This interview with Nikhyl Singhal, a product leader from Meta and Google, explores how the product manager role is changing in the AI era. He argues that the information-moving parts of PM work are disappearing, while builder skills and product judgment matter more than ever. The next two years may bring heavy churn, layoffs, and rehiring, but also a major opportunity for PMs who actively reinvent themselves.

1. A chaotic era and the changing PM role

Singhal says the skills that used to define product management are changing quickly. In the past, many PMs spent much of their time moving information between teams. Now that work is becoming less valuable, and the ability to build and judge product quality is becoming central.

He expects the next two years to be one of the most chaotic periods in the history of product management. There may be large cuts and large rehiring cycles at the same companies, with the new roles focused on AI-first capabilities.

The irony is that PM openings are also at a recent high. Demand exists, but it is for a different type of PM. Builders may have an unusually good period ahead. People who do not enjoy building may struggle.

This transition is exhausting. The pace of change means that a tool or pattern can become obsolete within months, which creates real fatigue for people trying to balance work, family, and continuous reinvention.

2. Builders versus information movers

Singhal divides the older PM role into two rough categories: builders and information movers. The information mover role is fading. AI can summarize, route, and update information faster than people can. The builder role is becoming more important.

Builders are benefiting because they can now create impact without depending on as many people. AI handles more of the repetitive work, which lets PMs focus on judgment and product creation.

Judgment means deciding whether a change is good or bad, whether a direction creates value for customers, and whether a business case is sound. As AI produces more ideas and changes, this selection function becomes even more important.

Singhal also predicts that bad software may become far less common because tools like Claude Code, Codex, and similar systems can improve code, security, and maintenance faster than traditional teams could.

3. Human resistance to change and how to move through it

People resist change, especially when they were successful in the previous game. Senior people may have the hardest time because admitting the old playbook no longer works is emotionally difficult.

Singhal suggests three ways PMs can adapt.

First, learn new tools aggressively. PMs who want to lead in the AI era need to use coding agents and agentic workflows directly, not just talk about them.

Second, find moments of joy. The first time a new tool solves a real personal problem, learning stops feeling like obligation and starts feeling energizing. That joy is an antidote to burnout.

Third, work to obsolete yourself. The best engineers automate their own work; PMs should do the same. If a recurring task can be handed to AI, the freed-up time should move toward more valuable judgment and strategy.

4. Future roles and core skills

Singhal highlights a provocative framing of future work: product engineers and vibe coders who build directly, security and infrastructure experts who keep systems safe, persuasive people who sell and support products, and grown-ups who bring judgment and experience.

PMs should pay attention to the grown-up role. Judgment, communication, and systems thinking still matter. Engineers may have an edge in scalable systems and automation. Designers may have an edge in aesthetics. Product people may have an edge in judgment and communication.

The PM role is therefore not disappearing, but it is becoming more technical, more build-oriented, and more dependent on the ability to guide teams through change.

5. Conclusion

Singhal's message is both stressful and optimistic. Product management is tired and chaotic, but the opportunity is real. PMs who become builders, use AI tools deeply, and keep making their own old tasks obsolete can thrive.

The practical advice is to search for the moment of joy with new tools, combine experience with AI leverage, and treat reinvention as part of the job rather than an interruption.