“What difference will AI summaries make to news media?” I asked Google, choosing the search engine’s new AI mode.

In a few seconds I received a summary paragraph, bullet-point arguments in four boxes titled “Benefit, Risk, Challenge and Opportunity”, and a summary of “the strategic outlook for news media”. There was enough material for me to copy and paste an entire slide presentation, had I needed one.

It also gave me links to 18 of the sources it had used, and a footnote that “AI responses may include mistakes.” Yes: as one of the cited articles found in February 2025, over half of AI chatbot summaries had problems with accuracy. But that’s not the only problem with Google’s AI tools.

The Financial Times has reported that publishers are scrambling to counter the “Google Zero” threat after a sharp drop in web traffic. Since Google introduced AI Overviews in May 2024, as an addition to standard search, users who see it are less likely to click on a link to a source article — half as likely, in fact. Pew Research found that encountering an AI summary cut the click rate from 15% to 8%. Members of digital media trade body DCN (Digital Content Next) reported that, in the week following the introduction of Google AI Overviews, news brands suffered a 16% drop in site visits via Google searches.

This is bad news for the media organizations producing the news that Google’s AI is summarizing. If readers never bother to click through, they’re neither paying for content nor seeing adverts. Without that revenue, journalists can’t be paid, and the sources will run dry. No wonder, then, that the owner of the Daily Mail has recently called for a crackdown on the tech company.

Google is not the only platform using generative AI summaries, of course, but as a search and advertising giant it has strategic market status. The Professional Publishers Association (PPA) has submitted data on the fall in traffic to its members’ sites as evidence to the UK’s Competition and Markets Authority (CMA), demanding that something be done to staunch the bleeding.

This isn’t the first time media companies have accused digital platforms of a parasitic relationship to their original work. When Facebook introduced Instant Articles in 2015, promising to make publishers’ content easier to access on mobile devices in particular, many worried that it handed more power, and ultimately more editorial control, to social media platforms. But Facebook did at least share revenue with the originators of the material.

Jason Kint, CEO of DCN, says the difference now is that AI summaries effectively replace the content from which they draw, discouraging the reader from ever following through to read the original material. Like AI-generated music, or visual artwork produced by training on human creations, the AI-generated news summary first devours human work and then takes its place. However, it’s hard to see AI-summarized news surviving for very long without a constant supply of fresh journalism to summarize.

What can media organizations do? Refusing permission to include their output in AI summaries would also remove their links from the results page altogether, resulting in an even bigger drop in traffic to their sites. The obvious answer is licensing agreements, giving Google AI and its ilk access to the original content in return for a share of revenue.

OpenAI already has licensing agreements with large media organizations, allowing it to train its Generative AI programs on their output. Google is perfectly placed to make similar agreements, which would allow it to use publishers’ output as both training data and raw material for news summaries that keep readers on its own pages.

Will such agreements safeguard the independence and financial security of news organizations? Of course not. But they may not have much choice. As Google AI mode told me: “For the news media, AI summaries are part of a larger power struggle with tech platforms over content and audience.”

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Timandra Harkness presents the BBC Radio 4 series, FutureProofing and How To Disagree. Her book, Technology is Not the Problem, is published by Harper Collins.

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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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