>at this point it does not matter what you believe about LLMs: in general, to trust LeCun words is not a good idea. Add to this that LeCun is directing an AI lab that as the same point has the following huge issues:
1. Weakest ever LLM among the big labs with similar resources (and smaller resources: DeepSeek).
2. They say they are focusing on open source models, but the license is among the less open than the available open weight models.
3. LLMs and in general all the new AI wave puts CNNs, a field where LeCun worked (but that didn't started himself) a lot more in perspective, and now it's just a chapter in a book that is composed mostly of other techniques.
Would be interesting to see opinion of antirez on this new release.
Not that I agree with all the linked points but it is weird to me that LeCun consistently states LLMs are not the right path yet LLMs are still the main flagship model they are shipping.
Although maybe he's using an odd definition for what counts as a LLM.
> LeCun consistently states LLMs are not the right path yet LLMs are still the main flagship model they are shipping.
I really don't see what's controversial about this. If that's to mean that LLMs are inherently flawed/limited and just represent a local maxima in the overall journey towards developing better AI techniques, I thought that was pretty universal understanding by now.
That is how I read it. Transformer based LLMs have limitations that are fundamental to the technology. It does not seem crazy to me that a guy involved in research at his level would say that they are a stepping stone to something better.
What I find most interesting is his estimate of five years, which is soon enough that I would guess he sees one or more potential successors.
In our field (AI) nobody can see even 5 months ahead, including people who are training a model today to be released 5 months from now. Predicting something 5 years from now is about as accurate as predicting something 100 years from now.
I don't understand what LeCun is trying to say. Why does he give an interview saying that LLM's are almost obsolete just when they're about to release a model that increases the SotA context length by an order of magnitude? It's almost like a Dr. Jekyll and Mr. Hyde situation.
LeCun fundamentally doesn't think bigger and better LLMs will lead to anything resembling "AGI", although he thinks they may be some component of AGI. Also, he leads the research division, increasing context length from 2M to 10M is not interesting to him.
If that's what he said, I'd be happy, but I was more concerned about this:
> His belief is so strong that, at a conference last year, he advised young developers, "Don't work on LLMs. [These models are] in the hands of large companies, there's nothing you can bring to the table. You should work on next-gen AI systems that lift the limitations of LLMs."
It's ok to say that we'll need to scale other mountains, but I'm concerned that the "Don't" there would push people away from the engineering that would give them the relevant inspiration.
> but I'm concerned that the "Don't" there would push people away from the engineering that would give them the relevant inspiration.
You have way more yay-sayers than nay-sayers, there is never a risk that we don't go hard enough into the current trends, there is however a risk that we go too hard into it and ignore other paths.
But ... that's not how science works. There are a myriad examples of engineering advances pushing basic science forward. I just can't understand why he'd have such a "fixed mindset" about a field where the engineering is advancing an order of magnitude every year
Well, having candles and kerosene lamps to work late definitely didn't hurt.
But in any case, while these things don't work in a predictable way, the engineering work on lightbulbs in your example led to theoretical advances in our understanding of materials science, vacuum technology, and of course electrical systems.
I'm not arguing that LLMs on their own will certainly lead directly to AGI without any additional insights, but I do think that there's a significant chance that advances in LLMs might lead engineers and researchers to inspiration that will help them make those further insights. I think that it's silly that he seems to be telling people that there's "nothing to see here" and no benefit in being close to the action.
Listening so Science Friday today on NPR, the two guests did not think AGI was a useful term and it would be better to focus on how useful actual technical advances are than some sort of generalized human-level AI, which they saw as more of a marketing tool that's ill-defined, except in the case of makes the company so many billions of dollars.
I mean they're not comparing with Gemini 2.5, or the o-series of models, so not sure they're really beating the first point (and their best model is not even released yet)
Is the new license different? Or is it still failing for the same issues pointed by the second point?
I think the problem with the 3rd point is that LeCun is not leading LLama, right? So this doesn't change things, thought mostly because it wasn't a good consideration before
>at this point it does not matter what you believe about LLMs: in general, to trust LeCun words is not a good idea. Add to this that LeCun is directing an AI lab that as the same point has the following huge issues:
1. Weakest ever LLM among the big labs with similar resources (and smaller resources: DeepSeek).
2. They say they are focusing on open source models, but the license is among the less open than the available open weight models.
3. LLMs and in general all the new AI wave puts CNNs, a field where LeCun worked (but that didn't started himself) a lot more in perspective, and now it's just a chapter in a book that is composed mostly of other techniques.
Would be interesting to see opinion of antirez on this new release.