Przemek Mroczek's critique of RTK — a tool claiming 60-90% token cost reduction by compressing CLI output for AI agents — lands a specific technical argument: the savings are measured on terminal output alone, which is not what's expensive; the compression happens silently without telling the agent context was stripped; and there's no published data on whether tasks actually succeed. The post is a useful diagnostic for a broader pattern in agent cost tooling.
Token prices are falling fast, but enterprise AI bills are rising. Uber burned through its entire 2026 AI coding budget in four months driven by Claude Code adoption. Goldman Sachs projects a 24× increase in token consumption by 2030. The Jevons paradox shows up again: efficiency gains don't reduce consumption — they expand it.
Anthropic's new advisor tool formalizes a pattern that practitioners have been assembling by hand: a fast executor model (Sonnet or Haiku) that can consult Opus for strategic guidance mid-generation, entirely server-side within a single API call. The benchmarks show real gains and the implementation is notably clean — but the more interesting shift is architectural: it treats Opus-level intelligence as a resource to be invoked selectively rather than paid for on every token.
