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← Getting five years of AI progress out of one year takes roughly eight…
17 connected korrents · 19 moments from 10 Dec 2015 to 11 Sept 2026.
Everything filed under scaling laws
scaling laws
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neural networks
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AI and science
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LLMs
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recursive self-improvement
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AGI
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team size
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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: Getting five years of AI progress out of one year takes roughly eight years of algorithmic progress, because the compute you would have had must be made up too. Getting five years of AI progress out of one year takes roughly eight years of algorithmic progress, because the compute you would have had must be made up too. Last stated 2 months ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: Once AI can do AI research, a single year should deliver four or five years worth of AI progress. — tap to centre the map on it Once AI can do AI research, a single year should deliver four or five years worth of AI progress. Last stated 2 months ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: Human-level AI will arrive in 5 to 20 years with 90% confidence, not in the decades-to-centuries range previously assumed. — tap to centre the map on it Human-level AI will arrive in 5 to 20 years with 90% confidence, not in the decades-to-centuries range previously assumed. Last stated 3 years ago
12 Aug 2023
YB
Yoshua Bengio — holds since 2023-08-12 — tap for who they are
Same subject: The current ease of building software with AI will be seen as normal and commonplace within 12 to 24 months, though few realize it yet. — tap to centre the map on it The current ease of building software with AI will be seen as normal and commonplace within 12 to 24 months, though few realize it yet. Last stated 3 months ago
15 Jul 2026
TY
Tynan — holds since 2026-07-15 — tap for who they are
Same subject: Powerful AI will compress the biological and medical progress of the next 50-100 years into 5-10 years. — tap to centre the map on it Powerful AI will compress the biological and medical progress of the next 50-100 years into 5-10 years. Last stated 2 years ago
1 Oct 2024
DA
Dario Amodei — holds since 2024-10-01 — tap for who they are
Same subject: Progress in AI takes a very strong dozen or two researchers, not hundreds or thousands of them. — tap to centre the map on it Progress in AI takes a very strong dozen or two researchers, not hundreds or thousands of them. Last stated 4 months ago
10 Jun 2026
MZ
Mark Zuckerberg — holds since 2026-06-10 — tap for who they are
Same subject: The performance gap between open and closed AI models has narrowed to roughly 4-6 months. — tap to centre the map on it The performance gap between open and closed AI models has narrowed to roughly 4-6 months. Last stated a month ago
11 Sept 2026
NL
Nathan Lambert — holds since 2026-09-11 — tap for who they are
Same subject: Most of the useful mathematics AI produces over the next five years will be filling in connections that only somebody expert in several fields could draw. — tap to centre the map on it Most of the useful mathematics AI produces over the next five years will be filling in connections that only somebody expert in several fields could draw. Last stated 4 months ago
30 Jun 2026
GS
Grant Sanderson — holds since 2026-06-30 — tap for who they are
Same subject: AI will not on its own compress decades of medical progress into a few years, because the binding constraint is human data, clinical trials and regulation rather than intelligence. — tap to centre the map on it AI will not on its own compress decades of medical progress into a few years, because the binding constraint is human data, clinical trials and regulation rather than intelligence. Last stated 3 months ago
21 Jul 2026
RT
Ruxandra Teslo — holds since 2026-07-21 — tap for who they are
JT
Jacob Trefethen — holds since 2025-08-02 — 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
Getting five years of AI progress out of one year takes roughly eight years of algorithmic progress, because the compute you would have had must be made up too.
Holds Read this korrent →
Once AI can do AI research, a single year should deliver four or five years worth of AI progress.
Holds
Human-level AI will arrive in 5 to 20 years with 90% confidence, not in the decades-to-centuries range previously assumed.
Holds
The current ease of building software with AI will be seen as normal and commonplace within 12 to 24 months, though few realize it yet.
Holds
Powerful AI will compress the biological and medical progress of the next 50-100 years into 5-10 years.
Holds
Progress in AI takes a very strong dozen or two researchers, not hundreds or thousands of them.
Holds
The performance gap between open and closed AI models has narrowed to roughly 4-6 months.
Holds
Most of the useful mathematics AI produces over the next five years will be filling in connections that only somebody expert in several fields could draw.
Holds
AI will not on its own compress decades of medical progress into a few years, because the binding constraint is human data, clinical trials and regulation rather than intelligence.
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