It’s too early to call a peak in the US stock market, but the signs of one are starting to appear. There’s been growing anxiety that the AI market has become a bubble, and a report out this week from an MIT research lab, which found that 95% of businesses applying AI have yet to see any return from their investment, underscored the worries. It didn’t help matters when OpenAI’s Sam Altman, of ChatGPT fame, added his thoughts that AI stocks were indeed in bubble territory. Following these reports, stock markets fell on Tuesday.

The crypto industry in particular may be the canary in the coal mine of any coming crash. Bitcoin fell sharply. That isn’t itself unusual, given its volatility, but it does bear monitoring because if it continues, it could become self-sustaining. Strategy (formerly MicroStrategy), Michael Saylor’s vehicle for pouring cash into Bitcoin, has tumbled 15% in the past month. On the way up, its rising value enabled it to sell shares, use the proceeds to buy Bitcoin, and drive the price of both up in a virtuous cycle. But if Bitcoin keeps falling and drags Strategy’s price down with it, the company may be forced to liquidate holdings to meet redemptions, causing the virtuous cycle to turn into a vicious downward spiral.

Investors will now be hoping that central banks rush to their rescue and cut interest rates. The Federal Reserve is coming under pressure from the Trump administration to do so, and investors are pricing in a cut at the Fed’s next meeting in September. The problem is that, although the US economy is clearly weakening, the data is not yet sufficiently compelling to suggest it’s at imminent risk of recession.

Meanwhile, inflation appears to be rising. With many economists expecting the hit from tariffs to begin only in the autumn, inflation may rise a fair bit too. Interest rate cuts, which stoke the market, could then add fuel to that fire.

In other words, inflation raises the stakes for the Fed shifting to an easy stance. And even if it does cut, it doesn’t follow that interest rates will fall. Bond yields aren’t declining in tandem with short-term rates because investors anticipate that inflation may return to stay if the Fed eases. If a perception takes hold that the Fed has lost control of inflation, things could get very ugly for both markets and the dollar.

The US may thus be headed into a perfect storm this autumn: a slowing economy, falling asset markets and rising consumer prices, the dreaded stagflation that made life so difficult in the Seventies. Central banks may have to stand by as markets continue falling, awaiting the point that they actually tip the economy into a slowdown sufficient to tame inflation. The danger is that they mistime their move, and a slowdown becomes a recession.

A theme of this year has been that every time institutional investors dumped their US share holdings, retail investors rushed in to buy the dip, driving the market back up. If they return to the market in the coming days, calm may settle for a while. But if they don’t, it may be a sign that the corner has been turned and a recession is on its way. Watch those signs closely, because they’re the tea leaves we can read to predict the autumn tides.

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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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