)
• Success without dignity: how we could win despite being stupid (03:00:58)
• Holden sees less dignity but has more hope (03:08:30)
• Should we expect misaligned power-seeking by default? (03:15:58)
• Will reinforcement learning make everything worse? (03:23:45)
• Should we push for marginal improvements or big paradigm shifts? (03:28:58)
• Should safety-focused people cluster or spread out? (03:31:35)
• Is Anthropic vocal enough about strong regulation? (03:35:56)
• Is Holden biased because of his financial stake in Anthropic? (03:39:26)
• Have we learned clever governance structures don't work? (03:43:51)
• Is Holden scared of AI bioweapons? (03:46:12)
• Holden thinks AI companions are bad news (03:49:47)
• Are AI companies too hawkish on China? (03:56:39)
• The frontier of infosec: confidentiality vs integrity (04:00:51)
• How often does AI work backfire? (04:03:38)
• Is AI clearly more impactful to work in? (04:18:26)
• What's the role of earning to give? (04:24:54)

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