The teams pulling ahead aren't the ones with the best models

About this episode

AI products are shipping faster than ever. But shipping isn’t impact. The teams pulling ahead aren’t the ones with the best models — they’re the ones who can prove their product moves the business. This edition is about that gap. How to measure what matters, where the biggest barriers to impact are hiding, and what the latest research says about getting AI products to actually drive growth. Because the real competitive advantage isn’t AI. It’s knowing whether your AI is working.

What You’ll Learn in This Edition

This edition cuts through the noise to focus on the measurement gap — the difference between shipping AI and proving AI drives growth.

* The Power/Speed/Impact/Joy bullseye — a calibration framework for AI products that actually drive growth

* A Nature paper reveals why removing friction from AI may be destroying the learning your team needs

* John Maeda on why design teams are being hollowed out — and why PMs are next

* Benedict Evans on why even OpenAI can’t solve product-market fit with capability alone

* Research that should change how your team thinks about AI-assisted skill building

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Episode 1: Why Your AI Metrics Are Lying to You - Framework for improving AI product performance

Your AI product might be fast, capable, and technically impressive — and still not drive the growth your business needs. In this episode, Brittany Hobbs and I introduce the Power, Speed, Impact, and Joy bullseye — a calibration framework borrowed from F1 racing. The teams winning aren’t shipping more features. They’re measuring different things entirely. We break down a three-layer eval approach and why most completion metrics are hiding the signals that matter.

“Success does not mean satisfaction. If someone stops engaging, does that mean they solved their problem — or that they were frustrated and left?” — Brittany Hobbs

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Your Role Isn’t Shrinking. It’s Being Hollowed Out.

John Maeda — Three major tech companies have restructured design teams into “prompt engineering pods.” Maeda’s #DesignInTech 2026 calls it what it is: the elimination of design judgment from the product process. “When you replace a designer with a prompt, you don’t lose the pixels. You lose the questions that should have been asked before anyone opened a tool.” Th