The industry has already exhausted the public web data used to train foundational AI models, a point underscored by the phrase "we've already run out of data." The next leap in AI capability and business value will come from harnessing the vast, proprietary data currently locked behind corporate firewalls.

The long-sought goal of "information at your fingertips," envisioned by Bill Gates, wasn't achieved through structured databases as expected. Instead, large neural networks unexpectedly became the key, capable of finding patterns in messy, unstructured enterprise data where rigid schemas failed.

The next leap in biotech moves beyond applying AI to existing data. CZI pioneers a model where 'frontier biology' and 'frontier AI' are developed in tandem. Experiments are now designed specifically to generate novel data that will ground and improve future AI models, creating a virtuous feedback loop.

To build a multi-billion dollar database company, you need two things: a new, widespread workload (like AI needing data) and a fundamentally new storage architecture that incumbents can't easily adopt. This framework helps identify truly disruptive infrastructure opportunities.

A major hurdle for enterprise AI is messy, siloed data. A synergistic solution is emerging where AI software agents are used for the data engineering tasks of cleansing, normalization, and linking. This creates a powerful feedback loop where AI helps prepare the very data it needs to function effectively.

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.

Beyond automating data collection, investment firms can use AI to generate novel analytical frameworks. By asking AI to find new ways to plot and interpret data inputs, the team moves from rote data entry to higher-level analysis, using the technology as a creative and strategic partner.

The most significant value from AI is not in automating existing tasks, but in performing work that was previously too costly or complex for an organization to attempt. This creates entirely new capabilities, like analyzing every single purchase order for hidden patterns, thereby unlocking new enterprise value.

Dr. Fei-Fei Li realized AI was stagnating not from flawed algorithms, but a missed scientific hypothesis. The breakthrough insight behind ImageNet was that creating a massive, high-quality dataset was the fundamental problem to solve, shifting the paradigm from being model-centric to data-centric.