No signs of AI payoff outside the tech sector

Commenters mostly treated the missing economy-wide payoff as a timing and deployment problem, not a clean verdict on AI’s usefulness. Software is the easiest first target: code has rich training traces, established tooling, automated tests, and engineers who can spot when a model goes off the rails. Physical industries, by contrast, need capital investment, workflow redesign, safety controls, and institutions willing to absorb risk. Several readers compared this lag to earlier general-purpose technologies whose gains appeared only after organizations rebuilt around them. But the thread was not uniformly patient. Skeptics argued that widespread instant access to LLMs weakens the infrastructure-lag analogy, and pointed to customer support, insurance administration, warehouses, and education as domains where polished demonstrations have not become reliable production automation. A recurring explanation was validation: coding agents can be fenced in by tests, while many real-world decisions have slow, ambiguous, or costly feedback. Some also worried that AI is accelerating low-value corporate output—more messages, documents, and dashboards—rather than outcomes anyone measures. There were concrete signs of value outside pure software, including clinical transcription, internal business automation, and falling costs in video and 3D asset creation. Yet anecdotal workflow failures, especially document extraction and unreliable chatbots, kept the mood cautious. The broad conclusion was that useful capabilities exist, but dependable, economy-moving adoption remains unproven.