If you’ve been reading technology news in late 2025 and thinking, “Why does every story feel like it’s secretly about chips?” you’re not imagining it. AI has dragged the entire stack into the spotlight: semiconductors, cloud services, data-center power, accounting practices, and international trade policy. What used to be a nerdy supply-chain beat is now a national-strategy beat.
Chips are the new leverage (and governments are using them)
The clearest signal came this week from Washington. Reuters reported that the U.S. administration announced plans to impose tariffs on Chinese semiconductor imports—then delayed the actual start until June 2027. That “announce now, implement later” move is revealing: chips aren’t only an import category anymore; they’re a negotiating chip (no pun intended) in a broader U.S.-China technology contest.
In the same report, Reuters noted parallel threads that define the moment: China restricting exports of rare earths critical to tech supply chains, and U.S. discussions touching tech-export rules and access to advanced AI chips. When both sides have tools that can hurt the other, policy becomes less about “free trade vs protectionism” and more like strategic pacing—when to tighten, when to pause, when to signal.
Nvidia’s dominance is being attacked from a surprising angle: software
Most casual readers assume Nvidia’s moat is “better GPUs.” In practice, the moat is also CUDA the software ecosystem that makes it easy to build and run AI models on Nvidia hardware.
Reuters highlighted a direct challenge to that advantage: Google is working to make its TPUs run more smoothly with PyTorch, the most common AI development framework, through an effort internally called “TorchTPU.” The significance is subtle but huge: if developers can use PyTorch on TPUs without painful rewrites, switching costs drop, and the world becomes less “Nvidia by default.”
Meta’s involvement matters too. Reuters reported Google is collaborating closely with Meta, which backs PyTorch because Meta also wants to reduce dependence on Nvidia and lower inference costs. In other words, the next phase of the chip war isn’t just hardware benchmarks; it’s “who controls the developer workflow.”
AI is colliding with the power grid
The AI boom is also becoming an electricity story, and the regulators have entered the chat. Reuters reported that the U.S. energy regulator FERC directed PJM (the largest U.S. grid operator) to launch clearer rules for connecting AI-driven data centers and other large loads that co-locate near power plants. Supporters claim co-location can reduce transmission needs; critics warn it can strain public supply and raise bills.
This is a turning point: the “cloud” is no longer metaphorical. It’s physical substations, interconnection queues, and political fights over who gets priority access to power. If you want to predict where AI infrastructure gets built in 2026, watch grid capacity and permitting more than keynote speeches.
The government isn’t just regulating AI it’s hiring and partnering to compete
Another underappreciated theme: states trying to build AI capability inside government, not merely oversee it.
Reuters reported that a new U.S. federal tech initiative (“Tech Force”) drew interest from about 25,000 applicants, with 1,000 slated for a two-year cohort working across agencies. Whether you love or hate the politics, the policy shift is clear: governments are treating AI talent like critical infrastructure.
And it’s not only staffing. Reuters also reported that the U.S. Department of Energy signed AI collaboration agreements with 24 organizations (including Microsoft, Google, Nvidia, OpenAI, Anthropic, and others) as part of its “Genesis Mission,” aimed at accelerating scientific research with AI. That’s AI positioned as an instrument of national research capacity closer to “space race” logic than “new productivity tool” logic.
Even accounting is part of the tech story now
The final twist is that the AI boom is so big it’s reshaping what investors scrutinize. Reuters flagged a growing debate over how major tech firms set depreciation schedules for assets changes that can make earnings look stronger on paper even if cash flows don’t change. In a market obsessed with AI capex, depreciation assumptions quietly become a narrative tool: they can soften the apparent cost of building the future.