The call for U.S. open sourced players
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Illustration: Brendan Lynch/Axios
Efforts to ban Chinese AI models in Washington, D.C., are meeting strong resistance in Silicon Valley from entrepreneurs and investors who fear it hands more power to Anthropic and OpenAI.
Why it matters: AI costs are ballooning, and companies don't want to be reliant on just one or two vendors for such critical tech.
What we're watching: There's increasing investor interest in one "prime" solution where the U.S. would have an open-source or open-weight alternative that is as powerful as the Chinese players.
- Closed models from OpenAI and Anthropic have taken the lead in the U.S. partly because of the economics.
- Closed models can make more money by charging for each query, but the pathway to monetization for open models is rockier.
State of play: It's still early days in the U.S. for open models.
- Thinking Machines, led by former OpenAI CTO Mira Murati, dropped its first model (an open-weights one), last week.
- The industry is also closely watching $25 billion Reflection AI, which has yet to release a model. Nvidia, meanwhile, has Nemotron.
- OpenAI also launched open-weight models last year.
What they're saying: "The open frontier is no longer just a Chinese story. It is an American one too," Benchmark's Bill Gurley wrote in a Washington Post op-ed. "Nearly everyone in the AI economy has a reason to prefer an open foundation — everyone, that is, except the big incumbents Anthropic and OpenAI, whose fortunes depend on keeping it closed."
Between the lines: Developments in new AI models are happening faster than ever.
- The conversation today has centered on Kimi K3, but the leading model in three to six months could be a whole different story.
- "The most important things to understand about the new AI race right now is that every metric is changing really fast right now," Artificial Analysis CEO Micah Hill-Smith tells Axios. Costs are going down inside of closed-source AI labs, while services companies are emerging aiming to also bring down bills.
The bottom line: Volatility is a big part of the AI industry right now. We're all just getting used to it.
