AI adoption was everywhere at London Tech Week 2026: in the keynotes, the breakout sessions, and the conversations between talks. So was the usual hiring agenda: curiosity, adaptability, T-shaped professionals, engineers with business sense. I took notes, nodded along, and heard plenty I’d expected. What I hadn’t expected was a recurring thread across multiple sessions: what the smartest companies had decided not to do with AI, and why.
The leaders who stood out weren’t the ones talking about which skills they were looking for. They were the ones asking a harder question: not which skills to hire for, but what happens to the people you already have.
The obvious move, when AI can demonstrably do the work of several people, is to reduce headcount. The logic is clean: same output, lower payroll, better margins. Plenty of companies are making exactly this calculation. But the leaders who gave me pause were the ones arguing the opposite: that committing to no AI-driven redundancies wasn’t just the ethical choice, it was the strategic one.
Their reasoning was straightforward. If your engineers suspect that every productivity gain they unlock is a step toward their own redundancy, they will stop unlocking productivity gains. Not consciously, perhaps, but the instinct for self-preservation is hard to override with a company memo about embracing change. Fear produces compliance, not curiosity. And curiosity, as every leader at the event was keen to stress, is the thing they most want and find hardest to hire for.
Psychological safety, in other words, isn’t a soft concept. It’s the precondition for everything else on the wishlist.
Which brings me to the continuous learning problem, because it’s more complicated than the conference circuit tends to admit.
Leaders say they want engineers who keep learning. What they mean (or what they should mean) is engineers who can genuinely rebuild their competence as the landscape shifts, not just engineers who have added a few lines to their LinkedIn.
Here’s what the honest version looks like from the inside. Most engineers I’ve known, myself included, have already rebuilt from scratch more than once. A language or framework you spent years mastering becomes legacy. You relearn. The tools change again. You relearn again. This isn’t continuous learning in the aspirational sense; it’s a recurring tax on expertise you’ve already paid for. At some point, the stamina runs out.
The university system offers no real solution. Curricula are designed to meet the needs of industry, but the industry now moves at the speed of model releases. By the time a graduate arrives in the job market, the core language they were taught may already be a second-best choice. The emergence of tools like Claude raises a harder question still: how much does syntax even matter any more? What exactly should a student be studying, and how would a course designer know? The gap between what education produces and what industry needs isn’t closing; it’s structural. It’s a problem nobody in education can solve quickly, which means the only environment where continuous learning can realistically happen is the one employers control.
AI does lower the friction here, and that matters. The paradigm shifts feel less brutal when you have something that can translate your existing knowledge into a new context. But there’s an obvious trade-off that doesn’t get named often enough: that engineers who lean heavily on AI assistance get shallower over time. The deep fluency that comes from wrestling with a problem yourself – from actually failing and debugging and understanding why – is quietly eroding. That might be an acceptable trade. But it should be a conscious one.
The companies that seem to understand all of this are doing something specific. They’re treating psychological safety not as a culture initiative but as an adoption strategy. If you want your engineers to experiment with AI, to find the places where it genuinely accelerates their work, and to be honest about where it doesn’t, you need them to feel that doing so won’t cost them their jobs. The commitment to no redundancies creates the conditions for the curiosity and adaptability that leaders claim to want. You can’t hire for those qualities and then manage them out of people once they’re through the door.
The ratio between technical depth and broader adaptability is shifting; that much is true, and London Tech Week confirmed it. But the shift isn’t simply a matter of engineers needing to broaden their horizons. It depends on whether the organisations employing them create the conditions for that broadening to happen at all.
The companies using AI to thin their headcount may hit their margin targets this year, although the evidence suggests many won’t even manage that. Forrester’s 2026 Future of Work report found that 55% of employers already regret AI-driven redundancies; cutting people for a technology that, in many cases, wasn’t ready to replace them. The ones who have made the opposite bet – keeping people, making them feel safe, and letting them experiment – are building something harder to replicate than a leaner payroll. They’re building teams that are actually capable of adapting. Whether that bet pays off at scale remains to be seen. But the alternative is looking shakier by the month.
Cutting for efficiency now might be exactly what prevents you from being efficient later.
