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

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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

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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.

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Connect with Jose Ramirez on LinkedIn → https://www.linkedin.com/in/joseramirez5/

Questions or comments? Email → podcast@pluralsight.com

Website → https://plrsg.ht/4rlhB5m

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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

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Questions or comments? podcast@pluralsight.com

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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

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Questions or comments? podcast@pluralsight.com

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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

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Connect with John Elliott on LinkedIn: https://www.linkedin.com/in/withoutfire/

Questions or comments? podcast@pluralsight.com

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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

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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?

Want to go deeper? Check out our weekly newsletters focused on Security, Cloud, and AI.

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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

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.

Questions or comments? podcast@pluralsight.com

www.pluralsight.com