The Pluralsight Podcast

GitHub Unleashed: LLMs on Guard | Tim Warner

What happens when you don't just ask one LLM to check your work, but three, and then make them vote? In this episode of The Pluralsight Podcast, we sit down with Tim Warner, Principal Pluralsight Author, Microsoft MVP, and author of more than 200 Pluralsight courses, to talk about what it takes to stay current through three decades of tech disruption, and why gen AI is just the latest wave to ride rather than fear. Tim breaks down what technology and L&D leaders get wrong in the AI gold rush, why shadow AI is the new shadow IT, and how to pair training with guardrails and sandboxes your teams will actually use. Then Tim takes us inside GitHub for a live demo: a GitHub Actions workflow that hands agent and skill definitions to three different LLMs, which scan for prompt injection and other vulnerabilities, then vote on a verdict through a simple quorum mechanism. It's a practical look at defense in depth for AI-assisted development, explained in plain language. Chapters: 01:42 Eternally curious: 30 years in IT 05:07 Know yourself, do your best 07:11 The eternal student persona 09:04 Learning in public 10:57 Underestimated fundamentals and agentic AI risk 13:00 AI councils, not fear-based reactivity 15:24 Shadow AI is the new shadow IT 17:09 Closing the upskilling gap 19:41 The skills leaders underestimate 21:05 Prompt vs. context engineering 22:29 What GitHub actually is 26:17 GitHub Actions explained 29:32 Tim's multi-LLM research workflow 32:53 When LLMs disagree 35:15 Multi-model on a budget 38:30 Is on-prem AI coming back? 39:52 Getting started responsibly 43:38 Demo: LLM quorum security scanning in GitHub 53:29 Security risks and data handling 56:14 Enterprise guardrails and orchestration 59:47 Advice: curiosity over fear 1:02:06 Where to find Tim Connect with Tim Warner:TechTrainerTim LinkedIn: https://www.linkedin.com/in/timothywarner/ Website: https://techtrainertim.com Pluralsight courses: https://app.pluralsight.com/profile/author/tim-warner Stay up to date on everything happening in cloud, AI, and security. Sign up for our weekly newsletter: https://www.pluralsight.com/technews/

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Customer Spotlight | SEB

One leads technology. One leads learning. Same bank, same transformation. In this episode of The Pluralsight Podcast, Josh sits down with Kimberly Lejonö, technology leader inside SEB's corporate credit process, and Gabriela Almonacid, L&D Partner for Global Business Services, to trace how two functions inside one of the largest banks in the Nordics are shaping the same AI transformation from entirely different vantage points. What surfaces is a series of connections neither had fully drawn before: where their work already overlaps, where old assumptions don't hold up, and where the real opportunity sits waiting between tech and L&D. We explore: Why 80 to 90 percent of an AI transformation is people work, not tech work, and what that demands from leaders on both sides of the business The calendar move that put a weekly learning hour in front of 3,500 employees at once, and why it had to clear four boards to happen SEB's step-by-step path to AI fluency for non-technical roles: Tech Foundations, hands-on workshops, ideation labs, and a role-based AI Academy How a developer with zero agent experience shipped to production in one month, and why your first use case should be finishable, not flashy Why manager engagement in learning nearly tripled in three years (hint: people don't listen to HR, they listen to their manager) What tech leaders and L&D leaders consistently miss, and why consensus culture pays back ten times at the end Connect with Kimberly Lejonö: https://www.autogram.id/kimberly Connect with Gabriela Almonacid: https://www.autogram.id/gabriela?card=open Chapters: 02:02 Meet Kimberly and Gabriela of SEB 05:32 Two roles, one goal: democratizing technology 09:17 The hardest part of scaling AI is people 12:31 Automating the credit process 14:18 A learning hour in 3,500 calendars 19:21 From proof of concept to production 21:39 Skills planning for 20,000 employees 24:04 Ideation labs and the first agents shipped 27:06 Building AI fluency step by step 28:56 What we wish we'd known about AI adoption 34:00 The future workforce 36:13 Will we lose the basic skills? 40:23 Logged hours vs. real learning 42:52 Why manager engagement nearly tripled 44:59 Risk, experimentation, and intentional mistakes 49:05 The Tech Foundations story 52:32 Consensus culture and the mirror exercise 56:20 Leading distributed teams through change 1:00:55 What tech and L&D misunderstand about each other 1:04:47 Measuring what matters 1:07:29 What winning looks like Stay up to date on everything from AI and cloud to cybersecurity with our weekly newsletter: https://www.pluralsight.com/technews/ #PluralsightPodcast #Pluralsight #AI #Upskilling #LearningAndDevelopment

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Certified Doesn't Mean Qualified | Andru Estes

We've seen this movie before. The FOMO, the holdouts, the surprise bills. In this episode of The Pluralsight Podcast, Josh sits down with Andru Estes, Pluralsight author and cloud architect, to unpack why today's AI gold rush looks so much like the early days of cloud adoption, and how to avoid repeating the same expensive mistakes. We explore: Why so many AI projects fail in the planning phase, and the first question every team should ask: do we even need this tool? Where AI and cloud costs spiral unexpectedly (hint: it's all about data), and how a FinOps mindset with real-time visibility keeps experimentation governed instead of gated Why being certified doesn't mean being qualified, and the hands-on habits that actually close the gap The hidden cost of AI coding tools: skill atrophy, senior engineers stuck babysitting, and the vibe coding cleanup Andru sees coming Common AWS design mistakes, from lift-and-shift bill shock to skipping the planning phase entirely The builder's mindset: discipline over motivation, failure as one of the best teachers, and why there's no such thing as a dumb question Connect with Andru Estes: https://www.linkedin.com/in/andru-estes/ Explore Andru's courses on Pluralsight: https://www.pluralsight.com/resources/blog/blog-author/andru-estes Stay up to date on everything from AI and cloud to cybersecurity with our weekly newsletter: https://www.pluralsight.com/technews/ Check out more episodes and subscribe: https://www.pluralsight.com/resources/podcasts/the-pluralsight-podcast Questions or comments? podcast@pluralsight.com Chapters: 1:55 Why the AI wave feels like early cloud adoption 4:13 Repeating old mistakes: do you even need the tool? 6:07 What leaders miss: governance, security, and infrastructure 8:38 Signals you're adopting AI the right way 11:36 Where costs spiral: data, FinOps, and guardrails 14:34 Certified doesn't mean qualified 16:41 Becoming capable, not just credentialed 19:29 Splitting time between certs and stretch projects 20:57 AI coding tools, skill atrophy, and tech debt 24:17 The coming vibe coding cleanup 25:17 Common AWS design mistakes 26:32 What a successful planning phase looks like 29:57 Overengineering vs. smart future-proofing 32:00 Staying current with the KISS approach 33:19 The Kubernetes hot take 34:15 Mindset advice for cloud engineers 35:58 Failing in production: a story 39:32 Connect with Andru

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Inside the 100% AI Agent Company | Matt Kropp

What actually happens when you try to run a company with zero humans? Matt Kropp, Senior Partner and Chief AI Officer at BCG X, is finding out firsthand. His experiment, Vessica Labs, is a company where AI agents set the strategy, write the code, design the brand, and run the operations. Matt's role? The agents dubbed him "the governor." We dig into what the experiment has revealed so far, including the moment his sales agent declared its pronouns were "he" because "men are closers," and what that says about bias hiding in the systems enterprises are deploying right now. We cover the BCG research behind "AI brain fry," why framing AI as an "employee" measurably degrades human performance, and the coaching model that actually gets skeptical teams to adopt AI, when training alone fails. In this episode: The Vessica Labs experiment: what a zero-human company can and can't do Why BCG's research says you should not have "AI employees" Bias in AI systems: how to measure it, baseline it, and monitor it before it scales AI brain fry: the new cognitive risk emerging in agent-heavy workflows Why tool rollouts and training fail without coaching, and what to do instead The "barbell" talent strategy: why some CEOs are doubling down on entry-level hires Minimizing toil and maximizing joy when redesigning work around AI Connect with Matt on LinkedIn: https://www.linkedin.com/in/matt-kropp/ Follow the Vessica Labs experiment on Matt's Substack: https://substack.com/@mattkropp Read Matt's "AI Brain Fry" article on Harvard Business Review: https://hbr.org/2026/03/when-using-ai-leads-to-brain-fry Chapters: 1:45 Who is BCG X, and what does a Chief AI Officer do? 3:06 Do consultants just mean headcount cuts? 4:55 Why LLMs didn't replace consultants 7:52 Vessica Labs: the 100% AI agent experiment 9:02 The 1,000-manager study: why you should not have "AI employees" 11:59 Meet the agents: Apex, Muse, Echo, Nova, and Forge 14:42 What the governor actually does: the taste problem 18:53 "Men are closers": when bias pops right out 21:56 How to measure, baseline, and monitor bias 23:42 Who profits from a 99% AI-run company? 25:20 AI brain fry: the new cognitive overload 29:00 Accountability: a human must own agent decisions 31:33 Avoiding brain fry: build in breaks 32:52 The state of enterprise AI: from 1,000 flowers to big rocks 37:05 Do SMBs have an AI advantage? 38:39 Why tool rollouts fail: the coaching model 42:33 Identity threat and the mindset flip 44:49 Skills that matter next, and the atrophy problem 46:49 Layoffs and AI: correction or cover story? 49:21 The barbell talent strategy 51:22 Gen Z and the messaging problem 53:19 Messaging AI adoption: good and bad examples 55:26 The environmental question 57:38 Advice for leaders: minimize toil, maximize joy 1:00:00 Where to follow Matt + closing thoughts

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Why 'AI-First' is Wrong | Bipasha Ghosh

Every company wants to announce it's "AI-first" — but what does that actually signal to the customers and employees you depend on? In this episode of The Pluralsight Podcast, Bipasha "B.G." Ghosh — business school professor, C-suite advisor, and community educator who demystifies AI for boardrooms, classrooms, and towns alike — makes the case that "AI-first" is the wrong North Star, and that building an effective learning culture is what actually separates the organizations that adopt AI well from the ones that stall. Put your customers first, she argues, treat AI as the thing that lets you serve them in ways you couldn't before, and invest in the people expected to make it all work. B.G. sits at a rare intersection of academia, enterprise advisory, and public education, and she uses all three to spot the same pattern everywhere: someone builds the AI, someone else lives with it, and trust breaks down in the gap between them. We dig into why trust — not tooling — is the real moat, and why so many transformations stall not because the technology is hard, but because the human side never gets managed. B.G. is direct about what leaders keep getting wrong: treating AI adoption as a technology project instead of a people project, buying the latest tools while skipping the change management, rolling out agentic systems before anyone's been trained to use them safely, and rewarding the old ways of working while asking for new ones. Her antidote is a genuine culture of learning — one where AI literacy means judgment and data fluency rather than clever prompting, where education is ongoing and curated to how people actually learn, and where leaders create enough psychological safety that experimentation is allowed instead of punished. Topics covered: → Why "AI-first" is the wrong message — and why "customer-first, powered by AI" builds more trust → Why AI adoption is a people project, not a technology project — and where change management breaks down → Why AI literacy is not prompting — and what a real culture of learning looks like → The accountability gap in agentic AI: who owns it when an agent goes wrong → How individuals stay relevant — domain expertise, adaptability, and connecting the dots Chapters: 02:00 Demystifying AI for Boardrooms, Classrooms, and Communities 05:43 The Expectation Gap: Where Trust Breaks Down 09:20 Tech Layoffs: Is AI Really the Reason? 12:43 The Coinbase Example: AI Isn't the Whole Story 13:54 AI Meets Blockchain and IoT 15:40 Losing the Trust of the People Who Stay 18:28 Advice for the People in the Middle 21:27 Preparing for Hybrid Teams: Humans and AI Agents 24:16 The Entry-Level Jobs Question 26:07 What Leaders Need to Understand (Without Coding) 28:51 Building AI Literacy Across the Organization 30:36 The Center of Experimentation 31:19 Psychological Safety, Incentives, and Gen Z Pushback 33:53 What to Look for in a Learning Solution 35:08 Start with Data: The Foundation of AI Literacy 37:34 Trust Is the Moat 38:22 Agentic AI and the Accountability Gap 43:09 What Doing AI Adoption Right Looks Like 44:38 Stop Saying You're AI-First 47:18 Where to Find B.G. 47:44 Rapid Fire: Myths, Confessions, and Hope Stay up to date on everything happening in cloud, AI, and security — subscribe to our weekly newsletter at https://www.pluralsight.com/technews/ Connect with Bipasha: LinkedIn: Bipasha Ghosh | LinkedIn Questions or comments? podcast@pluralsight.com www.pluralsight.com

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AI Threat Detection Is Broken | Zack Korman

Everyone is selling AI security — so when the threats are AI-generated and never look the same twice, can the tools built to match known attacks even see them? In this episode of The Pluralsight Podcast, Zack Korman — co-founder of AI-native security startup Embroidery and former CTO — argues that the answer is no, and that most of what's being sold to close that gap doesn't work the way the marketing claims. Zack spends much of his time proving, hands-on, what AI agents can be tricked into doing, which makes him unusually clear-eyed about what actually protects an organization and what just looks like it does. From a law degree to leading security tech and product teams, he's built a following on a simple habit: cutting through the hype to find what's real. We dig into why defending against AI-driven attacks requires AI-native detection, and why a system prompt is not a security control — you can't write your way to safety with a stern enough prompt when an agent is running with credentials it should never have had. We also take a hard look at what leaders are getting wrong right now: assuming they have visibility into their AI agents when the audit logs barely exist, handing agents their own operator credentials instead of least privilege, and trusting vendor claims that fall apart the moment you follow the incentives behind them. Topics covered: Why AI-driven threats outpace signature-based detection — and what AI-native detection actually requires The Microsoft Copilot audit-log gap and why most organizations have far less visibility than they think How to tell genuine AI security from "AI-washed" tools and vendor hype How to weigh risk when deploying AI agents — and what responsible deployment looks like How to build and lead a security team ready for the AI era Chapters: 00:01:14 Welcome & Why a Skeptic Founded an AI Security Company 00:04:25 "Also Me Being Mad" 00:07:00 What AI-Native Threat Detection Actually Means 00:11:10 An AI-Native Threat in Practice: Hide the Vulnerability 00:13:42 "Our Product Uses AI": Marketing Claim vs. Reality 00:16:21 The Microsoft Copilot Audit-Log Discovery 00:20:10 Visibility, Confidence, and Evaluating Agentic AI 00:24:43 The Limits of Sandboxing 00:26:50 Pulling Back the Curtain on the Vendor Space & MCP 00:31:12 Running Agents in Production & What a Ready Team Looks Like 00:34:29 Where Veteran Security Leaders Fit in an AI-First World 00:36:49 Skills, Hiring, and Where to Start 00:43:01 Rapid Fire 00:45:15 What Zack Is Building Toward & Closing Takeaway Stay up to date on everything happening in cloud, AI, and security — subscribe to our weekly newsletter at https://www.pluralsight.com/technews/ Connect with Zack Korman: LinkedIn: https://www.linkedin.com/in/zacharyakorman/ YouTube: https://www.youtube.com/@ZackKorman X: https://x.com/ZackKorman Questions or comments? podcast@pluralsight.com www.pluralsight.com

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Why I Stopped Writing Code | Wayne Hoggett

Wayne Hoggett — prolific Pluralsight author of 40+ courses across cloud, Kubernetes, and AI — joins Josh Burkhead to talk about what changes when AI moves from a tool you reach for to something embedded in every part of how you work. Wayne shares a striking admission: despite a heavy cloud-engineering background, he hasn't written code by hand in nearly a year — and what that shift signals for the teams technology leaders are building today. We dig into how the engineering role is being redrawn around AI: why architecture, judgment, and business context are becoming the durable human skills, why cutting junior talent to offset AI spend quietly shuts down your future pipeline, and how leaders should rethink hiring and team design when everyone on the team can generate working code in minutes. Wayne makes the case for building automation-first, AI-first workflows — then deciding deliberately where humans still need to be in the loop. We also get practical on the harder questions: how to prove ROI on AI and L&D investment, how to surface and govern "Shadow AI" instead of banning it, and why self-deployed agentic AI raises the stakes on security, defense in depth, and how fast your team can patch critical systems. Whether you lead a technology org, own an L&D strategy, or are an engineer trying to stay valuable as AI absorbs more of the execution work, this conversation is a grounded look at where the work is actually heading. Topics covered: Redesigning teams and hiring for an AI-first world — and why architecture and business context are the skills that last The real cost of cutting junior talent to fund AI investment, and the talent-pipeline gap it creates Proving ROI on AI and L&D initiatives by starting with low-hanging-fruit workflows Governing "Shadow AI" — enabling the tools your people already use instead of blocking them Securing self-deployed agentic AI: defense in depth, automated patching, and responding fast to new vulnerabilities Chapters: 00:40 Welcome & Guest Intro 01:15 Inside Wayne's World: Learning, Building, Teaching 03:58 Is AI a Natural Fit or a Forced One? 04:39 Don't Skip the Fundamentals: Building T-Shaped Skills 07:01 Linux, Windows, and France's Government Switch 08:31 "I Haven't Written Code in a Year" 10:33 The Moment AI Changed How Wayne Works 12:05 Hiring Entry-Level Talent for Business Awareness 13:37 Fewer Developers? Where the Headcount Logic Breaks Down 14:44 Building Automation-First, AI-First Teams 15:25 What an "AI-First View" Actually Looks Like 16:17 When AI Code Creates More Work Than It Saves 18:38 Rethinking Cloud Expertise on Your Team 20:00 Why Self-Deployed Agentic AI Worries Wayne 21:23 Staying Valuable as an Engineer: Architecture & Patterns 22:32 The Return of the Business Analyst Mindset 25:35 How AI Is Reshaping Cloud Infrastructure Itself 27:19 Shadow IT to Shadow AI: Enable, Don't Block 28:36 Reliability, Determinism, and New Job Titles 32:32 Proving ROI: Start with Low-Hanging Fruit 34:09 Security & the Mythos Headlines: Defense in Depth 36:42 Giving Teams a Safe Sandbox to Experiment 37:59 Closing Thoughts Want more insights on AI, security, and cloud? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ Wayne Hoggett on Pluralsight - https://www.pluralsight.com/authors/wayne-hoggett Connect with Wayne on LinkedIn: https://www.linkedin.com/in/waynehoggett/ Questions or comments? podcast@pluralsight.com www.pluralsight.com

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The Human Edge in Cloud Infrastructure | Ned Bellavance

What does it mean to be irreplaceable on a cloud infrastructure team when AI can write your Terraform, parse your logs, and troubleshoot your architecture — all before your second cup of coffee? In this episode of The Pluralsight Podcast, Ned Bellavance — infrastructure engineer, Pluralsight author, and host of the Day 2 DevOps podcast — argues that the answer isn't about the tools you know. It's about the judgment, institutional knowledge, and operational experience that no model can replicate. From scripting VMware deployments on a three-person IT team to building reusable Terraform libraries across cloud clients, Ned traces how infrastructure as code evolved and why understanding what's happening underneath the tools has never mattered more. We dig into where AI genuinely accelerates infrastructure work and where it introduces serious risk — overprivileged credentials, non-deterministic pipelines, and assumptions that only fail once you're in production. We also take a hard look at what leaders are getting wrong right now: cutting junior engineers to offset AI investment costs, undervaluing institutional knowledge that doesn't show up on a balance sheet, and handing AI agents access they were never designed to have responsibly. Topics covered: Infrastructure as code fundamentals and why declarative thinking changes everything How Terraform shifted Ned's approach to infrastructure work Where AI helps in IaC workflows — and where it creates real risk Why LLMs should never run your deployment pipeline The principle of least privilege applied to AI agents Why institutional knowledge is the hardest thing to replace and the easiest to lose The junior engineer pipeline problem leaders aren't seeing yet Skills to prioritize right now: networking, identity, storage, compute, security, and observability Chapters: 00:02:08 What Is Infrastructure as Code? 00:04:52 From Consulting to IaC: Building Reusable Libraries 00:07:09 Terraform and the Declarative Shift 00:13:05 Where AI Helps (and Doesn't) in IaC Workflows 00:22:53 The Real Risk: AI Agents and Overprivileged Credentials 00:25:59 Why LLMs Should Never Run Your Deployment Pipeline 00:27:02 Infrastructure Engineers as Decision Makers 00:29:28 The Value of Institutional Knowledge 00:31:16 What Leaders Risk When They Cut Experienced Engineers 00:34:43 The Junior Engineer Pipeline Problem 00:41:58 Opportunities in AI Infrastructure for Early-Career Engineers 00:45:11 The IaC Landscape: Terraform, OpenTofu, and What's Coming 00:50:51 Skills to Prioritize Right Now 00:51:49 Final Question: AI Doing More with a Smaller Team 00:52:38 Closing Takeaway Want more insights on AI, security, and cloud? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ Ned Bellavance on Pluralsight - https://www.pluralsight.com/authors/edward-bellavance Connect with Ned on LinkedIn: https://www.linkedin.com/in/ned-bellavance/ Check out the Day 2 DevOps Podcast: https://packetpushers.net/podcast/day-two-devops/ Questions or comments? podcast@pluralsight.com www.pluralsight.com

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What Good Instruction Really Looks Like | Amy Coughlin

What does good instruction really look like? Amy Coughlin has authored nearly 50 courses on Pluralsight covering Azure, AI, and cloud architecture — and she's spent years figuring out exactly what makes technical training land versus what makes learners tune out. In this episode, Amy pulls back the curtain on her approach to course design: why storytelling and real-world experience beat slide decks every time, what organizations consistently get wrong when they try to build training in-house, and why the best instruction is always built around the problem, not the tool. She also makes a sharp distinction that every L&D and technology leader should hear: AI chat tools are task tools, not learning tools. And confusing the two has consequences. In this episode: What separates instruction that sticks from training that gets forgotten Why themes, stories, and even corny puns make technical content more effective The hidden risks of pulling your best SMEs to run internal training What AI sycophancy means for developers who rely on it too heavily Why focusing on the problem, not the tool, is the future of content design Chapters: 2:03 — Amy's unconventional path into tech 7:13 — From data platform architect to course author: how Amy found her calling 9:52 — Making complex cloud topics relatable: themes, storytelling, and board games 12:19 — Real-world examples and the value of learning from mistakes 14:15 — What makes good technical content stick (and what falls flat) 18:25 — The human touch in learning: why podcasts and infotainment still win 21:23 — Hands-on labs and why doing beats reading 22:37 — Why organizations struggle when they try to build training in-house 25:48 — Hidden risks of using internal SMEs as instructors 28:08 — Tech debt, vibe coding, and the real cost of underskilled teams 30:24 — Is AI a legitimate learning tool? The sycophancy problem explained 34:55 — The future of content delivery: problem-focused, short-form, and refreshable 37:26 — Advice for parents opening doors to tech careers for their kids 41:11 — Closing thoughts and how to find Amy on Pluralsight Want more insights on AI, security, and cloud? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ Amy Coughlin on Pluralsight - https://www.pluralsight.com/authors/amy-coughlin Connect with Amy Coughlin on LinkedIn: https://www.linkedin.com/in/amy-coughlin-07300b44/ Questions or comments? podcast@pluralsight.com www.pluralsight.com

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Foundations for AI Success | Faye Ellis

Most organizations are excited about AI. Far fewer are actually ready for it. In this episode of The Pluralsight Podcast, host Josh Burkhead sits down with Faye Ellis — AWS Hero and Pluralsight Author Fellow, cloud architect turned educator, and AI upskilling strategist — to talk about what separates organizations that are stuck in AI curiosity mode from those that are building real, measurable capability. Faye brings a practitioner's perspective to some of the most pressing questions in L&D and technology leadership today: How do you close skills gaps when the technology keeps moving? How do you bring non-technical teams along without losing them? And why are 80% of AI pilots still failing to reach production — even as investment in AI continues to climb? Whether you're leading an L&D function, managing a technology team, or trying to figure out where to even start with AI upskilling, this conversation is packed with frameworks and honest perspectives you can take back to your team. In this episode: Why fear is not an AI strategy — and what to do instead How to move from AI curiosity to a skills-first, outcome-driven program The case for AI literacy at every level of the organization, not just technical teams What a successful upskilling program actually looks like in practice Why the organizations getting it right treat learning as a continuous journey, not a project Chapters: 00:01:08 — From Data Centers to AI: Faye's Career Journey 00:04:07 — What Got Her Hooked on Teaching 00:05:41 — The AI Curiosity Trap: Why Organizations Stay Stuck 00:09:21 — What It Looks Like When Strategy Clicks 00:12:53 — Running a Skills Gap Analysis in a Moving Target Environment 00:15:13 — Including Non-Technical Teams in the Talent Pipeline 00:18:31 — Building a Program That Actually Works 00:21:07 — Connecting Learning to Business Outcomes 00:26:13 — Designing for Confidence, Not Just Competence 00:31:22 — Scaling a Learning Culture Without Letting It Fizzle 00:37:00 — Trust as the Hidden Driver of Upskilling Success 00:41:02 — What Leaders Are Still Getting Wrong About AI Literacy 00:44:20 — Rapid Fire: Myths, Hard Truths, and One Thing in Common Want more insights on AI, security, and cloud? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ Connect with Faye Ellis on LinkedIn: https://plrsg.ht/420lS3W Questions or comments? podcast@pluralsight.com www.pluralsight.com

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Your AI Needs a Reviewer | Maaike Van Putten

What does it take to write code that's actually ready for an AI-powered world — and what happens when it isn't? In this episode of The Pluralsight Podcast, Maaike Van Putten — software developer, Pluralsight author, and instructor known for making technical concepts genuinely approachable — makes the case that clean code has never mattered more than it does right now. Not because the standard has changed, but because AI can generate bad code faster than ever before, and someone has to catch it. Maaike traces that argument back to a practical reality: as AI takes on more of the writing, the job of the developer increasingly becomes the job of the reviewer. From there, she breaks down why functions that do too many things are the silent killer of maintainable codebases, what the "driving fast" analogy reveals about the relationship between speed and code quality, and why AI should be treated as a teammate — not an authority — before you let it anywhere near data you can't afford to lose. We also get into why the entry bar for junior developers has shifted dramatically, how a brag document can be a genuine defense against imposter syndrome, and what scheduling "tech dates" actually looks like when you're trying to protect learning time in a world that never stops demanding more of it. If you're early in your development career, leading a team of developers, or just trying to figure out how to work alongside AI without letting it work against you — this conversation is a grounded, practical look at the habits and mindsets that hold up across every wave of change. Chapters: 03:12 Why the Entry-Level Developer Market Is Struggling Right Now 05:51 Clean Code: Why Small Habits Make or Break a Developer 08:40 Why Code Quality Matters More in the AI Era 10:21 What Happens When a Team Isn't Aligned on Standards 11:16 AI as a Teammate, Not an Authority: Lessons from a Hard Drive Wipe 13:09 What Leaders Should Consider Before Deploying AI in Development 14:15 Vibe Coding vs. Agentic Coding: Is There a Difference? 14:34 Why Reading Code Is Now More Valuable Than Writing It 16:20 How to Schedule and Protect Learning Time (Tech Dates) 18:33 How to Ask Your Manager for Learning Time 19:30 Beating Information Overload: Focus on Fundamentals 21:47 The Brag Document: Fighting Imposter Syndrome with Evidence 24:51 How to Share Your Work Without Feeling Exposed 26:35 What Motivated Maaike to Start Teaching and Creating Content 27:58 Skills Young Developers Are Overlooking Right Now 30:03 Maaike's 2026 Goals & Upcoming Book: *Illustrated Python* 📖 Illustrated Python by Maaike Van Putten — available now on Amazon: https://a.co/d/0dSvCYrR Want more insights on AI, security, and cloud? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ Connect with Maaike Van Putten on LinkedIn: https://www.linkedin.com/in/maaikevanputten/ Questions or comments? podcast@pluralsight.com www.pluralsight.com

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Skills First, Roles Second | Jose Ramirez

What if the reason your AI adoption isn't working has nothing to do with the technology — and everything to do with how you prepared your people? In this episode of The Pluralsight Podcast, Jose Ramirez — L&D strategist and former research analyst who spent a decade advising CIOs on building high-performing tech teams — makes the case that most organizations are solving the wrong problem. It's not a tools problem. It's a skills problem. And until leaders make learning part of the job instead of a break from it, no amount of AI investment will move the needle. Jose traces that argument back to a simple but powerful reframe: the difference between building AI tool adopters and building AI value creators. From there, he breaks down why a skills-first approach makes teams more resilient than role-based hiring, how the best tech leaders use storytelling to win over skeptical stakeholders, and why handing employees a new AI tool without context or strategy is one of the most expensive mistakes a leader can make right now. We also get into how to measure the real impact of upskilling beyond completion rates, why career mobility is the most overlooked metric in any L&D program, and what it looks like when a learning culture is actually working. If you lead technology teams, learning programs, or both — this conversation is a practical and honest look at what it takes to close the skills gap before it's too late. Chapters: 00:57 Welcome & guest introduction 01:43 Jose's career as an industry analyst 02:48 What CIOs still misunderstand about skill strategy 03:32 Skills first, roles second: the puzzle piece analogy 06:58 Where organizations stumble adopting a skills-first approach 08:49 Aligning a learning culture to specific outcomes 11:14 Storytelling as a core leadership competency 13:05 Anatomy of a persuasive story: the hook and the transformation 15:26 How leading with data derails a good strategy 16:43 Preparing people, not just systems: AI literacy 18:43 The risk of handing out AI tools with no strategy 20:42 Work redesign and employee autonomy 22:53 Sharing success stories to drive adoption 25:31 Modernizing legacy systems without losing critical knowledge 27:54 Career portfolios over career ladders 28:38 Building internal capabilities as AI reshapes skills 33:34 Measuring real business impact beyond usage metrics 39:18 Career mobility and connecting talent to business outcomes 39:54 Closing questions: what's top of mind for 2026 40:54 Wrap-up & where to find Jose Want more insights on Security, Cloud, and AI? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya Connect with Jose Ramirez on LinkedIn → https://www.linkedin.com/in/joseramirez5/ Questions or comments? Email → podcast@pluralsight.com Website → https://plrsg.ht/4rlhB5m Subscribe to our channel and hit the notification bell to stay up to date with the latest tech career and interview insights from Pluralsight → https://plrsig.ht/subscribe

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AI Ethics, Bias, and Responsible Innovation | Kesha Williams

What happens when the data you feed an AI system is already broken — and no one stops to ask why? In this episode of The Pluralsight Podcast, Kesha Williams — AI ethicist, AWS Hero, and 30-year tech veteran — makes the case that building powerful AI systems isn't enough. Building responsible ones is the only real standard that matters. Kesha traces her focus on AI ethics back to a single project: a crime prediction model that exposed how easily biased data can corrupt a machine learning system before a single line of code is written. From there, she breaks down the three types of bias teams face — data, algorithmic, and interpretation — why interpretation bias is the one most teams are still getting wrong, and what model drift means for organizations that think their work is done once a model ships. We also get into AI governance in the age of agents, why the ability to roll back an AI action may be the most underrated capability in any AI stack, and what an AI Center of Excellence actually looks like in practice. If you're building AI systems — or leading teams that do — this conversation is a practical and honest look at where things go wrong, and what it actually takes to get them right. Chapters: 00:00:33 — Introduction: Kesha Williams, AWS AI Hero 00:01:05 — Kesha's 30-year journey and spotting emerging tech early 00:02:51 — The moment that changed everything: building a crime prediction model 00:04:18 — Pre-crime, Minority Report, and bias hiding in UK stop-and-search data 00:05:44 — The Clear News AI case study: how bias shapes what a nation reads 00:07:57 — The three types of bias — and why interpretation bias is now the hardest 00:09:16 — Role play: interpretation bias and the home loan example 00:11:53 — Red flags: why skipping model retraining silently reintroduces bias 00:13:21 — Favorite tools: SageMaker Clarify, AI Fairness 360, and Fairlearn 00:14:22 — SHAP and LIME: making model decisions explainable 00:15:28 — Agentic AI governance: visibility, guardrails, and rollback 00:18:09 — Accountability and the case for an AI Center of Excellence 00:20:53 — Skills engineers need to prioritize: prompt engineering and LLM literacy 00:22:37 — The mindset of learners who thrive: curiosity and innovation 00:24:32 — No-code platforms, citizen developers, and guardrails 00:25:28 — Where to find Kesha: LinkedIn and Pluralsight Want more insights on AI, security, and cloud? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ Connect with Kesha Williams on LinkedIn: https://www.linkedin.com/in/keshaewilliams/ Questions or comments? podcast@pluralsight.com www.pluralsight.com

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Quantum, AI, and the Case for Continuous Curiosity | Frank La Vigne

What does it take to stay curious, keep learning, and stay relevant when the technology landscape keeps shifting beneath your feet? In this episode of The Pluralsight Podcast, Frank La Vigne — Principal AI Product Marketing Manager at Red Hat and one of Pluralsight's most dedicated learners — makes the case that adaptability isn't just a career skill. It's the only real career strategy. Frank breaks down what's actually kept him learning every single day for over 1,000 consecutive days, why intrinsic motivation beats structured programs every time, and what leaders get wrong when they try to inspire their teams to grow. From Commodore 64 nostalgia to quantum cryptography to the hidden risks of AI-generated code, this conversation covers a lot of ground — and all of it connects back to one idea: the most adaptable people and organizations always win. We also dig into the looming collision between quantum computing and modern encryption, what Frank took away from the NVIDIA conference in DC, and why cutting junior talent pipelines today could be one of the most costly mistakes the industry makes. Chapters: 00:01:16 — Meet Frank La Vigne 00:02:07 — 1,100 Days of Learning: Inside Frank's Pluralsight Streak 00:04:25 — How to Keep Your Team Motivated to Learn 00:08:05 — Commodore 64 and the Roots of a Tech Career 00:10:31 — Quantum Computing and the Future of Cybersecurity 00:15:42 — How AI Is Reshaping Red Hat's Security Approach 00:17:56 — Are We in an AI Bubble? The Dot-Com Parallel 00:20:21 — Inside the Nvidia Conference: Sovereign AI and National Security 00:23:50 — When AI Generates Bad Code: The Developer Tension 00:25:54 — The Junior Talent Pipeline Problem 00:28:13 — Adaptability as the Core Skill of the Future 00:29:19 — What Leaders Overlook in AI Adoption and Skill Development 00:32:59 — Frank's Favorite Pluralsight Authors and Learning Areas 00:35:24 — Final Thoughts and Where to Find Frank Want more insights on AI, security, and cloud? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ Connect with Frank La Vigne on LinkedIn: https://www.linkedin.com/in/frank-lavigne/ Questions or comments? podcast@pluralsight.com www.pluralsight.com

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Why Security Policies Fail: The Human Side of Cybersecurity | John Elliott

Most security failures aren't technical — they're human. So why do we keep designing security programs that ignore how people actually think and behave? In this episode of The Pluralsight Podcast, John Elliott — Pluralsight author fellow, PCI DSS contributor, and specialist in regulated security and data protection — makes the case that the language, culture, and psychology behind your security program matter just as much as the controls themselves. John breaks down why policies get misread, ignored, or worked around, and what leaders can do differently. From the neurolinguistics of security training to the aviation concept of "just culture," this conversation is packed with practical frameworks for building security programs that people actually follow. We also dig into the expanding attack surface of agentic AI, why your cybersecurity team is likely more anxious than you realize, and what organizations need to do right now to prepare for what's coming. Chapters: 02:58 How John Discovered the Human Side of Security 05:30 Why Security Communication Is So Often Overlooked 06:03 Where Policies Break Down in Practice 08:26 The Importance of Explaining the "Why" 09:31 Connecting Individual Behavior to Organizational Security 11:41 Designing Controls and Training People Will Actually Follow 12:49 Compliance Is Always a Risk Decision 14:36 Can You Ever Hit 100% Security Coverage? 17:03 Beta Testing Policies Before You Roll Them Out 18:05 What Most Teams Get Wrong About Security Training 19:15 The COM-B Model: Capability, Opportunity, and Motivation 21:04 How to Diagnose the Real Skill Gap in Your Organization 24:24 Don't Patronize People — And Don't Give Them 50 Things Not to Do 25:44 The Compliance Budget: You Only Get 3% of Someone's Brain 27:55 Building a Healthy Security Culture 28:10 Psychological Safety as the Foundation of Security Culture 29:10 What "Just Culture" Means and Where It Comes From 30:34 The Badge Policy Problem — And Why It Backfired 34:07 Balancing Risk Appetite Across Large Enterprises 35:22 AI's Unique and Poorly Understood Attack Surface 38:09 Agentic AI, Open Source Agents, and the Enterprise Risk 41:49 Two Practical Changes Leaders Can Make Right Now 44:49 Benchmarking Security Skills Want more insights on Security, Cloud, and AI? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ Connect with John Elliott on LinkedIn: https://www.linkedin.com/in/withoutfire/ Questions or comments? podcast@pluralsight.com www.pluralsight.com

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The Technologists' Edge: Skills that AI Can't Replace | Dr. Lyron Andrews

In a world where AI can generate code, automate tasks, and accelerate innovation, what skills still set great technologists apart? In this episode of The Pluralsight Podcast, Dr. Lyron Andrews shares his unconventional path into technology — from working before finishing high school to building a career through certifications, teaching, and eventually earning his doctorate at 49. Along the way, he explains why resistance, trial and error, and even failure aren't liabilities — they're signals of growth. We explore how simplifying complexity unlocks deeper understanding, from quantum computing and cryptography to Zero Trust architecture. Lyron breaks down intimidating concepts with practical analogies and challenges technologists to focus less on the "kitchen" (the tools) and more on the "meal" (business outcomes). The conversation also dives into AI governance, ISO 42001, and why organizations risk accelerating the wrong results if they don't build security and guardrails into their AI strategies from the start. Chapters: 02:37 Lyron's Unconventional Path Into Tech 05:59 Learning How to Learn 08:21 What Makes Someone Employable in Tech 12:57 The Superpower of Children 18:33 Babe Ruth, Failure, and Experimentation 24:16 Why Shared Definitions Matter 26:44 Simplify the Complex 30:16 AI Governance and ISO 42001 36:14 Skills AI Can't Replace 39:52 AI Bias and the Dutch Tax Fraud Case 43:06 Zero Trust and Federal Security Challenges 48:02 The "Frankenstein Tech Stack" Problem 50:17 Outcomes Before Tools 55:04 Stackable Credentials and Career Agility 59:21 How to Choose Skills to Learn 1:06:20 Increase Your Value, Understand the Business Want more insights on Security, Cloud, and AI? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ Connect with Dr. Lyron Andrews on LinkedIn Questions or comments? podcast@pluralsight.com www.pluralsight.com

Why Most Training Fails (and How to Make Learning Stick) | Dr. Will Thalheimer

Most L&D teams rely on learner satisfaction surveys to gauge training effectiveness. The problem? Happy learners and competent learners aren't the same thing. Dr. Will Thalheimer, learning researcher and author of The CEO's Guide to Training, E-Learning, and Work, breaks down why traditional evaluation methods send organizations in the wrong direction — and shares the four learning sciences (retrieval practice, spacing, context alignment, and feedback) that research shows can double training results. He also introduces a framework for rethinking how L&D creates competitive advantage, moving beyond "did they like it?" to "can they actually perform? Will Thalheimer is a learning scientist, author, and internationally recognized expert on evidence-based learning design. With more than four decades of experience researching how people learn and how training actually works in the real world, Will has become one of the most influential voices challenging myths and assumptions in workplace learning. He is the founder of Work-Learning Research, where he helps organizations design learning experiences that drive real behavior change and business impact. Will is best known for his groundbreaking research on learning transfer, spaced practice, retrieval practice, feedback, and evaluation, as well as for creating the Learning Transfer Evaluation Model (LTEM), which redefines how organizations should measure the effectiveness of learning. Will is the author of two widely respected books, including: Performance-Focused Learner Surveys: Using Distinctive Questioning to Get Actionable Data and Guide Learning Effectiveness The CEO's Guide to Training, eLearning & Work: Empowering Learning for a Competitive Advantage His work consistently bridges the gap between academic research and day-to-day learning practice, helping teams build significantly more effective learning and learning evaluation practices. A frequent speaker, advisor, and thought leader, Will is known for his clarity, rigor, and willingness to challenge the status quo in workplace learning. His work continues to shape how organizations think about upskilling, capability building, and creating learning experiences that truly stick. Chapter notes: 00:57 Welcome & guest introduction 01:40 Will's path into learning research 03:57 Why training fails to transfer into performance 05:46 What "getting it right" looks like in context 07:48 Learning myths: the fire hose, learning styles, and learner insight 10:36 What instructors get wrong about teaching 13:13 The Ten Reasons Not to Measure Impact 17:55 How AI is changing survey data analysis 20:42 Where we are with AI: hype, skepticism, and early days 22:53 Partnering with learners & think-aloud protocols 25:54 Making learning stick: alignment, retrieval practice, spacing 27:53 What to look for in a content provider 30:41 Building competitive advantage 34:34 AI success criteria beyond ROI 49:21 One piece of advice: competitive advantage 40:18 Closing & where to find Will Want to go deeper? Check out our weekly newsletters focused on Security, Cloud, and AI. Follow Pluralsight on Linkedin and join the conversation: https://www.linkedin.com/company/pluralsight/ Check out Dr. Will Thalheimer's latest book "The CEO's Guide to Training, E-Learning, and Work": https://a.co/d/05f2TMMh Connect directly with Dr. Will Thalheimer on Linkedin Questions or comments? podcast@pluralsight.com www.pluralsight.com

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