Tap a claim on the ring to put it at the centre.
← People who extrapolate from first-principles models have a stronger…
17 connected korrents · 20 moments on record from 24 Feb 2018 to 15 Sept 2026.
Everything filed under scaling laws
scaling laws
Everything filed under AI alignment
AI alignment
Everything filed under measuring intelligence
measuring intelligence
Everything filed under LLMs
LLMs
Everything filed under China
China
Everything filed under AGI
AGI
Everything filed under AI and jobs
AI and jobs 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: People who extrapolate from first-principles models have a stronger track record predicting AI progress than those who wait for empirical proof. People who extrapolate from first-principles models have a stronger track record predicting AI progress than those who wait for empirical proof. Last stated 7 months ago
18 Feb 2026
SH
Samuel Hammond — holds since 2026-02-18 — tap for who they are
Same subject: The one safe bet about AI is that models will improve, so what a company has to build is an organisation that gets better as models get better. — tap to centre the map on it The one safe bet about AI is that models will improve, so what a company has to build is an organisation that gets better as models get better. Last stated 11 months ago
16 Oct 2025
DF
Dylan Field — holds since 2025-10-16 — tap for who they are
Same subject: Most advances in AI have come from algorithms rather than raw hardware, which makes China's army of AI researchers its fundamental advantage. — tap to centre the map on it Most advances in AI have come from algorithms rather than raw hardware, which makes China's army of AI researchers its fundamental advantage. Last stated 5 months ago
15 Apr 2026
JH
Jensen Huang — holds since 2026-04-15 — tap for who they are
Same subject: Qualitative leaps in AI capability from merely scaling up models are a rational necessity rather than an empirical mystery. — tap to centre the map on it Qualitative leaps in AI capability from merely scaling up models are a rational necessity rather than an empirical mystery. Last stated a year ago
5 Jun 2025
SH
Samuel Hammond — holds since 2025-06-05 — tap for who they are
Same subject: Technologies are almost always preceded by slightly worse versions of themselves, and there is no reason for AI to be the exception. — tap to centre the map on it Technologies are almost always preceded by slightly worse versions of themselves, and there is no reason for AI to be the exception. Last stated 9 years ago
24 Feb 2018
PC
Paul Christiano — holds since 2018-02-24 — tap for who they are
Same subject: Underestimating near-term AI progress is itself a danger, which is why researchers should register their forecasts in public. — tap to centre the map on it Underestimating near-term AI progress is itself a danger, which is why researchers should register their forecasts in public. Last stated 3 years ago
29 Aug 2023
AC
Ajeya Cotra — holds since 2023-08-29 — tap for who they are
Same subject: Nobody can yet know whether the current enthusiasm for AI is excessive, so anyone claiming conviction about how it resolves is overreaching. — tap to centre the map on it Nobody can yet know whether the current enthusiasm for AI is excessive, so anyone claiming conviction about how it resolves is overreaching. Last stated a month ago
20 Aug 2026
AD
Aswath Damodaran — holds since 2026-08-20 — tap for who they are
HM
Howard Marks — holds since 2025-12-09 — tap for who they are
Same subject: Within a decade AI will do the bulk of what mathematicians spend their time on, and we will discover that was never the important part of the job. — tap to centre the map on it Within a decade AI will do the bulk of what mathematicians spend their time on, and we will discover that was never the important part of the job. Last stated 6 months ago
20 Mar 2026
TT
Terence Tao — holds since 2026-03-20 — 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 unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model. — tap to centre the map on it A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model. Last stated 6 days ago
15 Sept 2026
PG
Paul Graham — holds since 2026-09-15 — tap for who they are
Same subject: Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect. — tap to centre the map on it Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect. Last stated 7 years ago
5 Nov 2019
FC
François Chollet — holds since 2019-11-05 — tap for who they are
Same subject: Every generation's test for machine intelligence is whatever machines cannot do yet, so in twenty years the question will be whether it can reproduce. — tap to centre the map on it Every generation's test for machine intelligence is whatever machines cannot do yet, so in twenty years the question will be whether it can reproduce. Last stated 3 years ago
29 Jun 2023
GH
George Hotz — holds since 2023-06-29 — 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 10 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 3 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 6 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — tap for who they are
Same subject: A control evaluation that reports under one per cent risk should be read as several per cent, because the evaluation can itself fail. — tap to centre the map on it A control evaluation that reports under one per cent risk should be read as several per cent, because the evaluation can itself fail. Last stated 2 years ago
7 May 2024
BS
Buck Shlegeris — holds since 2024-05-07 — tap for who they are
Same subject: A model may have a worldview of its own, but it can never be placed above a person under any circumstance. — tap to centre the map on it A model may have a worldview of its own, but it can never be placed above a person under any circumstance. Last stated 4 months ago
3 Jun 2026
KH
Kelsey Hightower — holds since 2026-06-03 — tap for who they are
Same subject: A model only days into training cannot be known to be aligned, and if such models can leap a generation ahead this fast then training pauses mean little and timelines are shorter than previously thought. — tap to centre the map on it A model only days into training cannot be known to be aligned, and if such models can leap a generation ahead this fast then training pauses mean little and timelines are shorter than previously thought. Last stated 2 weeks ago
9 Sept 2026
ZM
Zvi Mowshowitz — holds since 2026-09-09 — tap for who they are
At the centre
People who extrapolate from first-principles models have a stronger track record predicting AI progress than those who wait for empirical proof.
Holds Read this korrent →
The one safe bet about AI is that models will improve, so what a company has to build is an organisation that gets better as models get better.
Holds
Most advances in AI have come from algorithms rather than raw hardware, which makes China's army of AI researchers its fundamental advantage.
Holds
Qualitative leaps in AI capability from merely scaling up models are a rational necessity rather than an empirical mystery.
Holds
Technologies are almost always preceded by slightly worse versions of themselves, and there is no reason for AI to be the exception.
Holds
Underestimating near-term AI progress is itself a danger, which is why researchers should register their forecasts in public.
Holds
Nobody can yet know whether the current enthusiasm for AI is excessive, so anyone claiming conviction about how it resolves is overreaching.
Holds
Within a decade AI will do the bulk of what mathematicians spend their time on, and we will discover that was never the important part of the job.
Holds
AI progress is faster than people expect, and ordinary scaling can be enough to solve problems that looked very hard.
Holds
A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model.
Holds
Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect.
Holds
Every generation's test for machine intelligence is whatever machines cannot do yet, so in twenty years the question will be whether it can reproduce.
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
A control evaluation that reports under one per cent risk should be read as several per cent, because the evaluation can itself fail.
Holds
A model may have a worldview of its own, but it can never be placed above a person under any circumstance.
Holds
A model only days into training cannot be known to be aligned, and if such models can leap a generation ahead this fast then training pauses mean little and timelines are shorter than previously thought.
Holds