How driftless reduces token spend

Tokens burn in rebuild loops

A coding agent that has to rework a feature three times consumes the tokens of building it three times: the context re-read, the rewrite, the re-test, and the review pass all run again on every round. Happy-path runs are a minor share of the spend. The lever that changes the bill is the number of times the same code gets built, so the methodology aims at rework.

Shaping removes the ambiguity that triggers rebuilds

An agent builds against what its directive says. When the directive is ambiguous, the agent guesses, and a wrong guess gets corrected in code, which is the expensive place to correct it: every correction is another rebuild loop. Shaping moves the decisions upstream instead of deferring them to the code. As documented on the cascades page, a week of cross-functional meetings becomes about 40 minutes of agent shaping that produces paired PRDs and tech specs with testable acceptance criteria, so the coding agent builds against a target from the first pass.

The spec gate catches failures before code exists

Shaped work passes through a spec gate before any task runs. The gate reviews every PRD and tech spec pair and catches contradictions and gaps before code exists. A fix at spec time costs hours; the same failure caught at code time costs weeks of rewriting plus rework across dependent tasks. Every rebuild loop the gate prevents is token spend never incurred.

The evidence: a low bug rate across a first month

Fewer bugs means fewer rebuild cycles, and fewer rebuild cycles means lower token spend. The worked example is Porchlight, a separate product built with the methodology, which went from concept to v0.5.0 within its first calendar month.

That month's build record, October 2026, month to date through October 20: 110 tasks shipped across 6 initiatives, 6 bugs detected, and a figure of 5.5% bugs as a share of tasks shipped, read from the project's build dashboard. The figures are the month's record as first published and refresh from the current dashboard on each republication. Brian, the builder who ran the month, describes it as "better than most human teams."

For industry context, TestDino's Bug Cost and Escape Rate Report puts the US industry-average defect escape rate at 10 to 15 percent and high-performing teams at 2 to 5 percent. The two figures measure different things, so each basis is named: 5.5% is bugs detected as a share of tasks shipped, and a defect escape rate is the share of defects that escape internal checks to reach users. With both bases on the table, 5.5% reads as right up there with a high-performing team, almost: it sits roughly half the 10 to 15 percent industry-average floor and just above the high-performing band's 5% ceiling, which is adjacency to the band, never membership in it. The same report's defect removal efficiency context matches what the gate does upstream: US teams catch about 85% of defects before production, and a catch that early costs hours the rebuild loop would have spent weeks repaying.

BYOK keeps model spend on your bill

driftless uses flat per-seat pricing with no usage metering. Inference runs on your own provider keys, and model choice stays yours, so routine work runs on inexpensive models and premium models run where the work warrants them. Key storage, transit, and lifecycle are documented on the BYOK page.