Open Weights, Closed Ranks: The AI Manifesto War
The AI industry has entered its manifesto era. Executives, researchers, and employees are issuing rival plans to keep advanced models safe. The fine print contains a less advertised question: Would those plans protect the public—or protect today’s leaders from the open models gaining on them?
That competition question starts with open-source AI models. These models make their source code, training methods, and trained parameters publicly available under a permissive license, allowing others to use and modify them. As Dirk Auer and I previously wrote, citing Susan Athey, then-chief antitrust economist at the U.S. Department of Justice (DOJ), a few strong open models may be enough to constrain proprietary large language models (LLMs):
… it is important not to neglect the role that open-source models currently play in fostering innovation and competition. As former DOJ Chief Antitrust Economist Susan Athey pointed out in a recent interview, the AI industry “may be very concentrated, but if you have two or three high quality — and we have to find out what that means, but high enough quality — open models, then that could be enough to constrain the for-profit LLMs.” Open-source models are important because they allow innovative startups to build upon models already trained on large datasets—therefore entering the market without incurring that initial cost. Apparently, there is no lack of open-source models, since companies like xAI, Meta, and Google offer their AI models for free…
The same reasoning applies to open-weight models. These models make their trained numerical parameters, or “weights,” freely available for download, even if their training data, full code, and methods remain private. The weights encode what a model has learned during training.
Open-weight models allow startups to build AI products and services without bearing the substantial upfront training costs that well-resourced incumbents can more readily absorb. Open-source and open-weight models therefore offer one reason, among others, for optimism about competition in AI markets.
That competitive role has made open-weight models a central target in the industry’s new manifesto war. Legitimate concerns about safety and potentially illegal conduct have prompted several private-sector regulatory proposals. The most concrete concern involves Chinese developers’ alleged large-scale distillation of proprietary models, a process in which one model learns from another model’s outputs. The proposals range from mandatory approval before release to targeted restrictions on open-weight and open-source development.
As Kristian Stout has argued, such restrictions are more likely to create problems than solve them. This post examines the competitive consequences of the most prominent proposals. Several could distort an AI market that has proved more open and competitive than the prevailing regulatory narrative suggests. The manifestos promise safer AI. Their fine print may promise today’s leaders a safer market.