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ai-philosophy
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Reality Cannot Be Turned Into Mathematics
Landgrebe and Smith argue non-ergodic systems can never be fully modeled, therefore AI will fail outside regular patterns. The physics is sound; the conclusion isn't. Their own combustion engine example defeats them: engineering succeeds at the macro-ergodic layer of non-ergodic systems, which is exactly what useful AI does. The buried insight is better than the headline thesis: every AI use case has an ergodic component and a non-ergodic component. The companies burning cash are the ones that can't tell which is which.
Wittgenstein's Apocalypse
Stern applies Wittgenstein's later philosophy to LLMs: the real threat isn't superintelligence but reinforcing a false mechanistic model of meaning. The strongest move in the piece is also its blind spot: "meaning is use" is the best argument against AI understanding and the best pragmatist defense of AI utility. If people use LLMs meaningfully, that's meaning on Wittgenstein's own terms. The critic's sharpest weapon cuts both ways.
What's left for us after AI? China's thinkers have very different answers
Bertrand argues the Western fear of AI rests on a culturally specific premise that intelligence is what makes us human, as in Homo sapiens. Liu Yongmou inverts it: humanity's biggest problem is the machinization of humans, not the humanization of machines. If coordination work is the first to go, as in Schmidt's flattened firms, the open question is what remains of a person's worth once usefulness is no longer the measure.
Four Direct, Reasonable, and Important AI Questions (Which the AI People Will Ignore)
deBoer holds AI maximalists to their own yardstick and finds daily life in 2026 barely distinguishable from 2021. His best argument is that cancer is limited by tumor recurrence, toxicity and patient dropout, and curing every cancer would add only about three years of life expectancy. That sets the ceiling for Schmidt's claim that disease could be cured in 15-20 years: where the constraint is physical, faster thinking buys little.