Several conversations I’ve had recently have circled around the debt that accumulates when teams build with AI. What I find interesting is how much of that conversation concerns the software itself, when some of the more consequential decisions may have happened before there was any code to review. A team can move very quickly from describing a problem to interacting with a working product without having fully understood what changed between the two.
There is an interpretation involved in every stage of building something. A person describes what they think is happening, someone translates that into a requirement, and the requirement becomes a particular interaction. Each step makes the idea more specific. It also introduces decisions about what matters, what can be left out, and what the person using the product is expected to do. AI can execute those decisions remarkably quickly, including the ones nobody has quite recognized as decisions yet.
This is the kind of debt I’ve been thinking about as translation debt. It accumulates when the meaning of the original problem changes as it moves through a specification, an implementation, and eventually a process of validation, without those changes being examined. The resulting product may be internally consistent. It may do exactly what its specification says. The uncertainty sits in whether that specification still describes the problem the team meant to address.
An onboarding flow is a useful example. If people are getting stuck during setup, a guided sequence with clearer instructions is one plausible response. But getting stuck could mean several things. Someone might not have access to the information being requested, might need another person’s approval, or might still be deciding whether the product is worth the effort. Turning that observation into a requirement for better guidance already commits the team to an explanation. Generating the flow makes that explanation tangible before it has necessarily become any more certain.
I think part of the difficulty is that a working interface changes what people pay attention to. Once there are screens to review, there are specific things to improve. A label could be clearer, a step could be shorter, an error could be handled more gracefully. Those are useful conversations, but they can gradually displace the question of why this particular flow exists. The team becomes more precise about the solution while the premise underneath it receives less scrutiny.
A recent study of a teacher-facing AI authoring system examines how professional intent travels through those stages. The researchers traced six build attempts across three accounts and found that two drafts met a stored package and security threshold while their correspondence to the original briefs remained unresolved. The study is small and does not establish how common this is across AI builders. What it makes visible is the distance between passing a particular check and establishing that an application preserves the intention behind it.
That distinction carries through to product validation. Testing whether someone can complete a generated onboarding flow tells us something about the flow. It does not necessarily tell us whether guidance was what that person needed. A technically successful build and a usable interaction can both be meaningful progress, while leaving the original explanation untested. The evidence becomes difficult to interpret when those different kinds of progress are treated as interchangeable.
The debt grows as other parts of the product begin to depend on that explanation. Completion becomes a metric, improvements to the flow enter the roadmap, and the team starts evaluating new decisions against the structure it has already built. Revisiting the original premise then means reconsidering more than a few screens. It may mean changing what is measured, what is prioritized, and what the team has come to believe about its users. That is where an assumption that was inexpensive to question can become expensive to unwind.
I don’t think the answer is to make every decision wait for certainty. Building can be one of the most useful ways to find out what a team has misunderstood. But it helps to preserve the difference between what was observed and what was inferred, so that encountering a working product does not erase the uncertainty that produced it. In the onboarding example, the observation is that people abandon setup when asked for information. The idea that they need a better explanation is still something to investigate, even after an explanation has been built.
For me, this is an important part of the product work around AI-assisted development. It means being able to follow a consequential decision back to the problem it was meant to address, understand which assumptions entered along the way, and ask whether the evidence actually speaks to them. That makes research useful throughout the build, particularly at the points where a plausible interpretation is becoming embedded in the product.
AI gives teams much more room to execute. I think the corresponding responsibility is to remain attentive to what they are executing, and to keep the original problem available for reconsideration as the product takes shape. Otherwise, the speed of building can become the speed at which an interpretation acquires the appearance of certainty.
