The Man Who Looked at TSMC and Thought: Fine, I’ll Build My Own
Musk's chip-factory ambition becomes a case study in impatience, vertical integration, and the difference between strategy decks and industrial action.
21 posts
Musk's chip-factory ambition becomes a case study in impatience, vertical integration, and the difference between strategy decks and industrial action.
Anthropic's labor research suggests AI is not replacing whole jobs so much as fragmenting knowledge work task by task.
Generative AI did not invent office busywork; it made the fakery cheaper, faster, and much harder to deny.
RSL 1.0 proposes a machine-readable licensing layer for the AI web, giving publishers a clearer way to state usage terms.
A loyal Apple user's impatience becomes an argument that Siri upgrades are not enough in the age of general intelligence.
In an age of ubiquitous knowledge, the post weighs adaptability against memory and asks what learning should still mean.
If AGI makes money less meaningful, why are AI companies raising so much of it? The contradiction becomes the story.
The AI boom is compared with dot-com excess, asking which parts are durable infrastructure and which are speculative heat.
AGI forces a hard look at universal basic income when work may no longer be society's main distribution mechanism.
AI hype is framed as an economic mirage, propping up confidence while hiding fragile assumptions beneath the spectacle.
AlphaEvolve suggests algorithmic discovery may reshape science and industry by evolving solutions humans would not design directly.
Uncensored models promise creative freedom and research access, but also expose the tradeoffs that safety layers usually conceal.
Saturation appears across markets, research, and models, revealing what happens when growth hits limits and novelty thins out.
DeepSeek R1 disrupts the AI cost narrative, challenging Silicon Valley's assumption that frontier capability requires extravagant spending.
The opening part of a benchmark series asks what LLM evaluations really measure and why the numbers often mislead.
Part two examines benchmark methods themselves, exposing the assumptions behind the scores used to compare language models.
Part three moves from benchmark scores to application areas, asking where LLM performance actually matters in practice.
Part four digs into the good, bad, and misleading sides of benchmark results and their interpretation.
Part five steps beyond scores to consider real-world limitations, reliability, and practical model behavior.
The final benchmark essay looks toward better evaluation methods that test usefulness rather than leaderboard theater.
Aleph Alpha and OpenAI are compared as two very different strategies in the market for language models.