What happened
Microsoft CEO Satya Nadella published a blog post warning that companies using frontier AI models are "paying twice"—once for token usage and again by surrendering proprietary data that trains the models. Palantir CEO Alex Karp echoed the concern on CNBC, saying enterprise clients are privately "livid" about AI vendors extracting proprietary data while charging premium prices. Both executives called for enterprises to build proprietary learning environments and retain data ownership.
Why it matters
Nadella argues that by sharing their data and corrections with AI labs, enterprises are unknowingly giving those labs the ability to one day compete with them—while the labs restrict enterprises from training on data the same way. This data imbalance has some companies considering switching to open-weight models (whose parameters are published publicly), which can offer more control and transparency, often at lower cost. A U.S. government block on access to Anthropic's Fable 5 models has also prompted companies to diversify away from single vendors to avoid service disruptions.
What to watch
Vercel reports open-weight models now account for 29% of traffic through its AI gateway, and Amazon's CTO Werner Vogels has observed companies shifting from frontier models to open-source options for cost control and transparency. Meanwhile, Anthropic CEO Dario Amodei personally donated $1 million（約1.6億円） to Public First, a super PAC pushing for mandatory AI safety rules, while OpenAI rank-and-file employees have donated more than $215,000 to Guardrails Alliance, a competing PAC seeking stricter AI regulation.
Summaries like this, in your inbox every morning.
The critique from Nadella, Karp, and others represents a significant shift in how established enterprise leaders are framing the relationship between Big Tech and frontier AI labs. While both Nadella (whose company has invested billions in OpenAI and Anthropic) and Karp (whose firm sells data-protection software) have obvious business incentives to push this narrative, the underlying concern taps into a genuine tension: enterprises are handing over sensitive data to train models they do not control, and those same labs are simultaneously improving their ability to compete in the enterprise software market.
The emergence of open-weight models as a credible alternative—particularly from Chinese labs that the article notes are now leading the open-weight space—has created a real option for companies wanting to reduce dependence on any single vendor. The U.S. government's block on Anthropic's Fable 5 models has reinforced this incentive: companies relying on critical AI workloads can no longer assume a vendor's models will remain available. The shift toward open models is already measurable (29% of Vercel's AI gateway traffic) and aligns with the broader push from figures like Karp and Vogels for data sovereignty and reduced vendor lock-in.
Pick your industry and the AI tools you use, and get news related to your work every day.
Free · 30 seconds with Google · unsubscribe anytimeWhat is AIToday? →
Ask AI anything about this article. The AI reads this article, earlier AIToday articles, and Wikipedia, and cites its sources. Q&As are published on this page for other readers too.
Chunghwa Telecom president Rong-shy Lin said the shift from conversational AI to autonomous AI agents is resha…
CoreWeave fell 5% in premarket trading Friday and was on track to finish the week down 15%, after rebounding f…
Apple and Meta said they would jointly release a new open-weight AI model designed to run on high-end Macs, ai…
Investors put $38.5 billion into Asian startups in Q3, per Crunchbase data
A longtime Anthropic investor told Fortune he expects shares priced around $1.8 trillion, below the company's…
In Q2, billionaires or their funds sold six 'Magnificent Seven' AI stocks
