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.

The most valuable intellectual property for companies will be their unique, private evaluation benchmarks. These evals allow them to "hill climb" any model, ensuring they retain control and are not locked into a single AI provider. The ability to switch models and improve performance is the key asset.

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.

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.

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.

Nadella introduces the 'harness'—the integrated system of data, tools, and context preparation surrounding a model. He posits this harness, which enables multi-model strategies and efficient execution, is where companies create unique value, rather than in the base model alone.

The rapid improvement of AI models is maxing out industry-standard benchmarks for tasks like software engineering. To truly understand AI's impact and capability, companies must develop their own evaluation systems tailored to their specific workflows, rather than waiting for external studies.

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.