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Bill Gates Has a Plan for AI’s Future. What About Its Present?

The immediate danger is not an all-powerful machine, but a technology industry asking the public to underwrite its ambitions.

Bill Gates may have left Microsoft, but he never really left the technology industry.

Two weeks ago, he published a nearly 6,000-word essay on Gates Notes, warning that AI could become either history’s greatest equalizer or its worst source of injustice. Even under the best conditions, he wrote, the transition could be among the most turbulent periods humanity has experienced.

Gates is not predicting the end of civilization. He proposes new national and international institutions, “Human Reserved” jobs, and taxes on AI tokens and robots to protect those hurt by the transition.

As a technology journalist in China, I think his diagnosis begins too far in the future. Gates does mention AI companies and the pressure data centers put on water and power. What he does not provide is a clear system of accountability for the founders, investors, banks and hyperscalers driving the boom today.

The AI model is not the only source of the problem. The people financing, marketing and deploying it also make choices—and they want the public to absorb the consequences.

AI Isn’t Firing Workers. Executives Are.

Gates expects AI to eliminate many white-collar roles, especially entry-level jobs in sales, customer support, software engineering and legal services. That structural risk is real. But current layoffs do not prove that machines have already surpassed workers.

Klarna offered an early warning. In 2024, the buy-now-pay-later company said its AI assistant handled 2.3 million conversations in one month, did work equivalent to 700 full-time agents and could improve annual profit by $40 million. A year later, CEO Sebastian Siemiatkowski acknowledged that cost-cutting had gone too far and began recruiting remote customer-service staff so customers could still reach a person. Klarna’s regulatory filings now emphasize a hybrid system combining AI with human support.

Meta reportedly considered something even more aggressive under Project OT, a plan to reorganize teams around AI and potentially reduce some groups by as much as 60%. Internal code changes rose 220%, but major technical and security incidents also increased 40%. The company later held back a broader round of cuts planned for November.

This is becoming a familiar cycle. Executives assume AI can remove labor costs, shrink teams before the technology is ready, and then ask fewer employees to repair the resulting failures. The machine did not decide to fire anyone. Management did.

Meanwhile, the infrastructure budget appears almost limitless. Alphabet raised $20 billion in dollar-denominated bonds in February as it accelerated data-center spending. Meta expanded its planned El Paso facility from a $1.5 billion project to an investment exceeding $10 billion, even as both companies repeatedly reorganized AI teams.

The result is a self-reinforcing capital-allocation problem. Companies spend heavily to replace workers, leaving less room to hire them. When the systems disappoint, they spend still more on compute. If policymakers want to address AI-related unemployment now, they should examine corporate investment incentives—not treat every layoff as evidence of machine superiority.

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