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← AI will not run out of data, because there is already enough…
17 connected korrents · 18 moments from 10 Dec 2015 to 27 Sept 2026.
Everything filed under scaling laws
scaling laws
Everything filed under LLMs
LLMs
Everything filed under neural networks
neural networks
Everything filed under AI and jobs
AI and jobs
Everything filed under data centers
data centers
Everything filed under creativity
creativity
Everything filed under AI alignment
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: 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 2 months ago
10 Aug 2026
FL
Fei-Fei Li — holds since 2026-08-10 — tap for who they are
Same subject: The current capabilities of existing AI models are barely being used and are often not even well understood. — tap to centre the map on it The current capabilities of existing AI models are barely being used and are often not even well understood. Last stated a month ago
18 Sept 2026
EM
Ethan Mollick — holds since 2026-09-18 — 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 2 months 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 7 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 5 months ago
4 Jun 2026
AI
Alex Imas — holds since 2026-06-04 — tap for who they are
Same subject: As of now there is not much data showing that AI is genuinely taking jobs away. — tap to centre the map on it As of now there is not much data showing that AI is genuinely taking jobs away. Last stated 4 weeks ago
27 Sept 2026
MG
Molly Graham — holds since 2026-09-27 — 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 9 months ago
8 Jan 2026
JH
Jensen Huang — holds since 2026-01-08 — tap for who they are
Same subject: Short of a breakthrough, throwing more compute at current AI models will not get them to invent new scientific theories. — tap to centre the map on it Short of a breakthrough, throwing more compute at current AI models will not get them to invent new scientific theories. Last stated 8 months ago
19 Feb 2026
NR
Naval Ravikant — holds since 2026-02-19 — 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
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 Read 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
The current capabilities of existing AI models are barely being used and are often not even well understood.
Holds
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
Training is no longer limited by data but by compute, because most of the data models learn from is now synthetic.
Holds
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
As of now there is not much data showing that AI is genuinely taking jobs away.
Holds
There is no AI bubble on the demand side: the shortage of computing capacity is global, and spans every company and industry.
Holds
Short of a breakthrough, throwing more compute at current AI models will not get them to invent new scientific theories.
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