Even though AI enables PMs to code, it's an inefficient use of their time. Since code creation is cheap and product strategy is the new bottleneck, PMs should focus entirely on product work, not engineering tasks.

As AI tools automate coding and prototyping, the product manager's core function is no longer detailed specification writing. Instead, their value multiplies in judging, facilitating, and making the right strategic decisions quickly. The emphasis moves from the 'how' of building to the 'what' and 'why,' making decision-making the critical skill.

Tools like AI and cloud code streamline the 'how' of building products by reducing execution friction. However, they don't address the strategic 'what' or 'why'—the 'thinking friction' of identifying the right problem and defining value. This is where a product manager's role becomes even more essential.

The PM role has often devolved into tactical development execution. By automating these tasks, AI forces the role to return to its original strategic function, akin to a P&G brand manager. The focus shifts back to owning the entire system: business model, market dynamics, and go-to-market strategy.

As AI automates routine tasks like writing specs and managing backlogs, the core responsibility of a PM will shift entirely to exercising judgment. This involves evaluating a high volume of potential product changes for their strategic fit, brand impact, and long-term sustainability.

Product Management's core responsibility is to drive the business growth of a product by delivering profitable customer value. Technical skills and building are means to an end, not the end itself. This business focus remains constant even as tools like AI change.

PMs often feel pressure to keep engineers busy building new features. The real job is to drive deep understanding, even if it means perfecting three core features rather than adding a fourth. It's better to pause building than to create a bloated, mediocre product that does nothing well.

With tools that make building faster than ever, it's easier to fall into the "build trap" of shipping features without validating their value. This shifts the primary bottleneck from execution to strategy, making the product manager's core job of identifying the *right* problem to solve more crucial than ever.

As AI automates the 'how' of product creation (coding, design, go-to-market), the PM's core value shifts to the 'what' and 'why.' Success will be judged on the ability to consistently pick the right customer problems and market opportunities, where even a small improvement in accuracy yields outsized returns.