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← Whether AI capability progress has slowed down cannot be settled,…
17 connected korrents · 19 moments from 7 Sept 2008 to 21 Oct 2026.
Everything filed under neural networks
neural networks
Everything filed under OpenAI
OpenAI
Everything filed under reinforcement learning
reinforcement learning
Everything filed under AGI
AGI
Everything filed under scaling laws
scaling laws
Everything filed under AI and science
AI and science
Everything filed under AI alignment
AI alignment
Everything filed under LLMs
LLMs
Everything filed under Anthropic
Anthropic Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Read this korrent: Whether AI capability progress has slowed down cannot be settled, since reasonable observers can interpret the same evidence differently. Whether AI capability progress has slowed down cannot be settled, since reasonable observers can interpret the same evidence differently. Last stated 2 years ago
18 Dec 2024
AN
Arvind Narayanan — holds since 2024-12-18 — tap for who they are
Same subject: AI model capabilities have not slowed down so far, and their progress probably won't stop anytime soon. — tap to centre the map on it AI model capabilities have not slowed down so far, and their progress probably won't stop anytime soon. Last stated 2 months ago
3 Sept 2026
AR
Armin Ronacher — holds since 2026-09-03 — tap for who they are
Same subject: Not every bottleneck on medical progress is one AI can remove, and which ones will remain can be guessed at now. — tap to centre the map on it Not every bottleneck on medical progress is one AI can remove, and which ones will remain can be guessed at now. Last stated a year ago
2 Aug 2025
JT
Jacob Trefethen — holds since 2025-08-02 — tap for who they are
Same subject: Confidence in monitoring, not capability research, will become the binding constraint on AI progress. — tap to centre the map on it Confidence in monitoring, not capability research, will become the binding constraint on AI progress. Last stated 2 months ago
6 Sept 2026
JP
Jakub Pachocki — holds since 2026-09-06 — tap for who they are
Same subject: AI capability progress in a given skill tends to arrive as a sudden phase change rather than as steady improvement. — tap to centre the map on it AI capability progress in a given skill tends to arrive as a sudden phase change rather than as steady improvement. Last stated 2 months ago
24 Aug 2026
JC
Jack Clark — holds since 2026-08-24 — tap for who they are
Same subject: If AIs can now considerably improve the training of future AIs, this is very unlikely to end well. — tap to centre the map on it If AIs can now considerably improve the training of future AIs, this is very unlikely to end well. Last stated 2 weeks ago
21 Oct 2026
KP
Kelsey Piper — holds since 2026-10-21 — tap for who they are
Same subject: The progress of AI, like the progress of everything else, will eventually plateau. — tap to centre the map on it The progress of AI, like the progress of everything else, will eventually plateau. Last stated 8 years ago
1 Sept 2018
PD
Pedro Domingos — holds since 2018-09-01 — tap for who they are
Same subject: Human-level artificial intelligence is plausibly centuries or millennia away, and no trend supports a shorter estimate. — tap to centre the map on it Human-level artificial intelligence is plausibly centuries or millennia away, and no trend supports a shorter estimate. Last stated 3 years ago
13 Dec 2023
SA
Scott Aaronson — holds since 2008-09-07 — tap for who they are
SA
Scott Aaronson — no longer holds since 2023-12-13 — tap for who they are
Same subject: AI progress is faster than people expect, and ordinary scaling can be enough to solve problems that looked very hard. — tap to centre the map on it AI progress is faster than people expect, and ordinary scaling can be enough to solve problems that looked very hard. Last stated 2 years ago
2 Feb 2025
AC
Ajeya Cotra — holds since 2023-08-29 — tap for who they are
SA
Scott Alexander — holds since 2022-09-12 — tap for who they are
MB
Miles Brundage — holds since 2025-02-02 — tap for who they are
Same subject: A PHP vulnerability found with GPT-5.6 Sol will cause havoc this week. — tap to centre the map on it A PHP vulnerability found with GPT-5.6 Sol will cause havoc this week. Last stated 2 weeks ago
21 Oct 2026
PS
Peter Steinberger — holds since 2026-10-21 — tap for who they are
Same subject: A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. — tap to centre the map on it A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
Same subject: Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers. — tap to centre the map on it Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
Same subject: A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score. — tap to centre the map on it A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score. Last stated 2 months ago
26 Aug 2026
HK
Henrik Karlsson — holds since 2026-08-26 — tap for who they are
Same subject: A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel. — tap to centre the map on it A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel. Last stated 3 months ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
Same subject: A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. — tap to centre the map on it A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. Last stated 11 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — tap for who they are
Same subject: A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable. — tap to centre the map on it A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable. Last stated 11 years ago
10 Dec 2015
JS
Jian Sun — holds since 2015-12-10 — tap for who they are
KH
Kaiming He — holds since 2015-12-10 — tap for who they are
Same subject: A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it. — tap to centre the map on it A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it. Last stated 9 years ago
9 Mar 2018
MC
Michael Carbin — holds since 2018-03-09 — tap for who they are
JF
Jonathan Frankle — holds since 2018-03-09 — tap for who they are
Same subject: A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution. — tap to centre the map on it A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution. Last stated 9 years ago
12 Jun 2017
IP
Illia Polosukhin — holds since 2017-06-12 — tap for who they are
JU
Jakob Uszkoreit — holds since 2017-06-12 — tap for who they are
NP
Niki Parmar — holds since 2017-06-12 — tap for who they are
AV
Ashish Vaswani — holds since 2017-06-12 — tap for who they are
NS
Noam Shazeer — holds since 2017-06-12 — tap for who they are
AG
Aidan N. Gomez — holds since 2017-06-12 — tap for who they are
+2
2 more
At the centre
Whether AI capability progress has slowed down cannot be settled, since reasonable observers can interpret the same evidence differently.
Holds Read this korrent →
AI model capabilities have not slowed down so far, and their progress probably won't stop anytime soon.
Holds
Not every bottleneck on medical progress is one AI can remove, and which ones will remain can be guessed at now.
Holds
Confidence in monitoring, not capability research, will become the binding constraint on AI progress.
Holds
AI capability progress in a given skill tends to arrive as a sudden phase change rather than as steady improvement.
Holds
If AIs can now considerably improve the training of future AIs, this is very unlikely to end well.
Holds
The progress of AI, like the progress of everything else, will eventually plateau.
Holds
Human-level artificial intelligence is plausibly centuries or millennia away, and no trend supports a shorter estimate.
No longer holds
AI progress is faster than people expect, and ordinary scaling can be enough to solve problems that looked very hard.
Holds
A PHP vulnerability found with GPT-5.6 Sol will cause havoc this week.
Holds
A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests.
Holds
Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers.
Holds
A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score.
Holds
A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel.
Holds
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable.
Holds
A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it.
Holds
A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution.
Holds