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\n Photo-Illustration: Intelligencer; Photo: Getty Images\n <\/p>\n
A little less than a year ago, the AI industry acknowledged<\/a> wider anxieties about a possible bubble. Sometimes, OpenAI\u2019s Sam Altman said, \u201csmart people get overexcited about a kernel of truth.\u201d Mark Zuckerberg allowed that a bubble is \u201cdefinitely a possibility, at least empirically,\u201d while Google\u2019s Demis Hassabis suggested that \u201csome parts of the AI industry are probably in a bubble.\u201d Jeff Bezos, while arguing that AI will \u201cchange every industry,\u201d pointed to signs of an \u201cindustrial bubble.\u201d<\/p>\n A few months later, this bubble-talk bubble was pricked by the arrival of a new generation of AI coding tools<\/a>, which were notably more useful for complex, real-world programming and quickly adopted by the tech industry. This had a few different consequences for AI firms. It refreshed the industry\u2019s narrative of inexorable progress, even if they hadn\u2019t quite anticipated how things would play out: The lines were still going up, and insiders once again described feelings of acceleration. One month, prominent AI figures were conceding that most AI-generated code was \u201cslop,\u201d and the next, they were delegating a significant portion of their work to Claude Code. In addition to a narrative reset, AI firms now had something to sell to other firms, which showed immediate interest \u2014 a plausible business model beyond getting as many users as possible. Lastly, frontier AI coding tools were extremely compute-intensive. They could write code but required enormous quantities of tokens to do so. This resulted in some sticker shock<\/a> among AI customers, as well as some unsustainable subsidies on the provider side, but also quelled, at least temporarily, concerns about overbuilding data centers. For the first half of 2026, capacity was scarce, major AI firms were selling as much access as they could provide, and plans for hundreds of billions of dollars of infrastructure buildout regained credibility among investors. Problem solved!<\/p>\n For a while. The anxious mood of late 2025 was caused, in part, by worries that model progress was at risk of slowing down. The arrival of agentic AI coding relieved that. But another contributor was the arrival of Chinese open-weight models, which were cheap to use, free to run on customers\u2019 own hardware, and anywhere from three to nine months behind the American frontier. In mid-2025, a model called DeepSeek R1 promised similar capabilities to OpenAI\u2019s GPT-4, accompanied by claims that it had been trained for a fraction of the price, briefly crashing American tech stocks. Now, in the post-AI-coding era, Chinese AI firms \u2014 which American companies have since accused<\/a> of \u201cdistilling,\u201d or ripping off, their frontier models \u2014\u00a0are once again entering the picture. About six months ago, Anthropic and OpenAI passed a new threshold in AI software development. Now, multiple open-weight competitors, according to AI researcher Nathan Lambert<\/a>, are passing it too. These include Moonshot AI, which makes a model family called Kimi, and Z.ai<\/a>, which just released an update to its GLM model:<\/p>\n Pretty much everyone I respect among the AI commentariat and researcher class has praised the model after using it personally. Such a focal point of discussion among the community has only been so clear with an open model release once before \u2014 DeepSeek R1. This is not a comparison I make lightly, and when I compared Kimi K2\u2019s release to a \u201cDeepSeek Moment,\u201d GLM-5.2 has well exceeded that\u2026\u00a0<\/p>\n<\/blockquote>\n GLM-5.2, he writes, is the first open-weight model \u201cthat feels right in coding harnesses as a general agent,\u201d which is to say it\u2019s the first of these vastly more affordable AI models to be able to do most of what people expect from using Claude Code with a recent Anthropic model, or the equivalent from OpenAI. For the moment, it appears as though these models are at least as good as anything Google has on offer, if not better, which is an outcome that would have sounded strange just a few months ago. (For its part, newer DeepSeek models are starting to show up<\/a> in corporate AI spending.)<\/p>\n The broader consequences of commoditizing circa-early-2026 agentic coding aren\u2019t easy to predict. For one, the frontier labs have kept moving. Since then, Anthropic released (and then was forced by the government to recall<\/a>) Mythos and Fable, a new generation of models from which another set of new capabilities, this time around cybersecurity, emerged unexpectedly, meaning that frontier models still had something unique, valuable, and expensive to offer to customers. (As of this week, however, OpenAI claims to be able to offer similar capabilities at a lower price just weeks<\/a> after Anthropic\u2019s release; Z.ai<\/a> claims<\/a> its own models will be there by the end of the year.) The arrival of cheaper, good-enough coding models could undercut offerings from American firms, which have raised many times more money than their Chinese counterparts. Alternatively, these cheaper models could end up inducing demand, making heavy coding-agent usage more attractive to a wider group of customers without cutting too much into the top end of the market, where the frontier labs could still dominate.<\/p>\n Then there\u2019s the vaguer prospect of another breakthrough that might render six-month-old capabilities less relevant to the bigger picture: AI companies are talking an awful lot about \u201cloops<\/a>\u201d these days and hinting at the possibility that their models are getting closer to \u201crecursive self-improvement,\u201d which they argue could solidify their leads in profound ways. It would be wrong to say that stranger things have happened \u2014\u00a0a self-improving AI that threatens to slip from human control is about as strange as things get \u2014 but things that are at least pretty strange have, and it would be imprudent to dismiss the possibility that, given the billions of dollars of research money and compute thrown at the problem, the next generation of models might be useful for something else the economy values as much as coding. At the very least, the industry is looking for its next big story.<\/p>\n But if you zoom out a bit, the basic dynamic here is punishing and carries undeniable risks for the industry. With each step-change in AI capabilities, affordable, flexible, and open models are pretty close behind, and what was briefly expensive to deploy becomes quite cheap. American AI firms are openly worried about this sort of thing, which they\u2019ve framed as a threat to national security, a form of intellectual property theft, and a source of uncontrolled AI risks, but which also clearly complicates their still-nascent business models. Bubble talk isn\u2019t quite back, but there are early signs \u2014\u00a0among tech leaders<\/a>, but also in markets, which are suddenly cooling on AI and chip stocks<\/a> \u2014\u00a0that vibes may be shifting once again, as they have a half-dozen times in the last three years.<\/p>\n Sign Up for John Herrman column alerts<\/p>\n Get an email alert as soon as a new article publishes.<\/p>\n Vox Media, LLC Terms and Privacy Notice<\/p>\n\n