Despite the wide availability of powerful AI models, a sustainable edge in the zero-sum game of investing comes from a combination of unique, curated data sets, bespoke technology for scale, and the experienced human context to ask the right questions of the models.

The effectiveness of AI agents is fundamentally limited by their data inputs. In the agent era, access to clean and structured web data is no longer a commodity but a critical piece of infrastructure, making tools that provide it immensely valuable. AI models have brains but are blind without this data.

Instead of solving underlying data quality issues, AI agents amplify and expose them immediately. This makes protecting and managing data at its source a critical prerequisite for maintaining trust and achieving successful AI implementation, as poor data becomes an immediate operational bottleneck.

Contrary to the belief that AI requires perfect, clean data, the biggest opportunity lies in building technology that can find signals in messy, diverse data sets across different modalities and organisms. The tech should solve the data problem, not wait for it to be solved.

With powerful LLMs, reasoning, and inference becoming commoditized, the key differentiator for AI-powered products is no longer the model itself. The most critical factor for success is the quality of the underlying data. Unifying, protecting, and ensuring the accessibility of high-quality data is the primary challenge.

The effectiveness of an AI system isn't solely dependent on the model's sophistication. It's a collaboration between high-quality training data, the model itself, and the contextual understanding of how to apply both to solve a real-world problem. Neglecting data or context leads to poor outcomes.

The core differentiator in AI application is shifting from the model itself to the quality of contextual data fed into it. An AI model is compared to a 'brain' that is useless without the 'eyes, ears, and legs' of integrated, proprietary data. This implies a company's data strategy is more critical to its competitive advantage than access to the latest frontier model.

The competitive advantage in pharma isn't the sophistication of an AI algorithm, which is often a commodity built on third-party models. The true differentiator is the quality, relevance, and end-to-end consistency of the proprietary data used to train and validate these models. Poor data invalidates even the best analytics.

While public AI can achieve 90% of a financial analysis, Goldman's competitive advantage lies in the final 10%. This edge is built on proprietary data, unique cross-asset class insights, global human intelligence, and expertise in complex products—factors external models cannot replicate.