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← Shrinking attention spans are reshaping real-world business models, not just digital media formats.
17 connected korrents · 17 moments on record from 5 Nov 2019 to 15 Sept 2026.
Everything filed under scaling laws
scaling laws
Everything filed under measuring intelligence
measuring intelligence
Everything filed under reinforcement learning
reinforcement learning
Everything filed under LLMs
LLMs
Everything filed under AI and human skill
AI and human skill
Everything filed under AI alignment
AI alignment
Everything filed under management
management
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: Shrinking attention spans are reshaping real-world business models, not just digital media formats. Shrinking attention spans are reshaping real-world business models, not just digital media formats. Last stated 7 months ago
16 Feb 2026
SW
swyx — holds since 2026-02-16 — tap for who they are
Same subject: Information is more fragmented than ever and simultaneously more available than ever, and language models widen that access further. — tap to centre the map on it Information is more fragmented than ever and simultaneously more available than ever, and language models widen that access further. Last stated 3 months ago
9 Jun 2026
BG
Bill Gurley — holds since 2026-06-09 — tap for who they are
Same subject: Models are enormous only because the internet is bad: most of that capacity does memory work, and a better dataset would need far less of it. — tap to centre the map on it Models are enormous only because the internet is bad: most of that capacity does memory work, and a better dataset would need far less of it. Last stated 11 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: Renouncing your own cognitive ability because the models are good is premature, because companies still pay an enormous premium for it. — tap to centre the map on it Renouncing your own cognitive ability because the models are good is premature, because companies still pay an enormous premium for it. Last stated 2 months ago
31 Jul 2026
PC
Patrick Collison — holds since 2026-07-31 — tap for who they are
Same subject: Chain-of-thought monitoring is getting less reliable, not more, as models grow more capable. — tap to centre the map on it Chain-of-thought monitoring is getting less reliable, not more, as models grow more capable. Last stated 2 weeks ago
6 Sept 2026
JP
Jakub Pachocki — holds since 2026-09-06 — tap for who they are
Same subject: Hybrid and sparse-attention model architectures will become more widely adopted as the ecosystem catches up. — tap to centre the map on it Hybrid and sparse-attention model architectures will become more widely adopted as the ecosystem catches up. Last stated 2 weeks ago
8 Sept 2026
NL
Nathan Lambert — holds since 2026-09-08 — tap for who they are
Same subject: Economic growth from AI waits on change management, not on model capability: the work artifact and the workflow have to change first. — tap to centre the map on it Economic growth from AI waits on change management, not on model capability: the work artifact and the workflow have to change first. Last stated 10 months ago
12 Nov 2025
SN
Satya Nadella — holds since 2025-11-12 — tap for who they are
Same subject: In a fast-changing industry, anyone genuinely keeping up will change their mind as the developments happen. — tap to centre the map on it In a fast-changing industry, anyone genuinely keeping up will change their mind as the developments happen. Last stated 2 weeks ago
7 Sept 2026
GO
Gergely Orosz — holds since 2026-09-07 — tap for who they are
Same subject: Some firms are laying people off mainly so as not to look behind on AI, in a cascade of keeping up with the Joneses. — tap to centre the map on it Some firms are laying people off mainly so as not to look behind on AI, in a cascade of keeping up with the Joneses. Last stated 4 months ago
4 Jun 2026
AI
Alex Imas — 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 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 6 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — tap for who they are
Same subject: A true artificial general intelligence cannot exist without being recognized as a moral subject. — tap to centre the map on it A true artificial general intelligence cannot exist without being recognized as a moral subject. Last stated a year ago
10 Jun 2025
SH
Samuel Hammond — holds since 2025-06-10 — tap for who they are
Same subject: A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model. — tap to centre the map on it A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model. Last stated 6 days ago
15 Sept 2026
PG
Paul Graham — holds since 2026-09-15 — tap for who they are
Same subject: Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect. — tap to centre the map on it Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect. Last stated 7 years ago
5 Nov 2019
FC
François Chollet — holds since 2019-11-05 — 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 2 months ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
Same subject: A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. — tap to centre the map on it A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. Last stated 10 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — tap for who they are
Same subject: Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model. — tap to centre the map on it Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model. Last stated 2 months ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
At the centre
Shrinking attention spans are reshaping real-world business models, not just digital media formats.
Holds Read this korrent →
Information is more fragmented than ever and simultaneously more available than ever, and language models widen that access further.
Holds
Models are enormous only because the internet is bad: most of that capacity does memory work, and a better dataset would need far less of it.
Holds
Renouncing your own cognitive ability because the models are good is premature, because companies still pay an enormous premium for it.
Holds
Chain-of-thought monitoring is getting less reliable, not more, as models grow more capable.
Holds
Hybrid and sparse-attention model architectures will become more widely adopted as the ecosystem catches up.
Holds
Economic growth from AI waits on change management, not on model capability: the work artifact and the workflow have to change first.
Holds
In a fast-changing industry, anyone genuinely keeping up will change their mind as the developments happen.
Holds
Some firms are laying people off mainly so as not to look behind on AI, in a cascade of keeping up with the Joneses.
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
A true artificial general intelligence cannot exist without being recognized as a moral subject.
Holds
A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model.
Holds
Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect.
Holds
A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel.
Holds
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model.
Holds