Welcome to Eye on AI. Beatrice Nolan here. In today’s issue:
- Satya Nadella warns enterprises they’re paying for AI twice.
- Thinking Machines Lab ships its first model.
- Google DeepMind loses another researcher.
- And, Anthropic’s CEO pours $1 million into a PAC pushing AI regulation.
Are Big Tech companies turning against frontier AI labs?
In the past few weeks, several executives and VCs have voiced concerns that the AI labs are gaining outsized access to their customers’ most sensitive business data.
This week, Microsoft CEO Satya Nadella weighed in. He published a blog post warning that companies using AI models like OpenAI’s and Anthropic’s are “paying twice”: once for tokens that do the work, and again by handing over the proprietary knowledge needed to make the models useful.
“Models learn from ‘exhaust,’” he wrote, “the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong. Every correction is distilled into institutional know-how.”
That’s a problem, Nadella argues, because enterprises are unsuspectingly handing the labs the ability to one day compete with them. While AI labs freely train on the open internet, he added, they restrict enterprises from doing the same to their models. His fix is for companies to retain ownership of their own data and build “proprietary learning environments” in the cloud, paired with “orchestration layers” that let them switch between AI providers instead of getting locked into one. Does Microsoft happen to sell exactly this kind of proprietary cloud environment and orchestration layer? Funny you should ask…
So sure, Nadella is being a bit self-serving. But it’s nonetheless a somewhat surprising argument to come from the CEO of a company that has poured billions into both OpenAI and Anthropic. But Nadella isn’t alone.
On CNBCearlier this month, Alex Karp, CEO of Palantir Technologies, argued frontier AI labs are quietly extracting the very thing that makes a business valuable—proprietary data, processes, and competitive edge—while charging premium token prices for tools he says often don’t deliver commensurate value.
Karp said enterprise clients are privately “livid” about their AI vendors, arguing frontier labs are more interested in “tokenmaxxing” than solving real business problems. Palantir followed up with a nine-point “AI sovereignty” manifesto declaring that “data retention is your treasure.”
Even from outside the Big Companies, people are piling on.
On his All In podcast, former White House AI and Crypto Czar, David Sacks, called Karp “exactly right” and argued that OpenAI and Anthropic have formed a duopoly that leaves enterprises with too little leverage over their own data and infrastructure.
Karp’s argument, like Nadella’s, is good for his own business. Palantir’s core pitch is that its Foundry platform lets enterprises use any model without exposing their “alpha”—the proprietary data, processes, and know-how that make up a company’s competitive edge.
The open-source argument
All of this rhetoric may also push companies toward open-weight models—models whose underlying parameters are published publicly, letting anyone download, run, and modify them—and away from close-sourced models such as GPT-5 or Claude. These open-source models can offer enterprises more control and transparency over their data, often at a fraction of the cost of frontier models.
Open-weight models—the most advanced of which are coming from Chinese-based companies—are also closing the performance gap with the frontier and seem to be gaining in popularity with users.
Vercel, for example, says open modelsnow account for 29% of traffic through its AI gateway. Amazon’s CTO, Werner Vogels, also recently told me that he was seeing companies shift from frontier model providers to open source options, both in an attempt to reign in cost and gain more transparency over the technology they are embedding into their companies.
The U.S. government’s recent decision to block access to Anthropic’s Fable 5 models has also made companies consider diversifying away from a single AI vendor. After all, if companies are relying on AI models to run crucial workloads, they want to be sure no one can pull the plug overnight.
With that, here’s more AI news.
Beatrice Nolan [email protected] @beafreyanolan
The post Big Tech’s top executives warn enterprises are giving away too much to AI labs appeared first on Fortune.
