“Our data show stability, not disruption, in AI’s labor market impacts, for now.”
Gino Ferrand, writing today from Austin, TX 🌞
A recent piece on LinkedIn made a comforting claim. AI is not taking jobs. According to the report, despite the rise of tools like ChatGPT, Copilot, and others, there’s no solid evidence of mass layoffs directly caused by AI adoption.
But like most soft truths about the future of work, there’s a footnote that deserves more attention.
“Not taking jobs” doesn’t mean “not changing them”
When we say AI isn’t replacing jobs, we are taking a statistical shortcut. It might be true in broad terms, across sectors or at the macro level. But it is not true for every role or every stage of a career.
In fact, other research points to a troubling trend: early-career workers appear to be getting squeezed. A study from Stanford’s Digital Economy Lab found that in roles most exposed to AI, employment for workers between the ages of 22 and 25 has declined by 13 percent since 2022. Meanwhile, mid-career and senior workers are holding steady or increasing.
The Yale/Brookings team hinted at this too. In their own words, they observed “a slight decline in recent graduate hiring” in occupations where generative AI is highly capable. The overall picture may be stable, but the entry points are tightening.
Why are early-career developers getting squeezed?
Three dynamics are already visible.
Entry-level, repetitive tasks are exactly the kind that tools like GitHub Copilot now automate with confidence. These were once the proving ground for juniors.
Tacit knowledge, organizational context, and architectural tradeoffs still require human experience. These qualities tend to sit with senior team members.
Junior engineers used to learn by doing, breaking things, and getting feedback. Now they are being asked to validate AI output they may not fully understand. It is a recipe for fragile growth.
The result is a modern kind of career gatekeeping. Veterans are trusted. Newcomers are questioned. Not because they lack talent, but because the space to build it has quietly disappeared.
So maybe AI isn’t a destroyer. It’s a filter.
Exactly. What’s emerging is not mass replacement, but a reconfiguration of the software job market. AI is not kicking people out, but it is keeping more people from getting in.
For engineering leaders, this creates a subtle but important dilemma. If your new hires can’t learn by starting with the basics, how do you help them grow? And if those basics are now automated, restructured, or invisible, where do they get the experience?
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How can leadership adapt?
Here are a few starting points:
Redesign entry-level roles - Don’t expect AI to do everything. Design workflows that combine automation with human analysis, where new team members can add value with context, creativity, or questions AI can’t yet ask.
Mentor with clarity and critical thinking - Train developers not just to use AI, but to challenge it. When to override. When to rewrite. When to reject. Human review is not a safety net. It is the core of AI fluency.
Track growth, not just headcount - Hiring is not enough. Measure how fast juniors ramp up, how often they push code with confidence, and how well they can explain what the AI did and why it might be wrong.
Protect learning loops - If entry-level roles become too shallow, the org will suffer. You lose future senior engineers. You lose diversity of thought. And you create teams where only those who already know how to build can survive.
The future isn’t jobless. It’s more selective.
Saying AI isn’t taking jobs is technically accurate. But for those just starting out, that truth can feel hollow.
The more urgent question is not whether AI replaces roles, but who gets access to them.
This shift isn’t happening at the top. It’s happening at the door.
More to come…
Recommended Reads
✔️ AI Is Eliminating Jobs for Younger Workers (Wired)
✔️ The End of Entry-Level Hiring? Experts Debate AI’s Real Impact (HR Executive)
– Gino Ferrand, Founder @ Tecla