Podcast

#232 – Andreas Mogensen on what we owe ‘philosophical Vulcans’ and unconscious AIs

By and · Published December 19th, 2025 ·

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.

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.

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

Continue reading →

#231 – Paul Scharre on how AI could transform the nature of war

By · Published December 17th, 2025 ·

In 1983, Stanislav Petrov, a Soviet lieutenant colonel, sat in a bunker watching a red screen flash “MISSILE LAUNCH.” The system told him the United States had fired five nuclear weapons at the Soviet Union. Protocol demanded he report it to superiors, which would almost certainly trigger a retaliatory strike.

Petrov didn’t do it. He had a “funny feeling” in his gut. He reasoned that if the US were actually attacking, they wouldn’t just fire five missiles — they’d empty the silos. He bet the fate of the world on a hunch that the 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, asks a terrifying 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 have launched 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.

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.

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

Continue reading →

#230 – Dean Ball on how AI is a huge deal — but we shouldn’t regulate it yet

By · Published December 10th, 2025 ·

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 — recently co-authoring Trump’s AI Action Plan before moving on to the Foundation for American Innovation.

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 concern 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 to regulate models when we should regulate companies instead), through the wrong institutions (bodies captured by AI interests), all while making it harder to build the actual solutions we’ll need (like open source alternatives or legal mechanisms newly enabled by AI).

But Dean is also a pragmatist: he opposed California’s AI regulatory bill SB 1047 in 2024, but — impressed by new capabilities shown by “reasoning models” — he supported its successor SB 53 in 2025.

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.

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

Continue reading →

#229 – Marius Hobbhahn on the race to solve AI scheming before models go superhuman

By · Published December 3rd, 2025 ·

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 when OpenAI’s reasoning models like o3 are inclined to “scheme” against the interests of users.

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.

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

Continue reading →

Rob & Luisa chat kids, the fertility crash, and how the ‘50s invented parenting that makes us miserable

By and · Published November 25th, 2025 ·

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.

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.

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

Continue reading →

#228 – Eileen Yam on how we’re completely out of touch with what the public thinks about AI

By · Published November 20th, 2025 ·

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.

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.

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

Continue reading →

OpenAI: The nonprofit refuses to die (with Tyler Whitmer)

By · Published November 11th, 2025 ·

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:

Not For Private Gain chart

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.

Host Rob Wiblin and Tyler discuss all that and more in today’s episode.

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

Continue reading →

#227 – Helen Toner on the geopolitics of AI in China and the Middle East

By · Published November 5th, 2025 ·

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.”

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 the above, plus:

  • Ways China exaggerates its chip production for strategic gain
  • The confusing and conflicting goals in the US’s AI policy towards China
  • Whether it matters that China could steal frontier AI models trained in the US
  • Whether Congress is starting to take superintelligence seriously this year
  • Why she rejects ‘non-proliferation’ as a model for AI
  • Plenty more.

CSET is hiring! Check out its careers page for current roles.

This episode was recorded on September 25, 2025.

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

Continue reading →

#226 – Holden Karnofsky on dozens of amazing opportunities to make AI safer — and all his AGI takes

By · Published October 30th, 2025 ·

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.

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.

All this low-hanging fruit is one factor behind his decision to join Anthropic this year. That said, his wife was also a cofounder and president of the company, giving him a big financial stake in its success — and making it impossible for him to be seen as independent no matter where he worked.

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.

Outside 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.

“I work at an AI company, and a lot of people think that’s just inherently unethical,” he says. “They’re imagining [that] everyone wishes they could go slowly, but they’re going fast so they can beat everyone else. […] But I emphatically think this is not what’s going on in AI.”

The reality, in Holden’s view:

I think there’s too many players in AI who […] don’t want to slow down. They don’t believe in the risks. Maybe they don’t even care about the risks. […] If Anthropic were to say, “We’re out, we’re going to slow down,” they would say, ‘This is awesome! Now we have a better chance of winning, and this is even good for our recruiting” — because they have a better chance of getting people who want to be on the frontier and want to win.

Holden believes a frontier AI company can reduce risk by:

  • Developing cheap, practical safety measures other companies might adopt
  • Prototyping policies regulators could mandate
  • Gathering crucial data about what advanced AI can actually do

Host Rob Wiblin and Holden discuss the case for and against those strategies, and much more, in today’s episode.

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

Continue reading →

#225 – Daniel Kokotajlo on what a hyperspeed robot economy might look like

By · Published October 20th, 2025 ·

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.

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.

This episode was recorded on September 9, 2025.

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

Continue reading →

#224 – Andrew Snyder-Beattie on the low-tech plan to patch humanity’s greatest weakness

By · Published October 2nd, 2025 ·

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 (now Coefficient Giving) 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.

As Andrew lays out, there are several ways this could happen, including:

  • A national bioweapons programme gone wrong (most notably Russia or North Korea’s)
  • AI advances making it easier for terrorists or a rogue AI to release highly engineered pathogens
  • Mirror bacteria that can evade the immune systems of not only humans, but many animals and potentially plants as well

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 person 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 now 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.

The approach exploits tiny organisms having 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.

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

Continue reading →

#223 – Neel Nanda on leading a Google DeepMind team at 26 – and advice if you want to work at an AI company (part 2)

By · Published September 15th, 2025 ·

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.”

This means creating as many opportunities as possible for surprisingly good things to happen:

  • Write publicly.
  • Reach out to researchers whose work you admire.
  • Say yes to unusual projects that seem a little scary.

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 discusses all that and some other hot takes from his four years at Google DeepMind. (And be sure to check out part one of Rob and Neel’s conversation!)

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

Continue reading →

#222 – Neel Nanda on the race to read AI minds (part 1)

By · Published September 8th, 2025 ·

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.

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 protection, 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.

In today’s episode, Neel takes us on a tour of everything you’ll want to know about this race to understand what AIs are really thinking. He and host Rob Wiblin cover:

  • The best tools we’ve come up with so far, and where mech interp has failed
  • Why the best techniques have to be fast and cheap
  • The fundamental reasons we can’t reliably know what AIs are thinking, despite having perfect access to their internals
  • What we can and can’t learn by reading models’ ‘chains of thought’
  • Whether models will be able to trick us when they realise they’re being tested
  • The best protections to add on top of mech interp
  • Why he thinks the hottest technique in the field (SAEs) are overrated
  • His new research philosophy
  • How to break into mech interp and get a job — including applying to be a MATS scholar with Neel as your mentor (applications close September 12!)

This episode was recorded on July 17 and 21, 2025.

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

Continue reading →

#221 – Kyle Fish on the most bizarre findings from 5 AI welfare experiments

By · Published August 28th, 2025 ·

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.

“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:

  • Dismissing AI consciousness entirely might mean ignoring a moral catastrophe happening at unprecedented scale.
  • But assuming consciousness too readily could hamper crucial safety research by treating potentially unconscious systems as if they were moral patients — which might mean giving them resources, rights, and power.

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.

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

Continue reading →

Rebuilding after apocalypse: What 13 experts say about bouncing back

By · Published July 15th, 2025

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

Continue reading →

#220 – Ryan Greenblatt on the 4 most likely ways for AI to take over, and the case for and against AGI in under 8 years

By · Published July 8th, 2025 ·

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

Continue reading →

#219 – Toby Ord on graphs AI companies would prefer you didn’t (fully) understand

By · 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

Continue reading →

#218 – Hugh White on why Trump is abandoning US hegemony – and that’s probably good

By · 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

Continue reading →

#217 – Beth Barnes on the most important graph in AI right now — and the 7-month rule that governs its progress

By · Published June 2nd, 2025 ·

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.

That’s both an exciting and troubling development, because it could radically speed up advances in AI capabilities, accomplishing what would have taken years or decades in just months. That itself could be highly destabilising, as we explored in a previous episode: Will MacAskill on AI causing a “century in a decade” — and how we’re completely unprepared.

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

Continue reading →

Beyond human minds: The bewildering frontier of consciousness in insects, AI, and more

By · Published May 23rd, 2025

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

Continue reading →