How leaders win over their team’s biggest AI skeptics | Superhuman’s Loic Houssier

Forget top-down mandates. How do you foster organic AI adoption on a skeptical, high-performing engineering team? Loic Houssier, Head of Engineering at Superhuman, joins us to share how he did just that. He explains his strategy for overcoming cynicism, which involved leveraging a highly respected internal champion, the Chief Architect, to re-evaluate the tools and prove their potential was no longer just buzz.Discover his team's biggest, unexpected productivity gains: dramatically faster ramp-up times on new codebases and the rapid creation of valuable internal tools, rather than just raw code generation speed. Loic details Superhuman's pragmatic, high-trust approach to measurement, blending qualitative feedback with simple signals like PR labels. He explains how they fostered this adoption with a bottom-up "AI Guild," empowering engineers of all seniorities without a heavy-handed mandate. He also reveals a stunning real-world example where AI turned a potentially days-long compliance task into a 90-minute win. This episode is a practical playbook for building genuine, bottom-up AI adoption.Get the guide: AI productivity guide for engineering leadersFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Connect with Loïc HoussierLearn more about Superhuman: superhuman.comSuperhuman was acquired by Grammarly: Read about the acquisitionReferenced in today's show:Amazon brain drain finally caught up with AWSClaude Skills are awesome, maybe a bigger deal than MCP How to Say NoThe Field Guide to AI SlopSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

Is your company using AI to trim your budget, or to multiply your team's impact? We're joined by Matt Culver, a senior engineering leader at Super.com, to discuss why the common view of AI as a tool for cost-cutting is a misguided "accounting mindset" that ultimately destroys trust. He argues that leaders should instead see efficiency gains from AI as a powerful opportunity to reinvest in their teams. This conversation reframes the AI debate by urging leaders to look beyond the coding loop to improve the entire product development lifecycle—from ideation to delivery.Matt explains that the key to successful AI adoption is aligning new initiatives with the developer's core incentives: removing friction and enabling the creative flow state that makes their job enjoyable. He provides a human-centric approach for channeling AI's power to solve upstream problems in product planning and market research, rather than just generating more code. Learn how to use AI not as a means to an end, but as a way to empower your developers, generate more value, and build a high-trust engineering culture.Bring AI into your code review process with LinearBFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Super: Super.comConnect with Matt: LinkedIn | [email protected] in today's show:A small number of samples can poison LLMs of any sizeRevisiting "Intelligence Drift"AI profiteering is now indistinguishable from trolling The AI water issue is fakeHTML’s Best Kept Secret: The <output> Tag Life in a World of Pervasive Immorality: The Ethics of Being AliveSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

With massive context windows and new agent frameworks, do vector databases still matter? Ram Sriharsha, CTO at Pinecone, joins the conversation to make the definitive case that they're more critical than ever. He explains that at the core of all AI is search, and externalizing this function is non-negotiable for security, auditability, and control.Ram offers a clear starting path for engineering leaders: begin with simple Retrieval-Augmented Generation (RAG) applications, but immediately implement a robust evaluation framework to manage hallucinations and ensure quality. He shares his perspective on the skills that matter most now, arguing that curiosity and the rise of the generalist engineer are critical in an AI-powered world. This episode is a guide to building the AI stack from the ground up, from using AI as a "good junior engineer" for testing to cultivating the engineering mindset of tomorrow.Bring AI into your code review process with LinearBFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Pinecone: pinecone.ioPinecone Tutorials & Blog: Explore guides on RAG, vector databases, and moreConnect with Ram Sriharsha: LinkedInReferenced in today's show:Your New 500K AI Coworkers Just ArrivedHow I influence tech company politics as a staff software engineerCoinbase says 40% of code written by AI, mostly tests and TypescriptCelebrating 1 Trillion Web Pages ArchivedSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

Vibe coding is a developer's dream, but in the enterprise, it can be a nightmare of risk and shadow IT. So how do you saddle the 'wild horse' of modern AI development? Dan Fernandez, VP of Product Management, Developer Services at Salesforce, joins the conversation to share the answer: a new category his team is pioneering called Enterprise Vibe Coding. This discussion reveals how to move beyond flashy greenfield AI demos and build for the reality of most enterprises, where the goal is to safely reuse existing systems, not reinvent them from scratch.Dan breaks down the specific guardrails Salesforce has built, from sandboxed environments for safe testing to automated "quality gates" that act as a bouncer for both human and AI-generated code. He shares the powerful lesson that building customer trust through policies like zero data retention is more important than any single feature. He explains why the real work of enterprise AI is more like secure "plumbing"—connecting the hardened systems you already have. This is an essential guide for any leader looking to apply the speed of AI to the complex reality of enterprise software.Get the guide: AI productivity guide for engineering leadersFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Unleash Your Innovation with Agentforce Vibes: Vibe Coding for the EnterpriseLearn more about Salesforce for Developers: developer.salesforce.comSalesforce Extensions for VS Code: VS Code MarketplaceSalesforce Code Builder: Learn more about the zero-install IDEConnect with Dan Fernandez: LinkedInReferenced in today's show:Future of tech leadership survey report 2025 - Riviera PartnersStop Avoiding Politics – Terrible Software Agentic Commerce I built ChatGPT with Minecraft redstone!Support the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

The Model Context Protocol (MCP) is evolving beyond local developer experiments and into the secure, remote infrastructure that will power the next generation of the internet. Brendan Irvine-Broque, Director of Product at Cloudflare, joins us to share a roadmap for this future. He explains how Cloudflare's "customer zero" philosophy of dogfooding their own tools provides a unique perspective on what it takes to scale MCP for production.Brendan makes the case for observability as the ideal starting point for enterprises and lays out the vision for MCP's ultimate destination: a universal protocol for agent-to-agent communication. The conversation explores how remote servers can create a decentralized layer for security and user memory, and what the exciting development of MCP UI means for the future of chat-based applications. This is an essential look at the next wave of agentic systems and the infrastructure required to build it.Check out:Watch Closing the AI gap: Surpassing executive expectations for AI productivityFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Cloudflare's work with AI: agents.cloudflare.comRead the latest from Cloudflare: The Cloudflare BlogCloudflare's Unique Primitives Mentioned: Durable ObjectsThe MCP UI Project: MCP UI on GitHub (Project by Ido Salomon)Observability Tools Mentioned: Datadog | HoneycombAI Tools Mentioned: Block/Square's "Goose" | CursorConnect with Brendan Irvine-Broque: X @irvinebroque | LinkedInReferenced in today's show:AI Has Won: Google’s DORA Study Shows Universal Dev AdoptionThe Theatre of Pull Requests and Code ReviewAI isn't replacing radiologistsIs it time to look for a new job? And how do I start?Support the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

What do you learn after spending 15 years at Apple and demoing your work directly to Steve Jobs? Ken Kocienda, Co-founder of Infactory AI and author of Creative Selection, joins us to share the answer. As a former Principal Engineer at Apple who helped create the iPhone keyboard and autocorrect, Ken discusses his incredible journey from a history major to a key figure in building technology used by billions. He explains his core philosophy of bridging the gap between the liberal arts and technology to create meaningful products, and why he believes AI is the next frontier for this mission. (BTW – we sat down with his co-founder Brooke, so if you like this episode be sure to check that one out!)The conversation dives into his disciplined, spec-driven approach to coding with AI and the power of "extractive AI" to unlock hidden value in data. Ken reveals the crucial lesson he learned from Steve Jobs—that "everything is provisional"—and how his "evolutionary design" process is perfectly suited for today's AI challenges. This episode is a deep dive into the timeless principles of design and a powerful argument for why the best technology is so intuitive, it makes technical literacy irrelevant.Check out:Register now: AI productivity guide for engineering leadersFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Infactory AI: infactory.aiConnect with Ken on LinkedInKen's Book: Creative Selection: Inside Apple's Design Process During the Golden Age of Steve JobsReferenced in today's show:MCP is probably the first protocol in tech history with more builders than users… or at least that’s how it feels.Albania appoints world’s first AI-made ministerSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

What does it take to transform a brilliant AI model from a research paper into a product customers can rely on? We're joined by Elizabeth Lingg, Director of Applied Research at Contextual AI (the team behind RAG), to explore the immense challenge of bridging the gap between the lab and the real world. Drawing on her impressive career at Microsoft, Apple, and in the startup scene, Elizabeth details her journey from academic researcher to an industry leader shipping production AI. Elizabeth shares her expert approach to measuring AI impact, emphasizing the need to correlate "inner loop" metrics like accuracy with "outer loop" metrics like customer satisfaction and the crucial "vibe check." Learn why specialized, grounded AI is essential for the enterprise and how using multiple, diverse metrics is the key to avoiding model bias and sycophancy. She provides a framework for how research and engineering teams can collaborate effectively to turn innovative ideas into robust products. Check out:Register now: Closing the AI gap: Exceeding executive expectations for AI productivityFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Contextual AI: Contextual.ai WebsiteFollow Contextual AI on Social Media: LinkedIn | X (formerly Twitter)Connect with Elizabeth: LinkedInReferenced in today's show:Throwing AI at Developers Won’t Fix Their ProblemsWhy language models hallucinatei ran Claude in a loop for three months, and it created a genz programming language called cursedSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

AI that talks is easy, but AI that acts securely is where everything breaks down. We're joined by Alex Salazar, CEO of Arcade, to confront the massive and often underestimated gap between a flashy AI demo and a production-ready system. Drawing from his team's own pivot from building agents to building the tools that secure them, he explains why a working demo is only 1% of the journey. Alex breaks down the four "demo killers" that cause most agent projects to fail: inconsistency, security flaws, prohibitive costs, and high latency.Alex reveals the counterintuitive solution his team discovered: the key to making non-deterministic AI reliable is to dial up determinism. Learn why giving an AI a constrained set of intention-based tools - like a calculator or a multiple-choice test - dramatically reduces errors and solves critical security challenges that plague open-ended systems. He explains why you can't just wrap existing APIs and must instead build custom, workflow-centric tools for your agents. This is an essential listen for anyone who wants to build AI that doesn't just talk, but acts securely on behalf of your users.Check out:Register now: Closing the AI gap: Exceeding executive expectations for AI productivityFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Arcade: Arcade.devArcade's YouTube Channel: Watch examples and walkthroughs on building agentsConnect with Alex Salazar: LinkedInReferenced in today's show:Google avoids break-up but must share data with rivals Welcoming The Browser Company to Atlassian "~40% of daily code written at Coinbase is AI-generated. I want to get it to >50% by October." on X Crushing JIRA tickets is a party trick, not a path to impact Support the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

For decades, the command line has been a developer's staple. But what if its future isn't to be a better terminal, but something else entirely? We're joined by Zach Lloyd, co-founder of Warp, to discuss this groundbreaking shift in developer tooling, sharing his bold vision that the future for developers is neither the IDE nor the terminal, but a new kind of platform built for launching and orchestrating AI agents. Zach explains how Warp is re-imagining the command line as the natural entry point for this evolution, transforming it from a place where you type commands to a place where you write prompts to solve complex problems.Zach dives into the new developer workflow, where the focus moves up a layer of abstraction from the minutiae of flags and syntax to higher-level problem-solving and guiding agents. He argues that by being the platform itself—not just an app running within it—a tool like Warp can provide a far richer and more effective user experience than traditional CLI agents. Discover the new skills developers need in this era, from problem decomposition to clearly expressing intent in natural language.Check out:AI code review tools: 2025 evaluation guideFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Try Warp: warp.devWarp's YouTube Channel: Check out Zach's live streams and product videosConnect with Zach: LinkedIn | X (formerly Twitter)Connect with Brooke: LinkedInReferenced in today's show:Team OKRs in ActionIn the Age of AI, some tech leaders think communications degrees may actually be more valuable than computer science degreesThoughts on Motivation and My 40-Year CareerAI, Ads & the Fight for Attention: Infactory Featured in Ad AgeIntroducing pay per crawl: Enabling content owners to charge AI crawlers for accessSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever

First, there was vibe coding. Now, get ready for "vibe entrepreneurship." Andrew McNamara, Director of Applied Machine Learning at Shopify, joins us to explain how his team is making this new era of business a reality. He shares the vision behind Shopify Sidekick, an AI co-founder designed to empower merchants by acting as their on-demand e-commerce expert. Drawing on his 16-year journey building AI assistants, Andrew reveals what it truly takes to create an AI tool that customers can trust with their livelihood.He shares a critical insight from his applied research background: the hardest and most important part of building production-ready AI isn't the model, but the evaluation ("eval") system. Andrew breaks down Shopify's innovative approach of using an LLM-as-a-judge to measure how well they're fulfilling user goals, not just executing features. This conversation is a definitive guide to creating high-trust AI systems and offers a powerful glimpse into the future of commerce.Check out:Register now: Closing the AI gap: Exceeding executive expectations for AI productivityFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Follow Andrew on XFollow Andrew on LinkedInAndrew's Talk at ICMLLearn more about Shopify SidekickFollow Fatima on LinkedInReferenced in today's show:The Economics of Software Innovation“AI First” and the Bus Factor of 0GitLab 18.3 released with Duo Agent Platform in Visual Studio (Beta) and Embedded viewsSupport the show: Subscribe to our Substack Leave us a review Subscribe on YouTube Follow us on Twitter or LinkedIn Offers: Learn about Continuous Merge with gitStream Get your DORA Metrics free forever