AI has changed the pace of product work in ways that can still feel slightly unreal. Features, prototypes, and working software can now be produced far faster than before. Once that kind of speed becomes the new baseline, it becomes easy to judge everything else against it. Anything that slows delivery is quickly labelled a bottleneck.

That is the part I want to question. The cost of building is falling in real time, but the conditions for making a good product have not changed nearly as much. A product is more than a set of features and flows. It is a system with its own structure, behaviours, and expectations. AI has made building much cheaper, but keeping that system coherent is still hard.

And yet the work that helps hold that coherence together is exactly the work now under pressure. Discussion, alignment, structure, and critique all become easier to dismiss as friction when they do not immediately produce something visible or shippable. But those are often the moments where product quality is actually decided. The easier it becomes to generate things, the more important it becomes to decide what should remain consistent, what should change, and why.

Cheap building changes the pressure

Cheaper building has clear benefits. More ideas can be explored, more prototypes can be tested, and smaller teams can produce more software in less time.

But it also changes what gets rewarded. As implementation speeds up, work that does not quickly turn into something visible is easier to dismiss as overhead. A conversation about structure, naming, edge cases, or system fit can look slow next to a prototype that already works.

The trouble is that these decisions do not disappear when teams skip them. They are postponed. The cost returns later as duplicated controls, conflicting patterns, inconsistent naming, and one-off behaviours that make sense only where they were introduced.

More output does not automatically improve the product

Once speed becomes the dominant expectation, progress starts to look synonymous with visible output: more screens, more flows, more variants, more features. More things that can be reviewed, tested, and shipped.

But product quality is not the sum of plausible parts. It depends on whether those parts work together clearly and consistently. Whether language holds across surfaces. Whether patterns remain legible. Whether each addition reinforces the system instead of making it harder to understand.

That is why a faster process can still produce a worse product. A team can become highly effective at generating options and shipping improvements while the product itself becomes more repetitive, more fragmented, and harder to learn. Local gains do not necessarily add up to systemic quality.

Speed multiplies drift

AI did not invent fragmentation, duplication, or feature creep. It lowers the cost of producing them.

When building becomes cheap, the threshold for adding something new drops. It is easier to create a new feature than to simplify an existing one. Easier to add another control than to resolve a structural problem. Easier to generate a plausible answer than to ask whether the problem needs a new surface at all.

A simple example is settings. Adding a new setting may now be trivial. The harder questions come afterwards. Does it belong in settings at all? Does it duplicate another control elsewhere? Does it use the same language as related features? Does it follow the same validation, saving, and recovery rules as the rest of the product?

This is how drift accumulates: not through a single dramatic mistake, but through many small decisions that solve local problems while weakening the overall system. Users encounter the product as a whole. Every duplicated control, inconsistent label, and special-case flow adds one more thing to learn.

AI beyond artefacts

Most current uses of AI in design still focus on direct production: more concepts, more mockups, more prototypes, more output. That is useful, but it may not be the most valuable use.

A more interesting role sits further upstream. AI can help clarify the problem, expose weak assumptions, surface edge cases, compare directions, pressure-test a flow, refine information architecture, tighten naming, and turn a vague idea into a sharper brief or spec.

The same logic already applies in engineering. Faster coding does not reduce the need for planning. If anything, it increases the cost of weak thinking, because weak decisions can now be turned into real software much faster.

That makes intention more valuable, not less. AI is most useful when it helps teams make clearer choices before they make more things: what problem is actually being solved, what belongs where, what should reuse an existing pattern, and what should not be added at all. One use expands output. The other strengthens intent. The scarcer thing is usually the second one.

Intention is all you need

AI has already changed product work in fundamental ways. It is pushing the cost of building towards zero, multiplying execution speed, and making it possible to turn ideas into software at a pace that would have seemed unrealistic not long ago.

That changes what teams can do. It also changes what teams optimise for. Much of the energy now goes into producing more, faster: more features, more prototypes, more things that can be shown and shipped. The harder question is whether that, on its own, leads to better products.

Product quality does not rise in proportion to output. It improves when speed is matched by judgement: when teams know what to simplify, what to keep consistent, what not to add, and where a new idea strengthens the system rather than fragments it.

Building is no longer the scarce part. Judgement is. Now that almost anything can be built, intention is all you need.