A powerful new open-source artificial intelligence model created by Chinese startup DeepSeek has shaken Silicon Valley over the past few days. Packed with cutting-edge capabilities and developed on a seemingly tiny budget, DeepSeek's R1 is prompting talk of an impending upheaval in the tech industry. To some people, DeepSeek's rise signals that the US has lost its edge in AI. But a number of experts, including executives at companies that build and customize some of the world's most powerful frontier AI models, say it's a sign of a different kind of technological transition underway. Instead of trying to create larger and larger models that require increasingly exorbitant amounts of computing resources, AI companies are now focusing more on developing advanced capabilities, like reasoning.

Meta will make its generative artificial intelligence (AI) models available to the United States' government, the tech giant has announced, in a controversial move that raises a moral dilemma for everyone who uses the software. Meta last week revealed it would make the models, known as Llama, available to government agencies, "including those that are working on defence and national security applications, and private sector partners supporting their work". The decision appears to contravene Meta's own policy which lists a range of prohibited uses for Llama, including "[m]ilitary, warfare, nuclear industries or applications" as well as espionage, terrorism, human trafficking and exploitation or harm to children. Meta's exception also reportedly applies to similar national security agencies in the United Kingdom, Canada, Australia and New Zealand. It came just three days after Reuters revealed China has reworked Llama for its own military purposes.

Meta has published an update on how its Llama large language models are performing, and they're apparently doing so well that they're now "approaching 350 million downloads to date." That's 10 times more than the downloads it accumulated compared to the same time last year. Approximately 20 million of those downloads took place in the last month alone, after the company released Llama 3.1, its latest LLM that it says can now rival OpenAI's and Anthropic's. The monthly usage of Llama grew ten times from January to July this year for some of Meta's largest cloud service providers, the company said. From May to July, in particular, hosted Llama usage on its cloud partners more than doubled by token volume.

Y Combinator is famed for its Demo Days, where portfolio companies pitch their apps and wares in hopes of growing from a fledgling company into the next AirBnB. But on Thursday, the startup incubator hosted a mélange of founders, venture capitalists, and US policy makers in its airy industrial space in San Francisco to tackle a defining topic for so many startups today: AI as the latest frontier in the battle between Big Tech and the little guys. For many early-stage tech entrepreneurs, questions around AI can carry existential weight. Ever since ChatGPT was unleashed in late 2022, OpenAI's technology, along with fast follows from Google's and Microsoft's AI teams, has dominated the conversation around this new era of artificial intelligence. But the increasing availability--and potency--of open source AI models has the potential to upend those dynamics.

Open model developers have emerged as key actors in the political economy of artificial intelligence (AI), but we still have a limited understanding of collaborative practices in the open AI ecosystem. This paper responds to this gap with a three-part quantitative analysis of development activity on the Hugging Face (HF) Hub, a popular platform for building, sharing, and demonstrating models. First, various types of activity across 348,181 model, 65,761 dataset, and 156,642 space repositories exhibit right-skewed distributions. Activity is extremely imbalanced between repositories; for example, over 70% of models have 0 downloads, while 1% account for 99% of downloads. Furthermore, licenses matter: there are statistically significant differences in collaboration patterns in model repositories with permissive, restrictive, and no licenses. Second, we analyse a snapshot of the social network structure of collaboration in model repositories, finding that the community has a core-periphery structure, with a core of prolific developers and a majority of isolate developers (89%). Upon removing the isolate developers from the network, collaboration is characterised by high reciprocity regardless of developers' network positions. Third, we examine model adoption through the lens of model usage in spaces, finding that a minority of models, developed by a handful of companies, are widely used on the HF Hub. Overall, activity on the HF Hub is characterised by Pareto distributions, congruent with OSS development patterns on platforms like GitHub. We conclude with recommendations for researchers, companies, and policymakers to advance our understanding of open AI development.

Jerome Pesenti has a few reasons to celebrate Meta's decision last week to release Llama 3, a powerful open source large language model that anyone can download, run, and build on. Pesenti used to be vice president of artificial intelligence at Meta and says he often pushed the company to consider releasing its technology for others to use and build on. But his main reason to rejoice is that his new startup will get access to an AI model that he says is very close in power to OpenAI's industry-leading text generator GPT-4, but considerably cheaper to run and more open to outside scrutiny and modification. "The release last Friday really feels like a game-changer," Pesenti says. His new company, Sizzle, an AI tutor, currently uses GPT-4 and other AI models, both closed and open, to craft problem sets and curricula for students.

Suddenly, "open source" is the latest buzzword in AI circles. Meta has pledged to create open-source artificial general intelligence. And Elon Musk is suing OpenAI over its lack of open-source AI models. Meanwhile, a growing number of tech leaders and companies are setting themselves up as open-source champions. But there's a fundamental problem--no one can agree on what "open-source AI" means.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. The open source technology movement has been having a moment over the past few weeks thanks to AI -- following a wave of recent large language model (LLM) releases and an effort by startups, collectives and academics to push back on the shift in AI to closed, proprietary LLMs. State-of-the-art LLMs require huge compute budgets – OpenAI reportedly used 10,000 Nvidia GPUs to train ChatGPT– and deep ML expertise, so few organizations can train them from scratch. Yet, increasingly, those that have the resources and expertise are not opening up their models -- the data, source code, or deep learning's secret sauce, the model weights -- to public scrutiny, relying on API distribution instead. That is where open source AI is stepping into the void to democratize access to LLMs.

By August, it had been cloned in the open by two master's students as OpenGPT-2 By November, OpenAI released their 1.5B parameter model, after a cautious staged release process May 2020: OpenAI released GPT-3 as a paper and a closed beta API in June 2020. Mar 2021: EleutherAI released their open GPT-Neo 1.3B and 2.7B models May 2022: Meta released OPT-175B for researchers (with logbook! and an open license) The Text-to-Image cycle took 4? months: Apr 2022: OpenAI announces DALL-E 2 with a limited "research preview" The timelines above are highly cherrypicked of course; the story is much longer if you take into account the longer development history starting from the academic papers for diffusion (2015) and transformer models (2017) and older work on GANs. But what is more interesting is what has happened since: OpenAI's audio-to-text model, Whisper, was released under MIT license in September with no API paywall. Of course, there is less scope for abuse in the audio-to-text domain, but more than a few people have speculated that the reception to Stable Diffusion's release influenced the open sourcing decision. Sufficiently advanced community is indistinguishable from magic.

The nonpartisan think tank Brookings this week published a piece decrying the bloc's regulation of open source AI, arguing it would create legal liability for general-purpose AI systems while simultaneously undermining their development. Under the EU's draft AI Act, open source developers would have to adhere to guidelines for risk management, data governance, technical documentation and transparency, as well as standards of accuracy and cybersecurity. If a company were to deploy an open source AI system that led to some disastrous outcome, the author asserts, it's not inconceivable the company could attempt to deflect responsibility by suing the open source developers on which they built their product. "This could further concentrate power over the future of AI in large technology companies and prevent research that is critical to the public's understanding of AI," Alex Engler, the analyst at Brookings who published the piece, wrote. "In the end, the [E.U.'s] attempt to regulate open-source could create a convoluted set of requirements that endangers open-source AI contributors, likely without improving use of general-purpose AI."