A Reality Check in the Silence
It started with the connectivity blackouts in Iran.
For a significant period, the internet was unavailable. Suddenly, the entire product operation at a leading InsurTech company—an environment that usually prided itself on speed—came to a complete standstill.
Watching the team during this period was revealing.
The real problem wasn't the lack of tools. It was something deeper: a collective inability to face a blank page.
The environment had become so accustomed to having AI generate the base structure of analysis, documentation, and exploration that when the infrastructure disappeared, something fundamental disappeared with it. The ability to start thinking from scratch.
What surfaced was a form of structural laziness—a professional condition where the mind, trained on instant outputs, struggles to manually construct deep analysis without a digital prompt.
The blackout didn't just expose a technological dependency.
It exposed a cognitive one.
Skipping the “Digestion” Phase
Before the age of AI, discovering patterns in product management was rarely instantaneous.
Insights emerged slowly.
A product manager would spend hours wrestling with SQL queries, cleaning messy datasets in Excel, testing hypotheses, discarding false assumptions, and gradually moving toward understanding.
This process was inefficient.
But it was incredibly valuable.
Because during that time, something subtle was happening: the brain was digesting the data.
Patterns were internalized.
Connections formed with past experiences.
Intuition began to develop.
A PM had to live with the problem long enough for meaning to emerge.
Today, AI often delivers an answer in thirty seconds.
The struggle disappears.
But so does the digestion.
The result is a strange new condition: teams possess answers they don't truly understand. They operate with conclusions whose roots they never explored.
And when the tools disappear, they discover something uncomfortable:
They aren't just professionals without software.
They are professionals who have become strangers to their own business.
The Question-to-Answer Trap
Healthy product thinking traditionally followed a path like this:
Question → Ambiguity → Struggle → Hypothesis → Answer
The most valuable stage in this process was ambiguity.
It was uncomfortable.
Slow.
Sometimes frustrating.
But ambiguity forced the mind to explore.
AI has quietly collapsed this journey into something much shorter:
Question → AI → Answer
The moment uncertainty appears, the instinct is to eliminate it immediately.
The machine provides a clean explanation. The anxiety of not knowing disappears.
But the moment we bypass ambiguity, we also bypass discovery.
Innovation rarely emerges from immediate clarity.
It emerges from the messy middle—those frustrating periods where easy answers fail and the mind begins searching for new connections.
The Commoditization of Thinking
AI systems are remarkable at identifying correlations.
They are far less capable of identifying causality.
Causality is the deeper layer of understanding that explains why something happens. It often lives outside databases—in user behavior, market psychology, and contextual knowledge.
When product teams become dependent on AI-generated reasoning, a subtle shift occurs.
They stop investigating causes.
They begin rewriting patterns.
Instead of asking why users behave this way, they accept whatever explanation the machine produces.
Over time, this leads to a dangerous professional transformation.
If a product manager's role becomes simply editing machine output, the strategic work disappears.
The PM becomes less of a strategist and more of a prompt operator.
And like unused muscles, analytical capabilities begin to weaken.
Bringing Back the Strategic Muscle
In the age of AI, speed is no longer a meaningful competitive advantage.
Machines have already won that race.
The real advantage now lies somewhere else:
The human capacity to tolerate ambiguity and perform independent analysis.
To rebuild this strategic muscle, teams need to rethink how they work.
First, tasks should be broken into smaller analytical steps rather than outsourced entirely to AI systems. This forces product managers to confront the underlying questions before jumping to automated outputs.
Second, organizations must recognize that AI increases output capacity while quietly reducing cognitive depth. Reducing task overload may actually improve strategic thinking by allowing time for real analysis.
And finally, teams should practice something simple but powerful:
The Ten-Minute Rule.
Before asking AI for an answer, spend ten minutes thinking through the problem independently. Form hypotheses. Write down assumptions. Explore possible explanations.
Walk in the dark for a moment before turning on the flashlight.
Because that short walk is where intuition forms.
Real Product Management Lives in the Questions
Real product management is not the art of generating answers quickly.
It is the discipline of asking questions that do not yet have a recorded answer.
AI can accelerate outputs.
But innovation still requires the slow work of thinking.
The struggle is not inefficiency.
It is the forge where strategy is formed.
And in a world that is becoming increasingly automated, the professionals who remain comfortable inside that struggle will be the ones who continue to create real value.
