Angel investor and All-In co-host Jason Calacanis stated that investors in cutting-edge model companies will quickly shift to a regulatory capture model. He predicts that vertical AI companies, governments, and enterprises, the largest token buyers, are embracing open source, which puts pressure on and caps the returns from investing in closed cutting-edge models. The goal of the entire safety mobilization is to block open source.

He wrote that if safety is truly the goal, there should be disclosure; open source is the ultimate disclosure process. He claims to have discussed this position on his podcast for two years. The concurrent context includes discussions between Anthropic and OpenAI about slowing down, on-site evaluations, and nationwide mandatory safety regulations. Calacanis attributes the migration of buyers to their unwillingness to hand over intelligence and data to private labs, stating that open source tokens are "dark tokens": their usage does not appear in the financial statements of model companies but only in cloud bills from AWS, Azure, GCP, etc.

He previously warned startups against exchanging free or discounted cutting-edge tokens for equity and data usage, stating that platforms will observe which applications perform best and then incorporate those features into their own products. He cited examples of some startups migrating inference to open source models on their own hardware, reducing costs by about 50% to 90%, with quality close to that of most tasks. Podcast partner David Sacks outlined the path: first, establish an AI regulatory body similar to the FDA; if political resistance is strong, shift to a self-regulatory organization like FINRA; then require open source and closed models to adhere to the same standards; once open source is released, it cannot be retracted or centrally monitored, and it will be pushed out of the compliant market under "equal treatment."

Chinese open source weight models like Kimi and Qwen are approaching the performance of closed cutting-edge models in some evaluations, with cheaper inference, intensifying this debate. Anthropic has previously accused industrial-scale distillation of constituting intellectual property theft to Congress. Calacanis and Sacks oppose punishing U.S. developers with bans, arguing that the source should address foreign entities, or else companies will be locked into the token tax of closed models. Brad Gerstner believes that open source and closed models are not a zero-sum game, and the U.S. needs revenue from both sides to fund annual computing infrastructure costs of about $1 to $2 trillion.

In market mechanisms, this is a battle for channels rather than the release of new models. Buyers are governments, industry software, and enterprise IT that want self-hosting and data to stay in-house; sellers are closed laboratory shareholders pricing APIs and future IPOs. Beneficiaries are open source inference, weight mass production, and privatized deployment tools in the cloud; those under pressure are laboratories whose valuations are tied to writing "safety standards equally applicable to open source" into legislation. Funding is shifting from closed interfaces billed by token to dark inference within enterprise clusters that do not keep accounts, making growth on reports appear as if demand has disappeared, while in reality, demand is just changing tracks.

Source: Public Information