Anthropic just closed a $65 billion Series H round at a valuation approaching one trillion dollars — and has crossed $30 billion in annualized revenue, driven largely by enterprise demand. Claude Code alone became generally available in May 2025 and reached $2.5 billion in annualized revenue in February 2026, with that figure more than doubling since the beginning of 2026.

Meaghan Choi, Head of Design for Claude Code and Cowork at Anthropic, was in that room. This conversation goes inside the operating model behind that growth.

What you'll learn:

• Claude Code's evolution from an internal feature into one of the fastest-growing revenue products in history
• Anthropic's secret sauce to shipping products at an incredibly high cadence while ensuring quality
• How product teams get structured into small pods of 5 AI Builders and a fleet of agents, where non-engineers ship code into production
• Driving enterprise adoption through PLG from technical teams
• How organizations can measure AI ROI beyond AI adoption and token usage
• Designing user interfaces for agentic capabilities, including CLI

Key takeaways:

• Titles and role boundaries matter less than contribution. At Anthropic, designers ship code and engineers design, and the pod owns the output collectively.
• Quality gates have moved downstream. The richest product learnings come from working software, not from reviewing mocks or PRDs.
• Managing a team now means managing both people and a fleet of AI agents. The skills are more similar than they appear.

Credits:
Host: Carlos Gonzalez de Villaumbrosia
Guest: Meaghan Choi

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Eric Ries wrote The Lean Startup — a book that has sold over 2 million copies and reshaped how a generation of founders and product teams build products. Fifteen years later, he's back with a new book, Incorruptible, and a harder question: not how to build a great company, but how to keep it that way.

What you'll learn:

• Why the forces destroying great companies are structural, not moral — and what that means for how you build
• How Saul Price built FedMart, and Costco's Jim Sinegal each solved half the problem, and why you need both halves
• How Anthropic used a purpose trust structure, the Long-Term Benefit Trust, to protect its safety mission from investor pressure
• Why values on the wall fail and what the Johnson & Johnson asbestos scandal reveals about how incentives quietly overwrite principles
• How builders at any level of an organization can start influencing governance without a title or authority

Key takeaways:

• Success makes you a target: the more valuable your company becomes, the more pressure it faces to betray the mission that made it valuable
• Ethos is the real moat: the intangible system of principles that makes a company trustworthy is harder to copy than any product or contract
• Governance is not a legal formality; it is the active, ongoing practice of protecting what you built from the forces that will try to extract it

Credits:
Host: Carlos Gonzalez de Villaumbrosia
Guest: Eric Ries

Social Links:

• Find out more about Product School here
• Follow our Podcast on TikTok here
• Follow Product School on LinkedIn here

Snowflake is the AI Data Cloud behind some of the world's largest enterprises — $4.68 billion in annual revenue, 29% year-over-year growth, and over 760 Forbes Global 2000 companies as customers. Baris Gultekin, VP of AI at Snowflake, leads the product efforts that sit at the center of how those enterprises actually operationalize AI. Before Snowflake, he co-founded Google Assistant and scaled it from 10 million to 500 million monthly users.

What you'll learn:

• Why our data isn't clean enough is a delay tactic — and the scoped approach to move past it
• What the semantic layer is and how it lets AI answer business questions accurately, not just fluently
• Why running AI next to data (instead of sending data to models) makes governance dramatically easier
• How Snowflake deployed AI internally: a CEO-level non-optional mandate combined with bottom-up access to their own Cortex coding agent
• Why context — not just data — is what agents need to operate reliably at enterprise scale

Key takeaways:

• Start with one scoped use case, build the semantic model around it, layer governance — don't wait for perfect data
• Context is a shared reality for agents: unified data + business semantics + codified workflows
• AI adoption compounds when leadership sets a hard mandate and simultaneously gives everyone a tool to experiment with

Credits:
Host: Carlos Gonzalez de Villaumbrosia
Guest: Baris Gultekin

Social Links:

• Find out more about Product School here
• Follow our Podcast on TikTok here
• Follow Product School on LinkedIn here