Companies with valuable proprietary data should not license it away. A better strategy to guide foundation model development is to keep the data private but release public benchmarks and evaluations based on it. This incentivizes LLM providers to train their models on the specific tasks you care about, improving their performance for your product.

Public internet data has been largely exhausted for training AI models. The real competitive advantage and source for next-generation, specialized AI will be the vast, untapped reservoirs of proprietary data locked inside corporations, like R&D data from pharmaceutical or semiconductor companies.

The key for enterprises isn't integrating general AI like ChatGPT but creating "proprietary intelligence." This involves fine-tuning smaller, custom models on their unique internal data and workflows, creating a competitive moat that off-the-shelf solutions cannot replicate.

Fears that universal tools reduce differentiation are misplaced. Instead of just leveling the playing field, open tools like OpenFold raise the entire industry's baseline capability. This shifts competition away from who builds the best foundational model to who can ask the most insightful scientific questions.

The choice between open and closed-source AI is not just technical but strategic. For startups, feeding proprietary data to a closed-source provider like OpenAI, which competes across many verticals, creates long-term risk. Open-source models offer "strategic autonomy" and prevent dependency on a potential future rival.

A common misconception is that Chinese AI is fully open-source. The reality is they are often "open-weight," meaning training parameters (weights) are shared, but the underlying code and proprietary datasets are not. This provides a competitive advantage by enabling adoption while maintaining some control.

As algorithms become more widespread, the key differentiator for leading AI labs is their exclusive access to vast, private data sets. XAI has Twitter, Google has YouTube, and OpenAI has user conversations, creating unique training advantages that are nearly impossible for others to replicate.

To escape platform risk and high API costs, startups are building their own AI models. The strategy involves taking powerful, state-subsidized open-source models from China and fine-tuning them for specific use cases, creating a competitive alternative to relying on APIs from OpenAI or Anthropic.

Rather than just consuming technology, members of the OpenFold consortium are building businesses on top of it. Companies are providing specialized services like federated learning tools and SaaS platforms, demonstrating how a pre-competitive open technology can spawn a new ecosystem of commercial service providers.