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← Token prices have stayed flat because labs deliberately kept models…
17 connected korrents · 16 moments from 10 Dec 2015 to 26 Sept 2026.
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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: Token prices have stayed flat because labs deliberately kept models smaller than expected in order to get more experimental cycles. Token prices have stayed flat because labs deliberately kept models smaller than expected in order to get more experimental cycles. Last stated 2 months ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: An AI as good as a top researcher will not be priced like one, because it competes against the other labs rather than against humans. — tap to centre the map on it An AI as good as a top researcher will not be priced like one, because it competes against the other labs rather than against humans. Last stated a year ago
15 Aug 2025
CH
Casey Handmer — holds since 2025-08-15 — tap for who they are
Same subject: Hundreds of billions of dollars of AI value rest on about a billion dollars of lithography tooling that cannot be scaled quickly. — tap to centre the map on it Hundreds of billions of dollars of AI value rest on about a billion dollars of lithography tooling that cannot be scaled quickly. Last stated 7 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — 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: Rationing AI tokens reflects a weird scarcity mindset when more tokens are easily affordable than one could ever use. — tap to centre the map on it Rationing AI tokens reflects a weird scarcity mindset when more tokens are easily affordable than one could ever use. Last stated 4 weeks ago
26 Sept 2026
DH
David Heinemeier Hansson — holds since 2026-09-26 — tap for who they are
Same subject: Not every token is worth the same amount, so AI will be sold by results and by the hour as well as by the token. — tap to centre the map on it Not every token is worth the same amount, so AI will be sold by results and by the hour as well as by the token. Last stated 8 months ago
13 Feb 2026
DA
Dario Amodei — holds since 2026-02-13 — tap for who they are
Same subject: The largest tech companies are not using fewer tokens; they are switching to cheaper, still capable open models instead of SOTA models. — tap to centre the map on it The largest tech companies are not using fewer tokens; they are switching to cheaper, still capable open models instead of SOTA models. Last stated a month ago
9 Sept 2026
GO
Gergely Orosz — holds since 2026-09-09 — tap for who they are
Same subject: Maxing out your token usage is optimising one node of the factory for utilisation instead of the end-to-end goal of shipping value that is stable and lasts. — tap to centre the map on it Maxing out your token usage is optimising one node of the factory for utilisation instead of the end-to-end goal of shipping value that is stable and lasts. Last stated 3 months ago
15 Jul 2026
DH
Dex Horthy — holds since 2026-07-15 — tap for who they are
Same subject: Nobody wants a slow model, which is why labs will not trade inference speed for cheaper memory even though they easily could. — tap to centre the map on it Nobody wants a slow model, which is why labs will not trade inference speed for cheaper memory even though they easily could. Last stated 7 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — 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
Token prices have stayed flat because labs deliberately kept models smaller than expected in order to get more experimental cycles.
Holds Read this korrent →
An AI as good as a top researcher will not be priced like one, because it competes against the other labs rather than against humans.
Holds
Hundreds of billions of dollars of AI value rest on about a billion dollars of lithography tooling that cannot be scaled quickly.
Holds
Tokens are not the true unit of AI inference, because better models yield more problem-solving per token.
Holds
Rationing AI tokens reflects a weird scarcity mindset when more tokens are easily affordable than one could ever use.
Holds
Not every token is worth the same amount, so AI will be sold by results and by the hour as well as by the token.
Holds
The largest tech companies are not using fewer tokens; they are switching to cheaper, still capable open models instead of SOTA models.
Holds
Maxing out your token usage is optimising one node of the factory for utilisation instead of the end-to-end goal of shipping value that is stable and lasts.
Holds
Nobody wants a slow model, which is why labs will not trade inference speed for cheaper memory even though they easily could.
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