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← Before building on a gap in the general models, work out whether that gap survives six months or three years.
17 connected korrents · 17 moments from 10 Dec 2015 to 10 Sept 2026.
Everything filed under scaling laws
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LLMs
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neural networks
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AI alignment
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: Before building on a gap in the general models, work out whether that gap survives six months or three years. Before building on a gap in the general models, work out whether that gap survives six months or three years. Last stated 3 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — tap for who they are
Same subject: A good framework for designing products is to design for six months out: presume model capabilities will get better and build for the world you would then be in. — tap to centre the map on it A good framework for designing products is to design for six months out: presume model capabilities will get better and build for the world you would then be in. Last stated a month ago
9 Sept 2026
EB
Ed Bayes — holds since 2026-09-09 — tap for who they are
Same subject: AI products should probably aim at where the models will be in two to three months, neither anchored in the present nor futuristic and unusable. — tap to centre the map on it AI products should probably aim at where the models will be in two to three months, neither anchored in the present nor futuristic and unusable. Last stated a month ago
10 Sept 2026
TS
Tara Seshan — holds since 2026-09-10 — tap for who they are
Same subject: The one safe bet about AI is that models will improve, so what a company has to build is an organisation that gets better as models get better. — tap to centre the map on it The one safe bet about AI is that models will improve, so what a company has to build is an organisation that gets better as models get better. Last stated a year ago
16 Oct 2025
DF
Dylan Field — holds since 2025-10-16 — tap for who they are
Same subject: Build where today's models succeed one percent of the time, not twenty: partial success means the capability is already arriving. — tap to centre the map on it Build where today's models succeed one percent of the time, not twenty: partial success means the capability is already arriving. Last stated 3 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — tap for who they are
Same subject: Build for the model six months from now, not the model of today. — tap to centre the map on it Build for the model six months from now, not the model of today. Last stated 8 months ago
25 Feb 2026
BC
Boris Cherny — holds since 2026-02-25 — tap for who they are
Same subject: A model only days into training cannot be known to be aligned, and if such models can leap a generation ahead this fast then training pauses mean little and timelines are shorter than previously thought. — tap to centre the map on it A model only days into training cannot be known to be aligned, and if such models can leap a generation ahead this fast then training pauses mean little and timelines are shorter than previously thought. Last stated a month ago
9 Sept 2026
ZM
Zvi Mowshowitz — holds since 2026-09-09 — tap for who they are
Same subject: Building for where AI models are now and building for where they will be in a year are equally wrong. — tap to centre the map on it Building for where AI models are now and building for where they will be in a year are equally wrong. Last stated 2 months ago
30 Aug 2026
TS
Tara Seshan — holds since 2026-08-30 — tap for who they are
Same subject: Designers should be designing right at the boundary of the current model, hoping the next one solves it; understanding what is possible and pushing the edges even if you get stuck is probably a good place to be right now. — tap to centre the map on it Designers should be designing right at the boundary of the current model, hoping the next one solves it; understanding what is possible and pushing the edges even if you get stuck is probably a good place to be right now. Last stated 6 months ago
14 Apr 2026
BL
Brian Lovin — holds since 2026-04-14 — 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
Before building on a gap in the general models, work out whether that gap survives six months or three years.
Holds Read this korrent →
A good framework for designing products is to design for six months out: presume model capabilities will get better and build for the world you would then be in.
Holds
AI products should probably aim at where the models will be in two to three months, neither anchored in the present nor futuristic and unusable.
Holds
The one safe bet about AI is that models will improve, so what a company has to build is an organisation that gets better as models get better.
Holds
Build where today's models succeed one percent of the time, not twenty: partial success means the capability is already arriving.
Holds
Build for the model six months from now, not the model of today.
Holds
A model only days into training cannot be known to be aligned, and if such models can leap a generation ahead this fast then training pauses mean little and timelines are shorter than previously thought.
Holds
Building for where AI models are now and building for where they will be in a year are equally wrong.
Holds
Designers should be designing right at the boundary of the current model, hoping the next one solves it; understanding what is possible and pushing the edges even if you get stuck is probably a good place to be right now.
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