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The AI Scapegoat: Why the Smartest Tech Companies Aren't Firing, They're Growing

Tech CEOs are pointing to AI to justify mass layoffs, but the data tells a different story. The real winners aren't cutting headcount. They're using AI to capture revenue that was always there but impossible to deliver.

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By Michael N. Rissover
views 7 min read

The AI Scapegoat: Why the Smartest Tech Companies Aren’t Firing, They’re Growing

Jack Dorsey cut 40% of Block’s workforce, roughly 4,000 people, and told the world it was because “intelligence tools have changed what it means to build and run a company.” Mark Zuckerberg declared 2026 the year AI would “dramatically change the way we work” while Meta axed hundreds in a single week. Amazon, Atlassian, Salesforce: the list keeps growing (BBC, 2026).

The narrative that AI is one of the root causes hides a lost growth opportunity that will differentiate leaders in the coming years.

The $650 Billion Question

Amazon, Meta, Google, and Microsoft have collectively committed to pouring $650 billion into AI infrastructure (BBC, 2026). That money has to come from somewhere, and payroll is the single largest line item on any tech company’s balance sheet. Investor Terrence Rohan put it bluntly: “Pointing to AI makes a better blog post. Or it at least doesn’t make you seem as much the bad guy who just wants to cut people for cost-effectiveness” (BBC, 2026).

The pattern raises questions. Block’s Jack Dorsey presided over two prior rounds of layoffs without mentioning AI. Salesforce’s Marc Benioff attributed cutting 4,000 support roles to “agentic AI agents,” though this followed years of aggressive hiring that Wall Street had been pressuring him to unwind (BBC, 2026; Crunchbase, 2026). It’s worth asking how much of this is genuine AI transformation and how much is capital reallocation wrapped in the buzzword of the moment.

In Q1 2026 alone, approximately 59,000 tech jobs were cut across 171 events, roughly 704 jobs per day. AI was explicitly cited in about 20% of those reductions (IBTimes, 2026). That means 80% of the cuts had other drivers entirely, even if AI gets the headline.

The Productivity Paradox Nobody Wants to Talk About

Here’s the awkward truth that undermines the entire “AI replaces workers” story: the productivity gains aren’t nearly as dramatic as the press releases suggest.

A Duke University/Federal Reserve CFO survey from March 2026 found that companies reported AI productivity gains averaging 1.8% in 2025. When researchers calculated the implied gains using actual revenue and employment data, the numbers were even smaller (Fortune, 2026a). This all sounds similar to economist Robert Solow’s famous 1987 observation: “You can see the computer age everywhere but in the productivity statistics” (Fortune, 2026b).

The gains are real. Developers using AI coding tools save an average of 3.6 hours per week, and 22% of merged code is now AI-authored across the industry (Index.dev, 2026; DX, 2026). But a 1.8% productivity improvement doesn’t easily justify eliminating 40% of your workforce. The gap between the measured gains and the scale of cuts suggests something else may be driving the decisions.

What Happens When You Replace Instead of Augment

For anyone who thinks wholesale replacement is the answer, look no further than Klarna. In 2024, Klarna’s CEO Sebastian Siemiatkowski publicly celebrated replacing roughly 700 customer service agents with AI, claiming human-equivalent quality. By early 2025, customer complaints had risen, satisfaction had dropped, and the AI couldn’t handle nuanced or empathy-requiring interactions. Klarna quietly began rebuilding its human support capacity and shifting to a hybrid model. Siemiatkowski later admitted: “We focused too much on efficiency and cost. The result was lower quality, and that’s not sustainable” (Tech.co, 2025).

Klarna has become the canonical cautionary tale. Every executive evaluating AI workforce strategy now has to explain how they plan to avoid “the Klarna outcome.”

The Revenue That’s Always Been on the Table

This is where the more compelling story lives, even if it doesn’t generate the same headlines as mass layoffs.

Many software companies have a backlog of potentially funded work they can’t deliver. Some of these items are difficult, complex, and for many other reasons the juice simply isn’t worth the squeeze. Every engineering organization has strategic initiatives that lose to capacity constraints quarter after quarter. The bottleneck has always been the same: not enough productive hours at a low enough cost to capture it.

AI changes that equation, not by eliminating the people who do the work, but by amplifying what each person can deliver and driving down the cost of the implementation. The companies that understand this distinction are building something fundamentally different.

The Divergence Is Already Visible

The data on this split is striking. An EY survey from December 2025 found that 96% of organizations investing in AI report productivity gains, and 57% call them “significant.” But here’s the number that matters: only 17% say those gains led to reduced headcount (EY, 2025).

What are the other 83% doing? Reinvesting. Into existing AI capabilities (47%), new AI capabilities (42%), cybersecurity (41%), R&D (39%), and upskilling employees (38%). Fifty-six percent report significant measurable improvements in overall financial performance (EY, 2025). They’re growing, and they’re funding that growth with the productivity gains AI delivers.

Shopify is the poster child for this growth. In 2025, Shopify grew revenue to $11.6 billion, a 30% increase. This was four points higher than the prior year while headcount only dropped 6% over two years (Shopify, 2026). CEO Tobi Lutke’s internal memo required teams to prove AI can’t do a task before requesting new headcount, and AI proficiency was built into performance reviews. Lutke later called the policy “extremely successful.” Teams built tools and workflows in response rather than lobbying for additional bodies. There was no dramatic slashing of headcount, and instead the numbers look much more like normal attrition without replacement.

30% growth with only normal attrition is a much better model, and prevents the dramatic swings that Klarna had to recover from. They used AI to make every person more capable, then captured revenue that their previous staffing levels couldn’t deliver.

The Next Generation of Standouts

The companies that will define the next era of technology are the ones that are finding ways to incorporate new technology into their proven structures. Instead of cutting 40% of their workforce and issuing press releases about AI transformation, they’re the ones that recognized that AI is a multiplier, not a replacement, for your workforce.

These organizations are using AI to make their teams more productive, and funding that investment by capturing the revenue that was historically impossible to deliver. Lower costs to development mean that work items that weren’t historically worth taking on are being delivered at a record pace. These changes to backlogs and projections are the real transformation, and they are going to change the way that software serves business enterprises in ways that are still becoming clear.

The EY data already shows the split, Shopify’s numbers prove it works, and the next 18 months will make it undeniable. The question every tech CEO should be asking isn’t “how many people can I cut?”, but instead “how much revenue have we been leaving on the table, and what happens now that our people can finally capture it?”

The answer to that question is worth a lot more than a press release about headcount reduction.

References

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