Memory prices have doubled, Macs and iPads have gone up, and iPhones are expected to follow. Ed Zitron, described by Politico as the AI boom's most "acerbic gadfly," argues the buildout driving those costs will never pay for itself. Zitron states that Large Language Models' costs run contrary to software selling models, as LLMs burn tokens at a per-million rate regardless of whether the user gets the desired response. He notes that AI companies sell monthly subscriptions with vague rate limits, allowing users to burn more in tokens than the subscription cost. SemiAnalysis found that users can burn hundreds of dollars on a $20-a-month subscription and thousands on a $200-a-month subscription. Zitron’s own reporting shows that OpenAI lost $20.9 billion on $13.07 billion in revenue in 2025. He adds that if Anthropic and OpenAI believed customers would pay the real cost, they would not give away 20 to 40 times the amount of tokens to subscribers. In March of this year, both moved enterprise customers to token-based billing, and Uber spent its entire annual token budget in a quarter, with its COO saying it was getting "harder to justify" the cost of AI. Sam Altman called it a "huge issue" but declined to say how it might be fixed. Zitron concludes that every AI startup is unprofitable because users do not want to pay the actual cost of AI