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← "Scaling" was powerful because it was one word: naming a research…
15 connected korrents · 14 moments on record from 28 Jul 2023 to 4 Sept 2026.
Everything filed under scaling laws
scaling laws
Everything filed under Google
Google
Everything filed under reinforcement learning
reinforcement learning
Everything filed under LLMs
LLMs 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: "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. Last stated 9 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: The age of scaling sucked the air out of the room and left the field with more companies than ideas, by quite a bit. — tap to centre the map on it The age of scaling sucked the air out of the room and left the field with more companies than ideas, by quite a bit. Last stated 9 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: Large language models start in exactly the wrong place, because they try to get by without a goal and therefore without any sense of better or worse. — tap to centre the map on it Large language models start in exactly the wrong place, because they try to get by without a goal and therefore without any sense of better or worse. Last stated 11 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: Large language models are a bad way to do science, because they have been fed so much that nobody knows what they already knew. — tap to centre the map on it Large language models are a bad way to do science, because they have been fed so much that nobody knows what they already knew. Last stated 11 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture. — tap to centre the map on it The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture. Last stated 3 days ago
4 Sept 2026
FL
Fei-Fei Li — holds since 2026-09-04 — tap for who they are
Same subject: Scaling down is half of scalability: cost and capacity should track load in both directions, right down to a service that is nearly free to run. — tap to centre the map on it Scaling down is half of scalability: cost and capacity should track load in both directions, right down to a service that is nearly free to run. Last stated 5 months ago
22 Apr 2026
MK
Martin Kleppmann — holds since 2026-04-22 — tap for who they are
Same subject: The Bitter Lesson is misread as being about scale; it is about refusing to add human priors, because a clever specific fix always loses to a simple scalable one. — tap to centre the map on it The Bitter Lesson is misread as being about scale; it is about refusing to add human priors, because a clever specific fix always loses to a simple scalable one. Last stated 2 years ago
3 Feb 2025
NL
Nathan Lambert — holds since 2025-02-03 — 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 2 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: After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. — tap to centre the map on it After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. Last stated 6 months ago
23 Mar 2026
JH
Jensen Huang — holds since 2026-03-23 — 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 10 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — tap for who they are
Same subject: Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago. — tap to centre the map on it Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago. Last stated 6 months ago
20 Mar 2026
AK
Andrej Karpathy — holds since 2026-03-20 — tap for who they are
Same subject: Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving. — tap to centre the map on it Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving. Last stated 11 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: A company's staff-engineer bar should be set against the best companies in the industry rather than against its own history, which is what makes title inflation a real cost. — tap to centre the map on it A company's staff-engineer bar should be set against the best companies in the industry rather than against its own history, which is what makes title inflation a real cost. Last stated 5 months ago
1 Apr 2026
TP
Thuan Pham — holds since 2026-04-01 — tap for who they are
Same subject: A crewed rocket cannot be made safe by making the booster reliable, so the only real way to improve safety is to carry an escape system. — tap to centre the map on it A crewed rocket cannot be made safe by making the booster reliable, so the only real way to improve safety is to carry an escape system. Last stated 3 years ago
14 Dec 2023
JB
Jeff Bezos — holds since 2023-12-14 — tap for who they are
Same subject: A monopolist that can no longer grow by winning new users can only grow by making its product worse for the users it already has. — tap to centre the map on it A monopolist that can no longer grow by winning new users can only grow by making its product worse for the users it already has. Last stated 3 years ago
28 Jul 2023
CD
Cory Doctorow — holds since 2023-07-28 — tap for who they are
At the centre
"Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next.
Holds IS Ilya SutskeverRead this korrent →
The age of scaling sucked the air out of the room and left the field with more companies than ideas, by quite a bit.
Holds IS Ilya Sutskever
Large language models start in exactly the wrong place, because they try to get by without a goal and therefore without any sense of better or worse.
Holds RS Richard Sutton
Large language models are a bad way to do science, because they have been fed so much that nobody knows what they already knew.
Holds RS Richard Sutton
The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture.
Holds FL Fei-Fei Li
Scaling down is half of scalability: cost and capacity should track load in both directions, right down to a service that is nearly free to run.
Holds MK Martin Kleppmann
The Bitter Lesson is misread as being about scale; it is about refusing to add human priors, because a clever specific fix always loses to a simple scalable one.
Holds NL Nathan Lambert
A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number.
Holds NB Noam Brown
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 DP Dylan Patel
After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute.
Holds JH Jensen Huang
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago.
Holds
Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving.
Holds
A company's staff-engineer bar should be set against the best companies in the industry rather than against its own history, which is what makes title inflation a real cost.
Holds TP Thuan Pham
A crewed rocket cannot be made safe by making the booster reliable, so the only real way to improve safety is to carry an escape system.
Holds JB Jeff Bezos
A monopolist that can no longer grow by winning new users can only grow by making its product worse for the users it already has.
Holds CD Cory Doctorow