When Efficiency Cannibalizes Itself: The Cost of Forgetting the Humans Who Built the Machine

Part 2 of 3 of AI in a Human World: Tools, Trust, and the Talent We Keep

AI is here, and regardless of what you may think about the AI bubble, it’s not going away in business any time soon. The burst of the dot-com bubble didn’t replace .com entirely; it made businesses prove value. Just like the digital business replaced paper and fax machines, AI is replacing our old ways of working. For anyone interested, TechNet did a great whitepaper on ways AI is shaping jobs (TechNet (2025). AI & the Workforce: 2025 White Paper.
https://www.technet.org/wp-content/uploads/2025/02/TechNet-AI-Workforce-White-Paper-2025.pdf).

I see this growing reliance on GenAI daily. When we give people a new license on a trial period, they may not always be able to articulate how the AI tool has helped beyond note-taking or deep searches, but they know they cannot go back to working without it, as shown in their deep resistance to giving it up. Industries are starting to lean into AI-driven changes too:

Technology: Already sprinting. Most large tech firms have early AI adopters organically in their workforce. They’re not debating whether to use it; they’re learning how to use it responsibly.

Finance: FinTech has been in algorithmic mode for years. Wealth protection, privacy, and compliance all depend on smarter automation.

Energy: The quiet revolution. As AI workloads grow, data centers are reshaping global power demand. Utilities that adapt early will own the next decade. Bloomberg (NEF) projects that AI-related data center energy use will double by 2027, accounting for nearly 4% of global electricity demand (Source: BloombergNEF (2025). Power for AI: Easier Said Than Built. You May Be Paying for It
https://about.bnef.com/insights/commodities/power-for-ai-easier-said-than-built/ ).

Education and Hospitality: These industries remind us why humans matter. AI can supplement, but it can’t nurture. Empathy is the differentiator, and technology can’t replicate it.

Across every sector, one truth holds: GenAI has opened new doors and new ways of working and frankly, brings new challenges to figure out. It’s time to shift our focus to how humans and AI will work together, and who will lead that conversation (Source: Korn Ferry Institute (2025). Defining and Developing the AI-Augmented Leader.
https://www.kornferry.com/institute/defining-and-developing-the-ai-augmented-leader).

Most of us don’t want a world run entirely by machines. I don’t know anyone saying we do, but I see a lot of leaders showing they do because they are laying off people under the commentary that they are doing an AI reorg. This is where human behavior is tricky.

Our gut says humans should stay relevant, but human talent costs money, thus human behavior creates counterintuitive scenarios. Rewards shape behaviors. If someone can make more money with less cost, and they are driven by money now, they will go that path so long as the risk doesn’t outweigh the reward. Translated as layoffs.

Layoffs don’t create agility. They create amnesia. We are not at a place where business experience and learnings are well documented and can be fed into the bots. Every time we cut talent instead of retraining it, we lose institutional knowledge that no algorithm can replace.

That said, most leaders don’t take layoffs lightly. They’re often the last resort when costs run too high and demand runs too low. Human capital is one of the greatest costs on a company’s balance sheet, and when budgets tighten, it’s also one of the few levers leaders can pull. Businesses have been doing this balancing budget act with resource costs since the rise of large-scale enterprises. Regardless of what you may read, it’s not caused by AI. It’s caused by basic economics. When demand is down and costs are up, then businesses must cut costs, and they often look to technology to help.

In many cases, layoffs are not about disregard. It’s about survival. And sometimes, when jobs change drastically, it can be more effective to rebuild than to rewire. Mindset and behavioral shifts are notoriously difficult to achieve at scale. John Kotter’s research at Harvard showed that fewer than 30% of large-scale change efforts succeed because people resist the discomfort of transformation (Kotter, J.P. (2012 reprint). Leading Change: Why Transformation Efforts Fail. Harvard Business Review Press.).

Labor economist Ron Hetrick (Lightcast, 2025) recently wrote a post in LinkedIn about this in the context of capitalism, arguing that our system is “cannibalizing itself” by pursuing efficiency at the expense of long-term capacity. When we reduce the workforce faster than we can reinvent it, we weaken the very market that sustains demand (Hetrick, Ron. (2025). I Really Hate Cannibalizing My Own Post (Capitalism Commentary).
https://www.linkedin.com/posts/ronlhetrick_i-really-hate-cannibalizing-my-own-post-yesterday-activity-7387514100945334272-AOlv)

The hard truth is that both realities coexist. Sometimes, starting over is the most efficient path forward; other times, it’s the easiest way to lose the very wisdom that could have fueled the change. I suggest that leaders don’t have to choose between starting over and transformation; you can balance both with precision.

In Part 3, I’ll unpack how trust, loyalty, and purpose are shaping the next chapter of work and why “The Talent We Keep” may be your company’s greatest competitive edge.

Until then, think about whatever is true, whatever is right, whatever brings you joy,