AI Can Help You Build the Wrong Product Faster

Speed of execution and correctness of direction are two different problems. A team can be moving fast and still be moving in the wrong direction, and for most of the history of software, that distinction did not matter as much as it should have, because building anything at all was slow enough to act as a natural check on ambition. A bad idea took months to prove itself bad. AI has removed that natural check. It has made execution dramatically faster without making direction any more reliable, and the gap between those two things is where AI product waste comes from: fully built, technically competent products that address a problem nobody actually has.

This is not an argument against using AI to build. It is an argument for noticing what AI actually changed, and what it did not.

Two Different Variables

Ask any engineering leader what has changed in the last few years and the answer is immediate: production got fast. A working prototype that once took two weeks can now take an afternoon. A full-featured application that once required a small team and a quarter can be scaffolded, styled, and connected to a database by one or two people in a matter of days. GitHub’s own data on Copilot usage and McKinsey’s research on generative AI’s economic impact both point in the same direction, concentrated productivity gains in software development and adjacent knowledge work. None of that is in serious dispute.

What is missing from that story is any claim that AI has made teams better at knowing what to build. Those are separate variables. One is a question of execution: given a decision, how fast can we produce the thing? The other is a question of direction: is this the right thing to produce, for this customer, for this reason? AI has moved the first variable enormously. It has done essentially nothing for the second, because the second variable was never a production problem in the first place. It was always a judgment problem, and judgment is not something a coding assistant supplies.

Velocity without direction creates waste faster. AI did not invent that risk. It just removed the friction that used to slow it down enough to notice.

Was Speed Ever the Binding Constraint?

It is worth asking honestly whether slow production was ever really the thing standing between most teams and product success. The evidence says no, and it said no well before generative AI existed.

CB Insights has repeatedly analyzed startup post-mortems, founder accounts of why their companies failed, and across those analyses, “no market need” is consistently the most cited reason, showing up in roughly 42 percent of the cases reviewed. Read that carefully. The most common cause of failure was not “we built it too slowly and a competitor beat us to it.” It was “we built something and it turned out the market did not want it.” That failure mode has nothing to do with production speed. A team that builds the wrong product in three months and a team that builds the same wrong product in three weeks have both failed for the identical reason. The second team just gets to find out sooner, which is only helpful if speed is paired with a willingness to actually check.

That pairing is the part that has not materialized automatically. GitClear’s 2025 research tracked a large body of code commits from 2020 through 2024, spanning the period during which AI coding assistants went from novelty to default tooling, and found rising code churn and a growing volume of duplicated code over that span. Code churn, work that gets rewritten or thrown away shortly after being written, tends to reflect work that was not well understood before it was produced. Rising duplication tends to show up when generating a new block of code is faster than taking the time to understand and extend an existing one. Neither trend is what should happen if AI assistance were, by itself, improving the thoughtfulness of what gets built. It looks much more like AI is making it cheaper to produce work quickly without first working out whether it is the right work, which is a reasonable working definition of AI product waste: more artifacts, produced faster, without a proportional increase in the number of those artifacts that actually move a customer outcome.

Put the two data points together and a pattern emerges. Production speed was rarely the binding constraint on product success. Understanding the customer well enough to build the right thing was. Removing the first constraint at scale, which is exactly what AI tooling has done, does not automatically loosen the second one. It can just as easily let a team run straight past it, faster than before.

A Scenario: Building the Full Feature Set Around One Untested Belief

Consider a hypothetical, illustrative case, not a real company, but a pattern familiar to anyone who has watched a product team move fast in the last two years.

A mid-sized B2B software company sells project tracking tools to operations teams. Leadership becomes convinced, based on a handful of enthusiastic conversations at a conference and a few pointed complaints in a support queue, that customers are frustrated by how rigid their approval workflows are. The belief crystallizes quickly: give operations managers a flexible, configurable, multi-level approval engine, with custom rules, conditional routing, and role-based escalation, and adoption of the core product will jump.

Nobody writes this belief down as an assumption to be tested. It gets treated, from the first planning meeting, as a known fact. There is no named core customer beyond “operations managers,” no attempt to separate the loud minority who complained from the larger group who never mentioned approvals at all, and no discussion of what “flexible” would actually need to mean to a manager who has never touched a rules engine before.

With AI-assisted development, the team moves remarkably fast. A backend engineer uses an AI coding assistant to scaffold a full rules engine with conditional logic in under a week. Another team member generates an admin dashboard for configuring approval chains, complete with drag-and-drop step ordering, in a few days. A third builds out role-based permissions and a notification layer so approvers get alerted at each stage. Within six weeks, what used to be a quarter-long initiative is not just prototyped, it is fully built: rules engine, dashboard, permissions, notifications, documentation, and a migration path for existing customers’ approval data. The team ships. Leadership is thrilled. The release notes describe a major platform upgrade.

Three months later, usage data tells a different story. Fewer than one in ten customers have configured a custom approval rule at all. Most who tried the feature during onboarding used the simplest possible setup, a single approver, and never returned to the configuration screen again. A round of customer conversations, the kind that arguably should have happened before any of this was built, surfaces the real problem: operations managers were not frustrated by rigid approval logic. They were frustrated by how long it took to get any approval at all, because approvers were slow to respond, not because the routing rules were too simple. The actual need or pain sat one layer earlier in the outcome chain than the feature that got built to address it.

Now the team faces the harder part. Unwinding a rules engine, a configuration dashboard, a permissions model, and a data migration is nowhere near as fast as building them was. Customers who did configure something, even a handful of them, cannot simply have it removed without a conversation and a transition plan. The documentation has to be revised or retired. Support has been trained on a feature that mostly needs to be walked back. None of this unwinding benefited from AI assistance the way the initial build did, because undoing a decision that shaped a data model and a customer-facing workflow is a product and communication problem, not a code-generation problem. The team built more, faster, around a belief nobody tested, and that speed is precisely what made the eventual correction more expensive than it would have been if the same untested belief had been explored slowly.

This is the shape AI product waste tends to take. It rarely looks like a failed prototype that quietly gets deleted. It looks like a fully realized feature set, technically solid, professionally documented, genuinely impressive as a piece of engineering, that has to be dismantled because the assumption underneath it was never checked against evidence before the building started.

The Unwind Is Bigger Than the Build

There is a specific reason AI-accelerated mistakes cost more to fix than slower mistakes did, and it is not really about the code. It is about everything the code drags along with it: data models that now hold real customer configuration, integrations that other systems have started to depend on, support documentation and training that reference the feature as if it is permanent, and customer expectations that were set the moment the release notes went out. Slow building used to create natural checkpoints where a half-finished feature could be quietly redirected before any of that accumulated. Fast building compresses those checkpoints out of existence. A team can go from idea to fully entangled feature before anyone outside engineering has had a real chance to ask whether the idea was sound.

A successful outcome should not hide an unacceptable side effect, and an impressive build should not hide an unvalidated premise. Both require someone to look past what was shipped and ask what actually changed for the customer.

Where the Outcome-Driven Product Design Framework Fits

None of this argues for building slowly on purpose. It argues for putting the validation work where it belongs, before the speed gets spent, not after. This is the specific discipline the Outcome-Driven Product Design Framework is built around: a sequence that starts with the customer and the priority need, moves to a stated desired outcome, and only then asks what evidence exists for the assumptions behind it, before any output gets built at all.

In the approval-workflow scenario, that sequence would have surfaced the mismatch in days rather than months. Instead of scaffolding a full rules engine, a team following assumptions and evidence discipline would have had to write down, explicitly, what was known versus assumed about why approvals felt frustrating, and would have had to name a validation action before committing engineering time. A Minimum Viable Output aimed at that same belief might have been nothing more than a short set of interview questions and a manual test, asking five operations managers to walk through their actual approval delays, with no code written at all until the real bottleneck was identified. An experiment written in the framework’s format, “we believe operations managers will report faster approval cycles by using X, and we will consider this supported when average approval time drops by a defined threshold within a defined period,” would have forced the team to define success before building toward it, and would have ended in an explicit decision to continue, improve, pivot, pause, or stop.

AI tooling would still have been useful in that version of events. It is an excellent accelerant for building the small, sharp test once the right question has been identified. What it cannot do, and what the framework exists to supply, is the judgment about which question deserves that speed in the first place. Teams that are serious about avoiding AI product waste, and about using AI well rather than just quickly, are worth a closer look at that sequencing question before the next roadmap gets written. Organizations working through this shift are welcome to explore the full framework, or to reach out through consulting if a structured review of a current roadmap would help.

Key Takeaway

AI has made execution fast in a way that is genuinely new. It has not made direction more reliable, because direction was never an execution problem to begin with, as CB Insights’ “no market need” data and GitClear’s rising churn and duplication figures both suggest in their own ways. The cost of an unvalidated assumption does not disappear when AI speeds up the build, it grows, because there is simply more product, more data, and more customer expectation built on top of it by the time anyone checks. The fix is not slower building. It is validating the outcome before the output, every time, regardless of how fast the output has become to produce.