What do machines know, and what are we projecting onto them?
Reasoning, language, learning, world models, and the inconvenient variety of things people call intelligence.
DeepMind reports longer task execution, self-correction, whole-body control, and collaboration among different robots.
Agency becomes more consequential when an error can move an object rather than produce a paragraph. Progress in embodied reasoning shifts questions of permission, interruption, and reversibility from interface design into physical space.
An agent that cannot remember remains useful in the way a brilliant stranger can be useful.
David Runciman with Jimmy Wales
Jimmy Wales and David Runciman discuss a public knowledge system built from argument, revision, rules, and improbable amounts of volunteer patience.
Wikipedia shows that trust can emerge from visible process, contestable decisions, and shared maintenance—not only from centralized authority or confident output.
Open the note →Demis Hassabis
Demis Hassabis discusses what today's models still lack: robust reasoning, planning, world models, and scientific discovery.
A frontier claim becomes more useful when its missing capabilities are named: reasoning, planning, world models, and discovery can then be examined rather than merely awaited.
Open the note →Dwarkesh Patel with Dario Amodei
Amodei presents the faster case: continued scaling and reinforcement learning could drive rapid capability growth and equally rapid economic diffusion.
This is the fast trajectory stated by someone spending billions to make it happen—important evidence, and also a fact worth keeping attached to the evidence.
Open the note →Dwarkesh Patel with Ilya Sutskever
Sutskever argues that current models generalize far worse than people and that the next gains may require ideas beyond brute-force pretraining.
It puts the central technical dispute plainly: are today's methods an unfinished recipe, or are they missing an ingredient?
Open the note →Dwarkesh Patel with Andrej Karpathy
Karpathy makes the case that continual learning, robust agents, and the ordinary miracle of human learning remain stubbornly unsolved.
This is the useful brake on very short timelines: a concrete inventory of what models still cannot learn, retain, and do without a human quietly repairing the road ahead.
Open the note →Ed Zitron
A four-part argument against the labor myths, compute demand, software-replacement claims, and financial assumptions surrounding generative AI.
The series supplies a comprehensive adversarial case against the boom's labor and economic narratives. Agreement is optional; answering the evidence is not.
Open the note →No Such Thing As A Fish with Rhys Darby
Robots, railways, lost witches, found phones, and Rhys Darby assemble themselves into something resembling an editorial philosophy.
Robots, railways, lost witches, and found phones do not need a common thesis. Their cheerful refusal to acquire one is the thesis.
Open the note →Dwarkesh Patel with Sholto Douglas and Trenton Bricken
Sholto Douglas and Trenton Bricken look beneath the forecasts at reinforcement learning, long-horizon agents, continual learning, and interpretability.
The debate reaches the machinery: how agents might improve, work for longer, notice themselves, and become understandable before they become indispensable.
Open the note →David Runciman with David Spiegelhalter
David Spiegelhalter explains how a once-suspect theory of probability became a practical way to revise belief.
AI systems turn uncertainty into outputs. Bayesian thinking offers a longer history of how evidence should change confidence—and why confidence is not the same thing as truth.
Open the note →Ed Zitron
DeepSeek becomes a challenge to assumptions about capital intensity, innovation, and the competitive moat of frontier AI laboratories.
A more efficient model tests whether giant capital commitments are an enduring moat or an expensive habit with unusually good public relations.
Open the note →Ed Zitron with Lily Ray
Google's generative search rollout becomes an observable case study in product quality, incumbent incentives, and performing innovation for investors.
This case can be tested without forecasting AGI. Billions already use Google, so the consequences of inserting generative answers into search are available for public inspection.
Open the note →Dwarkesh Patel with Demis Hassabis
Hassabis describes adding search and planning to language models, connecting the AlphaGo lineage to scientific discovery and more capable agents.
It connects the AlphaGo-to-LLM-to-agent story: prediction gains plans, plans gain tools, and a clever system begins to look suspiciously like an institution.
Open the note →No Such Thing As A Fish with Olga Koch
Joni Mitchell, competitive eating, the Dawn supercomputer, and conjugation meet without adult supervision.
Our surrealism generator can combine nouns. It still cannot reproduce the confidence with which this show moves from Joni Mitchell to supercomputing.
Open the note →Andrej Karpathy
Andrej Karpathy explains how language models learn, fail, and become something like a new operating system.
The operating-system metaphor connects language prediction to a general interface for computing, tools, and human intent.
Open the note →David Runciman with Gary Marcus and John Lanchester
A philosopher, a novelist, and a political thinker test what the word 'thinking' contributes to our understanding of machines.
Before debating machine minds, it helps to ask which human capacities have been bundled into the word 'thinking'—and which have quietly been left out.
Open the note →OpenAI
An early look at the chatbot becoming a programmable tool for knowledge work.
The demonstration catches the model leaving the chat box and entering workflows—the step from conversational novelty toward computing platform.
Open the note →Google DeepMind
A machine plays a move no human expected. The board remains still; our idea of intelligence does not.
AlphaGo made machine discovery legible: a system trained on human play found a move that expert intuition had not prepared to see.
Open the note →Conan O'Brien with Bill Hader
Two funny people examine antiquated language, performance, relaxation, crime-show narration, and the mechanics of getting out of their own way.
Two professional overthinkers discuss the importance of not thinking so much.
Open the note →Conan O'Brien with Jeff Goldblum
Goldblum and Conan produce procedural surrealism from pinky rings, funny children, marriage, elocution, and speaking in jazz.
No Such Thing As A Fish
An early title ages suspiciously well while the episode itself ranges across the Sun, fake baseball fans, giant birds' nests, and Hooverball.
The title has acquired unintended relevance. The giant birds' nests and Hooverball remain admirably resistant to trend analysis.
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