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The end of Microsoft Research's AI Frontiers group

What HN thinks

A thread about Microsoft closing a research group that quickly turned into a referendum on whether Yann LeCun deserved to be fired.

Scale transformers, skip side quests
Scaling is still paying off and resources like GPUs are scarce, so companies are right to drop exploratory research. Several commenters argue Meta was right to move on from LeCun, whose predictions about LLMs they say proved wrong.
Killing exploration is short-sighted
Industry side projects gave us the transistor, the laser and the transformer itself. Big firms can afford a skunkworks team, and whoever finds a cheaper architecture could upend the market.
Scaling is already hitting limits
Data shortages, Chinchilla-style limits, power-law costs and political resistance to data centers point to diminishing returns. Recent gains have come from RL and agent harnesses rather than bigger models.

Consensus: There is no consensus. Most commenters accept that this is an explore/exploit tradeoff, and that companies are currently going all in on exploiting transformers.

Recommended in the comments

Hutter Prize ↗ (other): Given as an example of a way to define and measure intelligence that doesn't depend on scale.
ARC Prize ↗ (other): Cited as another way of framing what intelligence is.
modded-nanogpt ↗ (tool): Mentioned as an efficiency-focused competition for ideas in model training.
METR Time Horizons ↗ (service): Cited as evidence that capability gains haven't plateaued yet.

Summarised by Claude from the comment thread 5 days ago.

Points: 477
Comments: 290
Best rank: #1
Posted: 2026-11-07 by srean
Category: AI & ML

microsoft-research john-langford ai-frontiers microsoft-ai ai-agents autogen scaling

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