The AI Productivity Paradox

Marty Cagan argues that the so-called AI productivity paradox, where teams ship faster but outcomes do not improve, is not really a paradox at all. Most organizations use AI to accelerate the old project model, which is designed around delivering output rather than outcomes. Strong product companies instead use AI to speed up discovery, testing solutions for both customer value and business viability, and only then accelerate building a commercial-quality product. Cagan notes that the gap between strong and weak product companies is widening rather than closing. He suggests many leaders will only accept the evidence once they see that their ideas are often not worth building at all.

01Key takeaways

• Using AI to speed up the existing project workflow mainly produces more output, not better outcomes.
• Use AI during discovery to rapidly explore and test solutions that create value for customers and viability for the business.
• Only accelerate delivery with AI once evidence shows the solution is worth building.
• Speed of building is rarely the real bottleneck; knowing what to build is the harder problem.
• Strong product culture, strategy, and discovery skills are the true differentiators in an AI-driven market.

02Key sections

The paradox is widely recognized

Industry reports from McKinsey and Atlassian highlight that AI adoption and speed are rising while measurable performance and ROI remain elusive. The author notes this pattern has long predated AI.

Output versus outcomes

The project model was never primarily slow; its core flaw is that it optimizes for delivering output instead of achieving outcomes. AI simply makes running faster in the wrong direction easier.

Why the equalizer hypothesis failed

Cagan expected AI to level the playing field, but the best engineers were also paired with strong product people, whose culture, strategy and discovery skills were the real advantage.

Building to learn versus building to earn

Strong teams use AI to accelerate discovery and validate solutions with customers before using it to accelerate delivery of a reliable, scalable product.

Leaders who resist the evidence

Many leaders believe faster building alone will fix results, and the author expects they will only change once the evidence that ideas are often not worth building becomes undeniable.

03From the post

“We’ve been writing a lot lately about product teams that are clearly leveraging AI to deliver faster, yet their outcomes are not improving. Today, this phenomenon, known as the “AI Productivity Paradox,” has been recognized by people from across the industry. From the latest McKinsey Quarterly: “The business world is grappling with an AI paradox:... The post The AI Productivity…”

04Frameworks mentioned

Summary and takeaways written by PM Atlas; quotes are short excerpts. © the original author.