Ship Happens

Docker

This is where serious devs level up. Each week, top engineers break down how they’re pushing productivity, locking down security, and building AI-first workflows—all in the cloud. It’s the real-world insight you won’t get from a blog post—straight from the people shipping at scale. We’re bringing you sharp minds, smart code, and battle-tested tactics. Hit play. Then out-build everyone else.

  1. Aug 25

    AI Changes the Bottleneck: Eric Bowman on Building the Right Thing

    In this episode of Ship Happens, host Per Krogslund talks with Eric Bowman, CTO of King, about how AI is changing the fundamental constraints of software development. As AI makes writing and producing code dramatically faster, Eric argues that the bottleneck is shifting: the harder problem is increasingly deciding what to build, why to build it, and whether you're building the right thing in the first place. Eric compares AI's impact on software development to the earlier acceleration brought by open source, while exploring what happens when teams can produce software faster than ever. They discuss the risks of the “myth of instant expertise,” how AI can reinforce existing cognitive biases, and why greater coding velocity doesn't necessarily translate into better products. The conversation also explores how AI could reshape engineering organizations, enabling smaller, more full-stack teams and pushing the platform and domain split higher in the stack. But while AI can accelerate development, Eric argues that operations, incident response, judgment, and understanding context remain deeply human. Per and Eric dig into the importance of prioritization, flow, and learning loops in an AI-powered development environment. Eric explains why teams need to get better at predicting outcomes, creating psychological safety, and shortening the distance between shipping something and learning whether it worked. He also looks ahead at how software development could change over the next five years, including his prediction that traditional code review may largely fade—and why human creativity could remain one of the hardest things for AI to replicate. What You'll Learn How AI is changing the bottlenecks in software development Why faster coding doesn't necessarily mean faster product development What AI has in common with the acceleration brought by open source The risks of the “myth of instant expertise” How AI can amplify both strong and mediocre engineering practices Why AI could enable smaller, more full-stack engineering teams How the platform/domain boundary may change as AI improves Why operations and incident response remain deeply human Why productivity metrics aren't enough to measure AI's impact How prioritization and flow become more important as development speeds up Why fast learning loops are essential for AI-powered teams What Eric expects software development to look like five years from now Why traditional code review may eventually disappear Why human creativity could remain difficult for AI to replicate Episode Chapters 00:00 AI Changes the Bottleneck 00:39 Meet Eric Bowman, CTO of King 01:34 Is Coding Still the Bottleneck? 04:15 AI and the Open Source Acceleration 06:10 Rethinking Engineering Teams 08:50 The New Platform and Operations Divide 10:46 Beyond Productivity: Are We Building the Right Thing? 14:00 Making Software Development Fun Again 15:39 Priorities, Flow, and Shipping What Matters 18:15 Shortening the Time to Learn 22:25 Building Better Learning Loops 26:42 Where Software Goes From Here 27:34 Why Creativity May Stay Human 28:50 Closing Thoughts     Key Takeaways The bottleneck is moving. When AI makes producing code easier, deciding what deserves to be built becomes increasingly important. Velocity isn't the same as value. Teams can ship more software without necessarily creating better products. AI makes good judgment and prioritization even more important. AI amplifies what already exists. Strong engineering practices can become more powerful with AI, but weak or mediocre practices can also scale faster. Learning speed matters. Teams that can make predictions, ship, get feedback, and adapt quickly will have an advantage as the cost of producing software continues to fall. Smaller teams may do more. AI could allow engineers to operate across more of the stack and reduce the need for some organizational layers. Some work remains fundamentally human. Incident response, judgment, context, and creativity aren't simply eliminated by faster code generation. The goal isn't to build faster. It's to build better. As AI changes the economics of software development, the teams that can consistently identify and build the right things may have the biggest advantage. About Eric Bowman Eric Bowman is the Chief Technology Officer at King, the game development company behind globally recognized titles including Candy Crush. He has spent his career building software organizations and platforms and exploring how engineering teams can work more effectively as technology and development practices evolve. In this conversation, Eric brings that experience to one of the biggest changes facing software engineering today: what happens when AI dramatically reduces the cost and time required to produce code? Resources & Links King Docker Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  2. Aug 11

    Building Search for AI, Not Humans | Saahil Jain, CTO of You.com

    As AI agents become increasingly capable of researching, reasoning, and completing complex tasks, traditional search engines aren't equipped to meet their needs. Saahil shares how You.com evolved from a consumer search engine into enterprise AI infrastructure powering more than one billion monthly queries. Together, they discuss why search built around human behavior breaks down for AI agents, how enterprises are moving beyond AI experimentation into operational AI, and what it takes to build reliable systems that balance speed, accuracy, and cost. From evaluation frameworks and multi-model architectures to continuous monitoring and the future of enterprise AI, this conversation offers an inside look at the infrastructure powering the next generation of intelligent software.     What You'll Learn Why traditional search engines weren't designed for AI agents How You.com is rethinking search for an agent-first future What separates AI adoption from true AI operations How enterprises balance cost, latency, and model accuracy Why evaluation frameworks are becoming critical AI infrastructure The benefits and challenges of multi-model AI architectures Why continuous monitoring matters as AI models evolve Where the next competitive advantages in AI will come from     Episode Timestamps 00:00 The Agentic Search Boom 00:43 Meet You.com’s CTO 01:39 Building Search for AI Agents 02:45 From Consumer Search to Enterprise AI 03:59 Why AI Agents Can’t Just Use Google 06:55 Rethinking Search for Enterprise AI 07:39 Giving Agents Control Over Search 09:05 Where Companies Are on the AI Adoption Curve 11:42 AI-Native vs. Traditional Companies 13:11 The Real Metrics Behind AI Operations 14:46 Why AI Evals Need to Go Beyond Benchmarks 17:13 Building for a Multi-Model World 19:42 Shipping AI: Gates, Drift, and Monitoring 21:17 Rethinking Org Design for the AI Era 22:53 What Actually Wins in AI Products 25:33 Avoiding AI’s Biggest Engineering Trap 27:58 Where the Next AI Moats Will Come From 30:55 Closing Thoughts   Key Takeaways AI agents require fundamentally different search experiences than human users. Building AI products means optimizing for reliability—not just intelligence. Operational AI depends on balancing accuracy, latency, and cost. Evaluation frameworks should evolve alongside AI models and user behavior. Multi-model strategies give organizations greater flexibility and resilience. Continuous monitoring is essential as models and workloads constantly change. Long-term AI differentiation will come from proprietary data, infrastructure, and customer workflows.   Notable Quotes "The future of search isn't just helping people find answers—it's helping AI agents get the right ones." "Shipping AI isn't the finish line. Operating it reliably is where the real work begins." "As models evolve, your evaluation strategy has to evolve with them."   Links & Resources You.com Docker.com   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  3. Jul 24

    Why Enterprise AI Is Really an Infrastructure Problem | Colton Shaw, Spectro Cloud

    Everyone is talking about AI models—but the hardest part of enterprise AI isn't choosing a model. It's building the infrastructure to run it securely, reliably, and at scale. In this episode, Docker's Per Krogslund is joined by Colton Shaw, Principal Architect at Spectro Cloud, for a conversation about the often-overlooked foundation of enterprise AI. They discuss why incidents like Log4Shell transformed software supply chain security, how Software Bills of Materials (SBOMs) provide visibility into application dependencies, and why AI-generated code introduces new governance challenges. Colton also shares lessons from deploying Kubernetes across highly regulated and air-gapped public sector environments, explains why edge AI is reshaping industries from defense to healthcare, and discusses how organizations can balance open-source innovation with enterprise-grade security, resiliency, and operational control. Whether you're modernizing cloud infrastructure, evaluating AI platforms, or preparing your organization for production AI, this episode offers practical insights into building systems that are secure, flexible, and ready for the future.     In This Episode You'll Hear Why enterprise AI is fundamentally an infrastructure challenge How Log4Shell changed software supply chain security forever What Software Bills of Materials (SBOMs) are and why they matter The risks AI-generated code introduces into enterprise environments How hardened and distroless containers reduce attack surfaces The realities of deploying Kubernetes in air-gapped environments Why edge AI requires a different operational mindset than cloud AI How model routing reduces vendor lock-in and improves resiliency The tradeoffs between proprietary and open-source AI models Why governance, auditability, and human oversight remain essential as AI scales     Timestamps (00:00) Welcome to Ship Happens (00:38) Meet Colton and Spectro Cloud (01:22) Why SBOMs Matter for Enterprise AI (03:57) The Log4Shell Wake-Up Call (07:19) Hardening Containers for AI Workloads (09:33) Security Requirements in Regulated Industries (14:16) What Spectro Cloud Does (15:54) Kubernetes in Air-Gapped Defense Environments (19:16) Why AI Infrastructure Is Different (20:45) The Current State of AI Operations (24:24) Budgeting for AI: Tokens vs. Talent (26:28) AI Sovereignty and Avoiding Vendor Lock-In (29:58) Building and Serving Your Own Models (32:54) Choosing the Right AI Model (36:52) Model Routing for Reliability and Resilience (40:33) Designing Invisible AI Failover (41:02) Managing Developer Complexity (43:45) AI Development Environments at Scale (45:04) Edge AI in the Real World (52:28) Why Human-in-the-Loop Still Matters (58:22) Governance, Audit Trails, and Responsible AI (01:05:20) Open-Source vs. Proprietary Models (01:11:46) Moving Beyond APIs to AI Strategy (01:17:39) Closing Predictions   About the Guest Colton Shaw is a Principal Architect at Spectro Cloud, where he helps public sector and enterprise organizations design, deploy, and secure Kubernetes platforms across cloud, edge, and air-gapped environments. His work focuses on modern infrastructure, software supply chain security, AI governance, and helping organizations operationalize AI in highly regulated industries, including defense and government. With deep expertise in Kubernetes, container security, and edge computing, Colton works with teams building resilient platforms that support mission-critical applications at scale.   Links & Resources 🌐 Learn more about Spectro Cloud: https://www.spectrocloud.com 🐳 Learn more about Docker: https://www.docker.com ☸️ Kubernetes Documentation: https://kubernetes.io 🛡️ Learn more about Software Bills of Materials (SBOMs): https://www.cisa.gov/sbom 📄 Learn more about the Log4Shell vulnerability: https://www.cisa.gov/news-events/cybersecurity-advisories/aa21-356a Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  4. Jun 24

    Security, AI Economics, Enterprise Incentives, and Agent Infrastructure

    As AI adoption accelerates, engineering organizations face a growing set of challenges that extend beyond code. This compilation episode highlights some of the most thought-provoking conversations from recent Ship Happens guests, covering topics ranging from security and privacy to enterprise software incentives and AI infrastructure. Sergey Katsev explores why AI-powered security tools can help teams move faster but can never replace the business context and judgment that developers bring to the table. Ruben Verborgh challenges today's economic model for AI and personal data, arguing that the current approach is unsustainable and proposing a future where algorithms travel to data instead of copying sensitive information into centralized systems. Brian Alvey offers a candid look at enterprise software purchasing, where buyers often aren't end users and success depends as much on compliance certifications and relationships as product capabilities. Vasek Mlejnsky discusses the infrastructure behind AI agents, including secure sandboxes that dynamically allocate resources while protecting credentials and sensitive information. The episode concludes with Ivar Østhus examining the impact of AI-generated code on software delivery, highlighting why stronger testing, governance, and reliability practices become even more important as development accelerates. Together, these conversations reveal a common theme: technology alone rarely determines outcomes. Incentives, trust, governance, and operational discipline matter just as much.     In This Episode You'll Hear Why AI security tools cannot replace developer expertise The hidden incentives shaping enterprise software purchases Why security is often treated as a cost center How AI changes the economics of personal data The concept of bringing algorithms to data instead of moving data Why compliance often beats product quality in enterprise sales How AI agent infrastructure manages resources and secrets securely The growing need for testing and governance around AI-generated code Why trust remains one of the biggest barriers to AI adoption The operational challenges created by increasing software velocity     Timestamps (00:00) Why Security Requires More Than AI Tools (01:05) Sergey Katsev: Security Needs Context, Not Just Automation (03:39) Ruben Verborgh: The Unsustainable Economics of AI and Data (04:34) Bringing Algorithms to Data Instead of Moving Data (09:24) Brian Alvey: Enterprise Software Is Driven by Incentives (11:57) Vasek Mlejnsky: Building Secure Infrastructure for AI Agents (13:56) Ivar Østhus: AI-Generated Code Demands Better Testing (16:34) Key Takeaways and Closing Thoughts     Featured Guests Sergey Katsev Co-Founder and CEO of Catchpoint, focused on observability, reliability, and the intersection of security and operational performance. Ruben Verborgh Computer scientist, professor, and researcher focused on decentralized data architectures, AI, and the future of the web. Brian Alvey Technology entrepreneur and industry leader known for his work in enterprise software, media technology, and digital infrastructure. Vasek Mlejnsky Founder and CEO of E2B, focused on secure cloud infrastructure and runtime environments for AI agents. Ivar Østhus Chief Evangelist at Unleash and a leading voice on developer experience, software delivery, and engineering effectiveness.     Links & Resources Per Krogslund on LinkedIn Docker Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  5. Jun 10

    AI Coding vs. Enterprise Reality Standards, Reliability, and the Future of DevOps

    While AI-powered development has made it easier than ever to build prototypes and accelerate coding tasks, guests throughout this compilation caution that enterprise software demands far more than speed. Security, compliance, scalability, maintainability, governance, and reliability remain critical concerns that AI alone cannot solve. The discussion explores the rise of "vibe coding," the growing importance of developer experience in AI-assisted workflows, and the challenges organizations face when introducing AI into production environments. Guests explain why governance, standards, golden paths, and clear exit criteria are essential for preventing the rapid automation of bad processes. The episode also examines how AI is helping teams navigate legacy codebases, automate upgrades, improve pull request reviews, strengthen security practices, and reduce cognitive load for developers. At the same time, speakers warn that increased AI adoption can create operational complexity, reliability risks, and new management challenges if organizations lack proper controls and testing strategies. From platform engineering and DevOps automation to running open-source LLMs in Kubernetes environments, this compilation highlights the opportunities, tradeoffs, and realities of building software in the age of AI. Topics Covered AI coding assistants and coding agents Vibe coding versus enterprise software development Developer experience (DevEx) DevOps automation and AI adoption Governance, standards, and golden paths Reliability and software delivery stability DORA research and AI productivity tradeoffs Pull request reviews and engineering bottlenecks Legacy code modernization Platform engineering best practices Running LLMs in production Kubernetes and GPU infrastructure Engineering leadership in the AI era Timestamps (00:00) Welcome to Ship Happens (01:05) Vibe Coding vs Enterprise (03:01) Developer Experience Still Matters (05:13) AI in DevOps Today (07:32) Governance and Golden Paths (09:15) Speed vs Stability Tradeoffs (13:19) Standards Bots and Reviews (16:11) AI Reshaping Management (17:54) Agents, Trust, and Responsibility (18:58) Running LLMs in Production (20:03) Costs, Testing and Wrap Up Key Takeaways AI coding tools are powerful for prototyping and MVP development, but enterprise software requires stronger controls and governance. Developer experience becomes increasingly important as AI-assisted workflows become more common. Reliability is emerging as a critical success metric in organizations adopting AI at scale. Without standards and governance, AI can accelerate poor processes just as quickly as good ones. Golden paths and platform engineering practices help teams balance speed, consistency, and security. AI can reduce cognitive load, modernize legacy systems, and improve operational efficiency when implemented thoughtfully. Running LLMs in production introduces infrastructure, operational, and cost considerations that organizations must carefully manage. Engineering leadership is evolving as AI changes how teams build, review, and maintain software. Organizations that combine AI adoption with strong testing, security, and reliability practices will be best positioned for long-term success. Key Takeaways AI coding tools are powerful for prototyping and MVP development, but enterprise software requires stronger controls and governance. Developer experience becomes increasingly important as AI-assisted workflows become more common. Reliability is emerging as a critical success metric in organizations adopting AI at scale. Without standards and governance, AI can accelerate poor processes just as quickly as good ones. Golden paths and platform engineering practices help teams balance speed, consistency, and security. AI can reduce cognitive load, modernize legacy systems, and improve operational efficiency when implemented thoughtfully. Running LLMs in production introduces infrastructure, operational, and cost considerations that organizations must carefully manage. Engineering leadership is evolving as AI changes how teams build, review, and maintain software. Organizations that combine AI adoption with strong testing, security, and reliability practices will be best positioned for long-term success. Resources & Links 🎧 Subscribe to Ship Happens: Apple Podcasts Spotify YouTube 🌐 Learn more about Docker: https://www.docker.com 📚 Referenced Topics & Technologies: DORA (DevOps Research and Assessment) Kubernetes Large Language Models (LLMs) Platform Engineering Developer Experience (DevEx) AI Coding Assistants & Agents Open Source AI Models Enterprise DevOps & Reliability Engineering   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  6. May 26

    Rethinking AI and Data: Incentives, Trust, and the Future of the Web with Ruben Verborgh

    In this Ship Happens episode, host Per Krogslund interviews Ruben Verborgh, a computer science professor at Ghent University and consultant about building a more responsible digital ecosystem where incentives align with ethics rather than exploitation. Ruben argues that today’s AI ecosystem is fundamentally unstable: companies often lack control over the models they deploy, sustainable business models remain unclear, and the industry continues relying on advertising-driven incentives that reward data extraction over trust. Instead of focusing purely on “ethical AI” messaging, Ruben says the real challenge is designing better economic systems and incentives. The conversation explores an alternative future for AI and personal data—one where raw user data stays under individual control and algorithms move to the data instead of endlessly collecting and centralizing it. Ruben explains how decentralized web standards like Solid aim to separate identity and storage from applications, giving users more portability, ownership, and freedom online. Per and Ruben also discuss mistrust in AI agents, why smaller task-specific models may ultimately be safer and more controllable than giant general-purpose systems, and why society must work alongside younger generations to shape technology rather than attempting to ban it outright. Chapters (00:00) Welcome and Guest Intro (00:32) Mission for Better Data (01:24) Incentives and AI Ethics (02:54) Packaging AI Like Utilities (05:14) Economic Model Will Implode (07:10) Beyond Ads and Big Data (08:36) Bringing the Algorithm to the Data (10:36) Consent Beats Tracking (11:45) The Myth of Retargeting Value (15:10) Trust and AI Agents (16:55) Guarantees and Smaller AI Models (18:03) Solid and the Decentralized Web (20:51) Why Identity Came Later (23:04) The Web as an Amplifier (26:22) Fix Incentives, Not Algorithms (27:53) There Is No “Responsible Tech,” Only Systems (29:06) Policy Shifts Beyond Finger Wagging (32:04) Believing in the Next Generation (33:11) Wrap Up and Outro   About the Guest Ruben Verborgh is a computer science professor at Ghent University and a researcher focused on decentralized web technologies, linked data, and data ownership. He works with Tim Berners-Lee’s Inrupt on the Solid project, which aims to separate applications from personal data storage to give users greater control, privacy, and interoperability across the web. Resources & Links Inrupt Official Website Solid Project Ghent University Ruben Verborgh Website Per Krogslund LinkedIn Docker Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  7. May 12

    Shipping Safely in the Age of AI: Feature Ops with Ivar Conradi Østhus

    Software delivery is getting faster—but also more unpredictable. In this episode of Ship Happens, we sit down with Ivar Conradi Østhus, CTO of Unleash, to unpack how AI is reshaping the way teams build, ship, and operate software. At the center of the discussion is “Feature Ops,” a philosophy that combines DevOps practices, feature flags, and real-world feedback loops to enable safer, faster delivery cycles. Ivar explains why production is not the end of the pipeline—but the beginning of learning. We explore how teams use DORA metrics to measure delivery performance, and why AI adoption—while increasing developer velocity—can introduce new risks around deployment stability and system reliability. To counterbalance this, Ivar breaks down the importance of runtime control, observability, and automated feature management as essential safeguards in modern engineering systems. The conversation also dives into how AI is already being used to automate operational tasks like feature flag cleanup across distributed systems. Finally, we discuss how enterprise development tools are evolving—moving closer to developer workflows like pull requests, CI/CD, and runtime decision-making—and why the future is likely a best-of-breed ecosystem rather than a single unified platform.  We discuss: What “Feature Ops” actually means in practice, How DevOps and DORA metrics measure real delivery performance, Why AI increases both velocity and risk in software delivery, The role of feature flags in safe production releases, Runtime control, observability, and production learning loops, AI-driven automation in DevOps workflows, Enterprise tooling integration into CI/CD pipelines, Platform engineering vs best-of-breed ecosystems, The future of AI in developer productivity and delivery safety. Whether you're building engineering systems, leading platform teams, or navigating AI adoption, this episode explores what it really takes to ship safely in the age of AI. Key Topics Discussed Feature Ops as a modern delivery philosophy Shipping safely with frequent production releases DevOps and DORA metrics in practice AI’s impact on software delivery speed and stability Feature flags and runtime control systems Observability and production feedback loops Automated cleanup and management of feature flags AI-assisted DevOps workflows and tooling CI/CD integration for enterprise engineering teams Platform engineering vs best-of-breed architecture The future of developer productivity in the AI era Episode Timestamps (00:00) AI Threat Framing (00:52) Welcome and Guest Intro (02:00) What Is FeatureOps (04:03) Experiments and Fast Feedback (06:06) DevOps Metrics and DORA (09:55) Funding and AI Risk (14:04) Runtime Control and Rollouts (17:14) Testing Renaissance and Trust (20:46) Accountability and AI Ethics (24:35) AI Automation and Flag Cleanup (28:41) Agents and Smarter Software (29:36) Natural Language Integrations (31:07) MCP Versus CLI Debate (31:56) Enterprise Security Concerns (33:51) Standards and AI Translators (37:16) SOAP Makes a Comeback (38:23) SaaS Valuations and AI Fear (41:28) What Great Dev Tools Do (44:54) Platforms Versus Best of Breed (47:48) Platform Teams and Enterprise Data (50:18) Measuring AI Productivity ROI (54:29) Pricing Reality and Model Choice (56:44) Wrap Up and Sponsor Thanks Guest Bio Ivar Conradi Østhus is the CTO and creator of Unleash, one of the leading open-source feature management platforms used by engineering teams worldwide. He has spent his career building developer tooling and scaling software delivery systems, with a focus on helping teams ship safely through feature flags, controlled rollouts, and runtime decision-making. Ivar is a leading voice behind “Feature Ops,” a modern approach to DevOps that emphasizes production-first learning, fast iteration, and strong operational guardrails in complex engineering environments. Links & Resources Per Krogslund’s LinkedIn Ivar Conradi Østhus’s LinkedIn Learn more about Unleash Learn more about Docker Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

  8. Apr 28

    Why CI/CD Is Broken (And How to Fix It) | Jignesh Patel, Harness

    In this episode of Ship Happens, host Per Krogslund sits down with Jignesh Patel, Field CTO at Harness, to unpack why CI/CD pipelines break down at scale—and what it takes to fix them. Drawing from his experience leading DevOps and cloud teams at major enterprises, Jignesh explains how fragmented tools, siloed teams, and heavy change management processes create what he calls “spaghetti pipelines”—complex, brittle paths to production that rely too heavily on individual expertise. They explore how DevOps should be viewed as a continuous journey focused on faster feedback loops, automation, and shifting security left. Jignesh breaks down how modern platforms like Harness aim to unify build, test, deploy, and security workflows—while introducing AI-assisted pipeline generation with guardrails to improve both speed and reliability. The conversation also dives into incident response, automated rollbacks, continuous verification, and why governance and human oversight are still critical—even as AI accelerates development workflows. For teams struggling with delivery bottlenecks, reliability issues, or scaling DevOps practices, this episode offers a practical look at what’s actually working in the enterprise today. In This Episode You’ll Hear: Why CI/CD pipelines break down in large organizations What “spaghetti pipelines” look like—and how to fix them How platform engineering simplifies DevOps at scale The role of AI in generating and optimizing pipelines Why DevSecOps requires shifting security earlier in development How automated rollback and verification improve reliability Why governance and human oversight still matter in AI workflows What metrics actually matter for developer experience and delivery Episode Timestamps: (00:00) Enterprise “Spaghetti Maps” in CI/CD (01:36) Podcast Intro & Guest Overview (02:32) Jignesh Patel’s Career Path to Field CTO (05:23) Proving DevOps ROI & Budget Value (06:05) Product Team Expectations vs Reality (08:48) Why CI/CD Pipelines Break Down (11:08) DevSecOps & Shifting Security Left (13:02) Fixing Release Chaos in Enterprises (15:08) What Harness Actually Does (19:17) Unified DevOps Platform vs Tool Sprawl (20:48) Reliability Engineering in the AI Era (21:50) Faster Rollbacks & Incident Recovery (22:34) Continuous Verification Explained (23:24) How AI Can Increase Chaos (24:40) Practical AI Use Cases for Developers (26:28) Governance, Guardrails & AI Safety (29:04) Reverse Demos & Hidden Workflow Gaps (31:43) Measuring Platform Adoption Success (33:24) Cloud Repatriation Trends Explained (34:55) AI Spend & ROI Reality Check (36:34) Risk, Accountability & AI Ownership (39:10) DevOps & AI: Next 5 Years Predictions (41:27) DevOps Strategy Advice for Teams (42:53) Closing Thoughts & Takeaways About the Guest: Jignesh Patel is the Field CTO at Harness, where he helps enterprises modernize software delivery through platform engineering, DevOps transformation, and AI-driven automation. With leadership experience at companies like United Airlines and Morningstar, Jignesh brings deep expertise in cloud infrastructure, CI/CD systems, and enterprise-scale DevSecOps. He is a strong advocate for simplifying delivery pipelines, improving developer experience, and building reliable, secure systems through better platform design. Links & Resources: Per Krogslund on LinkedIn Jignesh Patel on LinkedIn Learn more about Harness Learn more at Docker.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    Why CI/CD Is Broken (And How to Fix It) | Jignesh Patel, Harness
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out of 5
3 Ratings

About

This is where serious devs level up. Each week, top engineers break down how they’re pushing productivity, locking down security, and building AI-first workflows—all in the cloud. It’s the real-world insight you won’t get from a blog post—straight from the people shipping at scale. We’re bringing you sharp minds, smart code, and battle-tested tactics. Hit play. Then out-build everyone else.

Information

  • Creator
    Docker
  • Years Active
    2026
  • Episodes
    15
  • Rating
    Clean
  • Copyright
    © 2026 Docker
  • Show Website
    Ship Happens

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