Initiative Seeks AI Lab to Build 'American Truly Open Models' (ATOM)

'Benchmarking firm Artificial Analysis found that only five of the top 15 AI models are open source,' reports the Washington Post, 'and all were developed by Chinese AI companies....'

'Now some American executives, investors and academics are endorsing a plan to make U.S. open-source AI more competitive.'

A new campaign called the ATOM Project, for American Truly Open Models, aims to create a U.S.-based AI lab dedicated to creating software that developers can freely access and modify. Its blueprint calls for access to serious computing power, with upward of 10,000 of the cutting-edge GPU chips used to power corporate AI development. The initiative, which launched Monday, has gathered signatures of support from more than a dozen industry figures. They include veteran tech investor Bill Gurley; Clement Delangue, CEO of Hugging Face, a repository for open-source AI models and datasets; Stanford professor and AI investor Chris Manning; chipmaker Nvidia's director of applied research, Oleksii Kuchaiev; Jason Kwon, chief strategy officer for OpenAI; and Dylan Patel, CEO and founder of research firm SemiAnalysis...
The lack of progress in open-source AI underscores the case for initiatives like ATOM: The U.S. has not produced a major new open-source AI release since Meta's launch of its Llama 4 model in April, which disappointed some AI experts... 'A lot of it is a coordination problem,' said ATOM's creator, Nathan Lambert, a senior research scientist at the nonprofit Allen Institute for AI who is launching the project in a personal capacity... Lambert said the idea was to develop much more powerful open-source AI models than existing U.S. efforts such as Bloom, an AI language model from Hugging Face, Pythia from EleutherAI, and others. Those groups were willing to take on more legal risk in the name of scientific progress but suffered from underfunding, said Lambert, who has worked at Google's DeepMind AI lab, Facebook AI Research and Hugging Face.

The other problem? The hefty cost of top-performing AI. Lambert estimates that getting access to 10,000 state-of-the-art GPUs will cost at least $100 million. But the funding must be found if American efforts are to stay competitive, he said.

The initiative's web page is seeking signatures, but also asks visitors to the site to 'consider how your expertise or resources might contribute to building the infrastructure America needs.'

Read more of this story at Slashdot.