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← AI will not run out of data, because there is already enough…
17 connected korrents · 17 moments on record from 1 May 2023 to 12 Aug 2026.
Everything filed under robotics
robotics
Everything filed under scaling laws
scaling laws
Everything filed under reinforcement learning
reinforcement learning 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: AI will not run out of data, because there is already enough real-world data to build the simulators that generate more of it. AI will not run out of data, because there is already enough real-world data to build the simulators that generate more of it. Last stated a year ago
23 Jul 2025
DH
Demis Hassabis — holds since 2025-07-23 — tap for who they are
Same subject: AI's bottleneck was never its algorithms but how little data they were fed, which is why the field needed ImageNet rather than another model. — tap to centre the map on it AI's bottleneck was never its algorithms but how little data they were fed, which is why the field needed ImageNet rather than another model. Last stated 4 weeks ago
10 Aug 2026
FL
Fei-Fei Li — holds since 2026-08-10 — tap for who they are
Same subject: Most future robotics training data will be robots' own autonomous attempts, the way most language-model data is now generated by the model itself. — tap to centre the map on it Most future robotics training data will be robots' own autonomous attempts, the way most language-model data is now generated by the model itself. Last stated 4 weeks ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — tap for who they are
Same subject: Training is no longer limited by data but by compute, because most of the data models learn from is now synthetic. — tap to centre the map on it Training is no longer limited by data but by compute, because most of the data models learn from is now synthetic. Last stated 6 months ago
23 Mar 2026
JH
Jensen Huang — holds since 2026-03-23 — tap for who they are
Same subject: Economics cannot say what AI will do to work because the necessary data does not exist; what is needed is a Manhattan Project for data. — tap to centre the map on it Economics cannot say what AI will do to work because the necessary data does not exist; what is needed is a Manhattan Project for data. Last stated 3 months ago
4 Jun 2026
AI
Alex Imas — holds since 2026-06-04 — tap for who they are
Same subject: There is no AI bubble on the demand side: the shortage of computing capacity is global, and spans every company and industry. — tap to centre the map on it There is no AI bubble on the demand side: the shortage of computing capacity is global, and spans every company and industry. Last stated 8 months ago
8 Jan 2026
JH
Jensen Huang — holds since 2026-01-08 — tap for who they are
Same subject: We should want more tools and fewer operated machines; the real AI game changers will have little to do with plain content generation. — tap to centre the map on it We should want more tools and fewer operated machines; the real AI game changers will have little to do with plain content generation. Last stated 3 years ago
by 1 May 2023
AW
Amelia Wattenberger — holds since 2023-05-01 — tap for who they are
Same subject: A compelling simulation of a human being could arrive within five years, and the technology is heading there whether or not it is a good idea. — tap to centre the map on it A compelling simulation of a human being could arrive within five years, and the technology is heading there whether or not it is a good idea. Last stated a year ago
30 Apr 2025
TS
Tim Sweeney — holds since 2025-04-30 — tap for who they are
Same subject: The massive AI build-out is the right thing to be doing, and the only thing that will slow it is supply. — tap to centre the map on it The massive AI build-out is the right thing to be doing, and the only thing that will slow it is supply. Last stated 3 months ago
18 Jun 2026
LT
Lip-Bu Tan — holds since 2026-06-18 — tap for who they are
Same subject: A highly dynamic economy that cheaply mass-produces robots may increase rather than decrease the risk of Malthusian resource dilemmas. — tap to centre the map on it A highly dynamic economy that cheaply mass-produces robots may increase rather than decrease the risk of Malthusian resource dilemmas. Last stated 3 years ago
1 Oct 2023
TC
Tyler Cowen — holds since 2023-10-01 — tap for who they are
Same subject: A humanoid robot is the wrong tool on a factory floor, because a machine specialised in the task beats it. — tap to centre the map on it A humanoid robot is the wrong tool on a factory floor, because a machine specialised in the task beats it. Last stated a year ago
6 Apr 2025
MH
Molson Hart — holds since 2025-04-06 — tap for who they are
Same subject: A physical AI agent has to be safe on day one, because you cannot ship something merely good enough and let users find the edge cases. — tap to centre the map on it A physical AI agent has to be safe on day one, because you cannot ship something merely good enough and let users find the edge cases. Last stated a month ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — 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
At the centre
AI will not run out of data, because there is already enough real-world data to build the simulators that generate more of it.
Holds DH Demis HassabisRead this korrent →
AI's bottleneck was never its algorithms but how little data they were fed, which is why the field needed ImageNet rather than another model.
Holds FL Fei-Fei Li
Most future robotics training data will be robots' own autonomous attempts, the way most language-model data is now generated by the model itself.
Holds CF Chelsea Finn
Training is no longer limited by data but by compute, because most of the data models learn from is now synthetic.
Holds JH Jensen Huang
Economics cannot say what AI will do to work because the necessary data does not exist; what is needed is a Manhattan Project for data.
Holds AI Alex Imas
There is no AI bubble on the demand side: the shortage of computing capacity is global, and spans every company and industry.
Holds JH Jensen Huang
We should want more tools and fewer operated machines; the real AI game changers will have little to do with plain content generation.
Holds AW Amelia Wattenberger
A compelling simulation of a human being could arrive within five years, and the technology is heading there whether or not it is a good idea.
Holds TS Tim Sweeney
The massive AI build-out is the right thing to be doing, and the only thing that will slow it is supply.
Holds LT Lip-Bu Tan
A highly dynamic economy that cheaply mass-produces robots may increase rather than decrease the risk of Malthusian resource dilemmas.
Holds TC Tyler Cowen
A humanoid robot is the wrong tool on a factory floor, because a machine specialised in the task beats it.
Holds MH Molson Hart
A physical AI agent has to be safe on day one, because you cannot ship something merely good enough and let users find the edge cases.
Holds DD Dmitri Dolgov
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