For years, software delivery was constrained by engineering capacity.
Could we build it?
How long would it take?
How many people would it require?
AI is rapidly removing those constraints.
Engineering teams can now produce code at a speed that was unthinkable just a few years ago. From a pure execution standpoint, “Can we build it?” is no longer the hard question.
But software is still being built for humans.
And that changes what actually matters.
The Biggest Misconception I Hear from Leaders
Most leaders I speak with today are rightly focused on how AI is accelerating software development. But many are missing the more important constraint:
Our end users are still overwhelmingly human.
No matter how fast engineers can build, products still have to fit into real human workflows, solve real problems, and earn adoption. If the human end user is not actively represented as products evolve, teams risk building things that are technically impressive and commercially irrelevant.
That responsibility usually sits with the product team.
And it’s becoming more important, not less.
Strategy Is the New Bottleneck
As engineering becomes more efficient, the real bottleneck becomes strategy.
Are we truly understanding our users?
Are we connected to their real problems?
Are we solving the right thing in the first place?
When teams drift, it’s rarely because they lack talent or tooling. It’s because they stopped pressure-testing the core problem definition.
They moved forward quickly—without stopping to ask whether what they were building was still relevant or necessary.
AI doesn’t just accelerate delivery. It increases the cost of bad direction.
Talent on the Field, No Quarterback
When teams lose touch with the core problem they’re trying to solve, you don’t get bad engineering.
You get uncoordinated effort.
It’s like having a field full of talented players with no quarterback. Everyone is moving. Everyone is working hard. But no one is directing the play.
The result isn’t speed or progress.
It’s motion without momentum.
Most struggling software projects can be traced back to a lack of definition of the end goal.
Not a backlog problem.
Not a tooling problem.
Not even an execution problem.
A goal-definition problem.
When Iteration Turns into Drift
The same iterative development cycles that make teams nimble and adaptable can also cause them to lose sight of the end goal if that goal isn’t actively held and represented by the product team.
Without strong product leadership anchoring the work, teams drift from solving a problem to simply completing tasks.
A strong product leader holds the vision and constantly asks:
“Are the things we’re building still addressing the original problem we set out to solve?”
That question matters more now than it ever has.
The New Risk of Speed
What becomes riskier as teams build faster with AI isn’t technical quality in the narrow sense.
It’s losing sight of:
the real problem being solved
the human beings who are supposed to use what’s being built
Speed increases the danger of drift.
It becomes very easy to produce more and more software without stopping to ask whether any of it is actually making the product better for real users.
Product’s New Job Description
In an AI-accelerated environment, the real job of the product team becomes:
Developing a deep understanding of the end user and orchestrating a group of fast-moving producers in a single, coherent direction.
Product leaders will have to become more efficient themselves to keep up with delivery velocity. They’ll use AI to complete more of their own tasks.
But none of that changes the core truth:
Software is still being built to solve human problems.
And human brains don’t consume software the way machines produce it.
Our attention spans have narrowed. We’ve been conditioned to focus on quick, singular tasks. Which means product flow, prioritization, and restraint matter more, not less, in an AI-accelerated world.
From Buildability to Viability
For most of software history, the first product question was:
“Can we build it?”
That question is rapidly losing relevance.
The new first question has to be:
“Should we build it?”
Or more directly:
“If we build it, will they come?”
Product viability now means:
Is there a real human problem here?
Does this solve it in a way that fits into how people actually work?
Will anyone care enough to adopt this once it exists?
Just because we can build or add a feature doesn’t mean we should.
With the speed of engineering production, bloated products are becoming a real issue. You don’t want your best feature, or worse, your future revenue driver, buried under twenty “nice to have” features.
Advice to Leaders Embracing AI Speed
If I were advising a CEO or Head of Product who’s excited about AI-driven delivery, I’d tell them two things:
First:
Don’t lose sight of the core question:
Just because we can build it, should we?
Second:
Double down on product discipline.
Product definition and design now have to be centered around one simple test:
“If we build this, will it meaningfully improve the life of the person using it?”
And a caution for the AI era:
There’s a shiny new ball around every corner, and every tool, framework, or feature looks exciting. Not every ball is worth chasing. Leaders have to maintain focus on the problems that actually matter, not the ones that merely look innovative.
Because in a world where software can be produced almost endlessly, restraint, clarity of purpose, and human-centered strategy become the real competitive advantage.
