The strongest argument against optimizing token spend — taken seriously, and answered.
This is correct, and anyone dismissing it is not paying attention. We would go further: a company whose entire AI spend is a handful of engineers running coding agents should probably not buy anything from us yet.
But the argument proves less than it appears to, for three reasons.
The engineer-hour offset is real where an engineer is in the loop. It does not exist at all in the places where AI spend is actually concentrating: inference serving a live product, document and claims processing, classification, support deflection, agentic workflows running unattended overnight. There is no salary line sitting next to those tokens. They are the entire cost of the function.
A token is a token — same meter, same rate, same waste. What differs is whether a larger cost sits beside it making it look small. In the workloads that dominate enterprise AI budgets, nothing does.
The phrase deserves scrutiny, because it is being used in two directions at once. In the same week the objection above circulated, Cloudflare's chief financial officer told investors that on current trends, non-human traffic could reach as much as a thousand times human traffic within five years — and that humans would become, in his words, a rounding error on the internet. Cloudflare had forecast the crossover for 2027; it arrived in May 2026. Elon Musk publicly endorsed the projection as not a close call.
Independent measurement points the same way. HUMAN Security's 2026 report found automated traffic growing roughly eight times faster than human activity, with monthly AI-driven volumes up 187% across 2025. Imperva put automated traffic at 53% of the web in 2025. ARK projects AI agents could reach a quarter of digital spending by 2030, from about 2% in 2025.
Percentages do not care about framing. At a $50,000 annual spend, a quarter of the bill is $12,500 and reasonably ignored. At $5,000,000, the same quarter is $1.25M — a number that appears in a board deck and generates a request for an explanation. The rounding-error argument is an argument about scale wearing the costume of an argument about principle.
| Annual model spend | 25% waste | How finance treats it |
|---|---|---|
| $50,000 | $12,500 | Below the threshold of attention. |
| $1,000,000 | $250,000 | A funded initiative. |
| $5,000,000 | $1,250,000 | A board question with a named owner. |
Notice what the rounding-error argument addresses: whether the savings are worth chasing. Notice what it says nothing about: whether the number on the invoice is true.
This is why we decline the “AI gateway” category. A gateway competes on how much it saves, which puts it squarely in the path of this objection — and in the path of model providers cutting prices, adding native caching, and compressing the savings pool. Assurance competes on whether the record can be recomputed, which is a requirement that grows as spend grows and does not shrink when providers get cheaper.
We would rather say that plainly than sell into a poor fit and produce a case study nobody believes. Our marketing is constrained by our product: we can only publish figures our customers independently verified, so a bad-fit customer produces no publishable number and no reference.
Any vendor whose answer to the second question is “the same component, but we wouldn't do that” is asking for trust where a control belongs.
Participation in an open public process is not endorsement. No agency, standards body, consortium or company listed here endorses Atom Works™, its products or its claims.