Welcome to Eye on AI. Beatrice Nolan here. In today’s issue:
Google DeepMind is losing its grip on elite AI talent, new data shows. DeepSeek seeks $7.4 billion at a $74 billion valuation. OpenAI data-center head leaves amid broader exodus. And OpenAI releases more details on the Hugging Face hack.Enterprises are starting to look beyond America for their AI needs.
As Chinese open-weight AI models increasingly close the gap with their closed-source U.S. counterparts, more enterprises are warming to the idea of using Chinese alternatives. It’s easy to see why: Open-weight models offer companies more opportunities to fine-tune AI models, are much cheaper, and generally give enterprises more control, including more assurance that their data isn’t being used to train potentially competing products.
While Anthropic and OpenAI still dominate how American businesses buy AI, new spending data suggests that at least a minor shift is underway.
According to Ramp’s latest AI Index—which tracks token and subscription spend across its customer base—the share of businesses paying for model serving platforms, which give companies access to open-source and Chinese-developed models, rose to 6.1% of total AI-spending businesses in July, up from 4.5% in January 2026.
The shift may show increased enterprise interest in open-weight models such as Moonshot’s Kimi K3, which made waves on its release for being both an unusually large open-weight model but also one that showed coding and agentic performance close to leading proprietary systems.
Z.AI, another Chinese AI lab, this week confirmed that it built Ox Alpha, a previously anonymous model that had been performing well on AI benchmarks and gained enthusiastic reviews from AI developers over the weekend. The company has renamed it GLM-5.3-Flash and said it will charge $0.15 per million input tokens and $0.50 per million output tokens—another aggressively priced Chinese offering in a market where DeepSeek and Moonshot have already put pressure on rivals’ pricing. Z.ai is also thought to be close to releasing a larger version of GLM-5.3 that many believe will rival some of the best models from Anthropic and OpenAI in cyber capabilities, potentially a watershed event that many fear will usher in a new era of AI-powered cyber attacks for which the industry is woefully unprepared.
China is now clearly ahead in the open-model race. For example, Hugging Face found that, in almost every month of 2026, the largest and most capable open model came from a Chinese lab, while the U.S.’s most notable recent challengers have come from Thinking Machines Lab, and, more recently, Meta. But those American releases have generally not matched Kimi K3’s scale or developer pull.
Alex Brunicki, a partner at Backed VC, told me he’s already seeing a shift in how enterprises are approaching open-source models.
“We’ve seen a lot of companies…developing industry‑specific foundation models using open-source models that are then fine tuned on very particular data sets,” he said. “They’re not necessarily using the frontier models for all of the work that they’re doing. They’re actually using these open-source models which are free to use.”
At least two established organizations have recently publicly switched to open-source for some areas of their business. Thomson Reuters said this week it has built an in-house model, called Thomson-1, based on Snowdon, a system the company developed by adapting Alibaba’s open-source Qwen model.
The model will handle document-review tasks that previously ran on Claude. As part of the announcement, CTO Joel Hron said companies do not need ever-larger, more expensive models to get useful results, and that starting from a strong open foundation and specializing it deeply can produce capable AI at lower cost.
Harvey, the legal tech firm backed by OpenAI, Sequoia, and Andreessen Horowitz, also recently announced that its new model, Harvey Tenet, was post-trained on top of Moonshot AI’s open-weight Kimi K3 and that it outperformed both its base model and U.S. frontier systems, including Fable 5 and GPT-5.6 Sol, on complex legal agentic tasks. Harvey had previously built its product by customizing closed models from Anthropic, OpenAI, and Google.
Ramp’s lead economist, Ara Kharazian, wrote that recent growth of open-source has not yet dented spending on OpenAI or Anthropic directly. New AI buyers are still choosing the established American labs; for example, Anthropic gained the most ground among businesses in July, rising 1.1 percentage points to 43.5% market share, and OpenAI climbed just 0.23 points to 39.7%.
However, Anthropic’s Fable 5, thought to be the most advanced model on the market—so much so that the U.S. government briefly suspended foreign access to it for national security purposes—accounted for just 6% of tokens businesses purchase from Anthropic and 11.4% of dollars spent on Anthropic models overall, despite being priced at roughly $10 per million tokens, twice the cost of OpenAI’s GPT-5.6 Sol.
That model, by comparison, makes up 25% of OpenAI’s tokens and 23% of its spend. Kharazian argues Fable 5 has effectively found the market’s ceiling, and businesses are not willing to pay a premium for the best model on the market when a cheaper one is good enough.
Taken together, all this may suggest American frontier labs have found the limit on what customers will pay for the newest, most expensive model, and open-source may just be stepping in to fill that gap.
With that, here’s more AI news.
Beatrice Nolanbeatrice.nolan@fortune.com@beafreyanolan
This story was originally featured on Fortune.com
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