Clarity is the new bottleneck
For years we treated product building and software development as scarce skills. They were expensive, and business learned to live with missed deadlines, bugs, and compromises. Today shipping “something” is dramatically cheaper — many costs dropped by an order of magnitude.
But there has always been another half of the job: aligning requirements, matching expectations with reality, describing what needs to be done, and communicating it so people end up on the same page. In the past, engineers were usually the ones complaining about this — about managers changing requirements ten times and meaning something different from what they said. Now this part is no longer background noise. It’s the bottleneck.
Clarity Is More Expensive Than Implementation
Clarity has become more expensive than execution. Making your intentions understandable often costs more than building the thing you had in mind.
What exactly do we mean? What outcome counts as “good,” and what is “bad”? What constraints do we have? How do we keep scope from quietly inflating?
Even solo builders run into these questions now. In teams, the cost is higher because there’s more than one person, more stakeholders, more incentives, and a bigger price tag for misalignment.
Communication Isn’t the Problem. Convergence Is.
Most teams don’t struggle because they can’t communicate. They struggle because they increase the volume of communication and still can’t reach shared understanding fast enough.
You can have constant syncs, endless Slack threads, and documentation everywhere — and still end up with people operating on different internal models of the user, the goal, and the constraints.
That’s how you get a misunderstanding tax:
- doing the same work again starts being called “iterations”
- meetings multiply because context keeps shifting
- decisions that look logical still lead to the wrong outcomes
AI Scales Output — and Scales Ambiguity
AI doesn’t automatically fix this. In this area it often adds noise.
Agents can generate documents, code, reports, analytics, tasks — and it will all look neat. But they can’t magically infer what was actually needed, why certain decisions were made, or what the work is really trying to achieve.
If clarity is missing, an agent will scale the ambiguity. Faster, cheaper, and with better formatting.
So if your system relies on “smart people will figure it out,” it may break the moment generative output starts growing exponentially.
The Shift
Product work is shifting from “can we implement it correctly?” to “are we making the right decisions, and do we understand what we’re trying to do and why?”
The speed of business will be determined less by raw execution capacity and more by judgment: the quality of hypotheses, the ability to set boundaries, remove the unnecessary, and stay focused on value — without drowning in coordination and endless trade-offs.
In a world where building is cheap, misunderstanding is what gets expensive.