When building gets cheap, deciding gets expensive

Every product team has felt it this year: a feature that took a sprint takes an afternoon, and the backlog empties faster than anyone can refill it with things worth building. This article answers what that does to the product role, using the measurements published so far, with each figure’s sample and date attached.

Is building actually cheaper?

Yes, and the honest figure is smaller than the feeling. The 2025 DORA report, published by Google Cloud on 23 September 2025 from nearly 5,000 respondents, finds 90% using AI at work and, for the first time, a positive relationship between AI adoption and software delivery throughput. The same report finds that AI adoption continues to have a negative relationship with delivery stability: more change ships, and more of it breaks.

Lenny Rachitsky and Noam Segal’s survey of 1,750 product managers, engineers, designers and founders, published 23 December 2025, found more than half saving at least half a day a week on their most important tasks. The same survey found 92.4% reporting at least one significant downside to the tools. Both numbers are true at once, and a team that quotes only the first is planning on a fiction.

Why does the gain feel bigger than it is?

Because the people experiencing it misjudge it in a consistent direction. METR ran a randomized controlled trial, published 10 July 2025: sixteen experienced open-source developers, 246 real tasks from their own repositories, each task randomly assigned to allow or forbid AI tools. With AI allowed, the developers took 19% longer. Before the study they had forecast a 24% speed-up, and after living through the slowdown they still believed AI had made them 20% faster.

Sixteen developers is a small sample and the tools were early-2025 models, which the authors say themselves. What the trial establishes is narrower and more useful than a verdict on AI: perceived speed is an unreliable instrument. A product team that wants to know its real velocity has to measure it — lead time, change failure rate, the DORA four — with the tools on and off.

Where does the constraint move?

To judgment. When production stops being the limit, three decisions set the outcome, and none of them gets faster with a code assistant.

• What to build: which problem, for whom, how often it occurs, and what it costs to wait three months.
• How you will know it worked: the number that has to move, written down before the build, with the date it will be read.
• Whether a user can trust it: for an AI feature, the cases where it may answer, the cases where it must refuse, and how confidence is shown.

Marty Cagan wrote on 30 December 2024 that “the PM role becomes more essential but also more difficult with generative AI-powered products, not less”, reserving his enthusiasm for the combination of “someone with very strong judgement” and the tools. Teresa Torres said the same thing from the discovery side in April 2026: “Building is very cheap now. That doesn’t mean we should build every idea we have.” Both are describing a role whose scarce input is judgment about what deserves to exist.

What does a product team do with this?

Three practical moves follow, and each is cheap to start.

• Measure velocity with the tools on and off for a month, and read the change failure rate beside the throughput. DORA’s instability finding is the one most teams discover late.
• Write the decision before the build: one page with the problem, the alternatives on the table, what was set aside, and what has to be true in two months.
• Spend the freed hours on discovery and on evals. An interview synthesized by a person and then by a model, and an AI feature specified as test cases before the first prompt, are where the week now goes.

The prioritization method is written up in deciding what to build next. For a founder with a runway, the same logic decides what the first build has to prove, and the founders page sets out that sequence.

How the studio works with this

Torn Studio’s answer is a delivery model: one person holds the product role and builds with AI tooling, so a decision made on Monday is in code the same week. The cost is stated as plainly as the gain. One person’s week is the capacity, and a team that needs three product roles in parallel is better served by hiring them. Product Management is priced per engagement, with the scope set in advance and the price fixed before work starts.