Podcasts about engineering leadership

In this episode of Disruption/Interruption, host KJ sits down with Chris Dolezalek, Executive Vice President of R&D at Hum Capital. Chris shares his journey from Silicon Valley’s most highly funded startup to leading innovation in venture capital. He discusses the flaws in traditional VC, the power of resilience, and how technology and human insight are reshaping funding for diverse founders. Listeners will hear hard-won lessons from the edge of chaos, stories of near-failure and rebirth, and practical advice for founders seeking capital in uncertain times. Four Key Takeaways: Resilience is Essential [29:46]Chris emphasizes the importance of getting up after setbacks, sharing the Japanese saying, “Get knocked down seven times, get up eight,” and how resilience is a key trait for founders. Venture Capital Needs Disruption [8:15]Traditional VC relies on personal networks and outdated data, often excluding minority and female founders. Hum Capital is using real-time financial data and AI to democratize access to funding. Human Insight Complements AI [19:35]While AI can flag promising companies, Chris explains the need for human judgment—what he calls “AI whispering”—to make the best investment decisions. Learned Resilience and Growth [36:05]Chris introduces the concept of “learned resilience”—not just bouncing back, but learning from setbacks to come back stronger and smarter. Quote of the Show (32:52):“The truth about software is the Milli Vanilli effect. You don’t have to be able to sing. You just have to look good.” – Chris Dolezalek Join our Anti-PR newsletter where we’re keeping a watchful and clever eye on PR trends, PR fails, and interesting news in tech so you don't have to. You're welcome. Want PR that actually matters? Get 30 minutes of expert advice in a fast-paced, zero-nonsense session from Karla Jo Helms, a veteran Crisis PR and Anti-PR Strategist who knows how to tell your story in the best possible light and get the exposure you need to disrupt your industry. Click here to book your call: https://info.jotopr.com/free-anti-pr-eval Ways to connect with Chris Dolezalek: LinkedIn: http://www.linkedin.com/in/chrisdolezalek Company Website: https://humcapital.com How to get more Disruption/Interruption: Amazon Music - https://music.amazon.com/podcasts/eccda84d-4d5b-4c52-ba54-7fd8af3cbe87/disruption-interruption Apple Podcast - https://podcasts.apple.com/us/podcast/disruption-interruption/id1581985755 Spotify - https://open.spotify.com/show/6yGSwcSp8J354awJkCmJlDSee omnystudio.com/listener for privacy information.

Send us a textIn this conversation, Sarah Clatterbuck, a seasoned engineering leader with over 30 years of experience in tech, shares her journey at major companies such as Google, LinkedIn, and Yahoo. She discusses her recent decision to take a break from her career, her thoughts on leadership, the importance of authenticity, and the cultural differences she experienced after moving to Zurich. Sarah also reflects on the impact of COVID-19 on team dynamics and the challenges of executive leadership.Chapters00:00 Introduction to Sarah Clatterbuck01:13 Navigating Career Transitions05:03 The 30-Year Career Arc08:36 Transitioning into Engineering12:21 Growth at LinkedIn16:33 Challenges of Executive Leadership19:25 Leaning Out: A New Perspective21:11 Moving to Zurich: A New Chapter24:31 Cultural Differences in Leadership25:22 Building Culture During COVID28:59 Authenticity in Leadership32:20 Leaving the Google Bubble

This is a special episode, highlighting a session from ELC Annual 2025! OpenAI evolved from a pure research lab into the fastest-growing product in history, scaling from 100 million to 700 million weekly users in record time. In this episode, we deconstruct the organizational design choices and cultural bets that enabled this unprecedented velocity. We explore what it means to hire "extreme generalists," how AI-native interns are redefining productivity, and the real-time trade-offs made during the world's largest product launches. Featuring Sulman Choudhry (Head of ChatGPT Engineering) and Samir Ahmed (Technical Lead), moderated by Lawrence Bruhmeller (Eng Management @ Sigma). ABOUT SULMAN CHOUDHRYSulman leads ChatGPT Engineering at OpenAI, driving the development and scaling of one of the world's most impactful AI products. He pushes the boundaries of innovation by turning cutting‑edge research into practical, accessible tools that transform how people interact with technology. Previously at Meta, Sulman founded and scaled Instagram Reels, IGTV, and Instagram Labs, and helped lead the early development of Instagram Stories.He also brought MetaAI to Instagram and Messenger, integrating generative AI into experiences used by billions. Earlier in his career, Sulman was on the founding team that built and launched UberEATS from the ground up, helping turn it into a global food delivery platform. With a track record of marrying technical vision, product strategy, and large‑scale execution, Sulman focuses on building products that meaningfully change how people live, work, and connect.ABOUT SAMIR AHMEDSamir is the Technical Lead for ChatGPT at OpenAI, where he currently leads the Personalization and Memory efforts to scale adaptive, useful, and human-centered product experiences to over 700 million users. He works broadly across the OpenAI stack—including mobile, web, services, systems, inference, and product research infrastructure.Previously, Samir spent nine years at Snap, working across Ads, AR, Content, and Growth. He led some of the company's most critical technical initiatives, including founding and scaling the machine learning platform that powered nearly all Ads, Content, and AR workloads, handling tens of billions of requests and trillions of inferences daily.ABOUT LAWRENCE BRUHMELLERLawrence Bruhmuller has over 20 years of experience in engineering management, much of it as an overall head of engineering. Previous roles include CTO/VPE roles at Great Expectations, Pave, Optimizely, and WeWork. He is currently leading the core query compiler and serving teams at Sigma Computing, the industry leading business analytics company.Lawrence is passionate about the intersection of engineering management and the growth stage of startups. He has written extensively on engineering leadership (https://lbruhmuller.medium.com/), including how to best evolve and mature engineering organizations before, during and after these growth phases. He enjoys advising and mentoring other engineering leaders in his spare time.Lawrence holds a Bachelors and Masters in Mathematics and Engineering from Harvey Mudd College. He lives in Oakland, California, with his wife and their three daughters. This episode is brought to you by Span!Span is the AI-native developer intelligence platform bringing clarity to engineering organizations with a holistic, human-centered approach to developer productivity.If you want a complete picture of your engineering impact and health, drive high performance, and make smarter business decisions…Go to Span.app to learn more! SHOW NOTES:From research lab to record-breaking product: Navigating the fastest growth in history (4:03)Unpredictable scaling: Handling growth spurts of one million users every hour (5:20)Cross-stack collaboration: How Android, systems, and GPU engineers solve crises together (7:06)The magic of trade-offs: Aligning the team on outcomes like service uptime vs. broad availability (7:57)Why throwing models "over the wall" failed and how OpenAI structures virtual teams (11:17)Lessons from OpenAI's first intern class: Why AI-native new grads are crushing expectations (13:41)Non-hierarchical culture: Using the "Member of Technical Staff" title to blur the lines of expertise (15:37)AI-native engineering: When massive code generation starts breaking traditional CI/CD systems (16:21)Asynchronous workflows: Using coding agents to reduce two-hour investigations to 15 minutes (17:35)The mindset shift: How rapid model improvements changed how leaders audit and trust code (19:00)Predicting success: "Vibes-based" decision making and iterative low-key research previews (20:43)Hiring for high variance: Why unconventional backgrounds lead to high-potential engineering hires (22:09) LINKS AND RESOURCESLink to the video for this sessionLink to all ELC Annual 2025 sessions This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

As AI adoption accelerates across the software industry, engineering leaders are increasingly focused on a harder question: how to understand whether these tools are actually improving developer experience and organizational outcomes.In this year-end episode of the Engineering Enablement podcast, host Laura Tacho is joined by Brian Houck from Microsoft, Collin Green and Ciera Jaspan from Google, and Eirini Kalliamvakou from GitHub to examine what 2025 research reveals about AI impact in engineering teams. The panel discusses why measuring AI's effectiveness is inherently complex, why familiar metrics like lines of code continue to resurface despite their limitations, and how multidimensional frameworks such as SPACE and DORA provide a more accurate view of developer productivity.The conversation also looks ahead to 2026, exploring how AI is beginning to reshape the role of the developer, how junior engineers' skill sets may evolve, where agentic workflows are emerging, and why some widely shared AI studies were misunderstood. Together, the panel offers a grounded perspective on moving beyond hype toward more thoughtful, evidence-based AI adoption.Where to find Brian Houck:• LinkedIn: https://www.linkedin.com/in/brianhouck/ • Website: https://www.microsoft.com/en-us/research/people/bhouck/ Where to find Collin Green: • LinkedIn: https://www.linkedin.com/in/collin-green-97720378 • Website: https://research.google/people/107023Where to find Ciera Jaspan: • LinkedIn: https://www.linkedin.com/in/ciera • Website: https://research.google/people/cierajaspan/Where to find Eirini Kalliamvakou: • LinkedIn: https://www.linkedin.com/in/eirini-kalliamvakou-1016865/• X: https://x.com/irina_kAl • Website: https://www.microsoft.com/en-us/research/people/eikalliWhere to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact) https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro(02:35) Introducing the panel and the focus of the discussion(04:43) Why measuring AI's impact is such a hard problem(05:30) How Microsoft approaches AI impact measurement(06:40) How Google thinks about measuring AI impact(07:28) GitHub's perspective on measurement and insights from the DORA report(10:35) Why lines of code is a misleading metric(14:27) The limitations of measuring the percentage of code generated by AI(18:24) GitHub's research on how AI is shaping the identity of the developer(21:39) How AI may change junior engineers' skill sets(24:42) Google's research on using AI and creativity (26:24) High-leverage AI use cases that improve developer experience(32:38) Open research questions for AI and developer productivity in 2026(35:33) How leading organizations approach change and agentic workflows(38:02) Why the METR paper resonated and how it was misunderstoodReferenced:• Measuring AI code assistants and agents• Kiro• Claude Code - AI coding agent for terminal & IDE• SPACE framework: a quick primer• DORA | State of AI-assisted Software Development 2025• Martin Fowler - by Gergely Orosz - The Pragmatic Engineer• Seamful AI for Creative Software Engineering: Use in Software Development Workflows | IEEE Journals & Magazine | IEEE Xplore• AI Where It Matters: Where, Why, and How Developers Want AI Support in Daily Work - Microsoft Research• Unpacking METR's findings: Does AI slow developers down?• DX Annual 2026

In this episode, Brian Balfour (Founder & CEO @ Reforge) deconstructs the two core, interconnected challenges leaders face in the AI age: deciding what to build and evolving the Engineering, Product, Design workflow to deliver it. We cover why you should avoid “the local maxima trap” and siphon off "skunkworks" teams to take high-risk, AI-native bets. Brian provides the blueprint for the "Great Distribution Shift," detailing how to reshape your product from the ground up to avoid being left behind as platforms close, and how to emerge as a winner in the new AI landscape. Plus, learn how to rethink what to build, avoid commoditization, compress product discovery from weeks to hours, scale feature variations & prototypes, evolve products to solve harder classes of problems and shift specialist roles from "inboxes" to system builders. ABOUT BRIAN BALFOURBrian Balfour is the Founder & CEO of Reforge, which provides expert training and tools for AI-native product teams. Previously, he served as VP of Growth at HubSpot, spearheading launches like HubSpot CRM and building the growth team that propelled the company's next chapter. This episode is brought to you by Span!Span is the AI-native developer intelligence platform bringing clarity to engineering organizations with a holistic, human-centered approach to developer productivity.If you want a complete picture of your engineering impact and health, drive high performance, and make smarter business decisions…Go to Span.app to learn more! SHOW NOTES:Brian's reaction to the 5:1 gap between AI coding usage and actual product quality challenges (1:57)Why your system only goes as fast as the slowest part, and how hyper-optimizing engineering moves bottlenecks elsewhere (4:53)The "Local Maxima" trap: Why turning designers and PMs into mediocre developers is a waste of opportunity cost (6:04)Siphoning off "Skunkworks" Teams for AI-Native Innovation (7:53)Moving from exploring two solution paths to ten by simulating "product reps" through AI prototyping (13:24)Reforge's AI-native suite (Build + Research): Scaling prototypes, feature variations and compressing product discovery & validation from weeks to hours (15:43)Case Study: How Captions evolved to solve harder classes of problems, using a creator-tool wedge to fund custom AI emotion-models for the media studio market (19:54)Case Study: How Shopify reframed support agents as multimodal "Business Advisors" to provide outsized value (22:24)Navigating the great distribution shift: Understanding the lifecycle from open platforms to closed ecosystems (25:10)The lifecycle of distribution shifts: Navigating the "Open Phase" growth to "Closed Phase" monetization w/ examples from Facebook, Google, and Apple (29:30)OpenAI, memory & context as moat, and why you need to reshape your product from the ground up to win in this distribution shift (31:16)Strategic de-risking for EPD leaders: Building proprietary moats through memory, context, and specialized workflows (32:51)Optimizing EPD workflows and structures: Separate high-risk "skunkworks" from core product optimization, lean cross-functional teams for faster iteration / decisions, and avoiding too many specialized roles (35:25)Dissolving the "Octagon of Specialists": Shifting researchers and PMMs from "inboxes" to builders of self-serve systems (36:57)The five types of product work and why there is no "one-size-fits-all" system for EPD (41:25)Rapid fire questions (43:25)LINKS AND RESOURCESAbout Reforge: Expert training & AI-powered tools for product teamsReforge Build: The prototyping tool discussed for exploring multiple feature variations without designer constraints.Reforge Research: The AI-interviewer tool used to compress user discovery and validation from weeks to hours.Reforge Insights: The platform that aggregates qualitative customer feedback into a self-serve system for EPD teams.Brian Balfour's Research & FrameworksBrianBalfour.com: Brian's personal blog featuring deep dives into growth and product strategy.The Next Great Distribution Shift: The foundational article explaining the lifecycle of open vs. closed platforms.The Four Fits Framework: A refresher on the system of Product-Market Fit, Product-Channel Fit, Channel-Model Fit, and Model-Market Fit.Reforge Strategic Deep DivesAI Disruption Risk Assessment: A guide for engineering leaders to determine if their product is at risk of being commoditized.Product-Market Fit (PMF) Collapse: How to identify and avoid the risk of your core product losing relevance in the AI era.MentionsInvest Like the Best podcastThis episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of Wine After Work, Bryce Batts sits down with Anthony Fasano, founder of Engineering Management Institute, to talk about what leadership really looks like inside engineering and AEC firms today. Anthony has spent years helping engineers grow beyond technical excellence into confident, people-first leaders — and in this conversation, we unpack what firms get wrong about leadership development, why burnout is so common in AEC, and how engineers can build careers that feel both successful and sustainable. In this episode, we cover: Why technical skill alone isn't enough to advance in engineering leadership The biggest leadership blind spots holding AEC firms back How engineers can develop communication, confidence, and influence without losing credibility What career growth really looks like in today's AEC landscape How firms can retain talent by investing in leadership development earlier Whether you're an engineer navigating your next career move or a leader trying to build stronger teams, this episode offers practical insight and perspective you won't want to miss.   https://engineeringmanagementinstitute.org   https://www.linkedin.com/in/anthonyjfasano/  

Rajeev Rajan (CTO @ Atlassian) shares the leadership playbook he used to transform Atlassian's engineering culture, and how that cultural foundation directly powered the build and launch of Rovo (Atlassian's new AI powered app). We cover how they reduced ship time from 120 days to zero, why “developer joy” is the metric that matters, and how to create a community of developer productivity champions to scale DevEx transformation. Rajeev also breaks down his principles for systematizing autonomy and empowerment, including frameworks for giving direct reports more ownership. Plus, a look at the future of Atlassian's “Systems of Work”! ABOUT RAJEEV RAJANRajeev Rajan is the Chief Technology Officer (CTO) at Atlassian. Rajeev joined the company in May 2022 and is responsible for Atlassian Engineering, IT, Security and Trust, and the Engineering Operations teams. His focus areas include the company's continued transformation to Cloud, Developer Platform, and Product lines. Additionally, he is passionate about continuing to develop Atlassian's world-class engineering organization and making it a top choice for aspiring engineering talent worldwide.A long-time resident of Washington state, Rajeev previously acted as the Vice President and Head of Engineering for Facebook and Head of Office for Meta in the Pacific Northwest Region. Prior to Meta, Rajeev spent more than two decades with Microsoft, first joining as an intern in 1994. During his time there, he worked on many products, culminating in Office 365 where he built and led the team responsible for all of the Cloud Infrastructure for Office 365.Rajeev is married with two children and a spunky yellow lab named Rayna. He is very involved in and passionate about a number of efforts that uplift the local community, ranging from the arts to STEM programs. SHOW NOTES:The "Listening Tour": Grounding leadership in reality and identifying friction points (3:52)The Confluence Editor story: Reducing ship time from 120 days to 0 (6:26)Moving beyond productivity: Why "Developer Joy" is the metric that matters (8:45)Creating a community of Developer Productivity Champions and the power of a Productivity Summit (13:44)Elevating productivity to a company-level OKR and measuring qualitative sentiment (17:12)Leadership framework: Deciding when to "manage through people" vs. "manage through process" (19:05)How to give more direct ownership / responsibility to a DRI (23:03)Alignment conversations about prioritizing developer joy & productivity (24:22)Challenges faced during Atlassian's developer joy transformation journey (26:23)How the "Developer Joy" foundation enabled building Rovo in just 6 months (30:02)The "System of Work": Expanding Jira's utility beyond engineering to finance, marketing, and legal (33:22)Rapid Fire Questions (40:48) This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/5 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The common narrative suggests AI will make engineering leadership obsolete, but history - and the Industrial Revolution - suggests the opposite is true. Engineering executive Manoj Mohan joins the show live from ELC to argue that as code generation costs drop, the demand for high-level judgment and strategic oversight will only skyrocket. He breaks down why leaders must stop starting with models and start with customer pain points, utilizing his "3GF" framework to manage the risksLinearB: Measure the impact of GitHub Copilot and CursorFollow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's guest(s):Connect with Manoj: LinkedIn | SubstackOFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

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.

Ever wondered how an engineer's mindset can transform complex challenges into innovative solutions? In this conversation with Josh Tarbutton, PhD, PE, founder of Bravo Team, Cam and Otis explore the intersection of engineering excellence and entrepreneurial spirit."Engineering is about solving problems," Josh explains, drawing from his extensive experience as a military veteran, professor, and now leader of a premier engineering firm. From discussing the importance of custom machine design and automation to sharing insights about the Hero's Journey in professional development, this episode offers a deep dive into the world of advanced R&D.What makes this conversation particularly valuable is Josh's unique perspective on leadership and innovation. "The best solutions often come from understanding the narrative," he shares, emphasizing the role of storytelling in engineering and business. Whether you're an aspiring engineer, a business leader facing technical challenges, or simply curious about the future of automation, Josh's insights provide a roadmap for navigating complex problems with creativity and precision.More About Josh:Joshua Tarbutton, PhD, PE is an engineer, entrepreneur, and U.S. Army veteran, leading Bravo Team, a premier engineering firm specializing in custom machine design, automation, and advanced R&D. With a BSME from Georgia Tech and an MS andPhD in Mechanical Engineering from Clemson University, he spent nearly a decade as a professor, earning tenure at UNC Charlotte, publishing 53 research papers and securing millions in research funding. His eight years of military service instilled a disciplined, problem-solving mindset that drives his leadership. He is an Entrepreneur Organization member, where he has served as an Accelerator Coach and board member. Founded in 2018, Bravo Team partners with Fortune 500 companies, OEMs, and industrial manufacturers to solve complex engineering challenges. The firm excels in machine design, automation, PCB development, and software engineering, providing custom-built solutions where off-the-shelf options fall short. Joshua is dedicated to advancing engineering excellence, transformative automation, scalable innovation for industry leaders, and helping people find their narrative in the Hero's Journey.#10xyourteam #LeadershipDevelopment #EngineeringMindset #ProblemSolving #InnovationCulture #EntrepreneurialLeadership #VeteranLeaders #AutomationSolutions #AdvancedEngineering #RAndDInnovation #BusinessGrowthStrategiesChapter Times and Titles:From Military Service to Engineering Leadership [00:00 - 10:00]Introduction to Josh Tarbutton and Bravo TeamThe journey from Army veteran to engineering entrepreneurHow military discipline shapes problem-solvingCustom Solutions for Complex Challenges [10:01 - 20:00]The importance of custom machine design and automationWhy off-the-shelf solutions often fall shortPartnering with Fortune 500 companies for innovationThe Hero's Journey in Engineering [20:01 - 30:00]Understanding the narrative in problem-solvingHow storytelling enhances engineering solutionsThe role of the Hero's Journey in professional growthAdvancing Engineering Excellence [30:01 - 40:00]Josh's experience as a professor and researcherThe impact of publishing and securing research fundingBuilding a culture of innovation at Bravo TeamLeadership and Innovation in Practice [40:01 - 50:00]Balancing technical expertise with entrepreneurial visionLessons from serving as an Accelerator CoachEncouraging scalable innovation in industry leadersConnecting with Bravo Team [50:01 - End]How to learn more about Bravo Team's servicesFinal thoughts on engineering and entrepreneurshipContact information and resources for further explorationJosh Tarbuttonhttps://www.link

AI engineering tools are evolving fast. New coding assistants, debugging agents, and automation platforms emerge every month. Engineering leaders want to take advantage of these innovations while avoiding costly experiments that create more distraction than impact.In this episode of the Engineering Enablement podcast, host Laura Tacho and Abi Noda outline a practical model for evaluating AI tools with data. They explain how to shortlist tools by use case, run trials that mirror real development work, select representative cohorts, and ensure consistent support and enablement. They also highlight why baselines and frameworks like DX's Core 4 and the AI Measurement Framework are essential for measuring impact.Where to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-courseWhere to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda  • Substack: ​​https://substack.com/@abinoda  In this episode, we cover:(00:00) Intro: Running a data-driven evaluation of AI tools(02:36) Challenges in evaluating AI tools(06:11) How often to reevaluate AI tools(07:02) Incumbent tools vs challenger tools(07:40) Why organizations need disciplined evaluations before rolling out tools(09:28) How to size your tool shortlist based on developer population(12:44) Why tools must be grouped by use case and interaction mode(13:30) How to structure trials around a clear research question(16:45) Best practices for selecting trial participants(19:22) Why support and enablement are essential for success(21:10) How to choose the right duration for evaluations(22:52) How to measure impact using baselines and the AI Measurement Framework(25:28) Key considerations for an AI tool evaluation(28:52) Q&A: How reliable is self-reported time savings from AI tools?(32:22) Q&A: Why not adopt multiple tools instead of choosing just one?(33:27) Q&A: Tool performance differences and avoiding vendor lock-inReferenced:Measuring AI code assistants and agentsQCon conferencesDX Core 4 engineering metricsDORA's 2025 research on the impact of AIUnpacking METR's findings: Does AI slow developers down?METR's study on how AI affects developer productivityClaude CodeCursorWindsurfDo newer AI-native IDEs outperform other AI coding assistants?

Guest: Derek Baird, CEO & Co-founder, Switchboard HealthResources:Switchboard Health: https://switchboardhealth.com/Conduce Health: https://www.conducehealth.com/Connect with Derek: https://www.linkedin.com/in/debaird/Connect with Nick: https://www.linkedin.com/in/nick-crabbs-5674a233/ Product in Healthtech is community for healthtech product leaders, by product leaders. For more information, and to sign up for our free webinars, visit www.productinhealthtech.com.

Welcome to the 2025 season finale of Aurecon’s Engineering Reimagined podcast. Let’s look back on some of our favourite episodes of the year that all have one thing in common – the evolving technologies that affect or impact three critical areas of interest, including decision-making in the age of distraction, the energy transition and data centres. See omnystudio.com/listener for privacy information.

In this episode, Daniel Lereya (Chief Product and Technology Officer @ Monday.com) shares how they are evolving their engineering roles from developers to builders & system designers, where the lines between product, engineering, and design are intentionally blurred, and developers manage AI Agents as team members, tackling an ever-expanding list of projects. We explore the shift from "developer" to "system designer" and why managing AI agents requires the same skills as managing people. Plus, a case study where the Monday.com team leveraged AI agents to decompose a monolith, autonomously manage the project board and assign strategic / high-risk tasks to humans. ABOUT DANIEL LEREYADaniel Lereya has served as Chief Product and Technology Officer at monday.com since 2023. In this role, he focuses on advancing monday.com's multi-product vision and operational efficiencies while driving execution to support company growth. Previously, he was Vice President of R&D and Product, leading global teams in shaping and executing the company's product strategy through innovation and technology. Before joining monday.com, Daniel held leadership and engineering roles at IBM and SAP. SHOW NOTES:The three core principles of monday.com's culture: Ownership, Transparency, and Speed of Execution (3:59)How AI acts as an accelerant to implement these cultural principles at scale (8:36)Why the “Developer” role is evolving into a “Strategic Builder” and “System Designer” (13:47)Breaking silos: How the “Builder” role blurs the lines between product, engineering, and design (17:13)Real-world example: A designer using AI to submit code and fix UI issues independently (19:09)Case Study: The “Agent Factory” & how a weekend prototype by one leader shifted the product roadmap (21:25)Operationalizing transparency: Using internal tools (“Big Brain”) to align every builder on daily business impact (25:58)The “Kickoff Meeting” framework: A strict protocol for falling in love with the problem, not the solution (32:26)The new management paradigm with AI agents as team members (37:31)Rapid fire questions (42:09) This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This is a special episode, highlighting a session from ELC Annual 2025! The true promise of AI isn't in replicating human intelligence. It's in developing entirely new forms of non-human intelligence that perceive and understand the world in fundamentally different ways. Jamie Lien (Co-Founder and Chief Scientist @ Archetype AI) and Rashi Agarwal (Head of AI Engineering @ GoodLeap) explore the emergence of "Physical AI" - machines that sense the world through modalities beyond human biology to form internal representations free from our biases. This means building machines that can directly sense the physical world through modalities beyond human biology, form their own internal representations and interpretations free from our biases, and then translate that understanding back to us in human terms. ABOUT JAIME LIENJaime Lien, Ph.D. is Co-Founder and Chief Scientist at Archetype AI, a pioneering startup advancing Physical AI, artificial intelligence that understands the real world through real-time sensor data fusion.With over a dacade of experience in radar-based sensing, signal processing, and hardware engineering, Jaime's career bridges cutting-edge research and consumer-ready innovation. Before Archetype, she led radar sensing development for Google ATAP's Project Soli and contributed wireless communication and localization expertise at NASA's Jet Propulsion Laboratory. ABOUT RASHI AGRAWALRashi Agrawal is Head of AI Engineering at GoodLeap, where she leads enterprise-wide AI initiatives that deliver real business impact. An accomplished speaker, she covers the latest in AI, including context engineering, evaluations, and multi-agent collaboration, while driving Applied AI innovation in the enterprise. Previously, she scaled engineering teams at Yahoo, advancing its multibillion-dollar advertising business. A passionate world traveler to 40+ countries, Rashi brings global perspective and energy to her leadership and storytelling. SHOW NOTES:Archetype AI's mission: Building a foundation model for physical reality (2:24)The potential for discovery: Using AI to observe phenomena humans cannot perceive (3:36)Augmentation vs. Replacement: Giving humans "superpowers" rather than automating them away (5:48)The "Perfect Storm" for Physical AI: Transformers, self-supervised learning, and commodity sensors (6:04)Defining “Non-Human Intelligence” and removing the constraints of human labels (8:34)Why language is inherently lossy and insufficient for true physical understanding (10:28)Real-world application: How Physical AI aids safety decision-making in the solar industry (12:35)Use case: Improving pedestrian safety and traffic signaling in Bellevue (14:51)The biggest engineering leadership challenge: Embracing the “messiness” of real-world data (16:21)Q&A: Why we shouldn't teach AI physical laws, but let it discover them (18:50)Q&A: Validating models when there is a defined ground truth vs. subjective language (20:49)Q&A: Compute requirements and the future of active learning at the edge (22:05) LINKS AND RESOURCESVideo version of Jaime and Rashi's session at ELC Annual 2025 This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

KSBM Radio: The Voice of Townview presents a special panel event: “Designing for Connection: How Architecture, Interiors, and Leadership Create Spaces That Bring People Together.”Recorded live on Tuesday, November 11, 2025, this discussion features four talented professionals from the nationally recognized design firm Gresham Smith, a company dedicated to building healthy, sustainable, and thriving communities.✨ Meet Our Panelists:Mr. Carlson — Architect (Aviation Division); specializing in sustainability and airport design.Ms. Espinoza — Interior Designer; whose borderland upbringing shapes her culturally expressive spaces.Mr. Tavarez — Project Leader; known for uniting teams and building strong community-focused environments.Ms. White — Water Resources Engineer; specializing in water systems and infrastructure.In this episode, we explore:

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.

Diese Woche hatte ich ein Gespräch mit einem ambitionierten Ingenieur, der bei Tesla arbeitet.Ein Satz ist hängen geblieben:„Tesla hat einen Sense of Urgency – ein Gefühl der Dringlichkeit."In dieser Folge spreche ich über dieses Prinzip.Was es bedeutet, warum es so mächtig ist und wie du es in deinen Job und dein Unternehmen übertragen kannst.Show Notes:>> No Zero Days | Buch für Ingenieure: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠nozerodays.de⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠/buch⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Mentoring für Ingenieure: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠engineer-alliance.de⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Crashkurs: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠engineer-alliance.de⁠/crashkurs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Tim Schmaddebeck auf LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Hier klicken⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Buchempfehlungen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠mentorwerk.de/buecher⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Stichworte zur Folge:Move fast, schnelle Entscheidungen, Entscheidungszyklen, Sense of Urgency, Dringlichkeit, Tesla Arbeitskultur, Elon Musk Prinzipien, schnelle Umsetzung, iterative Verbesserungen, Fehlerkultur Ingenieure, Overthinking vermeiden, Two-Way Door Decisions, One-Way Door Decisions, reversible Entscheidungen, irreversible Entscheidungen, Geschwindigkeit im Job, Momentum aufbauen, Fortschritt steigern, Karriereentwicklung Ingenieure, Perfektionismus überwinden, Mut zu Entscheidungen, schnelle Iterationen, Engineering Leadership, Entscheidungsangst reduzieren, produktive Fehler, High-Performance Engineering, Umsetzung statt Planung, proaktives Arbeiten, Sichtbarkeit im Unternehmen, maniacal urgency, berufliche Wirksamkeit, Karrierebeschleunigung

Welcome to another episode of Road to CTO, our series where we sit down with some of the most experienced and influential technology leaders in the world.In this episode, we talk with Dorion Carroll, a veteran with 35 years of tech leadership experience, former CTO at Zynga, VP at Amazon, and one of the most insightful engineering minds in the industry.From scaling engineering teams from 280 to 3,600 people, to navigating billion-dollar decisions, to understanding what truly makes a great CTO, Dorion shares the lessons, stories and frameworks that shaped his extraordinary career.If you're an aspiring CTO, an engineering manager, or simply passionate about how world-class tech organisations operate behind the scenes, this is a masterclass.Support the show

Nathen Harvey leads research at DORA, focused on how teams measure and improve software delivery. In today's episode of Engineering Enablement, Nathen sits down with host Laura Tacho to explore how AI is changing the way teams think about productivity, quality, and performance.Together, they examine findings from the 2025 DORA research on AI-assisted software development and DX's Q4 AI Impact report, comparing where the data aligns and where important gaps emerge. They discuss why relying on traditional delivery metrics can give leaders a false sense of confidence and why AI acts as an amplifier, accelerating healthy systems while intensifying existing friction and failure.The conversation focuses on how AI is reshaping engineering systems themselves. Rather than treating AI as a standalone tool, they explore how it changes workflows, feedback loops, team dynamics, and organizational decision-making, and why leaders need better system-level visibility to understand its real impact.Where to find Nathen Harvey:• LinkedIn: https://www.linkedin.com/in/nathenWhere to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro(00:55) Why the four key DORA metrics aren't enough to measure AI impact(03:44) The shift from four to five DORA metrics and why leaders need more than dashboards(06:20) The one-sentence takeaway from the 2025 DORA report(07:38) How AI amplifies both strengths and bottlenecks inside engineering systems(08:58) What DX data reveals about how junior and senior engineers use AI differently(10:33) The DORA AI Capabilities Model and why AI success depends on how it's used(18:24) How a clear and communicated AI stance improves adoption and reduces friction(23:02) Why talking to your teams still matters Referenced:• DORA | State of AI-assisted Software Development 2025• Steve Fenton - Octonaut | LinkedIn• AI-assisted engineering: Q4 impact report

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."

James Reggio (CTO @ Brex) shares the story of "Brex 3.0", an 18-month journey behind their operational evolution. We explore how they rewound their org from a Series E to a Series C mindset, and replaced siloed OKRs with seasonal "marquee initiatives." James deconstructs the “Brex Hacker House”, an AI-focused startup within a startup experiment aimed to disrupt their core business. This conversation is all about evolving operational rhythms, layers of management, product building, and culture change! ABOUT JAMES REGGIOJames Reggio is Brex's Chief Technology Officer. James is a forward thinking technology leader who currently oversees Brex's entire Engineering org. James joined Brex in 2020 as Principal Engineer and has played a vital role in building the company's mobile app and AI capabilities. Prior to Brex, James had an extensive career as a Software Engineer at leading companies such as Microsoft, Salesforce, AirBnB, Stripe and more. Additionally, James founded two companies: Altair Management and Banter, a social discovery platform for podcasts that was later acquired by Convoy in 2018. James received his B.A. of Science from The University of Texas Austin. SHOW NOTES:The birth of Brex 3.0: Using a layoff as a "moment to refound the company" (3:38)Moving from a Series E to a Series C operational mindset (5:28)The problem with a GM model: How siloed OKRs and roadmaps created "deadlock" (6:07)New rituals: Why the CEO became "chief editor of the roadmap" (8:16)The impact on morale: "Folks just knew how their work fit into the bigger picture" (11:16)The challenge of the new model: Who do you hold accountable when you "win and lose as a team"? (13:43)The lesson for reintroducing systems: "Less is more" (15:43)The "Startup within a Startup": Launching an internal team to disrupt Brex (16:49)“What if we were founding Brex again today?” The 4 constraints for the "Hacker House" experiment (17:58)Questions eng leaders should ask when running a similar experiment to Brex (21:02)Aha moment: "With agentic coating, code is so cheap" (22:35)Managing the two narratives: "compounding" the core biz vs. “innovating" with AI (26:01)A surprising dynamic: Why the AI team struggled to see their impact (while the core team didn't) (29:38)Building alongside your customer to iterate / experiment faster (36:06)The turnaround is over: Brex hits 50% YoY growth and cash-flow positive (38:45)Rapid fire questions (42:10) This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

If you've ever shipped an AI feature that looked great in testing — only to watch it behave unpredictably in production — you're not alone.In this episode of IT Visionaries, host Chris Brandt talks with Lawrence Jones, Founding Engineer at incident.io, about the critical gap between AI that demos well and AI that works under pressure. Lawrence shares how his team designs tools that help engineers respond faster, learn from failure, and build systems that don't crumble when it counts. CHAPTERS / KEY MOMENTS00:00 - AI Chaos & The Mike Tyson Rule00:58 - Meet Lawrence Jones of Incident.io03:14 - From FinTech Outages to Incident Response06:22 - The Biggest Mistake in Incident Management09:08 - Training for Chaos: Game Day Simulations10:31 - Inside the AI SRE System13:01 - What SRE Really Means16:23 - From Prototype to Production AI20:27 - Keeping Up with AI's Rapid Evolution22:50 - Understanding Vector Databases & Embeddings28:34 - The Architecture Problem: Chaining Prompts at Scale36:11 - Measuring AI Performance & Reliability44:02 - The Future of SRE Meets AI52:10 - Lessons from Real Incidents56:42 - Final Thoughts: Building AI That Works -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, I talk with Dr. Feniosky Peña-Mora, Sc.D., P.E., F.ASCE, ASCE President and National Dean, School of Engineering and Sciences at Tecnológico de Monterrey, about civil engineering leadership and its future impact. We discuss how his upbringing in the Dominican Republic shaped his approach to leadership, the evolving demands of civil engineering education, […] The post How Civil Engineering Leadership Is Shaping the Future – Ep 300 appeared first on Engineering Management Institute.

In today's technology landscape, management as a skill is becoming more complex as teams become larger & managers must navigate the balance between relationships and strategic execution. So how can AI tools help managers level up their game? Jonathan Raymond (Founder & CEO @ Ren) shares insights that can help managers navigate their modern-day invisible cognitive loads. We cover how AI can be used to enhance – not replace – inherently human skillsets, the three components that make up an effective manager / employee relationship, product-building principles for building relational systems, and using AI to guide rather than provide concrete answers.ABOUT JONATHAN RAYMONDJonathan Raymond is the CEO at Refound and author of the award winning book, Good Authority. In 2018, he was named one of Inc. Magazine's top 100 leadership speakers. Refound trains leaders how to give effective feedback and create a culture of accountability. The former CEO of EMyth, Jonathan has led business transformation projects in technology, renewable energy, and the coaching industry. He's a half-decent barista, a bad-but-enthusiastic surfer, and will never give up on the New York Knicks.SHOW NOTES:Jonathan's perspective on the impossible cognitive load & colliding pressures of modern managers (2:31)The complicated workflow it takes to be a great manager (6:05)“Field Intelligence” and the need to ingest non-technical data such as (mood, sentiment, and alignment) to make better leadership decisions (9:52)The managerial matrix: high/low performers and the 10-person team that feels like 50 (12:46)The cost of mismanaging your team & why it's so easy to get it wrong (16:02)What's uniquely human vs. where AI provides leverage (18:01)AI's role: detecting signal and prompting human reflection (21:04)The “Growth Loop”: a 3-part system for effective leadership (27:20)Incorporating AI tools to enhance the manager / employee relationship (31:20)The future vision: an “in-ear” AI coach that closes the gap between learning and applying (33:09)Closing the gap between learning a new skill & it becoming an unconscious habit (35:59)Product Principle: Building a “Relational System,” not just a task manager (37:49)Product Principle: Why an AI coach must ask questions, not provide answers (40:50)How to harness AI tools for better emotional articulation / processing (45:11)A simple behavioral change to try this week (47:58)Rapid fire questions (49:57)LINKS AND RESOURCESGood Authority: How to Become the Leader Your Team is Waiting For - Jonathan's book in which he brings together what he has learned over a twenty-year journey as an executive, entrepreneur, team leader and leadership trainer.Entangled Life: How Fungi Make Our Worlds, Change Our Minds & Shape Our Futures - Merlin Sheldrake explores the spectacular and neglected world of fungi: endlessly surprising organisms that sustain nearly all living systems.Jonathan's session at ELC Annual 2025This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Highly skilled engineers often rise into leadership roles only to find themselves unprepared for the human complexities of guiding teams. In this episode, we explore the nuances of transitioning from a technical expert to a people leader, highlighting the soft skills that aren't typically taught in engineering school but are essential for success. Learn how to communicate effectively, inspire collaboration, and lead with influence without losing your technical edge. Guest: Dr. Tom Ulrich (Author of Leading Engineers and Co-Creator of a Leading Insulin Pump Tandem) In this episode: Dr. Emi Barresi, Tom Bradshaw, Dr. Thomas Ulrich, Nic Krueger, Lee Crowson, LindaAnn Rogers   I/O Career Accelerator Course: https://www.seboc.com/job Visit us https://www.seboc.com/ Follow us on LinkedIn: https://bit.ly/sebocLI Join an open-mic event: https://www.seboc.com/events   References:  Ulrich, T. R. (2017). A phenomenological inquiry into engineers' motivation to lead. Proceedings of the international annual conference of the American Society for Engineering Management, 1–10.

In this episode of Engineering Enablement, host Laura Tacho talks with Fabien Deshayes, who leads multiple platform engineering teams at Monzo Bank. Fabien explains how Monzo is adopting AI responsibly within a highly regulated industry, balancing innovation with structure, control, and data-driven decision-making.They discuss how Monzo runs structured AI trials, measures adoption and satisfaction, and uses metrics to guide investment and training. Fabien shares why the company moved from broad rollouts to small, focused cohorts, how they are addressing existing PR review bottlenecks that AI has intensified, and what they have learned from empowering product managers and designers to use AI tools directly.He also offers insights into budgeting and experimentation, the results Monzo is seeing from AI-assisted engineering, and his outlook on what comes next, from agent orchestration to more seamless collaboration across roles.Where to find Fabien Deshayes: • LinkedIn: https://www.linkedin.com/in/fabiendeshayesWhere to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro  (01:01) An overview of Monzo bank and Fabien's role  (02:05) Monzo's careful, structured approach to AI experimentation  (05:30) How Monzo's AI journey began  (06:26) Why Monzo chose a structured approach to experimentation and what criteria they used  (09:21) How Monzo selected AI tools for experimentation  (11:51) Why individual tool stipends don't work for large, regulated organizations  (15:32) How Monzo measures the impact of AI tools and uses the data  (18:10) Why Monzo limits AI tool trials to small, focused cohorts  (20:54) The phases of Monzo's AI rollout and how learnings are shared across the organization  (22:43) What Monzo's data reveals about AI usage and spending  (24:30) How Monzo balances AI budgeting with innovation  (26:45) Results from DX's spending poll and general advice on AI budgeting  (28:03) What Monzo's data shows about AI's impact on engineering performance  (29:50) The growing bottleneck in PR reviews and how Monzo is solving it with tenancies  (33:54) How product managers and designers are using AI at Monzo  (36:36) Fabien's advice for moving the needle with AI adoption  (38:42) The biggest changes coming next in AI engineering Referenced:Monzo The Go Programming LanguageSwift.orgKotlinGitHub Copilot in VS Code CursorWindsurfClaude CodePlanning your 2026 AI tooling budget: guidance for engineering leaders

Businesses are spending millions on AI tools hoping to accelerate time-to-market but aren't seeing organizational-level results. Laura Tacho (CTO @ DX) explains why an "individual productivity" mindset fails and how AI merely accelerates the condition of the system it enters. She provides a framework for leaders to shift to a systems-level approach, find high-leverage ROI by looking outside the 20% of time spent coding, and understand what sets high-ROI orgs apart. Plus Laura shares data literacy tools to cut through the "whiplash" of conflicting AI reports and provides key considerations for 2026 budgeting, detailing where and how companies are planning to strategically invest.ABOUT LAURA TACHOLaura Tacho is CTO at DX, a developer experience company. She previously led teams at companies like CloudBees, Aula Education, and Nova Credit. She's an expert in building world-class engineering organisations that consistently deliver outstanding results. Laura has coached CTOs and other engineering leaders from startups to the Fortune 500, and also facilitates a popular course on metrics and engineering team performance.SHOW NOTES:Downsides to approaching organizational outcomes from an individual task level (2:59)Why individual product gains don't always equate to systems-level improvements (4:56)How the quality of existing systems impacts the improvements AI can foster (7:26)Strategies for shifting mental models from the individual to systems level (9:09)Implement training & enablement techniques as an organizational lever (11:22)Common workflows that can unlock new problem-solving methods (14:46)Understanding what impact you want to see / getting the most ROI from AI (18:40)How to interpret the data when it comes to AI & its true ROI (21:22)AI data literacy for engineering leaders (23:06)Interpreting the meter study & what it means for engineers using AI (25:49)Quality vs. quantity when it comes to AI implementation on the org level (28:43)Characteristics that high-ROI companies possess when it comes to AI (30:35)Strategies to invest in that may lead to higher ROI (32:29)Laura's observations on time & money budgeting / investments for 2026 (35:28)Embracing cost savings & opportunity generation as an eng org (38:08)Tackling fear / uncertainty when it comes to AI adoption, budgeting, & ROI (40:01)LINKS AND RESOURCESPrevious Episode with Laura TachoIntroducing the AI Measurement Framework from DXAtlassian State of DevEx ReportMETR StudyDORA Report (2025)This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Anthony Reneaud, the Founder of Renant Antren Corp. and a licensed Professional Engineer in New York, New Jersey, Connecticut, and Rhode Island joins Enterprise Radio… Read more The post Building Success: How Anthony Renaud and Renant Antren Are Redefining Engineering Leadership appeared first on Top Entrepreneurs Podcast | Enterprise Podcast Network.

In this episode of The Tech Trek, Amir sits down with Michi Kono, CTO of Garner Health, to unpack what it really takes to scale engineering leadership inside a fast growing startup. Michi shares how he balances structure and speed, why formalizing processes too early can slow innovation, and how “the Garner way” blends lessons from big tech with first principles thinking. This is a conversation about leadership maturity, cultural design, and building systems that evolve with your company's growth.Key Takeaways• Leadership scale comes from knowing when to formalize processes, not just how.• “Six months is never”: waiting on fixes usually means they will never happen.• Feedback is a gift, and it is on leaders to create the safety for it to flow upward.• Borrowing from big tech only works when you adapt the principles, not the playbook.• Engineering leaders should measure success by business outcomes, not just delivery speed.Timestamped Highlights01:46 The first signals Michi looked for when stepping into the CTO role03:49 Turning ad hoc collaboration into structured dependency management06:36 Why delaying operational fixes is a silent killer for scaling teams08:38 Building standards only when they solve real, visible problems12:13 The art of forecasting leadership hiring and team design14:54 Lessons borrowed from Meta, Stripe, and Capital One, and when not to use them17:31 Defining “the Garner way” through first principles20:59 Judging engineering performance through business impact25:00 Creating true psychological safety for feedback across all levelsA Line That Stuck“If we can't execute on the roadmap that lets us actually build a successful business, then I failed as a leader. There are no excuses.”Pro TipsWhen you inherit a growing engineering organization, start by mapping dependencies, not hierarchies. Clarity around how teams interact is more valuable than adding headcount too early.Call to ActionEnjoyed this episode? Follow The Tech Trek on Apple Podcasts and Spotify, and connect with Amir on LinkedIn for more conversations on scaling teams, leadership, and engineering culture.

What happens to platform engineering when natural language becomes the primary interface to infrastructure? Miriam Aguirre (Co-founder & CEO @ Ingenimax) joins us to explore how AI is fundamentally reshaping platform strategy, team structures, and the very role of the platform engineer. We deconstruct the shift from tactical "how" to strategic "why" and explore what it means to lead and build resilient systems in this new paradigm.ABOUT MIRIAM AGUIRREMiriam Aguirre is the Co-Founder and CEO of Ingenimax, the company behind StarOps, an AI-powered platform engineering engine that helps teams deploy and manage kubernetes and other cloud-native systems in minutes, not months. Before Ingenimax, Miriam served as CTO and engineering leader at two startups that successfully scaled from early stage through IPO. Her career spans deep expertise in high-throughput, scalable systems and machine learning, with a focus on building the technical and organizational foundations for hypergrowth. Miriam is passionate about engineering leadership that turns complex technology into intuitive, reliable platforms, and about helping teams scale without losing their soul. ToolHive Unlocks the Full Value of MCP & Your AI AgentsSo you've invested in AI agents for code generation, but they're limited to experiments or even stuck on the shelf. To do real, valuable work, those AI agents need access to your data and systems.ToolHive helps you confidently connect the pieces by making it simple and secure for you to use the Model Context Protocol (MCP).ToolHive includes a pre-vetted registry of MCP servers, containerizes every MCP server for consistency and leans on built-in security to keep your secrets safe.Leaders trust ToolHive to put MCP into production and put their AI agents to work.ToolHive is open source, so get started for free at toolhive.dev SHOW NOTES:The Origin Story of Ingenimax (3:05)The recurring scaling problem: Why "scaling teams" means scaling systems first (5:23)How the age of AI forces platform strategy to evolve earlier in a company's journey (8:48)The decline of vendor lock-in and the rising appetite for experimentation with tech (10:56)The paradigm shift that breaks the old model: natural language as the new interface (14:11)Why deep knowledge of fundamentals is now more important than syntax (16:56)Shifting requirements conversations from tactical inputs to strategic outcomes (20:22)Balancing standardization and flexibility with guardrails in an AI-driven environment (22:58)The challenge of getting from an AI prototype to a polished product (26:42)How platform team roles will evolve to focus more on curation (29:32)How to become a great technology curator (37:30)Rapid Fire Questions (39:56)LINKS AND RESOURCESThe Book of George - From the author of the critically acclaimed Laura & Emma comes a The Love Affairs of Nathaniel P. for our times: Kate Greathead's razor-sharp but big-hearted excavation of millennial masculinity.Can Animals and Machines Be Persons?: A Dialogue - This is a dialogue about the notion of a person, of an entity that thinks and feels and acts, that counts and is accountable. Equivalently, it's about the intentional idiom --the well-knit fabric of terms that we use to characterize persons. Human beings are usually persons (a brain-dead human might be considered a human but not a person). However, there may be persons, in various senses, that are not human beings. Much recent discussion has focused on hypothetical computer-robots and on actual nonhuman great apes. The discussion here is naturalistic, which is to say that count and accountability are, at least initially, presumed to be naturally well-knit with the possession of a cognitive and affective life.This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of Engineering Enablement, Laura Tacho and Abi Noda discuss how engineering leaders can plan their 2026 AI budgets effectively amid rapid change and rising costs. Drawing on data from DX's recent poll and industry benchmarks, they explore how much organizations should expect to spend per developer, how to allocate budgets across AI tools, and how to balance innovation with cost control.Laura and Abi also share practical insights on building a multi-vendor strategy, evaluating ROI through the right metrics, and ensuring continuous measurement before and after adoption. They discuss how to communicate AI's value to executives, avoid the trap of cost-cutting narratives, and invest in enablement and training to make adoption stick.Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda  • Substack: ​​https://substack.com/@abinoda  Where to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro: Setting the stage for AI budgeting in 2026(01:45) Results from DX's AI spending poll and early trends(03:30) How companies are currently spending and what to watch in 2026(04:52) Why clear definitions for AI tools matter and how Laura and Abi think about them(07:12) The entry point for 2026 AI tooling budgets and emerging spending patterns(10:14) Why 2026 is the year to prove ROI on AI investments(11:10) How organizations should approach AI budgeting and allocation(15:08) Best practices for managing AI vendors and enterprise licensing(17:02) How to define and choose metrics before and after adopting AI tools(19:30) How to identify bottlenecks and AI use cases with the highest ROI(21:58) Key considerations for AI budgeting (25:10) Why AI investments are about competitiveness, not cost-cutting(27:19) How to use the right language to build trust and executive buy-in(28:18) Why training and enablement are essential parts of AI investment(31:40) How AI add-ons may increase your tool costs(32:47) Why custom and fine-tuned models aren't relevant for most companies today(34:00) The tradeoffs between stipend models and enterprise AI licensesReferenced:DX Core 4 Productivity FrameworkMeasuring AI code assistants and agents2025 State of AI Report: The Builder's PlaybookGitHub Copilot · Your AI pair programmerCursorGleanClaude CodeChatGPTWindsurfTrack Claude Code adoption, impact, and ROI, directly in DXMeasuring AI code assistants and agents with the AI Measurement FrameworkDriving enterprise-wide AI tool adoptionSentryPoolside

In this episode, we're addressing one of the biggest challenges current eng leaders are facing – balancing yesterday's constraints with tomorrow's potential! Chrystal Henke Ball (VP of Engineering @ Yahoo) shares insights on why it's important to constantly challenge your assumptions and how vision can sometimes work as a bottleneck for your organization. We dissect how the traditional product lifecycle is evolving to become more fluid and what that means for the collaborative relationship between product, eng, and design. Additionally, Chrystal defines grit, why it's important for leaders to model it, and strategies for cultivating the trait within your eng team in order to move past short-term challenges and focus on long-term goals! ABOUT CHRYSTAL HENKE BALLChrystal Henke Ball a seasoned engineering leader, currently serving as VP of Engineering at Yahoo, where she leverages her experience to accelerate product development across core products such as Yahoo.com and the Yahoo News app. Prior to Yahoo, she led engineering organizations at Google Search, Pandora, Pachama, and Arcadis, building highly available systems, guiding architectural transitions, spearheading novel solutions, and delivering delightful user experiences. Chrystal excels at designing purpose-driven, scalable architectures, streamlining development processes, and mentoring teams to work effectively and openly together. ToolHive Unlocks the Full Value of MCP & Your AI AgentsSo you've invested in AI agents for code generation, but they're limited to experiments or even stuck on the shelf. To do real, valuable work, those AI agents need access to your data and systems.ToolHive helps you confidently connect the pieces by making it simple and secure for you to use the Model Context Protocol (MCP).ToolHive includes a pre-vetted registry of MCP servers, containerizes every MCP server for consistency and leans on built-in security to keep your secrets safe.Leaders trust ToolHive to put MCP into production and put their AI agents to work.ToolHive is open source, so get started for free at toolhive.dev SHOW NOTES:Navigating the challenge of balancing constraint vs. innovation (3:05)Considerations for balancing current capabilities w/ your roadmap to change (4:34)Frameworks for categorizing what's fixed vs. in flux to aid decision-making (6:14)Conversation points for checking your assumptions (7:36)The new leadership challenge: vision as a bottleneck (14:45)Evolving feedback loops to address a more fluid product lifecycle (19:43)Defining product vision in today's fast-paced, fluid landscape (23:57)Defining grit as an essential trait & ways to cultivate it as an eng leader (31:57)Building AI-incorporated products with trust as a foundational principle (40:46)Rapid fire questions (43:01) LINKS AND RESOURCESTalking to Strangers: What We Should Know About the People We Don't Know - Malcolm Gladwell, host of the podcast Revisionist History and author of the #1 New York Times bestseller Outliers, offers a powerful examination of our interactions with strangers -- and why they often go wrong.Terrestrials - A show for people of all ages that explores the strangeness that exists right here on Earth. In each episode, host Lulu Miller (co-host of Radiolab) will introduce you to a creature or earthly phenomenon that will defy your expectations of how nature is supposed to work. Along the way, you'll encounter a chorus of experts, including scientists, surfers, hip hop artists and…a "Songbud" named Alan (indie punk musician Alan Goffinski) who creates original songs for key moments of confusion, discovery or awe. New episodes drop Thursdays. Listen in with your whole family. Or all alone. This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

I want to dive into the concept of Deliberate Practice, which sets the greatest apart in fields ranging from sports to writing to engineering. I'll explain why it's much more than just repetition or experience, and why applying it to your career can lead to rapid improvement. Most importantly, I will provide concrete ways you can apply deliberate practice to level up your engineering and leadership skills, especially in areas that are traditionally difficult to practice, such as communication and strategic decision-making.Differentiate Practice from Deliberate Practice: Understand that while repetition is part of practice, deliberate practice specifically involves engaging in a very narrow set of activities with the intentional goal of improvement, requiring very quick feedback for continuous incorporation.Identify Opportunities for Rapid Improvement: Learn why deliberate practice is much more effective at achieving rapid improvement than simply engaging in repetition.Apply DP to Leadership Skills: Discover how to incorporate deliberate practice into roles like engineering manager, tech lead, or IC (Individual Contributor) leader, where the activity of practice is often harder to pinpoint.Leverage Existing Work for Practice: I suggest a mindset shift where you begin looking at existing responsibilities, such as one-on-ones, as opportunities for practice. For example, you can focus on improving your clarity when providing constructive criticism and ask for specific feedback on that aspect.Generate Novel Value Through Practice: Explore how engaging in deliberate practice activities—like recording a video to communicate a technical concept or creating documentation—serves the primary goal of practice, while almost certainly creating unexpected value for your team (often net neutral or positive).Use Backwards Training for Strategy: Find out how to practice strategic decision-making and forecasting by using "backwards training". This involves reviewing past decisions or work scopes, creating your own rationale or estimate, and then calibrating it against the known reality.Simulate Difficult Conversations: Consider leveraging Large Language Models (LLMs) to engage in deliberate practice around language-heavy skills, such as modelling sensitive or difficult topics, or practicing receiving harsh feedback.

CEO Abi Noda is joined by DX CTO Laura Tacho to discuss the evolving role of Platform and DevProd teams in the AI era. Together, they unpack how AI is reshaping platform responsibilities, from evaluation and rollout to measurement, tool standardization, and guardrails. They explore why fundamentals like documentation and feedback loops matter more than ever for both developers and AI agents. They also share insights on reducing tool sprawl, hardening systems for higher throughput, and leveraging AI to tackle tech debt, modernize legacy code, and improve workflows across the SDLC.Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda  • Substack: ​​https://substack.com/@abinoda  Where to find Laura Tacho: • LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro: Why platform teams need to evolve(02:34) The challenge of defining platform teams and how AI is changing expectations(04:44) Why evaluating and rolling out AI tools is becoming a core platform responsibility(07:14) Why platform teams need solid measurement frameworks to evaluate AI tools(08:56) Why platform leaders should champion education and advocacy on measurement(11:20) How AI code stresses pipelines and why platform teams must harden systems(12:24) Why platform teams must go beyond training to standardize tools and create workflows(14:31) How platform teams control tool sprawl(16:22) Why platform teams need strong guardrails and safety checks(18:41) The importance of standardizing tools and knowledge(19:44) The opportunity for platform teams to apply AI at scale across the organization(23:40) Quick recap of the key points so far(24:33) How AI helps modernize legacy code and handle migrations(25:45) Why focusing on fundamentals benefits both developers and AI agents(27:42) Identifying SDLC bottlenecks beyond AI code generation(30:08) Techniques for optimizing legacy code bases (32:47) How AI helps tackle tech debt and large-scale code migrations(35:40) Tools across the SDLCReferenced:DX Core 4 Productivity FrameworkMeasuring AI code assistants and agentsAbi Noda's LinkedIn postMeasuring AI code assistants and agents with the AI Measurement FrameworkThe SPACE framework: A comprehensive guide to developer productivityCommon workflows - AnthropicEnterprise Tech Leadership Summit Las Vegas 2025Driving enterprise-wide AI tool adoption with Bruno PassosAccelerating Large-Scale Test Migration with LLMs | by Charles Covey-Brandt | The Airbnb Tech Blog | MediumJustin Reock - DX | LinkedInA New Tool Saved Morgan Stanley More Than 280,000 Hours This Year - Business Insider

BONUS: Jochen Issing on Building High-Performing Engineering Teams In this BONUS episode, we explore the fascinating journey of Jochen Issing, an engineering leader who brings unique insights from his background as a handball player and band member to building exceptional software development teams. From sports courts and music stages to engineering leadership, Jochen shares practical wisdom on psychological safety, team dynamics, and creating cultures where the best ideas win. From Sports and Music to Software Leadership "As soon as you complain about each other, you are starting to lose." Jochen's unconventional background as a handball player and band member has profoundly shaped his approach to engineering leadership. Drawing from team sports, he discovered that frustration leads to losing in both athletics and technology work. Great players in great teams optimize for the team's results, not individual glory. This translates directly to software development where great engineers slow down to make the team faster, recognizing that collective success trumps individual achievement. The lesson from the handball court is clear: when team members start blaming each other, they create a losing mindset that becomes self-fulfilling. Breaking the 10X Engineer Myth "It's not your success that makes our success, it's our success that makes your success." The mythology of the 10X engineer remains pervasive in software development, but Jochen challenges this with insights from team dynamics. The "hero culture" in companies often emerges when systems are already broken, requiring someone to step in and save the day. While we celebrate these heroes, we forget to ask the crucial question: how did we end up needing a hero in the first place? True high-performing teams don't require heroic individual efforts because they've built sustainable systems and shared knowledge. The goal isn't to eliminate talented individuals but to ensure that even the most skilled engineers can take time off without the organization grinding to a halt. Creating Psychological Safety Through Vulnerability "When psychological safety is missing, I try to ask ignorant questions - expose myself as being the least experienced person in the room." Building psychological safety requires intentional strategies that go beyond good intentions. Jochen employs a counterintuitive approach: when he senses team members hesitating to speak up, he deliberately asks "ignorant" questions to position himself as the least knowledgeable person in the room. This modeling behavior demonstrates that it's safe to admit uncertainty and ask questions. He also builds a culture of "challenging ourselves" by implementing ritualized dissent - assigning someone the specific job of finding flaws in proposed solutions. This prevents the dangerous harmony that can emerge when teams agree too quickly without proper scrutiny. The Power of the Expectation Sheet "I want people to share with me what might even drive them away from the company." Trust forms the foundation of effective team relationships, but building it requires explicit frameworks. Jochen uses an "expectation sheet" (See a prototype here Google Doc)- a document that formalizes mutual expectations between him and his team members. This tool establishes that he wants open, honest communication about everything, including situations that might drive someone to leave the company. The key principle is that he will never share confidential information or use personal disclosures against team members. This creates a relationship where he serves as both a representative of the company when necessary and a personal advocate for his team members when they need support navigating organizational challenges. Team-Centric Productivity and Collaboration "The team is the unit of productivity and delivery, not the individual." Effective engineering leadership requires balancing individual desires with team outcomes. Jochen emphasizes that while people naturally want to say "I did this," the focus must remain on team impact. This involves creating shared understanding of collective goals while still addressing individual needs and growth aspirations. Practical strategies include using on-call rotations to identify knowledge silos, implementing pair programming and mob programming to reinforce collaborative work patterns, and designing tasks that allow individuals to take ownership while remaining embedded in team efforts. The analogy to band dynamics is apt - when someone brings a song idea to the band, it evolves through collaboration into something different and usually better than the original vision. Building Sustainable High Performance "Great engineers slow down to make the team faster - which is how we get better teams." Sustainable high performance emerges when senior engineers invest in lifting the entire team rather than maximizing their individual output. This means senior staff level engineers focus less on their personal contributions and more on forming "tribes" across teams, coaching junior engineers, and building organizational capability. The measure of success shifts from individual heroics to collective achievement - if problems consistently require the same person to fix them, the team hasn't truly succeeded in building sustainable systems and shared knowledge. Recommended Resources for Further Reading Jochen recommends several foundational books for understanding team dynamics and engineering leadership. "The Culture Code" by Daniel Coyle explores the structure of high-performing teams and debunks myths about command-and-control leadership. "Product Development Flow" by Reinertsen provides the scientific foundation behind agile methodologies and explains what teams are really trying to solve. "The Culture Map" by Erin Meyer offers insights on working with diverse cultures and backgrounds to bring out the best in each team member. "Coaching Agile Teams" by Lyssa Adkins serves as a practical guide for developing coaching skills in technical environments. And our very own Scrum Master Toolbox podcast provides ongoing insights and real-world experiences from practitioners in the field. About Jochen Issing Jochen is an engineering leader who's all about building great teams and better developer experiences. From audio tech and cloud platforms to monorepos and feedback culture, he's done it all. A former bandmate and handball player, Jochen brings heart, trust, and collaboration into everything he builds with his teams. You can connect with Jochen Issing on LinkedIn and connect with Jochen Issing on Twitter.

In this episode, host Laura Tacho speaks with Jesse Adametz, Senior Engineering Leader on the Developer Platform at Twilio. Jesse is leading Twilio's multi-year platform consolidation, unifying tech stacks across large acquisitions and driving migrations at enterprise scale. He discusses platform adoption, the limits of Kubernetes, and how Twilio balances modernization with pragmatism. The conversation also explores treating developer experience as a product, offering “change as a service,” and Twilio's evolving approach to AI adoption and platform support.Where to find Jesse Adametz: • LinkedIn: https://www.linkedin.com/in/jesseadametz/• X: https://x.com/jesseadametz• Website: https://www.jesseadametz.com/Where to find Laura Tacho:• LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact) https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro(01:30) Jesse's background and how he ended up at Twilio(04:00) What SRE teaches leaders and ICs(06:06) Where Twilio started the post-acquisition integration(08:22) Why platform migrations can't follow a straight-line plan(10:05) How Twilio balances multiple strategies for migrations(12:30) The human side of change: advocacy, training, and alignment(17:46) Treating developer experience as a first-class product(21:40) What “change as a service” looks like in practice(24:57) A mandateless approach: creating voluntary adoption through value(28:50) How Twilio demonstrates value with metrics and reviews(30:41) Why Kubernetes wasn't the right fit for all Twilio workloads (36:12) How Twilio decides when to expose complexity(38:23) Lessons from Kubernetes hype and how AI demands more experimentation(44:48) Where AI fits into Twilio's platform strategy(49:45) How guilds fill needs the platform team hasn't yet met(51:17) The future of platform in centralizing knowledge and standards(54:32) How Twilio evaluates tools for fit, pricing, and reliability (57:53) Where Twilio applies AI in reliability, and where Jesse is skeptical(59:26) Laura's vibe-coded side project built on Twilio(1:01:11) How external lessons shape Twilio's approach to platform support and docsReferenced:The AI Measurement FrameworkExperianTransact-SQL - WikipediaTwilioKubernetesCopilotClaude CodeWindsurfCursorBedrock

How do you apply your leadership skills to a new, mission-driven industry and effectively lead teams across multiple technical domains? In this episode, Simone Kalmakis (VPE @ Viam) shares her playbook for successfully transitioning between industries from health-tech and climate to her current work in robotics and AI. We deconstruct the leadership models she uses to prioritize her time, manage multiple technical experts, and why she focuses on "depth with 1-2 teams > breadth". Plus, her framework for onboarding in a new domain, the lifecycle of a leadership "deep dive," and communication practices that build trust and empower your entire organization to stay aligned and motivated.ABOUT SIMONE KALMAKISSimone Kalmakis is the VP of Engineering at Viam, a platform unlocking AI, data, and automation for devices in the physical world. She has deep experience applying AI and machine learning to big data and big missions, and is known for building healthy engineering organizations that drive business value and real-world progress.Prior to Viam, Simone was Senior Director of Engineering at Arcadia, a climate tech company building an API platform for residential utility data to power solutions that fight climate change. Before that, she served as Director of Engineering at Flatiron Health, where she helped accelerate the development of cancer treatments through real-world data.Simone began her career at Microsoft, developing machine-learned relevance algorithms for Bing. She's also a successful founder––after Microsoft, she built and sold Symbi, a roommate-matching startup. She holds a degree in Mathematics and Economics from Yale University. ToolHive Unlocks the Full Value of MCP & Your AI AgentsSo you've invested in AI agents for code generation, but they're limited to experiments or even stuck on the shelf. To do real, valuable work, those AI agents need access to your data and systems.ToolHive helps you confidently connect the pieces by making it simple and secure for you to use the Model Context Protocol (MCP).ToolHive includes a pre-vetted registry of MCP servers, containerizes every MCP server for consistency and leans on built-in security to keep your secrets safe.Leaders trust ToolHive to put MCP into production and put their AI agents to work.ToolHive is open source, so get started for free at toolhive.dev Join us at ELC Annual 2025ELC Annual is the premier event for engineering leaders. This is our biggest event of the year: 1,000+ CTOs, VPs & Directors in San Francisco @ ELC Annual 2025 for two days of leadership breakthroughs, tactical peer learning & curated connections!

CTO Series: Toni Sallanmaa on Scaling Engineering Teams and Aligning Tech with Business Goals In this BONUS episode, we explore the journey of scaling technology teams and maintaining alignment between engineering and business objectives with Toni Sallanmaa, CTO at Funidata. Toni shares invaluable insights from leading the development of Sisu, a cutting-edge student information system serving over 100,000 Finnish university users, and discusses practical strategies for growing engineering organizations while preserving company culture. The Genesis of Leadership in Technology "I understood what I was really responsible for. I'm interested in the business we are running—the business adds meaning to the work." Toni's approach to technology leadership was fundamentally shaped by a pivotal moment early in his career when he first gained influence over system development and technology choices. After working with large-scale systems for 20 years, this moment of responsibility revelation transformed his perspective from purely technical to business-focused. He emphasizes that infinite curiosity drives success in tech businesses, and understanding the business context gives meaningful purpose to technical work. Bridging the Gap Between Tech and Product "Don't separate Tech from Product. We established a common language between product and technology people." One of Toni's most significant insights centers on eliminating the traditional divide between technology and product teams. As Funidata grew from a small startup to a 70-person organization, the challenges of maintaining alignment became apparent. Their solution involved several key practices: Teaching developers the language of the product domain  Banning confusing technical terms that create communication barriers Workshopping product language to ensure clarity Keeping entity names deliberately vague until true understanding emerges This approach draws heavily from Domain Driven Design principles, creating a unified vocabulary that enables seamless collaboration. Collaborative Planning and Transparency "We use transparency as a collaboration technique. Every team sees what's being proposed as a goal for the next quarter." Funidata implements a unique "marketplace of goals" approach during their quarterly big room planning sessions. Rather than using scaled agile frameworks, they focus on transparency and collaborative goal-setting. Teams present their high-level quarterly plans to each other, creating visibility across the organization. Product owners are embedded within teams, keeping communication distances short and ensuring alignment between technical execution and business objectives. Future-Forward Roadmapping "We talk about the higher level ideas regularly, but let them bubble up from the community. We hold internal hackathons." Toni's approach to roadmapping balances strategic vision with grassroots innovation. They maintain an internal technology roadmap that addresses emerging trends like AI, while allowing ideas to organically emerge from the engineering community. Internal hackathons serve as catalysts for innovation, providing structured opportunities for teams to explore new technologies and approaches that might inform future roadmap decisions. Scaling Challenges and Cultural Preservation "The biggest challenge is not technology, it was the rapid scaling of technology teams. When you scale up, keep the culture in mind." The most significant challenge Toni faced wasn't technical but organizational—rapidly scaling teams while preserving company culture. Growing from 10 to 50 people required evolving processes, from establishing internal forums for architectural discussions to implementing continuous integration flows. The key was identifying pain points proactively and maintaining open discussions with team members throughout the scaling process. Strengthening company culture became essential to successful growth. AI's Impact on Software Development "Productivity is on the rise. We see opportunities like generating test data, but we have strict requirements for cybersecurity, which puts pressure on code quality." Toni views AI's impact on software development with cautious optimism. While productivity gains are evident, particularly in areas like test data generation, the stringent cybersecurity requirements in their domain mean that AI hasn't yet significantly improved code quality where it matters most. The technology shows promise, but implementation must be carefully considered within the context of security and quality requirements. Measuring Engineering Success "We use DORA and SPACE framework. We measure how much of our work is KTLO (Keep The Lights On) and how much is elective development." Funidata employs both DORA and SPACE frameworks to measure engineering organization success. From SPACE, they particularly focus on measuring software team wellbeing, while also tracking the balance between "Keep The Lights On" (KTLO) work and elective development. Using JIRA connected to a data warehouse, they mine extensive data that serves both leadership decision-making and team improvement efforts, ensuring metrics benefit everyone in the organization. Influential Leadership Resources "The organizational books have been more influential to me than purely technical ones." Toni emphasizes that organizational leadership books have shaped his CTO approach more than technical resources. Two key influences stand out: "Team Topologies" for understanding how to structure and scale engineering teams effectively, and "Radical Candor" for building authentic, productive relationships within the organization. You can find a BONUS episode on Team Topologies with the authors Matthew Skeltton and Manuel Pais. About Toni Sallanmaa Toni leads technology and engineering at Funidata, developing Sisu—a cutting-edge student information system serving over 100,000 Finnish university users. Passionate about agile methodologies, system architecture, and software engineering, Toni specializes in technology management, software lifecycle, OOP, and relational databases to deliver innovative, scalable solutions in higher education tech. You can connect with Toni Sallanmaa on LinkedIn.

In this episode of Engineering Enablement, host Laura Tacho talks with Bruno Passos, Product Lead for Developer Experience at Booking.com, about how the company is rolling out AI tools across a 3,000-person engineering team.Bruno shares how Booking.com set ambitious innovation goals, why cultural change mattered as much as technology, and the education practices that turned hesitant developers into daily users. He also reflects on the early barriers, from low adoption and knowledge gaps to procurement hurdles, and explains the interventions that worked, including learning paths, hackathon-style workshops, Slack communities, and centralized procurement. The result is that Booking.com now sits in the top 25 percent of companies for AI adoption.Where to find Bruno Passos:• LinkedIn: https://www.linkedin.com/in/brpassos/• X: https://x.com/brunopassosWhere to find Laura Tacho:• LinkedIn: https://www.linkedin.com/in/lauratacho/• X: https://x.com/rhein_wein• Website: https://lauratacho.com/• Laura's course (Measuring Engineering Performance and AI Impact) https://lauratacho.com/developer-productivity-metrics-courseIn this episode, we cover:(00:00) Intro(01:09) Bruno's role at Booking.com and an overview of the business (02:19) Booking.com's goals when introducing AI tooling(03:26) Why Booking.com made such an ambitious innovation ratio goal (06:46) The beginning of Booking.com's journey with AI(08:54) Why the initial adoption of Cody was low(13:17) How education and enablement fueled adoption(15:48) The importance of a top-down cultural change for AI adoption(17:38) The ongoing journey of determining the right metrics(21:44) Measuring the longer-term impact of AI (27:04) How Booking.com solved internal bottlenecks to testing new tools(32:10) Booking.com's framework for evaluating new tools(35:50) The state of adoption at Booking.com and efforts to expand AI use(37:07) What's still undetermined about AI's impact on PR/MR quality(39:48) How Booking.com is addressing lagging adoption and monitoring churn(43:24) How Booking.com's Slack community lowers friction for questions and support(44:35) Closing thoughts on what's next for Booking.com's AI planReferenced:Measuring AI code assistants and agentsDX Core 4 FrameworkBooking.comSourcegraph SearchCody | AI coding assistant from SourcegraphGreyson Junggren - DX | LinkedIn

"If we were building Box today, what would we do?” Ben Kus (CTO @ Box) deconstructs their playbook for enterprise AI innovation. We cover their journey to reimagine & reorient the company to a new technical vision, how they run a “multi-speed” org that balances startup agility and & enterprise-grade stability, and their “platform first” approach to build AI features. Ben also explains why security/compliance was foundational from "day negative one" in their AI strategy, the evolution of agentic AI, determining the right guardrails for AI agents & the future of multi-agent systems, enterprise trends & more. ABOUT BEN KUSBen Kus is the Chief Technology Officer at Box, where he leads technology and AI strategy to help enterprises securely unlock insights from their unstructured data. Ben's career spans engineering, product leadership, and startup innovation—including co-founding Subspace (acquired by Box) and being an early employee at BigFix (acquired by IBM), where he later served as Chief Architect of Mobile Security. Ben holds a degree in Computer Science from UC Berkeley. ToolHive Unlocks the Full Value of MCP & Your AI AgentsSo you've invested in AI agents for code generation, but they're limited to experiments or even stuck on the shelf. To do real, valuable work, those AI agents need access to your data and systems.ToolHive helps you confidently connect the pieces by making it simple and secure for you to use the Model Context Protocol (MCP).ToolHive includes a pre-vetted registry of MCP servers, containerizes every MCP server for consistency and leans on built-in security to keep your secrets safe.Leaders trust ToolHive to put MCP into production and put their AI agents to work.ToolHive is open source, so get started for free at toolhive.dev Join us at ELC Annual 2025ELC Annual is the premier event for engineering leaders. This is our biggest event of the year: 1,000+ CTOs, VPs & Directors in San Francisco @ ELC Annual 2025 for two days of leadership breakthroughs, tactical peer learning & curated connections!

Engineering leadership is undergoing a seismic shift, requiring playbooks to be rewritten, in real-time. In this special episode, hosts Patrick Gallagher and Jerry Li give you an inside look at the ELC Annual 2025 experience, and how the two-day conference will equip you with new mental models, skills, and frameworks required to lead.Get a preview of tactical takeaways from deep operational dives into companies like OpenAI, Amplitude, and HeyGen. Discover how the conference will help you redesign your innovation engine, transform your team's workflows, and blur the lines between engineering, product, and business to drive impactful change. Through a unique mix of tactical sessions, peer-led roundtables, and curated mentorship, you'll learn how to find the community and coaching needed to lead through uncertainty and invest in your own career growth.To learn more & get tickets, go to sfelc.com/annual2025Use code podcast15 for 15% off tickets - group tickets / discounts available. ABOUT ELC ANNUALThe playbook for engineering leadership is being rewritten. ELC Annual 2025, happening September 10-11 in San Francisco, is where you'll gain the insights, strategies, and deep connections needed to lead in this new era. 50+ speakers, 50+ peer-led roundtables discussions, 1:1 matching to expand your network. Insights, connections & support.Join the community of engineering leaders who are co-creating the future of our field.Listener Discount → Use code podcast15 for 15% offGroup Tix → For teams looking to attend together, special group discounts can be found under the 'Tickets' section of our website!Secure your ticket at sfelc.com/annual2025 ToolHive Unlocks the Full Value of MCP & Your AI AgentsSo you've invested in AI agents for code generation, but they're limited to experiments or even stuck on the shelf. To do real, valuable work, those AI agents need access to your data and systems.ToolHive helps you confidently connect the pieces by making it simple and secure for you to use the Model Context Protocol (MCP).ToolHive includes a pre-vetted registry of MCP servers, containerizes every MCP server for consistency and leans on built-in security to keep your secrets safe.Leaders trust ToolHive to put MCP into production and put their AI agents to work.ToolHive is open source, so get started for free at toolhive.dev Join us at ELC Annual 2025ELC Annual is the premier event for engineering leaders. This is our biggest event of the year: 1,000+ CTOs, VPs & Directors in San Francisco @ ELC Annual 2025 for two days of leadership breakthroughs, tactical peer learning & curated connections!

This is the Engineering Culture Podcast, from the people behind InfoQ.com and the QCon conferences. In this podcast, Shane Hastie, Lead Editor for Culture & Methods, spoke to Thiago Ghisi about building engineering culture through leading by example, advancing careers by embracing "glue work" (non-technical but necessary tasks), taking full ownership of projects, and developing self-awareness to choose between technical and management career paths. Read a transcript of this interview: http://bit.ly/3Uen9jV Subscribe to the Software Architects' Newsletter for your monthly guide to the essential news and experience from industry peers on emerging patterns and technologies: https://www.infoq.com/software-architects-newsletter Upcoming Events: InfoQ Dev Summit Munich (October 15-16, 2025) Essential insights on critical software development priorities. https://devsummit.infoq.com/conference/munich2025 QCon San Francisco 2025 (November 17-21, 2025) Get practical inspiration and best practices on emerging software trends directly from senior software developers at early adopter companies. https://qconsf.com/ QCon AI New York 2025 (December 16-17, 2025) https://ai.qconferences.com/ QCon London 2026 (March 16-19, 2026) https://qconlondon.com/ The InfoQ Podcasts: Weekly inspiration to drive innovation and build great teams from senior software leaders. Listen to all our podcasts and read interview transcripts: - The InfoQ Podcast https://www.infoq.com/podcasts/ - Engineering Culture Podcast by InfoQ https://www.infoq.com/podcasts/#engineering_culture - Generally AI: https://www.infoq.com/generally-ai-podcast/ Follow InfoQ: - Mastodon: https://techhub.social/@infoq - X: https://x.com/InfoQ?from=@ - LinkedIn: https://www.linkedin.com/company/infoq/ - Facebook: https://www.facebook.com/InfoQdotcom# - Instagram: https://www.instagram.com/infoqdotcom/?hl=en - Youtube: https://www.youtube.com/infoq - Bluesky: https://bsky.app/profile/infoq.com Write for InfoQ: Learn and share the changes and innovations in professional software development. - Join a community of experts. - Increase your visibility. - Grow your career. https://www.infoq.com/write-for-infoq

In this episode of Engineering Enablement, DX CTO Laura Tacho and CEO Abi Noda break down how to measure developer productivity in the age of AI using DX's AI Measurement Framework. Drawing on research with industry leaders, vendors, and hundreds of organizations, they explain how to move beyond vendor hype and headlines to make data-driven decisions about AI adoption.They cover why some fundamentals of productivity measurement remain constant, the pitfalls of over-relying on flawed metrics like acceptance rate, and how to track AI's real impact across utilization, quality, and cost. The conversation also explores measuring agentic workflows, expanding the definition of “developer” to include new AI-enabled contributors, and avoiding second-order effects like technical debt and slowed PR throughput.Whether you're rolling out AI coding tools, experimenting with autonomous agents, or just trying to separate signal from noise, this episode offers a practical roadmap for understanding AI's role in your organization—and ensuring it delivers sustainable, long-term gains.Where to find Laura Tacho:• X: https://x.com/rhein_wein• LinkedIn: https://www.linkedin.com/in/lauratacho/• Website: https://lauratacho.com/Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda • Substack: ​​https://substack.com/@abinoda In this episode, we cover:(00:00) Intro(01:26) The challenge of measuring developer productivity in the AI age(04:17) Measuring productivity in the AI era — what stays the same and what changes(07:25) How to use DX's AI Measurement Framework (13:10) Measuring AI's true impact from adoption rates to long-term quality and maintainability(16:31) Why acceptance rate is flawed — and DX's approach to tracking AI-authored code(18:25) Three ways to gather measurement data(21:55) How Google measures time savings and why self-reported data is misleading(24:25) How to measure agentic workflows and a case for expanding the definition of developer(28:50) A case for not overemphasizing AI's role(30:31) Measuring second-order effects (32:26) Audience Q&A: applying metrics in practice(36:45) Wrap up: best practices for rollout and communication Referenced:DX Core 4 Productivity FrameworkMeasuring AI code assistants and agentsAI is making Google engineers 10% more productive, says Sundar Pichai - Business Insider

What if your engineering team didn't just write code, but owned product discovery, wrote the launch messaging, and handled early sales? In this episode, Michael Grinich, CEO and founder of WorkOS, deconstructs their playbook for collapsing the product/engineering stack: no design leads, only one PM, and engineers who own product end-to-end. Michael breaks down how they teach product thinking, build with deep customer insight, and why his most important job is often to "cut scope." You'll learn how to remove the "lossy translation layers" between teams, build a culture of curiosity and customer obsession, and ship higher-quality products, faster.ABOUT MICHAEL GRINICHMichael is the founder and CEO of WorkOS, a developer platform that enables companies to become Enterprise Ready through features like Single Sign-On (SAML). Their customers include many of the fastest-growing startups including Webflow, Drata, Loom, and +200 others. Before WorkOS, Michael co-founded Nylas and studied CS at MIT. ToolHive Unlocks the Full Value of MCP & Your AI AgentsSo you've invested in AI agents for code generation, but they're limited to experiments or even stuck on the shelf. To do real, valuable work, those AI agents need access to your data and systems.ToolHive helps you confidently connect the pieces by making it simple and secure for you to use the Model Context Protocol (MCP).ToolHive includes a pre-vetted registry of MCP servers, containerizes every MCP server for consistency and leans on built-in security to keep your secrets safe.Leaders trust ToolHive to put MCP into production and put their AI agents to work.ToolHive is open source, so get started for free at toolhive.dev Join us at ELC Annual 2025ELC Annual is the premier event for engineering leaders. This is our biggest event of the year: 1,000+ CTOs, VPs & Directors in San Francisco @ ELC Annual 2025 for two days of leadership breakthroughs, tactical peer learning & curated connections!

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: •

Influencing without authority is the hidden superpower of security leadership—and a crucial skill every engineering leader must master. In this episode, Srinath Kuruvadi (Head of Cloud Security @ JPMorgan Chase) breaks down how to influence without formal authority and advocate when ROI isn't immediately clear. We cover tactics for shaping problems from the POV of other stakeholders, Plus, strategies to establish shared outcomes, insights on optimizing your time, emerging AI x security trends, and how his team is operationalizing curiosity & experimentation through The Innovation Lab.ABOUT SRINATH KURUVADISrinath Kuruvadi is a globally recognized cybersecurity executive and cloud security leader with over two decades of experience driving security innovation at some of the world's most influential technology companies, including Netflix, Meta, Google, Lyft, and JPMorgan Chase. Currently serving as Managing Director and Head of Cloud Security at JPMorgan Chase, he leads the enterprise-wide security strategy across APIs, containers, and cloud platforms, shaping the future of banking technology.Srinath's approach blends deep technical expertise with executive-level risk management. At Netflix, he headed cloud security for one of the largest AWS environments globally, pioneering scalable governance and identity systems that supported massive data throughput. At Meta and Google, he led the development of custom infrastructure security systems protecting billions of users, including Facebook's Blackbird SIEM+SOAR platform.Beyond his executive roles, Srinath is a strategic advisor and angel investor, with five successful startup exits including Bridgecrew, Lightspin, Oxeye, Gem Security, and Kivera. He is also a trusted advisor to venture capital firms like YL Ventures and Glilot Capital Partners, and served on Amazon's Global CISO Advisory Council.He holds multiple patents in web application security, database protection, and abuse detection, and has authored research on algorithmic solutions in industrial systems. Srinath is also multilingual and committed to lifelong learning, exemplified by a sabbatical that took him to over 35 countries for cultural, linguistic, and creative growth.With a Master's degree in Computer Science from North Carolina State University and a Bachelor's from BITS Pilani, Srinath is known for transforming security from a blocker into a business accelerator.Join us at ELC Annual 2025ELC Annual is the premier event for engineering leaders. This is our biggest event of the year: 1,000+ CTOs, VPs & Directors in San Francisco @ ELC Annual 2025 for two days of leadership breakthroughs, tactical peer learning & curated connections!

In this episode, I talk with Everett Litton, P.E., Vice President at WSP in the U.S., about what powers major underground construction engineering projects across North America and beyond, from early career moments that shaped his leadership to the role of mentorship in driving the industry forward. Engineering Quotes: Here Are Some of the Questions […] The post Underground Construction Engineering Leadership Insights That Drive Purpose and Impact – Ep 293 appeared first on Engineering Management Institute.

Product Driven: The Book Launch - A Software Leader's Journey to Creating High-Performance Teams

Send us a textHow do you lead 950 engineers and still make time for mechanical watches, family, and learning? In this thoughtful and energizing episode from IT Nation Secure 2025, Joey Pinz interviews David Raissipour, Chief Technology & Product Officer at ConnectWise.