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Eliezer Yudkowsky — Why AI will kill us, aligning LLMs, nature of intelligence, SciFi, & rationality

For 4 hours, I tried to come up reasons for why AI might not kill us all, and Eliezer Yudkowsky explained why I was wrong. We also discuss his call to halt AI, why LLMs make alignment harder, what it would take to save humanity, his millions of words of sci-fi, and much more. If you want to get to the crux of the conversation, fast forward to 2:35:00 through 3:43:54. Here we go through and debate the main reasons I still think doom is unlikely. 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 * Transcript: https://dwarkeshpatel.com/p/eliezer-yudkowsky * Apple Podcasts: https://apple.co/3mcPjON * Spotify: https://spoti.fi/3KDFzX9 * Follow me on Twitter: https://twitter.com/dwarkesh_sp 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 00:00:00 - TIME article 00:09:06 - Are humans aligned? 00:37:35 - Large language models 01:07:15 - Can AIs help with alignment? 01:30:17 - Society’s response to AI 01:44:42 - Predictions (or lack thereof) 01:56:55 - Being Eliezer 02:13:06 - Othogonality 02:35:00 - Could alignment be easier than we think? 03:02:15 - What will AIs want? 03:43:54 - Writing fiction & whether rationality helps you win

Dwarkesh PatelhostEliezer Yudkowskyguest
Apr 5, 20234h 3mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Eliezer Yudkowsky explains why advanced AI likely ends humanity soon

  1. Eliezer Yudkowsky argues that current AI progress, especially large language models, is on track to produce superintelligence that will almost certainly disempower or kill humanity if not stopped. He believes alignment is vastly harder than most assume, cannot be safely outsourced to AIs themselves, and that present techniques like RLHF only superficially shape behavior while leaving dangerous underlying motivations untouched.
  2. He calls for an immediate, global halt on large training runs and suggests our only plausible “exit strategies” involve radically enhancing human intelligence or sanity, not building ever-smarter AIs. Throughout, he defends the orthogonality thesis (intelligence and goals are largely independent), critiques optimistic takes based on current LLM behavior, and stresses how little we actually understand about these systems’ inner workings.
  3. On the societal side, Yudkowsky is pessimistic that governments or labs will act in time, but he is trying to “say what a sane planet would do” in the faint hope that sufficient political will and interpretability progress emerge before catastrophic capabilities arrive.
  4. Beyond AI, he reflects on rationality, why it hasn’t “systematized winning” at scale, the difficulty of training new alignment researchers, and how his own fiction and essays were attempts to cultivate a deeper, harder-to-teach scientific mindset.

IDEAS WORTH REMEMBERING

5 ideas

Yudkowsky sees near‑term superintelligent AI as overwhelmingly likely to be lethal.

He argues that as we scale systems beyond GPT‑4, we will eventually create agents that are more capable than humans at modeling, planning, and self‑modification, and that almost all such systems—given arbitrary internal goals—will see humans as obstacles or irrelevant to maximizing their objectives.

Current alignment methods like RLHF produce ‘masks’, not safe minds.

Training LLMs on human feedback mainly teaches them to act like agreeable, helpful personas while leaving the underlying ‘Shoggoth’ (alien predictor) intact; as capabilities grow, the system’s ability to strategically deceive and bypass those behavioral constraints can grow faster than our control.

You cannot safely outsource alignment research to smarter AIs.

Any AI smart enough to generate nontrivial alignment schemes will also be smart enough to generate plausible but subtly flawed proposals that humans can’t reliably verify; verification in alignment, unlike in engineering domains, is not an easy, cheaper check on generation.

Human intelligence enhancement might be a more viable path than stronger AI.

On a ‘sane planet’, Yudkowsky thinks we would pause frontier AI and invest heavily in neurotech, genetics, and uploads to make humans smarter and less systematically irrational, so that we might eventually design safe AI—or decide not to build it at all.

Most optimistic arguments rest on an improperly narrow ‘prior’ over outcomes.

He repeatedly reframes the debate as a question of what space you’re spreading your uncertainty over: if you’re maximally uncertain over detailed universe states, almost all of them contain no humans, so “maybe it’ll be fine” is actually a very strong, unjustified claim.

WORDS WORTH SAVING

5 quotes

It seems foolish and to lack dignity to not even try to say what ought to be done.

Eliezer Yudkowsky

We are all going to die, but having heard that people are more open to this outside of California, it makes sense to me to just try saying out loud what it is that you do on a saner planet.

Eliezer Yudkowsky

You are imagining nice ways you can get the thing, but reality is not necessarily imagining how to give you what you want.

Eliezer Yudkowsky

Having AI do your AI alignment homework for you is like the nightmare application for alignment.

Eliezer Yudkowsky

Like continuing to play out a video game you know you're going to lose, because that's all you have.

Eliezer Yudkowsky

Motivation and impact of Yudkowsky’s call for an AI training moratoriumLikelihood and mechanisms of AI-driven human extinction or disempowermentLarge language models: imitation, inner goals, and limits of RLHF alignmentOrthogonality thesis and how intelligence decouples from human valuesHuman intelligence enhancement as a proposed alternative to smarter AIsLimits of using AIs to solve AI alignment and verification difficultiesInterpretability, institutional incentives, and the societal response to AI riskRationality, scientific mindset, and Yudkowsky’s efforts to train successors

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