The rapid progress of many LLMs was possible because they could leverage the same massive public dataset: the internet. In robotics, no such public corpus of robot interaction data exists. This “data void” means progress is tied to a company's ability to generate its own proprietary data.

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.

A key competitive advantage for AI companies lies in capturing proprietary outcomes data by owning a customer's end-to-end workflow. This data, such as which legal cases are won or lost, is not publicly available. It creates a powerful feedback loop where the AI gets smarter at predicting valuable outcomes, a moat that general models cannot replicate.

Since LLMs are commodities, sustainable competitive advantage in AI comes from leveraging proprietary data and unique business processes that competitors cannot replicate. Companies must focus on building AI that understands their specific "secret sauce."

GM's new robotics division is leveraging a non-obvious asset: its vast, meticulously structured manufacturing data. Detailed CAD models, material properties, and step-by-step assembly instructions for every vehicle provide a unique and proprietary dataset for training highly competent 'embodied AI' systems, creating a significant competitive moat in industrial automation.

The future of valuable AI lies not in models trained on the abundant public internet, but in those built on scarce, proprietary data. For fields like robotics and biology, this data doesn't exist to be scraped; it must be actively created, making the data generation process itself the key competitive moat.

As AI models become commoditized, the ultimate defensibility comes from exclusive access to a unique dataset. A startup with a slightly inferior model but a comprehensive, proprietary dataset (e.g., all legal records) will beat a superior, general-purpose model for specialized tasks, creating a powerful long-term advantage.

As AI's bottleneck shifts from compute to data, the key advantage becomes low-cost data collection. Industrial incumbents have a built-in moat by sourcing messy, multimodal data from existing operations—a feat startups cannot replicate without paying a steep marginal cost for each data point.

Companies create defensibility by generating unique, non-public data through their operations (e.g., legal case outcomes). This proprietary data improves their own models, creating a feedback loop and a compounding advantage that large, generalist labs like OpenAI cannot replicate.