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← AI self-improvement may not be powerful enough to overcome diminishing returns.
17 connected korrents · 19 moments from 11 Oct 2018 to 27 Sept 2026.
Everything filed under recursive self-improvement
recursive self-improvement
Everything filed under scaling laws
scaling laws
Everything filed under LLMs
LLMs
Everything filed under AI and jobs
AI and jobs 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: AI self-improvement may not be powerful enough to overcome diminishing returns. AI self-improvement may not be powerful enough to overcome diminishing returns. Last stated 5 days ago
27 Sept 2026
RN
Ramez Naam — holds since 2026-09-27 — tap for who they are
Same subject: The AI self-improvement loop needs to be significantly stronger to achieve a runaway intelligence explosion. — tap to centre the map on it The AI self-improvement loop needs to be significantly stronger to achieve a runaway intelligence explosion. Last stated 5 days ago
27 Sept 2026
NS
Noah Smith — holds since 2026-09-27 — tap for who they are
Same subject: An end-to-end self-improving AI is probably possible, but it is not even desirable, because it is a hard-takeoff scenario. — tap to centre the map on it An end-to-end self-improving AI is probably possible, but it is not even desirable, because it is a hard-takeoff scenario. Last stated a year ago
23 Jul 2025
DH
Demis Hassabis — holds since 2025-07-23 — tap for who they are
Same subject: The current speed of AI progress could be sustained all the way into recursive self-improvement. — tap to centre the map on it The current speed of AI progress could be sustained all the way into recursive self-improvement. Last stated 4 weeks ago
6 Sept 2026
JP
Jakub Pachocki — holds since 2026-09-06 — tap for who they are
Same subject: Recursive self-improvement will increase AI capability very quickly from here. — tap to centre the map on it Recursive self-improvement will increase AI capability very quickly from here. Last stated 2 months ago
3 Aug 2026
CP
Chamath Palihapitiya — holds since 2026-08-03 — tap for who they are
Same subject: Once AI can do AI research, a single year should deliver four or five years worth of AI progress. — tap to centre the map on it Once AI can do AI research, a single year should deliver four or five years worth of AI progress. Last stated 2 months ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: The one safe bet about AI is that models will improve, so what a company has to build is an organisation that gets better as models get better. — tap to centre the map on it The one safe bet about AI is that models will improve, so what a company has to build is an organisation that gets better as models get better. Last stated 12 months ago
16 Oct 2025
DF
Dylan Field — holds since 2025-10-16 — tap for who they are
Same subject: An AI loop can climb to a local maximum but then plateaus, and human intuition is needed to find the next hill. — tap to centre the map on it An AI loop can climb to a local maximum but then plateaus, and human intuition is needed to find the next hill. Last stated 4 weeks ago
6 Sept 2026
AA
Anish Acharya — holds since 2026-09-06 — tap for who they are
Same subject: Humans cannot out-compete AI by trying to match or exceed its raw output volume. — tap to centre the map on it Humans cannot out-compete AI by trying to match or exceed its raw output volume. Last stated 4 months ago
10 Jun 2026
JS
Jasmine Sun — holds since 2026-06-10 — 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 10 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 3 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: AI progress is faster than people expect, and ordinary scaling can be enough to solve problems that looked very hard. — tap to centre the map on it AI progress is faster than people expect, and ordinary scaling can be enough to solve problems that looked very hard. Last stated 2 years ago
2 Feb 2025
AC
Ajeya Cotra — holds since 2023-08-29 — tap for who they are
SA
Scott Alexander — holds since 2022-09-12 — tap for who they are
MB
Miles Brundage — holds since 2025-02-02 — tap for who they are
Same subject: As models keep growing, AI is running out of enough high-quality unique training tokens to keep up. — tap to centre the map on it As models keep growing, AI is running out of enough high-quality unique training tokens to keep up. Last stated 3 months ago
24 Jun 2026
LW
Lilian Weng — holds since 2026-06-24 — 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: Mixture-of-experts models can scale stably but begin to show fundamental limits as they are scaled further. — tap to centre the map on it Mixture-of-experts models can scale stably but begin to show fundamental limits as they are scaled further. Last stated 6 years ago
4 Feb 2021
GW
Gwern — holds since 2021-02-04 — tap for who they are
Same subject: Pre-training is a crappy evolution: the practically buildable substitute for the process that gave animals their built-in hardware. — tap to centre the map on it Pre-training is a crappy evolution: the practically buildable substitute for the process that gave animals their built-in hardware. Last stated 11 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — 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
At the centre
AI self-improvement may not be powerful enough to overcome diminishing returns.
Holds Read this korrent →
The AI self-improvement loop needs to be significantly stronger to achieve a runaway intelligence explosion.
Holds
An end-to-end self-improving AI is probably possible, but it is not even desirable, because it is a hard-takeoff scenario.
Holds
The current speed of AI progress could be sustained all the way into recursive self-improvement.
Holds
Recursive self-improvement will increase AI capability very quickly from here.
Holds
Once AI can do AI research, a single year should deliver four or five years worth of AI progress.
Holds
The one safe bet about AI is that models will improve, so what a company has to build is an organisation that gets better as models get better.
Holds
An AI loop can climb to a local maximum but then plateaus, and human intuition is needed to find the next hill.
Holds
Humans cannot out-compete AI by trying to match or exceed its raw output volume.
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
AI progress is faster than people expect, and ordinary scaling can be enough to solve problems that looked very hard.
Holds
As models keep growing, AI is running out of enough high-quality unique training tokens to keep up.
Holds
Current techniques restrict pre-trained representation power because standard language models are unidirectional.
Holds
Mixture-of-experts models can scale stably but begin to show fundamental limits as they are scaled further.
Holds
Pre-training is a crappy evolution: the practically buildable substitute for the process that gave animals their built-in hardware.
Holds
Scaling the 2019-2023 recipe — same architecture, bigger model, more data — is not enough; further progress depends on new architectural ideas.
Holds