[samrus]

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_]

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]

> 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_]

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.