Ever since I started my career, organizations have been largely constrained by IT production capacity. Building was hard, slow, and often expensive. We learned in business school to design operating models around that reality: specialized roles, phased tollgates, added governance and overhead, and many handoffs. The logic is simple. When capacity is scarce, you protect, coordinate, and optimize it.
In my profession, I believe and experience that the world is changing quickly every day. AI-assisted development and a high degree of automation across the software development lifecycle are collapsing the marginal cost of producing software products. More of my teams can produce more output with far less friction than before. Generating code and artifacts is moving from “craft” toward “commodity”, which I see as a product and business risk. I’ve never seen the responsibility of a product team as a commodity, and even in an AI-assisted world we shouldn’t.
Organizations and people’s capability to decide, review, and absorb change is not scaling at the same rate as software development. This mismatch in scaling is the product leadership challenge of today.
When building gets cheap, judgment becomes expensive, and the constraints move upstream to the Product Manager and the UX designer.
- When teams can build faster, our stakeholders naturally assume capacity is infinite. In my experience, product requests from stakeholders increase rapidly and become more solution-first. As a result, the backlog grows quickly. Product Managers have an important role here. Just because we can build it quickly doesn’t mean we should.
- AI coding software can draft requirements, generate code, propose tests, and iterate designs for us. But the more output the AI generates, the more you, as a human, need to validate. Engineering throughput increases faster than human attention can keep up. Review becomes the new constraint.
- Even if you can review and ship more, many B2B clients cannot absorb more. Today, documentation, training, change management, and go-to-market strategy have finite capacity. Extreme engineering velocity without time for absorption can lead to confusion and eroded trust from users.
This is why I believe product teams should remain obsessed with outcomes and measurable impact, rather than creating more artifacts that may make an impact at the cost of change fatigue. Creating a good product is not about becoming an engineering feature factory. This is the risk product leaders need to manage as production costs decline and the default response from stakeholders is to celebrate increases in velocity and ask for more.
It remains the responsibility of a product and engineering leader to design and operate an operating model in which increased output reliably translates into better outcomes. From capacity management of scrum teams to accelerating decisions for teams that are heavily supported by AI. AI compresses the coding work. Humans must elevate judgment, coherence, and responsibility.
The organizations that win won’t be the ones that ship the most; they’ll be the ones that decide best at speed. It’s been like this throughout my product career, and I really don’t believe it will change anytime soon.
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