Here's a confession that might surprise you: At 41, as VP of Engineering, I'm about to graduate with my first degree. Yes, you read that right. Despite having passed more than 60 university exams across three disciplines, 27 in Physics, some in Computer Engineering, and now 23 in Computer Science, I have exactly zero degrees to show for it.
Plot twist? This "academic nomadism" has become my secret weapon in leadership.
Let's demolish a dangerous myth right now. The tech industry loves its dropout heroes: Gates, Jobs, Zuckerberg. We've romanticized the garage startup story so much that formal education has become almost embarrassing to admit.
Here's the reality check: Walk through the corridors of today's leading AI companies. What do you see? PhD-heavy teams at OpenAI, DeepMind, Anthropic. Even NVIDIA, the hardware giant, is packed with advanced degrees. The dropout narrative is a statistical anomaly masquerading as a business strategy.
The data doesn't lie: In an AI-driven world, deep technical knowledge isn't optional, it's survival.
As engineering leaders, we face a brutal truth: How can you effectively lead what you don't fundamentally understand? I've watched too many VPs become glorified project managers, translating between technical teams and business stakeholders without truly grasping the underlying complexity.
My Computer Science journey wasn't born from career dissatisfaction or some midlife crisis. It was strategic necessity. To lead engineers building AI-powered products, I needed to understand not just what they were doing, but why certain approaches were fundamentally sound or flawed.
The consistency signal this sends to my team? Invaluable. When your VP is grinding through algorithms and machine learning theory alongside the junior developers, credibility shifts from positional to earned.
Here's what formal education forces that self-directed learning doesn't: intellectual humility. My CS program dragged me through subjects I actively disliked, relational databases, electromagnetism, and many others.
In the real world, we optimize for our interests. We dive deep into areas that excite us and skim over the rest. University programs are different. They're systematic. They force you to build foundations in areas you'd rather skip.
Those "boring" subjects? They've become my secret weapons in technical discussions. Understanding computational complexity helps me ask better questions about algorithm choices. Formal logic improved my approach to system design reviews.
Juggling a VP role, family life, cycling passion, and academic demands? It's been brutal. But here's the unexpected insight: This constraint forced me to become ruthlessly efficient with time management, a skill that immediately transferred to my leadership effectiveness.
When you have limited hours, every meeting must have clear outcomes. Every decision must be made with available information rather than perfect information. Every delegation must be precise.
The time pressure of academic deadlines while managing engineering teams taught me something crucial: Perfect is the enemy of done, but standards still matter. This balance is the essence of engineering leadership, or at least my idea of it.
We're living through an AI transformation that's fundamentally changing how software gets built. The engineering leaders who will thrive aren't those who can speak AI buzzwords at board meetings, they're the ones who understand the mathematical foundations, the training processes, the fundamental limitations.
My CS studies in machine learning and AI aren't just academic exercises. They're preparation for a world where every product decision will have AI implications. When your team proposes a new ML model, you need to understand the tradeoffs between accuracy and inference speed. When they discuss transformer architectures, you should grasp why attention mechanisms matter.
This isn't about becoming the smartest person in the room. It's about asking the right questions and understanding the answers. It is never nice to be in rooms where people speak about something they do not know, but it happens, especially in the AI era we live in.
My academic journey, Physics to Computer Engineering to Computer Science, might look unfocused. But it's taught me something powerful: Knowledge compounds across disciplines. Physics gave me mathematical rigor. Computer Engineering provided systems thinking. Computer Science is adding algorithmic sophistication.
Each transition was driven by curiosity about how my teams think and work. The lack of completed degrees wasn't too much about inability to finish (but also that in the past, you know: “I was young”, that's my excuse), it was about following intellectual curiosity wherever it led.
Now, finally completing this CS degree feels like closing a loop, not just collecting a piece of paper.
In a world where GPT models can write code and AI systems can design other AI systems, what's the role of human engineering leadership? It's not just about resource allocation or sprint planning anymore.
Modern engineering leaders need to:
Understand the technical implications of business strategies
Evaluate the feasibility and risks of emerging technology bets
Bridge the gap between cutting-edge research and practical implementation
Guide teams through technological transitions that could obsolete entire skill sets
This requires more than experience. It requires continuous, rigorous learning.
Here's your homework: Audit your learning strategy against the pace of change in your industry. Are you staying ahead of the technological curve, or are you managing yesterday's technologies while your competitors build tomorrow's?
The leaders who thrive in the next decade won't be those who coast on past experience. They'll be the ones who treat learning as a core leadership competency: formal, rigorous, and never-ending.
Remember: In a rapidly changing world, your expertise has a half-life. Continuous learning isn't just about staying relevant, it is about staying in the game.
Getting my CS degree at 41 isn't a mid-life course correction. It's an investment in leadership effectiveness. The combination of deep technical knowledge, management experience, and the discipline of formal learning creates a leadership profile that's increasingly rare and valuable. And yes, you might consider it too, as someone else would claim: “Just do it!”.
Until next time, keep learning like your leadership depends on it. Because it does.
Ciao,
Luca
P.S. 24 exams down in Computer Science, and yes, I'm still standing. The finish line is in sight, but the learning never stops. Feel free to write me if you need guidance to get started.
Thanks to Elena for inspiring me to write this article, and Alberto for reminding me my experience can be valuable to others.
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