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← Breakthroughs achieved with massive compute are typically achieved using it inefficiently.
17 connected korrents · 17 moments from 15 Jul 2021 to 19 Aug 2026.
Everything filed under scaling laws
scaling laws
Everything filed under AI and science
AI and science
Everything filed under software performance
software performance
Everything filed under innovation
innovation 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: Breakthroughs achieved with massive compute are typically achieved using it inefficiently. Breakthroughs achieved with massive compute are typically achieved using it inefficiently. Last stated 3 years ago
19 Dec 2023
SR
Sebastian Ruder — holds since 2023-12-19 — 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: Research needs some compute but it is far from obvious that it needs the largest amount of compute in the world. — tap to centre the map on it Research needs some compute but it is far from obvious that it needs the largest amount of compute in the world. Last stated 10 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: There will be no overnight intelligence explosion, because a model's best work takes so much test-time compute that time itself is the bottleneck. — tap to centre the map on it There will be no overnight intelligence explosion, because a model's best work takes so much test-time compute that time itself is the bottleneck. Last stated 3 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — 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 3 months ago
29 Jun 2026
CN
Cal Newport — holds since 2026-06-29 — tap for who they are
Same subject: A small performance gain is not worth the complexity added to achieve it, since complexity's costs compound rather than add. — tap to centre the map on it A small performance gain is not worth the complexity added to achieve it, since complexity's costs compound rather than add. Last stated 3 years ago
6 Dec 2023
RP
Rob Pike — holds since 2023-12-06 — tap for who they are
Same subject: General-purpose computing has largely run its course; domain-specific acceleration is the way computing keeps scaling. — tap to centre the map on it General-purpose computing has largely run its course; domain-specific acceleration is the way computing keeps scaling. Last stated 6 months ago
15 Apr 2026
JH
Jensen Huang — holds since 2026-04-15 — tap for who they are
Same subject: The history of machine learning is of proclaimed barriers dissolving inside the big blob of compute. — tap to centre the map on it The history of machine learning is of proclaimed barriers dissolving inside the big blob of compute. Last stated 8 months ago
13 Feb 2026
DA
Dario Amodei — holds since 2026-02-13 — tap for who they are
Same subject: Computing is an onion, and the more layers of it you can peel back and understand, the better you can build for the constrained machines real people are using. — tap to centre the map on it Computing is an onion, and the more layers of it you can peel back and understand, the better you can build for the constrained machines real people are using. Last stated a month ago
19 Aug 2026
AO
Addy Osmani — holds since 2026-08-19 — tap for who they are
Same subject: A compound's AI origin story is not by itself a reason to expect it to do better in the clinic. — tap to centre the map on it A compound's AI origin story is not by itself a reason to expect it to do better in the clinic. Last stated 2 years ago
13 May 2024
DL
Derek Lowe — holds since 2024-05-13 — tap for who they are
Same subject: A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck. — tap to centre the map on it A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck. Last stated 2 years ago
17 Sept 2024
AT
Alex Telford — holds since 2024-09-17 — tap for who they are
Same subject: A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known. — tap to centre the map on it A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known. Last stated 5 years ago
15 Jul 2021
DH
Demis Hassabis — holds since 2021-07-15 — tap for who they are
Same subject: Accurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics. — tap to centre the map on it Accurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics. Last stated 5 years ago
15 Jul 2021
DH
Demis Hassabis — holds since 2021-07-15 — tap for who they are
Same subject: AI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points. — tap to centre the map on it AI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points. Last stated 10 months ago
23 Nov 2025
EH
Elliot Hershberg — holds since 2025-11-23 — tap for who they are
Same subject: AI will not produce a big breakthrough in biological understanding any time soon, because biology has nothing like the clean, complete, high-quality data that made protein-structure prediction work. — tap to centre the map on it AI will not produce a big breakthrough in biological understanding any time soon, because biology has nothing like the clean, complete, high-quality data that made protein-structure prediction work. Last stated 2 years ago
19 Sept 2024
DL
Derek Lowe — holds since 2024-09-19 — tap for who they are
Same subject: Biology models are smaller than language models only because there is less data, not because biology is simpler. — tap to centre the map on it Biology models are smaller than language models only because there is less data, not because biology is simpler. Last stated 4 months ago
10 Jun 2026
MZ
Mark Zuckerberg — holds since 2026-06-10 — tap for who they are
Same subject: Digital biology is about to have its ChatGPT moment, on the back of multimodality, long context and synthetic data generation. — tap to centre the map on it Digital biology is about to have its ChatGPT moment, on the back of multimodality, long context and synthetic data generation. Last stated 9 months ago
8 Jan 2026
JH
Jensen Huang — holds since 2026-01-08 — tap for who they are
Same subject: In an intellectually dead research field, feeding AI decades of meaningless work only produces more meaningless output. — tap to centre the map on it In an intellectually dead research field, feeding AI decades of meaningless work only produces more meaningless output. Last stated 2 months ago
26 Jul 2026
PW
Peter Woit — holds since 2026-07-26 — tap for who they are
At the centre
Breakthroughs achieved with massive compute are typically achieved using it inefficiently.
Holds Read this korrent →
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
Research needs some compute but it is far from obvious that it needs the largest amount of compute in the world.
Holds
There will be no overnight intelligence explosion, because a model's best work takes so much test-time compute that time itself is the bottleneck.
Holds
The personal computer made countless individual tasks easier without delivering the productivity gains everyone expected, and AI may go the same way.
Holds
A small performance gain is not worth the complexity added to achieve it, since complexity's costs compound rather than add.
Holds
General-purpose computing has largely run its course; domain-specific acceleration is the way computing keeps scaling.
Holds
The history of machine learning is of proclaimed barriers dissolving inside the big blob of compute.
Holds
Computing is an onion, and the more layers of it you can peel back and understand, the better you can build for the constrained machines real people are using.
Holds
A compound's AI origin story is not by itself a reason to expect it to do better in the clinic.
Holds
A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck.
Holds
A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known.
Holds
Accurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics.
Holds
AI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points.
Holds
AI will not produce a big breakthrough in biological understanding any time soon, because biology has nothing like the clean, complete, high-quality data that made protein-structure prediction work.
Holds
Biology models are smaller than language models only because there is less data, not because biology is simpler.
Holds
Digital biology is about to have its ChatGPT moment, on the back of multimodality, long context and synthetic data generation.
Holds
In an intellectually dead research field, feeding AI decades of meaningless work only produces more meaningless output.
Holds