The new bottleneck in AI-assisted product creation
For a long time, digital innovation followed a fairly stable logic.
Ideas were translated into requirements. Requirements became concepts. Concepts became backlogs. Backlogs became software. And somewhere along the way, time, budget, technical complexity and organisational friction determined what was actually built.
This logic is now starting to change.
During a recent AI-assisted coding experiment, I translated several structured investment and advisory concepts into working digital tools within a very compressed timeframe. The most striking observation was not simply speed. Speed is visible, impressive and easy to communicate. But the deeper shift lies elsewhere.
The bottleneck no longer seemed to be software creation itself.
It increasingly moved to three other areas:
🔹Superior ideas
🔹Structured domain expertise
🔹Distribution
In other words, execution is becoming cheaper. Iteration is becoming faster. Prototypes can become usable products almost immediately.
That changes the strategic question.
Not:
Can we build it?But increasingly:
Should we build it?
From minimum viable product to maximum valid product
The traditional MVP logic was born in an environment where building software was expensive, slow and uncertain. The “minimum viable product” was a rational response to scarcity. Build only what is necessary, test the market, learn quickly, avoid overengineering.
This logic still has value.
But AI-assisted coding introduces a new tension. If relatively sophisticated systems can be created, tested, refined and documented in days or weeks, the boundary between prototype and product becomes less clear.
What once required a classical development cycle may now emerge from a structured prompt, domain logic, iteration and automated testing.
This may not eliminate the MVP. But it may change its meaning.
The relevant question is no longer only how little can be built to validate an idea. It is also how much can be meaningfully built when execution costs fall dramatically.
This points toward a different concept:
Not maximum because everything should be built.
But maximum because more of the actual product logic, user experience, analytics, controls and documentation can now be tested much earlier than before.
The strategic risk of building too much
The paradox is obvious.
When building becomes easier, the risk of building the wrong things increases.
- More functionality does not automatically mean more relevance. More automation does not automatically mean better judgement. More dashboards, simulations and features do not automatically create a stronger product.
In fact, the opposite may happen.
The ability to build quickly can create a false sense of progress. A tool may grow in scope because it can, not because it should. Features may be added because the marginal cost appears low. Complexity may return through the back door, hidden behind polished interfaces and impressive velocity.
This makes restraint more important, not less.
In an AI-assisted development environment, product discipline becomes a strategic capability. The value shifts from pure execution to judgement.
- What should be built?
- What should be simplified?
- What should remain outside the product?
- What should be tested, but not scaled?
- What creates genuine user value – and what merely demonstrates technical possibility?
Domain expertise becomes more important
There is another important implication.
If software creation becomes increasingly commoditised, domain expertise becomes more valuable.
AI-assisted coding can generate interfaces, logic, documentation and tests with impressive speed. But it does not automatically know which process matters, which assumption is realistic, which validation is necessary, or which output is useful for a professional user.
Especially in areas such as private banking, wealth management, risk management or advisory processes, the core value is not the code itself. The value lies in translating professional judgement into structured, explainable and usable systems.
This requires more than technical prompting.
It requires a clear understanding of processes, constraints, user behaviour, regulatory expectations, data quality, governance and practical implementation.
The stronger the domain logic, the stronger the product can become.
Distribution becomes decisive
There is also a commercial and strategic dimension.
If more people can build more tools faster, the market will not reward every functioning product. Execution alone will no longer be enough.
Distribution becomes decisive.
- Who has access to users?
- Who has trust?
- Who can explain the relevance?
- Who can embed tools into real workflows?
- Who can turn experiments into adoption?
This may be uncomfortable for organisations that historically relied on technical complexity as a protective barrier. If execution becomes easier to replicate, differentiation must come from insight, trust, positioning, workflow integration and credibility.
The software may be easier to build.
The relevance may not.
The new bottleneck: what not to build
The most important conclusion from the experiment is therefore not that AI makes building faster.
It is that AI changes the role of strategic judgement.
When the cost of execution falls, the cost of poor prioritisation rises.
In the age of AI-assisted coding, the discipline to decide what not to build may become one of the most valuable capabilities in digital strategy.
Not because ambition should shrink.
But because focus becomes harder when everything suddenly looks possible.
The new bottleneck is no longer only execution.
It is judgement.