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A CTO consultant shares practical advice for managing unreasonable AI productivity expectations in tech organizations. Using a real CEO client case, the author argues that comparing established engineering teams to small greenfield startups is a flawed apples-to-oranges comparison. Three frameworks are offered: (1) always contextualize comparisons rather than attributing differences to a single cause like AI adoption; (2) rely on independent data (e.g., a GetDX study showing only ~10% PR throughput improvement) rather than anecdotal hype; and (3) keep business outcomes and user needs at the center rather than chasing technology for its own sake. The author is skeptical of magical thinking around AI, drawing parallels to crypto hype, and urges leaders to define success metrics before investing in AI tooling.

Handling unreasonable expectations

All about Engineering Management, Leadership and Career Growth.

Engineering Leadership

Handling Unreasonable Expectations

<p>Handling Unreasonable Expectations</p>

<p>This was a great read, and I love the reminder not to compare two very different companies. I’ve seen something similar with agencies (I’ve worked with several over the years.) A five-person agency might see an immediate productivity boost thanks to AI, but then they don’t have the infrastructure, manpower, or money to scale it beyond that. A bigger agency might see slower adoption, but then it picks up speed. No two companies are exactly alike.</p>
