Critics questioned if the AI used Lean for verification.
Where did all the AI haters go? 🤔
You know, the ones screaming "it's just autocomplete!" and "it'll never be useful!"
They're real quiet now that AI is actually magical and transforming everything.
Almost like... they were wrong the whole time. 😏
Positive users highlight excitement over AI solving open math problems like Erdős challenges, while negative users direct insults at Gary Marcus as a clown or Altman as a scammer.
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is the new math result neurosymbolic with Lean, harnesses etc or a pure LLM?
I love AI, it’s pure LLMs I hate.
Pure LLMs *are* basically just autocomplete.
Recent progress (e.g. Claude Code) doesn’t show otherwise
Rather, lot of the progress in the last two years has come from *introducing* other things – mainly classic symbolic techniques and tools, to offset the weaknesses of pure LLMs.
Shame to see the tweet below muddle all of this.
If we want to make further progress we need to understand where the progress is coming from; mostly it is coming from leaving pure LLMs behind.
Where did all the AI haters go? 🤔
You know, the ones screaming "it's just autocomplete!" and "it'll never be useful!"
They're real quiet now that AI is actually magical and transforming everything.
Almost like... they were wrong the whole time. 😏
GOALPOST MOVED. "LLM must solve major conjecture without lean or data augmentation using lean. Otherwise its neuro symbolic AI, just as I predicted years ago. Gotcha."
i just want to shake people awake. this is it! the computers are speaking! they solve Erdos problems! they think for hours! code is no longer hand-written! wake up! gradient descent on deep neural networks shows no sign of plateau! this is it!
The pure LLM debate - which I had for many years, here and elsewhere - is indeed no longer relevant. Why?
Because I won; nobody uses pure LLMs anymore.
Nowadays all deployed objects are neurosymbolic, which was exactly the point of my infamous 2022 paper, Deep Learning is Hitting a Wall.
If you don’t know I won, it’s because you read the title and not the paper 🤷♂️
This is true in the narrow sense, but it also points to why the “pure LLM” debate is becoming less central.
The deployed object is not a pure LLM.
It is a language model embedded in a tool-using execution stack: retrieval, code, memory, verifiers, APIs, agents, symbolic constraints, workflow permissions, and external systems.
So the question is not whether autocomplete alone becomes intelligence.
The question is what happens when autocomplete becomes the interface layer for systems that can act, check, search, write code, call tools, route tasks, and operate inside institutional workflows.
That hybrid object is what matters.
Aviation was not transformed by “pure engines” either. It was engines plus control surfaces, navigation, fuel systems, airports, maintenance regimes, regulation, and logistics.
AI will likely be the same.
The model is not the civilization-level unit.
The stack is.
he is reaching fatal levels of copium, im legit worried for him
GOALPOST MOVED. "LLM must solve major conjecture without lean or data augmentation using lean. Otherwise its neuro symbolic AI, just as I predicted years ago. Gotcha."
Gary please stop digging your own hole😭
felt cute, might auto-complete all Erdös problems by 2030
I love AI, it’s pure LLMs I hate.
Pure LLMs *are* basically just autocomplete.
Recent progress (e.g. Claude Code) doesn’t show otherwise
Rather, lot of the progress in the last two years has come from *introducing* other things – mainly classic symbolic techniques and tools, to offset the weaknesses of pure LLMs.
Shame to see the tweet below muddle all of this.
If we want to make further progress we need to understand where the progress is coming from; mostly it is coming from leaving pure LLMs behind.
😂😂😂🤡🤡🤡
is the new math result neurosymbolic with Lean, harnesses etc or a pure LLM?
I wrote about the idea that "we're finding out what humans are bad at" last year: https://secondthoughts.ai/p/were-finding-out-what-humans-are.
"AI generating new knowledge and accelerating science will change the trajectory of humanity."
Welcome to the era of Knowledge Accelerationism.
This is the biggest deal in the history of AI so far. And it will look like a small deal at the end of the year.
I’ve spent countless hours on this problem as a PhD student. I genuinely cannot believe I’m alive to watch AI solve it.
AI generating new knowledge and accelerating science will change the trajectory of humanity. And we are unbelievably early.
I don't think the issue is whether problems will run out. It's whether human comparative advantage at solving problems, asking questions, or communicating solutions will run out. Current trends seem to suggest "yes" on all counts, and soon-ish.
Unit distances result is very exciting, but re: “math is solved” — humans regularly solve long-open problems, and yet infinitely more interesting open problems remain.
if you were paying attention this was apparent since davinci-003 api access
@GaryMarcus gary this is pure bullshit and i think you know this. your "paper" (which is actually a nautilus essay mostly filled with historical anecdotes) has *nothing* to do with modern reasoning models or agent harnesses, and those share nothing with "neurosymbolic" methods
Great post. My own sense is almost exactly like what Jason described here.
I suspect math will be like Chess and Go due to verifiability. The period of fruitful collaboration between humans and AI will be short (i.e. a few years or less, not a decade). Progress in math will be jagged, with harder to formalize fields coming last, but I suspect this jaggedness will be compressed in time -- I expect superhuman performance at (nearly?) all areas of math within a few years (a few = 2-3?).
AIs will also be better at asking pure math questions than humans, and will quickly develop theories beyond human comprehension.
Human theorists will have a recreational comparative advantage over other humans in understanding these theories, but AIs will be better at communicating these theories to applied researchers. Pure mathematicians will need to become applied researchers to do productive work, until applied research is also automated.
Confidence level for prediction: 50-65% for gist, 40-50% for all above claims being correct.
wild to see a somewhat disgraced politician comment on the technical side of AI as if he has any idea about the underlying computational questions.
and of course he dwells at the bottom of Paul Graham’s pyramid of argument, with a bunch of emoji rather than any sort substantive argument whatsoever.
😂😂😂🤡🤡🤡
I like how nobody (yet) has the conspiracy, that it was actually the group of openai employee that solved the conjecture and not the model, despite the number of IMO medalists and mathematicians OpenAI hired
I feel like it is legit conspiracy given how impossibly impressive the result is.
"Our most capable agent autonomously resolved 9 of 353 open Erdős problems at the per-problem cost of a few hundred dollars, proved 44/492 OEIS conjectures, and is being deployed in combinatorics, optimization, graph theory, algebraic geometry, and quantum optics research."
I think this was lost in the noise of all the unit distance problem solve news!
Paper from DeepMind: https://arxiv.org/abs/2605.22763v1
I think it's really great that OpenAI solved the unit distance math problem, but, as @ChrSzegedy and @KempeLab said in our podcast, we've known for a while that math is a solved problem (at least some aspects of it). Math is easy because it has verifiable outputs and a very deterministic, clean end-to-end judgment process. But what about social science? The big question right now is whether we see something similar there. My guess is not in the near future, but who knows…
the crazy part is that people are “clowning” me without knowing anything about the training or whether anything else other than scaled changed or how the model does on anything else. (or what it costs etc)
my claims have always been system level architecture and openai has said practically zilch.
if you are “clowning” me without knowing more the joke is actually on you, because it means you don’t understand the technical issues enough to ask.
I see a lot of people clowning gary for this but isn't he correct? regardless of the reason, AI labs should disclose everything about how they got a result when running a model, if they didn't, then say that too, I dislike ambiguity.
