The list is simply made up, as confirmed by John Carmack.
"""
John Carmack
@ID_AA_Carmack
I rather expected @ilyasut
to have made a public post by now after all the discussion of the AI reading list he gave me. A canonical list of references from a leading figure would be appreciated by many. I would be curious myself about what he would add from the last three years.
"""
That's a super nice story. Sometimes we tend to forget the contributions of AI for the visually impaired or hearing-impaired people—for example, subtitles on Meta glasses or audio descriptions and such.
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.
LeCun has had every advantage imaginable — and the scoreboard remains empty.
He joined Facebook (now Meta) in December 2013. That's over 12 years of access to one of the largest AI labs in the world, near-unlimited compute, and some of the best researchers money can buy.
He introduced I-JEPA in 2023, nearly 3 years ago. It was supposed to represent a fundamental shift in how machines learn — moving beyond generative models toward a deeper, more structured world understanding.
And yet: I-JEPA hasn't decisively beaten existing models on any major benchmark. No Meta product uses JEPA as a core approach. The research community hasn't adopted it — the field keeps pushing on LLMs and diffusion models. There's been no "GPT moment" for JEPA, no single result that made its value obvious to everyone.
So the question becomes simple: how many years, how many resources, and how many failed proof-of-concepts does it take before we're allowed to judge whether an idea actually works?
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.
The giant seed round proves investors were willing to fund Mira Murati, not that the company had built anything durable.
Within months, it had already lost cofounder Andrew Tulloch to Meta, then cofounders Barret Zoph and Luke Metz plus researcher Sam Schoenholz to OpenAI; WIRED also reported that at least three other researchers left. At that point, citing it as evidence of real competitive momentum feels weak.
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
The list is simply made up, as confirmed by John Carmack.
""" John Carmack @ID_AA_Carmack I rather expected @ilyasut to have made a public post by now after all the discussion of the AI reading list he gave me. A canonical list of references from a leading figure would be appreciated by many. I would be curious myself about what he would add from the last three years. """
https://x.com/ID_AA_Carmack/status/1622673143469858816