Token economics are serious AI business; API costs are out of control (theservitor.com)

AI’s “magic” is actually a hidden token economy—and that meter is running much hotter than most users realize. Tokens (text chunks that represent input and output) are the unit of compute and billing for LLMs: every prompt and every model reply counts, and follow-ups require reprocessing the entire conversation context. Modern “agents” compound this by internally prompting, searching, and reasoning across many steps, so a single high-level request can consume tens of thousands of tokens behind the scenes. GPUs are the scarce resource that process those tokens, creating a hard capacity and cost ceiling that can’t be scaled instantly. The practical consequence is a looming economics problem for providers and customers. Providers like OpenAI are reportedly losing money on heavy usage, prompting massive fundraising and infrastructure deals to secure GPU supply; yet global token processing capacity remains constrained. For businesses and startups this matters now: API bills are frequently underestimated (by 40–60%), and sudden pass-through of real costs could mean runaway price increases, throttled access, or fragile product economics. In short, AI’s capabilities are outpacing its sustainable cost model—pushing the community to rethink agent design, token efficiency, and who ultimately pays for large-scale, multi-step AI workflows.