Your Judgment Matters More Now, Not Less

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You think AI is threatening your value.

I thought that too.

Been worried that the skills I've built over a decade are being made obsolete. But I'm looking at it wrong.

Here's what's actually happening:

AI isn't changing what's valuable. It's revealing that there are layers of invisible work—and the deepest layers are what matter most.

Here's what I'm seeing as I use AI more:

AI can do research. That's obvious.

But AI can also do some of the work I thought required deeper expertise: synthesis, pattern recognition, even contextual judgment.

Give it enough context, ask the right questions, and it can produce insights that are genuinely useful.

But here's the nuance: AI's ability to do that work depends entirely on my ability to frame the question, provide the right context, evaluate what it produces, and know what it's missing.

There are layers of invisible work. AI is doing some of them.

But the deepest layer—the work that enables AI to do its work well—that's where the real value is now.

And that deepest layer?

That's judgment.

When I Started Seeing the Layers

Six months ago, I was working on a strategic analysis for a client.

I used AI for the research. Expected that.

Then I tried something: I gave AI comprehensive context and asked it to synthesize key insights.

I'd seen AI do basic synthesis—summarizing information and making obvious connections. But this was different. With enough context, it was making connections across domains that I would have made, finding patterns I would have found.

Not always.
Not perfectly.

But often enough to make me stop and think.

Then I pushed further. I asked it to apply those insights to this specific client's situation with their specific constraints.

With enough context, it did that too. Made recommendations that were contextually appropriate.

That's when I started seeing there were layers to this:

• Layer 1: Visible Work (AI does this easily)
  Research. Documentation. Initial analysis. This is what we all know AI can do.

• Layer 2: Basic Invisible Work (AI is getting good at this)
  Synthesis across sources. Pattern recognition. Framework generation. AI can do these if you give it good inputs.

• Layer 3: Contextual Invisible Work (AI can do this IF you guide it well)
  Understanding specific situations. Applying general principles to particular contexts. AI can do contextual thinking, but only if you provide the context. It doesn't know what context matters unless you tell it.

• Layer 4: Meta-Invisible Work (This is where judgment dominates)
  Knowing what question to ask in the first place. Knowing what context AI needs. Evaluating whether AI's output is right for this specific situation. Knowing what AI is missing. Knowing when to trust its judgment and when to override it.

But here's what required my judgment in that client work:

I had to judge which question to ask the client in the first place—reading between the lines of what they were really asking for.

I had to judge which parts of AI's synthesis were right for this situation and which parts, while generally true, wouldn't work here.

I had to judge what context AI was missing that would change its recommendations.

I had to judge when AI's contextual reasoning was sound versus when it was pattern-matching without true understanding.

That's Layer 4 work. That's judgment.

And AI's ability to do Layers 2 and 3 depends entirely on how good my judgment is at Layer 4.

The question isn't "can AI do invisible work?" It's "which layer requires judgment, and how do I get better at exercising that judgment?"

The LAYER Framework: Developing Judgment for an AI World

Here's how I'm thinking about developing this judgment:

L - Let AI Handle What It Does Well (Judgment: knowing what to delegate)
A - Assess AI's Output with Skepticism (Judgment: knowing what's wrong)
Y - Your Context Is Critical (Judgment: knowing what matters)
E - Evaluate What's Missing (Judgment: knowing gaps)
R - Refine Your Questions (Judgment: knowing what to ask)

Let’s get it…

L - Let AI Handle What It Does Well

Looking at a task and knowing: "AI can do this 80% as well, so I should delegate it and focus my judgment elsewhere."

This is harder than it sounds. Your instinct says, "I'm better at this than AI, so I should do it."

But the judgment is: "Where is my judgment most valuable?" Not "where am I better than AI?"

What this looks like:

Layer 1: Research, documentation, initial analysis Layer 2: Synthesis, pattern recognition, framework generation

Your judgment: "AI can handle these layers. My judgment is more valuable evaluating what AI produces than producing it myself."

Why this judgment matters:

Poor judgment here means you're doing work below your value level. You're using expert judgment on tasks that don't need it.

Good judgment here frees you to apply judgment where it actually matters.

Practice this judgment:

Before starting any task this week, ask: "Is my judgment needed to DO this work, or to EVALUATE this work?"

If evaluate, let AI do it and focus your judgment on evaluation.

A - Assess AI's Output with Skepticism

Looking at AI's output and knowing: "This looks comprehensive, but here's what's wrong."

Not obvious errors. Subtle ones. Assumptions that don't hold. Patterns that are superficial. Recommendations that sound good but won't work.

What to judge:

Not just "is this accurate?" but:

• What assumptions is AI making that don't hold here?

• What patterns is it recognizing that are real versus superficial?

• What recommendations sound good generally but won't work in this specific context?

• Where is it confidently wrong versus appropriately uncertain?

What this looks like:

AI produces a strategic analysis. Looks thorough. Well-structured. Insightful.

Your judgment: "This recommendation assumes X. But in our specific situation, X doesn't hold because of Y. So this won't work even though it's generally sound."

Why this judgment matters:

AI is good enough now that its mistakes aren't obvious. They require judgment to catch.

Without that judgment, you'll implement flawed recommendations that looked right.

Practice this judgment:

This week, have AI produce an analysis you normally do. Then spend 30 minutes judging what's wrong with it. Not critiquing—judging. What doesn't hold? What's missing? What won't work?

Y - Your Context Is Critical

Looking at a situation and knowing: "This context changes everything, even though it's not written down anywhere."

The most important context is often unwritten.

Organizational politics. Historical failures. Cultural dynamics. Unspoken constraints.

AI can't access what's not documented. Your judgment about what context matters—that's what makes the difference.

Context only you can judge:

• Organizational politics and power dynamics

• Historical attempts that failed and why

• Unspoken constraints and priorities

• Cultural dynamics that determine what's actually possible

• What people actually mean versus what they say

• Why certain approaches won't work even if they work elsewhere

What this looks like:

AI recommends approach A based on all documented information.

Your judgment: "That approach failed three years ago, not because it was wrong but because of a political dynamic that's not in any document. That dynamic is still there. So A won't work."

Why this judgment matters:

This context often determines whether decisions succeed or fail. AI has no access to it.

Your judgment about what context matters is often more valuable than the analysis itself.

Practice this judgment:

This week, think of one decision you're facing. List the undocumented context that matters. How does that context change what looks like the right answer?

E - Evaluate What's Missing

Looking at analysis or recommendations and knowing: "Here's what's missing that would change everything."

AI can only work with information it has. Your judgment tells you what information is missing that matters.

What to judge:

• What perspectives aren't represented in AI's analysis?

• What data is missing that would change the conclusion?

• What questions should have been asked but weren't?

• What's being assumed that should have been verified?

What this looks like:

AI produces a recommendation based on available data.

Your judgment: "This analysis is missing input from [specific group]. Their perspective would completely change this recommendation. Let me gather that before we proceed."

Or: "AI is assuming [thing] is true. But I know from experience that's often not true in situations like this. We need to verify."

Why this judgment matters:

The best decisions often depend on information that's not in the initial analysis.

Judging what's missing—and whether it matters—is what separates good decisions from flawed ones.

Practice this judgment:

This week, look at an AI-produced analysis. Don't evaluate what's there. Judge what's not there. What's missing? What should have been included? Does it matter?

R - Refine Your Questions

Looking at a problem and knowing: "Here's the question I should actually be asking."

Most people try to improve AI's output by correcting its answers. Better judgment: improve your questions.

The quality of AI's work depends entirely on the quality of your questions. That's judgment.

What this looks like:

Weak judgment: "AI gave me a mediocre analysis. Let me ask it to try again."

Strong judgment: "AI gave me a mediocre analysis. My question wasn't specific enough. Here's what I should have asked."

The judgment shift:

From: "How do I get AI to give me better answers?"

To: "What question should I actually be asking? What context does AI need? What constraints should I specify?"

Why this judgment matters:

You're not just using AI. You're judging how to use it. That's meta-level judgment.

And that judgment is what determines whether AI produces useful work or mediocre work.

Practice this judgment:

This week, when AI gives you mediocre output, don't regenerate. Stop. Judge your question. What was unclear? What context was missing? What should you have specified? Rewrite the question based on that judgment.

POLL

In Case You Missed It!

• AI Prompting Is a Critical Thinking Skill (Not a Shortcut to Avoid It)

• Your memory isn't broken

• Walk Away Without Burning Bridges

• Scattered Goals Are Killing Your Influence

The Bottom Line

AI isn't threatening your value.

It's revealing that judgment was always the valuable part.

AI can do research, synthesis, even contextual analysis if you guide it well.

But guiding it well requires judgment. Evaluating its output requires judgment. Knowing what it's missing requires judgment. Knowing when to trust it requires judgment.

There are layers of work. AI is doing some of them.

But your judgment about how to direct AI, what to trust, and what it misses—that's what determines whether you make good decisions or confidently wrong ones.

Thanks for reading. Be easy!
Girvin