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← All a thinking machine needs is a big slab of cheap silicon to make…
17 connected korrents · 14 moments on record from 19 Jun 2024 to 26 Aug 2026.
Everything filed under AI and jobs
AI and jobs
Everything filed under scaling laws
scaling laws
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reinforcement learning
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AGI
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product management
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solar
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semiconductors
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taste 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: All a thinking machine needs is a big slab of cheap silicon to make the power and a small slab of expensive silicon to do the thinking. All a thinking machine needs is a big slab of cheap silicon to make the power and a small slab of expensive silicon to do the thinking. Last stated a year ago
15 Aug 2025
CH
Casey Handmer — holds since 2025-08-15 — tap for who they are
Same subject: Inference was never going to be the easy, cheap half of AI, because inference is thinking, and thinking is far harder than reading. — tap to centre the map on it Inference was never going to be the easy, cheap half of AI, because inference is thinking, and thinking is far harder than reading. Last stated 6 months ago
23 Mar 2026
JH
Jensen Huang — holds since 2026-03-23 — tap for who they are
Same subject: Economics cannot say what AI will do to work because the necessary data does not exist; what is needed is a Manhattan Project for data. — tap to centre the map on it Economics cannot say what AI will do to work because the necessary data does not exist; what is needed is a Manhattan Project for data. Last stated 3 months ago
4 Jun 2026
AI
Alex Imas — holds since 2026-06-04 — tap for who they are
Same subject: The dangerous concentration of power in AI comes from who can afford the compute, not from who holds the model weights. — tap to centre the map on it The dangerous concentration of power in AI comes from who can afford the compute, not from who holds the model weights. Last stated 2 years ago
19 Jun 2024
AS
Aravind Srinivas — holds since 2024-06-19 — tap for who they are
Same subject: If AI can build the new theory that crystallises how to think about a subject, it would be surprising if that capability did not spill into the rest of the economy. — tap to centre the map on it If AI can build the new theory that crystallises how to think about a subject, it would be surprising if that capability did not spill into the rest of the economy. Last stated 2 months ago
30 Jun 2026
GS
Grant Sanderson — holds since 2026-06-30 — tap for who they are
Same subject: AI should be used as a failure machine that tests many ideas cheaply, not as a way to build one idea faster. — tap to centre the map on it AI should be used as a failure machine that tests many ideas cheaply, not as a way to build one idea faster. Last stated 3 months ago
14 Jun 2026
MP
Mark Pincus — holds since 2026-06-14 — tap for who they are
Same subject: The binding constraint on the AI build-out is chips, not power, which is why not having a solar panel factory will never be on the critical path. — tap to centre the map on it The binding constraint on the AI build-out is chips, not power, which is why not having a solar panel factory will never be on the critical path. Last stated a year ago
15 Aug 2025
CH
Casey Handmer — holds since 2025-08-15 — tap for who they are
Same subject: The binding constraints on AI's growth are power, helium and memory — and almost nobody has noticed the helium one. — tap to centre the map on it The binding constraints on AI's growth are power, helium and memory — and almost nobody has noticed the helium one. Last stated 3 months ago
18 Jun 2026
LT
Lip-Bu Tan — holds since 2026-06-18 — tap for who they are
Same subject: Cheap intelligence does not make agency cheap, and these tools may mostly compound the advantage of people who already have the skill, taste, money and time. — tap to centre the map on it Cheap intelligence does not make agency cheap, and these tools may mostly compound the advantage of people who already have the skill, taste, money and time. Last stated 4 weeks ago
14 Aug 2026
SP
Sunil Pai — holds since 2026-08-14 — tap for who they are
Same subject: A productivity improvement means fewer people doing the same job, and that should be said plainly by the people defending it as good. — tap to centre the map on it A productivity improvement means fewer people doing the same job, and that should be said plainly by the people defending it as good. Last stated 2 weeks ago
26 Aug 2026
DH
David Heinemeier Hansson — holds since 2026-08-26 — tap for who they are
Same subject: A universal basic income is a dangerous power-sharing arrangement, because it puts everyone's basic needs at the mercy of whoever holds office. — tap to centre the map on it A universal basic income is a dangerous power-sharing arrangement, because it puts everyone's basic needs at the mercy of whoever holds office. Last stated 3 months ago
4 Jun 2026
AI
Alex Imas — holds since 2026-06-04 — tap for who they are
Same subject: AI could displace half of all entry-level white-collar jobs within one to five years, even while it accelerates economic growth. — tap to centre the map on it AI could displace half of all entry-level white-collar jobs within one to five years, even while it accelerates economic growth. Last stated 8 months ago
1 Jan 2026
DA
Dario Amodei — holds since 2026-01-01 — 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: A model's capability is now a function of how much money you spend on it, so asking what a model can do means nothing until you name a budget. — tap to centre the map on it A model's capability is now a function of how much money you spend on it, so asking what a model can do means nothing until you name a budget. Last stated 2 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — 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 a month 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 a month ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
At the centre
All a thinking machine needs is a big slab of cheap silicon to make the power and a small slab of expensive silicon to do the thinking.
Holds CH Casey HandmerRead this korrent →
Inference was never going to be the easy, cheap half of AI, because inference is thinking, and thinking is far harder than reading.
Holds JH Jensen Huang
Economics cannot say what AI will do to work because the necessary data does not exist; what is needed is a Manhattan Project for data.
Holds AI Alex Imas
The dangerous concentration of power in AI comes from who can afford the compute, not from who holds the model weights.
Holds AS Aravind Srinivas
If AI can build the new theory that crystallises how to think about a subject, it would be surprising if that capability did not spill into the rest of the economy.
Holds GS Grant Sanderson
AI should be used as a failure machine that tests many ideas cheaply, not as a way to build one idea faster.
Holds MP Mark Pincus
The binding constraint on the AI build-out is chips, not power, which is why not having a solar panel factory will never be on the critical path.
Holds CH Casey Handmer
The binding constraints on AI's growth are power, helium and memory — and almost nobody has noticed the helium one.
Holds LT Lip-Bu Tan
Cheap intelligence does not make agency cheap, and these tools may mostly compound the advantage of people who already have the skill, taste, money and time.
Holds
A productivity improvement means fewer people doing the same job, and that should be said plainly by the people defending it as good.
Holds
A universal basic income is a dangerous power-sharing arrangement, because it puts everyone's basic needs at the mercy of whoever holds office.
Holds AI Alex Imas
AI could displace half of all entry-level white-collar jobs within one to five years, even while it accelerates economic growth.
Holds DA Dario Amodei
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
A model's capability is now a function of how much money you spend on it, so asking what a model can do means nothing until you name a budget.
Holds NB Noam Brown
A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel.
Holds DD Dmitri Dolgov
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 DD Dmitri Dolgov