The surprise price tag for AI tools has many companies rehiring their workforce: Why Canadian employees are suddenly worth more

And the numbers are striking. Organizations spent an average of US$1.2M on AI-first applications in 2025 — more than double the prior year, according to Zylo’s 2026 SaaS Management Index.

OpenAI CEO Sam Altman has acknowledged the issue directly. “People are really saying, you know, it’s kind of a meme now, but ‘My company spent my entire 2026 budget in Q1. Can you make this more efficient?’” Altman said. “That went from, at the beginning of this year, an issue that never came up (people were totally happy with the amount they were spending) to, all of a sudden, a huge issue.”

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Microsoft lets staff go — then lets Claude go

Last year, Microsoft announced it was prioritizing AI investment — piling US$80 billion (C$110 billion) into AI data centres and letting 15,000 people go worldwide, about 4% of its workforce. This April, Reuters confirmed Microsoft announced voluntary buyouts targeting employees at the senior director level and below whose combined age and years of service total 70 or more — roughly 7% of its US workforce.

Then came a second reversal: Microsoft cancelled its developers' Claude Code licenses after roughly six months, redirecting engineers to GitHub Copilot CLI — a move widely attributed to the unexpectedly high cost of agentic AI tool usage.

It’s not that Microsoft is abandoning AI. After all, the company has built Large Language Models (LLMs) into every aspect of its business and promotes AI with Microsoft 365 Copilot. But it’s doing its best to develop proprietary AI software to avoid being dinged on usage.

Prof G Markets co-host Ed Elson predicts that a growing number of developers at firms and start-ups will opt for cheaper, open-source AI models than high-end Claude and ChatGPT. Elson also notes that AI options made in China are up to 30 times less expensive. That could contain the soaring costs of developing AI applications at the front end. But Elson notes that in some cases the resulting AI products — the AI applications themselves — are costing more than the humans they were designed to replace.

That’s led to what observers call the “AI boomerang.”

The AI boomerang — bringing humans back… for now

The boomerang effect is well-documented in Canada. A recent Robert Half survey of 1,365 professional-services hiring managers found that 34% of those who cut staff after adopting AI have already reinstated those positions or similar ones — a trend the firm has labelled “AI correction hires.”

Lisa Cohen, an associate professor of organizational behaviour at McGill University’s Desautels Faculty of Management, explains why: most AI systems only replace slices of work, not whole jobs. “You can’t just take tasks in and out without changing the connected systems,” she says.

More research from Gartner shows that globally, 50% of businesses that laid off customer service representatives or people in operations roles will end up hiring them back by 2027.

One high-profile cautionary tale: Global fintech buy now, pay later company Klarna decided not to renew 700 customer service representative contracts and replaced them with an AI chatbot, giving the company US$10 million (C$13.7 million) in initial savings. But customers were so dissatisfied that they had to rehire its human workforce.

Back home, Statistics Canada confirms that the percentage of Canadian businesses using AI to produce goods or deliver services more than doubled from 6.1% during the 2023-to-2024 period to 12.2% during the 2024-to-2025 period. Yet of those AI adopters, only 6.3% reported a decrease in employment because of AI — a figure that remained unchanged even as adoption surged.

Not the last chapter

We’ve reached a new chapter in the story of the AI revolution, one in which AI can cost more than employing people, and companies are looking for cheaper AI solutions — weighing whether to invest in AI tokens, reduce headcount, or even bring people back.

But it’s not the last chapter. “The ‘humans are cheaper’ era has an expiration date,” warns technology analyst Rob Enderle. “The cost of AI is likely to follow a trajectory similar to solar power or flat-screen TVs: an initial period of ‘only for the rich’ followed by a precipitous drop.” He wrote in Technology News World that some data suggests AI labour could be 90% cheaper within the next 10 years, particularly as short language models — alternatives to LLMs — increase and the infrastructure is built out.

Gartner research supports a similar view, where a recent report projects that by 2030, inference costs on advanced AI models will fall by nearly 90% compared to 2025. The catch, analysts note, is that cheaper per-token pricing won’t necessarily translate to cheaper total AI bills: more capable “agentic” AI uses far more tokens for each task, and consumption tends to outpace the falling unit cost.

“At that point, a human being’s time will become a luxury good — something people pay extra for, like hand-stitched leather or artisanal sourdough,” Enderle wrote.

What this means for Canadian workers and their employers

Statistics Canada research makes clear that this isn’t an abstract concern. About 60% of the Canadian workforce is potentially highly exposed to AI-related job transformation — that’s roughly 12 million workers. But exposure doesn’t automatically mean displacement: About half of those workers are in jobs where AI is more likely to augment their work than replace them outright.

The real risk, as the boomerang stories show, is impulsive decision-making — cutting too fast, spending too much and then having to reverse course at a significant cost.

Next steps for Canadian workers and employers navigating the AI shift

Whether you’re an employee wondering about your future or an employer weighing an AI investment, here are some practical takeaways from the current moment:

For employees:

Know your exposure. Statistics Canada’s research distinguishes between AI-exposed jobs that are likely to be replaced and those likely to be augmented. Jobs involving complex judgment, communication and relationships — think nurses, teachers, trades people and senior analysts — are generally in the “augment” category.