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

Social Links:

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

Social Links:

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

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:

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