All three philosophical critiques of AI share the same structure: identify something AI can't do, conclude AI fails. What they keep finding instead is that the valuable layer isn't the hard part they're pointing at. It's the ergodic slice of a non-ergodic problem, or the use that constitutes meaning, or the infrastructure that translates a discovery into something institutions can actually touch.
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.
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.
Everyone's racing to build AI that does science. Nobody's building infrastructure for humans to use what it discovers. The bottleneck isn't discovery: it's deployment through human institutions. Chess went from centaur to post-human in 20 years; science will follow the same arc, but the output must still pass through labs, regulators, and clinical infrastructure that speak human. The entity that owns the translation layer between AI-generated and human-implementable science captures value from every breakthrough that needs to reach the physical world.
