Richard Sutton – Father of RL thinks LLMs are a dead end
Why Hacker News recommends it
HN commenters cite it as the serious skeptic's case against LLMs reaching general intelligence, usually in debates about whether scaling is enough.
What it is
Dwarkesh Patel interviews Richard Sutton, reinforcement learning pioneer, Turing Award winner and author of The Bitter Lesson. Sutton argues LLMs are a dead end because they cannot learn on the job, and that continual learning from experience will require a new architecture.
Recommended by 0 HN commenters across 0 different months, 2025 to 2026.
What Hacker News said
Cites gsf_emergency_4 · 2025-10 · on “Bitter lessons building AI products”
Rich Sutton, the guy behind both "reinforcement learning" & "the Bitter Lesson", muses that Tech needs to understand the Bitter Lesson better: [LINK] Longer analysis: [this video] To (try and) summarize those in the context of TFA: builders need to distinguish between policy optimisations and program optimisations I guess a related question to ask (important for both startups and Big Tech) might be: "should one focus on doing things that don't scale?"
Cites mbesto · 2025-10 · on “Claude Skills”
> Contrast with the way a human learns skills - as we gain experience with a skill, we get better at understanding when it's the right tool for the job. Which is precisely why Richard Sutton doesn't think LLMs will evolve to AGI[0]. LLMs are based on mimicry, not experience, so it's more likely (according to Sutton) that AGI will be based on some form of RL (reinforcement learning) and not neural networks (LLMs). More specifically, LLMs don't have goals and consequences of actions, which is the foundation for intelligence. So, to your point, the idea of a "skill" is more akin to a reference manual, than it is a skill building exercise that can be applied to developing an instrument, task, so
Cites j1mr10rd4n · 2025-11 · on “The Case That A.I. Is Thinking”
Geoffrey Hinton's recent lecture at the Royal Institute[1] is a fascinating watch. His assertion that human use of language being exactly analogous to neural networks with back-propagation really made me think about what LLMs might be able to do, and indeed, what happens in me when I "think". A common objection to LLM "intelligence" is that "they don't know anything". But in turn... what do biological intelligences "know"? For example, I "know" how to do things like write constructs that make complex collections of programmable switches behave in certain ways, but what do I really "understand"? I've been "taught" things about quantum mechanics, electrons, semiconductors, transistors, integra
Cites kbrkbr · 2026-05 · on “AI didn't delete your database, you did”
I think they are not actually intelligent. Fix all random seeds and other sources of randomness, and try the same prompt twice, and check how intelligent that looks, as a first approximation. On a more technical level very serious people have voiced doubts, for example Richard Sutton in an interview with Dwarkash Patel [1]. [1] [this video]
Cites solumos · 2026-07 · on “It Still Can't Do My Job: Four Years of Moving Goalposts (2022–2026)”
Google's definition is sufficient[0]. I'd also go so far as to say that LLMs aren't even AI, but that can be a bit pedantic. I align with Richard Sutton's reasoning, in that LLMs (and GPT models more generally) aren't "intelligent" in the same way that RL models are[1]. [0] - [LINK] [1] - [this video]
Cites mbesto · 2026-07 · on “A $500 RL fine-tune of a 9B open model beat frontier models on catalog review”
This is basically the entire argument by Richard Sutton happening in real time: [this video] Right now, we keep creating new models with larger and larger weights and throwing hardware at it. His argument is that this is a dead end and ultimately we'll eventually go back to purpose fit algorithms like we've always done in the history of AI development.