alphalist.CTO Podcast - For CTOs and Technical Leaders

Modernizing Yahoo in the private-equity era: legacy constraints, shipping velocity, and AI-native consumer products Yahoo is far more than a nostalgia brand—it’s a broad consumer platform operating at huge scale. In this episode, Tobias sits down with Lee Zen (CTO @ Yahoo) to explore how the company is modernizing in the private-equity era, and what “innovation” looks like when you’re also responsible for decades of legacy systems. We dive into: ** Yahoo’s consumer portfolio: Mail, Finance, Sports, News, Search—and what that implies for platform strategy What changes under private equity: priorities, focus, and modernization constraints Modernization in real terms: cloud adoption, legacy trade-offs, and sequencing big bets Shipping velocity as a leadership lever: faster learning cycles over perfect planning Organizational mechanics for rapid experimentation at consumer scale AI in engineering: where it helps, where it creates new bottlenecks, and “AI as a coworker” AI in the product (real use cases): mail catch-up, news key takeaways, finance and fantasy-sports insights The hardest problems at consumer scale: cost optimization, velocity, and quality without regressions Career reflections: imposter syndrome, self-belief, and staying hands-on as a CTO** Chapters: [00:00] Intro & welcome: Lee Zen (CTO @ Yahoo) [00:28] Lee’s journey & Yahoo’s evolution [01:14] Early computing: DOS, QBasic, and the builder origin story [03:43] Academic background: CS + AI foundations [05:20] Yahoo today: portfolio breadth & what private equity changes [08:54] Modernization: cloud, legacy constraints, and technical trade-offs [17:44] Leadership & org design: enabling speed and experimentation [22:52] AI integration: tool vs. coworker, and the future of engineering [31:38] AI across Yahoo’s consumer products: mail, news, finance, sports [32:21] Avoiding “AI labels”: enhancing UX with human + AI judgment [33:26] Consumer-scale engineering challenges: cost, velocity, and quality [34:28] Easter egg: side projects and reflecting on the past [35:34] Advice to his younger self: self-belief, self-love, imposter syndrome [36:28] Conclusion & final thoughts

Solving the "Outer Loop" bottleneck: Agentic coding, Stacked PRs, and the future of the CTO role with Graphite's Greg Foster The "Inner Loop" of coding (typing syntax) has been solved by AI. The new bottleneck is the "Outer Loop"—reviewing, testing, and merging the flood of code that agents generate. In this episode, Greg Foster (CTO of Graphite, now part of Cursor) explains why this shift drove their merger and what it means for the future of engineering. We dive into: The Outer Loop Crisis: Why fast code generation breaks traditional review processes (and how to fix it). Agentic Engineering: The 2029 vision where engineers become "architects of agents" and writing code trends to zero. Stacked PRs: Why smaller, dependent changes are the only way to maintain velocity at scale. Metrics that Matter: Why "PRs per engineer" is actually a valid proxy for velocity (velocity = speed + vector). The Cursor Merger: Combining the best text editor (Inner Loop) with the best review platform (Outer Loop). Provenance in Git: The need for storing "prompts" and AI attribution directly in commit history. Advice to Young Engineers: Why understanding the business model is as important as reading O'Reilly books. Chapters: [00:00] Intro: Greg's journey from iOS apps to Airbnb infrastructure [01:25] Graphite & The Pivot: Bringing "Phabricator-style" tools to GitHub [03:52] Reinventing the Pull Request: Stacked PRs and Merge Queues [13:20] The Velocity of Dev Tools: When Copilot/Cursor changed the game [20:41] Metrics: Is "PRs per engineer" a vanity metric or a velocity proxy? [24:22] Jevons Paradox: Why 2x productivity means more engineers, not fewer [26:30] The Merger: Why Graphite joined Cursor (Inner + Outer Loop) [33:30] Provenance: Storing AI prompts in Git history [52:00] The 2029 Vision: Writing code -> Zero. Reviewing Agents -> 100%. [56:17] Future of CI: Auto-generated sandboxes with agentic QA [59:26] Advice to his younger self: Understand the business, not just the code

In this discussion, Eric Bowman, CTO with a wealth of experience from King.com, TomTom, and Zalando, examines how AI is transforming programming and leadership. He shares insights on 'living on the edge' with AI tools and the difference between AI and human creativity. Eric also emphasizes the importance of time-to-value and learning in tech organizations, while advocating for voluntary AI adoption among engineers. He predicts a convergence of the CTO and product roles, reshaping the future of technology leadership.

Loïc Houssier, CTO of Superhuman Mail and veteran of tech acquisitions, shares invaluable insights on engineering leadership. He argues that clarity in team missions drives execution velocity more than new tools ever could. With practical metrics like PRs per engineer per week, he emphasizes tracking trends and minimizing dependencies. Loïc also favors a monorepo strategy, disdainfully dismissing microservices unless absolutely necessary. His unique perspective blends European experience with a fresh approach to building cohesive teams and delivering quality products.

Matthias Keller, Chief Product Officer at Kayak, shares his 12 years of insights into AI and travel tech. He delves into the strategic framework for evaluating emerging AI platforms like ChatGPT and vision technologies. Matthias discusses the lessons learned from Kayak's early Alexa experiments, the complexity of competing with giants like Google Flights, and the shift in user expectations. He also highlights how LLMs allow engineers to innovate without data scientists, emphasizing the balance between timely innovation and execution realities.

How single-node performance and embedded analytics are revolutionizing database architecture for modern applications Tradegate Direct: Europe's most direct online broker – trade for free, efficiently, and directly on the stock exchange. Trade directly here In this episode, we dive deep into the world of analytical databases with Mark Raasveldt, the co-founder and CTO of DuckDB Labs. Mark takes us through his fascinating journey from academic research to building one of the most talked-about databases in the industry. We explore: How DuckDB emerged from academic research at CWI Amsterdam The technical philosophy behind embedded analytical databases Why single-node performance still matters in the age of distributed systems The challenges of building a database system from the ground up Open source business models and the DuckDB Labs approach The evolution of columnar data formats and Parquet optimization How DuckDB is making high-performance analytics accessible to all developers The future of analytical data processing and embedded systems This conversation offers unique insights into database architecture, performance optimization, and the intersection of academic research and commercial success.

Solal Raveh, CTO of Product Infrastructure at Wiz, shares insights from his experience at one of the fastest-growing security companies, leading to a $32B acquisition by Google. He discusses the importance of specialized technical teams over geographic cloning, the success of remote collaboration, and a rapid POC development culture—highlighted by a swift response to the NPM exploit. Solal emphasizes customer connection, innovation metrics, and a people-first leadership approach, revealing how gamification makes security approachable for users.

From assembly lines to problem-solving teams: practical strategies for breaking development silos Here's the thing about agile transformations: they almost never work the way they're supposed to. Teams end up more siloed than before, chasing tickets instead of solving actual problems. Klaus Breyer has seen this pattern everywhere, and he's figured out some ways to break it. Klaus runs product and technology at Edding—yeah, the pen company—but his background is anything but traditional. He learned team coordination by managing 40-person World of Warcraft raids, ran a few startups, and now applies those lessons to building software at a 150-year-old German manufacturer. It's an unusual path that gives him a different perspective on how teams actually work together. We talked about Shape Up methodology, but honestly, the more interesting stuff was about changing how teams think about their work. Klaus has some pretty specific ideas about when teams are ready to ditch ticket systems entirely, how to spot the early warning signs of assembly-line thinking, and why most agile implementations fail at the mindset level. Tradegate Direct: Europe's most direct online broker – trade for free, efficiently, and directly on the stock exchange. Trade directly here Also, Edding is doing some wild stuff with technology—like building a driver license verification system using invisible conductive ink that smartphones can read. Who knew pen companies were this technical? What we covered: [00:51] Klaus's background and how Edding ended up doing serious tech [01:30] The invisible ink technology that got my attention [05:11] Why building cool tech is easier than building teams that work well together [06:05] Learning management from World of Warcraft raids (seriously) [08:40] The realization that most project failures aren't technical [09:29] The shift from "give me a ticket" to "let me solve the problem" [10:35] How Shape Up actually works in practice—6 weeks, small teams, single focus [11:26] Why tiny teams still end up with silos [13:22] Red flags that your team is in assembly-line mode [14:16] Late compromises as a symptom of poor collaboration [15:40] The magic number for team size and why bigger gets messy [16:28] Matching the right people to the right problems [18:17] Breaking down specialization barriers [19:23] How "business" ruined the original agile manifesto [20:35] Getting clear on what actually matters [22:28] The art of problem definition (harder than it sounds) [24:23] Having honest conversations about how much effort problems deserve [27:17] Building projects that can be cut at any point [29:41] When senior teams can just… work without tickets [32:17] What product managers actually do in this model [35:00] Conway's Law and organizing around what you're building [38:10] Dealing with matrix organizations and temporary teams [39:58] First steps for teams stuck in traditional agile [42:05] The question Klaus asks to cut through confusion [43:39] Remote collaboration tools and templates [45:46] Starting solution sessions with blank slates [48:19] Timeline from problem to working code [49:02] How you know when it's actually working Quotes worth remembering: "Almost all teams out there have silos. You can have silos in the smallest teams. You can have silos with three or four people if they are thinking about the work in the wrong way." [11:15] "One of the biggest signs is when you need to do tradeoffs because the time is running out. And then if you do tradeoffs because the time is running out, most of the times the tradeoffs are then done or led by the engineers because we don't have time to complete this feature." [13:22]

Kelsey Hightower, a self-taught engineer who became a Distinguished Engineer at Google and a key Kubernetes figure, shares insights on his 25-year tech journey and early retirement at 42. He discusses the complexities of infrastructure management and the misconception that new technologies replace old ones. Kelsey emphasizes the importance of business-driven engineering and understanding the hidden costs of complexity. He also reflects on the evolving role of AI and the significance of aligning engineering with revenue, all while advocating for a balanced view on work and fulfillment.

Peter Gostev, Head of AI at Moonpig, is a trailblazer in AI transformation within large engineering organizations. He discusses the challenges of bridging the gap between AI hype and real-world implementation, especially in a 600-person company. The conversation covers managing AI adoption among 150 engineers while emphasizing the need for effective communication and training. Peter delves into the realities of AI tool usage, the importance of collaboration, and crafting a three-pillar AI strategy that combines tools, automation, and experimentation.