By: Marley Peters
Long before titles like Chief Data and Engineering Officer, before leading hundreds of engineers, before thought leadership on AI and technical debt, Michael Privat was a kid in France getting dropped off at a computer store while his mother grocery shopped. Most kids would have treated that like a waiting room. Privat treated it like a lab.
He was six or seven, small enough to reach up awkwardly to the keyboards of the machines on display, teaching himself how to make screens do things they were never meant to do. First, it was simple code and ASCII art. Then scrolling visuals. Then increasingly advanced demos on old Commodores, Ataris, and Amigas. Eventually, he was learning Assembler and experimenting with 3D graphics on machines that could barely do basic math.
He was not playing games. He was building.
“I think of myself as a builder who has never stopped building,” Privat says. “I just changed what I built over time.”
Michael Privat is not one of those executives who drift upward and away from the work. He still sounds like an engineer who happens to lead at scale. Early in his career, he built systems and software. Now, as a Chief Data and Engineering Officer, he says he builds “the conditions under which 500 or so engineers can do their best work building.”
That shift from writing code to building organizations has shaped the leadership philosophy he now uses across large teams. He calls it “accountable autonomy.”
For Privat, autonomy without accountability can turn into drift. Accountability without autonomy can turn into micromanagement. He has seen both fail. When leaders hover too closely, nothing scales. When they step back without clear expectations, projects slide off course, communication breaks down, and eventually, the leader gets pulled back in to clean up the mess.
So his model is simple, but demanding. Ownership means owning outcomes. It means communicating early, especially when things go wrong. It means driving resolution without waiting to be told. It also means defining success, defining failure, and being explicit about what needs to be reported back and when. In Privat’s view, that is the only way a large engineering organization can move with effective speed.
He pushes his teams hard. He says he expects them to behave like pro athletes, showing up with professionalism and consistently raising the bar. That standard fits naturally with the tone of his writing as well. Across his recent Substack posts, Privat comes back to the same core belief from different angles. Companies can struggle when they cling to outdated assumptions. Technical debt is not just a mess to clean up, but a record of the decisions that created it. Features are easy to copy. Real value is much harder to replicate. And the biggest risks in any business are often the ones hiding outside the dashboard.
That mindset matters more now. Privat believes most companies are still running on rules that were written before the tools changed so dramatically.
He has been especially focused on the gap between what AI can do and how organizations are structured to use it. On his own time, coding for fun on weekends, he says AI makes him “100 to 1000 times faster.” But inside large companies, he does not see that same order-of-magnitude gain showing up in the output of engineering teams. His conclusion is blunt. The bottleneck is no longer just talent or technology. It is largely a process.
Many of the systems enterprises still use to organize work, including standard sprint cycles and planning rituals, were built long before AI existed. Yet companies keep acting as if the arrival of these tools should not fundamentally change how work gets scoped, paced, or measured. That, to Privat, is the issue. You cannot hand people radically different tools and expect the old operating model to somehow produce a radically different outcome.
That does not make him an AI evangelist without guardrails. He is practical about the risks. Yes, AI can leak data if people use it irresponsibly. But in his view, that is not a reason to retreat from it. It is a reason to lead it properly. He rejects the passive fear that AI will simply “take jobs.” The bigger risk, he argues, is people not adapting and failing to use their own full potential. His message to engineers is sharp: stop treating AI like a magic trick, and stop using your brain for the part a machine can already do better. Let AI type the code. Your job is to direct it, correct it, and think at a higher level.
That perspective ties directly back to how he leads.
He is not interested in maintaining systems that worked in the past. He looks for where they fail under new conditions. He pushes teams to question assumptions that have gone unchallenged for years. Not because it sounds good in theory, but because at scale, outdated thinking can become a bottleneck.
That is what separates operators from builders.
Builders look at a system and ask what it could become.
Privat has been doing that since he was a kid, standing in a computer store, pushing machines past their limits. The difference now is the scale. The systems are larger. The stakes are higher. The constraints are more complex.
But the instinct is the same. He is still building.
Only now, instead of wiring a homemade connection between two computers, he is rewiring how organizations think, operate, and scale in a world that is moving faster than most are prepared for.
Read more about Michael’s musings on tech, software, AI, and more on his Substack.
