The Product Podcast

Latest Episodes:

Snapchat SVP of Engineering on How a Billion-User App Lets Everyone Ship Code Without Breaking Quality | Saral Jain | E304 Jul 15, 2026

Show notes

In this episode of The Product Podcast by Product School, Carlos González de Villaumbrosia sits down with Saral Jain, SVP of Engineering at Snapchat, the last independent social platform operating at global scale, with 956 million monthly active users closing in on the one billion mark and a community that opens the app more than 30 times a day.
Saral joined Snap nine years ago, right around the IPO, after nearly a decade leading engineering teams at Amazon Web Services, and today leads all engineering for Snapchat, spanning product experiences, multi-cloud infrastructure, and the company's machine learning and generative AI platforms.
What you'll learn:

How Snap lets designers and product managers ship production code, with an AI agent running the first review pass on 90% of code within 5 minutes
What Casper is: the AI teammate any team at Snap can invoke from Slack or Jira to build a working prototype from a conversation
How Snap turned company-wide AI adoption into business impact after early prototypes were being built and thrown away
How lean startup squads mix engineers, designers, and data scientists to launch zero-to-one bets inside a mature platform

Key takeaways:

Quality control does not have to slow down who gets to ship, it has to change what reviews the work first
Widespread AI adoption is not the same as business impact, and the gap between the two is where most AI investment is being wasted right now
Small, cross-functional teams with blurred roles can move faster than traditional org structures, even inside a billion-user company

Credits:
Host: Carlos Gonzalez de Villaumbrosia
Guest: Saral Jain

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Axon CPTO on Building AI for Law Enforcement, Selling to Governments, and Using the Body Cam and Taser to Save Lives at Scale | Jeff Kunins | E303 Jul 08, 2026

Show notes

In this episode of The Product Podcast by Product School, Carlos González de Villaumbrosia sits down with Jeff Kunins, Chief Product Officer and Chief Technology Officer at Axon, the company that created the Taser and the body cameras federal agencies wear.

Axon ingests more video per year than YouTube, and with a market cap of approximately $32.9 billion and $2.78 billion in revenue, growing 33% year over year, it is one of the highest-growth companies in the S&P 500.
What you'll learn:

How law enforcement agencies are using AI inside body cameras and Tasers to save lives, not just hit metrics.
Why Axon declared a public moratorium on facial recognition AI for six years and what finally changed.
How Axon embeds external activists and researchers directly into product manager squads as a design input, not a compliance process.
Building first-party AI models for real-time license plate detection while using foundation LLMs for everything else.

Key takeaways:

Axon created the Taser and the body cam, and now ingests more video per year than YouTube. Most people have never heard of them.
Build only what you must to be differentiated. Everything else, license from the best available source.
Ethics review is not a compliance burden. When embedded in the product lifecycle, external critics help you see around corners and design better products.

Credits:
Host: Carlos Gonzalez de Villaumbrosia
Guest: Jeff Kunins

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Asana CPO on Why Every Employee Is Now an AI Eval and What That Means for How Enterprises Actually Capture AI ROI | Arnab Bose | E302 Jul 01, 2026

Show notes

In this episode of The Product Podcast by Product School, Carlos González de Villaumbrosia sits down with Arnab Bose, Chief Product Officer at Asana. Asana is the work management platform built for human and AI collaboration, trusted by over 170,000 customers including Accenture, Amazon, and Anthropic. The platform's Work Graph maps goals to portfolios to projects to tasks and serves as the foundation for Asana's AI Teammates: collaborative agents that operate inside the graph, learn from human decisions, and compound their intelligence with every cycle.
What you'll learn:

Why enterprise AI spend keeps returning zero productivity gains, and what is structurally breaking the loop
Why every employee approval, correction, or rejection of AI output is training data that makes the system smarter over time
How Asana wires its own processes through the Work Graph so that AI decisions write back automatically and compound rather than reset
How PLG, forward-deployed engineers, and AI agents all report to the CPO, each under a GM who owns a revenue number
Why the future of AI at work belongs to whoever has the richest shared context, not whoever has the best model

Key takeaways:

Individual AI productivity gains compound into zero enterprise ROI when decisions never write back into a shared system
Every human approval or correction is training data. The companies that capture it structurally will pull ahead of those that don't
PLG is an acquisition funnel, not a sales motion. Giving it a GM with a revenue number inside product changes the incentives entirely

Credits:
Host: Carlos Gonzalez de Villaumbrosia
Guest: Arnab Bose

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Typeform CEO on Why Breadth Beats Depth as an AI Moat and How to Build a Defensive and Offensive AI Strategy | Jay Choi | E301 Jun 24, 2026

Show notes

In this episode of The Product Podcast by Product School, Carlos González de Villaumbrosia sits down with Jay Choi, Chief Executive Officer at Typeform. Typeform is the AI engagement platform trusted by more than 150,000 customers, including 95% of the Fortune 500. Before Typeform, Jay spent seven years as Chief Product Officer and General Manager at Qualtrics, where the company scaled from $100M to over $1B in ARR.
What you'll learn:

Breadth of surface area as a stronger AI moat than depth of use case, and why going broad is the right strategic bet right now
The dual posture Typeform built: a defensive strategy to make their core product impossible to replicate, and an offensive strategy to expand into full customer workflows
Research Flow, their new product that compresses 50 customer interviews from weeks into hours using AI-moderated research
Being model-agnostic from day one, and what they learned when switching models without an observability platform in place
The pricing experiment framework Jay uses: 30 simulations before a single market goes live

Key takeaways:

When AI threatens to commoditize your core product, expanding surface area is a stronger defense than adding AI features to what you already have
Positioning AI capabilities in plain language, not technical terminology, is the difference between adoption and abandonment
Happy churners are a product problem, not a marketing problem: the fix is finding structurally always-on use cases.

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

Social Links:

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Mozilla Head of Firefox on The Future of Agentic Browsers and Fighting for the Open Internet Against Google Chrome, Apple Safari & Microsoft Edge | Ajit Varma | E300 Jun 17, 2026

Show notes

For episode 300 of The Product Podcast, Carlos Gonzalez de Villaumbrosia sits down with Ajit Varma, Head of Firefox at Mozilla, the nonprofit behind the original challenger browser that pioneered browser tabs, pop-up blockers, and browser extensions. With 210 million active users and $826 million in annual revenue, Firefox is the only major independent, open-source browser still standing against Google Chrome's 68% share, Apple Safari's 17%, and a new wave of agentic browsers.

Before Mozilla, Ajit spent six years at Meta leading monetization of WhatsApp and overseeing its business messaging platform. He has also held product roles at Google, Uber, and Square.
What you'll learn:

Why LLMs are making browsers more strategically important, and what that means for product teams building in an agentic world
Why "trust us" is no longer enough, and how open source changes the standard for privacy in AI products
- How to compete against trillion-dollar incumbents without abandoning your mission

Key takeaways:

Privacy claims without open-source inspectability are unverifiable, "trust us" is no longer a sufficient product strategy in the AI era
Competing against trillion-dollar companies is possible when mission clarity defines what you refuse to optimize for
The agent-driven internet will either democratize access or concentrate it, product choices made today will determine which

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Linear COO on Rebuilding the Product Development Lifecycle for Teams and Agents — From Issue Tracker to Shared Operating System | Cristina Cordova | E299 Jun 10, 2026

Show notes

In this episode of The Product Podcast by Product School, Carlos González de Villaumbrosia sits down with Cristina Cordova, Chief Operating Officer at Linear, the product development system built for teams and agents. Linear raised $82 million in a Series C round in June 2025 at a $1.25 billion valuation. The company has been profitable since 2021, and serves over 20,000 paid business customers, from seed-stage startups to Fortune 100 enterprises, with a team of just 140 people. Before Linear, Cristina joined Stripe as one of its first employees, and led Platform and Partnerships at Notion.
What you'll learn:

Why keeping headcount intentionally lean is a strategic advantage
Replacing traditional interviews with paid two to five-day projects
Why PMs are the fastest-growing power users of agentic tools

Key takeaways:

A small team is not a small business. Revenue, customers, and growth rate matter more than headcount.
If you fully delegate your AI thinking, you lose your native understanding of how these products actually work
Agentic workflows are now the default, not a feature. The companies that treat them that way will pull ahead.

Credits:
Host: Carlos Gonzalez de Villaumbrosia
Guest: Cristina Cordova

Social Links:

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Anthropic Head of Design on Claude Code's Evolution from an Internal Feature into the Fastest-Growing Revenue Product in History | Meaghan Choi | E298 Jun 03, 2026

Show notes

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

Social Links:

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The Lean Startup Author on New Book Incorruptible: Why Good Companies Go Bad and How Great Companies Stay Great | Eric Ries | E297 May 26, 2026

Show notes

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:

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Snowflake VP of AI on Why Enterprises Hide Behind Governance to Avoid Real AI Transformation | Baris Gultekin | E296 May 13, 2026

Show notes

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:

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Superhuman Mail CEO on Rediscovering Product-Market Fit in the Age of AI, Renaming Post-Grammarly Acquisition & Competing against Google Workspace | Rahul Vohra | E295 May 06, 2026

Show notes

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:

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