The Product Podcast

n8n's founder puts the company's GitHub repo, nearly 200,000 stars, right next to the paid signup button, and he's genuinely fine if you never pay. In this episode of The Product Podcast, Carlos (CEO at Product School) sits down with Jan Oberhauser, CEO of n8n, the open-source automation platform that's crossed $100 million in ARR at a $5.2 billion valuation. Jan breaks down the "fair-code" license bet that let him give the product away and still build a business, how that free version became the on-ramp into enterprises like Meta, Nvidia, Dell, Accenture, Vodafone, Deutsche Telekom, and Mercedes, and why he believes the people with the problem should build the automation themselves, not a centralized team or an outside agency.

He also walks through a live build of a personal AI agent (email and calendar), shows how n8n falls back from Claude to GPT via OpenRouter when a model isn't available, and explains how enterprises get automations into production faster because each agent can only do exactly what it's been permitted to do.

What you'll learn:
Why n8n rejected traditional open source for a "fair-code" license, and how it avoided the community backlash that burned other companies
Why trust and consistency, not features, are the real center of a community
How the free, self-hosted version drives bottom-up adoption inside major enterprises
Why "sprinkling AI on top" kills products, and what to build instead
How to chain agents so one agent's output becomes the next agent's input
Why n8n is the "connective tissue" between models, tools, and business systems
How guardrails (an agent can only do what it's explicitly allowed) speed up enterprise procurement and production
Why the people with the problem should own the building, not a centralized AI team
How 10,000+ community templates and 500+ integrations expand what non-technical builders can ship
How one company routes 75% of support through an n8n agent, with customers happier than with humans
Connect with Guest (Jan Oberhauser):
LinkedIn: https://www.linkedin.com/in/janoberhauser
X: https://x.com/JanOberhauser

Host: Carlos, CEO at Product School
LinkedIn: https://www.linkedin.com/in/villaumbrosia/

About Jan Oberholzer: Jan is the CEO of n8n, an open-source (fair-code) workflow automation and orchestration platform for building AI agents. He started the company over seven years ago, before LLMs went mainstream, and has grown it past $100M ARR at a $5.2B valuation.

About the Product Podcast: Product School's podcast brings you candid conversations with the founders and product leaders shaping tech.
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 Amit Zavery, President and Chief Product Officer at ServiceNow. The platform runs more than 75 billion workflows a year with around $15 billion in annual revenue growing over 20%. Its market cap is above $100 billion, yet the stock is down more than 30% this past year, while its AI business is on track for $1.5 billion, ahead of a $1 billion plan. 
Amit previously ran product and platform at Oracle for over two decades and was a VP and General Manager at Google Cloud.
What you'll learn:
Why the market can't yet tell AI winners from losers, and why companies that don't transform will get killed
Why the idea of one company becoming the single end-to-end enterprise orchestrator is a fallacy
The spare part approach that makes most enterprise AI projects fail, and what pacesetters do instead
Why access is shifting from user interfaces to agents, and what taking action actually requires
How to hold long-term conviction on platform bets while the market judges you on short-term sentiment
Key takeaways:
Transform or die: the market will separate AI-native platforms from legacy vendors
Interoperability beats domination in the agentic era
Governance only wins when it accelerates innovation, not when it blocks it
Credits:
Host: Carlos Gonzalez de Villaumbrosia
Guest: Amit Zavery
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 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
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 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
Follow Product School on LinkedIn here

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