alphalist.CTO Podcast - For CTOs and Technical Leaders

Bölümler

• #141 AI Pat Works Here Now: Why Agents Must Follow Human Rules with Pat Casey // CTO @ ServiceNow 02.07.2026 1sa 6dk

  Pat Casey was the first person besides founder Fred Luddy to write code at ServiceNow back in 2005, when it was called Glide and lived above a friend's restaurant. Twenty years later, he's CTO of a company where 85% of the Fortune 500 are customers, and until recently ran all of engineering: 10,000 people, 7,000 of them writing code. Almost nobody survives the journey from first engineer to public-company CTO. Pat did. Tobi and Pat dig into how ServiceNow actually works under the hood: a metadata processing engine running 90,000 single-tenant databases and over 25 billion queries an hour, why they bought a 15-person German database company and turned it into RaptorDB, and why tearing apart a 20-year-old monolith is harder than every senior engineer thinks. Then the conversation turns to AI. Pat bought 7,000 Windsurf licenses and measured a real, but unglamorous, 15% productivity bump, with a small subset of engineers going 5–6x while most barely changed. His thesis: AI coding is like playing five chessboards at once, and it's reshuffling the deck on who the top engineers will be. On agents, ServiceNow's answer is disarmingly simple: create a user called "AI Pat," assign it cases, and make it follow the exact same rules as humans because you should not trust an LLM more than you trust a human being. Topics covered: - From Atari 400 and floppy-disk jockey at Aldus to first engineer at ServiceNow - Scaling engineering from a stuffed fish on a monitor to 10,000 people — and the productivity trough at ~100 engineers - Single-tenant architecture: 90,000 databases, 25B+ queries/hour, and the monolith-to-Kubernetes migration - Why ServiceNow bought Swarm64 and built RaptorDB on a Postgres fork - 7,000 Windsurf licenses, Claude Code, and the real numbers on AI coding productivity - "AI Pat": the anthropomorphic model for enterprise agents outcomes, not toolkits - Whether AI kills seat-based SaaS, and why incumbents may have the inside track - Pat's advice to CTOs: this is not a time for excessive caution
• #140 From Stripe's Fifth Engineer to Serving Millions of Developers with Anurag Goel // Founder & CEO @ Render Goel 18.06.2026 1sa 12dk

  Before he founded Render, Anurag Goel was the fifth engineer at Stripe, where he watched roughly a fifth of the engineering team disappear into managing AWS, writing brittle, repetitive, error-prone infrastructure scripts that had nothing to do with the actual product. That experience became the seed for Render: a platform that automates away the undifferentiated DevOps work and lets application teams ship without standing up their own cloud team. Today, millions of developers build on it, and Render has raised over $260M from Bessemer and General Catalyst. In this episode, Tobi and Anurag get into what's actually changing as AI moves from hype to production. Anurag makes the case that agents are simply a new kind of application, long-running, stateful, tool-heavy, and a new kind of end user you have to design for. He explains why Render deliberately refuses the "AI cloud" label, what he's building with Workflows and sandboxes, and why the hardest part of shipping agents isn't building them but seeing inside them. The conversation also goes wide: how to hire executives when interviews lie, why short-lived keys and blast-radius thinking matter more than container escapes, how distribution is shifting from SEO to getting ChatGPT and Claude to recommend you, and why, despite all the "SaaS is dead" noise, specialization isn't going anywhere. Topics covered: Why ~20% of Stripe's engineers were stuck managing AWS and how that became Render "We're not the AI cloud, we're the application cloud," and why the distinction matters Agents, as a new type of application (and a new end user), you have to build for Render Workflows and sandboxes: the consolidated AI runtime Hiring executives when interviews are an imperfect signal Security as blast-radius management: short-lived keys over "admin forever" The shift from SEO to GEO, getting chatbots to recommend your product Why SaaS isn't dying, and specialization still wins
• #139 Your Future Job Is a Decision Inbox — Max Deichmann Built the Layer That Gets You There // Co-Founder @ Langfuse 04.06.2026 1sa 3dk

  Max Deichmann is the co-founder of Langfuse, the open-source LLM engineering platform that became the observability layer of choice for teams building production AI agents, before being acquired by ClickHouse. He started as a business student who taught himself to code via CS50 on a beach in Singapore, pivoted through Y Combinator, fired his own customers mid-batch, and built Langfuse out of a Sunday night conversation about what they'd actually want to build if nothing was in the way. In this episode, Tobi and Max dig into what it really means to build and operate AI agents in production, not the LinkedIn version, but the 3 am alert, copy-pasted into Codex version. They cover the full loop: from pre-production experimentation and prompt iteration, to tracing, online evaluation, and the emerging architecture of agentic incident response. Max is unusually honest about where Langfuse itself still falls short, and what the next 12 months of the engineer's job actually look like. What CTOs will take away: a clear mental model for LLM observability vs. traditional observability, a practical blueprint for agentic on-call workflows, and a grounded view of where agents are genuinely working in production today, and where the hype still outpaces reality. Topics covered: Why traditional observability tools fail for non-deterministic AI applications The Langfuse loop: pre-production testing, tracing, online evaluation, and iteration How the ClickHouse acquisition happened, and the half-page doc that decided it Open source as a go-to-market strategy: adoption without a sales team Agentic on-call: how Max's team handles 3 am incidents with Codex today The "decision inbox" model, what the engineer's job looks like when agents do the work Where agents are genuinely succeeding in production (and where LinkedIn is lying to you)
• #138 From Hacker News to W3C: How One Amazon Engineer Accidentally Shaped the Future of AI Browsers // Alex Nahas, MCP-B 21.05.2026 41dk

  Alex Nahas is 28 years old and has already initiated a W3C web standard. Working as a backend engineer at Amazon, he ran into a problem most enterprises face: MCP requires OAuth, but most enterprise infrastructure runs on SAML. His solution was elegant: run the MCP server in client-side JavaScript, letting AI agents use the browser's existing authentication context rather than rebuilding auth from scratch. What started as an internal tool became an open source project, then a viral Hacker News post published while under anesthesia, and ultimately an invitation from Google and Microsoft to help shape WebMCP as an official web standard. In this episode, Alex and Tobi explore what WebMCP actually is, why the browser is the most underestimated sandbox in AI development, and what the agentic web might look like two years from now. Topics covered: What MCP actually is and why it's just an RPC framework at its core Why OAuth is a dealbreaker for most enterprise infrastructure How WebMCP lets AI agents operate within existing browser authentication The Hacker News post that started it all, and why Alex doesn't remember posting it How Chrome is natively building WebMCP support The chicken-and-egg problem of standard adoption Real-time bidding for agents and what it means for digital advertising Why agents don't need their own identity Where the agentic web is headed in the next two years
• #137 - Only Three Search Engines Left Standing: One of Them Powers Your AI with JP Schmetz // Chief of Ads @ Brave 07.05.2026 1sa 33dk

  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
• #136 - AI Writes Code: Who Architects the Consequences? with Neal Ford // Software Architect & Author 23.04.2026 56dk

  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.
• #135 - From Legacy to Innovation: Yahoo's Modernization & AI with Lee Zen // CTO @ Yahoo 29.01.2026 37dk

  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.
• #134 - From Inner to Outer Loop: Agentic Coding, Stacking PRs, and the Cursor Merger with Greg Foster // CTO @ Graphite 15.01.2026 54dk

  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.
• #133 - Build the Learning Machine: AI Adoption, Flow Metrics, and the Future of the CTO Role with Eric Bowman 15.12.2025 57dk

  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.
• #132 - Clarity Over Tooling: Velocity & Building Teams Without Drama with Loïc Houssier // CTO @ Superhuman Mail 27.11.2025 54dk

  What drives execution velocity—better tools or better clarity? Loïc Houssier, CTO of Superhuman Mail (post-Grammarly acquisition), argues that most velocity problems stem from unclear team missions, not inadequate tooling. From steering DocuSign's French acquisition through complex carve-out negotiations to building Superhuman's offline-first architecture with a 100-millisecond interaction rule, Loïc shares hard-won lessons about engineering metrics that actually matter (PR per engineer per week trends over absolutes), when to resist microservices (until it's genuinely painful), and why promotion frameworks determine product quality. Technical leaders will learn how vertical team alignment eliminates dependencies, why guild structures maintain consistency without blocking speed, and how European safety nets create under-appreciated opportunities for technical risk-taking.
• #131 - AI Product Strategy: When to Build and When to Wait with Matthias Keller // CPO @ Kayak 13.11.2025 51dk

  Matthias Keller, Chief Product Officer at Kayak, shares hard-won lessons about AI product strategy and knowing when to invest in emerging platforms. With a PhD in computer engineering from ETH Zurich and 12 years at Kayak, Matthias has lived through multiple waves of AI hype—from Alexa voice skills in 2016 to today's LLM revolution. He discusses the strategic calculus of early platform bets, the painful lessons from experiments that didn't pan out, and how to recognize when technology has truly shifted. The conversation covers navigating distribution challenges when competing with giants like Google and ChatGPT, balancing first-mover advantage with execution realities, and how LLMs are democratizing AI development for engineering teams. Matthias emphasizes the critical framework: "if you build it, they may come—if you don't build it, they won't come."
• #130 - From PhD Research to DuckDB: Building the Next Generation of Analytical DBs with Mark Raasveldt // CTO @ DuckDB 16.10.2025 53dk

  Mark Raasveldt, co-founder and CTO of DuckDB Labs, shares his journey from academic research at CWI Amsterdam to creating one of the most innovative analytical databases of the last decade. Mark discusses the technical challenges of building DuckDB from scratch, the philosophy behind embedded analytical databases, and why single-node performance still matters in our cloud-first world. He provides insights into open source business models, the evolution of data formats like Parquet, and how DuckDB is democratizing high-performance analytics for developers everywhere.
• #129 - $32B Lessons: Building CTO Teams, Rapid Innovation, and Staying Customer-Connected with Solal Raveh 18.09.2025 48dk

  What does it take to build a company worth $32 billion? Solal Raveh, CTO Product Infrastructure at Wiz, shares hard-won lessons from scaling technical teams during one of the fastest-growing security companies in history. Learn how Wiz evolved their CTO office from traditional team building to rapid innovation incubation, why geographic team cloning failed spectacularly, and how staying customer-connected drives product decisions. Discover the three-fold mission of modern CTO roles, the shift from measuring finished features to tracking innovation velocity, and why technical leaders must balance automation expertise with people-first thinking. Technical leaders will gain insights into organizing global remote teams around domain expertise, implementing 3-hour threat response cycles, and building enterprise-ready infrastructure while maintaining startup agility.
• #128 - From Tickets to Problems: Klaus Breyer // Head of Product & Technology @ Edding 04.09.2025 55dk

  You know how agile transformations always promise better collaboration but somehow teams end up chasing tickets like a factory assembly line? Klaus Breyer from Edding has some thoughts on why this keeps happening—and what actually works instead. Klaus's path to leading product and technology at Germany's most famous pen company wasn't exactly traditional. Before Edding, he spent years managing 40-person World of Warcraft raids (yes, really) and running startups. Now he's applying those lessons to build software teams that actually solve problems instead of just completing tasks. The conversation digs into Shape Up methodology, but more importantly, Klaus explains the mindset changes needed to stop treating software development like an assembly line. His team at Edding has built some pretty cool stuff too—like a B2B driver license verification system using invisible conductive ink that smartphones can read. What you'll learn: • Why "give me a ticket" thinking kills collaboration (even in tiny teams) • How 6-week cycles help teams focus on one problem without distractions • The art of separating problems from solutions before jumping into code • Why late-stage compromises usually mean your team isn't really collaborating • When senior teams can ditch tickets entirely and just... work • Klaus's templates for getting everyone aligned on what problems are worth solving
• #127 - Kelsey Hightower's Unfiltered Truths: 25 Years of Infrastructure, DevOps, and Retiring at 42 07.08.2025 1sa

  What happens when a distinguished engineer who shaped the cloud-native landscape decides to retire at 42? Kelsey Hightower, a pivotal figure in the Kubernetes community and former Google engineer, shares brutally honest insights from his 25-year journey. This isn't a conversation about the next hype cycle; it's a masterclass in the timeless principles of infrastructure, maintenance, and technical strategy. From the fallacy of technology replacement to the hard business realities that should drive engineering decisions, Kelsey provides a minimalist's guide to navigating complexity. Learn why most companies should embrace managed services, why engineers who can't link commits to revenue are at risk, and what the future of AI really means for the systems we build and maintain. Technical insights for CTOs and engineering leaders: - 🏗️ System Accumulation: Why new technology rarely replaces the old, leading to a complex, multi-generational stack that must be maintained. - ☁️ Managed Services: The economic and expertise-driven argument for outsourcing infrastructure management. - 🔄 Evolutionary Architecture: How to avoid the trap of making permanent technology decisions on day one. - 💰 Business-Driven Engineering: The critical need for engineers to understand revenue, and for CTOs to use business metrics to guide technical priorities. - 🤖 The AI Reality: A grounded take on how AI will impact software, and the fundamental system evolution required for it to reach its true potential."
• #126 - AI Transformation at Scale: Practical Adoption Across 150+ Engineers with Peter Gostev // Head of AI @ Moonpig 24.07.2025 1sa 5dk

  How do you drive meaningful AI transformation across 150 software engineers without mandates or force? Peter Gostev, Head of AI at Moonpig, reveals the technical strategies and organizational approaches behind scaling AI adoption from 130 to 400+ users while navigating the gap between industry hype and implementation reality. From managing complex integration challenges where 80% of AI projects involve traditional software engineering to implementing three-pillar strategies (tool adoption, automation workflows, experimental features), Peter shares hard-earned insights on building AI capabilities through process re-engineering rather than simple automation. Technical insights for CTOs and engineering leaders: • 🏗️ Portfolio approach: balancing quick wins with experimental high-impact projects • ⚡ Prototype-first methodology for validating AI solutions before full development • 🤖 Reality gap between agentic AI hype and production deployment complexity • 👥 Organic adoption strategies that scale without top-down mandates • 🔧 Custom GPT frameworks for non-technical subject matter experts • 📊 Why most AI work is integration, scaffolding, and deployment—not just AI • 🔄 Process re-engineering with AI: changing workflows rather than automating existing inefficiencies
• #125 - Two CTO Dinosaurs vs. Today's Tech Hype with Raz Shuty // CTO @ auxmoney 10.07.2025 1sa 3dk

  What happens when two experienced CTOs sit down to debunk the latest tech trends? Raz Schweiger-Shuty, CTO at auxmoney, joins Tobi for an unfiltered discussion about the hypes, myths, and wastes of resources that plague modern tech companies. After taking over a 17-year-old fintech platform with no prior CTO, Raz made controversial decisions that flew in the face of conventional wisdom: stopping a microservices migration, questioning Kubernetes adoption, and focusing on measurable business value over engineering trends. His ""dinosaur CTO"" perspective offers a refreshing antidote to tech hype. This conversation cuts through the noise with practical insights on: • 🚫 Why every monolith-to-microservices story ends the same way (spoiler: badly) • 💰 Reducing cloud costs from €120k to €85k through systematic waste elimination • 🔧 When Kubernetes complexity becomes a liability rather than an asset • 📊 Using DORA metrics and cost-per-transaction instead of vanity metrics • 🏗️ Building modular monoliths with domain-driven design principles • 👥 Organizing engineering teams around business value streams, not technology stacks
• #124 - The Path to AGI: Inside poolside’s AI Model Factory for Code with Eiso Kant 27.06.2025 1sa 3dk

  How do you build a foundation model that can write code at a human level? Eiso Kant (CTO & co-founder, Poolside) reveals the technical architecture, distributed team strategies, and reinforcement learning breakthroughs powering one of Europe’s most ambitious AI startups. Learn how Poolside operates 10,000+ H200s, runs the world’s largest code execution RL environment, and why CTOs must rethink engineering orgs for an agent-driven future.
• #123 - From Nokia to AI-IoT: Engineering the Physical World with Bernd Groß // CEO @ Cumulocity 12.06.2025 1sa 3dk

  The physical world is becoming digital—and it requires fundamentally different technical architecture than traditional IT systems. Bernd Groß leads technical leaders through the evolution from enterprise software to industrial IoT, where real-time data from 30,000 wind turbines and millisecond-level decision-making define system requirements. As co-founder and CEO of Cumulocity, Bernd has navigated one of tech's most complex domains: connecting industrial hardware through standardized platforms. His journey from Nokia's early cloud computing initiatives to building Germany's leading IoT platform offers unique insights on technical leadership in physical-digital convergence. Technical leaders will gain valuable perspectives on: • 🏗️ Architecting speed-layer systems that handle 50TB monthly data flows while maintaining real-time responsiveness • 🔄 Managing technical debt across hundreds of industrial protocols while modernizing from monoliths to microservices • 🤖 Implementing "AI-IoT" strategies that bridge machine learning models with operational technology deployments • ⚡ Building edge-cloud hybrid architectures for regulated environments and latency-critical applications • 🛠️ Engineering platforms that scale from device management to data operationalization across industrial verticals
• #122 - Grid Control in Milliseconds: Engineering Energy Systems with Barbara Wittenberg // CTO @ 1KOMMA5° 16.05.2025 1sa 1dk

  Behind the renewable energy revolution lies complex technical infrastructure that CTOs across industries can learn from. Barbara Wittenberg leads a 250-person tech team at 1KOMMA5° that manages real-time data from 40,000+ connected energy assets while coordinating post-merger integration across 80+ companies in 7 countries. This episode unveils the technical architecture powering virtual power plants, where millisecond-level responsiveness can prevent grid failures and optimize energy usage. Barbara's journey from electrical engineering to Oracle and Google, then back to energy tech, provides unique insights on combining domain expertise with cutting-edge technology. Technical leaders will appreciate: - 🔄 How to manage distributed systems requiring real-time synchronization across numerous endpoints - 🧩 Strategies for standardizing operations while respecting existing successful processes after acquisitions - 🛠️ Practical applications of AI for automating complex technical explanations to customers - 🌐 Navigating complex regulatory environments that differ by country, region, and technical standards - 🚀 Building technical platforms that unite previously disconnected systems and data flows