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← Ordinary learning from experience makes organizations abandon things…
17 connected korrents · 16 moments from 29 Sept 2018 to 18 Sept 2026.
Everything filed under scaling laws
scaling laws
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open source
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OpenAI
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innovation
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management
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AI agents 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: Ordinary learning from experience makes organizations abandon things after early failure, so good things are dropped too soon Ordinary learning from experience makes organizations abandon things after early failure, so good things are dropped too soon
JM
James G. March — holds since c. 2013 — tap for who they are
Same subject: Learning from failure accumulates as psychic scar tissue that narrows what a person is willing to consider as they get older. — tap to centre the map on it Learning from failure accumulates as psychic scar tissue that narrows what a person is willing to consider as they get older. Last stated 5 years ago
25 Jan 2022
MA
Marc Andreessen — holds since 2022-01-25 — tap for who they are
Same subject: A company that does not learn from the work it does would probably eventually lose its advantage. — tap to centre the map on it A company that does not learn from the work it does would probably eventually lose its advantage. Last stated 3 weeks ago
10 Sept 2026
KS
Karri Saarinen — holds since 2026-09-10 — tap for who they are
Same subject: A system trained only on what is already known cannot produce genuine surprises, because a known thing stops being a surprise. — tap to centre the map on it A system trained only on what is already known cannot produce genuine surprises, because a known thing stops being a surprise. Last stated 2 weeks ago
18 Sept 2026
CD
Cory Doctorow — holds since 2026-09-18 — tap for who they are
Same subject: A business is more likely to fail from taking on too much complexity than from lacking resources. — tap to centre the map on it A business is more likely to fail from taking on too much complexity than from lacking resources. Last stated 8 years ago
29 Sept 2018
TG
Tren Griffin — holds since 2018-09-29 — tap for who they are
Same subject: In iterative problem-solving, each failure has value because it rules out one way that doesn't work. — tap to centre the map on it In iterative problem-solving, each failure has value because it rules out one way that doesn't work. Last stated 8 months ago
6 Feb 2026
AK
Arvid Kahl — holds since 2026-02-06 — tap for who they are
Same subject: Long-running agents fail because they drift off the distribution they were trained on, and degrade further the farther out they get. — tap to centre the map on it Long-running agents fail because they drift off the distribution they were trained on, and degrade further the farther out they get. Last stated 2 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — tap for who they are
Same subject: You only fail if you stop; iteration that keeps going is not failure, it is learning. — tap to centre the map on it You only fail if you stop; iteration that keeps going is not failure, it is learning. Last stated 4 months ago
7 Jun 2026
TF
Tony Fadell — holds since 2026-06-07 — 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 12 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. — tap to centre the map on it A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
Same subject: A technique that lets an AI model's reasoning shift outside its visible Chain of Thought is dangerous, both because it works and because a leading lab is willing to deploy it. — tap to centre the map on it A technique that lets an AI model's reasoning shift outside its visible Chain of Thought is dangerous, both because it works and because a leading lab is willing to deploy it. Last stated 4 weeks ago
3 Sept 2026
ZM
Zvi Mowshowitz — holds since 2026-09-03 — tap for who they are
Same subject: Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers. — tap to centre the map on it Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — 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: A business making hundreds of millions from an open source project owes it money or contribution, and can be refused its trademarks if it gives neither. — tap to centre the map on it A business making hundreds of millions from an open source project owes it money or contribution, and can be refused its trademarks if it gives neither. Last stated 2 years ago
26 Sept 2024
MM
Matt Mullenweg — holds since 2024-09-26 — tap for who they are
Same subject: A company that will not accept a project’s terms is free to go and use a more permissive project instead. — tap to centre the map on it A company that will not accept a project’s terms is free to go and use a more permissive project instead. Last stated 2 years ago
26 Sept 2024
MM
Matt Mullenweg — holds since 2024-09-26 — tap for who they are
Same subject: A contribution is a cost to the maintainer, not a gift: it is taking part, not merely using the code, that consumes a project’s scarcest resource. — tap to centre the map on it A contribution is a cost to the maintainer, not a gift: it is taking part, not merely using the code, that consumes a project’s scarcest resource. Last stated 5 years ago
17 Mar 2022
FV
Filippo Valsorda — holds since 2022-03-17 — tap for who they are
NA
Nadia Asparouhova — holds since 2019-03-14 — tap for who they are
At the centre
Ordinary learning from experience makes organizations abandon things after early failure, so good things are dropped too soon
Last stated c. 2013
Holds Read this korrent →
Learning from failure accumulates as psychic scar tissue that narrows what a person is willing to consider as they get older.
Holds
A company that does not learn from the work it does would probably eventually lose its advantage.
Holds
A system trained only on what is already known cannot produce genuine surprises, because a known thing stops being a surprise.
Holds
A business is more likely to fail from taking on too much complexity than from lacking resources.
Holds
In iterative problem-solving, each failure has value because it rules out one way that doesn't work.
Holds
Long-running agents fail because they drift off the distribution they were trained on, and degrade further the farther out they get.
Holds
You only fail if you stop; iteration that keeps going is not failure, it is learning.
Holds
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
A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests.
Holds
A technique that lets an AI model's reasoning shift outside its visible Chain of Thought is dangerous, both because it works and because a leading lab is willing to deploy it.
Holds
Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers.
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 business making hundreds of millions from an open source project owes it money or contribution, and can be refused its trademarks if it gives neither.
Holds
A company that will not accept a project’s terms is free to go and use a more permissive project instead.
Holds
A contribution is a cost to the maintainer, not a gift: it is taking part, not merely using the code, that consumes a project’s scarcest resource.
Holds