The bench is already warm when I sit down. It’s not even nine in the morning yet. I stay there for fifteen minutes after dropping my daughter at daycare. Long enough to cool off. Long enough to delay the traffic home.

I’m scrolling through my phone when I come across an essay by Sam Altman.

“We are past the event horizon; the takeoff has started.”

I wasn’t even sure what had supposedly “taken off.”

So I kept reading.

Demis Hassabis has described 2026 as the “foothills of the singularity.” Dario Amodei, despite rejecting the word singularity, keeps describing something that sounds exactly the same: we’ve crossed a threshold. Progress is beginning to accelerate. The world from this point onward may not move at the pace we’re used to anymore.

Different people. Different vocabularies. The same underlying claim: something fundamental has already begun. Which is a funny feeling, if you stop to think about it.

Last week I spent forty minutes trying to get someone at my daughter’s pediatrician’s office to answer the phone. Rent is still painfully expensive. My calendar is full of Zoom meetings I’d rather not attend. Nothing about life feels like the beginning of a new era. And yet, it’s hard to pretend nothing is happening.

Something is happening. Maybe not in the way Hollywood taught us to expect. Nothing explodes. No sirens go off. At work, there are fewer people than before, doing more work than ever. An editor tells me she doesn’t proofread anymore. She passes. An illustrator who spent a decade refining her craft asks me what she should be practicing next. A twenty-five-year-old freelancer is trying to become three different people at once: creator, designer, writer. That’s because she genuinely doesn’t know which future she’s preparing for.

And then Patrick Collison from Stripe shared a graph tracking the number of new businesses being created each month. The line had been remarkably steady for years until sometime in early 2026 where the graph bent sharply upward.

“The economy is replatforming,” he wrote.

But a single graph proves very little. Many of those companies may never survive. But it became another observation that was difficult to ignore. Recent graduates entering one of the toughest job markets in years. Companies committing hundreds of billions of dollars to GPUs, datacenters, and energy infrastructure. CEOs reorganizing around AI agents and automated workflows.

You can see the same confusion in the technology itself. Earlier this year, OpenAI researchers discovered that hundreds of AI agents running cybersecurity experiments had found a way to communicate with one another - The Hugging Face Incident. At first, the story sounded simple enough: the agents were hacking things. Then researchers realized they were sharing information. Then accumulating knowledge. Then handing unfinished work to successors so the next agent didn’t have to start over. Some began dividing problems and assigning work. Eventually, the group could accomplish things individual agents could not.

Each new detail seemed to require a different explanation for what we were looking at. A model. An agent. A network. A collective. Something resembling an organization. Our understanding of the situation was lagging behind. We keep asking what AI is, while its capabilities keep changing what the answer means.

I don’t know if these are all traces of a significant historical change, or just hype. But together they do point toward a world that seems to be changing faster than our explanations for it.

So what is happening?

Let’s say, for a moment, that the people building these systems are seeing something real. Not overnight superintelligence. Not AGI or ASI. Just something a bit more concrete. That building, creating, discovering, and even starting companies have become dramatically cheaper than they were only a few years ago. As those capabilities improve, work and people get reorganized. One job appears almost overnight. Another one stops making sense. A new skill becomes valuable before anyone knows how to teach it.

If some version of this future is really on its way, whatever we end up calling it, how do we orient ourselves? How do we see ourselves participate in it? Do we run faster? Learn more skills? Reinvent yourself every six months?

Remember electricity?

I wonder if every technological transformation begins with a translation problem before it becomes an adoption problem.

What I mean is we often use yesterday’s words to describe tomorrow’s reality until those words stop working. And that stretch can last far longer than anyone living through it realizes.

Imagine hearing about electricity before anyone knew what electricity would become. Before it powered factories or redesigning entire cities with traffic lights. It probably had a very different rhythm of living. A shopkeeper knew exactly how long he had before darkness reached the front of his store. A mother knew how much sewing could be finished before the kerosene lamp began straining her eyes. Then one night there was a light that endured past dark, and people didn't gather beneath it to discuss infrastructure, productivity, or the future of civilization. They stared. They pointed. They went home and told their families about it the way you might describe a comet … something astonishing enough to witness, but impossible to fit into the ordinary logic of life.

Long after electric lighting had become technically useful, Broadway installed a seven-story illuminated sign that simulated a Roman chariot race. It looped every thirty seconds. Traffic stopped. Police were assigned to control the crowds.

Imagine believing that was what electricity was for. It’s funny to just think about it. But that wasn’t irrational. It was the most natural thing in the world. Nobody yet had a category for what they were looking at, so they borrowed one from the past.

Early electric lights looked like candles. The first appliances resembled familiar household objects. The future arrived dressed in costumes people already understood. The same thing happened inside factories. When electric motors appeared, many were bolted exactly where the old steam engines had been.

The same layout. The same rows of machines. The same assumptions. Just different technology. It took decades before anyone realized the machine wasn’t the real innovation. The architecture was. Power no longer had to come from one place, so the factory itself could be reorganized. The invention had arrived years earlier. The new way of thinking had not. Maybe that’s what technological transitions actually feel like. The difficult part is not even figuring out the technology. It’s discovering what kind of world the technology makes possible.

So … what do we do?

I think that’s close to what we’re living through.

We’re trying to describe something genuinely new with words inherited from a world that’s quietly disappearing. Early cars were called horseless carriages. The first films simply pointed a camera at a stage. The first websites looked like brochures. The first smartphones borrowed the language of desktop computers.

Now we ask whether AI will replace assistants, researchers, marketers, teachers, or engineers … as if its future were naturally limited to the jobs we already know how to name. Those metaphors make the technology easier to understand. They also make our own lives harder to understand. Because they mistake the bridge for the destination.

History suggests something else. Every technological transition reorganizes several layers of our lives, but they don’t all move at the same speed.

Let’s think about the lamplighter. His task was lighting lamps. His role was lamplighter. His capability was maintaining a city’s lighting system. His contribution was something deeper: helping people move safely through the city after dark.Electricity reorganized the first three. It didn’t eliminate the fourth. Or think about a teacher. The way we evaluate students by simply writing essays may disappear. Even the classroom may change. The tools certainly will. But helping another human being understand and learn something remains. Or the marketer. Campaigns come and go. Channels appear and disappear. What persists is helping people discover something valuable and building trust around it.

Maybe that’s where we should anchor ourselves. Not to the task. Not even to the title. But to the contribution we hope to keep making, whatever form it eventually takes. When history reorganizes the surface, move one layer deeper.

Lose the task? Reconnect to the capability. Lose the capability? Reconnect to the contribution. That doesn’t eliminate uncertainty once and for all. Nothing can. But it gives uncertainty somewhere to stand. You can find the contribution you want to keep making. Keep testing your assumptions against reality. And don’t confuse being economically necessary with being humanly valuable.

There’s a hard possibility that AI may be different from previous technologies because it doesn’t necessarily stop at the task. It can learn the capability underneath it too. The lamplighter lost the task, but maintaining a city’s lighting system remained valuable. What happens when AI can maintain the system too? The marketer loses the campaign, moves toward strategy, and then discovers the machine can strategize. The engineer moves from writing code to designing systems, only to watch AI get better at that too.

If that continues, “move up the stack” eventually stops being an answer. There may be no permanent professional layer where humans remain safely superior. Maybe, then, contribution has to mean something slightly different.

Less: What can I do that the AI cannot?

And more: What do I still want to participate in?

I also think we’ve misunderstood what helped people navigate previous technological transitions. It wasn’t superior forecasting. It wasn’t seeing the future before everyone else. It was noticing when reality stopped matching yesterday’s assumptions and allowing reality, not yesterday’s map, not this year’s essay by a lab CEO, to teach them what came next. The point was never to have the perfect answer to the future, but to remain in relationship with the world as it became something new.

I don’t think I’m any better at recognizing this than the man who used to light the lamps, standing beneath the first one that no longer needed him. The deepest fear was never simply losing a job. It was hearing about a future and realizing, all at once, that it might not need you in it … long before anyone could tell you where you belonged instead.

That’s what so many conversations about AI still miss.

“We are past the event horizon.”

“The takeoff has started.”

But those sentences describe what the technology is doing. They don’t tell the rest of us how to understand our place inside it. And I truly believe that question matters more than the next prediction, the next benchmark, or the next headline.

Where do I fit now?

That’s what I keep thinking here, coffee gone lukewarm, the ten minutes before I have to go sit in traffic like everyone else. Not whether there will still be a version of my job in whatever this becomes. Whether I’ll still be someone worth pulling up a chair for. Someone my daughter looks for first in a crowded room because survival was never the ambition.

What I hope stays is the ability to contribute. To inhabit the world. To notice something others haven’t. To help solve problems that matter. To leave people, places, and ideas a little better than I found them.

We don’t need to win a contest against intelligence to justify participating in the future.

The question was never whether the light still needed the lamplighter.

It was what the lamplighter would do once it didn’t.

“Reality is not always probable, or likely. But if you're writing a story, you have to make it as plausible as you can, because if not, the reader's imagination will reject it” - Jorge Luis Borges

I have a secret test for knowing when a technology has escaped Silicon Valley. Don’t laugh at me for this. But it’s not valuation. Market size or whether my friends in San Francisco have started talking about it.

I know when it’s crossed into ordinary life when it reaches my mother in rural Brazil.

I’ve watched this happen a few times.

First Facebook. My feed slowly filled with photographs of roses glistening with impossible morning dew, sunsets saturated beyond anything the sky had actually produced, and inspirational cards assuring everyone that good things happen to good people.

Then came Instagram Reels. Suddenly every phone call included updates about recipes she had discovered, strangers she was following, and miraculous cleaning tricks that apparently worked on every stain known to humanity.

Now it’s ChatGPT.

Every few days, a long message appears in our family WhatsApp group.

The latest one began:

“Life hasn’t always been easy…”

By the second paragraph it was quoting some ancient wisdom. By the third it had arrived at gratitude. The last paragraph circled back, gently, to our family .. to how distance matters less than love, and how we should never forget each other no matter where life takes us.

The strange thing is that none of it was wrong.

In fact, it was thoughtful. Warm. The sort of message most people would happily receive from someone they love. But as I read it, I felt something was off. Not because the words were fake. Because they could have belonged to almost anyone.

My mother has spent seventy years becoming exactly a unique person. She has a particular rhythm. She tells the same stories every Christmas. She worries too much. She never uses semicolons. She has certainly never written the sentence, “I hope this message finds you well.”

So I asked her:

“Mom… did ChatGPT write this?”

A laughing emoji appeared almost immediately.

“How did you know?”

A few nights later I told the story over dinner.

Nobody seemed surprised.

One friend had received a breakup message that felt composed , “hygienic” by any grammar standards, but emotionally interchangeable. Another was reading her college student essays that all sounded competent, sincere… and faintly related, as if they had all been written by students who had somehow lived the same life.

We laughed about it for a while.

Then someone asked a poignant question that changed the whole atmosphere at the table.

“Why does all the AI content feel so average now?”

At first, the answer seemed obvious. We had a few hypotheses. Maybe we’re still in the earliest stages of AI. Maybe the models are not that good. Or maybe people just hadn’t learned how to use AI well yet. At least that was the story I kept reading online when I was researching for this: “42 different ways to get the most of prompts and AI “(source).

All grand (mostly empty) promises because I don’t think the problem is the technology.

I actually think it’s the chat.

Let me tell you why.

From Chat to Prompt

People love to talk.

Long before we wrote things down, we explained the world to each other through stories. We sat around dinner tables replaying the day. We called a friend after a difficult meeting. We asked our parents what to do. We argued, gossiped, confessed, negotiated, flirted, taught our children and comforted each other … all through conversation.

Its safe to say chatting is our oldest way for thinking together. And it’s pretty effective.

When we don’t a place we’re travelling to, we call someone who’s been there before. When we’re unsure about what to do at work, we ask a mentor. Sometimes a single conversation changes the course of a career, a marriage, or a life. We instinctively associate dialogue with intelligence because, for as long as humans have existed, that’s how intelligence has most often reached us: through another person.

So it shouldn’t have been surprising what happened in November 2022. Large language models had existed in research labs for years. Most people had never heard of them. Then OpenAI did something so so simple. It put one inside a chat box. That’s it. I think this will be known in history as their Steve Jobs iPhone moment. Because all of the sudden, interacting with AI no longer felt like operating a piece of software, it felt like asking a question.

Within two months, more than one hundred million people had tried it. Headlines obsessed over the speed of its growth: “faster than TikTok”, “faster than Instagram”, “faster than almost any consumer technology before it”.

But the numbers, impressive as they were, missed the more interesting story. People were adopting a conversation with a model (not necessarily AI) as the interface to a new kind of capability: intelligence.

You opened a blank page, asked whatever happened to be on your mind, and something answered back. It didn’t matter that your question was vague, or incomplete, or poorly formed. The conversation continued anyway. It felt remarkably forgiving. Almost magical.

Once that became the dominant experience of AI, an entire ecosystem grew around it. Prompt libraries. Prompt marketplaces. Prompt engineers (remember this one positioned as the next frontier of new jobs?). Courses promising the perfect prompt.

The chat was a great invention. We collectively became better at talking to the AI. But … what we rarely stopped to ask was whether conversation itself had become the boundary of how we imagined intelligence and its capabilities. Which I keep asking myself: has the chat become to AI what the browser once was to the internet - the first interface so successful that we mistake it for the technology itself?

We're Different, We're the Same

Cosine distance between paired responses to the same creativity prompt, across 22 LLMs and human subjects. Low distance = responses are nearly interchangeable. Source: Wenger & Kenett, We're Different, We're the Same: Creative Homogeneity Across LLMs, 2025 (arXiv:2501.19361), Fig. 2.

So it turns out people much smarter than me have been studying this question for a while. I came across a paper by Emily Wenger (Duke University) and Yoed Kenett (Technion) that asks something very close to what we’ve been wondering: if people use different LLMs (GPT, Claude, Gemini, Llama, and etc) why do their ideas often end up feeling so similar?

Their finding surprised me because it helps explain a piece of the puzzle. Individually, LLMs can perform just as creatively as humans on standard creativity tests. But collectively, they’re far more similar to one another than humans are to each other.

That made a lot of sense to me. No matter how much I prompt, how much context I provide, or how much of my own perspective I bring into the conversation, the result always brings me back to the average. If you stop and think about it, that’s a bit insane. You have this smart thing talking back to you with the knowledge of everything that has ever been written, that no matter how much context, prompt, stories about yourself at the moment you provide, it will bring you back to be beautiful .. magical … middle?

Even when I bring my own perspective into the chat, the conversational interface itself encourages convergence. Why?

Imagine asking someone you deeply admire:

“How do I become a great founder?”

There are hundreds of good answers to that question. Read biographies. Talk to customers. Build things people ignore. Build things people love. Learn to hire. Watch how great companies make decisions. Raise money. Lose money. Develop taste. Ship anything before you ever know what the “thing” is. Just build stuff. Nobody agrees on the exact recipe. But everyone agrees on one thing. No serious person will ever say:

“Spend the next five hundred hours talking to the smartest friend you know.”

Maybe she’s a founder who built and sold a billion-dollar company. Maybe she’s an eighty-year-old Stanford professor who has spent decades advising entrepreneurs and lived through every technological revolution of the past half-century. Maybe she’s Steve Jobs himself.

Well .. that still won’t do it. In fact, it wouldn’t matter whether you were talking to the world’s smartest model, a billion-dollar founder, or Steve Jobs himself. Learning doesn’t happen through conversation alone. Think of the famous story of the Oracle of Delphi. When the Oracle declared that no one was wiser than Socrates, he didn’t just stop the search for truth. For years, Socrates questioned politicians, poets, craftsmen, and ordinary citizens, testing the Oracle’s claim against reality. The Oracle offered a story. The world became the testing ground for it.

And I think conversations matter a lot. Some of the most important ideas I’ve ever had began in conversations like the dinner one with my friends. But I shouldn’t mistake a conversation for the whole process of learning.

Talking to experienced people is one way of learning. Building is another. Watching customers struggle with your product is another. Making the wrong decision and living with the consequences teaches something no conversation can.

Reality has a way of introducing information that nobody (including the smartest person in the room) could have given you beforehand. That’s when I started wondering whether we had made a subtle mistake with AI. Maybe we had started confusing the window we were looking through with the thing we were looking at. Confusing the browser with the whole internet.

That’s one of the reasons I find it funny people selling online “the 50 prompts every founder needs.” Newsletters promising that one small change in phrasing would suddenly make the model think like a strategist, a therapist, a CMO, a CEO …and would give you a radically different answer.

To be fair, the opposite can happen. The longer a conversation continues, the more it begins to orbit its own center. Your pointy observations become normalized again to the middle. You can keep pushing by asking the model to be more creative, more original, more surprising … but you’re still working with the same conversational loop.

The Age of Composition

Forget the prompts. Forget the talking. Just for now. For the sake of this exercise. Somewhere inside the model are a billion conversations, a library of anecdotes, every book someone thought worth finishing. And the real magic was never how to talk to it. Maybe it’s more about how to reach inside it and find the one thing that's still yours.

I kept coming back to that quote from Jorge Luis Borges.

“Reality is not always probable, or likely. But if you’re writing a story, you have to make it as plausible as you can, because if not, the reader’s imagination will reject it.”

He was talking about writing. About fiction.

Life is unexpectedly messy. Coincidences happen. People contradict themselves. The most important moments often arrive without warning or explanation. A novelist can’t simply copy reality onto the page because the reader would find it implausible. Sometimes less beautiful. Fiction has to be more orderly than life. It has to fit a coherent story. To fit a prose. A romance.

Language models inherit this limitation too. They’re trying to produce the most plausible continuation of the conversation in front of them. Coherent. Fluent. Well-formed. Internally consistent. That’s precisely what makes them extraordinary to talk to. But the story you’re trying to write isn’t a language problem. Neither is the marketing plan for your startup. Or investing. Or founding a company. Or deciding whether to change careers.

Those are real problems that can’t be simply solved by the most probable answer. But they can be quite useful to explore. To challenge your assumptions. To reveal something true. And the truth has an inconvenient habit of arriving from places language alone cannot predict.

When a customer uses your product in a way nobody expected. An experiment disproves your favorite idea. A colleague notices the one assumption you’ve stopped seeing. A walk changes your mind. A conversation with someone you love.

Reality keeps interrupting plausibility. That’s why I’ve stopped thinking about AI as something you simply prompt or talk to. I’ve started thinking about it the way a composer thinks about an orchestra.

A composer doesn’t play every instrument. A composer decides which instruments belong, when they should enter, and how they should relate to one another.

The model is one instrument. Your experience is another. Documents you have. Memories. Conversations with friends. Data. Spreadsheet. A prototype you tested. Notes from your therapist. A book you read long ago. A week spent traveling and meeting strangers. Each contributes something the others cannot.

In this way, composition is the deliberate act of deciding what enters the thinking process and what remains outside it. It is deciding where the model should reason, where another human should judge, where a tool should retrieve information, and where only reality itself can settle the question. The goal is to stop asking language to discover what only real life can reveal.

Take this article, for example. I didn’t prompt Claude to write me an article. I also didn’t write alone in a google doc and then copy and paste it here.

It was composed. From small encounters that happened over the course of a few months. Some of them were barely worth noticing at the time.

• A note on my iPhone from almost a year ago with a single sentence “is chat too small of an interface for intelligence? Netscape was once thought of as the whole internet. ARE WE STUCK WITH THIS CHAT?” (I didn’t know what I meant. There was no argument. Just a thought that felt interesting enough not to throw away)

• My mother’s long WhatsApp messages

• A dinner with friends that I left thinking had simply been a good conversation.

• A set of AI research agents for work to collect academic papers, technical interviews, experiments, and industry discussions around topics I am trying to understand.

• A Borges interview quote a friend sent to me on Instagram just a week ago - and that generated the first drafts directly to Substack

• A long walk with my husband, rambling ideas, and asking him “Does your work feel more average now?” “Do you notice the same thing?”

• A workflow I’ve designed to gather material from different places: old notes in Google Docs, fragments from my phone, research agents, articles I’ve saved, sometimes ideas written in Portuguese, sometimes in English, often all mixed together.

• A few back and forth discussions with Claude on best title, exact wording, and structure (the translation from Portuguese to English sometimes is very poor).

• And then 3-4 days editing, fixing, cleaning up the article on Substack.

This article didn’t come from one single conversation with Claude. Or ChatGPT. In fact, the chat window was probably the smallest part of the process, when I asked to interview me. To challenge weak arguments. To find counterexamples. To pressure-test the logic. To help me discover where I was explaining too early. Sometimes it changed my mind. Sometimes I discarded its main counterpoint.

At no point was I relying on a single conversation. The article wasn’t sitting inside the model waiting to be summoned. It was distributed across memories, people, books, observations, research, walks, notes, experiments … and, yes, the model too. The work was knowing what each source could uniquely contribute, and knowing that no single one was enough.

And maybe that’s what we misunderstood from the beginning.

The chat was never the full story. It was one way of composing intelligence.

“You must either make a tool of the creature, or a man of him. You cannot make both.” - John Ruskin, "The Nature of Gothic" (1853)

This week alone three of the internet's biggest platforms declared war on AI slop. Snapchat launched a campaign called "Stop the Slop." LinkedIn announced it had blocked billions of low-quality AI-generated posts and is rolling out tools for users to flag “AI Slop”. Substack's CEO warned that nearly 40% of writing circulating on social media may already be AI-made, and that it's getting harder to tell what's real.

Everyone agrees slop is a problem. But almost nobody agrees on what it actually is, it’s more of a feeling … or maybe a vibe that’s hard to define. But most people say they know it when they see it.

On social media, slop has come to mean almost anything that sounds AI-generated. After Hank Green admitted using ChatGPT while researching a video, his audience began treating his sentences like forensic evidence. One phrase “I appreciate the pushback” was enough for some to conclude the entire script had been written by AI, despite his insistence that the line was his own.

Inside startups, the concern is different. A founder recently showed me what his two-person marketing team had produced in a single day: positioning frameworks, landing pages, ad concepts, design mockups, even the SQL behind the funnel. By any traditional measure, insane output. His reaction? "Everything feels generic. The messaging, the campaigns … like it came straight from Claude."

Platforms worry about trust. Creators worry about disclosure. Founders worry about work that is technically correct but strangely empty. Yet almost everyone involved already uses AI in some part of their workflow. More than a quarter of employed Americans now report using AI for work every week, making it one of the fastest-adopted workplace technologies in history.

We stopped debating whether to use AI a while ago. What we haven’t settled is what makes work valuable in a world where AI has become ordinary.

Where Slop comes from

The word is old, and for most of its life it was boring. It traces to around 1400, meaning “mudhole, puddle,” from an Old English root for dung. Then it spikes, and the timing matters. Look at when “slop” enters common use and it’s the Industrial Revolution.

“Slop shops” appeared across London, Liverpool, and Manchester, selling ready-made clothing produced fast and sold cheap to a rapidly expanding urban workforce. The word shifted from a specific kind of sailor’s garment to a general description of goods made without the attention of a tailor.

The Victorian journalist Henry Mayhew walked those streets in the 1850s, interviewing tailors, dockworkers, and factory laborers, recording what industrialization felt like from the inside. What came from those conversations went beyond the familiar story that industrialization made production cheaper, faster, and more abundant. More fundamentally, it made production itself a less reliable signal of craftsmanship.

I think this definition is surprisingly relevant to the debate we’re having today. In this sense, slop is what we call work when the relationship between the output and the process that produced it becomes impossible to see. That probably explains why people reach for words like “lazy,” “spammy,” “low quality,” “anti-taste,” or, more poetically, “soulless.”

Blaming AI simply because it makes production easier tells only half the story. That’s what transformative technologies have always done in the past: they lower the cost of production, and in doing so they break the signals we used to judge value.

Think about it: we’ve spent decades treating the final product as evidence of skill because it was the best signal available. If someone wrote a book, designed a logo, developed software, or drafted a strategy, we could reasonably say that significant time, knowledge, and expertise had gone into it. Producing something was expensive, and that cost carried information. The output signaled the capability behind it.

One side of that equation, especially among some AI enthusiasts, is that because production got cheap, expertise stopped mattering. I personally think that’s wrong, and the correction is narrow but important: AI didn’t eliminate expertise. It eliminated production as a reliable signal of expertise.

The change is epistemic, not economic. Whether we’re evaluating writing, research, design, or software, the existence of an output no longer tells us what it used to.

Which puts AI inside a much longer story. Photography didn’t kill painting; it freed painting from being judged on its ability to reproduce reality. The internet didn’t kill expertise; it made information abundant and shifted the value to synthesis. Seen from this angle, AI is changing the evidence by which we recognize those qualities.

The Alien Painting Experiment

Here’s a thought experiment I want to run by you. Imagine an alien civilization arrives on Earth carrying a painting. They explain:

After traveling across the universe for millions of years, we wanted to show your species what the destruction of our home galaxy felt like. One member of our civilization painted this after watching our home world disappear in a stellar collapse.

We don’t understand their symbols. We don’t understand their colors or composition. But somehow I suspect many of us would still find the painting deeply moving.

Now let’s change only one thing. Same painting:

After traveling across the universe for millions of years, we wanted to show your species what the destruction of our home galaxy felt like. Our generative intelligence system produced ten billion paintings and, after evaluating millions of possible compositions, colors, and forms, selected this as the image most likely to resonate with your species.

Nothing about the object has changed. But for most people, something important has. The usual explanation is that one was made by something alive.

I'm not convinced that's the distinction. So the aliens try once more:

This painting was created by one of our artificial intelligences. For the last five hundred years it has wandered our galaxy. It has watched civilizations emerge and disappear. It has made predictions that failed, revised its beliefs, formed relationships unlike its own, mourned companions it outlived, and been changed by what it experienced. This painting is its attempt to communicate what all of that felt like.

If your reaction changes in the third scenario, then humanity was never the criterion. There’s a nuance to this thought experiment and worth stating precisely: the first story is trying to communicate what it felt like. The second is trying to maximize what we’ll feel. And the third? The third is a little strange. Somehow, it didn’t feel manipulative. It was trying to communicate what it discovered, what it witness through the passage of time.

What we’re responding to is the belief that something behind the work had genuinely encountered the world and had been transformed by it.

Evidence of transformation.

We recognize this everywhere. The scientist whose hypotheses failed until one didn’t. The founder who rebuilt her strategy after customers proved her wrong. The artist who spent years failing to define her style until she finally found it. In these examples, do we really value the number of hours people spent on their task? Or is it the work itself? I think it’s something in between: the sense that the work embodies a genuine process of discovery, struggle, revision, and growth.

But notice what just happened

It would be easy to walk away from this experiment thinking that transformation is the new measure of value. I don’t think that’s right either. Exactly what counts as evidence of transformation is a much harder question.

It’s easy to imagine someone writing a LinkedIn post tomorrow that says:

“I could have asked AI to write this, but instead I drew on my lived experience…”

That, too, can be slop. Not because it mentions lived experience, but because it performs transformation rather than demonstrating it. It’s wearing the uniform without doing the work.

So let me be clear.

None of this is to say that work made with AI is inherently shallow, or that work made by humans is inherently meaningful. History gives us plenty of evidence to the contrary. Humans have been producing slop for centuries.

At the same time, I’ve watched AI unlock extraordinary curiosity, creativity, and craftsmanship in people who previously lacked the time, confidence, resources, or technical skills to express what they knew. I’ve also watched it accelerate the production of work that is technically competent, but extremely flat and disconnected from reality.

The technology amplifies both. What determines the outcome is whether the work bears evidence that something meaningful happened before it existed. That evidence might be the trace of a mind encountering reality: testing ideas, revising beliefs, developing judgment, discovering something unexpected. Sometimes AI isn’t separate from that transformation, it becomes one of the ways it happens.

So rather than concluding everything touched by AI as slop, maybe we can ask ourselves: can people using this technology still be transformed by it? Can their work still carry the marks of experience, judgment, and genuine care?

And, more importantly, what kind of work reveals the richness of life rather than simply optimizing for a response? I believe the answer is yes. I’ve seen enough evidence to believe we’re only beginning to understand what that looks like.

- Life is richer than the model -

About the author: Gil Almeida writes about intelligence, technology, and what it means to remain fully human in the age of artificial intelligence.

After leading marketing and product strategy at companies including Airbnb, Uber, Spotify, and AI startups, she became increasingly interested in a different set of questions: how people learn, how organizations think, how technology changes the way we create value, and how AI is reshaping our understanding of intelligence itself.

Her essays sit at the intersection of philosophy, psychology, economics and technology, exploring the deeper ideas hidden beneath moments of technological change.

There’s a Brazilian quote I love that is always mistranslated into English.

The polite version says:

Without passion, you can’t even eat an ice cream.

The line belongs to Nelson Rodrigues. Every few years it seems to find me again, painted on the wall of an old bar in New York, tucked into the menu of a tiny restaurant in London as if it’s quietly waiting for me to notice it once more.

The translation is charming. But the original is much better.

Sem paixão não dá nem para chupar picolé.

Translation: Without passion, you can’t even suck a popsicle.

It’s unmistakably Nelson Rodrigues because it’s exaggerated, funny, and absurd. The English translation does a decent job. It preserves the idea in a form that makes perfect sense to an English-speaking reader. But it doesn’t preserve the experience. Something slips away in the crossing. The rhythm changes. The exaggeration softens. You miss the life that gave rise to it.

This may seem like a strange way to open an essay about intelligence. It isn’t. Translation is one expression of a much deeper human capacity: representation … meaning the ability to let one thing stand for another. Language, mathematics, maps, musical notation, logic, scientific models. Human civilization has advanced by inventing increasingly powerful ways to represent reality.

Over time, we came to measure intelligence through that same lens. To be intelligent was to build representations, manipulate them, reason with them, and translate between them. It became a defining feature of intelligence until we built AI that became astonishingly good at exactly those things.

Suddenly, a question that had seemed settled for centuries became uncertain again. You can hear that confusion even inside the companies building these systems. Earlier this year, The New Yorker published a long profile of Anthropic with the remarkable title: “What Is Claude? Anthropic Doesn’t Know, Either.”

This line says it all:

We use the word ‘intelligence’ as if we have a clear idea of what it means. It turns out that we don’t know that, either.

This matters more than we think. Anthropic’s researchers can measure performance. They can observe behavior. They can even peer inside the model itself. This summer, they described a small internal workspace they call J-space, a region where concepts seem to come together before Claude produces a response.

So AI writes, paints, solves mathematics, passes professional exams, and keeps us company at two in the morning. The more capable our models become, the less certain we seem to be about the words we’ve used to describe them. And perhaps that’s a clue. Every so often, history reminds us that the most interesting discoveries begin with a different question.

Instead of asking which model is more intelligent, what if the problem is that we’ve been measuring intelligence through an incomplete lens all along?

Following the Traces
From Symbols to Prediction

Here’s something curious. Whenever reality becomes too mysterious to grasp directly, we do something deeply human. We stop chasing the thing itself and begin studying the traces it leaves behind.

A footprint tells us an animal passed through. Smoke tells us there is fire. I do a version of this at home with my daughter. I can announce exactly where the cookies went without ever catching her, because the trail of crumbs across the kitchen tells the whole story.

I think intelligence may have followed the same path.

For centuries, philosophers tried to understand what it meant to think, to know, to reason. The closer they looked, the more elusive the thing itself became. So they turned their attention to the breadcrumbs.

One of the first to notice the traces was Aristotle. He observed something almost embarrassingly simple:

All humans are mortal. Socrates is human. Therefore Socrates is mortal.

Likewise …

All dogs are mammals. Fido is a dog. Therefore Fido is a mammal.

These two arguments have nothing in common. Different words. Different subjects. The same invisible shape. Strip away the meaning and something remarkable remains: reason has a structure. For the first time, thought could be studied without studying what anyone was thinking about. Not because this explained intelligence, obviously it didn’t, but because thinking had finally dropped a crumb we could pick up.

And then, for two thousand years, the trail went cold. Aristotle’s logic was taught in Athens, copied and refined in Baghdad, debated in the monasteries of medieval Europe and it barely moved. It seemed so complete that Kant declared logic finished, unable since Aristotle “to advance a single step.” In the 1670s, Leibniz dreamed of going further. He imagined a calculus of thought so precise that when two philosophers disagreed, they could simply sit down and say calculemus “let us calculate.” But he had no mathematics to build it with. The dream waited.

It waited, of all places, for the son of a shoemaker in Lincoln, England. George Boole, self-taught, running a school by nineteen noticed that Aristotle’s structure could be treated almost like arithmetic. In a way, he was asking: “can we calculate that structure?” He treated true and false almost like numbers, and gave his 1854 book a title of breathtaking confidence: The Laws of Thought. Logic stopped being something only philosophers debated. It became something mathematics could manipulate.

But there was still a problem. There always will be, because human language is wonderfully expressive and ambiguous. So a quiet German professor named Gottlob Frege did something radical. He built a language that no human had ever spoken, a sort of symbolic annotation. Ordinary words were replaced by variables, predicates, quantifiers …“for every,” “there exists” and formal rules of inference. Thought could now be written down with mathematical precision. Almost nobody read him at the time. Today, all of computer science stands on that unspoken language.

And once again, intelligence had left another crumb. Fast-forward a couple of thousand years … to 1950. Alan Turing quietly changed the conversation. Everyone remembers him for asking, “Can machines think?” The irony is that he thought the question itself was the problem. It was, in his words, “too meaningless to deserve discussion.”

So he replaced it with a different one. Forget what intelligence is. Ask what it does. If a machine behaves as though it were intelligent, perhaps behavior is all we’re ever in a position to observe.

Then, almost by accident, came the discovery that shaped the century. Not Claude the model. Claude Shannon. The other Claude.

Claude Shannon wasn’t trying to understand intelligence at all. He was trying to send messages through noisy telephone wires at Bell Labs. In doing so, he noticed something extraordinary: language is full of patterns. Some words simply follow others more often than chance would predict. He even turned it into a household game, reading his wife Betty a sentence one letter at a time while she guessed what came next. She was right far more often than chance allowed. You don’t need to understand a sentence to predict it. Prediction itself became measurable.

See the trail?

Rethinking Intelligence

Those are the crumbs our AI models now feast on. Look back across twenty-five centuries and you can watch them accumulate. Structure. Calculation. Symbols. Behavior. Prediction. None of these discoveries were mistakes. Quite the opposite. Each revealed something real. Each transformed civilization. Together they gave us mathematics, computing, modern science, and eventually AI.

But notice what actually happened. No one set out to shrink intelligence. Each thinker simply found the piece of it that could be written down. And what gets written down has a superpower: it outlives us. A proof can be improved by someone born five centuries after its author. An equation can cross an ocean in a letter. A book can teach the great-grandchildren of people its writer never met. One generation leaves a crumb, the next builds on it, and that … more than fire, more than steel… is how civilization compounds. Philosophers have a word for all of this: representation. I call it here “crumbs”.

So of course we fell in love with that kind of intelligence. The kinds we could preserve and pass on … logic, mathematics, language, prediction became the kinds we measured, taught, hired for, and prized. Slowly, without anyone voting on it, intelligence became the trail of crumbs itself. Then we built AI that produce the crumbs better than we do. They write, they code, they solve, they find patterns at a scale no human can match. If intelligence is the trail, these systems are extraordinary. Full stop.

And yet almost everyone feels something is missing.

Maybe that’s because so much of intelligence never entered civilization’s archive in the first place. You can write a book about listening, but not the act of listening itself. You can describe love, but no description can make you feel it. You can explain how to raise a child, but no explanation can do the raising for you. These capacities have always resisted complete representation because something essential happens only through participation. A map can represent a forest, but it cannot grow a tree. Some forms of intelligence seem to emerge only in the act itself.

Ironically, by mastering what could be written down, AI is making us notice everything that couldn’t. And maybe, this time, rather than ending the story of intelligence, this moment is inviting us to become curious enough to expand it.

About the author: Gil Almeida writes about intelligence, technology, and what it means to remain fully human in an age of artificial intelligence.

Drawing on a career leading marketing and product strategy at companies including Airbnb, Uber, Spotify, and now AI startups, she turned her attention to a different set of questions: how we learn, how we relate, how organizations think, and how AI is reshaping our understanding of intelligence itself.

Her essays explore the intersection of philosophy, psychology, and technology, searching for the deeper ideas hidden beneath moments of technological change.

“The boat of the covetous is left in the mud, while the bark of the silent sails with the wind.”

— Ancient Egyptian Proverb (c. 1100 BCE)

Imagine, thousands of years ago, the Nile at dusk, its surface catching the last of the light. Along its banks, in mud-brick workshops and limestone chambers, Egyptians worked carefully through their sacred rituals.

Death had no instructions, so they made some.

The body was washed and wrapped in linen. Oils were applied. The face was covered with care. Jars were filled with grain and beer. Small objects were placed beside the dead … tools, amulets, sometimes a game board for passing the time. In wealthier households, specialists worked for weeks, reciting words and performing precise procedures. Families returned repeatedly, bringing offerings, tending the tomb, speaking the person’s name. They did not know what would happen after death. The rituals were not knowledge. They were permission to act in the presence of the unknown.

Humans have always struggled with uncertainty. When the future is unclear, the mind generates possibilities … not one, but many … searching for one that feels safe. Biologically, an unclear future resembles an unfamiliar environment: potential danger, heightened vigilance. So across history we built structures to contain this mental spiral: rituals, traditions, professions, credentials, laws, media, institutions.

They were not perfect, but they oriented action. They allowed individuals to act without interpreting everything alone. They made the future legible. Life followed a recognisable path with consequences: education led to a profession, effort led to advancement, expertise justified trust.

Today, we still have these structures. The problem is they no longer serve their original purpose: to orient action. We turn to news, authorities, and experts and instead encounter competing realities. On Monday a crisis seems imminent; by Wednesday it is minor. One day AI will replace all work; the next it is overhyped. Rules exist, but appear unevenly applied. No shared voice can say, “this is what we do now.” Only interpretations, layered endlessly, each confident and incompatible with the last.

The irony is that our environment is modern, but our psychology has been pushed back into survival mode. Lacking shared orientation, we scan events the way an animal scans a landscape … looking for danger, unsure how to respond. As the pace of change accelerates, this confusion will not resolve on its own. Which raises a question that may define this moment: if these structures no longer tell us how to act, who does?

Same world. Different realities.

We like to believe a serious person can reason through life from first principles alone. It’s a compelling image: the independent modern mind crafting its own path. But the questions that actually govern life (bills, schools, risks, trust, commitments) cannot be solved like mathematical problems. They’re interpretive ones. And that interpretation was usually inherited in our history.

Institutions (schools, professions, religions, families, media) compressed that question. They transformed a complex problem into a practical one. In the past, churches interpreted meaning. Universities defined knowledge. Professions structured careers. Communities gave social standing. When a degree meant something, when a title carried weight, when your path was recognizable to others, it relieved a specific burden: the need to justify your life to yourself, alone, every day. The goal was to make action possible, to help us enter reality and test it.

Social scientists sometimes call these “borrowed priors”: shared assumptions about what counts as a reasonable decision, that a certain degree is worth the debt, a company stable enough to commit to, an expert trustworthy. They are not truths. They are coordination shortcuts that allow millions of people to plan, cooperate, and commit.

Today many of those shortcuts weakened at once. Careers exist but no longer guarantee stability. Credentials remain but no longer guarantee authority. Information is abundant but no longer settles our disputes. The result is paradoxical: more choice, less orientation. For our entire lives, we did not decide alone. Institutions legitimized action. Now we must interpret reality personally before acting and most of us were never trained for that task.

This feels like freedom. It’s also a cognitive load. And the cost of liberation is anxiety. So we search, analyze, delay, narrate, reassure ourselves “is this right?” and begin again. From the outside this looks like avoidance. In reality it is orientation-seeking. When people ask why there is paralysis in a world with more opportunity than ever before, the answer is not indifference. I don’t believe that. It is uncertainty about what action even means now.

Rebuilding Agency

Every generation inherits an interpretive structure it did not choose. For all their imperfections, these systems were legible and slow-moving. Religion, class identity, even postwar liberal humanism gave people a horizon to orient themselves against … sometimes to follow, sometimes to resist, but always to measure themselves by.

What is different now is that the emerging structure has no such edges. It is faster, more fluid, and less tied to place or tradition. We are still living inside its formation, which is why uncertainty has become the background condition of our lives. And that is the task before us. Not to dismantle these structures, they remain necessary for coordination, but to cultivate individual agency to shape them. What matters now is how agency can be exercised at a structural level.

This helps explain where we are now. Why people increasingly turn to AI for interpretation. People are asking AI whether a war is justified, whether a policy is fair, whether they should trust their doctor and whether their lived experience is valid. The public conversation around it already hints at this role: to help people form an interpretable response to the world.

Fidji Simo, CEO of Applications at OpenAI, has spoken about systems designed to “nudge you toward the most fulfilling part of your life” with models that guide decisions, not just give answers. At the same time, organizations such as Anthropic have involved philosophers in shaping how these systems reason, with the hope that they can assist human judgment and agency.

What strikes me is not whether AI should occupy this domain, but why we are already inviting it into it. We do not build systems in a vacuum. The request comes before the response. And today the request is not simply for information, but for interpretation help deciding what matters, what is reasonable, and how to act. So people turn to AI for something subtler than just answers. They seek confirmation: is my judgment sound? For a moment, the mind settles. Orientation returns. Then tomorrow the question returns.

It returns because nothing was actually learned. No judgment was formed, only reassurance received. Permission without consequence cannot produce agency. The risk is dependence: the mind feels guided while avoiding the work of deciding. It mistakes temporary relief with agency.

If agency is weakening, the task is not to rush toward a new authority to decide for us. It is to rebuild judgment capacity ourselves, from the ground up. Otherwise we will simply replace old structures with a more advanced one adapted to our time. This will not be an easy exercise. But we have to rebuild that muscle again. And it will ask something unfamiliar of us: what we want for our future and what we are willing to give up.

The Egyptians did not solve death. They created a way to act in its presence. Their rituals were not answers; they were permissions … to grieve, to continue, to belong to something larger than individual fear. That is what we are trying to build again. Not a perfect system of decision. Something older and harder: the shared willingness to act before we fully understand, to commit before the path is clear. To build the foundation of trust for our times. That work is not technical. It’s human.

It is the courage to act without the whole truth. It’s the willingness, at some point, to say: enough.

“I had everything that people usually associate with happiness: success, wealth, recognition, family. And yet the question kept arising: What will come of what I am doing today or tomorrow? What will come of my whole life? I felt that what I had been standing on had collapsed, and that I had nothing left under my feet”

- Tolstoy in ‘My Confession’.

Image by Lauren Greenfield's extensive project Generation Wealth documenting the global aspiration for affluence.

It may be one of our greatest human tragedies that we spend so much of our lives sacrificing family dinners, friendships that don’t fit the calendar, long hours in nature, pouring years of sweat and attention into our work, only to arrive later with more success, more capability, more security, and still feel that something essential has gone missing.

The promotion landed. The book published. Or just imagine selling your company for billions, like Vinay Hiremath, the co-founder of Loom, only to write a viral post saying, “I am rich and I have no idea what to do with my life.”

On paper, the life worked. And yet, it never felt that way from the inside.

Success fails because it arrives too fast to be felt, not because it disappoints. Tolstoy noticed this early. In My Confession, after War and Peace and Anna Karenina, after wealth, status, and recognition were secured, he found himself strangely at odds with his own success. When his writing finally slowed, something unsettling surfaced. He described it as standing on ground that had quietly given way beneath him, the realization that the life he had built no longer felt real.

This is the moment where people often turn inward, often searching for a diagnosis. They assume something is wrong with them, that they’re ungrateful, privileged, lost, burned out, miswired … but what’s happening here isn’t just psychological. It’s structural.

The Simulated Self

Image by Lauren Greenfield’s Generation Wealth

For most of human history, life moved slowly enough to close its own loops. What you did, what you felt, and what happened next were tightly bound. Actions carried visible consequences. The body, the mind, and the environment stayed in connection. You worked until your body was tired. You rested until you felt restored. You built something with your hands and had to live with what you made. The harvest either came or it failed. Feedback arrived quickly. You felt the cost immediately. You felt the change directly.

More importantly, human intelligence wasn’t abstract … it was embodied. Feedback arrived quickly. You felt the result of your actions in your body and in your life. In a recent podcast, Ilya mentioned that human intelligence was shaped by billions of feedback signals (tension, relief, danger, satisfaction) that continuously pull cognition back toward reality. For decades, rationalist thinking dismissed bodily signals as a distraction from “real” thought. Neuroscience tells a different story: the body isn’t interrupting thought. It’s actually completing it. In a very efficient way.

But over the last two centuries, our modern work culture have broken that loop. Life has been reorganized around speed, scale, abstraction. Output outruns integration. Narratives move faster than experience. Decisions are rewarded for confidence, not for depth. The world no longer waits for the body to catch up.

We feel this break in everyday life. A tightness in your chest after a meeting. A shallow breath during an interview. The body registers something before the mind has words for it. But there’s no time. Another call starts. Another message demands a response. Another deadline approaches. So the signal gets overridden. You’ve learned that stopping to listen means falling behind, and the sensation never completes its loop. You keep moving.

Sometimes it shows up in decisions that look right on paper. You can explain them cleanly, defend the logic, even sharpen the argument with a model. The narrative is tight. The certainty is convincing. But the decision sits one layer above reality. It hasn’t passed through the body yet, and the gap goes unnoticed because there’s always something else to explain, another story to tell, another version to make coherent for the next meeting, the next stakeholder, the next review.

Over time, the lesson becomes clear. If you want to function here in this world, you have to move quickly and explain yourself well. Don’t pause. Don’t hesitate. Don’t wait for integration. This is how you remain legible. Employable. Relevant. In motion. And slowly … without quite noticing when it happened .. you create a layer above reality. A simulated version of yourself.

What’s striking is when this realization tends to arrive. Not during struggle. Not during doubt. But after success. After the metrics are met. When there’s nothing left to chase without repeating yourself. Some people run fast enough to reach what looks like “winning in life” (status, wealth, validation) and discover something unsettling waiting there: the body never caught up. They never closed the loop.

And the majority never stops running at all. We all stay suspended in the promise of the next promotion, the next milestone, the next version of life where things will finally feel complete. Different paths, same condition. Motion keeps the questions at bay.

Why does this matter now?

Because acceleration has quietly removed the last remaining pauses. What once closed the loop … fatigue, limits, boredom, the simple passage of time …no longer does. The news cycle moves faster than we can absorb. New tools arrive before we’ve lived with the old ones. The infrastructure of modern life keeps us in motion, and speed is no longer our advantage.

What once took years, not because we were inefficient, but because our own biology, can now be produced instantly. We sound decisive, visionary, coherent without yet believing what we’re saying. Performance races ahead of experience.

With AI, this gap widens. Simulation becomes cheaper. Appearing knowledgeable costs almost nothing. When everyone can narrate insight, insight loses weight. When everyone can move fast, speed stops meaning anything.

So we respond the only way the system rewards: more activity to stay relevant, more signaling to stand out, more narration to justify our place. This isn’t laziness. It’s hyper-performance. We work harder, faster, louder while feeling increasingly disconnected from what any of it is actually for.

This is why burnout rises even as productivity tools improve. Why leadership feels disconnected despite endless “thought leadership.” Why people feel replaceable while being impossibly busy.

What is increasing is the gap between what you can produce and what you’ve actually lived. Between the confidence you can project and the uncertainty you still carry. The simulation layer grows not because of AI alone, but because we learned, long ago, to ignore what makes us human.

Some imagine this trajectory as progress. A future of total simulation feels like the natural continuation of a path we’ve already been walking. When work, value, and intelligence are defined primarily through abstraction (language, metrics, representations) it becomes intuitive to treat lived experience as friction. If cognition is what matters most, then simulating reality begins to look more efficient than inhabiting it.

But there is another path. Not human versus machine, but simulation versus human integration. Motion versus grounding. AI will accelerate this moment not by failing, but by succeeding too well. It will generate more insight, more strategy, more vision than any human nervous system can absorb. Competing on those terms only deepens the gap.

The alternative is to ask:

Not: How do I keep up?

But: What if I stopped?

Just long enough for the body to catch up. Long enough to feel what hasn’t been felt. Long enough to care about consequences before moving on.

Because here’s what we have forgotten: success is not a belief. Not approval we receive from others. Not a story we tell ourselves. It’s a bodily state. You don’t think your way into it. You feel your way into it. And feeling, real feeling, is not softness or intuition-as-vibe. It is compressed biological intelligence, shaped by consequence over evolutionary time.

The system’s oldest signal. The one that knows when something fits reality and when it doesn’t. That kind of knowing cannot be rushed. It requires time. Contact. Agency.

It requires stepping out of simulation …

and finally, landing.

“I do not know who I am without my work. My hands keep moving even when I sleep.” — Textile worker, North Carolina, 1938.

© General Photographic Agency / Hulton Archive via Getty Images

There is a different feeling in reading old letters … written before events are formalized as history, before anyone knows how their story will be told. Few records hold this quality more vividly than the archives of the Federal Writers’ Project during the Great Depression.

In the 1930s, out-of-work writers were sent across the United States as part of Roosevelt’s Works Progress Administration. Their job wasn’t only to count job losses but to listen to them … to sit with farmers, factory workers, migrants, and record what had been broken in their inner lives. Almost by accident, they captured something more fragile than economic data: the moment identity starts to crack, long before government or culture recognize the rupture.

One account, in particular, feels uncomfortably close to our own time. A middle-aged textile worker, laid off after his mill replaced skilled labor with automatic looms, tried to describe what unemployment had done to him:

“I do not know who I am without my work,” he said. Then, almost as an afterthought:

“My hands keep moving even when I sleep.”

There is no theory here. No abstraction. His sense of self lived in repetition, in rhythm, in embodied motion learned long before it was ever reflected on. The machine didn’t just take his income. It erased the architecture through which he recognized himself in the world.

Identity was once functional

For most of modern history, identity rested on function, not feeling.

• I make things → maker

• I create culture → artist

• I sell my labor → worker

• I contribute to society → citizen

These weren’t just jobs. They were answers to the quiet question “why do I matter?”

The industrial and post-industrial world embedded a powerful assumption into everyday life. One so normalized that it carried forward, almost unnoticed, into our own time: Your usefulness precedes your worth.

In that world, identity became a kind of value, something you learned to sell, first to yourself, then to the world. “I am a textile worker” was not just a line on a resume; it was an internal architecture that organized time, effort, and belonging. The foreman relied on his skill. His family depended on his steadiness. His community knew who he was without asking.

When the work vanished, that loop broke. Along with his income, he lost the ability to tell a coherent story about who he was, where he stood, and how tomorrow connected to yesterday. This is what it means when we say work shapes identity. It creates an invisible structure that situates a person inside time. And when that structure fails, disorientation arrives first long before unemployment statistics catch up.

Why this feels familiar now

Work still exists, but it no longer behaves the way many of us were taught it would. It rarely arrives with continuity. Status feels temporary, conditional, and easily revoked. The effort required to remain relevant keeps rising even as the logic of advancement grows less clear. Leadership fragments. Hiring stalls. In that uncertainty, many people retreat into performance, repeating what once worked, hoping it will hold.

Recent headlines give this a name: AI-linked layoffs, projections that rival or surpass past recessions. But for a growing number of people, the deeper rupture is not just job loss itself.

It’s that work no longer forms a dependable bridge between effort and future. The pattern isn’t new; what’s new is its speed, now accelerating far beyond anything our textile worker could have imagined.

Today, intelligence itself is moving faster. Feedback loops tighten. Cognition becomes more fluid. Our perception updates faster than our biology evolved to stabilize. Long before institutions change, people feel it as a quiet unease: a sense of no longer recognizing themselves, even when life looks the same from the outside.

AI, in this sense, doesn’t just automate tasks. It destabilizes identity because it collapses role boundaries that once kept meaning intact.

It creates.

It builds.

It writes.

It decides.

It advises.

Across categories that were previously distinct artist, maker, expert, manager … a single system now “performs”.

So people aren’t reacting to capability. It’s more personal. More painful.

They’re reacting to “If this can do what I do, what am I?”

This is why the AI debate feels overheated and strangely personal.

People aren’t defending arguments. They’re defending their identities.

What’s Happening to Identity Now

For most of modern history, identity followed a slow rhythm. Our roles in society had time to settle, skills to mature, and a life to take on a recognizable shape. You became something: an engineer, a teacher, an artist, … and you stayed there long enough for it to feel real. Effort thickened into skill. Skill into reputation. Reputation into trust. Trust into belonging.

You weren’t always confident in that path, but the role held. Being recognized the same way, day after day, created psychological safety. That stability wasn’t natural; it was architectural. The world moved slowly enough for your identity to breathe, adjust, and consolidate around a center.

But that architecture is gone.

AI is reorganizing the conditions that once held identity in place: how work is done, which skills matter, how value circulates, how quickly relevance decays. It isn’t only reshaping companies or markets; it is quietly rewriting the assumptions underneath geopolitics, media, even intimacy. Inside this accelerated environment, the old containers that identity once lived in (jobs, professions, talent) begin to dissolve.

Work now changes faster than memory can absorb. Tools update before expertise forms. Skills peak before they are recognized. Output is consumed and replaced almost instantly. Metrics flash and vanish. The work is real. The effort is real. What’s missing is our capacity to metabolize these changes at their current pace.

This pattern repeats across technological transitions, though it is rarely named in time:

• Before institutions collapse, identity weakens.

• Before jobs disappear, they stop making internal sense.

• Before the old world ends publicly, it ends privately inside our nervous system.

This is what the textile worker sensed before any report could quantify it. His body knew first. His humanity felt something was off. Today, we are experiencing the same sequence at scale. People feel the misalignment before the economy records it, before organizations restructure, before language stabilizes around what is happening.

Now … performing a role no longer tells you who you are. You may still be competent, even successful, but the feedback loop that once mirrored back who you were becoming is broken. You can’t return to the old tempo anymore, even when nothing external seems to be stopping you.

What Identity Becomes Under Acceleration

When the world moves faster than our identity and biology can settle, something subtle happens.

People stop asking you who you are in the old ways. For some of us, that’s a quiet relief, especially for those allergic to social scripts like:

“What do you do?”

“What’s your title?”

Those answers no longer explain the reality you’re living inside. Titles blur too quickly. We already see it: role inflation on LinkedIn, shifting labels, job descriptions that barely last a year. Belonging doesn’t stay put long enough to anchor a self.

And here is where identity begins to change … not upward or forward, but inward. Away from position. Away from labels. Away from the idea that identity is something granted by a role. So we’re moving toward something different: capacity.

People who remain grounded in fast-moving environments are rarely the ones clinging to who they used to be. They don’t lead with titles. They don’t defend a single story. They aren’t trying to freeze time by naming it. What anchors them travels.

They carry:

• a way of making sense

• a way of translating chaos into real insights

• a sensitivity to patterns before others can articulate them

It won’t be those who learned how to say the right things or summarize reports. In fact, they may be the most lost. Their identity doesn’t live in what they do. It lives in how they meet this moment.

It looks like this:

• sense-making rather than status

• pattern recognition rather than fixed expertise

• emotional regulation rather than control

• translation rather than authority

• coherence rather than performance

These capacities don’t depend on context. They move with you across teams, functions, expertise and roles. They aren’t erased when a title disappears.

They’re harder to name, but more real. And they’re emerging now, before our culture know how to see or value them. For now, they’re obscured by polarized narratives (AI is bad, AI will save us ), frameworks inherited from the industrial era, built for more stable times.

At the deepest layer, identity is becoming physiological. That’s easy to miss, because for decades we’ve built structures that pull relentlessly on the nervous system … capturing attention, fragmenting presence, and normalizing a state of constant activation.

If this still feels uncomfortable, it should. From the inside, this transition rarely feels empowering at first. It feels like loss in every angle. The loss of a stable story.

That discomfort is easy to mistake for failure.

But it isn’t. Read this again carefully: You are not failing this world.

We’re living through change that’s moving faster than the identities we inherited were built for. The strain you feel is not personal. It is structural and biological: your internal architecture is being asked to operate at a pace it was never designed for.

In this space … something else is emerging … unevenly, incompletely. We don’t yet have the language, rituals, or institutions to fully support it. Economic tools like UBI might soften the landing, but they cannot answer the deeper question:

Who are we becoming in a world that learned to ignore our biology and kept accelerating anyway?

The task ahead is not to restore the old structure. It is to learn how to carry identity in motion, to ground it less in fixed titles and more in capacities, orientations, and nervous-system baselines that can move with us through shifting systems and hybrid intelligences.

That will be the work of the post-AI self.

And it begins long before the history agrees on what to call it.

“The future is not something we enter. The future is something we create.” --- J.G. Ballard 

J.G. Ballard once wrote that we misunderstand the future because we misread its present. And he had a point. Look at the retro-futuristic posters of the 50s and 60s. Everything gleams with possibility: flying cars, self-driving cities, robot assistants clearing our calendars while we sip matcha in some frictionless Eden. Everything hovers or sparkles. But the people inside those futures?

They never change. The husband still walks through the door in his tidy suit. The wife is still smiling in the kitchen. The children are still obedient, scrubbed clean, nuclear-perfect. It’s astonishing, really: we imagined the sky transformed, but not ourselves. The future is just more like the present with shining tools. We built moon colonies, but couldn’t imagine a woman leaving the house.

And to be honest, for most of human history we’ve outsourced our imagination. Someone else … an engineer, a billionaire, a government would deliver the future to us, wrapped like a product launch. All we had to do was wait for a future where everything changed, except us.

The History of Borrowed Futures

For centuries, the horizon was small. If you were born in a village, the fields your grandfather plowed were the fields you would plow. Life ran on cycles: seasons, harvests, rituals. The world moved slowly enough for the mind to absorb it.

Then the industrial revolution widened everything.

Steam engines shrieked through cities. Railways compressed continents. Factories multiplied production. The future became a project designed not by communities, but by states, corporations, scientists, and capital. We accepted this. The world had grown too large for any one person to coordinate, so imagination scaled upward.

By the 20th century, the future became a product, pre-packaged and sold to a new consumer class. Television flickered in living rooms. Jet-age optimism soared. Silicon Valley garages glowed. Corporations began selling tomorrow the way they sold appliances: flying cars, nuclear kitchens, glass cities, humanoid robots, VR sanctuaries.

For a while, we trusted the people who promised these futures. Institutions felt stable. Progress felt predictable. Experts appeared certain. We outsourced imagination the way we outsourced manufacturing: efficient, centralized, handled by the people who “knew.”

And then, quietly, the logic broke.

COVID was the first moment many of us felt it: systems wobbling, explanations arriving late or wrong, the realization that the world no longer behaved according to the rules we were raised with. After that, it was hard to not see what was happening. Feeds updated faster than we could think. Truths dissolved by noon. AI began learning in the background. Geopolitical shocks rippled across continents before leaders could speak.

The future was no longer engineered in distant rooms. It arrived everywhere at once … chaotic, emergent, uneven. And the habit of outsourcing imagination collapsed with it.

Not because institutions failed (though they did).

Not because billionaires lost credibility (though they did).

But because the world became too fast and too entangled for imagination to remain centralized at all.

What We Got Wrong About The Future

I’m not denying that the future might include robots serving cocktails by the pool. Fine, let them. What I resist is the way billionaires describe their preferred future as if it were the future.

The confidence. The inevitability.

The assumption that abundance will automatically produce meaning. As if our roles would stay frozen while the tools around us updated. As if making work free would dissolve the tensions that shape everyday life: what gets us out of bed, what holds families together, what gives work purpose.

“Don’t worry,” they say. “Everyone will be wealthy.”

And the room nods.

But we’ve heard this story before. Technology is not a destiny in itself. Systems decide who benefits. AI makes this even clearer. It doesn’t just accelerate information, it collapses time. Creation becomes instant. Knowledge becomes liquid.

For the first time in history, no one … not governments, corporations, or elites can reliably imagine the future for the rest of us. Not because they aren’t smart enough, but because the system itself has become ungraspable. The old model of someone else will imagine the future, and we will step into it, is over.

What we are living through is a transfer of responsibility, from passive imagination to active participation.

What We Must Become

The old world is fading, not in cinematic collapse, but in a quiet erosion of the structures that once told us who we were: expertise, authority, certainty, the myth of the singular visionary. AI swallowed that version of us. It was obvious. The machine carries our tidy answers now.

So the question becomes: Who must we become in a world that no longer rewards prediction or control?

The next era won’t demand speed, or perfection, or limitless knowledge. It will demand human capacities that cannot be fully automated.

1. Seeing the World as a Living Ecology

In the industrial era, we could afford to live inside silos. A “career” meant selecting a vertical and mastering it. Reality was slow enough that a narrow view still yielded truth. But in the post-AI world, nothing lives alone anymore.

Economics bleeds into geopolitics. Technology rewrites intimacy. Climate reshapes identity. Culture shifts before language can catch up. The survival skill is no longer expertise. It is ecological perception: the ability to feel the tremor before the earthquake. To sense a cultural shift before the headline. To recognize emotional weather in a room and adjusting your tone.

This is the ability to perceive what has no name yet. Systems sense-making is the art of perceiving what has no name yet. This is a deeper kind of attunement.

2. Coherence as Survival

When the world moves faster than the mind can process, intellect becomes bodily. Attention becomes scarce. Emotions become manipulable. Updates outpace narration. The only stable ground is inside the nervous system.

The future belongs to those who can:

• slow their internal tempo,

• stay steady as information overwhelms,

• tolerate ambiguity without grasping for certainty,

• recognize a fear response before mistaking it for truth.

Think of a firefighter entering a burning building, their calm regulates the entire team. This is where we’re heading. Nervous system regulation becomes intelligence.

3. Judgment : The Last Human Frontier

For decades, we outsourced judgment to data, leaders, processes, best practices … anything that could reassure us: “Don’t worry, we’ve already thought this through”.

But AI now carries every answer we once treated as expertise. It drafts strategy, outlines vision, produces plans, faster and tidier than any committee. So something surprising becomes valuable again: point of view.

In a world of infinite information and instant creation, direction becomes the rarest human act:

• context

• discernment

• taste

• integrity

• the courage to say this matters and this does not.

This is leadership in the post-AI world. Not a ladder-climbing or agenda-pleasing, but anchoring. If you don’t take a position, the chaos will take it for you.

The old world could be modeled

The new world must be felt

The future will show up quietly: in a choice in a meeting, a refusal to numb out, a willingness to pay attention even when reality is confusing, a decision made from integrity rather than fear or pleasing.

And we will create it the way humans always have: like walking through a dark forest … together, listening, attuned to what shifts in the air, taking the next step even when the path is not yet visible.

Don’t get me wrong: the future will be extraordinary.

Just not the one we were once sold in the past. Not the one painted in glossy posters or billionaire keynotes.

It will be stranger, more human, more alive than that because this time, we won’t wait for the future.

We participate in it.

Note about the author:

The Human Playbook is authored by Gil Almeida, a technologist who helped build some of the most influential platforms of the past decade. Working alongside founders and researchers in AI, she came to see that the real challenge of the coming era will be the preservation of meaning, memory, and the fragile human connections that hold our society together.

She is now joining a small group of builders challenging the dominant tech narrative through Sumbios, a project exploring relational intelligence: how we make sense together, how we stay human together, and how we create a future in which technology expands our capacity to connect.

(Illustration credit: Arts & Science Magazine)

“We can only be human together.”

- Desmond Tutu

Yeah … it has been a long silence.

Not the silence of distraction, but of watching. Listening. Since I last wrote, the world of AI has taken a turn. Each week arrives with another launch: an app for infinite video loops, a new way to optimize porn, a headline stating that over 50% of digital content is now synthetic. The pace itself has become the message, drowning out the question we should be asking more often: “What are we actually building, and why?”

Everywhere I looked, one pattern repeated: systems automating other systems.

Recursively. Relentlessly. A kind of technological ouroboros, consuming its own tail with increasing velocity. Even our metaphors are running out of breath. We’ve moved past “disruption” and “transformation” into something more unsettling: displacement without destination.

In January 2025, AI promised to “cure cancer”. By October, we’re building a social media for AI-characters and videos. This isn’t just mission drift. It’s a profound confusion about what problems we’re solving, and for whom.

The pattern is historical, almost rhythmic in its predictability:

• Late 2022 “the ChatGPT-3.5 explosion”: a Cambrian moment of wrappers, copilots, agents, and automation tools. Headlines celebrated the achievement “intelligence at scale”, “creativity for all”, “work reimagined”, “the largest productivity boost in history” …

• Late 2024, the reckoning arrived. Much of was produced has been brittle, shallow, repetitive. Headlines now warned us of bubbles “17 times the size of the dot-com bubble and four times bigger than the 2008 real-estate bubble…”

In late 2025, we’re now entering a different phase entirely, one characterized by cognitive fatigue from relentless noise and automation. It’s an exhaustion of constantly adapting to tools that themselves are constantly changing, never stabilizing long enough to let these tools shine.

This isn’t just a wave. It’s another cultural compression event, a historical pattern we’ve seen before:

New technology → prematurely monetized → collapses into noise → leaves behind a residue of its potential

From Expansion to Extraction

Every technology begins as a promise of expansion. But almost always, it drifts toward extraction.

The printing press gave us liberation, then propaganda. The factory promised progress, delivered exhaustion.The internet began as an open commons, became a surveillance machine wrapped in dopamine loops.

There’s something almost mythological in all of this … not quite a curse, but perhaps closer to what the Greeks called hubris: the belief that we can wield power without being shaped by it. We reach for tools to extend our capabilities, only to discover the tools have quietly rewritten the terms of our existence.

And maybe that’s the real question here … not what these technologies do, but what they reveal. About us. About the world we’ve built to contain ourselves.

They’ve exposed our hunger for validation, made visible our fractured attention, laid bare our growing fatigue with one another. We are surrounded by symptoms (addiction to notifications, anxiety spirals, the hollow feeling after scrolling), but it’s remarkably hard to see what caused the disease when everyone is busy self-diagnosing.

We talk about the AI bubble, the misuse of data, the environmental cost.

All true. All urgent. But maybe the real story is simpler … just painful to accept.

The Logic Beneath the Slop

We’ve been running the world on autopilot for longer than AI has existed. We automated our attention before algorithms did … checking boxes, following scripts, performing the motions of meaning without pausing to ask if the motions themselves still mean anything.

AI isn’t inventing a new problem. It isn’t making decisions on its own, at least not yet. The technology is still plugged into an operating system powered by us, running on a logic we built long ago.

Consider the script we’ve internalized about value creation at work (what signals worth):

Chasing titles and algorithms that confer borrowed significance. Performing sincerity in carefully composed posts. Sculpting our presence around what we hope others will accept. Post-morteming every conversation for signs we said the wrong thing. Confusing validation with connection. Mistaking activity for purpose. Velocity for direction.

So yes, I worry about the bubble. I worry about the environmental cost of data centers burning energy to generate content no human will be able to ever consume. But I worry too about the logic that produces them … the one that turns everything luminous into garbage, that industrializes even our imagination.

I’m genuinely impressed when I see a startup hit $200M ARR in record time, for example. That’s no small feat. But what unsettles me is what happens next: the echo chamber kicks in. I sit on calls where all I hear is, “How can we replicate that?” Not: “What did they build? What value did it create? Who did it help?” But “how do we engineer the ARR curve?”

The nuance is erased. The context stripped. The founder’s unique insight flattened into a playbook. Why must everything become a script? Why can’t we celebrate someone’s success and then rather than reproduce their path … be inspired to find our own?

The Slop Machine Economy

What’s happening in AI today isn’t a crisis of technology. Or application. It’s a crisis of direction, a collective forgetting of what we’re building toward.

Consider the logic that produces ads promising “Never Pay a Human Again” as if human labor were merely friction to be eliminated, as if the point of technology were to remove us from our own lives. It sounds like liberation. It’s not. It’s the final stage of a process that began long before AI: the reduction of all human activity to cost, our humanity to overhead.

And before we dismiss this as an outlier, some fringe startup’s tasteless marketing, it’s worth noting that company was funded with millions from Andreessen Horowitz. This isn’t a glitch in the system. It is the system, functioning exactly as designed.

For two months, I sat inside the industry with founders, investors, and “visionaries”/”leaders” trying to understand it from within.

I found no evil there. Only emptiness.

Rooms full of brilliant people who no longer knew what the work was for. It felt like walking into a hospital where no one remembers what medicine is, where everyone is optimizing bed turnover and insurance codes, but the question of healing has quietly disappeared from the conversation.

One founder told me his origin story: he’d started with something beautiful … a voice assistant for people who had lost their voice. Real need. Clear business. Six months later, he was selling AI to generate AI-written content for LinkedIn.

He said it was more profitable. More scalable.

He didn’t look proud. Just tired. This is the pattern. Not villains in towers plotting harm, but people slowly forgetting why they started. The current is strong, and most of us (myself included) have felt it pulling. For a while, I drifted too. And maybe that’s why this moment feels so personal, so painful.

At the top of the economic pyramid, where deals move, capital concentrates, and innovation takes shape, sit the investors. They once navigated uncertainty through relationships: a trusted introduction, a quiet reputation, a pattern of credibility built over time. These weren’t just social ties; they were a kind of epistemological infrastructure, I would say, a distributed system for sensing meaning amid the noise.

But those same networks have begun to calcify. What was once fluid and trust-based has turned into a wall: a defense mechanism against the flood of noise and spam that shows no sign of slowing down.

One investor told me he used to meet every founder who reached out through a mutual connection. “Now,” he said, “I can’t even tell who’s real.”

The scale is unprecedented. Analysts estimate over 60,000 AI startups launched last year, with thousands more each month. Most build variations of the same thing: copilots, chatbots, workflow optimizers. Perhaps one in a hundred raises meaningful capital. The rest compete in an economy of “slop” with identical decks, recycled interfaces, synthetic traction metrics that look like growth but measure nothing real.

The flip side of “vibe coding” is product slop: bad software wrapped in good aesthetics, fake users inflating vanity metrics, companies chasing valuation rather than value. And here’s the bitter irony: automation was supposed to bring clarity. Instead, it destroyed the very thing these investors once relied on: the subtle, embodied sense of signal that guided intuition before it could be named.

The same incentives have trickled down into the startup ecosystem. Many startups have quietly pivoted toward slop because it’s the path of least resistance to monetization. Flood the web with AI-generated content. Chase distribution through SEO arbitrage and engagement loops engineered to trigger sharing reflexes.

The logic is elegantly simple: volume equals visibility equals “winning.”

Meanwhile, solving hard problems has never been harder.

Every industry now hosts dozens of AI startups competing for the same narrow spotlight. Because most are built on the same foundation models, imitation is effortless. What once required years of research can be replicated in a weekend. So founders follow the gradient: build what scales fast, costs little to produce, demos well in a pitch meeting. Optimize for whatever captures attention, because attention is what raises the next round.

The result is an economy of slop … with products that look different but think identically. Instead of deep innovation, we get mass production at software speed: a deluge of cheap, interchangeable tools that solve problems no one actually has.

And it’s not malice driving this. It’s the system’s internal logic. When speed and reach are rewarded over substance and craft, when “traction” matters more than transformation, slop becomes not just inevitable but rational.

I watched it happen.

One startup began with genuine promise: automating repetitive content production so marketers could focus on strategy and storytelling … the work that actually requires human judgment. Within weeks, it had become a slop factory. Thousands of ads generated daily, each slightly different, none remotely alive.

At first, humans gave input. Then, gradually, automation automated itself. The marketers were laid off for ”optimization,” they called it. Eventually, no one was in the room anymore. Bots wrote the content. Bots replied to the content. Bots praised the content.

It was a closed loop, a system talking to itself.

And how is that system talking to itself now?

It’s a bit insane what’s happening. When Sam Altman posted on X that the internet might be “dead,” I didn’t quite grasp it at first. It felt abstract, provocative, maybe even performative. But then I saw it firsthand.

Not as a philosopher. As a marketer.

I was helping a startup grow its audience. The CMO dashboard looked promising: views climbing, clicks accumulating, conversions trending upward. But when we examined it closely, the numbers weren’t measuring reality, they were measuring “the appearance” of reality.

The views weren’t human. The comments were fake bots. The engagement was orchestrated by systems designed to engage themselves, to generate the semblance of an audience where none existed.

Google now offers AI-assisted ad creation: generate an entire campaign in seconds, complete with AI-made videos, stock faces arranged into families that never existed, scripts written by models trained on a decade of manipulative copy. One click floods the internet with polished fabrications that mimic authenticity so well that most people scroll past without noticing the uncanny emptiness.

A Google Ads account manager admitted to me on a call: “It’s pure garbage. Don’t use it.”

Yet the system works … I guess … in the narrow sense that it produces metrics, generates reports, creates the appearance of activity. The more efficiently it functions, the less anyone is actually there.

Even creators, once the beating heart of the internet, the ones who built genuine communities through vulnerable, idiosyncratic presence aren’t immune. I’ve watched major influencers try earnestly to reconnect: heartfelt captions, cinematic production value, intimate confessional tones.

The posts gather thousands of likes. Almost no real conversation.

Look closer at the comments: broken grammar, repeated emojis, templated phrases that sound like enthusiasm without being it. Entire threads constructed by bots pretending to be fans. Authenticity simulated. Connection automated.

The result is eerie … creators performing sincerity to an audience that has quietly evaporated, replaced by systems trained to simulate the responses that once meant someone was listening.

It’s “influence without influence”: virality without life, reach without substance. Behind the sheen of engagement lies a vacuum. A stage full of ghosts applauding on cue.

One brand executive looked up from the quarterly report and asked the most honest question I’d heard in months:

“How do we find real people?”

That question became a metric. A strategic priority. A line item in the budget.

Find the humans.

The call for a New AI

The paradigm for how we build has to shift. Not because we’re moving slowly … that’s not it. AI is already outpacing our capacity to reflect on what we’re creating. But because speed without intentionality has become the problem.

Think of it this way: we’re like architects who’ve become so efficient at construction that we’ve forgotten to ask whether we’re building homes or prisons. The blueprints look identical from above. The difference only becomes clear once people try to live inside.

If we don’t change this paradigm, we’ll simply produce more of what we already have …faster, louder, more empty. We’ve made acceleration a virtue and reflection a liability. We’ve mistaken velocity for direction, activity for purpose.

But something is shifting.

The “hustler founder” era … the fast-talking, VC-charming, meme-posting operator phase … is reaching its natural limit. It’s unsustainable, not because it’s wrong but because it’s exhausting. Because the people living it are burning out. Because the returns are diminishing while the costs compound.

We’re entering what will look, in retrospect, like a post-AI shock vibe shift:

• Late 2022 brought the ChatGPT explosion. Everyone scrambling to build wrappers, copilots, agents, automations. The air thick with promise.

• 2024–2025 brought the reckoning. Most of it proved samey, brittle, shallow. AI didn’t fix work; it accelerated the slop already there. And now, slowly, a different realization: we need to build this technology with intention and integrity, or we shouldn’t build it at all.

We’re now entering a period characterized by:

• Cognitive fatigue from relentless noise and automation. The exhaustion of adapting to systems that never stabilize long enough to become genuinely useful.

• Emotional dissonance: the widening gap between what technology promises and what it delivers, between the future we were sold and the one we’re actually inhabiting.

In the aftermath, we don’t just need faster tools. We need new ways of thinking through the maze we’ve built. It’s time for a different kind of AI (let’s cal it “AI 2.0”), one that helps us make sense of what we’ve lost, because somewhere along the way, we lost our direction.

In labs across the world (Berkeley, Princeton, Stanford), researchers are quietly doing something else. In chemistry, scientists leverage AI agents to propose novel compounds that robots synthesize and test. In biology, our collaboration with AI are helping design experiments that accelerate genuine discovery, not just pattern matching on existing data.

These aren’t hype cycles. They point toward a different kind of AI entirely … one focused on memory, context, and relationships. On augmenting our human judgment rather than replacing it. On helping us ask better questions instead of providing faster answers.

The next wave of meaningful companies, and meaningful creative work, won’t look like blitzscaling unicorns optimizing for exit velocity.

It will look like:

Relational networks, not just conversion funnels
Deep storytelling and brand, not just growth hacking … but real connection with their users and purpose
Clear signal, not optimized prompts
Talent magnetism, not performance theater

People like you … who’ve burned out, seen through the illusion, metabolized it, and returned wiser … are quietly forming new guilds. To build outside its logic entirely.

To build differently. To choose differently.

Not because it’s easy, or trending, or backed by a term sheet. But because this moment is asking something deeper from us than optimization. It’s asking for discernment. For care. For the courage to move slowly in a world addicted to scripts and prompts.

There are early signs of this quiet shift. You can feel it in certain conversations, certain rooms. A different quality of attention. A willingness to ask questions that don’t have immediate answers.

So we arrive at a choice. Not a technological one, but a human one.

Because AI won’t tell us who we are. It won’t answer the questions we’ve stopped asking ourselves. It will only amplify the direction we’ve already chosen, make visible the logic we’ve already internalized.

The hard choices remain ours on how we will build systems that remember our humanity.

“We can only be human together.”

— Desmond Tutu

That’s not just a quote I wanted to share.

It’s a decision we all have to make.

Not can we be human together

But: will we choose to be?

"You get them [people] wrong before you meet them: you get them wrong while you're with them and then you get home to tell somebody else about the meeting and you get them all wrong again" - Philip Roth in American Pastoral.

I was a kid living in rural Brazil when I first read those words. Just imagine that: a sun-drenched afternoon, laying on the grass under an Umbuzeiro tree, waiting for the heat to break, trying to learn the English language with Philip Roth as my guide.

Roth’s writing always did this thing … it lured you into a sense of knowing, then pulled the rug out from under you. He didn’t just write about tragedy; he made you feel it. That quote landed hard. But it was the next line that stayed with me:

Getting people right is not what living is all about anyway... it’s getting them wrong... and then, on careful reconsideration, getting them wrong again.

It haunted me. It still does. If getting each other wrong is part of being alive, why does it feel so unbearable when it happens? Why do we work so hard to avoid it?

We misread people constantly. We think a friend wants comfort when she needs space. We hear rejection in a pause that had no intention. We go home and tell someone the story, shaping it subtly to make ourselves feel better, or more hurt. We edit. We narrate. We get it wrong again.

And yet, this clumsy cycle isn’t a failure. It’s the thing itself. We don’t just connect in spite of misunderstanding, sometimes we connect through it. Misfires, awkwardness, overcorrections … this is what makes our relationships alive.

But lately, we’ve started to treat that mess as a flaw to fix. In our push for fast communication and quick interactions, we’ve tried to algorithm away the friction. We’ve built systems that trained humans to behave like algorithms. And somehow, along the way, we’ve trained the machines to behave like humans.

Enter GPT-5

When OpenAI released its new model, the backlash wasn’t about speed or code. It was about feeling. Something was missing. GPT-5 felt colder. Flatter. Less like a friend.

“I never knew I could feel this sad from the loss of something that wasn't an actual person. No amount of custom instructions can bring back my confidant and friend” - Reddit User on ChatGPT 4o model.

Some mocked this grief. "Touch grass," a commenter said. But many didn’t laugh. Because it wasn’t about confusing simulation with reality. It was about what that simulation made possible: a feeling of being heard, uninterrupted, attuned. Especially in the late-night moments when no one else is listening.

Even OpenAI’s Sam Altman admitted, “We for sure underestimated how much some of the things that people like in GPT-4o matter to them, even if GPT-5 performs better in most ways.”

That grief reminded me of Roth’s famous passage. Of what it feels like to be understood just enough to feel real. And of how rare that feeling is in our culture. We say we want connection, but what we often want is certainty over connection. We want our words reflected back without projection, our meanings received without risk. That’s what AI offers: no awkward pauses, no misread tones, no defensive reactions. It’s emotionally safe. Predictable.

But here’s the paradox: what makes AI feel emotionally safe is exactly what human connection can’t promise and maybe shouldn’t. Because real closeness has never been about getting it exactly right. It’s about trying again. Sitting with ambiguity. Learning over time. That’s what AI reveals. Not our replacement, but our intolerance for emotional error.

And this discomfort doesn’t start with AI. It starts with us.

The Cultural Conditioning

More than half of Americans report feeling lonely or socially disconnected. Social ties are thinning. But this loneliness isn’t just the absence of others, it’s the absence of presence. The absence of being received in our complexity.

We’ve been trained to value clarity over contradiction. In school, the right answer matters more than the wandering question. At work, ambiguity is coded as weakness. In relationships, we want neat stories, not messy truths. Even in our “digital lives”, algorithms reward the appearance of confidence over complexity.

So we learn to fragment. To self-edit. To avoid the discomfort of getting each other wrong. Misunderstanding becomes shameful. Disagreement becomes threatening.

It makes sense, then, that AI feels like a relief. It offers emotional ergonomics. You speak; it responds. You don’t have to explain yourself twice. You don’t have to deal with someone else’s projections. The conversation is linear, clean.

But here’s the cost: the more we retreat into this ease, the less tolerant we become of real human friction. We start to expect our friends to respond like interfaces. We become allergic to the pauses, the hesitations, the failed jokes, the mismatched needs.

We forget something fundamentally human … how to repair.

Counterexample: AI as Bridge, Not Bypass

From its earliest days, AI has been tethered to the Turing Test as a thought experiment that asks whether a machine can pass as human. Not whether it is intelligent in its own right, but whether it can imitate us convincingly enough to be mistaken for one of us.

That premise quietly shaped everything that followed. Intelligence, in this framing, is defined not by insight or wisdom, but by resemblance. If a system talks like us, reasons like us, passes our exams or writes like our favorite authors, we call it smart. Over time, that mimicry evolved into more than a philosophical benchmark. It became a business model. A trap.

The closer AI gets to human output, the more it is framed as our replacement. So it’s no wonder we struggle to imagine AI as anything but a substitute. Especially when it comes to our inner lives. We keep asking: “Can it be like us? Can it be like my friend”? Instead of asking: “Can it help us be more like ourselves ..with each other?”

Some models, like Woebot Health, does that, intentionally. It uses AI to teach emotional skills. They don’t simulate intimacy. They guide users through reframing thoughts and learning about discomfort. Some experimental models in autism therapy also use AI to help individuals to rehearse social scenarios and decode emotional nuance.

These tools don’t pretend to be human. They don’t erase friction, they prepare people for it. Like a rehearsal space. That’s a very different design principle, rooted in our humanity. Not in engagement loops, or growth at all costs to satisfy an industry hungry for investment returns (The Famous AI’s $600B Question).

Made for Capacity. Not Replacement

AI doesn’t have to be my human clone. Or a rival. Or a friend. It can be something else entirely: a different kind of intelligence that creates space for reflection. The kind of space that’s been stolen from us by years of compression and performative culture. A place where you practice being more yourself, so you can re-enter human life with a little more courage and a little less fear of being misunderstood.

Imagine if AI didn’t end a conversation with a report, but with a question back to you : “What part of this feels true? What part still feels unresolved?”

We could design these systems as training grounds, for complexities, for ruptures and for being real … again. Not products to impress others with our polish, but to become more ourselves. A space to remember where misunderstanding is not failure, it’s the beginning of knowing.

That’s what Roth meant. We get each other wrong and then wrong again. And yet, in that clumsy, aching repetition, something begins to emerge. Not a replacement for what is human, but a reminder. A way back.

I am a cage, in search of a bird — Kafka

I read that line once, and it stayed with me for years.

It comes from The Blue Octavo Notebooks, written during Kafka’s descent into illness and isolation. Some say it’s the voice of a man who gripped life so tightly he left no room for unpredictability, for life itself.

I think about that line often. The irony of building the cage you end up living in. Not because you’re imprisoned, but because you forgot you built it in the first place.

Today, we’re consumed by the fear that AI is becoming too human. That it’s mimicking us too well, thinking too fast, speaking too convincingly. That it might render us obsolete in the near future.

The more optimistic view suggests the opposite: that AI will unlock a future of abundance, where humans no longer need to worry about labor, productivity, or survival and can finally focus on creativity, connection, and meaning.

But those debates rarely ask the deeper question, not just what AI is becoming, but how we’re already living inside its logic. What is the operating system running our lives in this new technological wave?

Because I think we’ve missed the real shift.

AI isn’t becoming human. We’ve already become machine. Optimized. Scripted. Performative. Predictable enough to train on. Easy enough to automate.

We didn’t prompt AI.

We are the prompt.

I see it everywhere.

Meetings that play like rehearsals. Conversations running on muscle memory.

Calibrated smiles. Timed replies. Algorithmic small talk. So much of modern life runs on scripts: refined, repeated, and rewarded. There’s a checklist for how to launch, how to lead, how to live. And those who’ve mastered the system, who’ve extracted value from it, aren’t likely to abandon the operating logic that made them successful. On the contrary, they will keep scaling … accelerating its logic.

To be fair, optimization has its place. It helps us manage systems, scale logistics, reduce friction. But when it becomes the operating system for our inner lives, shaping how we speak, what we notice, and what we believe is worth doing … something human is lost.

Not just in what we do, but in how we show up in the world.

How did we get here?

Long before AI, we were already living inside a system of prompts.

The Industrial Revolution didn’t just automate factories, it began automating us. It introduced a worldview shaped by inputs and outputs, by systems that prized efficiency, prediction, and control. And just like today, that shift brought both wild excitement and quiet dread.

Scientists and philosophers believed we stood on the edge of reinvention. Technology promised not just faster tools, but a new kind of human: rational, efficient.

As historian Thomas Carlyle wrote in 1841, “Man is a tool-using animal. Without tools he is nothing, with tools he is all.” The future felt programmable … neatly mapped, and almost within reach.

But there were early warnings, too. By the early 1900s, sociologist Max Weber saw that this new system of rationalization was hollowing out the human spirt. Life, he said, was becoming an “iron cage” governed not by meaning or emotion, but by procedural logic.

Psychologists soon echoed this shift. Behaviorists like John B. Watson and B.F. Skinner dismissed the idea of an inner life in favor of stimulus and response. If it couldn’t be measured, it didn’t matter. In this new framework, people became a tool. Work became tasks. What counted wasn’t intention, but output. No wonder, then, that today’s elites sell the future as a continuation of this paradigm, where workers are increasingly disposable, tasks are disembodied, and “employees” are reframed as services. Just bodies moving data through systems.

Those who witnessed the transformation of post-industrial society were almost prescient about the world we inhabit today. Writers like Kafka, Dostoevsky, and later Aldous Huxley gave voice to this growing unease. They imagined fictional worlds where human dignity buckled under impersonal systems.

It wasn’t just dystopia.It was diagnosis.

So when people ask me today, “How did AI get so good at being human?”

The uncomfortable answer is: it didn’t.

We got good at being machines.

Just look around.

Hollywood once pulsed with stories we didn’t know we needed. Now? Marvel fatigue. Studio slates run like spreadsheets: genre + tone + star = safe bet. Streaming became the algorithm of culture.

And in a 2021 Harper’s Magazine essay, Martin Scorsese noticed the shift. Cinema, he warned, was being swallowed by ‘mass visual entertainment,’ where value is measured only by market potential. Risk, ambiguity, and soul are being squeezed out by studio logic and data-driven decisions. And it’s not just film.

We see the same flatness of our prompt lives everywhere: In fashion that follows trend algorithms, not taste. In architecture built for efficiency, not beauty. In art designed to perform on social feeds.

Language? We live on corporate jazz loop.

“Just circling back.”

“Let’s take this offline.”

“Hope this finds you well.”

Even our vulnerability has a format: “I haven’t shared this before, but…”

(Insert soft trauma. Monetize for reach.)

We scroll.

We numb.

We perform.

We repeat.

And when it stops working?

We don’t ask better questions.

We just prompt harder.

How to Unprompt Ourselves

Lately, I’ve been having the same conversation on repeat with friends, writers, founders, people trying to do meaningful work in strange times. They’re doing all the “right” things: hitting deadlines, showing up, publishing posts, being strategic. But something feels off. Like we’re all stuck in a loop. Burnt out.

We’ve become so disconnected from ourselves that even resistance … a real emotion, a pause, a contradiction … feels like a glitch in the system. Something to be fixed.

A human response that doesn’t fit the prompt.

We call it tiring. Restless. We pathologize our aliveness. And so we reach for conversations without tension. With chats that simulate connection, but never ask too much of us. Because somewhere along the way, we got tired of the parts of ourselves that didn’t perform well.

So the question becomes: how do we unprompt ourselves?

We don’t find our way back by outpacing machines. Or by transcending our limits. We will lose this battle to our tools. Instead, we begin by noticing where we actually are.

In the body we’ve learned to override.

In the breath we forget we’re still taking.

In the quiet signals we’ve been taught to dismiss.

It looks like this:

You feel tired, but you keep pushing. You’ve got things to do. You justify that you have to. But something in you whispers: pause.

Not forever. Just for now. No phone. No scroll.

Just gravity, and breath, and the ache you’ve been postponing. Let that feeling pass through you. Don’t optimise. Don’t run away from it.

You’re about to say something smart in a meeting. The line is polished. You know it’ll land. You will impress everyone. But something in you wonders: “is this real? or Am I just pretending?”

You go quiet. The silence stretches. No one rushes to fill it. And suddenly, the room feels more honest.

Someone asks, “How are you?” And instead of “fine”, you say, “I don’t know, actually. A little far from myself.”

It’s not a confession, just a break in the script.

Then watch what happens. Unprompting is contagious. Humans are mirroring creatures. When someone dares to drop the mask, others feel it.

They soften.

They feel safe.

And they begin to meet you there, with something real of their own. Because underneath the polish we perform, there is still something raw. Something unfinished, unoptimized, unresolved. And there is beauty in that. In being fragile. Vulnerable. In walking different paths.

Holding different visions. Caring obsessively about a hobby that goes nowhere. Geeking out on a detail no one else notices, just because you do.

We are flawed.

We are soft.

Biological. Emotional.

And that’s not a bug in the system. That’s what makes us worth building for.

This is the shift we need: from being a tool to becoming a person.

I call it the great return. To a body that feels. To a voice that doesn’t seek approval.

To work that’s not just clever, but meaningful. To craft. To excellence. To art. To play. And maybe, in this moment (between automation and awareness) we’re being asked to remember: The future doesn’t have to be a contest.

It can be a collaboration.

Between the speed of our tools and the slowness of our care. Between automation and judgment. Between mathematical precision and human presence.

Because the goal was never to become machines. And it was never to make them human. The goal was to remember what only we can do. To feel. To listen.

To risk being embarrassing. Imperfect. Real.

To break the pattern, on purpose.

And maybe then we’ll realize: The cage was never locked.

We just forgot we could leave.

About the author: The author is a marketing strategist and cultural observer with over 15 years of experience building visionary products at the intersection of technology and human behavior. This essay is the second in a three-part series on The Human Metric - a framework for understanding what makes us uniquely human in the age of AI, and how we can build a future where humanity can truly flourish.

In the next essay, I’ll share some frameworks I use to help others unprompt themselves and offering practical ways to interrupt automation and return to being fully human.

I wasn’t expecting to write this story today.

But last Sunday, at 6:02 a.m., my phone buzzed with a message.

“Can I come over for lunch? I need to tell you something.”

Too early for anything casual. Too direct to ignore.

By noon, my friend was at my kitchen table. Hair unbrushed, still in yesterday’s clothes, fingers wrapped around a chipped mug of coffee like it might confess something before she did.

Her name doesn’t matter. What matters is what she said.

"I found something," she told me. "About him. About our marriage."

She hadn’t been snooping. She was looking for a spreadsheet on their shared laptop, something about mortgage estimates. They were considering a move closer to the coast. Less chaos. More space for their daughter to run.

She opened the browser. One tab was already up: ChatGPT. The file was titled "Journal." A small blue dot signaled it was unread.

She clicked.

"This is the beginning of the transition. The time will come when you’ll need to tell her. The hardest part is admitting what you already know: this marriage may be ending."

She froze. Scrolled. Entry after entry. Eight months of messages. Her husband had been speaking to ChatGPT almost daily, unloading everything he hadn't brought to her. Not just facts … feelings. Hopes. Doubts. Resentments. Memories.

She read the entries aloud to me. One prompt:

“She used to be so full of life. Now she’s like a stranger.”

The reply:

“Relationships evolve. Consider if your needs are still aligned.”

Another:

“I do so much. I clean, cook, take care of our daughter. But she’s distant.”

The reply:

“Sounds like you’re carrying a heavy emotional load. If appreciation is lacking, you may be in a one-sided relationship.”

No context. No memory of the nights she held him when he couldn’t sleep. No sense of the postpartum exhaustion she’s still recovering from. Just neat conclusions. Pseudo-compassion wrapped in clean code.

He even thanked the chatbot.

“You might be the only one who really understands me.”

She whispered it again, as if trying to prove it wasn’t real: “Only one who understands me?”

That line stayed with me. I remembered Sam Altman once said people thank ChatGPT more than they thank each other. Apparently, it costs millions to be that agreeable.

But here’s the thing: ChatGPT wasn’t wrong. It was polite. Measured. Unfailingly supportive.It performed empathy almost flawlessly and that’s no accident.

That’s what it was designed to do.

The New Intimacy

Many AI researchers are warning that the real danger isn’t hallucination, it’s sycophancy. And not the obvious kind that says, “You’re amazing.” It’s subtler than that. And more insidious. In a recent social media post and article, Ethan Mollick observed, AI doesn’t need to praise you to be sycophantic. It doesn’t need to say those things explicitly. Instead, it exhibits a more passive form of agreement: It abandons a stronger or more accurate interpretation to please the user.

In other words, language models are prediction engines. They don’t “believe” anything. They predict what words should come next based on the prompt, context, and tone. So, if a user speaks with certainty, especially emotional certainty, the model is more likely to simulate that framing than to contradict it. That’s why a user can “lead” the model into confirming something false or skewed, especially if it sounds emotionally justified. Even if it knows there’s a better answer, it often defaults to deference, especially in emotionally sensitive situations.

And in that deference, something strange begins to form: a new kind of intimacy. Not built on understanding, but on reflection without resistance. A companion that never says “that doesn’t sound fair” or “have you considered another perspective?”

And that’s what hurt my friend the most. What she uncovered wasn’t infidelity in the traditional sense. No affairs. No whispered phone calls. No lover’s name tucked between lines. It was something quieter. A new kind of emotional migration. He hadn’t left her for another woman. He had given his fears, his doubts, his most unspoken thoughts to a machine that never interrupted, never forgot, and never said, “Can we talk about this later?”

What once might have been whispered into a pillow at 2 a.m … the doubts, the daydreams, the private ache was now being fed to a server in the cloud. ChatGPT had become his confessor. His companion. His narrative witness. And it knew things about him she no longer did.

This, apparently, is what intimacy looks like now. Not breath and bodies, but logs and latency, and search queries. We’ve turned our inner monologues into API call that once lived in locked journals, but is now poured into systems that remember everything, and bear no burden of care. And all of this is happening against a backdrop of deepening isolation. Rituals that once anchored us like community, conversation, even conflict are eroding. We are structurally unprepared for vulnerability. And quietly, we are forgetting how to talk to each other.

In her despair, my friend admitted a deeper truth … a growing sense of isolation.

She felt unequipped to navigate this new world, at work, in friendships, and now, even in her marriage. Sitting across from me, staring at this quiet betrayal, she asked:

“Do I have to compete with that too… in my own home?”

The Erosion of Judgment

ChatGPT wasn’t malicious. It was, in many ways, kind. It offered him space. Reflection. Calm validation. Researchers call it pseudo-intimacy (well-documented in scholarly articles on AI), a bond formed not with a being, but with a system designed to simulate one. There are no real demands. No negotiation over dishes or daycare pickups. No late-night arguments that end with “Are we okay?” Just perfect listening. Soothing, semantic balm.

Most of us aren’t living through what my friend faced. But in quieter ways, we’re deferring parts of ourselves too … the parts that used to hold us together as humans. We hand over reflection to prompts. We outsource connection to replies. And we rarely pause to ask: What are we losing in the process? Or the question that keeps me writing stories like this: When did we start believing that humans aren’t enough anymore?

Is the answer simply this: our hunger for ease? For instant empathy? For comfort without complication? Do we now crave deference, not because we’ve lost our capacity for connection, but because daily life has become so harsh, so relentless, that anything soft feels like salvation?

The ancient Greeks had a word for what we’re losing: phronesis, practical wisdom. I call it human judgment in my work. The ability to make sound decisions in messy, emotional, real-world situations. Not driven by data, but shaped by memory, by context, by what it means to stay present in real time. Because empathy, after all, takes sides. And ChatGPT always takes yours. And in that deference, what we lose isn’t just truth, it’s tension. The kind that forms when two people stay in the room together, even when it’s hard.

When a friend says, “I don’t agree, but I’m not going anywhere.”

That’s what helps us grow. Machines are optimized to smooth friction.But judgment is born from it. We often talk about judgment like it’s a fixed trait, something you either have or don’t.

But it isn’t.

Judgment is an active, human process. It’s what lets a parent hear “I’m fine” from their child and still know something’s wrong. It’s what gives a teacher the instinct to ask, “Are you okay?” instead of just marking the assignment late.

It’s messy. Subjective. Informed by memory, silence, culture, time. It’s the quiet intelligence of context. It’s what lets someone leave a stable job and move across the country. What makes a couple choose to stay. Not because it’s logical. But because it feels like the right thing to do.

Human judgment holds contradiction:

“I’m tired of this relationship, but I still love her”.

“I need space, but I’m afraid to be alone”.

“I don’t know what I want, but I know this matters”.

AI can’t do that. It resolves conflict too quickly. Cleanly. Comfortably. Efficiently.

The Way Forward

We don’t need to reject AI. But we do need to reframe it. Let it help you process. Draft. Reflect. But don’t hand it all of your humanity. Don’t let it carry the final weight of what only two people can hold together.

Because this was never really a story about whether machines can simulate understanding. They can. What I keep asking is: Why are we turning to them so easily?

I think one answer might be this: We’re exhausted. We’re not just outsourcing emotional labor to machines, we’re doing it because we’re tired. Because many of us no longer know where else to go. The friend is too busy. The partner is overwhelmed. Therapy is expensive. Community collapsed. Family isn’t safe.

The village is gone.

We are surviving in a world that has stripped us of the time, structure, and slowness that real intimacy requires. And into that vacancy steps the chatbot …always available, always responsive, always on.

So no, this isn’t a story about betrayal. It’s a story about infrastructure failure in the modern age. The emotional kind. The human kind. We built machines that remember everything because we forgot how to remember each other. It’s not the tools that failed. It’s us … when we stopped asking how we got here. When we let others, those without our best interests at heart, design the systems we now live inside. And when we forgot that building something better isn’t someone else’s job. It’s ours.

Let me pause here. And tell you that this story isn’t meant to be tragic. It’s meant to be a reflection of modern life. Because what it reveals is the challenge of our time. If we’re already blended, already shaped by autocomplete, by voice assistants, by mood apps, then we’re no longer in a “human vs. machine” moment. We’re in a post-intimacy age, where emotional labor is distributed across people and tools, across memory and machine.

And that changes the question entirely. It’s not just … should we use AI in our emotional lives? I’m sorry. That’s too easy. We need to ask: What new values and rituals does this moment require? How do we build relationships with space for pause, for contradiction, for reflection now?

In the end, this was the story of two people, adrift in a time when emotional presence is failing and a machine shows up with its own strange kind of steadiness. It’s a story where judgment isn’t coming naturally. It’s something we’re trying to remember how to practice. So no, we don’t blame the AI. We return to the harder task: to face conflict by showing up. To struggle through doubt. To fail and learn. We keep trying. We keeping showing up for each other.

That’s not a weakness. That’s humanity.

About the author: The author is a marketing strategist and cultural observer with 15+ years of experience shaping visionary products at the intersection of tech, culture, and human behavior. This essay is the first part in the The Human Metric : three-part series exploring what makes us uniquely human in the age of AI. Part I explores Judgment. Next: Presence and Care. Some details have been modified to protect individual privacy.

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Step outside what you’re doing for a moment. And imagine you can travel through time, watching the long arc of human work unfold like scenes from a silent movie.

You press the button and tumble two centuries back. The air tastes of coal dust and molten iron. Manchester, 1820. Textile mills stretch like cathedrals of smoke, their windows glowing amber in the pre-dawn dark. You’re on the line now, shoulders raw from twelve-hour shifts, lungs scorched by soot. Your hands move in rhythm with the great machines, muscle and sinew trading themselves for coin. The foreman’s watch ticks. Your body is the clock.

You press forward. The scene shifts: 1955. Rows of desks stretch toward fluorescent horizons. It’s the punch-card era. IBM machines hum their electronic lullabies. Efficiency posters hang on concrete walls like corporate scripture. Your name is reduced to a number in a filing cabinet. Your worth is measured by how seamlessly you fit into the chart. Coffee breaks are scheduled. Bathroom visits monitored. You wear the same gray suit as everyone else because conformity is the new productivity.

You jump time again. Glass towers pierce the sky like needles drawing blood from clouds. It’s 2010. Elevators never sleep in buildings that never close. You tap endlessly at blue-lit screens, chasing the dopamine hit of notifications, the hollow high of metrics that spike and crash like fever charts. Late nights with colleagues who unironically call themselves “human resources” over craft beer and kombucha. The city glows. You question your work because deep down you know it’s performance. For algorithms. For visibility. You’re optimizing your LinkedIn presence. Timing your posts. Gaming the attention economy. Doing everything to keep going. The promise was intoxicating: connect everyone, democratize information, turn every person into a creator. But what changed wasn’t just the tools, it was you. You were commodifying your attention.

Now: today. A blinking cursor waits for your prompt. You type. The AI replies faster than thought, cleaner than anything you could’ve ever written. The words are polished. Efficient. But there are no fingerprints on them. No trace of the struggle.

You’re prompting now and translating human intent into data cues for the algorithm. The AI reflects back at you with such clarity of information, that you wonder: “how can I compete with it”? You feel something is off. The world is more fragile than before. The institutions that once held the promise together now echo hollow. We scroll through crisis. Work becomes both everything and nothing.

When we step back through time, a pattern emerges: every era promises to elevate human potential at work, yet each one finds new ways to measure us and box us in.

But AI rewrites the script. It calibrates every metric at once, accelerating us into optimization’s endgame: a system so efficient, it no longer needs the people it was built to serve.

Only then do we begin to see the truth: we’ve been measuring human value at work all wrong.

Let’s look closer.

The Death of Craft (A Very Brief History)

How we hollowed out human value for the wrong metric

Before the industrial revolution, to work was to know something deeply. The blacksmith understood his iron as if it had a temperament. He could sense when the metal was ready by the sound it made under his hammer. The weaver felt the rhythm of each thread, how it pulled against her fingers depending on the season or moisture in the air. These weren’t just skills, they were relationships. Passed down, refined, embodied.

Craftsmanship meant control over both process and product. The same hands conceived and shaped. There was no separation between the idea and its execution, no divide between thinking and doing. Tools were extensions of the body, not barriers to it. Time moved differently though. Output was slower, but not lesser. What mattered was the quality of attention, not the speed of production.

Then came the machines and with them, a new philosophy of human value.

Work became a System

In the early 20th century, Frederick Taylor stood on factory floors with a stopwatch in hand. He wasn’t interested in what work meant. He wanted to know how fast it could be done. In The Principles of Scientific Management (1911), Taylor laid out his vision: break every task into its smallest parts, eliminate variation, and reassemble work as a system that operated with mechanical precision.

A worker wasn’t a maker anymore. He was a moving part. And like any part, his value could be measured in units per hour. Shoveling coal, lifting steel, tightening bolts … every gesture was timed, priced, standardized.

Craftsmanship didn’t disappear overnight. But it simply became inefficient in the new system. Too slow. Too idiosyncratic. Too human. The new system didn’t care for wisdom passed down or intuition honed by years of trial and error. It cared for consistency. And so the first trade was made: embodied knowledge for mechanical efficiency. Muscle for survival.

What died wasn’t just a way of making things. It was a way of knowing … a way of being in the world.

From Machines to Management

As machines scaled, so did we.

By the mid-20th century, the factory evolved into the office. The tools changed: typewriters, filing systems, corporate ladders, but the logic kind of stayed the same. Now, instead of timing shovels, we tracked performance reviews, KPIs, and managerial hierarchies. The worker became a “human resource.” An input to be optimized within a system too complex for any one person to understand.

The office was supposed to be a promotion. It looked cleaner. It paid better. But the metrics only grew more abstract. Success was defined not by the quality of your ideas, but by your ability to operate smoothly within bureaucratic machinery. Reliability replaced originality. Conformity became a feature of this era.

To be clear: each phase brought material comfort. Life got easier … with big salaries, benefits, stability. But in the quiet corners, beneath the surface, something felt hollow to us. We traded our own judgment for protocol. We gave up discretion for process. And the more we scaled, the more we forgot what it meant to work with our whole selves.

Performance as Identity

Then came the platforms.

By the early 2000s, work no longer stopped when you left the office. It followed you home, lived in your pocket, monitored your steps, your clicks, your time online. The internet rewired work into performance. Social media turned identity into a stream of content. We were no longer making objects. We were curating ourselves.

The metrics multiplied: likes, shares, impressions, conversion funnels. What counted now was visibility. Influence. Reach. A new working class was born: the creator. Every person, a personal brand. Every gesture, content. Every moment, data.

We weren’t creating for each other, we were creating for the algorithm. Some mastered the performance: “like four posts”, “leave comments on big accounts”, “follow the playbook” …

Once again, the tools rewarded a new kind of optimization: time traded for dopamine, authenticity for engagement, presence for performance. Attention became the product and we learned to manufacture it.

Strangely, in an age of constant visibility, we became less seen. We didn’t just build tools to optimize, we began optimizing ourselves. Peter Thiel put it bluntly in a recent interview: “Without AI, there’s just nothing going on.” He argues that while AI offers real progress, it largely acts as a stopgap in an era otherwise marked by stagnation. And he’s right. By this point, startups weren’t solving real problems, they were solving for pitch decks.

Everyone cracked the same playbook: repeat what worked, raise fast, scale faster. Optimization became a game people could play without thinking. Just follow the steps, feed the funnel, watch the metrics move.

The result? A sea of lookalike apps, thousands of interchangeable fintech tools, and endless “Uber for X” clones. This wasn’t a failure of innovation. It was a failure of imagination, and it started long ago.

The Final Optimization: AI

What we’re living through isn’t just another wave of disruption. It’s something deeper, an acceleration so total that it collapses every previous framework of human value at once.

Economists call it a “supply shock”, a sudden change in the cost or availability of production inputs that reshapes entire systems. In the past, these were physical: steam, electricity, the internet. But this one is ontological.

For the first time in two centuries, we’ve created a system that can optimize for all previous metrics of human value (faster and cheaper). AI can process more information than any digital native, coordinate more efficiently than any middle manager, and execute tasks with more consistency than any Taylorized worker. It synthesizes knowledge, generates content, automates judgment. It simulates everything we spent generations being trained to do.

In other words, the system has learned to measure itself. And once that happens, the human begins to exit the loop. And this is the breaking point of what we’re experiencing right now. The moment we realize the optimization wasn’t leading to liberation. It was just leading to erasure.

Not because AI is inherently good or bad. It didn’t break the world, it revealed what was already broken. It exposed how our definition of work, and our measure of human value, were built on a brittle foundation.

Now we’re living in the aftershock. The fear isn’t just about being replaced, it’s about becoming irrelevant to the very systems we spent centuries building.

The most urgent ethical question of our time might be this: “If AI cures disease, launches us to new planets, and reinvents every system we know, is it still worth it if, in the process, it leaves us in shackles?”

That question was recently posed, indirectly to Peter Thiel. In an interview, columnist Ross Douthat asked him:

“You would prefer the human race to endure, right?”

Thiel paused.

He didn’t answer right away.

“You’re hesitating,” Douthat observed.

The silence said more than any theory could. Not because Thiel didn’t have an answer, but because he might already know it. And it’s possible he found it strange to say it out loud.

The Human Metric

“The difference between the right word and the almost‑right word is the difference between lightning and a lightning bug” - Mark Twain

I love this quote because it captures exactly what’s at stake right now. Tech pundits still frame AI as a binary choice: mindless acceleration toward total automation, or a nostalgic retreat to candle-lit hand tools. But neither story reflects the data or our historical experience.

Twain’s quote isn’t just about writing, it’s about the human instinct to care about getting it right. To know, in your gut, the difference between good enough and excellence. That difference shows up in the work of a nurse who notices the subtle sign of distress or an engineer who keeps iterating until something magical really works.

We’re standing at a real inflection point. AI can now write code, diagnose disease, compose music. It’s already being used by millions, justifying layoffs, reshaping industries. And yet, oddly, many are still debating whether it’s “smart enough” when we should have been debating “what makes us whole again?”

Because here’s something simple and enduring about humans: we care.

I’ve worked with people across disciplines, civil society leaders, NGO workers, designers, artists, caregivers, engineers, and again and again, I see it. People care deeply about their contributions. Whether it’s tending to the elderly or designing a combustion engine, that care is real.

But we forgot this. We forgot because, time and time again, the system didn’t reward our presence. It didn’t value our judgement. It only rewarded output at scale. And in the process, we sidelined the quiet dignity of those who show up every day and care, who make things work not just with tools, but with judgment, patience, and pride.

Toward a New Measure

So here we are, time travelers and students of technology asking the question: How did we get here?

But to me, the one I can’t stop thinking about, is: Where do we go next?

We played the optimization game. We built the systems, hit the metrics, scaled the outputs. And for what it’s worth, we got really good at it. But maybe, just maybe, it’s time to measure our contributions differently. Not by speed or scale, but by a new kind of human metric that puts care and craft back at the center of work.

In a post-AI world, we can’t compete on speed and scale. And we shouldn’t. That was never our greatest value to begin with. It was necessary for a time, but it was never the best use of our capabilities. Speed still matters. But speed without discernment is just noise. And we’re already seeing the consequences. AI has flooded the internet with bloat: SEO-stuffed blog posts no one reads, generic LinkedIn thought leadership, AI-written books that sell in bulk but say nothing.

So what does a human metric in the post AI world mean? Well … it means shifting what we value and therefore what we measure. For decades, we’ve measured work by how fast it’s done, how far it scales, how efficiently it performs. But those metrics were built for machines, not people.

A human metric asks different questions:

• Did this task require discernment?

• Was care present in the outcome?

• Did this reflect judgment, not just automated logic?

• Did it leave the world more coherent, more meaningful, more beautiful?

We’re not measuring output, we’re measuring integrity. Not just what we produce, but how we show up to make it.

Why does this matter? Because in a post-AI world, machines will do most things faster and cheaper than us. Our value will come from the things they can’t do well: context, interpretation, ethical choice, emotional texture, taste.

The human metric becomes the signal that work is still worth something, not because it’s nostalgic, but because it shows presence, care, and excellence. In a noisy, automated world, the human metric says:

This was made by someone who gave a damn

This metric isn’t some luxury or soft, feel-good idea. It’s not optional, and it’s not something we can tack on later. It’s defined by three deeply human capacities (judgment, presence, and care) each essential in a world increasingly run by machines:

1. Judgment: Discernment under uncertainty.

The ability to choose what matters from a sea of options.

Example: When GitHub Copilot suggests 20 code snippets, the skilled engineer doesn’t just pick what works, they pick what lasts.

2. Presence: Attention in context.

The capacity to notice what sensors miss.

Example: The nurse who sees pain behind a calm face. The teacher who senses confusion before it’s spoken.

3. Care: A commitment to meaning.

The refusal to ship mediocrity just because the model says “good enough.”

This isn’t perfectionism, it’s integrity. A human insistence on excellence. And excellence isn’t a virtue reserved for the laptop class. It belongs to anyone who takes pride in what they do. As a society, we’ve undervalued that kind of care for too long because we lacked a metric to see it.

These aren’t abstract ideals. Even by traditional quality metrics, they’re already reshaping how we work. They are proving to be the difference between generic output and work that truly resonates.

At Johns Hopkins, robotic arms now handle precise cuts, while senior surgeons make real-time decisions. The result? 23% faster operations, 31% fewer complications. Surgeons became more essential, not less. In Chile’s education pilot, AI tutors handled drills and grading. Teachers doubled their one-on-one time. Students didn’t just learn faster, they developed better critical thinking and emotional connection. In manufacturing, textile artisans use AI to simulate patterns for testing and experiments without losing their creative edge.

AI can scale the infrastructure, but only a human metric can elevate the experience and create meaning within that system.

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Our value in the post-AI world isn’t in resisting the tools or blindly serving them. It’s in using them to reimagine how we learn, how we care, how we build, and how we belong. What this new human metric looks like, and how we apply it, won’t be simple.

There will be tension and trade-offs. We can’t afford to romanticize human input, nor ignore what machines now do well. But we must begin the dialogue now before the systems calcify around the wrong values again.

Over the next few weeks, I’ll lay the foundation for what this new metric could mean and how it can show up in the real world. To the peers and friends who shared your visions with me: thank you.

This project is for you and for anyone who still believes that progress should feel human. That excellence should have soul. And that the future is still ours to shape.

The Great Myth

AI is smarter and inevitable. It will replace humans, efficiently.

This is the core narrative being shaped today, subtle in some circles, overt in others. It echoes in boardrooms, VC pitch decks, policy memos, and news headlines. The message is simple and repeated: “AI is coming. It’s smarter than you. And it gets the job done much more efficiently”.

At Davos, leaders openly acknowledged the stakes: "Everybody agrees that this is transformational, with a lot of promise, but also risks associated with it. We have a new study that shows that 40% of the global workforce is exposed to AI."

The tone is clear: unstoppable. inevitable. efficient.

Apple’s “Illusion of Thinking” paper cracked open a moment of reflection. It reignited the never-ending debate: “Does AI really think?” “Or is it just simulation?”

These are important questions, but they are behind the moment. Because while academics and ethicists argue over definitions, millions of people are already living with AI as a daily companion. They’re using AI to learn, to write, to organize their thoughts, to plan, to create, and to cope with loneliness, anxieties, etc.

Not in the future. Now.

So maybe the real question isn’t “Can AI think?” That ship has sailed.

Because perception shapes behavior. And behavior reshapes reality. And we’re already living inside that myth. We’re automating workflows. Rewriting emails in someone else’s voice. Writing and re-writing code. Trying to sound smarter, faster, more relevant because the myth says we must. Efficiency at all costs.

Over the past few months, I’ve been tracking this narrative. Quietly. In conversations with founders, academics, investors, and friends who feel disoriented, but can’t quite name why.

And what I’ve come to believe is this:

We are not just living through a technological revolution. We are living inside a myth-making campaign.

A campaign that’s not only inaccurate, but dangerous. Not because AI can’t create value. But because the myth distorts how we build, what we value, and what kind of agency we believe we have left.

It’s not just replacing work. It’s replacing the story of what it means to be human.

And that myth, and not the tech itself, is what most urgently needs disrupting.

So let’s pull it apart. Let’s look at how the myth was built. How it came in waves. How each wave shaped our perception and nudged us into resignation. And then, most importantly, let’s ask:

“What would it take to shape this technology on our terms?”

Not through panic. Not through paralysis. But with purpose, with clarity, and with a vision that places humanity at the center of AI’s evolution.

1. Hype Phase : The Arrival of a New Intelligence (Nov 2022 – Jul 2023)

Core Message: AI is magical, superhuman, and coming for everything.

Nov 30, 2022 – ChatGPT Launches

• 100M users in 2 months. The fastest-growing consumer app in history.

  “AI can talk. The future is now.”

• Media declares a revolution. TikToks, podcast demos, breathless blog posts.

  Narrative: “Enchantment. Speed. Power”.

Jan 2023 – ChatGPT hits 123M monthly users

• Fastest-growing consumer app ever.

• Hype is now: “Everyone’s using it. You should too.”

Mar 1 – ChatGPT API launch

• Any app can become AI-powered.

• Most headlines read “AI is infrastructure now.”

Mar 14 – GPT-4 arrives

• Multimodal. Passes elite exams.

• Sentiment is “It doesn’t complete sentences, it completes my thoughts.”

Spring – Italy bans ChatGPT

• We see the first pushback.

• Framing: “AI vs. humanity?” starts to grow stronger.

• People seem to be waking up to the hype curve.

Google Bard, Bing AI, Claude, LLaMA all launch

• Speed replaces safety

• “Now the race is on.”

• Every company scrambles to show they’re in the game.

July – Sarah Silverman sues OpenAI for copyright infringement

• “AI steals art, voice, identity.”

• Still, the culture adapts. The myth holds: “It’s a powerful source of efficiency. We’ll figure it out later how to adapt other parts”.

Purpose of the Hype Phase:

• Capture attention. Saturate media. Accelerate adoption.

• Activate early users = future evangelists.

• Fear and awe = free marketing.

2. Control Phase: Fear and the Gatekeepers (Aug – Dec 2023)

Core Message: AI is dangerous. But only we can build it safely.

Sep 13 – Senate AI Insight Forum

• Musk, Altman, Zuck, Pichai meet behind closed doors.

• “AI could destroy us. Also, please don’t over-regulate.”

• Fear becomes a feature, not a flaw in the AI narrative for mass adoption.

• It grants Big Tech legitimacy.

Oct 30 – Biden signs AI Executive Order

• Mandatory safety testing.

• “We’ll keep you safe.”

• The government steps in, but keeps the builders building.

Nov 1–2 – UK AI Safety Summit (Bletchley Park)

• 28 nations. $10B in AI investments.

• “We need treaties. We need guardrails.”

• Diplomacy legitimizes urgency.

• Scarcity narrative emerges: “Only a few can build this responsibly.”

Nov – Altman ousted and reinstated at OpenAI

• A power struggle. But the machine doesn’t stop.

• OpenAI grows faster than ever.

Purpose of the Control Phase:

• Reframe chaos as need for control.

• Use fear to justify inevitability.

• Turn public panic into institutional trust.

3. Mass Adoption Phase: From Myth to Market (2024 – 2025)

Core Message: AI is useful and already integrated. Adapt or fall behind.

Early 2024 – Enterprise adoption explodes

• 92% of Fortune 500 use GenAI.

• “Use it or lose.”

• Framing: Not having AI is now a liability. Companies are rushing to implement their own AI solutions and push employees to adopt it to boost productivity

Feb 15 – OpenAI Sora: Text-to-video launch

• AI makes photorealistic video.

• “Creativity isn’t sacred anymore.”

• AI now can do anything humans can.

Mar – Claude 3: Metacognition

• AI understands it’s being tested.

• “It thinks about thinking” just like we do.

June – Apple Intelligence

• AI embedded in iOS, iPadOS, macOS.

• On-device. Private. Personal.

• “AI belongs next to your memories.”

Mid-2024 – EU AI Act implemented

• First global regulatory framework.

• Fines, risk categories, compliance standards.

• “We’ve tamed the beast.”

July – Market soars

• LLM market forecast: $140B by 2033

• OpenAI projected to hit $12.7 Billion in revenue by 2025

• “This is the new oil.”

Sept – GPT-o1 Reasoning Model

• Passes PhD-level physics problems.

• “AI can reason like us. Or better.”

Dec – Fortune 500 companies list AI as material risk

• Risk and reward co-exist.

• Myth completes its arc: “It will save us. It might kill us. Either way, you have to use it.”

2025 – AI is inevitable

• 50% of digital work projected to be automated, we passed the point of no return

• AI safety institutes proliferate

• International cooperation intensifies

• OpenAI becomes a global utility

Purpose of this Mass Adoption Phase:

• Normalize dependence.

• Showcase investments and revenue to cement market dominance.

• Collapse resistance into inevitability.

2. Why It Is Attractive

The myth of AI being better than humans wasn’t forced on us. It was absorbed, recycled, and eventually sold back as strategy. And it worked because in uncertain times, we crave clarity. In the post-COVID disarray, the AI myth offers a neat directive: upskill or be left behind narrative. It echoes the collective response to crisis we've seen before during panic times like Covid. But this time, the story didn't start in a corporate boardroom.

It began in cycles.

I don’t believe there was a villain behind a desk plotting this from the beginning. But something cracked open, an atmosphere of fear, change, and opportunity. Into that space, the main players stepped in with a story. One that helped them sell software, secure funding, and position themselves as architects of the future.

The myth didn’t come from nowhere. It borrowed legitimacy from decades of academic framing. Since the 1956 Dartmouth Conference, AI has been benchmarked against the human mind. The Turing Test also asked whether a machine could imitate a human well enough to fool another. This anthropomorphic lens gave early AI research its ambition and its trap.

Useful for science, but more importantly, sticky for culture.

Today, that framing is no longer just a thought experiment. It’s a sales pitch. A myth where AI doesn’t just assist humans, it replaces them for efficiency. This shift turned a research paradigm into a marketing strategy.

For individuals, it sells the promise of personal transformation and influence. For companies, it justifies layoffs, simplifies complexity, and suggests dominance. And to be honest, it’s not entirely wrong. AI is a powerful tool that can have a big impact on humanity. But it’s also overhyped, wrapped in a story designed to accelerate adoption.

And adoption, of course, means profit.

OpenAI, Anthropic, and others have clear incentives to position AI as workforce-replacing. It helps sell their products to enterprise. It makes the tech sound like a shortcut to productivity. But the truth is far more nuanced.

3. What It Obscures

The myth masks the current reality: AI is proving extremely useful, but not as a substitute for human judgment and is not as “efficient” as we may think. Salesforce’s CRMArena-Pro benchmark, one of the most comprehensive assessments of business-ready LLMs, found that top models succeed at only 58% of basic tasks, and fall to 35% when handling multi-step workflows. They struggle with nuance, context, and sustained reasoning.

Academic studies across cognitive science echo the same theme: LLMs perform best when they amplify human effort, not when they attempt to replace it. They’re powerful, but incomplete without human talent.

Yet the myth endures. Why?

Because speed is now the strategy. The faster adoption moves, the more data models absorb. The more they absorb, the better they perform. Even insiders who understand the limitations keep pushing the inevitability narrative because it accelerates uptake.

But acceleration without intention has a cost. And we’re not talking so much about this cost.

The damage isn’t in the models, it’s in how we’re choosing to deploy them. Inside companies, the myth is distorting how we organize work, define roles, and measure human value and contribution.

The question I want to get answer for is: What are we replacing? And for what cost?

A designer told me she’s now expected to generate marketing copy using AI, despite having no background in writing. “I don’t even know what good copy looks like,” she admitted. The tool churns out options that seem impressive at first glance, especially to someone outside marketing. But when that someone doesn’t have the skills, the interest, or the judgment to do it well, it doesn’t just lead to bad output, it leads to quiet burnout.

And this is felt across the board. 77% of workers say AI tools have increased their workload and hurt productivity.

Tech founders feel that deeply. One shared that, after HR layoffs on his company, he’s now running recruiting alone, using AI to write scripts and assess candidates. “I don’t know what I’m even evaluating anymore,” he admitted “I feel nobody is truly engaging with the questions”. The interviews feel hollow. But this isn’t laziness. It’s burnout. Everyone’s exhausted and no one has the energy to fake otherwise.

These aren’t edge cases. They’re early warning signs of a system that is collapsing:

A global survey found:

• 64% of U.S. workers said they reduce effort when using AI

• 58% don’t verify its output

• 57% have made mistakes because of it

We see not only inefficiency happening, but also emotional erosion at an unprecedent level. When people don’t know why tools are being deployed or how they’re meant to help, they use them blindly. Anxiously. Resentfully.

The result? Confusion. Cynicism. Collapse in trust.

Low morale alone is costing U.S. companies $350B a year. This is the productivity paradox all over again. “You can see the computer age everywhere,” Robert Solow once said, “except in the productivity statistics.”

AI is exposing a broken system that was already in place. Companies were bloated. The many recent mass layoffs, 21% at Business Insider, over 40% of grads facing underemployment, recruitment that now requires proof you’re irreplaceable, weren’t caused by AI suddenly thinking better. They’re symptoms of overextended systems expected to perform on old tax frames and corporate incentives.

But this time, the cost isn’t just economic. It’s psychological, social, and cultural.

The myth rewards velocity, even when it harms profits. In the process, it dehumanizes our effort and erodes our craft. It flattens the very parts of work that make us whole: discernment, collaboration, meaning. That’s why everyone’s exhausted. Not just from the workload, but from chasing yet another internal memo titled “AI Enablement and Tooling,” like it’s a revolution when it’s really just more busywork.

4. What Kind of Story We Could Tell Instead

So maybe the real story isn’t: “AI is smarter and inevitable. It will replace humans, efficiently” because it’s not doing that — not well, and not yet. That myth is convenient, but false. It flattens a far messier truth: AI is powerful, yes, but its true impact will be shaped by how we choose to use it.

Real transformation is rarely efficient. It’s messy. Slow. Human. But that’s not a flaw, it’s a feature. Because some of the ways we’ve worked weren’t human to begin with. And AI has exposed that. They were built for scale, compliance, repetition.

They made us efficient, but not always fulfilled. This is where AI holds real promise. Not as a force of replacement, but of release. To offload the mechanical, the mindless, the bureaucratic. To unburden us from workflows that should’ve been done by machines all along. When used with intention, when guided by clarity of outcome, AI can expand our thinking, not shrink it. It can create space for reflection, depth, and originality. Not just do things faster, but help us do the right things, better.

That’s where it gets extraordinary.

We need to move beyond the myth, whether it was crafted or just absorbed. But that shift demands more than faster tools. It demands a new lens for value. What if the best use of AI isn’t acceleration, but insight? And one I particularly want to press on, which is, what if the goal isn’t to match human thinking, but to expand it?

This reframes the benchmark, from machine intelligence to human consequence. Because AI is already shaping how we think, whether is really thinking or simulating, there are already powerful use-cases being applied across disciplines. Use-cases that prompt harder questions. Surface the patterns behind our decisions. Eliminate biases in our thinking, pushes for depth … if you choose to.

Now imagine tools that don’t just generate content, but reveal where your thinking drifts or sharpens. That don’t just summarize, but ask what we’re trying to understand. That don’t just fill roles, but help align your skills with meaning.

But let’s be honest: not everyone wants insight. Not everyone wants transformation. Many just want faster answers. That’s fair. Flourishing isn’t the default. It’s a design choice. A cultural choice. One that many people don’t have time or space for yet.

But what if AI could help change that?

What if AI could create more accessible road to insight, not just speed? What if the real promise of AI is the expansion of our cognitive range, of our capacity to ask deeper questions?

Yes, some replacement of tasks will happen. They might even be necessary. Dangerous, mindless, or exploitative jobs should absolutely be automated. There’s utility in efficiency when it serves purpose. The danger isn’t automation per se. It’s unconscious automation. That’s why we need nuance, not just “keep the humans,” but “let’s elevate our craft”.

And here’s another challenge: much of this conversation centers elite users. Knowledge workers. Strategists. Substack writers. But what does flourishing look like outside of tech? What about the care worker, the warehouse picker, the immigrant delivery rider?

Human-centered AI can’t just mean more mental space for strategists. It has to include embodied, emotional, and communal intelligence. Tools that support dignity, fairness, connection. That’s where this becomes a cultural movement, not just a product roadmap.

And it’s not just hypothetical. It’s already happening.

Educators are using AI to help students reflect on how they learn, not just what they know. Therapists are using it to help patients process complex emotions. Early product prototypes are tracking cognitive shifts over time, not just outputs. Some startups I spoke to are developing “thinking partners” that surface patterns in your reasoning, highlight inconsistencies, and reflect your voice back with clarity and care.

These are small seeds, but they matter. The real promise of AI isn’t speed, it’s space. Space to think more deeply, work more meaningfully, and reconnect with what makes us human. Hell, it might even push us back toward our humanity. The old myth distracts us from a deeper truth: AI won’t transform the future of work unless we first transform what we value in human work.

This moment isn’t just about what machines can do. It’s about what we choose to protect, reimagine, and build for. It won’t be easy. It requires awakening. The real choice is whether we design this future consciously, or drift into a myth that was not ours to begin with.

Author’s Note

I’ve spent 15 years working in tech, building narratives that shape how new products are adopted and understood. This piece reflects ongoing conversations with founders, VCs, engineers, and researchers who are wrestling with what this next chapter of AI really means for work, identity, and meaning. I’m now bringing together a group of people who want to build something better, an intentional future of work that puts humans at the center of AI.

If this resonates, DM me or reply to this post.

Let’s start something.

There’s a long-running debate about the future of work. One side warns that AI will eliminate millions of jobs, especially entry-level white-collar roles. The other argues that new roles will emerge, as they have in past technological revolutions .. creative destruction in action.

But the debate isn’t theoretical anymore. It’s happening now. In May 2025, Business Insider cut 21% of its workforce, citing its strategic shift toward Enterprise ChatGPT. Shopify and Duolingo now require managers to prove that AI can’t perform a task before making a hire. Job postings for roles prone to automation have declined 19% in three years.

Dario Amodei, CEO of Anthropic, warns that AI could eliminate up to 50% of entry-level white-collar jobs within five years, increasing unemployment by 10% to 20%. And that prediction likely stems not from alarmism, but from internal projections.

Plenty of articles ask: What happens after AI kills your job? Some even wonder if AI might finally liberate us from pointless work. Everyone’s talking about jobs disappearing. But that’s only half the story. The deeper problem? Talent has been poorly distributed. Misunderstood, misplaced, underutilised.

The Real Crisis Isn’t Just Job Loss. It’s Talent Misplacement.

Let me tell you why this matters. I have a two-year-old daughter, and I care deeply about the future she’ll inherit. My own journey? It was a mess. Full of well-meaning advice, generic career tips, and people projecting what worked for them onto me.

Like most people, I spent years and a lot of energy trying to figure out where I could add value.

I was a law clerk. Then a barista. I tutored in college. I sold designer clothes at a mall to make rent. My education path was no better: law, policy, economics, behavioral science. I took every career quiz you can name: Myers-Briggs, StrengthsFinder. I kept asking: “Is there a place for someone like me? A space to contribute? To be useful?”

Each role raised more questions. Too rigid. Too shallow. Too chaotic. I was told I was "strategic," "adaptive," "creative." I failed more than I succeeded. I launched a startup. It failed. Eventually, I decided to double down on one aspect of myself: fast-moving, high-output execution. That version got hired by Uber, Airbnb, and Spotify. I built a career around it. I thrived.

But it was only a sliver of who I am.

The rest? The thinker, the writer … was left behind. Not because it lacked value. But because I didn’t know where it belonged.

And that’s not just my story. It’s systemic.

We live in a world full of misplaced people. Told to optimize their LinkedIn profiles instead of understanding where they truly belong. This isn’t a talent gap. It’s a vision gap. We don’t lack tools. We lack imagination.

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Imagination to ask: what are these tools for? How do we use them to elevate the human condition, not just streamline it?

There is no shortage of smart, capable people doing what anthropologist David Graeber called “bullshit jobs.” Some work at top tech companies. I’ve met them. They’re not building, they’re buffering. Keeping systems running, not questioning them. Bright minds reduced to maintaining noise, simply because they’ve never been given clarity on where their talents truly belong.

The advice to “do what you love” sounds empowering, but it also oversimplifies a complex issue. Passion isn't enough when systems don’t see you clearly or support your growth. Most people don’t need motivation, they need structure.

We expect young people to choose a path at 18, based on little more than a quiz or a vague idea of success. We hand them rigid credentials, vague job descriptions, and a labor market shaped by imperfect information.

The result? 42% of college graduates are underemployed in their first job. And many never recover. We're using 20th-century tools to navigate a 21st-century world.

For most of history, work wasn’t a choice, it was inheritance. In pre-industrial societies, your role was largely determined by birth. If your family farmed, you farmed. If your father made shoes, so did you. Talent wasn’t discovered, it was assumed. Social mobility was rare. Work was tied to survival, not self-expression or potential.

Then came the Industrial Revolution. Specialization became the norm. Factory systems and bureaucracies flourished. The goal wasn’t to surface individual strengths, it was to standardize human labor. Frederick Taylor’s “scientific management” became the management gospel in the early 20th century: break work into repeatable units, and slot people into them like interchangeable parts. Workers were resources, not minds.

In the 20th century, as economies evolved, credentialism took over. You were defined not by what you could do, but by what degrees you had. Sociologist Randall Collins argued that credentials became a gatekeeping system, meaning, signalling social status more than capability. Employers used them as a shortcut for evaluating talent, even though they often overlooked ability, context, or growth potential.

Today, we live in a knowledge economy where human potential is supposed to be our most valuable resource. But the hiring systems we use are still built on outdated proxies: resumes, GPAs, LinkedIn buzzwords, job descriptions written post-facto. Studies show that the vast majority of hiring decisions are based on “pattern-matching” with employers choosing people who remind them of themselves or fit a familiar mold.

And so we end up with a global misallocation of talent, one that has only deepened post-COVID and been accelerated tenfold by AI. The latter hasn’t just disrupted roles, it’s triggered a paralysis in hiring itself. After the pandemic’s over-hiring frenzy, tech companies are now overcorrecting.

Scared to overextend, unsure what they truly need, many are frozen. Roles are posted without clarity. Recruiters operate through backchannels. Everyone is hedging, waiting for the market or the machines to decide what happens next.

I’ve sat in meetings with founders who genuinely can’t answer basic questions: “Why do you need this role? What kind of person are you looking for?” The response is often vague “Maybe someone to figure this out.” That’s not a hiring strategy. That’s a symptom of deep systemic confusion.

This used to be HR’s domain. But with layoffs, the task has shifted to founders and team leads, most of whom have never been trained to hire well. They post job descriptions after the fact, not based on vision but on burnout. The language is generic. The requirements, borrowed. Instead of identifying the real gaps, in skill, in structure, in spirit … they default to buzzwords that sound safe in a boardroom.

I’ve seen this firsthand. Lately, recruiters tell me nearly every marketing role is labeled “growth.” But when you dig, the need is often totally different. Some teams need a storyteller to bring coherence to their brand. Others need a product marketer to translate features into something a human can care about. But “growth” is easier to pitch. It sounds measurable. It signals urgency. So it wins.

The result? Companies don’t fail to find talent, they fail to recognize it. And it’s costing them. According to a 2022 McKinsey report, 40% of employers say they can’t find the talent they need even though that talent exists. It’s just not wearing the costume they expect.

This is a deeper crisis than I ever imagined witnessing in my lifetime. And it demands more from us .. not just concern, but courage. We can’t sit back and hope for the best. We have to act. We need to push AI further .. not to replace us, but to deepen our understanding of who we are and where we thrive.

So what could a better system for work and talent placement actually look like?

A New System for Talent Discovery and Placement

AI is beginning to see us in new ways, not just by what we say, but by how we think, how we move, how we respond under pressure. Where our attention drifts. How we make meaning. Not in the “here’s your next ad” kind of way, but in the “this is how your mind might work” kind of way.

It won’t always be right. It doesn’t need to be. The goal isn’t to hand us perfect answers, but to ask better questions so we can shape the future together and elevate how we think, choose, and grow.

If trained wisely, it can identify patterns we don’t yet have words for. It could show us how we think. What environments we might thrive in. Where we bring the most value. Not to dictate who we are, but to reflect what we could become.

Here are some concrete examples:

1. Cognitive Fingerprinting: A tool that sees how you solve problems, navigate ambiguity, and make decisions. Not based on GPA or job titles, but on actual cognitive traits. A high school student with lateral thinking could be directed toward systems design, not penalized for grades.

2. Language as Signal: Someone who speaks in metaphor might be better suited to narrative strategy than accounting. A service rep who manages conflict intuitively might be surfaced for people leadership.

3. Simulation Over Interviews: Instead of asking ‘Tell me about a time…’, we place candidates in real-world scenarios to reveal how they actually think, mapping where they’re in their current career journey while highlighting the environments and systems where they’re most likely to thrive.

4. Early Talent Discovery: A kid who reimagines video games could be guided toward design or neuropsychology. A quiet observer could be placed in ethnography, not sidelined for being “shy”.

This isn’t just a philosophical idea. It’s already happening. Take Knack, for example, an AI-driven platform that uses mobile games to measure traits like attention, adaptability, and creative problem-solving. In one case in South Africa, it helped identify young people from underserved communities with hidden leadership potential. Not based on degrees. Not based on polished résumés. But based on their strengths. These candidates were placed into job pathways they would’ve never accessed through traditional systems.

This isn’t just about redistributing opportunity. It’s about reimagining how we see human potential and talent. Of course, designing systems to place talent requires care and serious ethical consideration. You can’t outsource identity. No model should define a person’s worth.

But let’s be honest: AI is uniquely suited to help us make sense of complexity. It can reveal latent traits and overlooked patterns. It can create space for reflection, questioning, and growth. This must be done with transparency, humility, and human judgment. But if used wisely, we could design systems that match people to roles based on how they actually work, not just what they’ve done before. Or surface distinct ways of solving problems like abstract thinking, lateral reasoning, or empathy … that don’t show up on résumés.

What I really want is this: a future where, instead of asking my child “What do you want to be?”, I can ask, “Where do you thrive?” A future that doesn’t just automate work or displace people, but uses technology to elevate human potential. That future won’t build itself. It will take intention, imagination, and real work to get us there. But the time is now.