Jeremy Bradley-Silverio Donato is a writer and the COO at Zama.
While the world races to capture value from artificial intelligence, a silent crisis is unfolding inside the companies driving this innovation. Visionary founders and charismatic commercial leads dominate the headlines: securing funding, building narratives and attracting talent. But behind the scenes, technical managers are burning out, stalling or quietly dropping out.
These mid-level leaders—such as team leads, engineering managers and applied research leads—are the unsung translators of deeptech. They operate at the fault line where science meets shipping deadlines, where foundational models meet flaky APIs and where the theoretical promise of AI must survive the brutal realities of implementation. Without them, the pipeline from R&D to product collapses. And yet, many organizations treat technical managers as afterthoughts.
It’s a blind spot that AI startups and scaleups can no longer afford.
The Forgotten Middle Of Deeptech
In traditional industries, middle management has long been the butt of cynicism—seen as bureaucratic or replaceable. But in AI-driven companies, technical managers are not cogs. They are multilingual systems thinkers who must translate visionary goals into granular execution, all while managing teams of experts with rare and often fragile skill sets.
Here’s what they’re expected to do:
• Decode ambiguous product roadmaps and make them technically feasible.
• Prioritize and allocate scarce compute, data and engineering resources.
• Coach highly specialized individual contributors without necessarily being their intellectual peer.
• Navigate the ever-evolving complexity of ML tooling, privacy regulation and ethical constraints.
• Mediate between visionary founders and increasingly stretched engineering teams.
And yet, unlike founders or senior researchers, technical managers are rarely offered the time, training or trust to develop as leaders.
In AI organizations, prestige still flows to code and charisma. Managerial work, especially in the middle, remains undervalued. The “invisible leaders” of AI teams carry enormous weight but remain largely unrecognized.
Manager Burnout And The Consequences For Innovation
The cost of ignoring technical managers is not just internal; it’s strategic. In a 2022 McKinsey report on AI, organizations seeing the highest returns from AI were more likely to be following a number of strategy- and leadership-related best practices, which I’ve found technical managers are critical in helping to execute.
A recent analysis covered in the Harvard Business Review emphasized the role of middle managers as collaborators, not just supervisors, due to “constant change and innovation.”
When these managers exit, whether through burnout or quiet disengagement, they take with them vital institutional memory and informal power. Replacing them is not just costly; it also destabilizes the very bridge between ambition and execution.
In the AI gold rush, everyone wants to be the prospector. Few want to maintain the roads to the mine.
Why Leadership Development Fails Technical Managers
Traditional leadership programs often fall flat for technical leads. Why?
1. They’re too generic. Courses designed for “managers” often fail to address the specific dilemmas of leading in scientific, experimental environments.
2. They ignore the dual identity. Most technical managers still write code or run experiments. They’re not “pure” people managers, and they shouldn’t be forced to choose.
3. They don’t account for epistemic humility. In deeptech, managers often lead people who know more than they do about core domains. Authority can’t be asserted—it must be earned differently.
The result is a gap: Technical managers are expected to grow into leadership roles without guidance tailored to the unique pressures they face. Many end up over-functioning, doing both technical and managerial work, and poorly supported in either until they break. It’s time to realize that middle managers are not blockers to innovation; they are its linchpins.
What Can Be Done: Three Levers For Founders And Execs
If you’re leading a deeptech company as a founder, CTO or COO, here are three practical levers to support and retain your technical managers:
1. Design role clarity, not just titles.
Instead of promoting engineers into management by default, create dual tracks that are equally valued. Then, for those who do take the management path, clarify what success looks like—and make sure it includes leadership behaviors, not just delivery metrics.
Introduce role templates like:
• Technical Lead: 70% individual contributor (IC) work; 30% mentorship/project oversight.
• Engineering Manager: 50% delivery leadership; 50% people development
• Group Lead: 30% technical context; 70% strategic leadership
Clarity can reduce the stress of identity conflict and help managers evolve with confidence.
2. Build micro-communities of practice.
I’ve found that technical managers often feel isolated, especially in hybrid or remote teams. Create spaces where they can regularly share dilemmas, swap notes and ask for help. This could be a monthly “manager engineering forum,” a dedicated Slack channel or cross-functional manager roundtables.
3. Offer deeptech-specific leadership development.
Don’t settle for off-the-shelf training. Partner with coaches or programs that specialize in leading technical teams, or build your own internal curriculum that covers:
• Leading through uncertainty
• Feedback in high-autonomy teams
• Navigating conflicting epistemologies (research vs. product vs. business)
• Psychological safety in experimental environments
The Leadership Gap That Could Break AI
As the AI landscape matures, differentiation will not come from bigger models or flashier demos. It will come from sustained, coherent execution across diverse teams. And that coherence depends on the invisible labor of technical managers.
They are not footnotes in your org chart. They are the load-bearing beams of your company’s future.
If you’re a founder, don’t just ask your technical managers to “step up.” Ask yourself: Have you built an environment where they can thrive, lead and stay?
Because in the end, the AI gold rush won’t be won by vision alone but by the people who make it real—line by line and decision by decision.
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