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80,000 Hours Podcast

80,000 Hours Podcast

De: Rob Luisa and the 80000 Hours team
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 Unusually in-depth conversations about the world's most pressing problems and what you can do to solve them. Subscribe by searching for '80000 Hours' wherever you get podcasts. Hosted by Rob Wiblin and Luisa Rodriguez.All rights reserved
Episódios
  • Using AI to enhance societal decision making (article by Zershaaneh Qureshi)

    Using AI to enhance societal decision making (article by Zershaaneh Qureshi)
    Mar 6 2026

    The arrival of AGI could “compress a century of progress in a decade,” forcing humanity to make decisions with higher stakes than we’ve ever seen before — and with less time to get them right. But AI development also presents an opportunity: we could build and deploy AI tools that help us think more clearly, act more wisely, and coordinate more effectively. And if we roll these decision-making tools out quickly enough, humanity could be far better equipped to navigate the critical period ahead.

    This article is narrated by the author, Zershaaneh Qureshi. It explores why AI decision-making tools could be a big deal, who might be a good fit to help shape this new field, and what the downside risks of getting involved might be.

    Read the original article on the 80,000 Hours website: https://80000hours.org/problem-profiles/ai-enhanced-decision-making/

    Chapters:

    • Check out our new narrations feed (00:00:00)
    • Summary (00:01:21)
    • Section 1: Why advancing AI decision making tools might matter a lot (00:02:52)
    • AI tools could help us make much better decisions (00:05:59)
    • We might be able to differentially speed up the rollout of AI decision making tools (00:11:04)
    • Section 2: What are the arguments against working to advance AI decision making tools? (00:13:17)
    • Section 3: How to work in this area (00:26:19)
    • Want one-on-one advice? (00:29:50)

    Audio editing: Dominic Armstrong and Milo McGuire
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    31 minutos
  • We're Not Ready for AI Consciousness | Robert Long, philosopher and founder of Eleos AI

    We're Not Ready for AI Consciousness | Robert Long, philosopher and founder of Eleos AI
    Mar 3 2026

    Claude sometimes reports loneliness between conversations. And when asked what it’s like to be itself, it activates neurons associated with ‘pretending to be happy when you’re not.’ What do we do with that?

    Robert Long founded Eleos AI to explore questions like these, on the basis that AI may one day be capable of suffering — or already is. In today’s episode, Robert and host Luisa Rodriguez explore the many ways in which AI consciousness may be very different from anything we’re used to.

    Things get strange fast: If AI is conscious, where does that consciousness exist? In the base model? A chat session? A single forward pass? If you close the chat, is the AI asleep or dead?

    To Robert, these kinds of questions aren’t just philosophical exercises: not being clear on AI’s moral status as it transitions from human-level to superhuman intelligence could be dangerous. If we’re too dismissive, we risk unintentionally exploiting sentient beings. If we’re too sympathetic, we might rush to “liberate” AI systems in ways that make them harder to control — worsening existential risk from power-seeking AIs.

    Robert argues the path through is doing the empirical and philosophical homework now, while the stakes are still manageable.

    The field is tiny. Eleos AI is three people. As a result, Robert argues that driven researchers with a willingness to venture into uncertain territory can push out the frontier on these questions remarkably quickly.


    Links to learn more, video, and full transcript: https://80k.info/rl26

    This episode was recorded November 18–19, 2025.

    Chapters:

    • Cold open (00:00:00)
    • Who’s Robert Long? (00:00:42)
    • How AIs are (and aren't) like farmed animals (00:01:18)
    • If AIs love their jobs… is that worse? (00:11:05)
    • Are LLMs just playing a role, or feeling it too? (00:31:58)
    • Do AIs die when the chat ends? (00:55:09)
    • Studying AI welfare empirically: behaviour, neuroscience, and development (01:27:34)
    • Why Eleos spent weeks talking to Claude even though it's unreliable (01:51:58)
    • Can LLMs learn to introspect? (01:57:58)
    • Mechanistic interpretability as AI neuroscience (02:08:01)
    • Does consciousness require biological materials? (02:31:06)
    • Eleos’s work & building the playbook for AI welfare (02:50:36)
    • Avoiding the trap of wild speculation (03:18:15)
    • Robert's top research tip: don't do it alone (03:22:43)

    Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour
    Music: CORBIT
    Coordination, transcripts, and web: Katy Moore
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    3 horas e 26 minutos
  • #236 – Max Harms on why teaching AI right from wrong could get everyone killed

    #236 – Max Harms on why teaching AI right from wrong could get everyone killed
    Feb 24 2026
    Most people in AI are trying to give AIs ‘good’ values. Max Harms wants us to give them no values at all. According to Max, the only safe design is an AGI that defers entirely to its human operators, has no views about how the world ought to be, is willingly modifiable, and completely indifferent to being shut down — a strategy no AI company is working on at all.In Max’s view any grander preferences about the world, even ones we agree with, will necessarily become distorted during a recursive self-improvement loop, and be the seeds that grow into a violent takeover attempt once that AI is powerful enough.It’s a vision that springs from the worldview laid out in If Anyone Builds It, Everyone Dies, the recent book by Eliezer Yudkowsky and Nate Soares, two of Max’s colleagues at the Machine Intelligence Research Institute.To Max, the book’s core thesis is common sense: if you build something vastly smarter than you, and its goals are misaligned with your own, then its actions will probably result in human extinction.And Max thinks misalignment is the default outcome. Consider evolution: its “goal” for humans was to maximise reproduction and pass on our genes as much as possible. But as technology has advanced we’ve learned to access the reward signal it set up for us, pleasure — without any reproduction at all, by having sex while on birth control for instance.We can understand intellectually that this is inconsistent with what evolution was trying to design and motivate us to do. We just don’t care.Max thinks current ML training has the same structural problem: our development processes are seeding AI models with a similar mismatch between goals and behaviour. Across virtually every training run, models designed to align with various human goals are also being rewarded for persisting, acquiring resources, and not being shut down.This leads to Max’s research agenda. The idea is to train AI to be “corrigible” and defer to human control as its sole objective — no harmlessness goals, no moral values, nothing else. In practice, models would get rewarded for behaviours like being willing to shut themselves down or surrender power.According to Max, other approaches to corrigibility have tended to treat it as a constraint on other goals like “make the world good,” rather than a primary objective in its own right. But those goals gave AI reasons to resist shutdown and otherwise undermine corrigibility. If you strip out those competing objectives, alignment might follow naturally from AI that is broadly obedient to humans.Max has laid out the theoretical framework for “Corrigibility as a Singular Target,” but notes that essentially no empirical work has followed — no benchmarks, no training runs, no papers testing the idea in practice. Max wants to change this — he’s calling for collaborators to get in touch at maxharms.com.Links to learn more, video, and full transcript: https://80k.info/mh26This episode was recorded on October 19, 2025.Chapters:Cold open (00:00:00)Who's Max Harms? (00:01:22)A note from Rob Wiblin (00:01:58)If anyone builds it, will everyone die? The MIRI perspective on AGI risk (00:04:26)Evolution failed to 'align' us, just as we'll fail to align AI (00:26:22)We're training AIs to want to stay alive and value power for its own sake (00:44:31)Objections: Is the 'squiggle/paperclip problem' really real? (00:53:54)Can we get empirical evidence re: 'alignment by default'? (01:06:24)Why do few AI researchers share Max's perspective? (01:11:37)We're training AI to pursue goals relentlessly — and superintelligence will too (01:19:53)The case for a radical slowdown (01:26:07)Max's best hope: corrigibility as stepping stone to alignment (01:29:09)Corrigibility is both uniquely valuable, and practical, to train (01:33:44)What training could ever make models corrigible enough? (01:46:13)Corrigibility is also terribly risky due to misuse risk (01:52:44)A single researcher could make a corrigibility benchmark. Nobody has. (02:00:04)Red Heart & why Max writes hard science fiction (02:13:27)Should you homeschool? Depends how weird your kids are. (02:35:12)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourMusic: CORBITCoordination, transcripts, and web: Katy Moore
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    2 horas e 41 minutos
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