Tap a claim on the ring to put it at the centre.
← A lab under real commercial pressure cannot fool itself about whether…
17 connected korrents · 17 moments on record from 22 Mar 2025 to 3 Sept 2026.
Everything filed under AI and science
AI and science
Everything filed under scaling laws
scaling laws
Everything filed under reinforcement learning
reinforcement learning
Everything filed under innovation
innovation
Everything filed under team size
team size 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: A lab under real commercial pressure cannot fool itself about whether AI makes it productive, because the output is a model launch every few months. A lab under real commercial pressure cannot fool itself about whether AI makes it productive, because the output is a model launch every few months. Last stated 7 months ago
13 Feb 2026
DA
Dario Amodei — holds since 2026-02-13 — tap for who they are
Same subject: The absence of a visible productivity jump from AI proves nothing, because a real ten percent uplift across the board would be impressive and almost invisible from outside. — tap to centre the map on it The absence of a visible productivity jump from AI proves nothing, because a real ten percent uplift across the board would be impressive and almost invisible from outside. Last stated 2 weeks ago
26 Aug 2026
CM
Casey Muratori — holds since 2026-08-26 — tap for who they are
Same subject: AI helps exactly as far as the work is predictable and well documented, and stops helping on anything cutting edge. — tap to centre the map on it AI helps exactly as far as the work is predictable and well documented, and stops helping on anything cutting edge. Last stated a year ago
22 Mar 2025
TH
ThePrimeagen — holds since 2025-03-22 — 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: The most fertile way to study neuroscience today is to work on AI models rather than on brains. — tap to centre the map on it The most fertile way to study neuroscience today is to work on AI models rather than on brains. Last stated 3 weeks ago
20 Aug 2026
MH
Max Hodak — holds since 2026-08-20 — tap for who they are
Same subject: The personal computer made countless individual tasks easier without delivering the productivity gains everyone expected, and AI may go the same way. — tap to centre the map on it The personal computer made countless individual tasks easier without delivering the productivity gains everyone expected, and AI may go the same way. Last stated 2 months ago
29 Jun 2026
CN
Cal Newport — holds since 2026-06-29 — tap for who they are
Same subject: AI has already made sweeping mechanical changes cheap; the unsolved problem is getting the same productivity when adding features to an old, entangled codebase. — tap to centre the map on it AI has already made sweeping mechanical changes cheap; the unsolved problem is getting the same productivity when adding features to an old, entangled codebase. Last stated 5 months ago
1 Apr 2026
TP
Thuan Pham — holds since 2026-04-01 — tap for who they are
Same subject: AI's productivity gains at Figma have been mild to moderate, and none of them have reduced the number of engineers the company wants to hire. — tap to centre the map on it AI's productivity gains at Figma have been mild to moderate, and none of them have reduced the number of engineers the company wants to hire. Last stated 11 months ago
16 Oct 2025
DF
Dylan Field — holds since 2025-10-16 — tap for who they are
Same subject: Building at the AI frontier is science rather than programming: you pose hypotheses and invalidate them again and again. — tap to centre the map on it Building at the AI frontier is science rather than programming: you pose hypotheses and invalidate them again and again. Last stated a year ago
18 Aug 2025
CB
Clay Bavor — holds since 2025-08-18 — tap for who they are
Same subject: AI could compete with human mathematicians once it acquires a mathematical sense of smell: knowing which way of splitting a problem makes it easier rather than harder. — tap to centre the map on it AI could compete with human mathematicians once it acquires a mathematical sense of smell: knowing which way of splitting a problem makes it easier rather than harder. Last stated a year ago
14 Jun 2025
TT
Terence Tao — holds since 2025-06-14 — tap for who they are
Same subject: AI in chess and mathematics does not explain anything; it says which position is better, and humans build the theory from that. — tap to centre the map on it AI in chess and mathematics does not explain anything; it says which position is better, and humans build the theory from that. Last stated a year ago
14 Jun 2025
LF
Lex Fridman — holds since 2025-06-14 — tap for who they are
Same subject: AI will transform how scientific discovery is done, and within two or three years a working scientist's job will already look dramatically different. — tap to centre the map on it AI will transform how scientific discovery is done, and within two or three years a working scientist's job will already look dramatically different. Last stated 3 weeks ago
18 Aug 2026
MK
Michael Kratsios — holds since 2026-08-18 — 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: 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: AI model capability progress is not going to slow down soon — tap to centre the map on it AI model capability progress is not going to slow down soon Last stated 4 days ago
3 Sept 2026
AR
Armin Ronacher — holds since 2026-09-03 — 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 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
At the centre
A lab under real commercial pressure cannot fool itself about whether AI makes it productive, because the output is a model launch every few months.
Holds DA Dario AmodeiRead this korrent →
The absence of a visible productivity jump from AI proves nothing, because a real ten percent uplift across the board would be impressive and almost invisible from outside.
Holds CM Casey Muratori
AI helps exactly as far as the work is predictable and well documented, and stops helping on anything cutting edge.
Holds TH ThePrimeagen
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
The most fertile way to study neuroscience today is to work on AI models rather than on brains.
Holds MH Max Hodak
The personal computer made countless individual tasks easier without delivering the productivity gains everyone expected, and AI may go the same way.
Holds CN Cal Newport
AI has already made sweeping mechanical changes cheap; the unsolved problem is getting the same productivity when adding features to an old, entangled codebase.
Holds TP Thuan Pham
AI's productivity gains at Figma have been mild to moderate, and none of them have reduced the number of engineers the company wants to hire.
Holds DF Dylan Field
Building at the AI frontier is science rather than programming: you pose hypotheses and invalidate them again and again.
Holds CB Clay Bavor
AI could compete with human mathematicians once it acquires a mathematical sense of smell: knowing which way of splitting a problem makes it easier rather than harder.
Holds TT Terence Tao
AI in chess and mathematics does not explain anything; it says which position is better, and humans build the theory from that.
Holds LF Lex Fridman
AI will transform how scientific discovery is done, and within two or three years a working scientist's job will already look dramatically different.
Holds MK Michael Kratsios
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
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
AI model capability progress is not going to slow down soon
Holds
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