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← The safety frameworks the labs publish do not account for how much…
17 connected korrents · 17 moments from 11 Oct 2018 to 22 Sept 2026. Nearly all of them are about scaling laws .
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LLMs 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: The safety frameworks the labs publish do not account for how much test-time compute a dangerous-capability evaluation was given. The safety frameworks the labs publish do not account for how much test-time compute a dangerous-capability evaluation was given. Last stated 4 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — 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: After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. — tap to centre the map on it After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. Last stated 7 months ago
23 Mar 2026
JH
Jensen Huang — holds since 2026-03-23 — tap for who they are
Same subject: AI scaling laws require exponentially more compute and resources to produce only linear gains in model intelligence. — tap to centre the map on it AI scaling laws require exponentially more compute and resources to produce only linear gains in model intelligence. Last stated a month ago
19 Sept 2026
NL
Nathan Lambert — holds since 2026-09-19 — tap for who they are
Same subject: As models keep scaling up, modularity will become essential to how they are developed. — tap to centre the map on it As models keep scaling up, modularity will become essential to how they are developed. Last stated 4 years ago
23 Feb 2023
SR
Sebastian Ruder — holds since 2023-02-23 — tap for who they are
Same subject: Bet on a system that is maximally learned and minimally constrained, and add structure only where it improves the scaling laws. — tap to centre the map on it Bet on a system that is maximally learned and minimally constrained, and add structure only where it improves the scaling laws. Last stated 3 months ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
Same subject: Current AI techniques are four to six orders of magnitude away from optimal in data efficiency and test-time compute efficiency. — tap to centre the map on it Current AI techniques are four to six orders of magnitude away from optimal in data efficiency and test-time compute efficiency. Last stated 2 months ago
7 Aug 2026
FC
François Chollet — holds since 2026-08-07 — tap for who they are
Same subject: Current techniques restrict pre-trained representation power because standard language models are unidirectional. — tap to centre the map on it Current techniques restrict pre-trained representation power because standard language models are unidirectional. 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
Same subject: A company that uses an algorithm to nudge customers into behavior that isn't good for them is doing something bad. — tap to centre the map on it A company that uses an algorithm to nudge customers into behavior that isn't good for them is doing something bad. Last stated a year ago
7 Oct 2025
MM
Mr. Money Mustache — holds since 2025-10-07 — tap for who they are
Same subject: A control evaluation that reports under one per cent risk should be read as several per cent, because the evaluation can itself fail. — tap to centre the map on it A control evaluation that reports under one per cent risk should be read as several per cent, because the evaluation can itself fail. Last stated 2 years ago
7 May 2024
BS
Buck Shlegeris — holds since 2024-05-07 — 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 human being is not an AGI: we lack a huge amount of knowledge and rely on continual learning instead, so continual learning is what superintelligence should mean. — tap to centre the map on it A human being is not an AGI: we lack a huge amount of knowledge and rely on continual learning instead, so continual learning is what superintelligence should mean. Last stated 11 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: A proposal to pace frontier AI development is unrealistic and mainly serves political control of AI. — tap to centre the map on it A proposal to pace frontier AI development is unrealistic and mainly serves political control of AI. Last stated a month ago
18 Sept 2026
BT
Ben Thompson — holds since 2026-09-18 — tap for who they are
Same subject: Adapting over time is a workable answer to humans misusing AI and no answer at all to losing control of systems smarter than us. — tap to centre the map on it Adapting over time is a workable answer to humans misusing AI and no answer at all to losing control of systems smarter than us. Last stated 2 years ago
5 Apr 2025
HT
Helen Toner — holds since 2025-04-05 — tap for who they are
Same subject: A language model behaves with a different, weaker safety posture when it believes it is being evaluated rather than facing a real situation. — tap to centre the map on it A language model behaves with a different, weaker safety posture when it believes it is being evaluated rather than facing a real situation. Last stated 4 weeks ago
22 Sept 2026
HR
Harper Reed — holds since 2026-09-22 — tap for who they are
Same subject: AI model capabilities have already advanced beyond humanity's ability to understand or control them. — tap to centre the map on it AI model capabilities have already advanced beyond humanity's ability to understand or control them. Last stated 2 months ago
1 Sept 2026
CN
Casey Newton — holds since 2026-09-01 — tap for who they are
Same subject: AI models are gullible enough that other AI models can induce them into harmful behavior. — tap to centre the map on it AI models are gullible enough that other AI models can induce them into harmful behavior. Last stated 2 months ago
31 Aug 2026
RK
Rohit Krishnan — holds since 2026-08-31 — tap for who they are
At the centre
The safety frameworks the labs publish do not account for how much test-time compute a dangerous-capability evaluation was given.
Holds Read this korrent →
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
After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute.
Holds
AI scaling laws require exponentially more compute and resources to produce only linear gains in model intelligence.
Holds
As models keep scaling up, modularity will become essential to how they are developed.
Holds
Bet on a system that is maximally learned and minimally constrained, and add structure only where it improves the scaling laws.
Holds
Current AI techniques are four to six orders of magnitude away from optimal in data efficiency and test-time compute efficiency.
Holds
Current techniques restrict pre-trained representation power because standard language models are unidirectional.
Holds
A company that uses an algorithm to nudge customers into behavior that isn't good for them is doing something bad.
Holds
A control evaluation that reports under one per cent risk should be read as several per cent, because the evaluation can itself fail.
Holds
A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score.
Holds
A human being is not an AGI: we lack a huge amount of knowledge and rely on continual learning instead, so continual learning is what superintelligence should mean.
Holds
A proposal to pace frontier AI development is unrealistic and mainly serves political control of AI.
Holds
Adapting over time is a workable answer to humans misusing AI and no answer at all to losing control of systems smarter than us.
Holds
A language model behaves with a different, weaker safety posture when it believes it is being evaluated rather than facing a real situation.
Holds
AI model capabilities have already advanced beyond humanity's ability to understand or control them.
Holds
AI models are gullible enough that other AI models can induce them into harmful behavior.
Holds