Cheap prototypes, expensive mistakes
As AI collapses the cost of software production, competitive advantage shifts entirely from execution speed to rigorous strategic decision-making.
The Cost of Building Is Falling. The Cost of Building the Wrong Thing Isn’t.
The cost of building software has collapsed, fundamentally changing the economics of product development. Where a working prototype once took weeks to stand up, AI now makes it possible in hours. Developers can explore ideas before a business case even exists, and designers can test directions long before anyone agrees they are the right ones. This newfound speed is incredibly useful, but it introduces a distinct, hidden risk. When building gets cheaper, the temptation is simply to build more. We end up with more concepts, prototypes, and features because the reduced cost of trying lowers the threshold for approval. Yet the cost of a bad decision remains exactly the same.
The bottleneck has moved
For years, the sheer practical constraints of development forced teams to make hard choices. Ideas competed fiercely for engineering time, proposals required strict estimates, and the sheer expense of building provided a natural filter against weak concepts.
That filter is now gone.
When anyone can spin up a working prototype in an afternoon, the central question shifts entirely from whether we can build something to whether we should. Answering that second question is significantly harder. It requires a deep understanding of the underlying problem, the human context surrounding it, and the existing alternatives already in the market. It demands knowing exactly which assumptions carry the most risk and what specific evidence would invalidate them.
While AI can accelerate this investigation by summarising research and making abstract ideas tangible, generating a plausible answer does not prove that the answer is actually worth pursuing. In reality, sheer speed often obscures weak assumptions. A highly polished prototype masquerades as tangible progress, making a half-baked idea much easier to sell to stakeholders. The effort invested in building the thing becomes a defensive justification for keeping it, even when the core problem remains poorly understood. At that point, the prototype merely proves that the team is capable of building, rather than proving they ever should have.
More options don’t automatically mean better decisions
Consider a product team exploring an AI customer support assistant. Within a single day, they might produce four entirely plausible directions: one that drafts responses, another that summarises customer history, a third recommending actions, and a fourth attempting to resolve the ticket entirely. The easy, dangerous response is to simply look at the demos and pick the most impressive one.
Doing so completely ignores the actual work of product strategy. You still need to know where the current process breaks down and whether the real bottleneck is search time, agent confidence, or system navigation. You have to define what happens when the AI hallucinates an answer, and establish exactly when an agent should ignore a recommendation. Those foundational questions dictate what the product actually needs to do, while the prototypes only provide an illusion of shape. Skip that step, and teams will spend their cycles executing brilliantly on the wrong problem.
Design becomes a way to reduce uncertainty
This is where design plays a critical role, but not the one traditionally assigned to it. Design is not the downstream function that makes an AI-generated prototype look usable. It is the discipline that defines what the team must actually achieve, identifies which assumptions carry the most risk, and establishes what a valuable outcome actually looks like before the first screen is ever drawn.
It means surfacing competing interpretations of a problem and isolating the core decision that needs to be made. Sometimes the right artifact to resolve that uncertainty is a working interface, but other times it is a service blueprint, a customer conversation, or a deliberate choice to build absolutely nothing. A prototype only holds value if it actively changes what the team knows. If it merely confirms existing biases, it is just an expensive way to reach a predetermined conclusion.
Change what you reward
Organisations must adapt their incentive structures to match this new reality. If leadership continues to reward shipped features, prototype volume, or mere AI adoption, teams will naturally optimise for output. Volume will spike, but business outcomes will remain flat.
The solution is not adding layers of governance or reverting to the slow approval cycles of the past, but fundamentally redefining what counts as progress. Early on, progress might mean discovering a problem is not worth solving or killing an approach before investing engineering time into it. Later, it means proving a solution actually works for real users and scales in production. Metrics must reflect the specific strategic decision the team needs to make at that exact moment.
The advantage isn’t building faster
AI will inevitably continue to drive down the effort required to turn ideas into software. The winners in this landscape will not be the teams that ship the most features, but the ones that use this newfound capacity to make better strategic decisions. They will test critical assumptions instead of polishing irrelevant details, and they will recognise when an idea is worth scaling versus when it needs to be killed.
The cost of building is falling rapidly, offering an unprecedented opportunity to learn faster and waste less effort. But that advantage only materialises if the time saved on production is reinvested into deciding what is actually worth building.