Via AI News & Strategy Daily | Nate B Jones

The training market has bifurcated into just those two poles: the [Class] 101 Basics and the [Class] 401 level – technical implementation. It has skipped the middle entirely and the middle is where most of the productivity gains for most people actually live. The [Class] 201 level is where the question shifts from “How do I use this tool?” to “Where does this tool fit in my workflow and how do I know when the output is trustworthy?”

This is applied judgment. It’s not about writing better prompts per se. It’s about knowing which parts of your work AI ought to do, which parts you ought to do, and how to verify the relationship between them. The strategic insight that most organizations miss is that this is not a technology adoption problem. It gets dressed as one, but it’s really an organizational capability problem.

— Why Your Best Employees Quit Using AI After 3 Weeks (And the 6 Skills That Would Have Saved Them), at 2:36

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Nate B. Jones (AI Strategist & Product Leader) notes that corporate AI training has bifurcated into two poles: the basic 101 Classes, which offer a tour of basic tool usage (“here’s how to write a prompt”), and the advanced 401 Classes that go deep into technical implementation (API integrations, RAG architectures, fine-tuning).

The Class 201 middle ground, where most workers and most productivity gains actually live, tends to be skipped entirely.

The Class 201 level is where the question shifts from how do I use this tool to where does this tool fit in my workflow, and how do I know when the output is trustworthy? That’s applied judgment, and it requires an entirely different kind of training.

Nate identifies six judgment-layer skills that separate people who achieve sustained, effective AI use from those who quit. Notably, prompt engineering, tool-specific features, and technical implementation are absent from the list.

• Context assembly: Knowing what information to provide and why. 101-level users dump entire documents or provide almost nothing; 201-level users curate the right background, constraints, and examples.
• Discerning Quality: Knowing when to trust AI output and when to verify across task types (high-stakes legal vs. low-stakes drafts) and within a single output (AI can hallucinate in the same paragraph as accurate information).
• Task decomposition: Breaking work into AI-appropriate chunks rather than throwing an entire task at the tool—the same skill you’d use to manage a new team member.
• Iterative refinement: Treating the first draft as a starting point. The 101-level user either accepts AI slop or abandons the effort entirely; the 201-level user knows how to move from 70% to 95% through structured passes.
• Workflow integration: Embedding AI into how work actually gets done, not treating it as a “side activity I’ll try later.”
• Frontier recognition: Knowing when you’re operating outside AI’s capability boundary, which is the key skill that saves you from wasting time trying to get AI to do things it can’t.

As Ethan Mollick puts it, the best AI users are good managers and good teachers. The skills that make you effective with AI are people skills.

At major organizations, the effective power users are not the most technically savvy employees. They’re the senior executives and distinguished engineers—people with strong domain knowledge and management skills.

What are the implications?

• Your AI training problem may be a management development problem in disguise
• Your AI champions should be your best managers, not your most technical people
• Skills like task decomposition, quality assessment, and iterative refinement are management skills—not tool skills.

These are all skills that can be taught and learned.

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Practical Things You Can Try Doing Today

For individual contributors

• Audit one recurring work task and identify which subtasks AI should handle vs. which require your judgment
• Treat AI’s first output as a starting point—build a habit of at least one structured refinement pass before accepting output
• Document one case where AI produced a wrong or low-quality result; keep a personal “failure log” to sharpen your frontier recognition skills

For managers and team leads

• See your team’s best managers (not most technical people) as AI champion candidates
• Get a sense: can your people name one task type where AI reliably helps and one where it reliably fails? If not, that’s your starting point

For L&D and HR leaders

• Audit your AI training offering: does it skip the 201 middle? Plan content specifically around context assembly, quality judgment, and iterative refinement
• Design a junior employee development track that preserves domain judgment-building even as AI takes over routine tasks

For organizations broadly

• Create lightweight AI labs that include non-technical employees alongside technical ones
• Run a low-stakes “workflow improvement” competition to surface practical use cases and create social proof
• Build a shared repository for AI failure cases—not just wins—so boundary knowledge spreads