Exclusive: Paying for frontier AI models buys 4-month head start at 5x the cost

The current reality is that “most of the open models the world runs on are Chinese,” Krikorian said. “The Chinese labs are running the same playbook the Americans ran with Android—give it away, but own the ecosystem around it,” he explained.

But Krikorian is not so concerned about “Chinese models” as he is about the risk of concentration. At the moment, the best open models are concentrated in China, whereas the best closed frontier models come from US companies. He argued for US and European labs to start competing in the “same open lane, so that no single country sets the world’s defaults.”

“The uncomfortable truth is that the plural ecosystem around open-weight AI is largely funded by Chinese capital right now,” Krikorian said. “That’s a plurality and a concentration at the same time.”

The history of development for open source software such as Linux may offer some lessons for how an “alternative coalition” could come together, Krikorian said. Such open source software infrastructure was funded by a coalition of organizations that “each needed the commodity layer to exist,” including neutral foundations.

Any coalition for building more open AI models would likely consist of “institutions with a mission rather than a market,” as opposed to frontier AI labs, Krikorian said. He described it as follows:

We need public compute programs funding fully open reference models. Switzerland’s national compute producing Apertus is an example. Foundations need to hold the same neutral ground that they held for the internet’s open protocols, extended into models, harnesses, and agentic standards. Companies that benefit from commodity models have to participate. And philanthropy has to cover what none of the others will—evaluation and audit infrastructure, especially.

Krikorian also called for the alternative coalition to go even further than open-weights models by embracing more of the open source approach. “It’s hard to fully trust a model with decisions if you can’t tell how it was trained or what it was evaluated against,” he said.

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