A wave of slide‑deck promises—“our tool makes data scientists 40% more productive”—has become routine in AI product pitches. The author, a consultant with a math PhD, argues those figures often mask reality. By focusing on a single task’s speedup, marketers inflate the claim to suggest a company‑wide efficiency boost, even when the underlying improvement touches only a tiny slice of daily work.

Consider a tool that accelerates model parameter selection by 20%. Surveys may show that specific activity improves, yet data scientists spend roughly 40% of their time on core analytics, and parameter tuning occupies only about 10% of that block. The net effect translates to roughly 1% of total work time, barely perceptible once learning curves are factored in.

Instead of chasing marginal speed gains, the piece suggests measuring cognitive load. A competing solution that keeps task duration constant but eases mental effort can extend effective work hours, delivering a few percent more output while boosting morale. Asking marketers to quantify both the time slice affected and the mental burden reveals whether a touted “40% productivity” claim is meaningful or merely marketing fluff.