1. A fact first: the coach wasn't cut — he mattered more
At the 2024 Tour de France, the Visma-Lease a Bike team put an AI race-analysis system in its team car. According to research published jointly by McKinsey and QuantumBlack, it sped up race analysis by 36 times.
Common sense would say: if efficiency jumps 36x, cut some people. The opposite happened — the coach wasn't let go. He became more critical, not less.
The reason is simple. AI can compute in a fraction of a second that "a headwind on stage three pushed the peloton's energy use up 12%, a rider's heart-rate variability is dropping, fatigue is entering the orange zone." But a cyclist moving at 60 km/h can't absorb that much detail while riding. The coach's job is to compress dozens of pages of analysis into one line in the earpiece: "Hold on, there's a feed station coming up — take salt."
That's the first reason AI can't replace you: it can generate an answer, but it can't "translate" that answer into something you can trust and act on. Someone has to do the translating.
2. First reason: it can speak, but it can't translate itself
AI produces technical output; the human does the translating — turning high-density data into a low-dimensional, high-trust, executable instruction. Translation here isn't about language; it's about turning information into action.
This translation goes beyond the race. A CEO at an energy-equipment company told McKinsey his customers' budgets had tripled or quadrupled, yet they weren't asking for "cheaper stuff" — they wanted "a more credible strategy." Customers don't understand the technical specs of high-voltage DC transmission; they need someone to translate those specs into "a plan for the next decade of electricity growth."
Translation runs on three layers, and all three matter:
- Internal translation: how employees understand and trust AI output well enough to act on it. The Visma coach and the high-performing teams on Sonar sit at this layer.
- External translation: how a company turns technical specs into a promise customers can believe.
- Manager translation: how a leader turns raw anxiety into concrete moves. That CEO said the busier he gets, the more he "walks the dog" — not to slack off, but to leave himself room to think in the right direction.
Between "take salt" and a page of analysis sits this layer of translation. AI doesn't occupy it yet.
3. Second reason: it can "use," but it can't "orchestrate"
Using AI has three levels, and most people stay on the first.
- Level one, tooling around: use AI to write reports, autocomplete code, translate email. Each saves a few minutes, but the structure of the work doesn't change.
- Level two, redesigning the workflow: embed AI into a specific node of the process and re-divide responsibility between the human and the machine. In Sonar's data, teams at this level saw 50%–80% productivity gains.
- Level three, orchestrating systems: multiple AI agents collaborate on their own, and the human only sets boundaries and goals, stepping in at the edges.
The surprise comes at level three: the bottleneck shifts from "generating" to "verifying." Code gets written faster, but code review doesn't keep up. The scarce skill becomes orchestrating and managing these agents — the Harvard Business Review noted in May 2026 that treating AI agents like "employees" backfires, because they don't behave the way human intuition expects.
Moving from level one to level three isn't a tool upgrade; it's a shift in how you think. AI won't make that shift for you.
4. Third reason: it has no share of "indispensability"
In a defense-industry analysis McKinsey did for Sweden, one number stands out: in a core defense domain, roughly 15% market share is the threshold for long-term survival. Below it, you may not be replaced — but you'll stop winning the resources for the next round of R&D.
Fifteen percent is an analogy, not a universal rule. Its real meaning: your capability has to be scarce enough that people prioritize you when allocating resources.
How to check? Three dimensions, each scored 1 to 8:
- Depth: are you better than most people around you in a narrow field.
- Differentiation: is your skill combination unique, or could anyone replicate it with the same hours.
- Exportability: is your capability still worth something outside your current company, industry, or city. In the Swedish defense report, only 20% of firms could export — the other 80% struggled once they left the home market.
Score below 12 total, and your capability structure may be fragile. AI didn't cause that, but it will make it much easier to see.
5. Fourth reason: it has no resilience to rebuild
When a lane shrinks or disappears, only the human can rebuild from scratch.
Climate research offers a useful frame: resilience has three layers.
- Absorptive capacity: how much disruption you can take before you fail. In career terms, "how long can you last as your current lane shrinks."
- Adaptive capacity: whether you can recombine your existing resources when the shock exceeds what you can absorb. In career terms, "can you switch quickly into an adjacent field."
- Transformative capacity: when the old lane is gone, can you rebuild an edge in a new one.
Each layer is built differently. Absorption comes from reserves; adaptation from cognitive flexibility; transformation from a learning system that keeps building new knowledge.
That CEO who walks his dog is talking about the same thing — the core of resilience isn't "enduring," it's continuously recalibrating direction, which takes the deliberate slowing-down you have to carve out for yourself.
6. Turn these four into a flywheel, and start with the lowest step
Translate, level up, own a niche, stay resilient — these aren't parallel; they form a loop.
Translation lets you capture AI's gains faster and move up a level; a higher level lets you build depth and scarcity faster; the security from scarcity frees up cognitive room to build resilience instead of burning energy on anxiety; and resilience lets you build new translation ability quickly in a new field.
The hard part is starting the flywheel. Don't try to push all four at once — that just overloads you. Start with translation; it has the lowest entry bar. You don't need to become an AI expert. You just need to learn to compress, evaluate, and build trust after you receive AI's output.
One small start: don't ask AI for a single answer. Ask for several versions, then choose and revise. That one move is the first brick of translation.
AI will keep doing more. But "what do you do beyond what AI can do" — the earlier you answer that, the sooner you can breathe easy.
References
- McKinsey & QuantumBlack — "Scaling Agentic Development: The Sonar AC/DC Framework," McKinsey Digital, 2026
- McKinsey Sweden — "Enhancing Swedish Security Through Deterrence and Prosperity," McKinsey & Company, 2026
- Bernard Marr — "5 ChatGPT Prompts To AI-Proof Your Career In 2026," Forbes, 2026
- Ryan Roslansky (LinkedIn CEO) — "AI Can't Replace These 5 Skills," CNBC, May 2026
- The New Stack — "The AI Verification Bottleneck: Developer Toil Isn't Shrinking," 2026
- Kropp, M. et al. — "Research: Why You Shouldn't Treat AI Agents Like Employees," Harvard Business Review, May 2026
- McKinsey — "Europe on the Move: A Conversation with Hitachi Energy's CEO," McKinsey Interview Series, 2026
- McKinsey Global Institute — "Advancing Climate Adaptation in Asia: Protecting People, Protecting Growth," MGI, 2026
- PwC — "No More Pyramids: Rethinking Your Workforce for the Agentic AI Era," PwC Workforce Research, 2026
- Boston Consulting Group — "AI Will Reshape More Jobs Than It Replaces," BCG Publications, 2026
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