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← Inference was never going to be the easy, cheap half of AI, because…
17 connected korrents · 19 moments from 10 Dec 2015 to 22 Sept 2026.
Everything filed under scaling laws
scaling laws
Everything filed under LLMs
LLMs
Everything filed under neural networks
neural networks
Everything filed under AI alignment
AI alignment
Everything filed under energy
energy
Everything filed under reinforcement learning
reinforcement learning 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: Inference was never going to be the easy, cheap half of AI, because inference is thinking, and thinking is far harder than reading. Inference was never going to be the easy, cheap half of AI, because inference is thinking, and thinking is far harder than reading. Last stated 7 months ago
23 Mar 2026
JH
Jensen Huang — holds since 2026-03-23 — tap for who they are
Same subject: AI excels at certain tasks but cannot substitute for human thinking. — tap to centre the map on it AI excels at certain tasks but cannot substitute for human thinking. Last stated 2 months ago
1 Sept 2026
NM
Nick Maggiulli — holds since 2026-09-01 — tap for who they are
Same subject: Tokens are not the true unit of AI inference, because better models yield more problem-solving per token. — tap to centre the map on it Tokens are not the true unit of AI inference, because better models yield more problem-solving per token. Last stated a month ago
15 Sept 2026
PG
Paul Graham — holds since 2026-09-15 — tap for who they are
Same subject: No one fully understands the consequences of how capable AI is becoming. — tap to centre the map on it No one fully understands the consequences of how capable AI is becoming. Last stated 2 months ago
1 Sept 2026
SA
Sam Altman — holds since 2026-09-01 — tap for who they are
Same subject: Real reasoning will have arrived when spending more compute at inference time reliably buys a dramatically better answer. — tap to centre the map on it Real reasoning will have arrived when spending more compute at inference time reliably buys a dramatically better answer. Last stated 2 years ago
19 Jun 2024
AS
Aravind Srinivas — holds since 2024-06-19 — tap for who they are
Same subject: AI alignment is a red herring not worth the effort currently devoted to it. — tap to centre the map on it AI alignment is a red herring not worth the effort currently devoted to it. Last stated 2 months ago
8 Aug 2026
MW
Matt Webb — holds since 2026-08-08 — tap for who they are
Same subject: Inference, not training, is now the constraint, and specialized low-energy hardware will beat general-purpose GPUs and TPUs on latency. — tap to centre the map on it Inference, not training, is now the constraint, and specialized low-energy hardware will beat general-purpose GPUs and TPUs on latency. Last stated 3 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — tap for who they are
Same subject: Work on large language models is not progress toward artificial general intelligence, because the models do not understand what they read. — tap to centre the map on it Work on large language models is not progress toward artificial general intelligence, because the models do not understand what they read. Last stated a year ago
30 Jul 2025
AG
Alexey Guzey — holds since 2024-08-09 — tap for who they are
AG
Alexey Guzey — no longer holds since 2025-07-30 — tap for who they are
Same subject: The 'machine god' metaphor for AI is unhelpful and limits thinking about the technology. — tap to centre the map on it The 'machine god' metaphor for AI is unhelpful and limits thinking about the technology. Last stated 4 weeks ago
22 Sept 2026
BD
Brad DeLong — holds since 2026-09-22 — 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 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: 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
Inference was never going to be the easy, cheap half of AI, because inference is thinking, and thinking is far harder than reading.
Holds Read this korrent →
AI excels at certain tasks but cannot substitute for human thinking.
Holds
Tokens are not the true unit of AI inference, because better models yield more problem-solving per token.
Holds
No one fully understands the consequences of how capable AI is becoming.
Holds
Real reasoning will have arrived when spending more compute at inference time reliably buys a dramatically better answer.
Holds
AI alignment is a red herring not worth the effort currently devoted to it.
Holds
Inference, not training, is now the constraint, and specialized low-energy hardware will beat general-purpose GPUs and TPUs on latency.
Holds
Work on large language models is not progress toward artificial general intelligence, because the models do not understand what they read.
No longer holds
The 'machine god' metaphor for AI is unhelpful and limits thinking about the technology.
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 physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel.
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