Guest: Alex Ratner, CEO & Co-founder, Snorkel AI — snorkel.ai
Host: Solomon Hailu, Partner, March Capital

For years, AI researchers treated data as someone else’s problem — janitorial work, beneath the real science of models and architectures. Alex Ratner bet his career against that assumption. It’s now a $3.5 billion company.

In this episode, March Capital partner Sol Hailu sits down with Alex Ratner, CEO and co-founder of Snorkel AI, fresh off a $350M Series E fundraise and a year in which Snorkel grew nearly 20x.

Alex traces the arc from the Snorkel open-source project at the Stanford AI Lab to the shift he calls Data 1.0 to Data 2.0: from throwing volume at the wall to designing expert curricula and RL environments for models already operating at “PhD+” level. Along the way — why the data spec is increasingly the product spec, why no enterprise will hand its most differentiated knowledge to an off-the-shelf model, and what it means that models have become eval-aware, able to tell when they’re being tested.

He and Sol get into the jagged frontier — why a model can crack a Millennium Prize problem and still fumble an expense report — and land on what Alex calls the defining problem of the moment: for the first time in AI history, our ability to measure what models can do is lagging the capabilities themselves. His answer isn’t fewer benchmarks. It’s more of them, built in the open and harder to game.

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Snorkel is hiring and expanding its Open Benchmark Grants program — researchers working on open benchmarks are invited to get in touch.

The Montgomery Summit returns to Santa Monica on March 9-10, 2027.

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00:00 Introduction to AI and Snorkel
02:56 The Evolution of AI Data and Snorkel's Journey
06:04 Data-Centric AI: The New Paradigm
08:56 Data 1.0 vs Data 2.0: Shifting Focus
11:52 Collaborative Data Design and Customer Engagement
15:08 The Importance of Data in AI Applications
18:08 Navigating AI Safety and Model Development
21:12 Trends in Open vs Closed AI Models
24:06 Defining Scope for AI Implementation
26:58 The Evolution of AI Deployment
27:28 AI Safety and Its Challenges
29:02 Understanding AGI and Its Implications
30:08 The Jagged Frontier of AI Capabilities
32:34 Evaluating AI: The Need for Robust Benchmarks
36:13 Pacing AI Development for Safety
39:13 Funding Growth: Series E and Future Prospects
43:00 Exciting Applications of AI in Healthcare
46:18 Shifting Beliefs in AI Development