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← The gap between chimps and humans is no evidence that a small increase…
17 connected korrents · 13 moments from 24 Feb 2018 to 25 Sept 2026.
Everything filed under AGI
AGI
Everything filed under scaling laws
scaling laws
Everything filed under evolutionary biology
evolutionary biology
Everything filed under LLMs
LLMs 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 Same subject Read this korrent: 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. 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
Cannot both be true: Intelligence is going to scale by exactly one thing, and that thing is compute. — tap to centre the map on it 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
Same subject: Humanity's advantage is accumulated shared knowledge rather than higher intelligence — and AI models, which vanish with their context window, have none of it. — tap to centre the map on it Humanity's advantage is accumulated shared knowledge rather than higher intelligence — and AI models, which vanish with their context window, have none of it. Last stated 4 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: Cultural evolution, not biology, is the source of whatever makes humans distinctively special. — tap to centre the map on it Cultural evolution, not biology, is the source of whatever makes humans distinctively special. Last stated 4 weeks ago
25 Sept 2026
RH
Robin Hanson — holds since 2026-09-25 — tap for who they are
Same subject: Model intelligence should speciate like the animal kingdom rather than converge on one oracle that knows everything. — tap to centre the map on it Model intelligence should speciate like the animal kingdom rather than converge on one oracle that knows everything. Last stated 7 months ago
20 Mar 2026
AK
Andrej Karpathy — holds since 2026-03-20 — tap for who they are
Same subject: Because we do not have good alignment technology, we are choosing to build an alien mind with its own values and gamble on it instead of building a tool. — tap to centre the map on it Because we do not have good alignment technology, we are choosing to build an alien mind with its own values and gamble on it instead of building a tool. Last stated 2 months ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: That people are good at maths and coding, which evolution never selected for, shows human learning is not a complicated evolutionary prior but something more fundamental. — tap to centre the map on it That people are good at maths and coding, which evolution never selected for, shows human learning is not a complicated evolutionary prior but something more fundamental. Last stated 11 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: AI capabilities are jagged rather than smooth, so the different definitions of human-level AI will be met years apart rather than together. — tap to centre the map on it AI capabilities are jagged rather than smooth, so the different definitions of human-level AI will be met years apart rather than together. Last stated 7 months ago
6 Apr 2026
HT
Helen Toner — holds since 2026-04-06 — tap for who they are
Same subject: Technologies are almost always preceded by slightly worse versions of themselves, and there is no reason for AI to be the exception. — tap to centre the map on it Technologies are almost always preceded by slightly worse versions of themselves, and there is no reason for AI to be the exception. Last stated 9 years ago
24 Feb 2018
PC
Paul Christiano — holds since 2018-02-24 — 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's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human. — tap to centre the map on it A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human. Last stated 2 years ago
22 Apr 2024
SC
Sean Carroll — holds since 2024-04-22 — tap for who they are
Same subject: A selection signal can be validated without a significance threshold, by measuring how far it is enriched for mutations an entirely independent study says affect a trait. — tap to centre the map on it A selection signal can be validated without a significance threshold, by measuring how far it is enriched for mutations an entirely independent study says affect a trait. Last stated 5 months ago
8 May 2026
DR
David Reich — holds since 2026-05-08 — tap for who they are
Same subject: Adaptive evolution tends to achieve even a simple, fixed objective through complicated behavior. — tap to centre the map on it Adaptive evolution tends to achieve even a simple, fixed objective through complicated behavior. Last stated 5 months ago
4 Jun 2026
SW
Stephen Wolfram — holds since 2026-06-04 — 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: Reinforcement learning from verifiable rewards does not reliably carry a model's pretrained ethical understanding into its actual behavior. — tap to centre the map on it Reinforcement learning from verifiable rewards does not reliably carry a model's pretrained ethical understanding into its actual behavior. Last stated 2 months ago
11 Aug 2026
SH
Samuel Hammond — holds since 2026-08-11 — tap for who they are
Same subject: The safety frameworks the labs publish do not account for how much test-time compute a dangerous-capability evaluation was given. — tap to centre the map on it 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
At the centre
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 Read this korrent →
Intelligence is going to scale by exactly one thing, and that thing is compute.
Holds
Humanity's advantage is accumulated shared knowledge rather than higher intelligence — and AI models, which vanish with their context window, have none of it.
Holds
Cultural evolution, not biology, is the source of whatever makes humans distinctively special.
Holds
Model intelligence should speciate like the animal kingdom rather than converge on one oracle that knows everything.
Holds
Because we do not have good alignment technology, we are choosing to build an alien mind with its own values and gamble on it instead of building a tool.
Holds
That people are good at maths and coding, which evolution never selected for, shows human learning is not a complicated evolutionary prior but something more fundamental.
Holds
AI capabilities are jagged rather than smooth, so the different definitions of human-level AI will be met years apart rather than together.
Holds
Technologies are almost always preceded by slightly worse versions of themselves, and there is no reason for AI to be the exception.
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's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human.
Holds
A selection signal can be validated without a significance threshold, by measuring how far it is enriched for mutations an entirely independent study says affect a trait.
Holds
Adaptive evolution tends to achieve even a simple, fixed objective through complicated behavior.
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
Reinforcement learning from verifiable rewards does not reliably carry a model's pretrained ethical understanding into its actual behavior.
Holds
The safety frameworks the labs publish do not account for how much test-time compute a dangerous-capability evaluation was given.
Holds