Generative AI's Impending Death by a Thousand Rake-Smacks (www.whatwelo.st)

Independent analyst Ed Zitron argues generative AI faces an economic reckoning: the sector is losing money at scale with profits concentrated among hardware suppliers (Nvidia, Dell, Samsung, SK Hynix) while model operators burn capital. Zitron highlights OpenAI’s reported $300 billion, five‑year compute commitment with Oracle—a back‑of‑envelope cost of roughly $5 billion per month—which, under plausible revenue mixes (≈30% API, 70% subscriptions), would require untenable consumer uptake (roughly 125 million $20/month subscribers) or massive, unprecedented enterprise spending. The Bank of England has already warned valuations for AI‑focused tech look stretched and vulnerable to a sharp market correction if capability, adoption, supply chains (power, data, chips), or costs diverge from expectations. Beyond economics, the piece stresses a faith problem: enterprise demand is often driven by hype rather than rigorous evaluation, and LLMs remain unreliable—hallucinations and factual errors are intrinsic risks. High‑profile failures (e.g., Deloitte refunding A$440k for a flawed generative‑AI report) erode trust and raise the prospect of more consequential incidents (security breaches, data leaks, misinformed decisions). Technical implications include continued dependence on specialized compute infrastructure, fragile unit economics for consumer models, and an urgent need for provable reliability and enterprise economics to avoid broad disillusionment and market contraction.