When AI makes building cheap, judgment becomes expensive

For much of the technology industry's history, building was expensive.

Software required substantial engineering effort.

High-fidelity prototypes required specialized craft.

Research took time.

Content took time.

Analysis took time.

Even testing an idea could require enough investment that organizations became careful about which ideas reached production.

AI is changing that constraint.

Code can be generated.

Interfaces can be prototyped rapidly.

Research can be synthesized.

Content can be produced at enormous volume.

Data can be explored conversationally.

A small team can create what once required a much larger one.

That is extraordinary leverage.

It also creates a less obvious problem:

When producing things becomes cheap, organizations can produce a tremendous amount of the wrong thing.

Output is no longer the scarce resource

Many management systems were designed around the assumption that production capacity was constrained.

Scarcity forced prioritization.

AI weakens some of those constraints.

But it does not remove the need for prioritization.

It makes prioritization more important.

If ten ideas can now be prototyped in the time previously required for one, someone still has to decide which prototype deserves to become a product.

If a team can produce fifty pieces of content, someone still has to decide whether any of them are worth a customer's attention.

If AI can generate dozens of interface alternatives, someone still needs enough taste and product understanding to recognize the right one.

The bottleneck moves from production toward judgment.

"Can we build it?" becomes less interesting

Technology teams have always been tempted by capability.

A new platform appears.

A new model becomes available.

A competitor launches something impressive.

Someone asks:

"What can we do with this?"

That can be a useful exploratory question.

It is a dangerous investment question.

As building becomes easier, the ability to produce a solution is weaker evidence that the solution deserves to exist.

The better questions remain stubbornly human:

AI does not eliminate these questions.

It increases the number of opportunities competing for them.

Taste becomes operational

Taste is sometimes treated as a visual-design concept.

In Product leadership, taste is broader.

It is the ability to recognize quality before every dimension can be reduced to a metric.

Those are judgments.

Some can eventually be validated quantitatively. They still have to be made before the evidence is complete.

AI makes that ability more important because it dramatically increases the number of plausible options.

The challenge is no longer generating enough alternatives.

It is choosing among them.

More experiments require stronger stopping rules

Cheap experimentation sounds unambiguously positive.

Usually it is.

But the ability to test more ideas creates a new temptation: continuing to test ideas that should already have been abandoned.

Teams can always make another prototype.

Try another prompt.

Add another workflow.

Target another segment.

Create another variation.

The cost of one more experiment appears small.

But organizational attention is not free.

Every additional experiment competes with something else for interpretation, iteration, deployment, maintenance, and leadership attention.

This makes stopping criteria increasingly important.

Before an experiment begins, teams should understand:

Without those questions, rapid experimentation can become sophisticated procrastination.

The organization is producing evidence without making decisions.

Speed magnifies weak strategy

AI can help a strong team move faster.

It can also help a confused organization become confused at extraordinary speed.

If the problem definition is weak, AI accelerates execution against the wrong problem.

If the customer is poorly understood, AI produces more polished assumptions.

If ownership is unclear, AI generates more artifacts for people to disagree about.

If the product strategy is incoherent, AI increases the rate at which incoherence reaches production.

Technology does not remove the need for organizational clarity.

It amplifies whatever clarity already exists.

This is why leadership becomes more, not less, important as production accelerates.

The job shifts from pushing work through a constrained production system toward creating the conditions in which a much faster system makes good decisions.

Quality cannot be delegated to the model

One of the seductive qualities of generative AI is plausibility.

The first output is often surprisingly good.

That can make mediocre work harder to detect.

The language is polished.

The interface is complete.

The analysis sounds confident.

The prototype behaves.

But "looks finished" and "is good" are not the same thing.

As AI raises the baseline quality of output, teams need stronger review mechanisms rather than weaker ones.

That includes domain expertise.

Product judgment.

Design critique.

Technical review.

Customer validation.

Evaluation.

Editorial judgment.

And sometimes the simple willingness to say:

"This is not good enough yet."

The role of experts changes.

They may produce fewer first drafts personally.

They become more responsible for defining standards, spotting subtle failure, making tradeoffs, and improving the system that produces the work.

That is not the disappearance of craft.

It is craft moving up a level.

The value of expertise changes

When AI can perform parts of a specialist's work, it is easy to assume the specialist becomes less valuable.

Sometimes that will be true.

But expertise contains more than production technique.

An experienced practitioner has accumulated patterns.

They know which questions matter.

They recognize when an answer is technically correct but practically useless.

They notice missing context.

They know which compromise will become expensive later.

They understand which edge case is actually a core case wearing an inconvenient costume.

AI can make some of that expertise available more broadly.

It also increases the value of the people capable of evaluating what the system produces.

The scarce expertise shifts from:

"I know how to create this artifact"

toward:

"I know what good looks like, why it matters, and what to do when the obvious answer is wrong."

That distinction is particularly important for senior leaders.

Leadership becomes the design of attention

Organizations have always had more possible work than capacity.

AI increases the imbalance.

There will be more ideas.

More prototypes.

More analyses.

More customer signals.

More generated recommendations.

More experiments.

More data.

More things that could plausibly deserve attention.

The leader's job increasingly becomes determining what actually receives it.

That is resource allocation, even when the resource is no longer primarily production capacity.

Attention becomes capital.

AI raises the premium on coherence

The most effective products rarely succeed because every individual decision is optimal.

They succeed because the decisions fit together.

The customer is clear.

The problem matters.

The value proposition matches the product.

The experience reinforces the value.

The operating model supports delivery.

The commercial motion reaches the right customer.

The economics justify continued investment.

AI can accelerate each component.

It cannot guarantee that they form a coherent whole.

That is a leadership problem.

It requires someone to move across levels:

from customer need,

to product,

to experience,

to technology,

to organization,

to market,

to economics,

and back again.

Not because one leader should perform every function.

Because someone has to understand enough of the system to make consequential tradeoffs across it.

What becomes expensive

As AI makes more kinds of production cheap, the expensive things become easier to see.

Customer trust is expensive.

Organizational attention is expensive.

A bad strategic commitment is expensive.

Complexity is expensive.

Rework is expensive.

Poor adoption is expensive.

A product nobody needs is expensive.

A decision made confidently on weak evidence is expensive.

Losing the trust of a user after an autonomous system makes the wrong consequential decision is expensive.

The cost did not disappear.

It moved.

The opportunity for Product leadership

This shift creates an enormous opportunity for Product leaders.

The role can become less about coordinating production and more about increasing the quality of organizational judgment.

Clarify the problem.

Create useful constraints.

Make tradeoffs explicit.

Design experiments that can actually change a decision.

Connect customer evidence to product investment.

Define quality.

Build evaluation into the product system.

Connect what ships to what gets adopted.

Know when to accelerate.

Know when to stop.

And preserve enough curiosity to recognize when the original thesis was wrong.

AI gives teams extraordinary leverage.

But leverage multiplies direction.

The defining question is increasingly not:

How much can we produce?

It is: