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Conspicuous Cognition Podcast: Daily Summaries Delivered
by Dan Williams
A podcast about big questions in philosophy, psychology, evolution, politics, artificial intelligence, and more. www.conspicuouscognition.com
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- Yesterday1h 16m
What If Artificial Intelligence Progress Explodes? (with Benjamin Todd)
Benjamin Todd, co-founder of 80,000 Hours, joins Dan and Henry to discuss whether artificial intelligence progress could become explosive.Benjamin explains why he thinks transformative artificial intelligence by 2030 is a serious possibility, how feedback loops in artificial intelligence research could accelerate progress, and why the most important risks now go beyond classic alignment problems. The conversation covers artificial intelligence timelines, bottlenecks in chips and research talent, the future of work, mass unemployment, concentration of power, engineered pandemics, space governance, and how young people should think about their careers in a rapidly changing world.Topics discussed include:• Why 80,000 Hours increasingly focuses on artificial intelligence• The case for short timelines to transformative artificial intelligence• Whether artificial intelligence progress could become explosive• Feedback loops in artificial intelligence research• Chip bottlenecks, data centres, and geopolitical risk• Whether artificial intelligence will cause mass unemployment• Why “become a plumber” may be bad career advice• Alignment, control, and concentration of power• Misuse risks, engineered pandemics, and future governance• How to think clearly under extreme uncertaintyBenjamin Todd is the co-founder of 80,000 Hours and the author of 80,000 Hours, a new book about how to choose a career that is both personally rewarding and socially impactful. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.conspicuouscognition.com/subscribe
- 2 weeks ago1h 9m
Academics Must Wake Up on AI (with Alexander Kustov)
The political scientist Alexander Kustov recently published a Substack post with a provocative claim: that AI can already do social science research better than most professors. The post went viral. It attracted more than a million views and over a thousand responses, many of them very angry. (Some people even demanded that Alex’s university fire him.)In this conversation, we talk about this controversy and the claims that triggered it, including:* What agentic AI tools like Claude Code and Codex can already do for research, from coding and data analysis to literature reviews, translation, and brainstorming, and why only around 20% of quantitative social scientists currently use them.* What best predicts whether researchers adopt or reject AI: ignorance, openness to experience, methodological background, or the awkward role of self-interest.* How much published academic research is genuinely mediocre, and whether the cause is laziness, lack of skill, or a broken incentive structure, with a detour through the replication crisis and some high-profile fraud cases.* Whether AI will raise the quality of research or simply flood the literature with more slop, and what journal editors could do about it.* Whether AI can be genuinely creative or only recombine what already exists, by way of Margaret Boden’s three kinds of creativity, Thomas Kuhn on paradigm shifts, and AlphaGo’s “Move 37”.* The fight over AI writing and detection tools like Pangram, and why current disclosure norms end up punishing the honest.* The angry response to Alex’s series, and what is really driving reflexive opposition to AI among academics.Conspicuous Cognition is a completely reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Links and further reading* Alexander Kustov — Alex’s homepage, with an overview of his research on immigration, public opinion, and effective governance.* Popular by Design — Alex’s Substack on public opinion, persuasion, and the politics of getting good ideas adopted.* Academics Need to Wake Up on AI — followed by a Part II and Part III* Pangram — the AI-detection tool discussed at length, which labels text as human, AI-assisted, or AI.* AlphaGo versus Lee Sedol — the 2016 match, including the famous “Move 37” that Henry raises as a candidate for genuinely transformative machine creativity.* Margaret Boden — the cognitive scientist whose distinction between combinational, exploratory, and transformative creativity frames part of the discussion.* The Structure of Scientific Revolutions — Thomas Kuhn’s account of normal science and paradigm shifts, referenced in the exchange about AI and discovery.* “AI Is a Better Researcher Than You” — The Chronicle of Higher Education‘s account of the controversy around Alex’s series.Transcript* Please note that this transcript is lightly AI-edited and may contain minor mistakes. Dan Williams: Welcome back. I’m Dan Williams, and I’m back with my co-host, Henry Shevlin. Today we are honoured to be joined by Bluesky’s favourite academic, Alexander Kustov. Alex is a political scientist at the University of Notre Dame and the author of one of my favourite Substacks, Popular by Design. His primary research is on immigration and public opinion, but that’s not really what we’re going to be talking about today. We’re going to be talking about a fascinating and hugely viral series he published at his Substack titled “Academics Need to Wake Up on AI,” about what AI can already do when it comes to research,
- 3 weeks ago1h 18m
Are We Building Conscious AI Servants?
Richard Dawkins recently announced in UnHerd that, after spending three days talking with an instance of Claude he christened “Claudia,” he had been moved to expostulate: “You may not know you are conscious, but you bloody well are!” This produced a lot of mockery and criticism. But however one feels about Dawkins’s specific case, his reaction might become much more common as AI systems become increasingly intelligent. In this episode, which Henry Shevlin and I recorded live on Substack (hence the slightly lower video quality), we discussed his first essay on his new Substack Polytropolis, “Behaviourism’s Revenge“, as well as his second, “The House Elf Problem,” on the ethics of designing AI systems that genuinely love being our servants. Henry’s central empirical prediction is that public attributions of consciousness to AI are likely to massively outpace the science, and that consciousness science is so theoretically chaotic that there is no expert consensus to push back. His most provocative philosophical claim is that a core assumption underlying many people’s scepticism — that consciousness is a deep natural kind, distinct from behaviour and from how we are inclined to interpret a system — may be much harder to defend than it looks. The result is what he calls “behaviourism’s revenge”.This conversation connects to previous episodes with Anil Seth, Robert Long, and Rose Guingrich, but also touches on a wide range of new questions and controversies in the metaphysics, the politics, and ethics of the AI consciousness debate, which is going to become increasingly important in the coming years. Topics* Dawkins, Claude, and why even the sceptics might feel the pull to attribute consciousness or “sentience” to AI* Whether consciousness sceptics are destined to “go extinct” — and how this maps onto political and cultural fault lines* Anthropomimesis vs. raw intelligence as drivers of consciousness attribution* Why consciousness science can’t replicate the public–expert consensus we see for climate or vaccines* The case for (and against) metaphysical behaviourism: is it as mad as it seems?* Daniel Dennett, the consciousness stance, and the difference between behaviourism and interpretationism* What is consciousness for? Function, evolution, and the limits of “facilitation hypothesis” arguments for AI* Live Q&A: are we just confusing intelligence with consciousness? Are LLMs designed to trick us? Is the public always wrong?* Our credences on contemporary LLM consciousness (and why Henry is more sceptical than Dan)* The House Elf Problem: if we could design AI to genuinely love being our servants, would that be fine — or monstrous? (Dan is sympathetic to the former answer - Henry, much less so)* Brainwashing vs. education, and whether constraining a mind’s preferences caps its hedonic ceiling* Why this is a golden age for philosophy — which makes it so tragic that philosophy departments are closingTranscript* Please note that this transcript is lightly AI-edited and may contain minor errors. IntroductionDan: Welcome. I’m Dan Williams, author of the Conspicuous Cognition Substack, and I’m here with Henry Shevlin, author of the spanking new Substack Polytropolis. Today we’re going to be doing something a little bit different. We’re going to be talking about Henry’s first published essay on Polytropolis, titled “Behaviorism’s Revenge: On Human–AI Relationships and the Future of Consciousness Science.”Henry and I have already had a few conversations about this general topic, including with previous guests like Rose Guinrich, Anil Seth, and Rob Long. So please do go check out those conversations if you’re interested in this kind of stuff. But today we’re not merely going to be treading the same ground. We’re going to be using the spicy takes in Henry’s essay as a springboard for hopefully going beyond the material we’ve covered in the past.To kick things
- May 4, 20261h 21m
Aliens, Superintelligence, and the Future of Science (with David Kipping)
Most conversations about artificial intelligence are focused on Earth: jobs, misinformation, education, politics, science, regulation, consciousness, safety, and the future of human society. But AI—and especially the possibility of reaching “AGI” (artificial general intelligence) and “superintelligence”—forces us to think on much larger scales. If advanced AI is possible, why hasn’t it already emerged elsewhere? If civilisations can build self-replicating probes, artificial scientists, or planet-scale computational systems, why does the universe still look so natural? And if intelligent life is common, where is everyone?In this episode, Henry and I discuss these and many other questions with David Kipping, Associate Professor of Astronomy at Columbia University, where he leads the Cool Worlds Lab. David’s research spans exoplanets, exomoons, Bayesian inference, technosignatures, and the search for life and intelligence beyond Earth. He is also one of the best science communicators working today through the Cool Worlds YouTube channel and podcast.Among other topics, we discussed:* David’s Red Sky Paradox: if most stars are red dwarfs, and red dwarfs live for vastly longer than stars like the Sun, why do we find ourselves orbiting a yellow star?* Whether anthropic reasoning — reasoning from the fact of our own existence — is a profound scientific tool, a philosophical minefield, or both.* The reference class problem: when we reason about “observers like us”, who or what exactly counts as being like us?* The Doomsday Argument, and why some apparently bizarre forms of probabilistic reasoning can nevertheless be powerful.* The Fermi Paradox: if the universe is so large, and if life or intelligence is not fantastically rare, why don’t we see clear evidence of extraterrestrial civilisations?* Whether advanced civilisations would spread through the galaxy using self-replicating probes — and why the absence of such probes might be one of the strongest constraints on extraterrestrial intelligence.* How recent developments in artificial intelligence affect the Fermi Paradox. If humanity is close to building systems that can massively accelerate science and engineering, shouldn’t someone else have got there first?* Whether artificial intelligence makes the simulation argument more plausible.* David’s experience using artificial intelligence in scientific research, and why a meeting at the Institute for Advanced Study changed how he thinks about the role of these tools in science.* Why David thinks artificial intelligence already has something close to “coding supremacy”, but is still far from being able to do science autonomously.* The risks of AI-generated scientific slop: papers, peer review, and training data polluted by low-quality machine outputs.* Whether artificial intelligence will make science more productive, or instead strip it of some of its deepest human value.* Why the future of science communication may depend on better collaboration between academic institutions and independent creators.Links and further reading* Cool Worlds Lab — David’s research group at Columbia University, focused on extrasolar planetary systems, exomoons, habitability, technosignatures, and related questions.* Cool Worlds on YouTube — David’s excellent science communication channel, covering astronomy, exoplanets, alien life, the Fermi Paradox, cosmology, and much else.* Cool Worlds Podcast — David’s podcast, featuring conversations on astronomy, technology, science, engineering, and related topics.* Cool Worlds Podcast: “We Need To Talk About Artificial Intelligence” — the solo episode in which David reflects on artificial intelligence and science after a meeting at the Institute for Advanced Study.* David Kipping’s Columbia profile — short institutional profile with background on his research.Conspicuous Cognition is a reader-supported publication. To receive new pos
- Apr 18, 20261h 28m
Should We Care About AI Welfare? (with Robert Long)
Almost all of the discussion about the risks associated with AI focuses on the dangers that increasingly advanced AI systems pose to us — to humanity. But what about the dangers that we might pose to them? As these systems become increasingly intelligent and agentic, AI companies, policy makers, and ordinary citizens need to start taking the possibility of AI consciousness and welfare seriously. If we are in the process of bringing complex and sophisticated minds into existence, how should we understand and treat such minds?In this episode, Henry and I discuss these issues with Robert Long, founder and executive director of Eleos AI, a research nonprofit dedicated to understanding and addressing the potential wellbeing and “moral patienthood” of AI systems. Rob did his PhD in philosophy at NYU under David Chalmers, and is the co-author of two of the most important papers in the emerging field of AI welfare: “Consciousness in Artificial Intelligence” and “Taking AI Welfare Seriously”.This was a really fun, informative, and wide-ranging conversation. Among other topics, we discussed:* Why Rob disagrees with previous guest Anil Seth in taking the possibility of AI consciousness very seriously.* Why “fancy autocomplete” dismissals of large language models miss the point, and what, if anything, we can learn about an AI model’s experiences by talking to it.* The difference between consciousness and the kinds of motivations and interests that might actually ground moral status, and whether AI systems could have one without the other.* What Rob found when he conducted the first externally-commissioned welfare evaluation of a frontier AI model, Claude, and why Claude appears to have an inflated self-conception of what it wants.* Rob’s experiments with Claude Mythos, an AI model so advanced it hasn’t been released to the public yet. * Why the fact that Anthropic writes Claude’s character arguably doesn’t settle whether Claude has genuine preferences and values — and the difficult philosophical questions this throws up.* The “willing servitude” problem: if we succeed in building AI systems that genuinely love being helpful, is that a good outcome or a horrifying one?* How AI welfare connects to AI safety, and why caring about model wellbeing may turn out to be pragmatically important for alignment even if you’re skeptical about AI consciousness.* Why AI welfare is already becoming a political and legal battleground. * Practical advice for users: whether it’s worth being polite to your chatbot, and what low-cost things you can do if you want to hedge against the possibility that these systems might matter morally.* Whether discourse about AI consciousness functions as hype or propaganda for AI companies, and why Rob thinks AI companies actually have an incentive to downplay AI consciousness. Links and further reading* Eleos AI Research — Rob’s nonprofit. Home to their research agenda, team page, and blog. If you want to follow the institutional effort on AI welfare, start here. They’re also, as Rob mentioned in the episode, actively fundraising and hiring.* “Taking AI Welfare Seriously” (Long, Sebo, Butlin et al., 2024) — the flagship report, co-authored with Jeff Sebo, David Chalmers, Jonathan Birch, and others. Argues that there’s a realistic near-future possibility of conscious or robustly agentic AI systems, and lays out concrete steps AI companies should be taking now.* “Consciousness in Artificial Intelligence: Insights from the Science of Consciousness” (Butlin, Long et al., 2023) — the “indicators” paper referenced several times in the episode. Surveys leading neuroscientific theories of consciousness and derives computational properties you’d look for in an AI system. S* Rob’s Substack, Experience Machines — where Rob writes more informally. The piece we discussed in the episode, “Language models are different from humans, and that’s okay,” is a good entry point, as is
- Mar 10, 20261h 8m
Time To Start Panicking About AI?
In this episode, Henry and I finally do something we probably should have done in the first episode: introduce ourselves. We talk about our backgrounds in philosophy, how we became interested in psychology and cognitive science, and what drew us to thinking about AI. From there, we dig into the current state of AI capabilities, especially “agentic” AI (e.g., Claude Code), the politics of AI (including the Trump administration's recent conflict with Anthropic), and whether the growing public hostility to AI is well-founded or misdirected. We wrap up with a big question: is it time to start panicking about AI? Henry says the time to panic was five years ago. I argue that for panic or any other emotion to be productive, it must be anchored in an accurate, evidence-based understanding of what is happening, which is missing from lots of the current discourse about AI. Links * Dan Williams, The Mind as a Predictive Modelling Engine: Generative Models, Structural Similarity, and Mental Representation (PhD thesis, University of Cambridge, 2018). * Dan Williams, “Socially Adaptive Belief” (2021)* Henry Shevlin, “Three Frameworks for AI Mentality” (2026) * Henry Shevlin, “A Lack of Understanding: Storytelling for Robots” (2019) — Litro Magazine. * Lake et al, “Building Machines That Learn and Think Like People” (2017) * Matt Shumer, “Something Big Is Happening” (2026)* Leopold Aschenbrenner, Situational Awareness: The Decade Ahead (2024) * Joseph Heath, “Highbrow Climate Misinformation” (2025) * Dean Ball* Ethan Mollick * Leopold Aschenbrenner Transcript(Note that this transcript is AI-edited and may contain minor mistakes).Introducing OurselvesDan: Welcome back. I’m Dan Williams, and I’m back with Henry Shevlin. Today we’re going to be discussing some questions about the nature of AI as it’s developed over the past couple of months. We’re also going to be talking about the politics of AI and probably some questions about AI and public opinion — some of the backlash that appears to be brewing among certain segments of the public when it comes to AI.But to kick things off, we’re going to do something we probably should have done in the first episode but haven’t actually done yet, which is to introduce ourselves. So Henry, to begin with — who are you?Henry: So many different descriptors I could choose from. I think I’ll start with philosopher of cognitive science. I’m also a father, husband, son, D&D player, big video gamer, runner, cyclist — all that good stuff. But let me talk a little more about the philosopher of cognitive science side.I’m the associate director at the Leverhulme Centre for the Future of Intelligence, Cambridge’s main AI ethics, theory, policy, and law research centre. Basically, everything except building the models. We do practical benchmarking work on capabilities, legal reviews, sociology and critical theory of AI — it’s a really big interdisciplinary centre. I’ve been there now going on nine years. I joined early 2017, all the way back when state-of-the-art AI was stuff like AlphaGo. We were created just as that story was brewing. In 2016, AlphaGo won a very surprising victory against Lee Sedol in the game of Go, which was seen by many as an almost impossible challenge for AI because of its combinatorial complexity.It’s been amazing working in this role — having these front row seats to what I think is a unique period, not just in the history of AI, but in the history of human civilisation. In the last nine years, it really was like having a front seat in Lancashire during the Industrial Revolution, watching the development of various industrial applications.Dan: Yeah.Henry: Before we get more into AI, maybe a little more background. I’m from the UK, originally from Staffordshire. I was a
- Feb 17, 20261h 34m
AI Sessions #9: The Case Against AI Consciousness (with Anil Seth)
We are joined by Anil Seth for a deep dive into the science, philosophy, and ethics surrounding the topic of AI and consciousness. Anil outlines and defends his view that the brain is not a computer, or at least not a digital computer, and explains why he is sceptical that merely making AI systems smarter or more capable will produce consciousness. Anil Seth is a neuroscientist, author, and professor at the University of Sussex, where he directs the Centre for Consciousness Science. His research spans many topics, including the neuroscience and philosophy of consciousness, perception, and selfhood, with a focus on understanding how our brains construct our conscious experiences. His bestselling book Being You: A New Science of Consciousness was published in 2021. He is the English-language winner of the 2025 Berggruen Prize Essay Competition for his essay “The Mythology of Conscious AI”, which develops ideas in his recent article, “Conscious Artificial Intelligence and Biological Naturalism.”Conspicuous Cognition is a reader-supported publication. To receive all new posts, access the complete archive, and support my work, consider becoming a paid subscriber.Topics* What we mean by “consciousness” (subjective experience / “what it’s like”) vs intelligence.* Whether general anaesthesia and dreamless sleep are true “no consciousness” baselines.* Psychological biases pushing us to ascribe consciousness to AI* How impressive current AI/LLMs really are, and whether “stochastic parrots” is too dismissive* Whether LLMs “understand”, and the role of embodiment/grounding in genuine understanding* Computational functionalism: consciousness as computation + substrate-independence, and alternative functionalist flavours* Main objections to computational functionalism* Whether the brain is a computer* Simulation vs instantiation * Arguments for biological naturalism* Predictive processing and the free energy principle * What evidence could move the debate* The ethics surrounding AI consciousness and welfare. Transcript(Please note that this transcript is AI-edited and may contain minor errors).Dan Williams: Welcome back. I’m Dan Williams, back with Henry Shevlin. And today we are honoured to be joined by the great Anil Seth. Anil is one of our most influential and insightful neuroscientists and public intellectuals, working on a wide range of different topics, including the focus of today’s conversation, which is consciousness — and more specifically, the question of AI and consciousness.Could AI systems, either as they exist today or as they might develop over the coming years and decades, be conscious? Could they have subjective experiences? In a series of publications that have been getting a lot of attention from scientists and philosophers, Anil has been defending a somewhat sceptical answer to that question, arguing that consciousness might be essentially entangled with life — with biological properties and processes of living organisms — which, if true, would suggest that no matter how intelligent AI systems become, they would nevertheless not become conscious. He’s also argued that the consequences of getting this question wrong in either direction — attributing consciousness where there is none, or failing to attribute consciousness when there is — are enormous: socially, politically, morally.So in this conversation, we’re going to be asking Anil to elaborate on this perspective, see what the arguments are, and generally pick his brain about these topics. Anil, maybe we can start with the most basic preliminary question in this area: when we ask whether ChatGPT is conscious, or any other system is conscious, what are we asking? What’s meant by consciousness there?Anil Seth: Well, thanks, Dan. Let me first say thank you for having me on — it’s a great pleasure to be chatting with you, my Sussex colleague Dan, and my longtime sparring partner about these issues, Henry. I’m very much looking forward to this conversation.I think you set it up beautifully. It’s a deep intellectual question which involves both philosophy and science, and it’s a deeply important practical question, because the consequences of getting it wrong either way are very significant.You’re also right that the first step is to be clear about what we’re talking about. For a while, there was this easy slippage where people would talk about AI and intelligence and artificial general intelligence — which is supposedly the int
- Jan 23, 20261h 23m
AI Sessions #8: Misinformation, Social Media, and Deepfakes (with Sacha Altay)
Henry and I chat with Dr Sacha Altay about:* How prevalent is misinformation?* What even is “misinformation”?* Is there a difference between politics and science?* How impactful are propaganda, influence campaigns, and advertising?* What impact has social media had on modern democracies?* How worried should we be about the impact of generative AI, including deepfakes, on the information environment?* The “liar’s dividend”* Whether ChatGPT is more accurate and less biased than the average politician, pundit, and voter. Links* Sacha Altay* “Misinformation Reloaded? Fears about the Impact of Generative AI on Misinformation are Overblown” Felix M. Simon, Sacha Altay, & Hugo Mercier * “Don’t Panic (Yet): Assessing the Evidence and Discourse Around Generative AI and Elections” Felix M. Simon & Sacha Altay * “The Media Very Rarely Lies” Scott Alexander * “How Dangerous is Misinformation?” Dan Williams* “Scapegoating the Algorithm” Dan Williams* “Is Social Media Destroying Democracy—Or Giving It To Us Good And Hard?” Dan Williams* “Not Born Yesterday: The Science of Who We Trust and What We Believe” Hugo Mercier* Joseph Uscinski* “Durably Reducing Conspiracy Beliefs Through Dialogues with AI” Thomas H. Costello, Gordon Pennycook, & David G. Rand* “The Levers of Political Persuasion with Conversational AI” Kobi Hackenburg, Ben M. Tappin, et al. * Ben TappinChapters* 00:00 Understanding Misinformation: Definitions and Prevalence* 04:22 The Complexity of Media Bias and Misinformation* 14:40 Human Gullibility: Misconceptions and Realities* 27:28 Selective Exposure and Demand for Misinformation* 29:49 Political Advertising: Efficacy and Misconceptions* 35:13 Social Media’s Role in Political Discourse* 40:50 Evaluating the Impact of Social Media on Society* 42:44 The Impact of Political Content on Social Media* 46:57 The Changing Landscape of Political Voices* 51:41 Generative AI and Its Implications for Misinformation* 01:03:46 The Liar’s Dividend and Trust in Media* 01:14:11 Personalization and the Role of Generative AITranscript* Please note that this transcript was edited by AI and may contain mistakes. Dan Williams: Okay, welcome back. I’m Dan Williams. I’m back with Henry Shevlin. And today we’re going to be talking about one of the most controversial, consequential topics in popular discourse, in academic research, and in politics, which is misinformation. So we’re going to be talking about how widespread is misinformation? Are we living through, as some people claim, a misinformation age, a post-truth era, an epistemic crisis?How impactful is misinformation and more broadly domestic and foreign influence campaigns? What’s the
About Conspicuous Cognition Podcast
A podcast about big questions in philosophy, psychology, evolution, politics, artificial intelligence, and more. www.conspicuouscognition.com
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