When he nominated Kevin Warsh to be the new chairman of the Federal Reserve earlier this year, President Donald Trump left no doubt that he wanted a central bank head who’d do things his way. In particular, he wanted his chairman to work more closely with the administration and to meet his demand for lower interest rates.

For a president who measures his success by the performance of the stock market, the appeal of cheap credit is obvious: it stimulates the economy and boosts asset values. But to economists, cutting interest rates at a time when inflationary pressures are rising and the economy is humming would unleash an inflationary spiral that could only end badly.

At his public debut as chairman of the Federal Reserve this week, holding the press conference after his first meeting of the rate-setting Federal Open Market Committee, Warsh conveyed that some things would in fact change during his term of office. In particular, he signalled that he’d be a less voluble chairman than his predecessor Jerome Powell. The statement put out after the meeting was less than half as long as Powell’s had typically been. Warsh made it clear he’d be taciturn with the press, and wouldn’t provide details about what informed the committee’s decision-making and the likely future course of interest rates.

Central bankers have become very communicative over the last two decades, but it wasn’t always thus. There was a time when committees would set the interest rate, inform the public of their decision, and leave it at that. The open-book approach was pioneered during a time of volatile markets and ultra-cheap credit, when central bankers wanted to prepare investors for any upcoming change in direction. Whether or not that approach did any good, it has come in for increasing criticism, and so Warsh’s innovation has been well-received.

But when it comes to the bread and butter of central banking, namely monetary policy, Warsh indicated a continuity that is unlikely to please Trump. Beyond a possible tweak to the definition of inflation and a consequent modest increase in the Fed’s target for price increases, the new chair made clear that his primary job was not to boost the markets — something which for Trump is a sine qua non.

Additionally, while he didn’t join his colleagues in indicating his expectations for interest rates on the so-called dot plot of future moves, Warsh didn’t object to the exercise and cited the plot as evidence that the Fed was leaning more towards hiking than cutting rates at future meetings. It sounded a lot like he was adopting the role of good cop, aiming for the same outcome as his colleagues yet opting for slightly different tactics. Or maybe it was just that he wanted to let Trump know that, much as he might like to do his bidding and slash rates, he has no way to overrule his colleagues.

That’s true, in that the chairman can influence other board members but can’t dictate to them. Yet it also didn’t sound like Warsh much wanted to do so, either. As Trump has tried to bend the institutions of the federal government to his will, the Fed is a holdout that has largely maintained its independence and continuity. Further change may be coming, but it doesn’t look like Trump will get the Fed he wanted anytime soon.

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John Rapley is an author and academic who divides his time between London, Johannesburg and Ottawa. His books include Why Empires Fall: Rome, America and the Future of the West (with Peter Heather, Penguin, 2023) and Twilight of the Money Gods: Economics as a Religion (Simon & Schuster, 2017).

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China is trying to catch up with America on artificial intelligence. The Wall Street Journal has reported that Zhipu AI — one of China’s six “AI tiger” LLMs — has released a new model that can rival leading US systems, including Anthropic’s Mythos, in cybersecurity tasks such as pinpointing security bugs. While this marks a milestone in China’s drive to catch up with Western AI capabilities, strong performance on a single benchmark does not mean it has taken the lead. Chinese models still lag behind their Western counterparts in broader capabilities, such as autonomous operation. Skepticism is therefore warranted before resorting to hysterical conclusions, but complacency about the geopolitical implications of China’s AI advances would be an even greater mistake.

On the infrastructure side, Chinese AI is still constrained by access to advanced chips, with American labs way ahead in computing capacity as well as investment. Analysis from earlier this year suggests that Chinese models are likely to be at least a few months behind those in the US. But they are still continuing to make progress, or that the geopolitical importance of AI will be decided only by whose LLM has ventured deeper into the technological frontier. The practical applications of AI, countries’ to capture foreign markets, and the application of AI into the real economy will matter just as much.

Here, China may hold an advantage. As with its dominance across many critical supply chains, Beijing may not need to produce the most advanced AI systems — only those that are affordable and widely deployable. In doing so, it could consolidate global influence by supplying functional, low-cost AI at scale.

Beijing seems to be pursuing exactly that path, developing an AI “open-source” strategy that offers affordable, widely available AI models for companies and individuals to use and modify as they wish. The production of the DeepSeek AI model, which matched the performance of Silicon Valley tools such as ChatGPT at a fraction of the cost for users, created goodwill among Chinese models with developers.

The four most popular models on OpenRouter, an AI hardware platform for developers, are now all Chinese. The goal for China is not only to win the frontier-model race, but to make its systems the default layer of AI adoption across industries and global markets. For most economies, the choice is increasingly between an affordable tool they can deploy now and a more robust one that may be out of reach.

And while the countries adopting Chinese models may be exposed to political pressure and cyber threats from Beijing, safer and more capable alternatives matter little if they are unaffordable. American AI companies are already under pressure to monetize products whose operating costs are rising. If Chinese open-source models become the cheap default for startups, universities, governments and businesses across the developing world, then America’s AI lead will be eroded from below.

Perhaps more concerning for America in the long run is how AI can give Chinese manufacturing even more strength, through the ongoing integration of AI as a general-purpose technology. China’s new Five-Year Plan mentioned AI more than 50 times and includes an “AI+” action plan aimed at spreading AI across the economy.

Beijing has been pioneering automation of its critical infrastructure for years, with promising recent results in increasing warplane production capacity. In that regard, China’s open-model strategy and manufacturing dominance will reinforce each other. Cheap, adaptable models accelerate deployment across the real economy while those deployments generate real-world data and use cases that can feed back into further model improvement.

The United States should not dismiss the importance of its lead in the AI race. That lead worries Beijing, not least because a more automated Chinese economy would also become more vulnerable to AI-generated cyber threats. But nor should Washington assume that China cannot catch up with American capabilities over time.

This AI competition represents part of a broader struggle over tech supply chains and geopolitical influence. Decisions over whether to adopt US or Chinese models could produce a more fragmented global reality, with different regions relying on different cloud providers, chips and security structures. The result will likely be a global economy which is divided into competing spheres, rather than one which produces a single winner.

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Miquel Vila is a political and geopolitical risk consultant focusing on industrial strategy, critical infrastructure and global supply chains.

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