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← The capability gap between smaller, locally-run AI models and larger…
17 connected korrents · 19 moments from 10 Dec 2015 to 24 Sept 2026. Nearly all of them are about LLMs .
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scaling laws
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OpenAI 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: The capability gap between smaller, locally-run AI models and larger models will narrow over time as compute becomes more accessible. The capability gap between smaller, locally-run AI models and larger models will narrow over time as compute becomes more accessible. Last stated 5 months ago
27 Apr 2026
JS
Jon Seager — holds since 2026-04-27 — tap for who they are
Same subject: The capabilities gap between open and closed AI models has decreased over the last three years. — tap to centre the map on it The capabilities gap between open and closed AI models has decreased over the last three years. Last stated 2 weeks ago
21 Sept 2026
NL
Nathan Lambert — holds since 2026-09-21 — tap for who they are
Same subject: There is a large and growing gap between open-weight AI models and frontier AI models. — tap to centre the map on it There is a large and growing gap between open-weight AI models and frontier AI models. Last stated 6 months ago
29 Mar 2026
CW
Chris Wellons — holds since 2026-03-29 — tap for who they are
Same subject: The performance gap between open and closed AI models has narrowed to roughly 4-6 months. — tap to centre the map on it The performance gap between open and closed AI models has narrowed to roughly 4-6 months. Last stated 3 weeks ago
11 Sept 2026
NL
Nathan Lambert — holds since 2026-09-11 — tap for who they are
Same subject: Models are far more differentiated from one another than clouds are, so AI will not commoditise the way cloud did. — tap to centre the map on it Models are far more differentiated from one another than clouds are, so AI will not commoditise the way cloud did. Last stated 8 months ago
13 Feb 2026
DA
Dario Amodei — holds since 2026-02-13 — tap for who they are
Same subject: The local AI niche will end up dominated by whichever open-weight model runs practically fast on high-end consumer hardware, like a Mac or a 'GPU in a box' rig. — tap to centre the map on it The local AI niche will end up dominated by whichever open-weight model runs practically fast on high-end consumer hardware, like a Mac or a 'GPU in a box' rig. Last stated 5 months ago
14 May 2026
SS
Salvatore Sanfilippo — holds since 2026-05-14 — 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 2 weeks ago
18 Sept 2026
EM
Ethan Mollick — holds since 2026-09-18 — tap for who they are
Same subject: Local AI models will eventually catch up to frontier models once frontier progress hits diminishing returns. — tap to centre the map on it Local AI models will eventually catch up to frontier models once frontier progress hits diminishing returns. Last stated 8 months ago
8 Feb 2026
DC
David Crawshaw — holds since 2026-02-08 — tap for who they are
Same subject: AI capabilities are jagged rather than smooth, so the different definitions of human-level AI will be met years apart rather than together. — tap to centre the map on it AI capabilities are jagged rather than smooth, so the different definitions of human-level AI will be met years apart rather than together. Last stated 6 months ago
6 Apr 2026
HT
Helen Toner — holds since 2026-04-06 — 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 10 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 3 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: 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: The ChatGPT moment for robotics already happened a couple of years ago; what remains is the unglamorous post-training work. — tap to centre the map on it The ChatGPT moment for robotics already happened a couple of years ago; what remains is the unglamorous post-training work. Last stated 2 months ago
26 Jul 2026
JH
Jensen Huang — holds since 2026-07-26 — tap for who they are
Same subject: A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. — tap to centre the map on it A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
Same subject: The gains from extra simulated layers in AI are much smaller than the gains from extra real layers. — tap to centre the map on it The gains from extra simulated layers in AI are much smaller than the gains from extra real layers. Last stated a week ago
24 Sept 2026
SA
Scott Alexander — holds since 2026-09-24 — 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
The capability gap between smaller, locally-run AI models and larger models will narrow over time as compute becomes more accessible.
Holds Read this korrent →
The capabilities gap between open and closed AI models has decreased over the last three years.
Holds
There is a large and growing gap between open-weight AI models and frontier AI models.
Holds
The performance gap between open and closed AI models has narrowed to roughly 4-6 months.
Holds
Models are far more differentiated from one another than clouds are, so AI will not commoditise the way cloud did.
Holds
The local AI niche will end up dominated by whichever open-weight model runs practically fast on high-end consumer hardware, like a Mac or a 'GPU in a box' rig.
Holds
The current capabilities of existing AI models are barely being used and are often not even well understood.
Holds
Local AI models will eventually catch up to frontier models once frontier progress hits diminishing returns.
Holds
AI capabilities are jagged rather than smooth, so the different definitions of human-level AI will be met years apart rather than together.
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
Scaling the 2019-2023 recipe — same architecture, bigger model, more data — is not enough; further progress depends on new architectural ideas.
Holds
The ChatGPT moment for robotics already happened a couple of years ago; what remains is the unglamorous post-training work.
Holds
A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests.
Holds
The gains from extra simulated layers in AI are much smaller than the gains from extra real layers.
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