Episode #42: The "Winners" in AI Today, DaaS Is Not Venture Backable, Seed Rounds Grow As Startups Shrink

This week on The Learning Corner, we dive into three sharp pieces exploring the evolving shape of venture. We kick off with Elad Gil’s take on why AI markets are finally gaining clarity, with key winners emerging across foundational models, code, legal, and customer experience. We ask: can new entrants still break through if they’re not one of the early, well-capitalized names? Next, we unpack Auren Hoffman’s argument that data businesses, while solid, often aren’t built for venture scale. As more investors rethink their appetite for pure-play DaaS models, we explore where data companies can still attract VC interest, especially as AI blurs the line between tooling and infrastructure. Lastly, we discuss Tomasz Tunguz’s piece on why seed rounds are getting larger even as startups shrink. From inflated pre-seed valuations to AI-powered team efficiency, we reflect on the new expectations being placed on early-stage companies and where those pressures might lead next.

AI Market Clarity (Elad Gil) Data is Actually Not a Great VC-Backed Business (Auren Hoffman) Why Seed Rounds Are Growing as Startups Shrink (Tomasz Tunguz)

(0:00) Introduction and welcome (0:41) AI markets ripe for disruption and impact of not being a big-name AI fund (1:30) Conviction in capital and customer markets (3:11) Portfolio company conflicts in venture capital (5:22) Emerging markets, challenges in AI, and data businesses in venture capital (10:05) Challenges and successes in data business scalability and market trust (13:18) Seed rounds: growth, driving forces, and expectations (17:18) Benefits and challenges of larger seed rounds (18:41) Closing remarks and gratitude