Max Headroom 
A colleague told me something recently that I keep thinking about.
She said, unprompted, that she appreciated seeing both sides of my AI conversations. Not just the output. The full thread. My prompts, the AI’s responses, the back and forth, the dead ends, the iterations. She said it made her trust me more.
This piece is an example of that. The conversation that produced it exists. A raw transcript would be longer, messier, and significantly less useful than what you’re reading now. What you’re reading is the annotated version, the part where judgment entered the artifact. That’s not a disclaimer. That’s the argument.
I’ve been transparent about using AI in my work from the start. Partly because I wrote a book on data ethics and hiding it felt wrong. Partly because I’ve spent 25 years watching technology adoption go sideways when the human dimension gets treated as an afterthought. But her comment made me realize something more specific was happening when I showed the conversation rather than just the output.
It’s worth unpacking why.
An old problem, a new incarnation
In the 1990s, Harvard Business School professor Dorothy Leonard introduced the concept of “deep smarts” in her book Wellsprings of Knowledge: the experience-based expertise that accumulates over decades of practice, the kind of judgment that lives in people’s heads and doesn’t reduce to documentation. She also introduced a companion concept that has stayed with me: core competency as core rigidity. The very depth that makes expertise valuable also makes it hardest to transfer. Experts often can’t fully articulate what they know because they’ve stopped experiencing it as knowledge. They experience it as just seeing clearly.
Leonard’s work was about organizational knowledge transfer: how companies preserve institutional wisdom when experienced people retire or leave. That’s been a challenge since the first consultant ever billed an hour. What’s different right now is that the tools to actually solve it have arrived simultaneously with the largest demographic wave of executive retirement in American history.
What’s interesting about this particular moment is that the same dynamic is now showing up at the individual level in how practitioners interact with AI. The tacit knowledge at stake isn’t a retiring VP’s intuition. It’s your own judgment, your own expertise, your own hard-won understanding of what a project or organization actually needs. And the question isn’t how to transfer it before you walk out the door. It’s whether you can see it clearly enough to know when the AI is substituting for it.
The instinct gets it backwards
The natural impulse is to clean up the AI interaction before sharing anything with a collaborator, a team, or a stakeholder. Show the polished output, not the messy process. You don’t want them thinking you just handed your work to a machine.
That instinct produces a disingenuous outcome.
When you hide the process, the people you’re working with have no way to evaluate how the work was made, what judgment calls went into it, or where your expertise ended and the AI’s pattern-matching began. You’ve made the process invisible. And invisible AI processes erode trust, slowly and quietly, over time.
The instinct to hide is also, if we’re honest, a little defensive. It assumes the people in the room can’t tell the difference between AI output and practitioner judgment. Most of them can. And the ones who can’t yet will figure it out. Hiding the seams doesn’t make the work more credible. It just defers the reckoning.
The deeper problem: It’s not just about appearances
Here’s what took me longer to see.
Hiding the process doesn’t just affect how others perceive you. It erodes your own clarity about where your expertise is actually operating.
To understand why, it helps to be precise about what AI actually is. AI is a pattern matcher, a deeply sophisticated one, trained on more human-generated content than any single person could read in a thousand lifetimes. That’s its power (core competency) and its limitation (core rigidity) simultaneously, and the two are inseparable. The very scale that makes it extraordinary is also the boundary that defines what it cannot do. It is extraordinarily good at producing the most likely next thing given what came before. What it cannot do is know what you actually need, when the obvious answer is the wrong one, or when the stated goal isn’t the real goal. It has no judgment about context, relationship, or organizational reality. It has patterns. Incomprehensibly vast ones. But patterns.
That distinction matters because of what happens when you stop paying attention to it.
I’ve watched it happen in my own work. You share a draft with someone and they’re impressed. They quote a formulation back at you, something that sounds sharp and considered. And you realize, tracing it back, that the formulation came from the AI. Not because the AI invented it, but because you said something rougher and less precise earlier in the conversation, and the AI reflected it back in cleaner language. The idea was yours. The AI gave it a polish you then forgot to account for. The person quoting it back thought they were seeing your judgment. They were seeing your thinking laundered through a pattern matcher and returned to you at higher resolution.
That’s the subtler version of the problem. Not that AI invents things. It’s that it can reflect your own thinking back with more confidence and clarity than you put in, and that gap is easy to mistake for the AI contributing something it didn’t.
When you route everything through a polished output layer, you stop noticing the moments where you pushed back, redirected, rejected the first three versions, reframed the question entirely. Those moments are where your judgment lives. They’re the difference between using AI and being used by it. It’s Leonard’s core rigidity problem, applied inward: The very fluency that makes AI feel useful can make your own expertise invisible to you.
When the process stays hidden, the knowledge stays local and static. When it’s visible, it becomes something you and the people around you can actually work with and build on. The reason transparency benefits your audience is the same reason it benefits you: It keeps the scope of your judgment visible and therefore expandable. That’s not just an ethical argument. That’s the amplification mechanism.
Which is also what makes the upside real rather than consoling. When you stay in the process rather than just collecting outputs, work that would have taken days now takes hours. Your thinking gets sharper because you have to articulate it precisely enough for the AI to be useful. The people developing fastest right now aren’t the ones offloading the most. They’re the ones using AI as a thinking partner and staying in the conversation.
Here’s the paradox at the center of it: The more clearly you see the AI as a pattern matcher, the more human you have to be in working with it. The more human you are, the more useful the output. The tool doesn’t replace the practitioner. It reveals them.
Transparency isn’t just an ethical practice. It’s a cognitive one.
Radical AI transparency in practice
I’ve started calling this radical AI transparency. Not a policy, not a compliance framework, not a disclosure checkbox. A practice. Something you can actually do Monday morning.
Here’s how it shows up concretely:
Have the conversation before you need to.
Before you’re deep in a project or collaboration, surface how you use AI and genuinely explore how others do. Not as a disclosure (“I want you to know I use AI tools”) but as a real exchange. What are you using? What do you trust it for? Where are you still skeptical? The comfort level and sophistication in the room will vary more than you expect, and knowing that before you’re mid-deliverable matters.
This is also how you build the psychological foundation for showing your work later. If the people you’re working with have never heard you talk about AI before and you suddenly share a full chat thread, it lands differently than if you’ve already had the conversation.
Track the full threads.
This is partly an orchestration problem and I won’t pretend otherwise. There’s cutting and pasting involved. The tools haven’t caught up to the practice yet, which is itself worth naming honestly when the topic comes up.
A few approaches that help: a running document per project where you paste key threads as they happen (not retroactively, you’ll never do it retroactively), dated and labeled by what you were working on. Claude and most other major AI tools now offer conversation export, which produces a complete record you can archive. The low-tech version, a single shared document per engagement, is underrated for its simplicity.
The reason to do this isn’t just for sharing. It’s for your own reference. Being able to go back and see what you asked, what the AI produced, what you changed and why, builds a record of your judgment over time. That record is professionally valuable in ways that are hard to anticipate until you have it.
Annotate before you share.
Not every thread is self-explanatory to someone who wasn’t in it. Context is everything, and raw transcripts without context are a lot to ask anyone to parse.
A sentence or two before the thread begins. A note at the moment where the direction changed. A brief flag on what you rejected and why. This is where your voice enters the artifact, and it transforms a raw AI exchange into a demonstration of judgment. The annotation is the work. It’s where you show what you saw that the AI didn’t, what you knew that the prompt couldn’t capture, and what made the third version better than the first two.
This is also where the most useful material for future reference lives. Annotations are the deep smarts layer on top of the raw exchange. They’re what makes a conversation a record.
Be real about the errors.
AI makes mistakes. It conflates, confabulates, and hallucinates. It gives you the confident wrong answer with the same tone as the confident right one. It misses context that any competent person in the room would have caught.
These aren’t bugs to apologize for or hide. They’re the clearest window into what the tool actually is. AI makes mistakes in a specifically human way because it was trained on human output. Think of it as rubber duck debugging at professional scale. The AI is a duck that talks back, which is useful and occasionally misleading, which is exactly why you have to stay in the room. When you’re transparent about the errors, and even a little good-humored about them, you’re teaching the people around you something true about the technology. That’s more useful than pretending it’s a black box that either works or doesn’t.
The people who build the most durable trust around AI are usually the ones most comfortable saying: “The first version of this was wrong and here’s how I caught it.”
The bigger picture
What I’ve described so far is an individual practice. But the same principles scale.
Teams and organizations adopting AI face a version of the same problem. The impulse to treat AI outputs as authoritative, to make the process invisible to colleagues and stakeholders, to optimize for the appearance of capability rather than its actual development, produces the same trust erosion. Just at greater scale and with less ability to course-correct.
The teams that will navigate AI adoption well are the ones that treat transparency not as a risk to manage but as a methodology. Where the process of building with AI, including the corrections, the overrides, the moments where human judgment superseded the model, is part of how the organization learns what it actually believes and values. That’s Leonard’s knowledge transfer problem at institutional scale, and the practitioners who understand both dimensions will be the ones leading those conversations.
That’s a much larger conversation. But it starts with the same Monday morning practice.
Show the conversation. Not just the output.
What you’re actually demonstrating
When you show your AI conversations, you’re not demonstrating that you needed help.
You’re demonstrating that you understand what you’re working with. AI is a pattern matcher, trained on more human-generated content than any single person could read in a thousand lifetimes. What it cannot do is know what you need. That requires judgment, context, relationship, and the kind of hard-won expertise that doesn’t reduce to pattern matching, no matter how good the patterns are.
You’re demonstrating that you know the difference between the pattern and the judgment. That you were present enough in the process to know when to push back, when to redirect, when to throw out the output entirely and start over. That you understand, precisely, what the tool can and cannot do, and that you stayed in the room to do the part it can’t.
That’s a meaningful professional signal. It says: “I am not confused about what AI is. I am not outsourcing my judgment. I am using a very powerful pattern matcher as a thinking partner, and I know which one of us is doing which job.”
That’s the work. That’s always been the work.
The tool just makes it visible now. That’s not a threat. That’s an opportunity.
Claude is a large language model developed by Anthropic. Despite having read more human-generated content than any person could consume in a thousand lifetimes, it still required significant editorial direction, at least three rejected drafts, and occasional reminders about em-dashes. The full conversation transcript is available upon request. It is longer, messier, and significantly less useful than what you just read. Which was rather the point.
AI & ML, Commentary
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