Nadella posits a future where the winner isn't the company with the best model. Instead, value accrues to the platform that provides the data, context, and tools (the 'scaffolding') that make any model useful, especially as capable open-source alternatives proliferate.

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.

The competitive advantage for vertical AI isn't just data, but creating increasingly difficult, proprietary evaluation benchmarks. By creating and continuously improving performance against a moving target for specific tasks, vertical AI companies build a durable product advantage that general models cannot easily replicate.

Standardized benchmarks for AI models are largely irrelevant for business applications. Companies need to create their own evaluation systems tailored to their specific industry, workflows, and use cases to accurately assess which new model provides a tangible benefit and ROI.

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.

Microsoft's decision to promote Anthropic models on Azure as aggressively as OpenAI's reflects a core belief from CEO Satya Nadella. He anticipates AI models will become commoditized, making the underlying intelligence interchangeable and the cloud platform the primary point of differentiation and value capture.

As enterprise spend on AI workflows explodes, companies will create custom evaluation benchmarks (evals) for each specific use case. These evals act as a system of record to hot-swap between different models based on price-performance, enabling perfect competition and ultimately commoditizing the API layer.

If a company and its competitor both ask a generic LLM for strategy, they'll get the same answer, erasing any edge. The only way to generate unique, defensible strategies is by building evolving models trained on a company's own private data.

The rapid release of new AI models makes it crucial for companies to move beyond industry benchmarks. Developing internal evaluation systems ("evals") is necessary to test and determine which model performs best for unique, high-value business use cases, as model choice is becoming extremely important.