The Pluralsight Podcast

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 [https://plrsg.ht/3MZ78ya] Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ [https://www.linkedin.com/company/pluralsight/] Amy Coughlin on Pluralsight - https://www.pluralsight.com/authors/amy-coughlin [https://www.pluralsight.com/authors/amy-coughlin] Connect with Amy Coughlin on LinkedIn: https://www.linkedin.com/in/amy-coughlin-07300b44/ [https://www.linkedin.com/in/amy-coughlin-07300b44/] Questions or comments? podcast@pluralsight.com www.pluralsight.com [http://www.pluralsight.com/]

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 [https://plrsg.ht/3MZ78ya] Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ [https://www.linkedin.com/company/pluralsight/] Connect with Faye Ellis on LinkedIn: https://plrsg.ht/420lS3W [https://plrsg.ht/420lS3W] Questions or comments? podcast@pluralsight.com www.pluralsight.com [http://www.pluralsight.com/]

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 [https://a.co/d/0dSvCYrR] Want more insights on AI, security, and cloud? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya [https://plrsg.ht/3MZ78ya] Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ [https://www.linkedin.com/company/pluralsight/] Connect with Maaike Van Putten on LinkedIn: https://www.linkedin.com/in/maaikevanputten/ [https://www.linkedin.com/in/maaikevanputten/] Questions or comments? podcast@pluralsight.com www.pluralsight.com [http://www.pluralsight.com/]

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. Want more insights on Security, Cloud, and AI? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya [https://plrsg.ht/3MZ78ya] Connect with Jose Ramirez on LinkedIn → https://www.linkedin.com/in/joseramirez5/ [https://www.linkedin.com/in/joseramirez5/] Questions or comments? Email → podcast@pluralsight.com [podcast@pluralsight.com] Website → https://plrsg.ht/4rlhB5m [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 [https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa0Y4ai1QR18tX0J4SVV6WVQyZldoTlJXUlpCUXxBQ3Jtc0tucUFfUndPMG9CM0FqellCdVVXd29Tai14S1JiY2hEQXMxLURMOExjdDg4czgxTmdETHpmTFU4MUxMcS02WjN1R1dBa0U4VVE1eGJxdm1oZy1FOVpfUF9GR3U3WmVndVJ3YldfNlNXcG5TVnhhWEM0aw&q=https%3A%2F%2Fplrsig.ht%2Fsubscribe&v=WYFGfZfsvYI]

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 [https://plrsg.ht/3MZ78ya] Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ [https://www.linkedin.com/company/pluralsight/] Connect with Kesha Williams on LinkedIn: https://www.linkedin.com/in/keshaewilliams/ Questions or comments? podcast@pluralsight.com [podcast@pluralsight.com] www.pluralsight.com [http://www.pluralsight.com/]