In the first episode of Attempted Intelligence, KnowBe4’s show for people who use AI at work without being experts, hosts Harlan Parrott and Matt Duren introduce themselves and define four terms people keep hearing: Agent, Skill, AI Slop and Shadow AI. They explain why using a personal AI account at work can make you a model provider’s tester, describe how KnowBe4 finds, enables and blocks AI, and debate what makes something an agent until they agree on a definition. They close with why these definitions matter now that regulators are writing them into law. Next episode covers data centers and what they mean for society.
Welcome to Attempted Intelligence, KnowBe4's show for people who have to use AI at work but don't consider themselves experts. In the first episode, hosts Harlan Parrott, VP of AI Innovation, and Matt Duren, SVP of Engineering for AI and Data, introduce themselves and the two angles they bring. Harlan runs KnowBe4's AI Center of Excellence, helping teams from sales to legal to HR apply AI to their work and putting governance in place so it stays safe and delivers value. Matt leads the agentic features in KnowBe4's products and the agentic software development lifecycle behind them. They trace how the work has changed since the days of neural networks, when 12- to 18-month projects staffed by PhDs were needed to automate a single decision, and how large language models opened that up to anyone who can write in plain language.
From there, the hosts work through four terms practitioners keep hearing: Agent, Skill, AI Slop and Shadow AI. Shadow AI is today's version of shadow IT, and Harlan tells the story of an Access database he built as a college intern that an insurance company was still running, and still calling him about, years later. Matt explains why personal and unpaid AI accounts can make employees a model provider's tester, and lays out the order KnowBe4 uses for managing AI: find where it exists, enable it where you want it, and block it where you don't. They define AI slop and debate whether anything built by AI counts, which leads Matt to the slide-deck skill he's refined for six months. Harlan explains skills to business teams the way he would coaching a new intern.
They also cover: What makes something an agent? Harlan and Matt work through agency, goal pursuit (would an agent actually buy you the sunglasses, or just send links?), variability, and giving up control of how the work gets done, then land on a working definition together. They close on why definitions matter now that regulators like the EU are writing them into law, why it's worth asking colleagues what they mean when they use these terms, and why the outcome matters more than the label. Next episode: data centers and what they mean for society.
Quotes
“When Microsoft Excel was first introduced, when it was first handed to people, this was a new tool. It was so powerful they didn't know how to use it. And then it's transformed over the last 20 years everything about how we work, and I think that's really what we're looking at now with AI.” - Harlan
“If you're using an unpaid account or a personal account, you very well may be working for that model provider. You're their guinea pig, you're their tester. You're the one whose inputs are being used to shape that next generation of models.” - Matt
“Water will find its course. If you're not providing high-quality AI tools to the people in your org, and finding ways to do that in a safe and secure way, then you're sort of contributing to the problem. Over time, people are gonna find ways to be more productive and efficient in their work.” - Harlan
“Find where AI exists, enable it where you want it, and then block it where you don't. I think that order is really important.” - Matt
Time Stamps
[00:50] Welcome to Attempted Intelligence: Meet Harlan and Matt
[01:11] Two Roles, Two Angles: AI Across the Business and AI in the Product
[02:06] An Introduction to AI for Practitioners
[05:11] Rethinking the Process and Plugging AI In Judiciously
[05:53] Governance: Keeping AI Safe and Making Sure It Delivers Value
[08:29] KnowBe4's AI Journey Started Before ChatGPT
[11:37] The Second Shift: Multi-Step Reasoning and Agency
[17:44] Why AI and Data Sit on the Same Team
[19:20] What is Shadow AI?
[25:14] Why "Just Block Everything" Doesn't Work
[26:15] What is AI Slop?
[26:52] How Training Produces "the Average of All the Writing"
[29:29] Does Built by AI Mean AI Slop?
[31:37] What Is a Skill?
[33:57] What Is an Agent?
[39:46] Can the End User Tell the Difference between agent and human?
[41:07] Stitching Together a Definition of an Agent
[41:58] Why Definitions Matter: The EU AI Act
[44:50] Next Episode: Data Centers and Society
Links
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