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← In verifiable domains model capability scaling should remain…
17 connected korrents · 17 moments from 10 Dec 2015 to 4 Sept 2026.
Everything filed under scaling laws
scaling laws
Everything filed under neural networks
neural networks
Everything filed under benchmarks
benchmarks
Everything filed under reinforcement learning
reinforcement learning
Everything filed under self-driving cars
self-driving cars
Everything filed under cybersecurity
cybersecurity 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: In verifiable domains model capability scaling should remain unbounded, because the space of enumerable patterns is infinite by construction. In verifiable domains model capability scaling should remain unbounded, because the space of enumerable patterns is infinite by construction. Last stated 2 months ago
27 Aug 2026
FC
François Chollet — holds since 2026-08-27 — tap for who they are
Same subject: Scaling laws are still working, but the models people actually use run a few months behind the maximum capability Google can deliver, because the biggest model is too slow and expensive to serve. — tap to centre the map on it Scaling laws are still working, but the models people actually use run a few months behind the maximum capability Google can deliver, because the biggest model is too slow and expensive to serve. Last stated a year ago
5 Jun 2025
SP
Sundar Pichai — holds since 2025-06-05 — tap for who they are
Same subject: The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture. — tap to centre the map on it The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture. Last stated 2 months ago
4 Sept 2026
FL
Fei-Fei Li — holds since 2026-09-04 — tap for who they are
Same subject: The least verifiable part of AI research is the judgement call about what goes into the one big training run. — tap to centre the map on it The least verifiable part of AI research is the judgement call about what goes into the one big training run. Last stated 2 months ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: Models leak and algorithms get copied, but hundreds of millions of audited autonomous miles cannot be replicated — earned trust is the real moat. — tap to centre the map on it Models leak and algorithms get copied, but hundreds of millions of audited autonomous miles cannot be replicated — earned trust is the real moat. Last stated 3 months ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
Same subject: Mixture-of-experts models can scale stably but begin to show fundamental limits as they are scaled further. — tap to centre the map on it Mixture-of-experts models can scale stably but begin to show fundamental limits as they are scaled further. Last stated 6 years ago
4 Feb 2021
GW
Gwern — holds since 2021-02-04 — tap for who they are
Same subject: The scaling curve really is S-shaped, but there are at least two cycles left in it, which means models sixteen times smarter and all knowledge work subsumed. — tap to centre the map on it The scaling curve really is S-shaped, but there are at least two cycles left in it, which means models sixteen times smarter and all knowledge work subsumed. Last stated 7 months ago
11 Mar 2026
SY
Steve Yegge — holds since 2026-03-11 — tap for who they are
Same subject: Running a model until its performance plateaus is no longer a usable evaluation rule, because a well-scaffolded model keeps improving for weeks. — tap to centre the map on it Running a model until its performance plateaus is no longer a usable evaluation rule, because a well-scaffolded model keeps improving for weeks. Last stated 4 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: The tradeoff in cyber-capable models cannot be designed away: the model that can do something nefarious is the same model that hardens the system against it. — tap to centre the map on it The tradeoff in cyber-capable models cannot be designed away: the model that can do something nefarious is the same model that hardens the system against it. Last stated 2 months ago
18 Aug 2026
MK
Michael Kratsios — holds since 2026-08-18 — 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: A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel. — tap to centre the map on it A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel. Last stated 3 months ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — 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 11 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — 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
Same subject: A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution. — tap to centre the map on it A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution. Last stated 9 years ago
12 Jun 2017
IP
Illia Polosukhin — holds since 2017-06-12 — tap for who they are
JU
Jakob Uszkoreit — holds since 2017-06-12 — tap for who they are
NP
Niki Parmar — holds since 2017-06-12 — tap for who they are
AV
Ashish Vaswani — holds since 2017-06-12 — tap for who they are
NS
Noam Shazeer — holds since 2017-06-12 — tap for who they are
AG
Aidan N. Gomez — holds since 2017-06-12 — tap for who they are
+2
2 more
Same subject: A benchmark that ranks Claude Code last while it stays first in use is measuring the wrong thing, and has been for a year. — tap to centre the map on it A benchmark that ranks Claude Code last while it stays first in use is measuring the wrong thing, and has been for a year. Last stated 2 months ago
3 Sept 2026
DR
Dax Raad — holds since 2026-09-03 — tap for who they are
Same subject: A carmaker's claim to be the safest is mostly an artefact of comparing a new car against a fleet average twelve years old. — tap to centre the map on it A carmaker's claim to be the safest is mostly an artefact of comparing a new car against a fleet average twelve years old. Last stated 3 years ago
19 Dec 2023
PK
Philip Koopman — holds since 2023-12-19 — tap for who they are
Same subject: A company should define what great looks like for its own role rather than borrow another company's definition. — tap to centre the map on it A company should define what great looks like for its own role rather than borrow another company's definition. Last stated 2 months ago
9 Aug 2026
AW
Adam Ward — holds since 2026-08-09 — tap for who they are
At the centre
In verifiable domains model capability scaling should remain unbounded, because the space of enumerable patterns is infinite by construction.
Holds Read this korrent →
Scaling laws are still working, but the models people actually use run a few months behind the maximum capability Google can deliver, because the biggest model is too slow and expensive to serve.
Holds
The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture.
Holds
The least verifiable part of AI research is the judgement call about what goes into the one big training run.
Holds
Models leak and algorithms get copied, but hundreds of millions of audited autonomous miles cannot be replicated — earned trust is the real moat.
Holds
Mixture-of-experts models can scale stably but begin to show fundamental limits as they are scaled further.
Holds
The scaling curve really is S-shaped, but there are at least two cycles left in it, which means models sixteen times smarter and all knowledge work subsumed.
Holds
Running a model until its performance plateaus is no longer a usable evaluation rule, because a well-scaffolded model keeps improving for weeks.
Holds
The tradeoff in cyber-capable models cannot be designed away: the model that can do something nefarious is the same model that hardens the system against it.
Holds
A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score.
Holds
A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel.
Holds
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
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
A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution.
Holds
A benchmark that ranks Claude Code last while it stays first in use is measuring the wrong thing, and has been for a year.
Holds
A carmaker's claim to be the safest is mostly an artefact of comparing a new car against a fleet average twelve years old.
Holds
A company should define what great looks like for its own role rather than borrow another company's definition.
Holds