alphalist.CTO Podcast - For CTOs and Technical Leaders

What Wiz's $32B acquisition teaches about scaling CTO teams, rapid innovation, and customer-centric leadership Tradegate Direct: Europe's most direct online broker – trade for free, efficiently, and directly on the stock exchange. Trade directly here What does it take to build a company worth Google's $32 billion acquisition? Solal shares the hard-won lessons from scaling technical teams during one of the fastest-growing security companies in history. Key leadership insights from the episode: • CTO Office Evolution: How Wiz split technical leadership into 3-4 specialized tracks focused on domain expertise rather than geography • The Geographic Cloning Failure: Why hiring locally for technical roles created dissatisfaction and duplication instead of excellence • Remote Team Success: Building global CTO teams around container security, API infrastructure, and runtime protection expertise • Incubation Philosophy: Moving from building teams to rapid POC development - like their 3-hour response to the Shy Hulud NPM exploit • Customer-Centric Engineering: How every CTO team member stays connected to customer challenges rather than waiting for inbound requests • Innovation Metrics: The challenge of measuring incubation success vs finished features, plus P99 performance tracking for enterprise readiness • People-First Leadership: Why focusing on people and customer problems trumps pure technical automation • Security Industry Insights: Making security "not scary" through gamification and community engagement Technical Context (18% of episode): • Agentless API scanning that maps entire cloud environments in minutes vs weeks • Graph database visualization of attack paths from code credentials to AWS admin access • Risk contextualization: Why a CVSS 9.9 vulnerability on unused images can wait, but the same vulnerability across 10,000 live VMs demands immediate action • AI agent "Mika" that correlates threat intelligence with specific infrastructure data Chapters: [01:49] - What makes Wiz worth $32 billion: People and technology combined [04:08] - Technical architecture: Agentless scanning to graph databases to agent validation [10:56] - Personal journey: From assembly coding to customer-focused engineering [14:18] - CTO office structure: Splitting technical leadership into specialized domains [17:30] - Three-fold CTO mission: Foresight, gray areas, and team incubation [19:35] - Evolution from team building to rapid POC development [23:30] - Security industry paradigm shifts: Vulnerabilities, identities, and AI challenges [25:30] - Log4Shell response: Community support and agentless advantage [34:17] - Major failure: Why geographic CTO team cloning doesn't work [40:09] - CTO metrics challenges: Measuring innovation vs finished features [43:16] - Missing hands-on work: The balance between leadership and building [45:44] - Time travel advice: Focus more on people than automation

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.

Unfiltered insights on microservices myths, cloud cost reality, and pragmatic technical leadership Two veteran CTOs tear down today's tech hype with brutal honesty and hard-won experience. Raz Schweiger-Shuty shares his controversial approach to technical leadership at auxmoney, where he stopped a microservices rewrite and focused on business outcomes over engineering trends. From his early days as a QA engineer to running 80-person engineering organizations, Raz demonstrates why sometimes the best technical decision is the boring one that actually works. This conversation offers a masterclass in pragmatic technical leadership for an age obsessed with the latest frameworks and architectures. Key insights for technical leaders: • 🏗️ Domain-driven design as prerequisite for any architectural decision • 💰 FinOps strategies that connect engineering decisions to business metrics • 🔄 Modular monolith patterns for teams of 40+ engineers • 📊 Why velocity metrics are vanity and DORA metrics matter • 🚫 Avoiding Kubernetes complexity when elasticity isn't needed • 👥 Conway's Law applied to value stream organization • 🔧 Platform engineering pitfalls and centralization nightmares TIMESTAMPS: [00:01:00] Show premise: Two CTO Dinosaurs vs. Today's Tech Hype [00:03:56] Raz's unconventional path: QA to CTO via pragmatic learning [00:08:31] Junior vs senior engineers in the AI era [00:11:01] auxmoney: 17 years without a CTO, then Raz arrives [00:13:45] Stopping the microservices migration: building trust through disagreement [00:15:08] Language choices: PHP works, Rust creates hiring problems [00:19:35] Modular monolith strategy using domain-driven design [00:21:56] Value stream teams and Conway's Law alignment [00:22:26] Kubernetes reality check: elasticity vs complexity overhead [00:29:53] FinOps deep dive: €120k to €85k AWS cost reduction [00:32:13] Cost-per-transaction metrics for engineering accountability [00:35:00] Platform engineering centralization dangers [00:40:22] Velocity metrics are broken: focus on DORA instead [00:43:54] Meantime to recovery as team health indicator [00:48:34] Bottom-up AI adoption: Cursor rollout strategy [00:54:16] Fintech security: ISO 27001 and AI supply chain risks [00:56:59] Leadership lessons: building trust through authentic communication

Building Human-Level AI for Code: Model Factories, RL at Scale, and Distributed Teams Technical Deep Dives: Poolside’s model factory: end-to-end automation from raw data to production models Scaling RL from code execution: 800,000+ containerized repos, millions of agent tasks Immutable versioning with Apache Iceberg for full traceability Distributed team structure: 120+ engineers across US/EU, monthly in-person sprints Hardware orchestration: 10,000+ H200s, hot swap failover, dynamic allocation Leadership: dividing responsibilities, low-ego culture, and the MIT principle Future of software: managing agent workforces, context window strategies, continual learning "Our model factory runs thousands of experiments before a single production model is trained. It’s an empirical science—every component, from data ingestion to evals, is versioned and traceable." – Eiso Kant Chapters: [00:04:28] Poolside’s unique approach to foundation models [00:13:02] Scaling hardware: 10,000+ H200s and orchestration [00:17:42] RL, agents, and the future of developer tools [00:24:56] Immutable versioning and evaluation frameworks [00:36:04] Distributed team structure and monthly sprints [00:40:26] Leadership, decision-making, and low-ego culture [00:45:54] Lessons for CTOs: breaking process dogma, preparing for agent-driven orgs [00:50:54] The next 3 years: AGI, agent workforces, and the end of manual coding [00:53:44] Context window, continual learning, and model memory [00:56:20] Everything collapses into the model: product, research, and daily life [00:59:46] Advice to a younger self: scale compute, trust RL+LM, and the four-minute mile

Bridging operational technology and cloud architecture for industrial-scale systems The convergence of AI and IoT is reshaping how technical leaders approach industrial systems architecture. Bernd Groß, CEO and co-founder of Cumulocity, shares insights from building Germany's leading IoT platform—from its origins as a Nokia spin-off to becoming a critical infrastructure for companies managing tens of thousands of connected industrial assets. This conversation explores the unique engineering challenges of industrial IoT, where systems must process 50 terabytes of monthly data from wind turbines while maintaining real-time control capabilities. Bernd discusses the evolution from monolithic architectures to microservices, the complexity of supporting hundreds of industrial protocols, and the emerging "AI-IoT" paradigm. Key technical insights: • 🏗️ Speed-layer architecture design for real-time data processing with cloud data lake offloading • 📡 Managing protocol complexity through certified partner ecosystems (320+ PLC protocols) • 🔄 Microservices transformation strategies for industrial platforms on Kubernetes • 🤖 AI model deployment and operationalization across distributed industrial assets • ⚡ Edge-cloud hybrid architectures for latency-critical and regulated environments • 🛠️ Technical debt management during platform evolution and acquisition cycles Thx 2 our partner SoSafe for sponsoring this Episode. Or start a free safety simulation with SoSafe TIMESTAMPS: [00:01:04] Introduction to Bernd Groß and Cumulocity's Industrial IoT Platform [00:03:45] Early Programming: From ZX Spectrum to PC Building [00:07:07] The Nokia Rejection: Language Barriers in Early Career [00:11:51] Nokia's Innovation Incubator and Cloud Computing (2010) [00:17:24] Meeting Werner Vogels: Early AWS and Cloud Architecture [00:20:07] Spinning Off Cumulocity from Nokia Networks [00:21:21] IoT Device Management: Original Platform Vision [00:23:56] Real-Time Architecture vs. Legacy VPN Systems [00:26:33] From APIs to Applications: Learning Customer Needs [00:31:19] Software AG Acquisition: Strategic Partnership [00:34:03] Management Buyback from Silver Lake Partners [00:37:53] AI's Impact on IoT: The Emergence of "AI-IoT" [00:42:03] Speed-Layer Architecture and Data Lake Integration [00:46:46] Edge vs. Cloud: Hybrid Deployment Strategies [00:48:53] Technical Debt: Protocol Complexity and Modernization [00:52:37] Partner Ecosystem: Managing 320+ Industrial Protocols [00:56:59] Leadership Lessons: Trust and Authentic Communication QUOTES: • "When you compare that to the generation, the technology which was available before, there was really a VPN system where you needed to dial into the wind turbine… And today it's very, very different with real-time analytics happening in the background." - Bernd Groß [00:23:56] • "Our platform does data acquisition, data normalization, contextualization of the data. So you need to help structure the data because that is the foundation for your training for your AI model training." - Bernd Groß [00:42:03]

Real-time system architecture lessons from the edge of the energy transition Technical infrastructure enables the modern energy transition. Barbara Wittenberg, CTO at 1KOMMA5°, reveals the engineering challenges behind connecting and optimizing 40,000+ household energy assets while building a unified tech platform after acquiring 80+ companies across 7 countries. Tech leaders will find valuable parallels to their own challenges as Barbara discusses how her team modernized legacy systems, managed complex integrations, and deployed advanced optimization algorithms that must respond in milliseconds to prevent grid failures. Key technical insights include: 🏗️ Building infrastructure-as-code and standardizing operations across acquired companies 📊 Creating data pipelines that connect real-world physical devices to cloud optimization systems ⚡ The millisecond-level responsiveness required for grid stabilization services 🔄 Managing the product transition from siloed applications to integrated platforms 🤖 Leveraging AI to automate complex technical explanations to end users 🧠 Post-merger integration strategies that preserve valuable domain knowledge Thx 2 our partners Surfshark & SoSafe for sponsoring this Episode. Go to surfshark.com/alphalist or use code ALPHALIST at checkout to get 4 extra months of Surfshark VPN! Or start a free safety simulation with SoSafe TIMESTAMPS: [00:00:51] Introduction to Barbara Wittenberg & 1KOMMA5° [00:02:52] Barbara's Journey from Electrical Engineering to CTO [00:05:13] Early Virtual Power Plant Development at E.ON [00:07:05] The Transition from Energy to Tech and Back [00:11:02] Explaining 1KOMMA5°'s Business Model & Technology [00:13:00] The Physics of Energy Systems and Real-Time Control [00:16:41] Grid Operators and the Complex Energy Landscape [00:19:47] Smart Meters and Market Differences Across Countries [00:22:32] Battery Storage and Energy Trading Economics [00:25:35] Building Software Systems for Installation Companies [00:28:54] Tech Stack and Integration of Acquired Companies [00:33:19] Managing 2,500 Employees Across 80+ Locations [00:37:21] Data Standardization and Business Intelligence [00:42:10] Using AI to Explain Energy Decisions to Customers [00:49:22] Female Representation in Energy & Engineering [00:54:28] Closing Thoughts on Consumer Energy Solutions QUOTES: ""We send live signals in two-second intervals to 40,000 assets in the field. In Sweden, we need to respond to grid frequency changes in milliseconds - you can't send faxes, you can't even send an email. You need real-time control of your assets."" - Barbara Wittenberg [00:13:50] ""If you want to bring the best solution, you need to have all assets connected in one system."" - Barbara Wittenberg [00:12:12]

Insights into hypergrowth, late-stage career frameworks, AI adoption, and the tech powering Canva's collaborative editor. In this episode, Tobi chats with Adam Schuck, Senior Engineering Director at Canva, a company that has scaled to over 5,000 employees, 2,000+ engineers, and 230 million MAUs while remaining profitable. Adam shares his journey through startups (including acquisitions by Twitter and Canva) and large tech companies like Google, leading to his current role managing 220 engineers at Canva. They dive deep into the challenges and strategies behind Canva's hypergrowth, including: Scaling engineering teams from 150 to over 2000. Implementing a career framework (Growth & Development Framework) relatively late at 1000+ engineers, moving beyond "minimum viable structure." Canva's approach to AI: Viewing it as a tailwind, fostering experimentation ("AI Impact"), providing broad access to tools (Cursor, Copilot, LLMs), and emphasizing human responsibility ("humans as shepherds"). The core technology decisions enabling Canva's success, particularly the operational transformation logic for real-time concurrent editing and the strategic shift to a unified web-based mobile experience (WebX). Maintaining a startup culture of adaptability despite massive scale. Adam's personal productivity hacks for leaders, focusing on ruthless calendar management and clear goal setting. TIMESTAMPS: [00:00:51] Introduction to Adam Schuck & Canva's Scale [00:03:28] Adam's Personal Nerd Journey (Full Stack, Startups, Google, Twitter) [00:10:00] Canva's Business: Growth, Revenue, and Profitability [00:11:32] AI as a Tailwind: Canva's Strategy and Magic Studio [00:15:36] From PLG to Enterprise: Evolving Canva's Go-to-Market [00:17:21] Conway's Law & Org Structure at Scale (Matrix, Groups, Specialties) [00:19:45] Defining the "Paved Road": Tech Governance and Foundations Teams [00:24:23] AI Impact: Augmenting Engineers, Tooling (Cursor/Copilot), and Responsibility [00:31:29] Implementing Career Frameworks Late: The Growth & Development Framework Story [00:42:10] Tech Secrets: Concurrent Editing (Operational Transformation) Deep Dive [00:46:24] Tech Secrets: The Journey to a Unified Mobile Experience (WebX) [00:52:26] Personal Productivity Hacks for Leaders [00:57:19] Advice to Past Self QUOTES: "We recognized that maybe we were now at the point where it was actually causing more harm than good to not have these kind of frameworks and structure in place." - Adam Schuck [00:33:50] "We're really clear with our messaging to our engineering team that we don't, we're not entitling our team to abdicate themselves of responsibility. That ultimately every line of code that is shipped at Canva, it's a person responsible for that." - Adam Schuck [00:28:46]

Georg Zoeller, Co-Founder of the Centre for AI Leadership and a former Meta expert, dives deep into the reality of AI's rapid evolution. He warns against the blind adoption of AI technologies, highlighting the risks of prompt injection and the economic strains facing the industry. The conversation touches on the commoditization of software engineering driven by vast training data, and Georg emphasizes the crucial need for CTOs to invest in understanding and governance. He also discusses geopolitical risks tied to Big Tech's influence and offers actionable advice for navigating this complex landscape.