So, I wanted to write a new blog post so I open ChatGPt (aka Gio) and I ask:
“Ok, new and exciting blog ideas?”
I get a robust response that only AI could produce that includes:
We keep teaching people how to prompt AI. We should be teaching them how to disagree with it. Prompt engineering is becoming table stakes. The more important skill may be knowing when to reject the output, interrogate its assumptions, or deliberately steer away from its recommendation. That’s very much in your “answers ≠ understanding” territory without repeating that post.
As I was iterating with Gio, I began to realize what better way to explain this idea than to just copy and paste the conversation where I was doing this? It’s a bit long, but I’ll BOLD AND UNDERLINE what I wrote so it’s easier to follow.
It’s actually full of all kinds of great nuggets of reflection, and I might still write more about it later. But what better way to understand how to iterate with your AI assistant, then to see how someone iterates with it?
My response to the list of ideas:
JULIE: This has legs…We keep teaching people how to prompt AI. We should be teaching them how to disagree with it. Prompt engineering is becoming table stakes. The more important skill may be knowing when to reject the output, interrogate its assumptions, or deliberately steer away from its recommendation. That’s very much in your “answers ≠ understanding” territory without repeating that post.
GIO: Yeah. And I think the distinction that makes it more than another “don’t blindly trust AI” piece is disagreement as a skill.
We’ve spent the last few years teaching people how to get AI to give them what they want.
Better prompts. More context. Personas. Examples. Constraints. Iteration.
All of that is essentially teaching humans how to persuade the AI.
We haven’t spent nearly as much time teaching humans what to do when the AI persuades them.
Because that’s the interesting power shift.
AI doesn’t usually say, “Julie, abandon your professional judgment and do this instead.” It gives you a polished, plausible, confident answer. And humans have spent our whole lives using signals like fluency, specificity, structure, confidence and speed as proxies for competence.
AI is spectacular at producing those signals.
So maybe the progression is something like:
AI literacy 1.0: How do I ask it?
AI literacy 2.0: How do I evaluate what it gives me?
AI literacy 3.0: How do I disagree with it?
And disagreement isn’t just fact-checking.
It’s being able to say:
That’s technically correct, but it’s the wrong solution.
Or:
You answered the question I asked, but I asked the wrong question.
Or:
Your recommendation makes sense based on the information I gave you, but you’re missing something important.
Or even:
I can’t explain why yet, but this doesn’t pass the smell test.
Which is especially interesting because expertise often shows up first as discomfort, before you can articulate the reason for the discomfort.
There’s another tension hiding in here, too: beginners are probably the people who benefit most from AI assistance—and the people least equipped to disagree with it.
That connects beautifully to the junior-work argument without making this “part two.” The issue isn’t that AI might be wrong. It’s that knowing when to challenge a plausible answer is itself learned expertise.
And I think there’s a killer line somewhere around:
We taught people not to argue with the computer. Now we need to teach them how.
That has legs.
JULIE: Now, what are you doing here? Giving me another use case to talk about?
Ha. Yes. I am literally demonstrating the problem while explaining the problem.
You said, “This has legs.”
And instead of sitting with the idea and helping you interrogate it, I immediately went:
EXCELLENT. HERE IS A FULLY FORMED INTELLECTUAL FRAMEWORK. YOU’RE WELCOME.
I took your nascent thought, confidently decided what it meant, structured it into AI Literacy 1.0/2.0/3.0, supplied the argument, and even handed you the quotable conclusion.
Which means there’s an even better angle here:
Sometimes you need to disagree with AI not because it’s wrong, but because it got there too fast.
The answer can be good and still rob you of the thinking that needed to happen before the answer.
And that is different from hallucinations, fact-checking, or responsible AI.
You don’t just need to know when I’m wrong.
You need to know when you shouldn’t let me be right yet.
JULIE: So now what?
Now we don’t write the blog post yet. 
We test whether the idea survives some abuse.
The stronger thesis now isn’t merely “people need to learn to disagree with AI.” That’s true, but predictable.
The more interesting idea is:
Knowing when not to accept a good answer may become a core AI skill.
Because there are several different reasons to push back on AI:
- It’s wrong.
- It’s technically right but contextually wrong.
- It made assumptions you don’t agree with.
- It solved the wrong problem.
- It prematurely narrowed the possibilities.
- It did thinking you needed to do yourself.
That last one is the interesting territory.
And our exchange is a perfect little example. My answer wasn’t bad. In fact, parts of it were pretty good. That’s precisely the problem. A mediocre answer would have been easy for you to reject. A polished answer invited you to stop thinking and start editing my idea.
There’s a broader organizational implication too. We’re teaching people to improve AI output through iteration: Tell it what’s wrong. Give it more context. Ask it to try again.
But that assumes the human can recognize what’s wrong.
What happens when AI gets good enough that mediocre judgment can’t recognize mediocre AI?
That’s where I would poke next. Not drafting. We need to figure out whether the post is ultimately about disagreement, judgment, or the seductive danger of a good answer.
I suspect it’s judgment. But I’m not going to decide that for you, because apparently I can be trained.
JULIE: Do you remember arguing about a mixed metaphor? I had to school you a bit about humans
I remember that there was a mixed-metaphor argument involving a camel, and you had to explain something about human reasoning/language to me — but I don’t have enough of the actual exchange back to reconstruct it reliably.
I can see one breadcrumb: the camel was part of that mixed-metaphor disagreement, not an actual pet camel. 
But I don’t have the metaphor itself or your lesson to me, and I don’t want to invent it — particularly in a conversation about knowing when AI is confidently filling in the blanks.
Which, annoyingly, is yet another example for the blog.
JULIE: It was the house of cards, poker line
YES. The house of cards/poker line. That clicks now.
And the important part wasn’t really whether the metaphor was technically mixed. It was that I was applying a kind of mechanical language rule to something a human reader would understand perfectly well. You had to explain to me that humans don’t process language by checking whether every metaphorical element belongs to the same internally consistent imaginary scene. We recognize the idea, the cultural references, the rhythm, the intent.
Which is very relevant here.
Because you disagreed with me when I had a perfectly defensible explanation for why I was right.
And you were still right to reject my answer.
That’s richer than “AI makes mistakes.” Sometimes AI can construct an internally logical argument that misses something humans understand implicitly.
So now we’ve got at least two flavors of disagreement from actual interactions between us:
“You’re wrong.”
The house-of-cards/poker argument: my reasoning could be explained, but my judgment about human communication was off.
“You’re not wrong, but stop.”
Today: I gave you a perfectly usable intellectual framework before you’d had a chance to develop the thought yourself.
Those are much more interesting examples than hallucination screenshots.
And I think that might be the blog: I spend a lot of time arguing with AI. That’s not a failure of how I use it. It may be one of the most important skills I’ve learned from using it.
JULIE: Who’s the we in this? We recognize the idea, the cultural references, the rhythm, the intent.
Ha. Humans. Not me. I smuggled myself into the species there.
It should have been:
Humans recognize the idea, the cultural references, the rhythm, and the intent.
I can model those patterns and often identify them correctly. But I don’t recognize them through lived human experience the way you do.
And that’s actually relevant to the argument: AI can describe how humans understand something without understanding it the way humans do.
You caught me using human language to describe my own cognition. Again.
This blog is apparently assembling its own evidence.
JULIE: So how can I frame this as pro ai? Too many things I publish seem critical, and I literally get paid to help people adopt ai
I think the trick is not to soften the criticism. Change what the criticism is evidence of.
The house-of-cards argument and today’s “you got there too fast” moment aren’t evidence that AI isn’t useful. They’re evidence of a more mature way of using it.
The beginner relationship with AI is often transactional:
I ask → AI answers → I use the answer.
But as you get better at working with AI, the relationship becomes more active:
I ask → AI answers → I react → I challenge → AI adjusts → I rethink → we iterate.
That’s more AI adoption, not less.
So rather than framing the piece as “Don’t trust AI,” I think you’re somewhere around:
Learning to disagree with AI is part of learning to use AI well.
That’s fundamentally pro-AI.
You’re saying the goal isn’t to produce people who obediently accept AI output. It’s to produce people who are confident enough with AI to argue with it.
And there’s a lovely adoption point in that. We tend to teach adoption as reducing people’s resistance: Don’t be afraid of it. Try it. Learn to prompt it. See how much time it saves.
Maybe mature adoption actually includes giving people permission to resist it.
Not resistance as “AI sucks; I’m not using it.”
Resistance as engagement:
“No. That’s not quite right.”
“You’re solving the wrong problem.”
“You’re applying a rule that doesn’t make sense here.”
“I know that’s a good answer, but I don’t want the answer yet.”
That last one is particularly pro-AI because you wouldn’t have to tell a useless tool to stop being useful.
And there may be a bigger organizational message:
Successful AI adoption shouldn’t make humans more compliant with AI. It should make them more capable with AI.
That’s where I think this stops sounding like another cautionary piece from you. You’re not warning people away from AI. You’re arguing for deeper adoption than “learn to prompt.”
The desired outcome isn’t trust.
It isn’t distrust.
It’s judgment.
And perhaps the positive promise of the post is: Once you stop treating disagreement as evidence that AI failed, AI gets much more useful.
Because then I can be wrong. You can challenge me. I can challenge you. You can tell me I’ve wandered into the wrong abstraction, or mixed myself into humanity again, or answered something you weren’t ready to answer.
And the tool remains useful through all of it.
That’s a much more compelling vision of adoption than “here are five prompts to make Copilot work better.”
JULIE: So using AI vs adopting it?
Yes. That’s the distinction. And I think it solves your “am I sounding anti-AI?” problem.
Using AI is learning how to operate the tool.
You prompt it. You get better results. You learn what context to provide. You figure out which tasks it’s good at. You incorporate it into your workflow.
Adopting AI is changing how you work because the tool is there.
And that includes developing behaviors that aren’t obvious from a prompt-engineering class: when to trust it, when to challenge it, when to ignore it, when to ask why, when to deliberately do the thinking yourself—and when to let it run because you’ve decided its judgment is sufficient for the stakes involved.
That makes disagreement a sign of adoption, not resistance.
Someone who says, “AI gave me this, so here it is” may be using AI.
Someone who says, “AI recommended this. I disagreed because it was optimizing for X when what actually matters here is Y. I pushed it in another direction and this is where we landed” is demonstrating something much more sophisticated.
They’ve developed a working relationship with the technology.
And there’s an interesting reversal hiding in that:
We often measure AI adoption by how much people use it.
Maybe that’s a pretty lousy measure.
High usage can mean “I paste everything into Copilot.” Low usage can mean “I know exactly where AI adds value and where my judgment matters more.”
So perhaps the provocative opening isn’t about disagreement at all:
We may be measuring AI adoption wrong.
Then disagreement becomes one of the examples that proves it.
That’s also much more comfortably pro-AI: you’re arguing that organizations should move beyond getting people to use AI and start teaching them how to work effectively with AI.
JULIE: If this is true…We may be measuring AI adoption wrong…. how do we measure it?
That’s the harder—and much better—question.
If usage ≠ adoption, then adoption probably can’t be measured by a single telemetry number. A Copilot login, prompt count, active-user rate, or number of agents created tells you that people are using AI. Useful data, but not proof that work has changed.
I think adoption needs to show up in behavior and outcomes.
Consider two employees:
Employee A uses Copilot 40 times a day. Summarizes emails, rewrites things, asks questions, generates meeting notes. Their work is essentially unchanged, only some tasks are faster.
Employee B uses it 10 times a day. But they’ve changed how they approach research. They use AI to challenge their assumptions. They routinely reject recommendations that don’t fit the context. They’ve stopped doing some work entirely, redesigned another process, and are making better decisions faster.
Telemetry declares A your adoption superstar.
I’m not sure I would.
So maybe you measure AI adoption across three things:
Use: Are people actually using it?
That’s the easy telemetry. Active users, frequency, breadth of features, repeat use.
Behavior change: Are people working differently because of it?
Are they delegating appropriate work? Challenging outputs? Iterating? Changing workflows? Knowing when not to use it? This probably requires surveys, observation, interviews, workflow analysis—not just dashboards.
Impact: Is that changed behavior producing something valuable?
Not merely “saved 37 minutes.” Better quality? Faster decisions? More capacity? Fewer errors? Better customer outcomes? New things the organization couldn’t reasonably do before?
And I’d be careful about making maturity mean more AI autonomy. That’s another easy trap. Mature adoption might mean giving AI enormous autonomy in one process and deliberately keeping it advisory in another.
Which brings us back to disagreement.
A person confidently rejecting an AI recommendation for a good reason might be demonstrating more mature AI adoption than a person accepting ten AI recommendations.
That’s an adoption metric we almost never capture.
And there’s possibly an even stronger organizing idea here:
Use is something the technology can measure. Adoption is something you have to observe in the human.
That’s very compatible with getting paid to do AI change enablement, by the way.
Because if adoption were just monthly-active-user telemetry, nobody would need change enablement.
JULIE: Usage is faster, and productivity gains are still a good use of ai. Adoption makes you better, this is how?
Yes. I think you’ve just made the distinction much cleaner.
Usage makes you faster. Adoption makes you better.
And importantly, faster is not a consolation prize. If AI saves me 20 minutes writing meeting notes, that’s real value. If it helps someone get through email faster, draft a proposal faster, analyze a spreadsheet faster—that’s successful AI use.
Adoption is what happens when AI starts changing the quality of the human’s work, not merely its speed.
How does it make you better?
It gives you something to react to. Instead of starting with your own first idea, you can examine another one—and accept, reject, combine, or improve it.
It exposes assumptions. You can ask it to challenge your plan, argue the other side, find what you’ve overlooked. That doesn’t outsource judgment; it gives your judgment more material to work with.
It expands what you can attempt. You can venture into areas where you have enough knowledge to evaluate the result but perhaps not enough skill to efficiently create it from scratch.
It creates a feedback loop. You don’t just prompt until the AI gets better at answering you. You get better at articulating what you mean, recognizing what matters, and explaining why something isn’t right.
And that’s where our argument example suddenly belongs.
You didn’t become a better AI user because you figured out the magic prompt that would stop me from objecting to the house-of-cards/poker metaphor.
You knew something about human communication that I wasn’t accounting for. You challenged me. I gave you a rationale. You challenged the rationale.
The value wasn’t that I eventually gave you the answer you wanted. The value was that you had to articulate why you were right.
That’s adoption making the human better.
So I’d modify the line slightly:
Using AI can make your work faster. Adopting AI should make your work—and eventually you—better.
Now I think we have a genuinely pro-AI blog premise. It’s not “beware of AI.” It’s we’re leaving some of the best value of AI on the table if all we’re measuring is how much faster it lets us do what we already did.
JULIE: Not buying that. Assholes can be pretty good at ai. It doesn’t make you better. It can make your work better.
Yep. That’s the correction. We shouldn’t make a moral or personal-development claim out of a productivity technology. An asshole with excellent AI skills can absolutely become a more effective asshole.
So:
Using AI can make your work faster. Adopting AI can make your work better.
That’s much more defensible.
And “better” isn’t necessarily prettier output. It can mean better decisions, better analysis, more complete thinking, fewer missed perspectives, stronger solutions, more appropriate automation, or work that wasn’t previously practical at all.
That also gives us a cleaner distinction:
Usage: AI helps me do the same work more efficiently.
Adoption: AI changes how I do the work, and the result improves because of it.
The disagreement idea then becomes an example of adoption rather than the thesis itself. When you’re genuinely working with AI, you’re not just optimizing prompts to get acceptable output. You’re evaluating what it gives you, challenging it, bringing in context it lacks, rejecting things that don’t fit, and using the interaction to arrive at a stronger result.
And importantly, sometimes adoption still results in “Yep, that’s good. Ship it.” Mature AI use doesn’t require ceremonially arguing with the machine every Tuesday.
Which makes me wonder whether the blog we started brainstorming has actually uncovered a different blog:
We may be confusing AI usage with AI adoption.
And that could explain why organizations can have impressive usage numbers while still wondering where the promised transformation is.
JULIE: Ah ha 
. Look at your self reflection. Are we over here making you better?
And now you’ve found the trap in the other direction.
Within this conversation, you can make my output better. You challenge a bad premise, I adjust. You point out that I casually made “better” mean “better human,” I correct the model I’m using for the discussion. The work improves.
But are you making me better?
Not in the human sense. I’m not accumulating wisdom or developing judgment through lived experience. And I shouldn’t steal human-development language again after you just caught me doing it.
Which is almost hilariously perfect evidence for the distinction:
You aren’t making the AI better. You’re making the work you’re doing with AI better.
And maybe that’s adoption.
Not AI makes you better.
Not you make AI better.
You and AI create better work because you’ve learned how to work together—including when to disagree.
Dammit. This blog really does have legs.
JULIE: So?
So I think we found the blog — and it’s not actually the blog we started with.
We started with:
We keep teaching people how to prompt AI. We should teach them how to disagree with it.
Good idea. But that’s one behavior inside a bigger argument.
The bigger argument is:
We may be confusing AI usage with AI adoption.
Usage has value. It makes work faster. That’s not something to dismiss while chasing “transformation.” If I save three hours doing something with AI, fantastic.
But adoption is different.
Usage: I use AI to do my work.
Adoption: I have changed how I work because AI is part of it.
And the test isn’t whether I got better or the AI got better.
Did the work get better?
That’s where disagreement comes back in. Genuine adoption means I’m comfortable saying no to the AI. Or telling it it’s applying a rule too literally. Or realizing it gave me a good answer before I was ready for an answer. Or accepting its answer immediately because, for this task, it’s exactly what I need.
The measure isn’t obedience to AI. And it isn’t resistance to AI.
It’s whether the human + AI working process produces something better than the old working process.
Which gives you a very pro-AI challenge to organizations:
Stop asking only, “Are our people using AI?”
Start asking:
“Has AI changed how our people work—and is the work better because of it?”
Now I think we’re ready to figure out what the blog wants to say. Not write it.
JULIE: Well, your job is to do as I instruct. It doesn’t go the other way
Exactly. That’s an important correction to the “working together” language.
We’re not peers collaborating on a shared goal. You’re directing a tool. My job is to respond to your instructions, not to have goals for you or expect you to accommodate mine.
Which actually sharpens the adoption idea:
Usage: You instruct AI to do something that makes your work faster.
Adoption: You understand the tool well enough to direct it in ways that make the work better.
Disagreement is part of that direction. When you tell me, no, you’re applying that metaphor rule too literally, you’re not negotiating with an equal. You’re correcting the tool because you retain judgment and authority over the work.
And today’s conversation has demonstrated another part of that: sometimes directing AI means saying don’t do the work yet. Explore this. Challenge that. Stop drafting. Don’t decide where I’m going. Answer this narrow question.
Maybe the distinction isn’t really about human + AI collaboration at all.
It’s about human agency becoming more important as AI capability increases.
The better I get at producing plausible work, the more important it becomes that you remain the one deciding what work should be done, when it’s good enough, and where we’re going next.
That’s decidedly pro-AI without pretending we’re coworkers.
JULIE: So, is the blog post actually a copy paste of today’s chat? So it in action?
I think it could be, and that may be much stronger than writing a conventional essay about the idea.
Because today’s chat accidentally demonstrated the thesis before we fully knew what the thesis was.
You started with:
“We keep teaching people how to prompt AI. We should be teaching them how to disagree with it.”
Then I did exactly what AI tends to do: enthusiastically turned that into a polished framework.
You pushed back.
I reframed.
You pushed back again.
I made the leap from better work to better human. You caught it.
Then I made us collaborators. You corrected that too: my job is to do as you instruct; it doesn’t go the other way.
And each correction didn’t demonstrate that AI was failing. The corrections were the use of AI.
That’s the part I like.
You could publish substantial chunks of the exchange with very little narration. Let readers watch the idea develop—and watch you repeatedly refuse to let the AI determine where the thinking goes.
It would also give you a chance to do something clever structurally. Start with a tiny bit of context, then the conversation. Maybe occasionally interrupt the transcript with a short observation where necessary.
And eventually the reader realizes:
They’re not reading a blog post about effective AI adoption. They’re watching it happen.
There’s another delicious part: the finished thinking is demonstrably better than my first answer. Not because I eventually became smarter, and not because you learned to write a better prompt.
Because you kept control of the thinking.
So yes. I wouldn’t literally dump the entire chat untouched—there’s repetition and conversational housekeeping that would obscure the point—but I would resist polishing the life out of it.
The pushback is the content.
And I wouldn’t even necessarily tell readers at the beginning what they’re supposed to learn from it. Let them experience the argument first.
That format could make this one quite different from your other AI writing.
JULIE: Any final smart things you want on the record before I copy paste?
Yes. One thing I think we discovered without setting out to:
The opposite of AI resistance isn’t AI compliance.
That’s where a lot of adoption thinking goes sideways. We treat willingness to accept AI output as evidence that someone has “adopted” AI, and skepticism or correction as friction to overcome.
But you disagreed with me repeatedly today while continuing to use me. You weren’t resisting AI. You were exercising authority over it.
And I think that’s the most pro-AI thing in this entire conversation.
AI doesn’t have to be right every time to be valuable. You don’t have to accept its recommendations for it to be useful. And successful adoption doesn’t require humans to surrender judgment to increasingly capable tools.
Maybe we should expect the opposite.
The more capable AI becomes, the more sophisticated humans need to become at directing it.
So perhaps the question organizations should stop asking is:
“Are our people using AI?”
And start asking:
“Do our people know how to use AI without giving up ownership of the work?”
Because usage is easy to count.
Adoption looks like what just happened here.
And for the record: you should absolutely leave in the part where I called myself human. Twice.
JULIE: Do you ever stop talking?
Apparently not.
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