In a recent New York Times article, Noam Scheiber raised an important question: “Which Workers Will A.I. Hurt Most: The Young or the Experienced?” Having spent over 20 years guiding technological transformations as a CTO, I believe this provocative framing opens the door to an even deeper and more essential discussion.
This issue isn’t about who AI might “hurt” — it’s how AI will redefine expectations and transform everyone’s role.
The Misunderstood Math of AI Efficiency
In my experience integrating AI across teams — including engineering, analytics, marketing, legal, and operations — I’ve observed significant variability in efficiency gains, largely dependent on specific job functions. Some roles, especially certain engineering tasks, might achieve gains of 50% or more, while others experience far smaller improvements. When viewed collectively across an entire organization, leaders should realistically anticipate an average cumulative efficiency gain of about 30%.
However, both executives and employees frequently misinterpret this efficiency gain. Executives might assume it means they can immediately reduce their workforce by 30%, while employees might fear that such efficiencies inevitably put targets on their backs.
Here’s the reality check: if you achieve around 30% efficiency gains but correspondingly cut your team by 30%, your total cost savings shrink significantly once you factor in the total cost of AI ownership — including implementation, infrastructure, training, and ongoing maintenance. Industry analyses confirm that organizations often underestimate these hidden expenses, leading to actual savings frequently only half of the initial efficiency gain. This leaves companies delivering the same output at marginally lower costs — a clear recipe for market irrelevance rather than competitive advantage.
The Real Change: Customer Expectations
What many discussions seem to miss is AI’s transformative impact on customer expectations. The Internet didn’t merely digitize existing workflows — it changed what customers demanded: instant access, rapid responses, and customized experiences. AI amplifies this trend exponentially.
Projects that previously had months-long timelines are now expected within days or hours. Periodic reporting becomes real-time analytics. Personalized customer experiences, once optional, are now essential. Companies adopting AI as a cost-cutting measure alone quickly lose market share to competitors leveraging AI to enhance customer responsiveness, experience, and innovation significantly.
Seeing AI as a Capability Multiplier
From my vantage point as a technology leader, AI is not about workforce reduction — it’s about exponential capability enhancement. The objective shouldn’t be smaller teams, but rather significantly empowered teams capable of delivering greater innovation and value more efficiently.
This applies equally, irrespective of age or tenure. Junior developers harnessing AI can deliver sophisticated solutions earlier in their careers. Experienced architects use AI to accelerate design and amplify their impact. Marketers harness predictive analytics to craft campaigns more precisely than ever before.
Flexibility, Not Age, Determines Success
The individuals truly at risk in this new AI landscape are neither specifically junior nor senior employees — it will be those resistant to change. Workers unwilling to adapt, learn new skills, or embrace AI-driven workflows will inevitably struggle.
Adaptability isn’t tied to experience level or tenure. I’ve seen professionals at all career stages both struggle with and enthusiastically master AI tools. The real differentiator is mindset.
Managing the Transition Wisely
There’s no doubt: the AI transition period will necessitate strategic workforce optimization. Thoughtful leaders won’t approach AI costs as mere budget items to offset with layoffs. Instead, they’ll strategically invest in AI capabilities, prioritizing workforce development and skill enhancement to maximize productivity and innovation.
The successful organizations will invest strategically in AI integration, upskill their entire workforce, and focus on significantly enhancing output rather than solely minimizing expenses.
Redefining Trust and Leadership
An insightful comment on my earlier article raised the question: “Who trusts a supervisor who can’t perform the job of their subordinate?” Trust in leadership isn’t fundamentally about replicating every team member’s skill — it’s about clear guidance, strategic vision, and reliable support. Effective leaders build trust by fostering adaptability, resilience, and continuous learning throughout their teams.
The Path Forward
Ultimately, the central question isn’t “which workers will AI hurt,” but “which organizations will adapt the quickest?” Companies embracing AI as a tool to expand capabilities, rather than merely optimizing costs, will dominate. Others will remain stalled, debating layoffs while competitors surge ahead.
In the AI-driven future, adaptability isn’t just valuable — it will be essential.