Will AI Coding Make Product Managers Obsolete?

By Simon Sovič, Senior Product Manager

AI coding assistants are accelerating how fast teams ship features. But shipping faster doesn't mean you need fewer product managers. It means you need smarter ones.

The confusion comes from mixing two separate roles: engineers writing code and product managers deciding what code to write. AI is solving the first problem. It's not touching the second.

What is AI Coding and How Does It Change Development?

AI coding tools like GitHub Copilot and similar assistants auto-generate code based on prompts and context. They reduce the time engineers spend on boilerplate, debugging, and routine implementations. A feature that took three weeks might now take two. This is genuine productivity gain. But faster execution exposes a different bottleneck: knowing what to build and why. That's the product manager's job.

Why AI Coding Actually Increases Demand for Product Managers

When engineering cycles compress, product decisions become more visible and frequent. Teams can prototype ideas faster, test user feedback quicker, and iterate more often. This requires clearer thinking about priorities, faster roadmap adjustments, and better communication between product and engineering. These are core product manager responsibilities. Rather than making the role obsolete, AI coding makes bad product management more expensive.

What Problems Can AI Coding Not Solve?

AI coding cannot research user problems, validate market assumptions, or decide which features matter most. It cannot negotiate trade-offs between different user groups or forecast revenue impact. It cannot build cross-functional alignment or manage product strategy during uncertainty. It cannot talk to customers and understand their unspoken needs. These decisions require judgment, experience, and context that AI cannot replicate. Product manager skills focus on exactly these areas.

Are Some Product Manager Tasks Becoming Automated?

Possibly. AI tools may eventually help with roadmap visualization, analytics dashboards, or basic market research synthesis. But automation of tasks is not the same as obsolescence of the role. When finance teams got spreadsheets, they didn't disappear. They stopped doing manual calculation and started doing strategic analysis. Product managers will likely shift away from routine documentation toward higher-use work: deeper customer discovery, harder strategy decisions, and better cross-functional leadership.

How Should Product Managers Adapt to AI Coding?

Learn what your engineers are now capable of building in weeks instead of months. Spend that time understanding user needs better and testing assumptions faster. Your day-to-day work shifts toward discovery and validation Rather than timeline negotiation. Develop stronger skills in decision-making under uncertainty, because faster shipping means faster feedback loops and more frequent directional choices. Work closely with engineering to understand what's actually technically feasible now, not what seemed impossible last year.

What If Your Company Replaces Engineers With AI?

If AI actually replaces most coding work, product strategy becomes even more critical. Someone still needs to decide which problems users have, which ones matter most, and how to measure success. That someone is a product manager. Product strategy guides the entire direction of a business. Code is just the medium for executing it. The medium changing doesn't eliminate the need for the strategy.

Will Product Manager Headcount Stay the Same?

Probably not, but not because of AI coding. Teams might ship faster with the same product manager handling more features or larger teams. Some roles may consolidate. But the skill gap between great product managers and average ones is widening, not shrinking. Companies that invest in better product management while adopting AI coding tools will outpace those that assume faster engineering means fewer product people needed.

The real risk for product managers isn't obsolescence. It's irrelevance.If you treat AI coding as a threat instead of an opportunity to do deeper work, you'll become less valuable. If you use it as cover for lazy prioritization or poor customer understanding, your engineering team will notice and work around you. The bar is moving up, not down.