When Building Was the Advantage
Early in my career, I thought product progress looked like shipping.
I spent the first chapter of my career in teams where engineering was the strength—strong builders operating in a culture of fast delivery and high quality. If a customer had a problem, we could turn it into a feature quickly. The roadmap stayed full. Releases stayed frequent. It felt like we were winning.
Later, I stepped into a GTM-heavy environment. Sales, marketing, and delivery teams running a motion—chasing demand, shaping messaging, and closing deals. Watching that system up close revealed something I hadn’t fully appreciated before: markets don’t reward the team that builds the most. They reward the team that concentrates around a clear wedge of demand. Concentration makes the system easier to tune: messaging gets sharper, onboarding gets simpler, sales cycles shorten, and the feedback signal becomes easier to interpret. Without that concentration, speed doesn’t create clarity. It simply creates more surface area for confusion.
That shift gave me a different lens. Building fast is useful. Building without concentration is expensive. In practice, concentration means refusing adjacent feature requests, broader demos, and custom exceptions when they weaken the clarity of the segment you are actually learning from.
This pattern showed up repeatedly: building was rarely the constraint. In engineering-heavy teams like ours, customer problems quickly turned into roadmap items. The roadmap moved, releases were frequent, and internally velocity started to feel like competence.
But velocity alone doesn’t tell you whether you’re getting sharper—or simply getting bigger. And in one product, that difference became impossible to ignore.
The PMF Island We Didn’t Concentrate On
And yet, the signal was there.
At first it didn’t look like product-market fit. It looked like a statistical anomaly. One slice of users retained meaningfully better. They converted faster, experienced less onboarding friction, and their usage patterns were far more consistent than the rest.
Individually, each signal looked explainable. Together, they pointed to something coherent. It wasn’t dramatic and it wasn’t viral. It was simply consistent.
That was our PMF island.
We noticed it, but we didn’t fully respect what it meant. Instead of concentrating there, we expanded. We generalized the product. We built for adjacent use cases and optimized for broader demos and larger conversations. At the time, the move felt rational: adjacent demand was real, larger conversations looked like progress, and broadening the product felt easier to justify than narrowing it.
The product became more powerful, but it also became less sharp.
Nothing collapsed. Revenue didn’t implode. Engineers stayed busy. From inside the company it looked like growth. The hardest part was not obvious failure. It was the growing gap between visible activity and weakening conviction.
But something subtle was happening beneath the surface. Focus diluted, positioning blurred, and the signal got noisier. We weren’t slow. We were distributing our conviction across too many directions.
And because we had no explicit wedge thesis, no shared record of what we believed, no clear falsification criteria, and no common rule for what counted as confirming evidence, every new feature looked like progress even when it compounded strategic noise.
Rethinking Velocity
That experience reshaped how I evaluate velocity.
It also forced me to start thinking about how product teams could preserve clarity while moving fast. That question has shaped much of my work since.
I stopped asking, "What should we build next?" and started asking, "What must be true for this direction to hold?"
Velocity stopped being the indicator of progress. Evidence-backed conviction did.
At the time, there was still a natural limiter on how much damage misalignment could do. Building was expensive. Engineering capacity was finite. Even confused roadmaps moved slowly because the act of building itself imposed friction.
That friction quietly protected teams from their own strategic noise.
Today, that limiter is disappearing.
When AI Removes the Build Constraint
AI-assisted development is collapsing many of the constraints that once slowed product teams down. Routine coding effort is shrinking. Lead times are shorter. Experiments that once required weeks of engineering work can now be attempted in days.
A small example made this concrete for me: I recently built this website in Next.js, with a working content system, in under three hours. Six years ago, when I was still closer to code, I would not have expected to do that in a day or two, let alone after being away from hands-on development for years.
This does not remove coordination, rollout, or adoption complexity. But it materially lowers the cost of turning intent into working software.
For teams with strong product judgment, this is a real unlock.
But it changes what actually constrains product development.
The failure pattern I described earlier, expanding before concentrating and mistaking motion for clarity, becomes far more dangerous when building is no longer the limiting factor.
Because AI does not just increase speed.
It lowers the cost of iteration, which means teams can change more things at once. As simultaneous changes increase, the number of interacting variables rises with them. Attribution gets weaker. Learning gets noisier. And when learning gets noisier, concentration gets harder to maintain.
The Rise of Decision Entropy
Now imagine how this dynamic plays out inside a fast-moving product team.
In one week you adjust pricing. The next week you introduce an AI-assisted onboarding shortcut. A week later the dashboard layout changes to surface a new capability. None of these changes feel dramatic on their own. They are the normal, incremental moves of a team trying to improve the product.
But a few weeks later the numbers begin to move.
Activation ticks upward. Revenue per account nudges higher. At the same time, churn increases in a particular cohort.
Now the interpretations begin.
Growth attributes the improvement to onboarding. Sales points to pricing. Product believes the new layout made key capabilities easier to find. Customer success blames the new workflow for the churn.
All of these explanations sound plausible. None of them are provable.
The system changed in multiple dimensions at once, and the feedback signal is now contaminated by overlapping causes.
That is decision entropy.
Decision entropy is what happens when the pace of change exceeds a team’s ability to tell what is actually driving outcomes. Too many things move at once. Causes start to overlap. The signal gets harder to read. And once the team can no longer tell what produced the result, interpretations split and roadmap decisions become reactive or political.
Speed assumes clean feedback. Entropy breaks that assumption.
Where Speed as a Moat Breaks
The common speed argument is that the fastest team learns the fastest. That only holds when attribution remains coherent. If the pace of change rises faster than a team’s ability to interpret outcomes, learning does not accelerate—it fragments. Speed still matters, but it compounds whatever direction the system is already moving in. When the system is concentrated around a clear direction, speed sharpens advantage. When it is diffused, speed amplifies dilution. Execution speed still matters. It is just becoming easier to access. The scarcer capability is preserving clarity while the system moves quickly.
The New Scarcity: Clarity Under Speed
In an AI-accelerated world, execution speed is easier to access. The scarcer advantage is preserving decision traceability as teams move faster.
Most existing stacks are much better at measuring outcomes than preserving the chain between belief, decision, change, and result across overlapping moves. That is why dashboards, experiment tools, and reviews often help teams observe the system without helping them stay causally clear inside it.
What becomes valuable is the ability to move fast while preserving a traceable record of why the team made the move, what it expected to happen, and what would prove the bet wrong.
This is not primarily a discipline problem. It is a systems constraint. Even strong teams fail here because the workflow rarely preserves decision lineage across product, GTM, and strategic changes.
As the pace of change increases, so does the number of interacting variables. Without a way to preserve attribution across those changes, even smart teams lose the ability to reason clearly about what is actually working.
What looks like a governance gap is often an attribution gap: governance usually enforces process, but attribution preserves the causal reasoning needed to know what is actually working.
Under entropy, the goal is to preserve attribution as the system moves faster. In the pre-AI era, misalignment accumulated slowly. In the AI era, it compounds. The founders who win will not be those who ship the most. They will be those who preserve clarity while shipping.
Toward an Explicit Operating Model
Over the past few months, I’ve been working toward a more explicit operating model for this. It treats every major product move as a traceable bet, not just an execution task.
At minimum, each bet should preserve six things: the segment it is meant to serve, the belief behind it, the evidence supporting that belief, the change being made, the outcome expected, and the condition that would prove the bet wrong.
That does not sound complex. But once product, GTM, and strategy begin moving in parallel, most teams lose the thread between those elements faster than they realize. That is where speed stops compounding learning and starts compounding confusion.
That is also why this cannot be another governance ritual added on top of execution. It has to operate close enough to the workflow that teams can preserve clarity without paying for it in speed. What makes this moment different is that the same AI that accelerates change can also help preserve traceability, if it is embedded into the workflow rather than layered on afterward.
The founder’s job, then, is not just to increase velocity. It is to preserve the system’s ability to stay causally clear while the pace of change accelerates.