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← Bigger is smarter, always: bringing your special domain knowledge to…
17 connected korrents · 17 moments on record from 1 Oct 2024 to 19 Aug 2026.
Everything filed under AI and human skill
AI and human skill
Everything filed under scaling laws
scaling laws
Everything filed under reinforcement learning
reinforcement learning
Everything filed under market efficiency
market efficiency 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: Bigger is smarter, always: bringing your special domain knowledge to the model is the wrong side of the bitter lesson. Bigger is smarter, always: bringing your special domain knowledge to the model is the wrong side of the bitter lesson. Last stated 6 months ago
11 Mar 2026
SY
Steve Yegge — holds since 2026-03-11 — tap for who they are
Same subject: “Work on what you know” is the worst advice there is: knowledge can be acquired, but what you love cannot be changed. — tap to centre the map on it “Work on what you know” is the worst advice there is: knowledge can be acquired, but what you love cannot be changed. Last stated a month ago
28 Jul 2026
BS
Blake Scholl — holds since 2026-07-28 — tap for who they are
Same subject: The Bitter Lesson is misread as being about scale; it is about refusing to add human priors, because a clever specific fix always loses to a simple scalable one. — tap to centre the map on it The Bitter Lesson is misread as being about scale; it is about refusing to add human priors, because a clever specific fix always loses to a simple scalable one. Last stated 2 years ago
3 Feb 2025
NL
Nathan Lambert — holds since 2025-02-03 — tap for who they are
Same subject: The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out. — tap to centre the map on it The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out. Last stated 11 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: Copying a trained digital mind into the next one matters far more than learning from people, because no child can inherit another child's learning. — tap to centre the map on it Copying a trained digital mind into the next one matters far more than learning from people, because no child can inherit another child's learning. Last stated 11 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to. — tap to centre the map on it A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to. Last stated 2 months ago
15 Jul 2026
DH
Dex Horthy — holds since 2026-07-15 — tap for who they are
Same subject: Intelligence is becoming a choice rather than a byproduct of daily work: there will still be smart people, but only those who choose to be. — tap to centre the map on it Intelligence is becoming a choice rather than a byproduct of daily work: there will still be smart people, but only those who choose to be. Last stated 10 months ago
18 Nov 2025
PG
Paul Graham — holds since 2024-10-01 — tap for who they are
SY
Scott H. Young — holds since 2025-11-18 — 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 2 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: Even the most advanced AI cannot be followed blindly in investing, where value added is zero-sum and what is widely known is therefore worth little. — tap to centre the map on it Even the most advanced AI cannot be followed blindly in investing, where value added is zero-sum and what is widely known is therefore worth little. Last stated 3 months ago
10 Jun 2026
RD
Ray Dalio — holds since 2026-06-10 — tap for who they are
Same subject: AI can be a shortcut around thinking or a way of thinking more rigorously, and for your core job function only the second one is wanted. — tap to centre the map on it AI can be a shortcut around thinking or a way of thinking more rigorously, and for your core job function only the second one is wanted. Last stated 4 weeks ago
12 Aug 2026
CM
Charity Majors — holds since 2026-08-12 — tap for who they are
Same subject: AI should be treated as an instrument to play rather than a tool to use, because there is no purpose to better machines if they do not also produce better humans. — tap to centre the map on it AI should be treated as an instrument to play rather than a tool to use, because there is no purpose to better machines if they do not also produce better humans. Last stated 11 months ago
20 Oct 2025
FC
Frank Chimero — holds since 2025-10-20 — tap for who they are
Same subject: Heavy AI use erodes understanding first and judgement second: cognitive debt turns into cognitive surrender, where the model's answer simply becomes your answer. — tap to centre the map on it Heavy AI use erodes understanding first and judgement second: cognitive debt turns into cognitive surrender, where the model's answer simply becomes your answer. Last stated 3 weeks ago
19 Aug 2026
AO
Addy Osmani — holds since 2026-08-19 — 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 2 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: 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 6 months ago
23 Mar 2026
JH
Jensen Huang — holds since 2026-03-23 — 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: Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago. — tap to centre the map on it Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago. Last stated 6 months ago
20 Mar 2026
AK
Andrej Karpathy — holds since 2026-03-20 — tap for who they are
Same subject: Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving. — tap to centre the map on it Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving. Last stated 11 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
At the centre
Bigger is smarter, always: bringing your special domain knowledge to the model is the wrong side of the bitter lesson.
Holds SY Steve YeggeRead this korrent →
“Work on what you know” is the worst advice there is: knowledge can be acquired, but what you love cannot be changed.
Holds BS Blake Scholl
The Bitter Lesson is misread as being about scale; it is about refusing to add human priors, because a clever specific fix always loses to a simple scalable one.
Holds NL Nathan Lambert
The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out.
Holds
Copying a trained digital mind into the next one matters far more than learning from people, because no child can inherit another child's learning.
Holds RS Richard Sutton
A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to.
Holds DH Dex Horthy
Intelligence is becoming a choice rather than a byproduct of daily work: there will still be smart people, but only those who choose to be.
Holds SY Scott H. Young
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 NB Noam Brown
Even the most advanced AI cannot be followed blindly in investing, where value added is zero-sum and what is widely known is therefore worth little.
Holds
AI can be a shortcut around thinking or a way of thinking more rigorously, and for your core job function only the second one is wanted.
Holds CM Charity Majors
AI should be treated as an instrument to play rather than a tool to use, because there is no purpose to better machines if they do not also produce better humans.
Holds FC Frank Chimero
Heavy AI use erodes understanding first and judgement second: cognitive debt turns into cognitive surrender, where the model's answer simply becomes your answer.
Holds AO Addy Osmani
A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number.
Holds NB Noam Brown
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 DP Dylan Patel
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 JH Jensen Huang
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago.
Holds
Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving.
Holds