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
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Superhuman Mail users respond to 72% more emails per hour and save an average of four hours every week — numbers backed by a case study from one of the Big Three strategy consulting firms. Rahul Vohra, CEO at Superhuman Mail, built the world's fastest email engine over three years without launching, held the line until the product was ready, and then productized product-market fit into a repeatable, measurable science. Following Superhuman's acquisition by Grammarly in 2025, Rahul is now steering the company toward a unified AI-native productivity suite spanning email, calendar, tasks, and agents.

What you'll learn:

• The 5-step PMF Engine: how to survey, segment, analyze, implement, and track your way to product-market fit with a numerical score
• Why you should ignore the not disappointed and most somewhat disappointed users — and which signals actually tell you who to build for
• How to use the High Expectation Customer (HXC) framework to narrow your market without changing your product
• Why PMF is a moving target and how to defend it against commoditization and copy-cat competition
• How Rahul operates as the editor of the product — using 20 verbatim quotes to push PMs and designers to sharper decisions

Key takeaways:

• If more than 40% of your users would be very disappointed without your product, you have an initial PMF — and you can measure your way there
• Changing your market is faster than changing your product — segmentation alone can jump your PMF score 10 points overnight
• Building for your highest-expectation customer is not the same as building for your ICP — confuse the two, and you'll optimize for the wrong signal

Credits:
Host: Carlos Gonzalez de Villaumbrosia
Guest: Rahul Vohra

Social Links:

• Find out more about Product School here
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GoFundMe has facilitated over $40 billion in help since 2010, powering a community of more than 200 million people across 20 countries. Arnie Katz is the Chief Product and Technology Officer there — and a three-time CPTO, having previously led product and engineering at StubHub and TheRealReal. In this episode, he brings the rare perspective of someone who has built and scaled marketplaces at every stage, across multiple industries.

What you'll learn:

• The three failure modes every marketplace must solve — cold start, imbalance failure, and false positive growth — and how to fix each one
• How GoFundMe is using AI agents to reduce friction for fundraisers, resulting in an expected $125 million in additional funds raised
• Why AI is driving revenue growth at GoFundMe, not just developer productivity — and how they sequenced that deliberately
• The real trade-offs of the CPTO model: what you gain in speed, and what you have to mitigate through hiring
• How GoFundMe is building demand-side and matching mechanisms to grow donation volume beyond viral sharing

Key takeaways:

• Marketplace liquidity isn't just about having enough supply — it's about designing the right matching and demand mechanisms at every stage of scale
• AI unlocks revenue opportunities that were previously uneconomical to pursue, especially when the customer is already in a vulnerable, high-friction state
• The CPTO structure enables faster decision-making, but requires consciously strong functional leaders underneath to offset the natural lean toward one side

Credits:
Host: Carlos Gonzalez de Villaumbrosia
Guest: Arnie Katz

Social Links:

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