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    Technology

    alphalist.CTO Podcast – For CTOs and Technical Leaders

    This podcast features interviews of CTOs and other technical leadership figures and topics range from technology (AI, blockchain, cyber, DevOps, Web Architecture, etc.) to management (e.g. scaling, structuring teams, mentoring, technical recruiting, product etc.).
    Guests from leading tech companies share their best practices and knowledge.
    The goal is to support other CTOs on their journey through tech and engineering, inspire and allow a sneak-peek into other successful companies to understand how they think and act. Get awesome insights into the world‘s top tech companies, personalities with this podcast brought to you by Tobias Schlottke.

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    Copyright: © alphalist

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    Latest Episodes:
    #128 - From Tickets to Problems: Klaus Breyer // Head of Product & Technology @ Edding Sep 04, 2025
    Show notes

    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.

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    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]


    #127 - Kelsey Hightower's Unfiltered Truths: 25 Years of Infrastructure, DevOps, and Retiring at 42 Aug 07, 2025
    Show notes

    Unfiltered lessons on infrastructure, career strategy, and the art of knowing when to stop.

    What does 25 years in tech teach you? Kelsey Hightower, a self-taught engineer who rose to become a Distinguished Engineer at Google and a key figure in the Kubernetes community, sits down with Tobi to share unfiltered insights from his extensive career and his recent decision to retire at 42.

    This conversation goes beyond the hype, focusing on the timeless principles of technical leadership, system maintenance, and career strategy. Kelsey breaks down the reality of infrastructure management, the hidden costs of complexity, and why the most important engineering skill is understanding the business.

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    Technical insights for CTOs and engineering leaders:

    • The Lifecycle of Technology: Why new tech is additive, not a replacement, leading to an ever-growing collection of systems to maintain.
    • The Value of Managed Services: Understanding when to build vs. when to buy, and why most companies should lean on experts.
    • Evolutionary Architecture: The danger of making permanent technology decisions on day one and the importance of evolving your stack.
    • Business-Driven Engineering: Why engineers who can't link their work to revenue are at risk and how CTOs can use business metrics to guide priorities.
    • A Grounded View on AI: How the evolution of underlying systems is a prerequisite for AI to reach its full potential.

    TIMESTAMPS: [02:52] Kelsey's self-taught journey into tech [05:30] From computer store owner to Google data center technician [09:17] Why infrastructure and complexity look the same after 25 years [10:25] The reality of technology accumulation vs. replacement [12:28] The myth of choosing the ""perfect"" tool (Kubernetes vs. Firebase) [14:15] Why managed services are the right choice for most companies [18:00] The biggest mistake: failing to evolve your tech stack [22:47] Why engineers must link their work to revenue to survive layoffs [26:04] How CTOs can use business metrics to guide engineering priorities [31:13] The future of software: Will AI change the fundamentals? [38:17] The evolution required for AI to manage entire systems [41:30] Why indie hackers and small SaaS will survive the AI wave [48:12] The philosophy behind retiring at 42 to ""learn how to live"" [56:00] Advice to his younger self: ""You're on the right track""

    QUOTES:

    • ""I think the only thing that is wrong is when you fail to evolve… They try to make a permanent decision on day one."" - Kelsey Hightower [18:00]
    • ""If you're ignoring all of that signal [about what's important to the business], then you're going to be in for a surprise… make my own career pivot before someone decides to make it for me."" - Kelsey Hightower [24:47]

    #126 - AI Transformation at Scale: Practical Adoption Across 150+ Engineers with Peter Gostev // Head of AI @ Moonpig Jul 24, 2025
    Show notes

    Navigating AI hype versus implementation reality in large engineering organizations

    The AI transformation challenge isn't just technical—it's organizational. Peter Gostev, Head of AI at Moonpig, discusses building practical AI capabilities in a 600-person company while navigating the gap between AI hype and implementation reality.

    From managing adoption across 150 software engineers to partnering with non-technical teams, Peter shares insights on driving meaningful AI transformation that goes beyond superficial tool usage. This conversation explores the contrast between industry claims of widespread AI adoption and the nuanced reality of implementing AI at scale.

    TIMESTAMPS: [00:00:51] Introduction: Peter Gostev, Head of AI at Moonpig [00:03:49] First Computer Memory: Pentium 2 and the Golden Age of Learning [00:05:28] Career Evolution: From Gaming to Data Science and Machine Learning [00:07:01] AI Hype Cycle: Underestimating Future Potential While Overhyping Present [00:09:41] Agent Reality Check: Why Full Agentic Systems Are Harder Than Expected [00:13:51] The Decade of Agents: Learning from Autonomous Driving Timelines [00:15:09] Developer Tool Adoption: The Skill Gap in AI-Assisted Programming [00:18:40] Organizational Change: Teaching 150 Engineers New Workflows [00:20:51] Claude Code Success: Finding Product-Market Fit in CLI Tools [00:24:33] Context Management: Why Proper AI Usage Requires Intuition [00:26:07] Three-Pillar AI Strategy: Tools, Automation, and Experimentation [00:28:01] Team Structure: AI Engineers vs Data Scientists [00:30:10] Partnership Models: Building vs Enabling Other Teams [00:32:51] Adoption Management: Direct Collaboration vs Ambassador Networks [00:35:45] Prototype Power: One Evening of LLM Work vs Weeks of Traditional Development [00:38:02] Subject Matter Expertise: Custom GPTs for Non-Technical Users [00:42:45] Writing with AI: Research vs Synthesis and the Importance of Human Thinking [00:47:08] Tool Strategy: ChatGPT Rollout Without Internal Marketing [00:49:58] Adoption Reality: 400 Users But Still Only 5-10% of Potential Usage [01:02:06] Training Challenges: From Individual to National Level Education [01:05:10] Future of Development: What Changes and What Remains Human [01:08:24] Developer Productivity: Higher Ambition, Not Less Work [01:16:50] University Concerns: Are Students Prepared for AI-First Development? [01:18:47] Career Advice: Do Hard Technical Things Early While You Can

    QUOTES:

    • ""I think there's never really been more exciting time in my career versus now and every time we can we can just do more and more and more, which is just such a cool position to be in."" - Peter Gostev [00:06:39]

    • ""I think people really underestimating where we are and or how much more there is to go… we are really just at the beginning of these investments starting to come through."" - Peter Gostev [00:07:01]

    • ""On any given project, most of the work is actually non-AI work… most of it is integrations, scaffolding, deployment, UI, all of the things like that. And then the AI part of it is normally maybe 10, 20% of the actual work."" - Peter Gostev [00:28:01]


    #125 - Two CTO Dinosaurs vs. Today's Tech Hype with Raz Shuty // CTO @ auxmoney Jul 10, 2025
    Show notes

    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


    #124 - The Path to AGI: Inside poolside’s AI Model Factory for Code with Eiso Kant Jun 27, 2025
    Show notes

    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


    #123 - From Nokia to AI-IoT: Engineering the Physical World with Bernd Groß // CEO @ Cumulocity Jun 12, 2025
    Show notes

    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.

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    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]


    #122 - Grid Control in Milliseconds: Engineering Energy Systems with Barbara Wittenberg // CTO @ 1KOMMA5° May 16, 2025
    Show notes

    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]

    #121 - Canva's Playbook: Scaling Teams, Tech, and AI with Adam Schuck // Senior Engineering Director @ Canva May 01, 2025
    Show notes

    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]

    #120 - AI's Singularity & Commoditization: Navigating Hype vs. Reality with Georg Zoeller // Co-Founder @ C4AIL Apr 17, 2025
    Show notes

    Debunking AI hype, exploring security risks, and strategies for CTOs in the age of rapid change.

    In this episode, Tobi talks with Georg Zoeller, Co-Founder of the Centre for AI Leadership and mercenaries.ai, about the turbulent landscape of AI. Georg, with his background at Meta and deep expertise in AI strategy, cuts through the hype surrounding AI's capabilities and economic impact.

    They discuss the 'singularity' we're already in, driven by rapid, open-source AI development, and why this makes future predictions impossible. Georg argues that software engineering is being commoditized due to the vast amount of training data available (Stack Overflow, GitHub), making AI adept at code generation but raising profound security concerns like prompt injection.

    Explore:

    • Why Georg believes blindly adopting AI early is a 'terrible mistake' for most companies.
    • The fundamental security flaws in LLMs (prompt injection) and why they're currently unsolvable for open input spaces.
    • The questionable economics of AI: high costs, self-cannibalizing business models, and the reliance on performative fundraising.
    • How AI tools impact engineer productivity, shifting the bottleneck to decision-making and validation.
    • The geopolitical risks and diminishing trust associated with Big Tech's AI dominance.
    • Actionable advice for CTOs: Invest in understanding, focus on governance beyond the tech team, and consider the strategic value of local/open-source alternatives.

    TIMESTAMPS: [00:00:51] Introduction to Georg Zoeller [00:02:16] Georg's Fascination with Complexity and Computers [00:03:54] The Commoditization of Software via AI [00:05:13] Training Data: Software Engineering's "Terrible Mistake" [00:11:00] The Dilemma of Operating in Exponential Change [00:12:09] Why Adopting AI Early Can Be a Mistake [00:16:41] Deep Dive: The Unsolved Security Problem of Prompt Injection [00:23:15] The Questionable Economics and Business Models of AI [00:32:23] AI for Information Discovery vs. Decision Making [00:47:08] Geopolitical Risks and the Growth Limits of Big Tech [00:55:46] Recommendations for CTOs in 2025 [01:01:08] Looking Back: Advice for Younger Self

    QUOTE: "Software engineering profession made a terrible mistake. We uploaded everything we know onto two websites, Stack Overflow and GitHub… our entire profession, our entire knowledge… they're all uploaded… And when it's in the training data… the technology gets really good at creating results." - Georg Zoeller


    #119 - Navigating Ambiguity and AI's Impact on Engineering feat. Ivan Kusalic // CTO @ Enpal Apr 04, 2025
    Show notes

    Leading in ambiguous environments and the impact of AI on engineering practices

    In this episode, Tobi talks with Ivan Kusalic, CTO of Enpal, who leads a team of 250 engineers at one of Germany's leading solar energy companies. Ivan shares insights from his extensive technical leadership journey and his recent return to coding after seven years due to his excitement about AI.

    Ivan discusses how he navigates complexity and ambiguity in the renewable energy sector, where Enpal builds systems to help households manage solar panels, batteries, EV chargers, and heat pumps as integrated energy solutions. He explains the challenges of coordinating with Germany's fragmented energy grid infrastructure and how Enpal's Virtual Power Plant stabilizes the grid by coordinating household energy consumption in real-time.

    Discover:

    • 🧠 How Ivan uses intuition as a leadership tool while managing complex technical organizations
    • 🌞 The technical challenges of building integrated renewable energy systems for households
    • ⚡ How Enpal's Virtual Power Plant (VPP) helps stabilize the energy grid through coordinated home energy management
    • 📱 Ivan's personal productivity system and thought management techniques
    • 🤖 Insights on AI's impact on engineering productivity and the future of coding
    • 🚀 Practical tips for managing complexity and making decisions in ambiguous environments

    TIMESTAMPS: 00:00 Introduction to Alphas Podcast 00:54 Meet Ivan Kusalic, CTO of NPAL 02:00 Ivan's Journey into Engineering and Leadership 07:05 The Role of NPAL in the Renewable Energy Sector 14:54 Navigating Ambiguity in Complex Systems 16:30 The Importance of Intuition in Leadership 23:24 Tools and Techniques for Managing Thoughts and Productivity 26:01 The Future of AI in Engineering 29:07 Experimenting with AI in Coding 29:57 Productivity Gains with AI Tools 30:40 Challenges with AI in Complex Projects 31:37 AI's Randomness and Managing It 32:24 Real-World Examples of AI Limitations 35:08 Setting Up AWS Infrastructure with AI 38:10 The Evolution of Tech Stacks 44:35 Adopting AI in Large Organizations 51:01 Reflections and Advice for Engineers 54:28 Conclusion and Contact Information

    QUOTE: "Intuition is an impulse to explore. It triggers the right thoughts for exploration in a moment that you would otherwise miss." - Ivan Kusalic, CTO of Enpal


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