alphalist.CTO Podcast - For CTOs and Technical Leaders

Most people assume the web runs on Google. The reality is more concentrated: only three companies on earth operate truly independent search indices — Google, Bing, and Brave. Jean-Paul Schmetz helped build one of them.

In this episode, Jean-Paul traces the arc from writing appointment software in a Belgian Radio Shack in 1981, through founding and selling Clix — a European search engine backed by Burda — to his current role as Chief of Ads at Brave, where he now sells search infrastructure to the AI companies that need it most.

For CTOs, this is a rare look inside an infrastructure layer most take for granted: how search indices are actually built, why it takes decades and hundreds of millions to do it properly, and why the entire AI grounding market quietly runs on infrastructure a small group of engineers spent their careers building.
Topics covered:

- Why only Google, Bing, and Brave have truly independent global search indices
- How AI companies use search grounding — and what happens when Google and Bing cut them off
- The SERP API gray market and why it probably has a two-year shelf life
- What it actually costs to crawl and index the web at scale
- The advertising model that will eventually come to AI — and why it's inevitable
- Jean-Paul's Stanford years: machine learning with Andrew Ng, and what was obvious in 2013 that took until 2022 to matter
- Build vs. buy for search infrastructure in 2025

Neal Ford: software architect, author, speaker, and independent consultant (formerly 20+ years at ThoughtWorks), joins Tobias to explore what happens to software architecture when AI agents write the code.

We unpack the critical distinction between behavior and capabilities: why everyone focuses on what code does, but too few think about scalability, security, and responsiveness. Neal introduces architectural fitness functions as the essential guardrail for agentic systems, and explains why non-deterministic code generation demands deterministic tests.

Finally, we dig into legacy modernization, the Dreyfus scale applied to LLMs, ephemerality as the new architectural dimension, and why AI is a multiplier, not a replacement, for experienced engineers.

Lee Zen, CTO of Yahoo, joins Tobias to unpack what it takes to modernize one of the internet’s most iconic consumer portfolios—Mail, Finance, Sports, News, and Search—while operating with real legacy constraints at massive scale.

We talk about Yahoo’s evolution from its public days to private equity ownership, how modernization actually happens (cloud, platform bets, experimentation), and why shipping velocity becomes the most honest forcing function when you’re rebuilding the engine mid-flight.

Finally, we go deep on AI: where it meaningfully improves consumer experiences (mail catch-up, news takeaways, fantasy insights), how teams should avoid “AI labels” without user value, and what it means when AI becomes a tool—and increasingly a coworker.

Greg Foster, Co-founder and CTO of Graphite (recently acquired by Cursor), joins the podcast to discuss the massive shift occurring in software engineering: the move from maximizing "Inner Loop" speed (writing code) to solving "Outer Loop" bottlenecks (reviewing, testing, merging).

With AI generating code faster than humans can review it, the traditional Pull Request model is under pressure. Greg explains how "Stacked PRs" and agentic review workflows are essential for high-performing teams, and why he believes the role of the software engineer is evolving into an "architect of agents."

We also cover the strategic rationale behind the Graphite/Cursor merger, the controversial "PRs per engineer" metric, and why he predicts that by 2029, manual code writing will be near zero—but demand for engineers will be higher than ever.

Eric Bowman (CTO @ King.com, previously CTO at TomTom and VP Engineering at Zalando) returns to the alphalist podcast to unpack what “agentic engineering” really means in practice—and how to introduce it to teams without turning it into a mandate.

We talk about the uncomfortable trade-offs behind “YOLO mode” tooling, why adoption should feel voluntary even when you set explicit goals (like “five AI-assisted commits” as a company-level key result), and why the real opportunity isn’t just faster coding—it’s building a learning system that relentlessly reduces time-to-learning and time-to-value.

The conversation spans practical rollout patterns, DORA/value-stream thinking, Toyota’s Andon-cord mindset applied to software, multi-agent decision support with MCP, and why the CTO role may keep converging with product as AI pushes organizations to optimize for iteration speed over output volume.