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← An exponential feels like it cannot possibly keep going, which is…
17 connected korrents · 19 moments from 10 Dec 2015 to 11 Oct 2026.
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scaling laws
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neural networks
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innovation
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recursive self-improvement
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mathematics
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AI alignment 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: An exponential feels like it cannot possibly keep going, which is exactly why people keep expecting AI progress to stop. An exponential feels like it cannot possibly keep going, which is exactly why people keep expecting AI progress to stop. Last stated 4 months ago
10 Jun 2026
MZ
Mark Zuckerberg — holds since 2026-06-10 — tap for who they are
Same subject: The thing worth finding is the exponential with the steepest curve that will run the longest — decades ago that was Moore’s law, and today it is AI progress. — tap to centre the map on it The thing worth finding is the exponential with the steepest curve that will run the longest — decades ago that was Moore’s law, and today it is AI progress. Last stated 3 months ago
29 Jul 2026
AW
Alexandr Wang — holds since 2026-07-29 — tap for who they are
Same subject: We are already in an intelligence explosion and have been for decades, so AI will show up as more of the same exponential rather than a break in it. — tap to centre the map on it We are already in an intelligence explosion and have been for decades, so AI will show up as more of the same exponential rather than a break in it. Last stated a year ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: The current speed of AI progress could be sustained all the way into recursive self-improvement. — tap to centre the map on it The current speed of AI progress could be sustained all the way into recursive self-improvement. Last stated 2 months ago
6 Sept 2026
JP
Jakub Pachocki — holds since 2026-09-06 — tap for who they are
Same subject: People with business theses based on the idea that we are not yet at a certain technological threshold will be disrupted by AI's exponential progress faster than they think. — tap to centre the map on it People with business theses based on the idea that we are not yet at a certain technological threshold will be disrupted by AI's exponential progress faster than they think. Last stated 2 weeks ago
11 Oct 2026
PL
Pieter Levels — holds since 2026-10-11 — tap for who they are
Same subject: A deliberately slower pace of AI model releases would still feel faster than the pace of AI improvement that came before it. — tap to centre the map on it A deliberately slower pace of AI model releases would still feel faster than the pace of AI improvement that came before it. Last stated a month ago
14 Sept 2026
BH
Byrne Hobart — holds since 2026-09-14 — tap for who they are
Same subject: Expecting technology to keep accelerating is a bias of our particular moment; for most of human history the tools did not change at all. — tap to centre the map on it Expecting technology to keep accelerating is a bias of our particular moment; for most of human history the tools did not change at all. Last stated 2 years ago
22 Dec 2024
AF
Adam Frank — holds since 2024-12-22 — tap for who they are
Same subject: What AI still cannot do in mathematics is build cumulatively on partial progress; it is trial, error and brute force rather than an idea improving through use. — tap to centre the map on it What AI still cannot do in mathematics is build cumulatively on partial progress; it is trial, error and brute force rather than an idea improving through use. Last stated 7 months ago
20 Mar 2026
TT
Terence Tao — holds since 2026-03-20 — 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: "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. — tap to centre the map on it "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. Last stated 11 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. — tap to centre the map on it A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. Last stated 4 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. — tap to centre the map on it A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. Last stated 7 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — tap for who they are
Same subject: One pre-trained robot model now matches or beats the specialists that were fine-tuned with reinforcement learning for the very tasks they were built for. — tap to centre the map on it One pre-trained robot model now matches or beats the specialists that were fine-tuned with reinforcement learning for the very tasks they were built for. Last stated 2 months ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — tap for who they are
Same subject: RL environments exist to make a model generalise, not to teach it each skill one at a time — exactly as pre-training does. — tap to centre the map on it RL environments exist to make a model generalise, not to teach it each skill one at a time — exactly as pre-training does. Last stated 8 months ago
13 Feb 2026
DA
Dario Amodei — holds since 2026-02-13 — 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: Scaling the 2019-2023 recipe — same architecture, bigger model, more data — is not enough; further progress depends on new architectural ideas. — tap to centre the map on it Scaling the 2019-2023 recipe — same architecture, bigger model, more data — is not enough; further progress depends on new architectural ideas. Last stated 2 years ago
20 Dec 2024
FC
François Chollet — holds since 2024-12-20 — tap for who they are
GM
Gary Marcus — holds since 2024-10-11 — 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
At the centre
An exponential feels like it cannot possibly keep going, which is exactly why people keep expecting AI progress to stop.
Holds Read this korrent →
The thing worth finding is the exponential with the steepest curve that will run the longest — decades ago that was Moore’s law, and today it is AI progress.
Holds
We are already in an intelligence explosion and have been for decades, so AI will show up as more of the same exponential rather than a break in it.
Holds
The current speed of AI progress could be sustained all the way into recursive self-improvement.
Holds
People with business theses based on the idea that we are not yet at a certain technological threshold will be disrupted by AI's exponential progress faster than they think.
Holds
A deliberately slower pace of AI model releases would still feel faster than the pace of AI improvement that came before it.
Holds
Expecting technology to keep accelerating is a bias of our particular moment; for most of human history the tools did not change at all.
Holds
What AI still cannot do in mathematics is build cumulatively on partial progress; it is trial, error and brute force rather than an idea improving through use.
Holds
AI capability progress in a given skill tends to arrive as a sudden phase change rather than as steady improvement.
Holds
"Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next.
Holds
A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number.
Holds
A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from.
Holds
One pre-trained robot model now matches or beats the specialists that were fine-tuned with reinforcement learning for the very tasks they were built for.
Holds
RL environments exist to make a model generalise, not to teach it each skill one at a time — exactly as pre-training does.
Holds
A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score.
Holds
Scaling the 2019-2023 recipe — same architecture, bigger model, more data — is not enough; further progress depends on new architectural ideas.
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