pen source AI might be catching up to its proprietary rivals.

Chinese AI firm Moonshot, the developer of the Kimi open source model family, is reportedly targeting a $10 billion valuation in an expansion of its current funding round, according to Bloomberg.

The company raised $500 million at a $4.3 billion valuation last month. The round’s existing backers include Alibaba, Tencent and 5Y Capital, which have already committed more than $700 million to the company.

And Moonshot isn’t the only company seeing open source success. Last week, Paris-based Mistral AI, which provides a suite of open-source models, announced a $1.4 billion commitment to AI data centers in Sweden as it hit an annualized revenue run rate of more than $400 million. The company had raised roughly $2 billion at a more than $13.8 billion valuation in September.

Adoption of these models, too, are starting to pick up pace, with Alibaba’s Qwen model family raking in hundreds of millions of downloads on Hugging Face.

Still, these figures are drops in the bucket next to the high-flying valuations of US-based proprietary model developers, with OpenAI eyeing an $830 billion valuation in its upcoming twelve-figure funding round; Anthropic hitting $380 billion after its $30 billion round; and xAI sitting at upwards of $230 billion prior to its acquisition by SpaceX.

However, money might not be everything. Scott Bickley, advisory fellow at Info-Tech Research Group, told The Deep View that these valuations are more a function of structural economic differences, rather than being indicative of model performance.

• That difference is geopolitical: The US relies on massive companies to push frontier AI research via capital concentration, he said.
• Meanwhile, China – where most of the open source AI development is concentrated – favors a large swathe of small companies to conduct research cheaper, said Bickley.

“This valuation gap belies the fact that open-source models are rapidly closing the intelligence gap,” Bickley told The Deep View.

Our Deeper View

Though there is a common notion that open-source AI still lags behind proprietary competitors, the race may no longer be who can make the most performant AI model, but rather it’s a “race to the bottom,” said Bickley, the winner being the lab that can make the AI model that’s most efficient and affordable. But the benefits of open-source go beyond being cheap: Open ecosystems promote and democratize collaborative innovation, if developers can find ways around the lack of safeguards and security issues that these models tend to pose.