Jun 12, 2025

AI Is Not Coming for Your Job, Management Is

100% real, not at all fake photo of horses having an argument about the Ford Model T in 1908

1908. New York City. The Ford Model T had just been introduced and violent discussions erupted in stables, with horses on one side saying the future would be brighter because cars would augment their capabilities, and horses on the other side saying they were doomed because cars were coming for their jobs.

Cars didn't 'come for' horses' jobs. Humans chose to replace horses with cars because it served their purposes better, not because of 'technological inevitability.' The horses had no say.

Over 264,000 tech workers lost their jobs in 2023, 238,000 in 2024, and at least 71,000 more in just the first months of 2025^[1]—a workforce crisis explicitly driven by automation, AI-driven job displacement, and cost-cutting strategies to fund AI investments^[1:1].
Workers debate whether AI will augment or replace them, while missing the real point: they are the horses in this story.

The timeline reveals everything

Workforce transformations like these take 12-36 months to plan and execute.

When IBM started implementing AI-driven layoffs in 2023, those decisions were made when AI capabilities were roughly equivalent to advanced spell-check.

That timing exposes the fundamental issue: AI provides compelling narrative cover for decisions that are actually about quarterly earnings pressure, over-hiring corrections, and cost optimization.
"We're investing in AI transformation" sounds forward-thinking. "We need to hit Wall Street targets" sounds callous.

Take Meta's elimination of 21,000 positions while citing "efficiency" and AI capabilities^[2]. I've seen the actual roles eliminated: content moderators, recruiters, marketing coordinators, software engineers. These weren't positions being replaced by sophisticated AI systems—they were eliminated to achieve financial targets.

The same pattern appears across major technology companies: Amazon cut 18,000 jobs while expanding AI initiatives^[3], Google eliminated 12,000 positions during its "AI-first" transformation^[4], Twitter reduced its workforce by 75% while promising AI improvements^[5].

Here's the evidence that exposes this as opportunistic rather than strategic: 55% of companies now regret their AI-driven layoffs because the AI couldn't actually do the work^[6].

While companies rarely admit to direct AI replacement, the pattern is unmistakable: IBM eliminated 8,000 HR roles with AI systems^[7], Microsoft cut 6,000 workers as AI writes 30% of their code^[8], Chegg slashed 22% of staff after students abandoned them for ChatGPT^[9], Intel plans 21,000 cuts (20% of workforce) while pivoting to AI chips^[10], Canva fired technical writers nine months after pushing AI tools^[11], and Meta cut Reality Labs staff while restructuring around AI^[12].

Watch what they do, not what they say

The 55% regret rate provides clear insight into executive decision-making. When technology fails and companies reverse course, their original messaging stands exposed.

The contradictions become visible when examining specific executive statements alongside their actual decisions. Sebastian Siemiatkowski from Klarna perfectly demonstrates this contradiction. While implementing AI systems that eliminated 700 customer service positions, he expressed concern for societal impact: "Maybe society can have the luxury to at least support individuals that are affected by these changes, because not everyone will be able to just retrain into something different."
He advocated for "empathy and care" in managing technological displacement^[13].

At the same time, Siemiatkowski celebrated workforce reduction as technological achievement, positioning Klarna as OpenAI's "favorite guinea pig" and claiming their AI chatbot "handled 2.3 million conversations in its first month, replacing 700 agents"^[14].

When questioned about employment impact, he stated: "We have simply communicated to our employees that we're gonna shrink"^[15].

The contradiction resolved when Siemiatkowski admitted that "cost unfortunately seems to have been a too predominant evaluation factor" and acknowledged the AI-only approach resulted in "lower quality" customer service^[16].
Despite advocating for societal care, his company prioritized cost reduction over both employee welfare and service quality.

IBM's Arvind Krishna demonstrates similar messaging inconsistency. In December 2023, Krishna announced "massively upskilling all of our employees on AI," presenting IBM as committed to worker development^[17]. Seven months earlier, in May 2023, he had told Bloomberg: "I could easily see 30% of that [26,000 non-customer-facing roles] getting replaced by AI and automation over a five-year period"^[17:1].

Krishna's statements became increasingly contradictory. In August 2023, he told CNBC: "It's absolutely not displacing — it's augmenting... We should all feel better about it"^[18], while simultaneously planning approximately 7,800 position eliminations. By early 2025, Krishna had acknowledged that IBM's AI agent "automated 94% of routine HR tasks, replacing several hundred HR employees"^[19].

Like Klarna, IBM had to reverse course, rehiring thousands when their AI systems failed to match human performance.

The pattern is accelerating in 2025: DBS Bank plans 4,000 cuts over three years as AI "takes over key tasks"^[20], and Business Insider eliminated 21% of staff while "going all-in on AI"^[21]. But the regret statistics and quiet rehiring reveal the gap between corporate AI rhetoric and actual capabilities.

The workers getting laid off today aren't losing their jobs because an AI can do their work better. They're losing their jobs because a spreadsheet says their salaries represent an opportunity to boost quarterly profits, and AI provides a socially acceptable excuse.

See the pattern here?

Time to reframe this entirely

Executive narratives about "structural necessity" while reporting record profits reveal the truth: they're optimizing for quarterly earnings, not long-term capability.

Executive compensation structures tie directly to short-term financial metrics. Activist investors demand immediate cost reductions. Wall Street consistently rewards compelling narratives over operational substance, particularly when those narratives justify immediate financial gains. Understanding these underlying motivations—quarterly earnings pressure, investor expectations, stock price optimization—provides clarity for navigating their decisions.

The AI transformation narrative serves executive interests by positioning job elimination as technological inevitability rather than management choice.

But what makes this situation uniquely damaging is the broken social contract. These massive, resource-rich corporations had both the capability and the implicit responsibility to manage the transition thoughtfully, retraining workers for AI augmentation, creating hybrid roles, piloting programs, but instead chose to facilitate the cull.

Call it what it is

The horse analogy isn't about technological destiny. It's about agency.

Horses didn't choose their replacement. You can choose which skills to develop, which organizations to work for, and how to position yourself in an economy where management uses AI as cost reduction justification.

Organizations that implement thoughtful AI augmentation—featuring gradual integration, comprehensive worker training, and hybrid role development—do exist. While they don't represent the majority approach, they demonstrate that AI displacement isn't inevitable. Seek organizations that view AI as capability enhancement rather than workforce replacement justification.

What this actually means for you

Stop debating whether AI will replace you. That's the wrong conversation—you're having the horses' debate while humans make decisions about your future.

The evidence reveals a clear pattern: companies are eliminating workers immediately after AI tools become available, not after those tools prove superior.

This distinction matters because it changes how you should respond: stronger worker protections, corporate governance reform, and recognition that this is about power, not technology.

Your leverage is stronger than they want you to know

That 55% regret rate proves companies moved too fast. When organizations have to quietly rehire workers after AI systems fail, they demonstrate that human capabilities remain essential—despite what executives claim publicly.

Your institutional knowledge, judgment, and adaptability aren't easily replaceable. Companies spending billions on AI infrastructure while laying off workers reveal their priorities: they'd rather pay for expensive technology than invest in human capability.

This creates opportunity. When their AI systems fail to deliver promised results, workers who understand both the technology and the business become invaluable.

Focus on management's actual motivations, not their stated ones

Executives claiming "structural necessity" while reporting record profits are optimizing for quarterly earnings, not long-term capability. When they promise "upskilling" while freezing hiring, they're managing public perception, not worker development.

The AI transformation narrative serves executive interests by making job elimination appear inevitable rather than chosen. When you recognize this as corporate strategy rather than technological destiny, you can respond accordingly.

Prepare for the reversal wave

As more companies join the 55% AI regret club, opportunities will emerge for workers who have maintained relevant capabilities. Position yourself strategically by developing expertise where AI consistently underperforms: complex problem-solving, creative adaptation, interpersonal communication, institutional knowledge application.

When companies realize they eliminated essential human capabilities, you'll be positioned to return on stronger terms.

And lastly…

The next time someone frames this as inevitable technological progress, remind them of something: 55% of companies regret their AI-driven layoffs. That's not inevitability. That's bad management disguised as innovation.

References