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> But this is not an applied AI company. There is absolutely no doubt about Yann's impact on AI/ML, but he had access to many more resources in Meta, and we di

by Oras 7mo ago

> But this is not an applied AI company. There is absolutely no doubt about Yann's impact on AI/ML, but he had access to many more resources in Meta, and we didn't see anything. It could be a management issue, though, and I sincerely wish we will see more competition, but from what I quoted above, it does not seem like it. Understanding world through videos (mentioned in the article), is just what video models have already done, and they are getting pretty good (see Seedance, Kling, Sora .. etc). So I'm not quite sure how what he proposed would work.

the_real_cher 7mo ago
He was suffocated by the corporate aspect Meta I suspect.
_giorgio_ 7mo ago
I can’t reconcile this dichotomy: most of the landmark deep learning papers were developed with what, by today’s standards, were almost ridiculously small training budgets — from Transformers to dropout, and so on. So I keep wondering: if his idea is really that good — and I genuinely hope it is — why hasn’t it led to anything truly groundbreaking yet? It can’t just be a matter of needing more data or more researchers. You tell me :-D
- samrus 7mo ago
  Its a matter of needing more time, which is a resource even SV VCs are scared to throw around. Look at the timeline of all these advancements and how long it took Lecun introduced backprop for deep learning back in 1989 Hinton published about contrastive divergance in next token prediction in 2002 Alexnet was 2012 Word2vec was 2013 Seq2seq was 2014 AiAYN was 2017 UnicornAI was 2019 Instructgpt was 2022 This makes alot of people think that things are just accelerating and they can be along for the ride. But its the years and years of foundational research that allows this to be done. That toll has to be paid for the successsors of LLMs to be able to reason properly and operate in the world the way humans do. That sowing wont happen as fast as the reaping did. Lecun was to plant those seeds, the others who onky was to eat the fruit dont get that they have to wait
  - _giorgio_ 7mo ago
    If his ideas had real substance, we would have seen substantial results by now. He introduced I-JEPA in 2023, so almost three years ago at this point. If he still hasn’t produced anything truly meaningful after all these years at Meta, when is that supposed to happen? Yann LeCun has been at Facebook/Meta since December 2013. Your chronological sequence is interesting, but it refers to a time when the number of researchers and the amount of compute available were a tiny fraction of what they are today.
    - samrus 7mo ago
      > If his ideas had real substance, we would have seen substantial results by now This is naive. Like saying if backprop had any real substance, it would have had results within 10 years of its publication in 1989 > Your chronological sequence is interesting, but it refers to a time when the number of researchers and the amount of compute available were a tiny fraction of what they are today. Again. Those resources are important. But one resource being ignored is time. Try baking a turkey at 300 for 4 hours veruss at 900 for 1 hour and see how edible each one is
      - _giorgio_ 7mo ago
        Backprop kept producing wins. That bought it time. “Wait longer” is not a blank check. In 2026, with Meta-scale talent, data, and compute, serious ideas should show strong intermediate results, not just theory. Time is necessary, but it is not evidence. More compute does not replace insight, but it does speed up falsification. So no, skepticism is not naive. If a research program still cannot point to a clear empirical advantage after years, “it just needs more time” stops sounding like science and starts sounding like insulation from the scoreboard.
boccaff 7mo ago
llama models pushed the envelope for a while, and having them "open-weight" allowed a lot of tinkering. I would say that most of fine tuned evolved from work on top of llama models.
- oefrha 7mo ago
  Llama wasn’t Yann LeCun’s work and he was openly critical of LLMs, so it’s not very relevant in this context. Source: himself https://x.com/ylecun/status/1993840625142436160 https://x.com/ylecun/status/1993840625142436160 (“I never worked on any Llama.”) and a million previous reports and tweets from him.
  - alecco 7mo ago
    > My only contribution was to push for Llama 2 to be open sourced. Quite a big contribution in practice.
    - oefrha 7mo ago
      Sure, but I don't that's relevant in a startup with 1B VC money either. Meta can afford to (attempt to) commoditize their complement.
  - rockinghigh 7mo ago
    He founded FAIR and the team in Paris that ultimately worked on the early Llama versions.
    - oefrha 7mo ago
      FAIR was founded in 2015 and Llama's first release was in 2023. Musk co-founded OpenAI in 2015 but no reasonable person credits ChatGPT in 2022 to him.
stein1946 7mo ago
> There is absolutely no doubt about Yann's impact on AI/ML, but he had access to many more resources in Meta, and we didn't see anything. That's true for 99% of the scientists, but dismissing their opinion based on them not having done world shattering / ground breaking research is probably not the way to go. > I sincerely wish we will see more competition I really wish we don't, science isn't markets. > Understanding world through videos The word "understanding" is doing a lot of heavy lifting here. I find myself prompting again and again for corrections on an image or a summary and "it" still does not "understand" and keeps doing the same thing over and over again.
- GorbachevyChase 7mo ago
  Do not keep bad results in context. You have to purge them to prevent them from effecting the next output. LLMs deceptively capable, but they don’t respond like a person. You can’t count on implicit context. You can’t count on parts of the implicit context having more weight than others.
torginus 7mo ago
Most folks get paid a lot more in a corporate job than tinkering at home - using the 'follow the money' logic it would make sense they would produce their most inspired works as 9-5 full stack engineers. But often passion and freedom to explore are often more important than resources
YetAnotherNick 7mo ago
> we didn't see anything. Is it a troll? Even if we just ignore Llama, Meta invented and released so many foundational research and open source code. I would say that the computer vision field would be years behind if Meta didn't publish some core research like DETR or MAE.
- famouswaffles 7mo ago
  You should ignore Llama because by his own admission, >My only contribution was to push for Llama 2 to be open sourced.
  - rockinghigh 7mo ago
    He founded the team that worked on fasttext, llama and other similarly impactful projects.
- koolala 7mo ago
  Did he work on those vision models?
nashadelic 7mo ago
Your take is brutal but spot on
andreyk 7mo ago
"and we didn't see anything" is not justified at all. Meta absolutely has (or at least had) a word class industry AI lab and has published a ton of great work and open source models (granted their LLM open source stuff failed to keep up with chinese models in 2024/2025 ; their other open source stuff for thins like segmentation don't get enough credit though). Yann's main role was Chief AI Scientist, not any sort of product role, and as far as I can tell he did a great job building up and leading a research group within Meta. He deserved a lot of credit for pushing Meta to very open to publishing research and open sourcing models trained on large scale data. Just as one example, Meta (together with NYU) just published "Beyond Language Modeling: An Exploration of Multimodal Pretraining" (https://arxiv.org/pdf/2603.03276 https://arxiv.org/pdf/2603.03276) which has a ton of large-experiment backed insights. Yann did seem to end up with a bit of an inflated ego, but I still consider him a great research lead. Context: I did a PhD focused on AI, and Meta's group had a similar pedigree as Google AI/Deepmind as far as places to go do an internship or go to after graduation.
- Oras 7mo ago
  I wasn't criticising his scientific contribution at all, that's why I started my comment by appraising what he did. Creating a startup has to be about a product. When you raise 1B, investors are expecting returns, not papers.
  - magicalist 7mo ago
    >> but he had access to many more resources in Meta, and we didn't see anything > I wasn't criticising his scientific contribution at all, that's why I started my comment by appraising what he did. You were criticising his output at Facebook, though, but he was in the research group at facebook, not a product group, so it seems like we did actually see lots of things?
  - JMiao 7mo ago
    they are not expecting returns at 1B+, just for some one to pay more than they paid six months ago
  - overfeed 7mo ago
LarsDu88 7mo ago
That's such a terrible take. For a hot minute Meta had a top 3 LLM and open sourced the whole thing, even with LeCunn's reservations around the technology. At the same time Meta spat out huge breakthroughs in: - 3d model generation - Self-supervised label-free training (DINO). Remember Alexandr Wang built a multibillion dollar company just around having people in third world countries label data, so this is a huge breakthrough. - A whole new class of world modeling techniques (JEPAs) - SAM (Segment anything)
- Oras 7mo ago
  > - Self-supervised label-free training (DINO). Remember Alexandr Wang built a multibillion dollar company just around having people in third world countries label data, so this is a huge breakthrough. If it was a breakthrough, why did Meta acquire Wang and his company? I'm genuinely curious.
  - airstrike 7mo ago
    People make stupid acquisitions all of the time.
  - LarsDu88 7mo ago
    Wang fits the profile of a possible successor ceo for meta. Young, hit it big early, hit the ai book early straight out of college. Obviously not woke (just look at his public statements). Unfotunately the dude knows very little about ai or ml research. He's just another wealthy grifter. At this point decision making at Meta is based on Zuckerberg's vibes, and i suspect the emperor has no clothes.
dabeeeenster 7mo ago
> It could be a management issue, though Or, maybe it's just hard?
lee 7mo ago
In an interview, Yann mentioned that one reason he left Meta was that they were very focused on LLMs and he no longer believed LLMs were the path forward to reaching AGI.
htrp 7mo ago
this is absolutely an applied ai company, the only question is whether the applied AI will be subordinated to the research

by Oras 7mo ago

the_real_cher 7mo ago

He was suffocated by the corporate aspect Meta I suspect.

_giorgio_ 7mo ago

I can’t reconcile this dichotomy: most of the landmark deep learning papers were developed with what, by today’s standards, were almost ridiculously small training budgets — from Transformers to dropout, and so on. So I keep wondering: if his idea is really that good — and I genuinely hope it is — why hasn’t it led to anything truly groundbreaking yet? It can’t just be a matter of needing more data or more researchers. You tell me :-D

samrus 7mo ago
Its a matter of needing more time, which is a resource even SV VCs are scared to throw around. Look at the timeline of all these advancements and how long it took Lecun introduced backprop for deep learning back in 1989 Hinton published about contrastive divergance in next token prediction in 2002 Alexnet was 2012 Word2vec was 2013 Seq2seq was 2014 AiAYN was 2017 UnicornAI was 2019 Instructgpt was 2022 This makes alot of people think that things are just accelerating and they can be along for the ride. But its the years and years of foundational research that allows this to be done. That toll has to be paid for the successsors of LLMs to be able to reason properly and operate in the world the way humans do. That sowing wont happen as fast as the reaping did. Lecun was to plant those seeds, the others who onky was to eat the fruit dont get that they have to wait
- _giorgio_ 7mo ago
  If his ideas had real substance, we would have seen substantial results by now. He introduced I-JEPA in 2023, so almost three years ago at this point. If he still hasn’t produced anything truly meaningful after all these years at Meta, when is that supposed to happen? Yann LeCun has been at Facebook/Meta since December 2013. Your chronological sequence is interesting, but it refers to a time when the number of researchers and the amount of compute available were a tiny fraction of what they are today.
  - samrus 7mo ago
    > If his ideas had real substance, we would have seen substantial results by now This is naive. Like saying if backprop had any real substance, it would have had results within 10 years of its publication in 1989 > Your chronological sequence is interesting, but it refers to a time when the number of researchers and the amount of compute available were a tiny fraction of what they are today. Again. Those resources are important. But one resource being ignored is time. Try baking a turkey at 300 for 4 hours veruss at 900 for 1 hour and see how edible each one is
    - _giorgio_ 7mo ago
      Backprop kept producing wins. That bought it time. “Wait longer” is not a blank check. In 2026, with Meta-scale talent, data, and compute, serious ideas should show strong intermediate results, not just theory. Time is necessary, but it is not evidence. More compute does not replace insight, but it does speed up falsification. So no, skepticism is not naive. If a research program still cannot point to a clear empirical advantage after years, “it just needs more time” stops sounding like science and starts sounding like insulation from the scoreboard.

boccaff 7mo ago

llama models pushed the envelope for a while, and having them "open-weight" allowed a lot of tinkering. I would say that most of fine tuned evolved from work on top of llama models.

oefrha 7mo ago
Llama wasn’t Yann LeCun’s work and he was openly critical of LLMs, so it’s not very relevant in this context. Source: himself https://x.com/ylecun/status/1993840625142436160 https://x.com/ylecun/status/1993840625142436160 (“I never worked on any Llama.”) and a million previous reports and tweets from him.
- alecco 7mo ago
  > My only contribution was to push for Llama 2 to be open sourced. Quite a big contribution in practice.
  - oefrha 7mo ago
    Sure, but I don't that's relevant in a startup with 1B VC money either. Meta can afford to (attempt to) commoditize their complement.
- rockinghigh 7mo ago
  He founded FAIR and the team in Paris that ultimately worked on the early Llama versions.
  - oefrha 7mo ago
    FAIR was founded in 2015 and Llama's first release was in 2023. Musk co-founded OpenAI in 2015 but no reasonable person credits ChatGPT in 2022 to him.

stein1946 7mo ago

> There is absolutely no doubt about Yann's impact on AI/ML, but he had access to many more resources in Meta, and we didn't see anything. That's true for 99% of the scientists, but dismissing their opinion based on them not having done world shattering / ground breaking research is probably not the way to go. > I sincerely wish we will see more competition I really wish we don't, science isn't markets. > Understanding world through videos The word "understanding" is doing a lot of heavy lifting here. I find myself prompting again and again for corrections on an image or a summary and "it" still does not "understand" and keeps doing the same thing over and over again.

GorbachevyChase 7mo ago
Do not keep bad results in context. You have to purge them to prevent them from effecting the next output. LLMs deceptively capable, but they don’t respond like a person. You can’t count on implicit context. You can’t count on parts of the implicit context having more weight than others.

torginus 7mo ago

Most folks get paid a lot more in a corporate job than tinkering at home - using the 'follow the money' logic it would make sense they would produce their most inspired works as 9-5 full stack engineers. But often passion and freedom to explore are often more important than resources

YetAnotherNick 7mo ago

> we didn't see anything. Is it a troll? Even if we just ignore Llama, Meta invented and released so many foundational research and open source code. I would say that the computer vision field would be years behind if Meta didn't publish some core research like DETR or MAE.

famouswaffles 7mo ago
You should ignore Llama because by his own admission, >My only contribution was to push for Llama 2 to be open sourced.
- rockinghigh 7mo ago
  He founded the team that worked on fasttext, llama and other similarly impactful projects.
koolala 7mo ago
Did he work on those vision models?

nashadelic 7mo ago

Your take is brutal but spot on

andreyk 7mo ago

"and we didn't see anything" is not justified at all. Meta absolutely has (or at least had) a word class industry AI lab and has published a ton of great work and open source models (granted their LLM open source stuff failed to keep up with chinese models in 2024/2025 ; their other open source stuff for thins like segmentation don't get enough credit though). Yann's main role was Chief AI Scientist, not any sort of product role, and as far as I can tell he did a great job building up and leading a research group within Meta. He deserved a lot of credit for pushing Meta to very open to publishing research and open sourcing models trained on large scale data. Just as one example, Meta (together with NYU) just published "Beyond Language Modeling: An Exploration of Multimodal Pretraining" (https://arxiv.org/pdf/2603.03276 https://arxiv.org/pdf/2603.03276) which has a ton of large-experiment backed insights. Yann did seem to end up with a bit of an inflated ego, but I still consider him a great research lead. Context: I did a PhD focused on AI, and Meta's group had a similar pedigree as Google AI/Deepmind as far as places to go do an internship or go to after graduation.

Oras 7mo ago
I wasn't criticising his scientific contribution at all, that's why I started my comment by appraising what he did. Creating a startup has to be about a product. When you raise 1B, investors are expecting returns, not papers.
- magicalist 7mo ago
  >> but he had access to many more resources in Meta, and we didn't see anything > I wasn't criticising his scientific contribution at all, that's why I started my comment by appraising what he did. You were criticising his output at Facebook, though, but he was in the research group at facebook, not a product group, so it seems like we did actually see lots of things?
- JMiao 7mo ago
  they are not expecting returns at 1B+, just for some one to pay more than they paid six months ago
- overfeed 7mo ago

LarsDu88 7mo ago

That's such a terrible take. For a hot minute Meta had a top 3 LLM and open sourced the whole thing, even with LeCunn's reservations around the technology. At the same time Meta spat out huge breakthroughs in: - 3d model generation - Self-supervised label-free training (DINO). Remember Alexandr Wang built a multibillion dollar company just around having people in third world countries label data, so this is a huge breakthrough. - A whole new class of world modeling techniques (JEPAs) - SAM (Segment anything)

Oras 7mo ago
> - Self-supervised label-free training (DINO). Remember Alexandr Wang built a multibillion dollar company just around having people in third world countries label data, so this is a huge breakthrough. If it was a breakthrough, why did Meta acquire Wang and his company? I'm genuinely curious.
- airstrike 7mo ago
  People make stupid acquisitions all of the time.
- LarsDu88 7mo ago
  Wang fits the profile of a possible successor ceo for meta. Young, hit it big early, hit the ai book early straight out of college. Obviously not woke (just look at his public statements). Unfotunately the dude knows very little about ai or ml research. He's just another wealthy grifter. At this point decision making at Meta is based on Zuckerberg's vibes, and i suspect the emperor has no clothes.

dabeeeenster 7mo ago

> It could be a management issue, though Or, maybe it's just hard?

lee 7mo ago

In an interview, Yann mentioned that one reason he left Meta was that they were very focused on LLMs and he no longer believed LLMs were the path forward to reaching AGI.

htrp 7mo ago

this is absolutely an applied ai company, the only question is whether the applied AI will be subordinated to the research