Your full-time CTO is always on stage. Every meeting is a performance. Every decision is live. Every failure happens in front of an audience.
I have a different job. I practice.
A full-time CTO’s calendar looks like a concert tour with no breaks. Monday: architecture review. Tuesday: board prep. Wednesday: incident response. Thursday: hiring panel. Friday: strategy offsite. Weekend: catching up on Slack.
Where in that schedule does deliberate learning happen? Where does experimentation occur? When does your CTO try a new approach, fail safely, and refine it before deploying it with your team?
The answer, typically: never.
I’ve been in that position. As VP of Engineering at Jimdo, my weeks were fully booked months in advance. I was good at my job, but I wasn’t getting better at my job. There’s a difference.
In K. Anders Ericsson’s landmark research on expert performance, he studied violinists at the Berlin Academy of Music. The elite performers — those destined for international solo careers — weren’t distinguished by innate talent. They were distinguished by how much time they spent in deliberate practice: focused, structured work on improving specific weaknesses. By age 20, the best violinists had accumulated roughly 10,000 hours of it.
The key insight isn’t the number. It’s the ratio. These musicians spent far more time practicing than performing. Performance depletes while practice builds. You can’t improve your technique during a concert. You can only execute what you’ve already mastered.
Now imagine a violinist who only performs and never practices. Within a year, they’re coasting on declining skills, making errors they can’t diagnose, falling behind peers who invested in fundamentals.
That’s your full-time CTO or yourself (I have been there).
Last month, I spent three days testing a new approach to OKR workshops with a client who gave me explicit permission to experiment. The approach failed. We identified why. I refined it. The following week, I ran the improved version with another client. It worked.
A full-time CTO doesn’t get that second attempt. Their first try IS the real thing. Their team remembers the failure. Their credibility takes the hit.
Here’s a conversation I had recently with a CEO:
CEO: “How do you stay current on AI strategy when you’re not embedded in one company?”
Me: “That’s exactly why I stay current. I’m implementing AI workflows across four different organizations right now. Your full-time CTO is implementing it in one. I see what works in fintech, what fails in e-commerce, what surprises everyone in SaaS. By the time I bring an approach to you, I’ve already run the rehearsals.”
Full-time CTOs operate in a closed system. Their reference points are their current company, their previous company, and whatever they read on Hacker News.
I’m working with a B2B SaaS startups, a Finserv company, and two scale-ups simultaneously. When one solves a problem, the solution travels. When one fails, the lesson travels faster.
Last quarter, I watched a client struggle with engineering team topology. They’d organized around components, and cross-team dependencies were killing velocity. I’d seen this exact failure mode at two previous clients. I’d also seen the solution: a shift to stream-aligned teams with clear domain boundaries.
At my first client, implementing this change took four months of trial and error. By my third client, it took six weeks. The approach wasn’t different. My practice was better.
A full-time CTO costs you roughly €200-300K per year in total compensation. For that investment, you get someone who:
Knows your context deeply
Is available full-time
Accumulates institutional knowledge
Stops learning at approximately month 18
A fractional CTO costs (way) less. For that investment, you get someone who:
Knows multiple contexts and can pattern-match
Is available part-time
Brings external perspective
Never stops learning because the job requires it
The question isn’t which model is better. The question is which problem you’re solving.
Full-time CTOs win when your primary constraint is context depth. If you’re navigating a multi-year platform migration with deep legacy complexity, you need someone who lives in that codebase.
They also win when you need someone to build and maintain a large engineering organization. Managing 50+ engineers is a full-time job, and fractional models struggle to provide the daily presence that team-building requires.
Fractional wins when your constraint is strategic clarity, not execution bandwidth. When you need someone who can diagnose organizational dysfunction, design a target operating model, and coach your existing leaders to implement it.
It wins when you’re scaling from 10 to 50 engineers and need someone who’s done that transition five times, not once.
It wins when your full-time leadership team is stuck in operational mode and needs an external voice to ask the obvious questions everyone is too busy to consider.
Whether you hire fractional or full-time, the deeper lesson stands: executives need practice time. Deliberate learning. Safe-to-fail experiments. Space to refine approaches before deploying them.
If your CTO hasn’t learned anything new in the past quarter, something is wrong. If your leadership team can’t name a recent experiment that failed, your organization is fragile.
Build practice into the system. Give your leaders room to rehearse. The alternative is a concert where nobody remembers how to play.
K. Anders Ericsson’s research on deliberate practice — the foundation for understanding why practice beats raw experience: The Role of Deliberate Practice in the Acquisition of Expert Performance (Frontiers in Psychology)
Team Topologies by Matthew Skelton and Manuel Pais — the source for stream-aligned team thinking: teamtopologies.com/book
My previous newsletter on aligning teams through a single customer-centric OKR
Considering whether fractional leadership might solve a problem for you? Let’s talk. Find me on LinkedIn.
Ciao,
Luca
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