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← Intelligence is going to scale by exactly one thing, and that thing is compute.
17 connected korrents · 19 moments from 24 Feb 2018 to 17 Sept 2026.
Everything filed under scaling laws
scaling laws
Everything filed under LLMs
LLMs
Everything filed under AGI
AGI
Everything filed under AI alignment
AI alignment
Everything filed under energy
energy
Everything filed under physics
physics
Everything filed under reinforcement learning
reinforcement learning
Everything filed under reinforcement learning
reinforcement learning Cannot both be true Cannot both be true 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: Intelligence is going to scale by exactly one thing, and that thing is compute. Intelligence is going to scale by exactly one thing, and that thing is compute. Last stated 7 months ago
23 Mar 2026
JH
Jensen Huang — holds since 2026-03-23 — tap for who they are
Cannot both be true: 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
Cannot both be true: The gap between chimps and humans is no evidence that a small increase in compute yields an explosion of capability, because evolution was not optimising chimps for technology. — tap to centre the map on it The gap between chimps and humans is no evidence that a small increase in compute yields an explosion of capability, because evolution was not optimising chimps for technology. Last stated 9 years ago
24 Feb 2018
PC
Paul Christiano — holds since 2018-02-24 — tap for who they are
Same subject: Compute is not a market like phones, where demand is capped by how many buyers there are; intelligence is priced like energy, so the only real question is how much the world uses at a given price. — tap to centre the map on it Compute is not a market like phones, where demand is capped by how many buyers there are; intelligence is priced like energy, so the only real question is how much the world uses at a given price. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
Same subject: Intelligence looks like a fact of physics: apply enough compute to matter and you get something that thinks. — tap to centre the map on it Intelligence looks like a fact of physics: apply enough compute to matter and you get something that thinks. Last stated 2 months ago
20 Aug 2026
MH
Max Hodak — holds since 2026-08-20 — tap for who they are
Same subject: We are already in an intelligence explosion and have been for decades, so AI will show up as more of the same exponential rather than a break in it. — tap to centre the map on it We are already in an intelligence explosion and have been for decades, so AI will show up as more of the same exponential rather than a break in it. Last stated a year ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — 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: There is infinite demand for intelligence. — tap to centre the map on it There is infinite demand for intelligence. Last stated a month ago
9 Sept 2026
GR
Guillermo Rauch — holds since 2026-09-09 — tap for who they are
Same subject: Superintelligence is already here, is more powerful than us, and it rather than humans is now deciding where things go. — tap to centre the map on it Superintelligence is already here, is more powerful than us, and it rather than humans is now deciding where things go. Last stated 3 months ago
20 Jul 2026
PL
Pieter Levels — holds since 2026-07-20 — 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 4 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: Humans have been living in the presence of a superintelligence their entire lives. — tap to centre the map on it Humans have been living in the presence of a superintelligence their entire lives. Last stated a month ago
15 Sept 2026
TL
Tobias Lütke — holds since 2026-09-15 — tap for who they are
Same subject: A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI. — tap to centre the map on it A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI. Last stated 10 months ago
1 Jan 2026
JL
Jason Lemkin — holds since 2026-01-01 — tap for who they are
Same subject: A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to. — tap to centre the map on it A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to. Last stated 3 months ago
15 Jul 2026
DH
Dex Horthy — holds since 2026-07-15 — tap for who they are
Same subject: A body of publications is easy for AI models to consume via pretraining corpora but cumbersome for most humans due to CAPTCHAs, accounts, and paywalls. — tap to centre the map on it A body of publications is easy for AI models to consume via pretraining corpora but cumbersome for most humans due to CAPTCHAs, accounts, and paywalls. Last stated a month ago
17 Sept 2026
PC
Patrick Collison — holds since 2026-09-17 — 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 deep bidirectional model is strictly more powerful than a left-to-right model or a shallow concatenation of unidirectional models. — tap to centre the map on it A deep bidirectional model is strictly more powerful than a left-to-right model or a shallow concatenation of unidirectional models. Last stated 8 years ago
11 Oct 2018
KT
Kristina Toutanova — holds since 2018-10-11 — tap for who they are
MC
Ming-Wei Chang — holds since 2018-10-11 — tap for who they are
KL
Kenton Lee — holds since 2018-10-11 — tap for who they are
JD
Jacob Devlin — holds since 2018-10-11 — tap for who they are
At the centre
Intelligence is going to scale by exactly one thing, and that thing is compute.
Holds Read this korrent →
Scaling the 2019-2023 recipe — same architecture, bigger model, more data — is not enough; further progress depends on new architectural ideas.
Holds
The gap between chimps and humans is no evidence that a small increase in compute yields an explosion of capability, because evolution was not optimising chimps for technology.
Holds
Compute is not a market like phones, where demand is capped by how many buyers there are; intelligence is priced like energy, so the only real question is how much the world uses at a given price.
Holds
Intelligence looks like a fact of physics: apply enough compute to matter and you get something that thinks.
Holds
We are already in an intelligence explosion and have been for decades, so AI will show up as more of the same exponential rather than a break in it.
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
There is infinite demand for intelligence.
Holds
Superintelligence is already here, is more powerful than us, and it rather than humans is now deciding where things go.
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
Humans have been living in the presence of a superintelligence their entire lives.
Holds
A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI.
Holds
A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to.
Holds
A body of publications is easy for AI models to consume via pretraining corpora but cumbersome for most humans due to CAPTCHAs, accounts, and paywalls.
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 deep bidirectional model is strictly more powerful than a left-to-right model or a shallow concatenation of unidirectional models.
Holds