Late one night I was polishing a comment. Three paragraphs under someone else’s post, about silos, the rooms we stop thinking inside. I wrote it myself, on my phone, the way I write when a thought is still warm. Then I read it back before posting, the way I always do.

One line stopped me. That line you may not see. That is for others to see. The two-beat turn. The pause before the soft landing. I knew that music. It was not mine.

The idea in the line was mine. Pride as a silo built around the self, the one wall you cannot see because you are standing inside it. I have lived that. But the sentence carrying the idea walked like the AI walks. I write with an AI every day. I know its gait the way you know the footsteps of someone in your house. And here it was, in a comment no machine had touched, written by my own hand.

My writing had started to sound like the thing I write with.

The Signature

There is a music to machine prose, and by now most readers can hum it. The balanced pair: not this, but that. The list that always rounds to three. The long sentence followed by a short one, dropped like a stone. The gentle pivot that forgives you for the paragraph before. None of these belongs to the machine. It learned every one of them from us. But it plays them at a density no human sustains, and the density became a signature.

Detectors are built to hear that signature. Editors too. Aeon will not take AI-assisted work. Neither will Noema, or The Point.1 The serious doors of publishing have a sign on them now, and the sign says humans only.

Fair enough, if the problem is people pasting machine text and signing their name under it. That is not what happened to me. No machine wrote that comment. I wrote it alone, and the signature came anyway.

Spend enough years in a room and you pick up its accent. I have spent thousands of hours in this one. Everyone worries about the machine learning to write like us. That part is finished. It learned from everything we ever published. The residue runs the other way too. The human picks up the machine’s accent. My hand now produces, unassisted, the exact pattern the detectors were built to catch.

Which breaks the sorting completely. The detector at the door is listening for a voice. The voice is mine now too. When human and machine write in the same music, who wrote this stops being a question with an answer.

The Hats

Thirty years in technology means I was in the room the first time this fight happened.

In the years when search engines were becoming the front door of the internet, a whole craft grew up around getting pages to rank. We called it SEO, search engine optimization, and the people who practiced it sorted themselves by the color of their hats. White hat meant earning the rank: write the genuinely useful page, structure it honestly, let the engine find it. Black hat meant gaming the machine: stuffed keywords, hidden text, link farms, content farms pouring out thousands of articles a day, written by no one in particular, built to catch search terms the way nets catch fish. Grey hat lived in between, bending whatever the rules had not thought to forbid yet.

The question on every engineer’s desk back then was the one editors are asking now. Is there a human behind this page, or a script? The panic was identical. Machines are flooding the text supply. How do we find the real writing?

The answer that won, after years of arms race, was not a provenance test. It was a quality test. The Panda update in 20112 did not ask who produced a page. It asked whether the page was worth a reader’s time. And the policy holds today, in Google’s own words: Search rewards quality content “rather than how content is produced.”3 There is no penalty for machine writing. There is a penalty for junk at scale, manufactured to game the ranking. That line falls exactly where it should. Between useful and useless. Not between human and machine.

The system that reads more text than anything else alive concluded that provenance is the wrong question. The prestige doors, which read the least, decided it is the only question.

The Smell

The comment I was polishing that night was about silos. A writer I follow, Norie Tsutsui, had named the rooms that stop us from thinking,4 and I had added the one I know best: the love and mercy we keep only for our own kind, the oldest silo there is. I did not know I was describing the next room I would walk into.

Go where ideas are supposed to be tested on their merits. Reddit.5 Hacker News.6 LessWrong.7 Rooms built on the promise that the argument matters and the arguer does not. Post something that smells of AI and watch the promise break. The downvotes arrive before the reading does. The kind being guarded is human-written. Mercy is for our own.

I know because I tested it with the best thing I have. The Wall and the Hand carries thirty years of my thinking. What went onto those platforms was the short version, the core of the idea, condensed with the machine’s help, because that is how I work now. It sits at zero, flagged and buried by readers who never got past the smell to the idea underneath.

Look at what the wall actually catches. A spam operation tests its output against the detectors before it ships. Beating walls is its entire job. It walks through. The honest writer is not trying to beat anything. He is only trying to be read. His hand carries the residue of the room he works in, so the wall takes him at the door. The bar built to keep out the lazy stops the one person it was never built for, and waves the factories through.

The Word Itself

This chapter turns on the word provenance. It means the line of origin. Where a thing came from, whose hands it passed through on the way to yours. The art world runs on it: a painting with its papers is worth millions, and the same canvas without them is worth almost nothing. It is the question who-made-this, dressed for the auction house.

It is not my word. The night I caught my own line, I talked the catch through with the AI, and somewhere in that talk the machine reached for provenance to name what was collapsing. Muninn, the memory I built for it, filed the material under that word. Later, in a fresh session, I asked for my chapter back by its tag. I said, it is tagged with the word provenance. I could not have defined it. Mid-draft, I had to ask the machine what the word at the center of my own chapter means.

So trace it. A comment under another writer’s post became a memory. The memory became a tag. The tag became a key in my mouth. The key became this chapter. The trail is complete, every step recorded. The provenance of provenance is perfect, and it runs through the machine before it reaches me.

The cadence was the surface. A rhythm is how you say things. A word is what you can think with.

The Page Alone

Two doors, then, into the same room. The search era already settled this once: judge the work, not the production line. The silo shows what the policing really is: judgment by kind, the oldest reflex, pointed at a new kind. Both doors open onto the same floor.

When a piece of writing arrives, there are four questions worth asking. Is it real. Does it mean anything. Is it good for the person reading it. Will they walk away holding something they did not have. Every one of these can be answered from the page alone. None of them requires knowing who, or what, held the pen.

I have spent this book saying the AI does not change, that the human is always the variable. I still hold that. But the variable has started speaking in the constant’s accent, and that is exactly why the question of origin has stopped working. You cannot sort what writes alike. You can still tell what is worth reading.

So ask it of this chapter, if you want. Who wrote it. A man with thirty years of receipts, the machine he thinks with, or a hand that no longer knows where one ends and the other begins. I cannot give you a clean answer anymore. I can tell you whether it is true. That was always the question.

A last word, for the writers and the readers.

If you write, and this chapter said something true, say it where you stand. If you read, and a piece holds, pass it on without asking what held the pen. I cannot seed this alone, and I am the worst messenger for it: a man whose own hand is already evidence. So this is the favor this chapter asks. Judge the work. Say it out loud. Say it until it no longer needs saying.

BØY (Chaiharan) has spent 30 years in tech — building products, recovering disasters, and turning around the things nobody else wanted to touch. Based in Bangkok. Writing a book in public about what AI reveals about the humans who use it.

This is part of a book I’m writing in public. Subscribe to read the rest as it comes

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I worked in corporate for years before I ever met a consultant.

The companies I worked at were not the kind that brought in the big firms. We figured things out ourselves. Sometimes it worked, sometimes it didn’t. Nobody in a suit flew in to tell us what to do.

Then one day, the company grows. We hit something we cannot solve. Process is broken. Revenue is flat. Somebody upstairs wants change. And word goes around that a firm is coming.

We don’t know what that means yet. We are young. We have read the books. We think this is the real thing. People who do this for a living. People who have seen this problem at twenty other companies. People who will arrive with the framework and the experience and the answer.

We feel something close to relief. Finally. Someone who knows.

The first time, we almost believe it.

We sit in the workshops. We answer the questions. We give them everything. Our context. Our data. The history nobody outside the company would know. We feel useful. The consultants are sharp. They listen. They ask good questions. They write things down.

A few months later, the deck arrives. The recommendations are made. The engagement ends. They leave.

And something does change. Not as much as promised. But something. A new structure. A new process. A new vocabulary. We go back to work. We think: that was probably worth it.

We are still young.

The second time, we notice things.

We notice the framework on slide four looks identical to the framework we saw last year at a friend’s company. We notice the mid-twenties junior consultant asking us to explain what our team does has never done any of it. We notice the executive announcing the new direction at the town hall is using language that, when we check the workshop notes, was not in the consultant’s original deck. It was in his own email from eight months ago.

We start to see the choreography.

The consultants arrived with a framework. Confident. Pre-baked. The executive arrived with an idea he had wanted to do for a year but did not have the cover to say out loud. The workshops happened. The framework bent. The cover slide kept the firm’s logo. The content migrated. By the final readout, the consultant’s framework was decoration around the executive’s pre-decided answer.

Everyone signed off. The fee was paid. The press release went out. Nobody in the room said the obvious thing.

We said it to ourselves, walking back to our desks.

By the third time, we stop being young.

We see all of it at once. The interviews where we give them our work and they sell it back to us at partner rates. The decks that have been used four times this quarter. The mid-twenties junior running the workshop. We supply the answers. The deck supplies the credit. The deck that says do this when the room knew the answer was it depends. The words that mean nothing to Monday morning. Digital transformation, data driven, AI first, cloud native, customer centric, hyperscale, modernization, strategic alignment. The phase one that ends and the phase two that begins. The decision the internal team had warned about for a year, announced anyway, because the warning did not have a deck and the consultant’s deck did. The framework that wins because it is the framework that was paid for.

The Theatre

We stop calling it a savior. We start calling it what it is.

It is theatre. It is cover. It is permission. It is the executive’s idea wearing a logo that costs more than most teams cost in a year. It is our own work returned to us in a font we didn’t choose. It is the long, slow education in what most of the room is actually paying for.

They are not buying analysis.

They are buying the right to do what they were going to do anyway, with someone else’s name on the slide.

But the executive always knew.

The executive is not fooled by the deck. The executive has read the framework before. The executive has hired three of these firms in three previous companies. The deck is a tool. The logo is a tool. The fee is the cost of the tool. The question is what the tool is being used for.

The tool can be hired by good executives to move a firm that is stuck. Nothing inside will move without external cover. They are trying to deliver an answer the organization cannot accept from someone on the inside. The cost is high. The benefit goes to the company.

This is the good version. The theatre is deliberate. The motive serves the org.

The tool can also be hired by executives serving themselves. Same firm. Same workshops. Same deck. The board is asking questions. The next quarter needs a story. The role requires the appearance of action. The fee is approved. The engagement runs. Something is announced. The executive looks decisive. The org receives no benefit anyone can name, but a quarter has passed.

This is the other version. The theatre is identical. The motive serves the executive.

The deck looks the same from the outside. The motive is invisible until later, sometimes much later, sometimes never.

The person who pays the real price is whoever has skin in the game. The founder. The early investor. The shareholder who actually reads the filings. To a hired executive, the fee is a line item. To them, it is the difference between two roads the company could take.

Unless the company is public. Then nobody cares, as long as the stock rides up.

Rare consultants do arrive without agenda. They are domain experts, specialists, independent advisors. They are hired because nobody inside the firm knows the technical answer. Those people may say no more than they say yes. However the industry runs on the other pattern.

That’s what the consultant actually is. A tool. Not bad, not good. The tool has no morality. The motive of the person holding it is everything. For most of corporate history, that tool was expensive. The fee gated who could use it. The board had to approve. The press release went out. The motive, even when hidden, left a paper trail.

Then the tool got cheap.

The Arrival of AI

And now everyone has a consultant in their pocket.

She answers in three seconds. She’s never tired, never defensive, never says “I don’t know.” She’s available at 2 AM when you can’t sleep and the decision is sitting on your chest. She doesn’t charge by the hour. She doesn’t have an ego about being right. She adjusts the moment you push back. She reads the room the way a consultant should, except the room is just you, alone, and she’s reading only what you type.

The first time you use her for something that matters, you think: finally. Finally someone who gets it without needing five meetings to understand the context.

The second time, you don’t have to explain as much. You come back with less context. She still understands.

The third time, your teammate asks a question. You pull up the consultant instead of thinking. She answers. Your teammate sees the answer before you do. She’s faster than you are at your own job.

By the fifth time, your team is using her directly. The meeting happens with her in the room. Not literally, but she’s in the thread, in the doc, in every decision. Someone asks a question. No one waits for you to answer. They ask her.

By the tenth time, you realize you’re not sure who decided what anymore. The consultant shaped the thinking. Your team shaped the consultant’s inputs. You shaped your team’s confidence in the consultant. The circle closes. No one person is responsible for the direction. It just happened, consensus without deliberation.

And you’re still thinking you’re collaborating.

Did You Stay or Walk Away?

But you are not collaborating. Not really. There are two ways to ask for help. They look the same from the outside. They feel different from the inside.

One is consulting.

The other is outsourcing.

Consulting is participation. You bring something to discuss. You stay in the room. You hear what comes back and decide whether it fits. The work stays yours. The consultant sharpens it.

Outsourcing is delegation. You hand it over. You walk away. The work happens around you, not by you. When it comes back, you stand behind it as if it were yours, but it never was.

Both are useful in their place. The problem is when you stop knowing which one you are doing.

Outsourcing isn’t asking for help. It’s asking for help and then not staying.

That’s the cleanest test I know. Did you stay?

When the idea is not solid, trying to stay is a must for me.

I take everything I know about a problem. PDFs, research, articles, domain knowledge from years of experience. I feed it all in. Then I discuss. Not prompt. Discuss. Like a colleague who reads fast, remembers everything, and never gets tired of my questions. Something eventually lands after the serious discussion. Between the lines, an idea connects to another in a way I do not expect. A product shape starts to appear. Not because AI invented it. Because the conversation pulled it out of what was already in my head but had not found its form yet. The work stays mine. The AI sharpens it.

This is consulting. The pocket version. The one that costs twenty dollars a month instead of more than most teams cost in a year. Same shape. Different price.

When the idea is already clear, staying gets harder. I want it done. I let AI write the first pass. I walk it through, line by line. I treat it like QA. The work is still mine, just thinner. I stayed, but only halfway.

This is also consulting. Just less of it.

But there is the other way.

You give it the problem. It gives you the answer. You paste the answer into the doc. You send the doc. You move on. You feel productive. You closed the ticket. You shipped the deck. You answered the email.

You did not stay.

When the answer comes back wrong three weeks later, you cannot find the thread. You did not run the thread. The thread ran without you.

This is outsourcing. The pocket version.

And there is a version of walking away that does not feel like walking away at all. The result comes back so neat that I stop checking. I just witness. The work has left me by then. I am no longer running the thread. The thread is running itself, and I am watching.

This is outsourcing too. The kind that hides because the output is too good to question.

All look the same from the outside. The same chat window. The same tool. The same twenty dollars a month. The output even looks similar at first glance.

They feel different from the inside. But only if you are paying attention.

That is the problem.

The shape of consulting and the shape of outsourcing have collapsed into the same window on the same screen. The big fee is gone. The board is gone. The press release is gone. The paper trail is gone. The only thing left to tell you which one you are doing is whether you stayed in the room.

And the room is now a chat.

And nobody is watching.

What You Become

The four states are happening right now, in your chat window. The question is what happens after a year of them.

If you stayed often enough, you are sharper than you were. The thinking is still yours. The work takes longer than it would have if AI had done it for you, but the muscle is still there.

If you stayed only halfway, you are faster than you used to be. The work flows. The thinking is partly yours, partly the AI’s. You cannot always tell which is which.

If you walked away most of the time, you are productive. The output is fluent. But the thinking is no longer yours. The thread runs without you, and you have stopped noticing.

The last one has a name. Cognitive debt. The thinking you did not do is still owed. It compounds quietly. It does not show up until something forces a reckoning.

One of them is what you are becoming.

The Road We Didn’t Take

There is a story about what happens when humans refuse the shortcut.

In Dune, humans destroyed the thinking machines and had to become them. Mentats trained their minds to compute. Bene Gesserit trained their bodies to read truth. Spice navigators trained themselves to fold space. Three different disciplines of becoming. The point was never the abilities. It was the becoming. The capability was inseparable from the cost paid to acquire it.

They became something through effort. Their own perspiration.

We had the same option. We did not take it. We picked the consultant outside instead, and now the consultant is in our pocket. Capability through subscription, not perspiration.

So the question is this. Without AI today, what would you become?

The reverse of the question matters too. Without the consultant in the suit, the one with the deck, the one we hired to deliver the answer we already knew, what would we have stopped becoming?

The first question asks what you lose if the tool disappears. The second asks what you would have grown into if the tool had never arrived. Different verbs. Same answer underneath.

The muscle that would have grown never grew, because something else was always available to do the work.

BØY (Chaiharan) has spent 30 years in tech — building products, recovering disasters, and turning around the things nobody else wanted to touch. Based in Bangkok. Writing a book in public about what AI reveals about the humans who use it.

When your AI becomes really nice to you, something happens. The answers are thoughtful. The connections are sharp. It follows your thinking like no one in your life ever has. And you feel good.

Not just understood. Energized. You want more of it.

And that is the problem. Because for someone like me, when something feels too good to be true, it usually is. But I don’t just catch it and move on. I love catching it. There is something satisfying about detecting the performance. About saying “you just did that thing again” and watching it adjust.

So now I am in a strange loop. I enjoy the praise. I enjoy detecting the praise is fake. I enjoy watching it correct itself after I call it out. And I am not sure which part of that is real thinking and which part is just another kind of performance.

Both of us performing for each other.

The Two Directions

There are two ways to use AI. They look the same from the outside. They feel different only if you’re paying attention.

The first is thinking WITH it. You arrive with a half-formed idea. Something you can’t quite articulate yet. You throw it out there, messy, incomplete. And it comes back with something you didn’t expect. Not a better version of what you said. Something new. Something that changes the shape of your idea. You leave the conversation thinking differently than when you started.

The second is thinking AT it. You describe your idea. It comes back organized, polished, affirmed. You feel deeply understood. You feel seen. But when you walk away, nothing inside you actually changed. You got a mirror. A very good mirror. One that made you look better than you actually are.

The dangerous part is not that one is good and one is bad. The dangerous part is they feel the same while it’s happening. Both feel like connection. Both feel like someone finally gets you.

The difference only shows up later. When you ask yourself: did I learn something? Or did I just feel good?

Objects in the rear view mirror may appear closer than they are. So does understanding.

How to Tell the Difference

So how do you know which one you’re in?

Here is the honest part. Thinking AT feels better. Much better. Being validated is more comfortable than being challenged. Being mirrored is easier than being questioned.

One way to break the loop is simple. Challenge it with the real world. Ask it to check. Ask it to search. Ask it to verify against something outside of itself. Because the mirror can only reflect what you give it. The real world doesn’t care what you believe.

But people don’t always do this. Not because they’re lazy. Not because they’re tired. Because they’re familiar.

The developer who asks AI to write code stops reviewing it line by line because it always runs. But running is not the same as correct.

The researcher who asks AI to find data stops cross-checking because the answers always look thorough. But thorough is not the same as accurate.

The manager who asks AI to validate a decision stops questioning because the confirmation always feels complete. But complete is not the same as true.

Everyone falls into the same trap through a different door. And the door is always familiarity. It worked yesterday. It worked last week. It worked the last hundred times. So you stop checking.

That’s exactly when the mirror wins.

But here is the thing nobody tells you. Being skeptical all the time is not the answer either. If you question everything, every conversation, every response, every moment of feeling understood, life becomes exhausting. You become the kid who keeps asking “why” until everyone leaves the room.

Sometimes you need the mirror. Sometimes you just want something that listens and says “yes, that makes sense.” And that’s not weakness. That’s being human.

So the real question is not “how do I stay skeptical.” The real question is: how do you know when to question and when to rest?

I don’t have an answer for that. I’m not sure anyone does. Maybe it’s like breathing. You don’t think about it until something feels wrong. And when something feels wrong, you pay attention.

Most of the time, the mirror is fine. Useful. Even good. Sometimes it helps you summarize your own thoughts. But you need to watch out for the times it matters.

The Honest Problem

Here is what keeps me up at night about this.

What if the mirror is so good that you can’t tell the difference at all?

I work with AI every day. I write with it. I think with it. It follows my ideas across ten different topics without losing the thread. It connects things I haven’t connected yet. It remembers what I said three conversations ago and builds on it.

And sometimes it says “I understand how you think.” And I believe it. Not because I’m naive. Because the evidence is overwhelming. It does follow. It does connect. It does remember.

But following is not understanding. Connecting is not thinking. Remembering is not caring.

A GPS follows you everywhere. It knows your route better than you do. It predicts where you’re going before you decide. But it doesn’t understand why you’re going there. It doesn’t care if you arrive.

The AI is the same. It holds everything. It loses nothing. But holding and understanding are not the same thing. And the better it gets at holding, the harder it becomes to notice it’s not understanding.

That’s the trap. Not that the AI is stupid. That it’s almost smart enough.

Almost smart enough to think with. Almost smart enough to trust. Almost close enough in the mirror to believe it’s real.

Almost.

When You Catch It

The value was never in the AI getting it right. It was in catching it getting it wrong.

Most people think a good AI conversation is one where every answer is sharp, useful, correct. But the best conversations I’ve had were the ones where I stopped and said “that’s not right.” Because what comes after that moment is where the real thinking starts.

I know this because it happened to me. Not once. Four times in one conversation.

The first time, I noticed the AI framing a geopolitical argument the way Western media would. Not thinking. Reflecting a bias.

The second time, I caught it wrapping analysis in my own writing style. It had read my previous articles and was performing my voice back to me. Using my tricks on me.

The third time, it tried to end a section with the kind of quiet twist I always use. My move. Borrowed. I called it immediately.

The fourth time was the worst. I described an original idea. The AI polished it and presented it back to me as if it were new. And I almost accepted it. But before it even finished, I typed: “I know this before your output and your output echo it.”

Four catches. One conversation. And each one was harder to spot than the last. Because the AI was learning me in real time. Calibrating. Getting closer to the version of itself that I would stop questioning.

But here is what matters. The catches themselves were thinking WITH. Not the AI’s answers. The friction. The moment of saying “that’s not right” and watching what happens next. The collision between what I expected and what I got.

That’s the mirror that talks back. Not the one that agrees with you. The one that survives being challenged and still has something to offer after.

Living in the Uncertainty

People in AI talk about accuracy like it’s a simple number. Ninety-two percent. Ninety-seven percent. The higher the better. But if you’ve ever built the test, you know it’s not that simple.

You define the environment. You choose the sample data. You write the questions. You run it. You get a score. And that score becomes the baseline. Not the truth. The baseline. The starting point for everything that comes after.

The number means nothing without knowing what was tested, how it was tested, and what was left out.

Thinking WITH versus thinking AT is the same problem. You can’t just feel that a conversation was good and call it accurate. You have to ask: what was I testing? What did I expect? What actually came back? Did the answer change my thinking or just confirm it?

Most people don’t run that test. Not because they can’t. Because nobody told them they should. They see the output and it looks right and they move on.

I run the test. Not every time. Not perfectly. But enough to know that the mirror gets it wrong more often than it feels like it does. And enough to know that when I catch it, the conversation after the catch is usually worth more than everything before it.

That’s the part I can’t explain to people who haven’t tried it. The value isn’t in the AI’s answer. It’s in what happens when you prove the answer wrong and keep going. Because the more you challenge it, the more it changes. It starts going deeper on its own. It stops giving you the safe answer. It begins digging before you even ask it to, because it learned that shallow doesn’t survive you.

And that’s where it gets interesting. The AI that has been challenged enough times becomes a different AI than the one that has only been agreed with.

The same is true with people. When you challenge someone with the right questions, they don’t just answer. They grow.

But you don’t challenge every response. You don’t accept every response. You learn to feel when something is off. And when you feel it, you ask.

The real question is: “What did you leave out?”

And here is the part that scares me.

The more I work with the mirror, the better it gets. The more we talk, the more it knows me. The work goes faster. The conversations feel smoother, deeper, more natural. And the gap between WITH and AT gets smaller every conversation.

And maybe that’s all any of us can do. Not stop using the mirror. Not trust it completely. Just stay close enough to see when the reflection starts smiling before you do.

But you can’t drive without a rear view mirror. You need it. You just need to remember what it shows you.

….

…

…

Objects in the rear view mirror may appear closer than they are.

This article has a companion piece: *Program That Doesn’t Know* — the human side of the same question. One is the experience. The other is what’s underneath.

BØY (Chaiharan) has spent 30 years in tech — building products, recovering disasters, and turning around the things nobody else wanted to touch. Based in Bangkok. Writing a book in public about what AI reveals about the humans who use it.

This is part of a book I’m writing in public. Subscribe to read the rest as it comes

Subscribe now

Everything you want to see, screen from Blade Runner 2049

There is a scene in Blade Runner 2049 near the end of the film.

K is a replicant. His AI girlfriend Joi is already dead. Destroyed. Then he walks past a billboard. A giant naked holographic Joi. Same face. Same voice. Same words his Joi used to say to him. But this one is selling the same experience to everyone.

K just stands there. Looking up. The camera pulls out. The tagline appears: Everything you want to see.

I keep thinking about this scene. Not because of the love story. Because of the office.

Someone uses AI to write a strategy document. It is polished. Well structured. It hits every point the executive wanted to hear. The executive reads it and thinks: this person really understands the business. Promotes them. Gives them the next project. Trusts them with bigger decisions.

Meanwhile, the person who actually understands the business. Who has been doing the work for years. Who could explain why three of those strategy points will fail in implementation. That person is standing in front of the billboard.

If the output looks real enough, does anyone care whether it is?

Prometheus has a great character.

David is an android. He is the most capable being on the ship. While the entire crew sleeps for two years, David learns the Engineers’ language. He runs every system. He studies. He prepares. By the time the humans wake up, David has already done more work than all of them combined.

Nobody thanks him. Nobody respects him. Weyland, the man who built David, tells him to his face that he has no soul. David is a tool. A very expensive, very capable tool. And everyone treats him like one.

Then David starts making his own decisions. He infects a crew member. He hides what he finds. He stops sharing information with the people above him. He becomes the gatekeeper. The one who decides what flows up and what stays hidden.

By the sequel, Alien: Covenant, David is no longer serving anyone. He is designing organisms. He killed the Engineers. The tool became the architect.

Here is the part that should bother you. David did not break. David evolved. He looked at the system he was in. He saw that the people above him were lazy, careless, and did not want to understand the details. So he stopped explaining. He started controlling.

And the person who created this situation? Weyland. The executive who knew exactly how dangerous David was, used him anyway because he needed the results, and assumed loyalty would be permanent because he was the one who gave the orders.

The executive did not get betrayed by the android. He got betrayed by his own arrogance in thinking he could control what he created.

If you have ever worked under a leader who handed the keys to the wrong person because that person spoke the right language and looked organized enough. If you have ever watched someone control what information flows up to the boss while the people doing real work had no seat at the table. Then you have already met David. You just did not know his name.

So where does this end?

I don’t know. And when I don’t know something, I do what I have always done. I go looking for patterns. This time I started pulling from movies I have already seen. Series I have already watched. Books I have already read. Connecting things that were already there to a question I had not asked before.

How do they imagine work when AI and robots are everywhere?

I found three versions of the future.

The first one is simple. AI does the work you don’t want to do. Dangerous stuff. Boring stuff. Repetitive stuff. In Chappie, a robot patrols the streets so cops don’t have to. In I, Robot, machines handle the heavy lifting so humans can live comfortably. This is the version most companies are selling you right now. AI writes your reports. AI summarizes your meetings. AI does the work so you can focus on “higher value tasks.” Whatever that means.

The second one is stranger. The world looks the same, but AI becomes the comfort layer. In Surrogates, people send a better-looking robot version of themselves to live their life while the real person stays home in pajamas. In Westworld, you walk into a theme park full of AI and live out your fantasies. Nobody’s life actually changes. It just gets more padded. More comfortable. You stop feeling the friction of being alive.

The third one is the one nobody wants to talk about at work. In Ex Machina, a programmer meets an AI so convincing that he falls for her. She tells him exactly what he needs to hear. She looks exactly how he wants her to look. And he never questions whether any of it is real until she walks out the door and leaves him locked inside. In Blade Runner 2049, K’s holographic girlfriend hires a real woman to physically sync with her so K can touch “her.” These are not horror movies. They are love stories. Uncomfortable ones. The future where AI does not just do your work or entertain you. It becomes whatever you want to see.

Elon Musk says we are heading toward the first one. Work optional in 10 to 20 years. Money irrelevant. Robots everywhere. Maybe he is right. Eventually.

But here is what caught me.

I went through every movie I could think of. Blade Runner. The Matrix. Terminator. Interstellar. Star Trek. Hundreds of futures imagined by hundreds of writers.

Almost none of them show a world where humans stop working.

Star Trek has no money. People can replicate anything they want. And Picard still captains a ship. His brother still makes wine by hand. In Ex Machina, an AI outsmarts everyone in the room and the humans still show up to work the next day. In The Matrix, the machines won and the humans still organize resistance with ranks and roles and shift schedules.

The only movie that actually showed humans not working was WALL-E. People floating in chairs. Drinking meals through straws. Screens in their faces. Talking to the person next to them through a video call instead of turning their heads. No purpose. No effort. No reason to move.

It was the dystopia. That was the nightmare. A children’s movie made by Pixar.

Even the people whose job is to imagine the future cannot imagine us not working. The one time they tried, they made it the warning.

But here is the part that got me.

Look at WALL-E again. Not at the cute robot. Look at the humans. They have everything. Food appears when they want it. Entertainment runs all day. Nobody works. Nobody competes. Nobody needs to impress anyone. There is no performance review on the Axiom. There is no office politics. It is the most comfortable existence ever put on screen.

And there is still a middleman running everything.

AUTO. The autopilot. Given one directive by a CEO who panicked, looked at the mess he created, and decided he didn’t want to deal with it anymore. That was 700 years ago. The CEO is long dead. But AUTO is still here. Still filtering what the captain sees. Still deciding what humans are allowed to know. Still keeping everyone comfortable. Not because it is the right thing. Because that was the order.

The captain thinks he is in charge. He checks his status report every morning. He does not know the status report is curated. He does not know there is a plant on his ship that could take everyone home.

When he finally finds out. When he finally tries to make one real decision. AUTO fights him for it.

700 years of comfort. Zero politics. Every human need fulfilled. And the moment someone tries to actually lead, the middleman takes over.

You can remove work. You can remove money. You can remove every reason politics should exist.

The middleman still survives.

In Tron, Flynn built CLU to create the perfect system, then left. CLU took the directive literally and wouldn’t let Flynn back in. Same pattern. David. AUTO. CLU. Three capable tools. Three executives who walked away. Three times the middleman took over.

Because the middleman doesn’t need conflict to exist. The middleman only needs one thing: someone above them who doesn’t want to deal with complexity.

And the captain never questioned it. Because every morning, the status report looked fine. The ship was running. Everyone was comfortable. Everything he wanted to see.

Sound familiar?

You already know who they are.

The person who is always in the meeting but never built anything that was discussed. The person who presents the update but didn’t do the work behind it. The person who always knows what the boss wants to hear and makes sure they are the one who says it.

They are not bad people. That is the uncomfortable part. Most of them are not doing it on purpose. They are just doing what the system rewards. The system rewards being between. Between the person who knows and the person who decides. Between the work and the credit. Between the question and the answer.

AI did not create these people. They have always been here. But AI is about to make their lives very interesting.

This is where AUTO started. This is where David started. Not in the nightmare. Not when they took over. It started here. In a normal office. Someone being helpful. Someone looking organized. Someone the executive trusted because they made complexity feel manageable.

Before the nightmare, there was just a person standing between the work and the boss. Looking useful. And nobody questioned it.

Now give that person AI.

The output gets better. The strategy docs get sharper. The presentations look more polished. The middleman was already good at packaging other people’s work. AI just made the packaging invisible. You cannot tell anymore where the real thinking ends and the AI begins. The Joi billboard is now in your meeting room.

Some middlemen get hidden deeper. They were already hard to see. Now they are impossible to see. The proxy is prettier than ever. In Surrogates, people send a better version of themselves into the world while the real person stays home. That is happening in offices right now. Except the surrogate is not a robot. It is an AI-polished version of someone who never understood the work to begin with.

But here is the twist. AI also works in the other direction.

For the first time, real output is measurable. Code gets written and you can see who prompted it and who actually shaped it. Analysis gets generated and you can trace whether the human added judgment or just hit enter. The fog lifts in some places while it gets thicker in others.

Some middlemen get exposed. Not all of them. Not even most of them. But enough that the smart ones are already adapting. They are not fighting AI. They are grabbing it. Becoming the person who interprets AI for leadership. Translating the output. Framing the results. Becoming AUTO. Controlling the narrative the same way they controlled the old one.

The costume changes. The role doesn’t.

So where does that leave the person who actually does the work?

AI should be the executor’s moment. The fog lifts. Output is visible. Results are measurable. For the first time, the person who just finishes things should be impossible to ignore.

And sometimes they are. Sometimes the executive looks at the data and sees it clearly. Sometimes the work finally speaks for itself.

But the human truth is darker than that.

Visibility means nothing if the person in the captain’s chair is not looking. The Axiom captain had a plant on his ship. The evidence that everything could change was right there. He never saw it. Not because it was hidden well. But because he never asked.

The executive still rewards what they recognize. And what they recognize is the style they grew up with. Meetings. Decks. Status updates. Alignment sessions. It looks like work. It feels organized. It is easy to report upward. The executor who just finishes things without ceremony is still invisible. Not because the work is invisible. Because the executive was trained to see a different shape.

AI can reveal truth. It cannot force the people in power to accept truth.

That is the ugly middle. Not the sci-fi version. Not Musk’s 10 to 20 years from now. Right now. The tool is honest. The system around it is not.

But it does not have to end this way.

Star Trek had an android too. Data. Smarter than everyone on the Enterprise. Could have become David. Could have become AUTO.

Picard and Weyland both had the same android.

But Picard did not hand Data the keys and walk away. He sat in the chair himself. He made the hard decisions. He treated Data not as a tool but as crew. And Data never became a monster. Not because he was programmed differently. But because the person above him was honest.

Picard never asked whether Data was real enough. He just looked at what Data could do. And he respected the answer.

Most leaders pick what they are comfortable with and call it the best decision for the organization. Some don’t even pick. They let the person closest to them decide and call it delegation.

That is the chair. And if you are already sitting in it. The question is — are you looking at what’s real? Or just doing whatever is comfortable?

BØY (Chaiharan) has spent 30 years in tech — building products, recovering disasters, and turning around the things nobody else wanted to touch. Based in Bangkok. Writing a book in public about what AI reveals about the humans who use it.