AI tools are making code development 10-20x faster. However, the 'why we should build' (customer research) and 'getting it to customers' (adoption) phases remain bottlenecked by human interaction speed. This creates an imbalance that modern product teams must manage.

Historically, the 'build' phase was the primary bottleneck in software development. With AI making building nearly instantaneous, the critical path to success has shifted. Mastery of the 'define' (scoping) and 'feedback' (learning) stages is now what separates winning teams from the rest.

AI has compressed development cycles from weeks to days, but it hasn't equally accelerated human coordination. The new bottleneck is getting stakeholders aligned on strategy, planning user communication, and managing the "fuzzy" aspects of a launch. While coding saw a 100x speed-up, these coordination problems remain.

The traditional PM function, which builds sequential, multi-month roadmaps based on customer feedback, is ill-suited for AI. With core capabilities evolving weekly, AI companies must embed research teams directly with customer-facing teams to stay agile, rendering the classic PM role ineffective.

In AI, low prototyping costs and customer uncertainty make the traditional research-first PM model obsolete. The new approach is to build a prototype quickly, show it to customers to discover possibilities, and then iterate based on their reactions, effectively building the solution before the problem is fully defined.

With AI accelerating development, the key challenge is no longer building faster; it's getting completed features through legal, marketing, and other operational hurdles. Organizations must now re-engineer these internal processes to match the new pace of creation.

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.

The proliferation of AI has dramatically reduced development time, shifting the primary constraint in product delivery from engineering capacity to the customer's ability to learn and integrate new features into their workflow. More output no longer guarantees more value.

With AI accelerating development, the limiting factor for shipping value is no longer engineering speed. The real challenge has shifted to the customer's capacity to adopt, implement, and train users on the constant stream of new features, making customer success and enablement paramount.