The economics of running AI: pricing, unit costs, and whether the spend pays for itself.
7 articles · Updated
81% of enterprise tech leaders say AI-generated code is causing more production failures, while 92% still think their code was ready to ship. The gap between those two numbers is the whole problem.
Tesla, Uber, Meta, Amazon, and Walmart have all put hard caps on employee AI spending. Token-based billing turned every prompt into a variable cost, and the tools shipped without the guardrails to contain it.
Claude Code's price ranges from $20 to over $1,200 a month for identical work. The sticker price on the pricing page is the wrong number. Here is what actually determines your Claude Code cost.
AWS gave AI agents a way to spend money autonomously. The payment was never the hard part; authorizing, capping, and auditing a non-human spender is, and most enterprises have not built for it.
The price on the vendor quote is a fraction of what a production agent actually costs. Here is the full bill, layer by layer, the unit that should replace cost-per-token, and where the math breaks.
Your dashboard says 90% cache hits. Your bill says otherwise. The gap is almost always three specific patterns inside the system prompt that don't show up in any log, and you can fix them in an afternoon.
Opus 4.8 lists at $5/MTok in and $25 out (a third of the prior Opus generation) with cache hits at $0.50. Here's what that does to the real per-request math for production teams.
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