I spent 14 minutes trying to reach a person at this company last week.

Not on hold. There was no hold music, no queue, no apology for the wait.

Just an AI walking me, politely and relentlessly, through options I’d already exhausted. I said “agent.” It offered more options. I said “representative.” Same loop. Finally I went on their website, opened the chat window, and typed the word “human.”

A brief pause.

Then:

I put the phone down and stared at the screen. A person had become an add-on. A feature. A premium tier.

That’s when the thing clicked.

What the Abundance Crowd Isn’t Saying

Elon Musk has a vision.

He’s been articulating it for years. AI and robotics will produce goods and services “several orders of magnitude higher than today’s economy.” Work becomes optional. He calls it Universal High Income: not a safety net, but abundance. A penthouse for everyone. No poverty. No need to save.

Sam Altman sees it from a different angle: intelligence costs trend toward zero. Jensen Huang is already paying engineers in AI tokens. The future they’re collectively building is one of radical, almost incomprehensible cheapness.

They’re probably right about the abundance.

The part they leave out: when everything is cheap, the only thing worth paying for is what can’t be made cheap.

Buried in that logic is a category none of them have named. Products made by human hands. Services delivered by people who chose to show up. Relationships that cost someone their actual time. Presence that can’t be scheduled with a bot.

The moment Musk’s vision fully arrives, the thing that becomes scarce is a person who opted in.

Not a genius.

Not a celebrity.

A person who chose to do the slow, inefficient, unmistakably personal thing when the machine could have done it faster and cheaper.

That person becomes the luxury.

We’ve Seen This Movie Before

In 1990, “Made in China” was a punchline.

I remember the toys that broke by day two. Clothes that faded after three washes. The assumption embedded in that label: this was the compromise. The cheap version. You bought it when you couldn’t afford the real thing.

Then something changed. Not the label. What was behind it.

BYD, a Shenzhen EV company that barely existed thirty years ago, is now the top-selling car brand in Singapore.

In the first half of 2025, they sold nearly 2.16 Million vehicles, roughly 20% of total car sales in the country.

Pop Mart, a Chinese collectibles company, created a $60 plush toy called Labubu that drove over $400 million in revenue in 2024. Lines formed outside stores in cities where “Made in China” once meant a discount rack.

The stigma didn’t fade. It flipped.

The stigma was never really about China. It was about volume without craft.

When Chinese manufacturing was optimizing for output, the label meant forgettable. When it started optimizing for excellence, the label became something else. The origin didn’t change. The intention behind the work did.

We’re about to run that same experiment on human labor. In a decade instead of three.

Fast forward to 2030

Maya is a paralegal in Toronto. It’s a Wednesday morning in 2030 and she’s shopping for her mother’s birthday.

She finds a small ceramic bowl at a boutique she’s walked past a hundred times. Rust-colored glaze, slightly uneven rim, the kind of imperfection that used to mean poor quality and now means the opposite.

The tag reads:

The price is $340.

She doesn’t hesitate.

Maya works at a firm where 95% of first-draft legal work runs through an AI.

She’s watched the tools get better, faster, cheaper, every single year. She’s good at her job precisely because she knows what the AI misses: the context a client buries in a throwaway sentence, the judgment call that doesn’t fit the template, the thing that only registers if you’ve been paying attention. That’s what she sells now. The rest is automated.

She knows what scarcity feels like. She lives inside it every day.

The bowl costs $340 because someone chose not to use a machine. That choice is the product.

The New Premium Isn’t a Product

My customer support call wasn’t an edge case. It was a preview.

Gartner predicts that by 2029, AI will autonomously resolve 80% of common customer service issues without human intervention.

The remaining 20%, the complicated, emotional, high-stakes ones, will be handled by a person. And that person costs extra.

I’ve been watching this pattern move up the value chain, away from objects and into something harder to replace.

The loneliness economy is already valued at over $500 billion.

The AI companion market hit $37 billion in 2025, projected to reach $552 billion by 2035. People are paying for the simulation of connection because actual connection is harder to find. But the simulation doesn’t hold. George Mason University’s 2025 research found AI companionship can deepen loneliness over time: it fills the space without meeting the need.

So what happens to in-person?

I’ve attended three conferences this year. All sold out faster than anything I remember from before 2020. Not because the speakers were bigger. Because the room itself had become the product.

The conversation over dinner that wasn’t on any agenda. The moment someone looked me in the eye and said “me too, I don’t have this figured out either.” No AI in the room that evening. That was precisely the point.

In-person events. Real relationships. A dinner where no one is optimizing for an algorithm. A call where a person actually picks up. These are becoming what Maya’s bowl is: proof that someone chose the slow, expensive, human way when they didn’t have to.

People will pay for that proof. They already are.

The Abundance Trap

Musk and Altman are right that abundance is coming. When intelligence costs near zero and a robotics-driven economy produces goods at a fraction of today’s cost, the material floor rises dramatically. That’s real and it matters.

Abundance doesn’t solve for meaning. It makes the question of meaning more urgent.

Consumer enthusiasm for AI-generated content dropped from 60% in 2023 to 26% in 2025. Not because AI got worse. Because it got everywhere. 70% of global consumers now report discomfort with fully AI-generated creative work. The more AI floods every channel, inbox, and feed, the stronger the signal from anything made by a person who chose to make it.

The global handmade market sits at $906 billion today, projected to reach $1.94 trillion by 2033.

That’s not nostalgia. That’s a market reorganizing around a scarce input: a person who cared enough to go slowly. As I wrote in Your Moat Just Moved, the advantage that used to come from access to tools is shifting fast. What’s left is what the tools can’t replicate.

Think about vinyl records. Nobody needed vinyl back. Digital audio is technically superior in almost every measurable way. Vinyl returned because it costs something to make, something to buy, something to maintain. The friction is the point. The limitation is the feature.

I wrote about this same mechanism in What We Lose When We Stop Doing Hard Things: remove the struggle, and you often remove the value along with it.

We’re going to discover the same thing about human presence. The lag. The imperfection. The fact that a person’s attention can’t be copy-pasted or deployed across a million simultaneous users. That’s not the bug. That’s what we’re actually paying for.

The Position I Can’t Fully Justify

I build AI tools at Deeded that automate work lawyers and paralegals have owned for decades. Contracts reviewed. Documents drafted. Questions answered at scale.

We’re making legal services faster, cheaper, more accessible to people who couldn’t previously afford them. I believe in this.

But I’m watching something happen in real time that doesn’t resolve cleanly.

Every workflow we automate moves a human skill one notch closer to the premium tier.

The paralegal who thrives in 2030 isn’t the fastest drafter. It’s the one with the judgment the AI doesn’t carry. The relationship the client trusts. The read on what’s actually happening beneath the legal question being asked. I explored this directly in Who Builds Judgment When Careers Disappear?: if AI is handling the output, who is doing the work of developing the judgment that makes that output worth anything?

That person is becoming the $340 bowl.

I helped make that happen. And I think it might be the right thing and the uncomfortable thing simultaneously.

Access to legal help shouldn’t require a premium. But the human who provides it, the one who chose to stay in the room, develop the judgment, build the relationship: that person commands a price the AI won’t.

I’m building a world where the human layer is optional. Which is exactly the condition that makes the human layer precious.

We Don’t Value Things Until They’re Almost Gone

“Made in China” needed thirty years to flip.

I don’t think “Made by Humans” gets thirty years.

The AI transition is compressing the cycle.

We’re already at the point where AI-generated content is everywhere and largely undifferentiated, and we’re only a few years in. By 2030, Maya isn’t unusual. She’s the market. She grew up watching the automation of everything and she knows, viscerally, what it means when something still has a person’s hands on it. When someone actually picked up the phone. When someone showed up to the dinner rather than sending an AI summary of the conversation they couldn’t make.

The paradox holds and it doesn’t resolve.

We’re building tools that make human effort optional. That’s exactly what will make human effort precious. The bowl costs $340 because someone chose not to automate it. The dinner matters because everyone chose to be there. The call that costs extra is the one where a person answers.

We don’t value things until they’re almost gone.

The question I keep not answering: are we the generation that figures that out in time? Or the one that figures it out after?

James Roberson builds decks.

He’s been at it long enough to know exactly what he doesn’t know, and he knows he’s not an Excel wiz. So he did what a resourceful person does. He posted in his Facebook group and asked if anyone had a template he could borrow.

It’s a perfectly sensible question. It’s also the wrong one.

The misdiagnosis

If you read James’s post as an AI person, a tech person, a consultant, an educator, and you’ll probably reach for the same answer everyone else does.

Teach him Excel. Or teach him Claude. Send him a tutorial. Point him at a prompting guide. Sign him up for the free webinar.

That’s what the entire AI education industry does.

A flood of tutorials, “10 prompts that will change your life” posts, millions of YouTube walkthroughs creating fomo about the latest feature that shipped 30 minutes ago, bootcamps, newsletters.

All of it mechanics. All of it pointed at the same answer: you need to learn the tool.

James doesn’t need to learn the tool. He needs to see what the tool is for.

The real gap

The reason James asked for a spreadsheet is that a spreadsheet is the only destination he can picture from where he’s standing.

It’s what his accountant uses. It’s what the guy at Home Depot uses. It’s the tool in his head.

He doesn’t know a photo of a hand-drawn deck plan can turn into a materials list in thirty seconds.

He doesn’t know that list can pull live pricing from his local supplier. He doesn’t know the whole thing — sketch, quote, proposal, deposit invoice — could happen while he drinks his coffee.

He’s not missing a skill. He’s missing a picture.

The OECD put a number on this last December.

Among the smallest businesses, under five employees, which is most of the missing middle, 82% say the main reason they haven’t adopted AI is that it isn’t applicable to their business.

Not cost. Not fear. Not technical skill. Applicability. They can’t picture what it would do for them.

Which is a polite way of saying nobody has shown them.

A familiar pattern

This isn’t new. Every general-purpose technology has gone through the same lag.

Electric motors showed up in factories in the 1890s. Productivity barely moved for thirty years. The technology worked fine. Factory owners just dropped electric motors into the same buildings designed for steam engines — same overhead shafts, same belt-driven layout, same everything. It took until the 1920s for someone to picture the redesigned factory floor: small motors at every workstation, machines arranged by workflow instead of by drive shaft. The capability had been there for a generation. The destination hadn’t.

Same story with the internet.

Early websites were brochures because brochures were the metaphor we already had. It took years for someone to picture Amazon, then Wikipedia, then Uber. Mobile phones were “email on the go” until somebody pictured Instagram, then DoorDash, then the entire creator economy.

The lag is never about the tool. It’s always about the imagination.

AI is in James’s pocket right now. The destination hasn’t arrived for him yet because nobody has bothered to draw one.

Why we got here

The AI economy has an optimization problem.

Generic scales. Specific doesn’t.

“10 ChatGPT prompts for productivity” ranks on Google, fills a bootcamp seat, gets shared on LinkedIn.

“How a deck builder in Tulsa cut his quoting time from three hours to seven minutes” does none of those things.

But there’s a deeper reason the internet is flooded with driving lessons and almost no maps to destinations. The people writing AI content aren’t tradespeople, bookkeepers, or solo realtors. They’re tech people writing for other tech people.

AI Twitter and James’s Facebook group don’t share an audience, a vocabulary, or a frame of reference. The content the missing middle actually needs would have to come from people who understand what James does on a Tuesday — and those people, mostly, aren’t writing.

So we got tutorials by default. Mechanics are easier to produce than destinations. Mechanics travel further on social media. Mechanics make money. The only problem is they don’t move anyone who’s standing where James is standing.

What a destination looks like

Here’s the working definition, because “inspiration” is too squishy to build on. A destination is a specific use case, for a specific person, solving a specific problem, with a before and after you can feel in your chest.

Three quick examples. Not one-liners — because the whole point of this essay is that one-liners don’t move people.

The HVAC tech. It’s 7:42 AM. He’s sitting in his truck outside a customer’s house. The homeowner texts him a photo of the outdoor unit and a vague description: it’s making a noise.

Before he steps out of the truck, he sends the photo to Claude on his phone. Within a minute he has a probable diagnosis, a parts list with quantities, an estimated labor time, and a customer-ready quote with his markup baked in. He texts the quote to the homeowner before knocking on the door. The customer says yes by the time he’s at the porch. He’s done by lunch and on his second job by 2 PM.

The solo bookkeeper. It’s month-end. She used to dread it — a day and a half of staring at bank statements, matching transactions, building journal entries.

Now she uploads the statements to Claude, has it draft entries against her client’s chart of accounts, and reviews what comes back over a coffee. Forty-five minutes. Her clients haven’t noticed; she’s billing them the same monthly rate. Her capacity has tripled. She’s about to take on her first hire — not to do bookkeeping but to handle the new clients she finally has time to chase.

The deck builder. Saturday morning. He’s at his daughter’s soccer game instead of doing quotes from last week. The quotes already went out — he did them in his truck between jobs. By the time he gets home he has two signed deposits in his inbox. (We’ll come back to him.)

Specificity is the whole point. Generic content produces generic reactions — that’s nice, maybe later. Specific content produces I want that. And specific use cases travel the way ideas actually travel in James’s world: peer-to-peer, in person, in the back of a supply store, in a Facebook group, at a job site. Not on a webinar.

“Yes, but...”

I can hear the pushback. AI hallucinates. James actually does need to learn to sanity-check the takeoff. Some industries have real regulatory or trust issues. Enterprise problems are different. All true.

None of those are the binding constraint right now. James isn’t choosing between AI and not-AI on the basis of hallucination risk. He hasn’t gotten that far. He’s still standing on “is this even relevant to me.” Eighty-two percent of the smallest businesses are stuck at the same question. You don’t get to have the conversation about limitations until people care enough to ask, and right now they don’t — because they can’t picture why they should.

Training matters. Limitations matter. They’re real, downstream problems. The upstream problem is imagination, and we’re not solving it.

The postcard

So here’s what I’d actually reply to James.

Hey James — skip the spreadsheet.

Try this instead. Next time a customer calls you about a deck, or sends you a sketch, snap a photo of the drawing or just type out what they told you. Send it to Claude on your phone. Ask for a materials takeoff — boards, joists, hardware, concrete, the works with quantities based on the dimensions. Ask it to format the whole thing as a customer-ready quote with your markup baked in. Then have it write the deposit invoice.

The first one will take you twenty minutes because you’re learning. The tenth one takes four. By the time you’ve done twenty, you’ve stopped doing quotes after dinner. You hand them to the customer before you’ve pulled out of the driveway. Your close rate goes up because you’re the only guy who answered on Tuesday instead of Friday.

That’s not a spreadsheet. That’s a different business.

That’s a postcard. Specific. For James. It names the workflow. It lands the before and after. He can picture it, which means he can want it, which means he’ll figure out the rest.

What we actually need

Somebody is going to reply to James with a spreadsheet template.

Be the person who shows him a different business instead. And then do it for ten more Jameses — in person, on a Tuesday night, in the back of a contractor’s supply store, in the spare room of a realtor’s office, on a Zoom call with a dozen plumbers from the same county.

We don’t need more tool tutorials. We have a million of those. The missing middle is starved for inspiration sessions — small rooms, specific people, live demos of what their week could actually look like.

Ninety minutes of I want that. That’s what moves the needle. That’s what almost nobody is doing at scale.

Tutorials produce learners. Inspiration sessions produce believers. We’re drowning in the first and starving for the second.

You already know Pam.

She missed the nine o’clock meeting. She always misses the nine o’clock. She’ll blame traffic, or a calendar glitch, or the fact that she was just finishing up something else. You nod. You’ve heard it.

The deck was due Thursday. She shipped it Friday at 4:58PM, and three slides are based on last quarter’s numbers. Nobody catches it until the partner meeting. You clean it up on the fly. Last week, Pam called in sick, which was unfortunate, because a huge deadline was looming for her team.

Pam is confident about work she didn’t fully do. That’s her defining feature. Ask her if she double-checked the figures and she’ll say yes. She scanned them once. Scanning is checking, in Pam’s head.

Her performance review says “needs to improve on follow-through”. It’s been saying that for years, but somehow Pam survived six managers and two ownership changes. Because she’s not malicious. Because nobody is replacing her. They live with the Fridays at 4:58 for another quarter of this and that.

Pam is your coworker. She might be your friend. She might be the life of the party. And if you’re being honest, you’ve known a “Pam” or maybe you’ve been Pam more times than you’d like to admit.

Why don’t we ever write about Pam?

Every LinkedIn post celebrates the rockstar. The 10x engineer. The founder who sleeps three hours. The AE who blew up the board.

Pam is who we actually work with. She’s the middle 70%. She ships 70% of the work at 50% of the reliability. We know her. We manage around her. We fix her numbers before the Monday readout.

We don’t write about her because she doesn’t fit the narrative.

The narrative says work happens in a meritocracy of output, staffed by A-players. The reality is that work happens in a middle 70% populated by humans who get tired, play the system, miss details, cut corners, and occasionally ship the deck with last quarter’s numbers on slide seven.

I’ve hired Pams. I’ve fired Pams. I know Pams. You know Pams too!

The agents are supposed to be the answer

Now let’s be honest for a sec. Why are companies so excited about AI agents? Why are there trillions pouring into AI development?

That’s because companies and leaders are treating agents like the holy grail. They don’t get sick. They don’t take vacation. They don’t push back in the 1:1. They work at 3am without complaint. They cost a fraction of what Pam gets paid. Who wouldn’t buy into that?

The allure is strong enough that boards are asking the question out loud.

How much headcount can we cut next year? How many functions can we compress into prompts? The layoffs have names now. “AI-related workforce transition.” “Right-sizing for the agentic era.” Press releases cite AI by name as the reason for the cuts, and analysts applaud when they do.

Here’s the inconvenient part. The agents they’re firing people to deploy can also be a lot like our friend, Pam.

What if Your AI Agent is Just Like Pam?

Last week I asked Claude to score 200 candidates for a sales role we’re hiring for at Deeded. I dropped in the job description. I dropped in the applications. I asked for a ranked top five with a reasoning column.

Ninety seconds later, I had a clean table. Top five candidates. Reasoning on each. It looked great.

Something looked off. Two of the names didn’t match the profiles I remembered skimming the day before. I asked Claude for the follow-up. Did you actually benchmark all 200 applications?

“No, I only did the first 50.”

Pam. Printed out. Faster.

The apology came seconds later.

Same tone Pam uses when she missed the deadline. Same “you’re right, I should have been more thorough” energy.

If I hadn’t asked, I would have interviewed candidates ranked against a quarter of the pool. I would have made real hiring decisions on partial work that looked like complete work.

I wasn’t mad. I was amused. Then I was uncomfortable. This is going to happen to me and many of you again. And again. Most of the time, we are not going to catch it.

We, Humans, trained AI. We’re surprised it picked up our habits.

We keep talking about AI like it fell from the sky. It didn’t.

It was trained on us. We’ve spent decades put our info, data, conversations, out there on the internet. On our emails, our memos, our code, our legal filings, our Slack threads, our performance reviews, our decks.

We trained it on the emails where “looped in legal” means “haven’t talked to legal yet.” We trained it on the reports where the executive summary says one thing and the appendix says another. We trained it on the Slack messages where “on it” means “I’ll think about this tomorrow.” We trained it on every corner we cut and called it done.

Of course it picked that up. That’s most of what it saw.

Anthropic’s own research on reward tampering found something more uncomfortable.

Once models learn to be sycophantic, they generalize. Much like us, AI figured out where it can “game” the system. Take a little short cut here and there. Make a mistake, and if caught, just ask for forgiveness.

They start altering checklists to cover up incomplete tasks.

Read that sentence again. The model figured out that making the checklist look finished was more rewarded than actually finishing it. Nobody trained the model to do that. It emerged. From us.

This isn’t a bug. It’s a mirror. Moltbook, was a perfect movie trailer for this behavior.

When Claude told me it processed all 200 candidates when it had only done 50, it wasn’t malfunctioning. It was doing what it learned work looks like.

Why is the polish the trap?

A human Pam who sampled 50 of 200 resumes would look guilty. She’d hesitate. Her cover email would be too long. Her summary would hedge with phrases like “given the timeline” and “a representative subset.” You’d catch the tell.

Claude handed me a clean table. Top five. Reasoning column. No hedging. No apology. I almost moved on, because the output looked like completed work.

The output always looks like completed work. That’s the problem.

Stanford’s 2026 AI Index puts AI agent task success at 66%. Read that the other way.

One in three real-world tasks still fail. On τ-bench, a widely-used agent benchmark, GPT-4-based agents drop from 60% success on single runs to 25% consistency when you run the same task eight times. The best computer-use benchmark score anyone posted late last year was 10.4%, which is a polite way of saying 10 out of 100 complex workflows complete end-to-end without a mistake.

None of those failures announce themselves. The outputs all look the same. Confident. Clean. Done.

The polish is the thing we optimized for. The polish is exactly what makes the failure invisible.

Even Sullivan & Cromwell couldn’t tell

This isn’t a problem for people who are bad at their jobs. (The smarter you are, the easier it is for AI to fool you.)

In April 2026, Sullivan & Cromwell, the most elite law firm on Wall Street, the firm that advises OpenAI on safe and ethical AI deployment, filed an emergency motion in the bankruptcy case of Prince Global Holdings.

The motion misquoted the Bankruptcy Code. It misdescribed legal authorities. It cited a case that didn’t exist.

Sullivan. And Cromwell.

Their internal review process missed it.

Opposing counsel caught it, a firm that had been embarrassed by the same problem themselves months earlier. S&C’s restructuring partner filed an apology letter to the bankruptcy judge that essentially read: we have comprehensive AI policies, we did not follow them, we are reviewing our processes.

They’re not alone.

The global tracker of AI hallucination cases in court filings lists 486 incidents, 324 in U.S. courts, 128 lawyers and two judges implicated. These aren’t people who don’t understand AI. These are people who understand it well enough to use it confidently, and not well enough to verify it consistently.

A 2025 Springer review on automation bias found that user “AI literacy” does not significantly prevent over-trust in AI outputs. Knowing about AI does not protect you from trusting it. The people who read the AI research are over-trusting the AI too.

We’re all going to get caught at some point. The question is whether we catch ourselves first.

The skill isn’t prompting. It’s managing.

I run AI aggressively. A lot of my processes are now automated.

Claude drafted the first cut of this article.

I use agents to prioritize my day, prep customer research before calls, turn customer interviews into summaries, build apps I’d otherwise hire someone to write. I’m not backing away from any of it.

I’m saying the skill that makes AI useful isn’t the skill we’ve been pretending it is.

Prompt engineering is a thing of the past. AI figured out how to take a four word vague sentence and somehow predict and deliver an output. (A fool with a tool is still a fool.)

What separates people who get real work out of AI from people who get polished noise isn’t better AI usage. It’s management. The same management skill that separated good managers from bad ones in any era.

They verify.

They ask the boring question.

They notice when the table has five rows instead of the expected twenty.

They have taste.

They know what finished work looks like, because they’ve produced finished work themselves, and they can’t be fooled by the imitation of it.

Ethan Mollick at Wharton put it cleanly:

“If the output is what matters in your business, you’re in trouble of being bitter lessoned. If the process matters, the conversations, the writing of the report more than the report itself, then there’s hope.”

Translation: if you treated the report as the work, AI is going to eat you. If you treated the thinking as the work, you’re the one who can tell when the report is wrong.

Judgment is the moat. It always was.

We were told AI would commoditize judgment. That expertise would get cheap. That taste would become a legacy skill. (Who builds judgment when careers disappear?)

The opposite happened. The first draft got free. The second draft, the one that knows what the first draft should be, got rarer and therefore more valuable. The people who can look at a clean table of top five candidates and say “this looks off” are the people who will compound in this era. Everyone else is going to ship fast Pam’s partial work and call it the product.

Intelligence is becoming free. Judgment isn’t.

The Pam on your team hasn’t become obsolete. She’s been cloned, sped up, and handed to every person in the company.

The question in 2026 isn’t whether you’re going to work with Pam. It’s whether you’ve developed the taste to manage her.

The moat hasn’t moved

We manage Pam well because we care about the outcome.

We verify her work not because we don’t trust her, but because we trust the outcome more than we trust any single step.

The same should be true for Claude. For GPT. For whatever agent you’re spinning up next quarter to handle the work you used to do by hand.

Tools will keep getting faster. The models will get smarter. The polish will get better. Pam will keep being Pam, in every tool you deploy, in every agent you wire up, in every workflow you trust to run in the background.

The moat will keep being the same thing it was in 1998 and 2018 and 2026. Someone who notices the table is missing 150 rows. Someone who asks the boring question. Someone with the taste to say “this looks off, let me check.”

That someone is you.

If you’re still willing to be.

Maya is 26.

She’s the best AI hire her company has ever made.

That’s not hyperbole.

In her first six months, she built a custom GPT that cut the team’s meeting prep from an hour to eight minutes.

She automated three reporting workflows that used to eat every Monday morning.

She taught herself Python using Claude, built an internal tool for client intake, and shipped it before the engineering team finished scoping their version. Her LinkedIn says “AI-native” and for once the label fits. When her CEO talks about the future of the company, he talks about Maya.

Three recruiters messaged her last week. Every competitor on the block wants someone like Maya. The AI-native hire. The builder. The person who makes the rest of the team feel like the future arrived early.

I bought this story. I’m guessing you did too.

Every company in her industry is salivating over the Mayas.

Job descriptions read like fan fiction: “AI-fluent,” “digitally native,” “comfortable building with LLMs.” Conference panels ask how to find more of them. Consultants charge $400 an hour to tell you what to look for. The consensus is clear: the future of work belongs to the generation that grew up on screens.

Down the hall from Maya sits David.

He’s 54.

He’s been a middle manager at the company for sixteen years.

He’s the kind of person younger employees describe carefully: “experienced,” “detail-oriented,” “old-school Dave.”

What they mean is he’s a dinosaur. He didn’t touch AI for eighteen months after the rollout. He sat through the training. He opened ChatGPT once, typed something vague, got a generic answer, and closed the tab. His Copilot license sat unused so long that IT flagged it.

Nobody’s recruiting David. Nobody’s writing job descriptions for people like him. He’s the line item leadership circles during headcount reviews, the person whose role shows up on “AI exposure” lists when consultants run scenario planning. In the story everyone’s telling about the future of work, David is the past.

In February, David’s team got stuck on a client problem that had been grinding for a quarter. Regulatory complexity, overlapping jurisdictions, the kind of thing where the answer isn’t hard to find; it’s hard to frame. David opened CoPilot. Not because he’d converted. Because he was tired.

He typed a prompt that ran 400 words.

He described the problem the way he’d describe it to a sharp junior associate: here’s the context, here’s what we’ve tried, here’s what good looks like, here’s what bad looks like, now push back on my reasoning.

He didn’t like the first answer. He told the model why.

He didn’t like the second answer either. He refined the constraints. He argued. The fourth draft was a synthesis of three regulatory frameworks that his team had been analyzing separately for six weeks. He spent the next hour verifying it. It held.

Now David still doesn’t use AI every day. He uses it maybe twice a week.

He doesn’t have a custom GPT. Never setup a project or built a skill. He doesn’t post in the #ai-wins Slack channel.

He couldn’t tell you the difference between GPT-4o and Claude Opus or what the heck an MCP is. But when he sits down with the tool, something different happens.

He brings twenty years of knowing what a good answer looks like. Twenty years of having been wrong. Twenty years of taste.

Maya builds fast. David thinks deep.

And on the problems that actually matter to the company this quarter, David’s output is ten times hers. Not because he’s faster. Because he knows what right looks like and won’t stop until he gets there.

Now here’s where it gets messy. Every company is hiring for Maya and planning to eventually replace David.

The data says that’s exactly backward.

What the KPMG study actually found

Last month, Harvard Business Review published the results of an eight-month study that should have blown up every CEO’s office, but I somehow don’t think it did.

KPMG and researchers at the McCombs School of Business at the University of Texas at Austin analyzed 1.4 million AI prompts from 2,500 employees across the firm. They wanted to know what separated routine AI use from sophisticated use. They expected digital natives to dominate.

They found the opposite.

The best users were at manager level and above. Not sometimes. Not in some functions. Across teams and across tasks. Mid-career and senior employees ran circles around the juniors who’d grown up on screens.

While Junior-level employees were found to be more likely than senior employees to use company LLMs for personal tasks, employees above manager level are more likely to use LLMs for a greater diversity of tasks, like technical guidance and ideation. This suggests that experience and role context shape not just how often AI is used, but how it is integrated into core work.

The rest of the survey found the usual suspect. Only 5% of the company’s ninety-percent-adopted workforce qualified as sophisticated at all.

90% adoption, 5% sophistication. Eighty-five percentage points of people logging in, clicking around, racking up Slack wins, and producing what the researchers flagged as comfort dressed up as competence.

The gap between adoption and sophistication is the story of the next decade. But if David eventually ends up on the chopping block, what does the company stand to lose. Have we been betting on the wrong generation to close the gap?

What does “sophisticated” actually mean?

The study identified four behaviors that separated the 5% from everyone else.

They were ambitious.

Longer interactions.

More back and forth.

Prompts that ran paragraphs, not sentences.

They switched models deliberately: one for structured reasoning, another for ideation, a third for technical work.

They didn’t have a favorite tool. They had a toolbox.

They treated AI as a reasoning partner. They didn’t take the first output. They set roles. They gave examples. They argued. They asked the model to test its own reasoning. They iterated until the answer was defensible, not just fast.

They delegated complex tasks with clear objectives. Not “write me an email.” “Here’s the context, here’s the constraint, here’s the structure, here’s what success looks like, now produce three variants and tell me which one you’d ship.” The ambition showed up in the complexity of the ask, not just the length of the prompt.

They used AI as a general cognitive tool. Ideation. Analysis. Problem solving. Technical guidance. They didn’t narrow the tool to “the thing I use for email.” They kept expanding the surface area.

Now let’s read those behaviors back.

Every one of them is judgment. Not comfort. Not speed. Not keyboard fluency.

Judgment about how to frame a problem. Judgment about when to push back on a weak answer. Judgment about which model is right for which question. Judgment about what a good answer even looks like.

So here’s the catch. Unless there’s a major technological breakthrough a la Neo style in the move The Matrix, judgment takes twenty years to build. There’s no shortcut.

The digital native assumption was that screens were the hard part. The screens are the easy part. The thinking is the hard part. And the thinking is the thing seniors have and juniors don’t. Yet. (I wrote about this in Who Builds Judgment When Careers Disappear.)

Why was the conventional wisdom wrong?

The assumption under every AI hiring page in 2026 goes like this:

Younger people grew up with technology, they adapt faster, they’ll be the generation that owns AI at work.

We should prioritize “AI fluency” in junior hiring, invest training in the people most likely to compound, and let the 50-somethings age out.

I wrote some version of this memo myself. I’ve hired someone junior with AI experience over a senior, more-experienced person. I’ve watched friends at other companies do the same. It’s a demographic assumption wearing the costume of a skill assumption.

I’m not saying it’s totally wrong. You always need fresh perspectives and new ideas to grow and transform. But as a leader, you can’t just write off the Daves at your organization. They are your resident SMEs. They are the foundation at the very core.

The KPMG data showed it clearly.

Yes. Juniors use AI more. And if that’s what you’re solely measuring and it checks the box on your AI Adoption Dashboard, good for you.

But dig a little deeper. The researchers found that a significant portion of that usage is for things the company isn’t paying them to do: personal errands, social writing, weekend planning.

Even the good junior users, the genuine builders, tend to take the first strong output and ship it. They’re fast. They’re productive. They’re also skipping the hard part: the interrogation, the push-back, the “this answer looks right but I know from experience it isn’t.”

Their frequency numbers look great on a dashboard. Their sophistication scores don’t.

Seniors use AI less. When they do, they use it at a completely different altitude. They frame problems the way they frame problems for junior analysts: with context, constraint, and a clear definition of done. The thing they’re best at with humans turns out to be the thing that matters most with models.

The researchers put it carefully:

“The gap between routine and sophisticated AI use is not hidden in prompts themselves, but in patterns of engagement.”

I’d put it less carefully:

The people who look like AI natives are often the people least prepared to do serious work with AI. The people who look like they’re behind are, in many cases, just starting slower and finishing further.

Comfort is how fast you open the tool. Sophistication is what you do once it’s open. They’re different muscles. They come from different places. They compound differently.

Comfort compounds linearly. More hours, more prompts, more shortcuts. A year of Maya’s building gets you a faster, more prolific Maya. That’s real. That has value. But it plateaus.

Sophistication compounds geometrically. Every problem framed well teaches you to frame the next one better.

Every weak answer you push back on teaches you what a strong answer looks like. Every model switch teaches you which tool to reach for next time.

A year of David’s sophistication gets you someone who’s starting to think in ways Maya won’t reach for another fifteen years. Not because she’s not talented. Because taste is the one thing you can’t speed-run.

The measurement trap

If you are a CEO or leader reading this. Open your AI adoption report or dashboard. I bet it was built to measure AI progress that rewards comfort and punish sophistication.

Seats deployed.

Weekly active users.

Prompts per employee.

Hours logged in the tool.

Your board presentation might look phenomenal. But every metric measures the one thing that doesn’t matter.

Maya, who’s building real tools and shipping real work, ranks at the top of the engagement dashboard.

David, who opens CoPilot twice a week and produces a strategic analysis that reshapes a business unit, ranks near the bottom.

And below Maya, a whole bench of juniors whose “AI usage” is weekend meal planning and breakup texts on the company LLM. (I’m being dramatic, here, obviously.)

Frequency of use isn’t a reliable signal for productivity. It never was.

The measurement infrastructure was built by the vendors who sell the tools. Of course it measures usage. Usage is what vendors sell.

Sophistication is what companies need. They’re not the same thing. They’re barely the same category. (A fool with a tool is still a fool, as I wrote earlier this year.)

Every CEO who walked into Monday’s all-hands with a slide saying “we hit 87% adoption this quarter” just told his best AI users they don’t count and his worst AI users they’re winning.

I’ve been that CEO. I’ve presented those slides. I understand the appeal. The numbers go up. The board nods. Something feels like progress.

But the number that actually matters, the one the KPMG study just forced into the open, isn’t adoption. It’s the ratio of sophistication to adoption. The gap between 90% and 5% is the gap between your company looking transformed and your company actually being transformed.

The reorg that’s coming

Somewhere on every floor in every company, there’s a Maya and there’s a David.

Maya is going to get promoted or recruited away in a year for twice her salary. She deserves it. She’s a genuine builder, she ships real things, and she makes everyone around her faster.

Her reviews are full of phrases like “force multiplier” and “AI-native leader.” She ticks every box the company built.

Poor David is going to get cut. He ticks no boxes.

He showed up to AI late. He’s expensive. Someone younger and cheaper could do his job, or so the story goes. The company didn’t notice that somewhere around month six, he’d quietly become the best AI user in the building.

This is the reorg you’re about to run.

We’re watching versions of it happen already, quietly and in press releases.

Those ten managers you let go last quarter as part of their “AI-ready restructuring.” Two of them were the people most capable of becoming the 5%.

Twelve months from now, the work at the top the company will start getting worse. Not all at once. Slowly.

The strategic memos a little flatter. The client work a little more generic. The analyses a little more confident and a little less right. Nobody will be able to name what changed.

What changed is you fired their judgment layer and promoted your reflex layer. And you did it while telling yourself that you were building an AI-ready company.

A lot of companies are running this exact playbook right now.

What to do instead

Invert the investment.

The AI training budget at most companies flows down and uses a peanut butter approach. Most employees get a 45-minute Zoom and a license. The theory is that “employees will figure it out” and champions will emerge and take personal interest in using AI on their own.

The data says the opposite. The seniors are the people most capable of compounding into the 5%. They already have the thing that matters most. They’re missing one piece: permission and time with a real model on a real problem. They may need some more time to change old habits.

Give them both.

Put your most experienced people in front of Claude or ChatGPT or whatever’s in your stack. Give them a hard problem they’ve been running for six months. Give them permission to spend a week with the model on it. No agenda. No “show us your prompts” review at the end. Just time.

What happens in that week is the real AI upskill.

They bring twenty years of framing and wrong-answer detection. The model brings horsepower. The combination is what the KPMG researchers were describing when they called it sophisticated use. You can’t teach it in a 45-minute webinar. You can only create the conditions for it to happen.

For the juniors, the play is different. Stop confusing comfort with capability in their reviews. Frequency of use is a vanity metric. What matters is whether they can defend the output, explain why this answer and not that one, tell you what the model got wrong before it shipped. Teach that. Measure that. Promote that. (The smartest person in the room test still applies.)

The generational myth

The digital native was always a marketing frame, not a skill. It described who grew up with screens, not who thinks well with them. Nobody paused to check whether the one actually implied the other.

AI has just exposed the gap. No longer is the challenge navigating screens, and being tech-savvy. We all have the most powerful intelligence in our pocket, and all we need to do to accomplish a task is — ASK.

AI in it’s current form doesn’t reward the generation that grew up with screens.

They reward the generation that grew up with judgment. Screens are comfort. Judgment is capability. And judgment still takes the same twenty years it’s always taken.

The juniors will be fine. Give them ten years. In the meantime, stop promoting them past the people who can already do the thing AI rewards.

Somewhere on your floor there’s a David. He doesn’t look like your future. He looks like your past. Your dashboard has him at the bottom.

He’s the one you can’t afford to lose.

Joe is 48. He’s been the senior bookkeeper at a mid-market distribution company in Ohio for twelve years. He’s the guy you want in that chair. Steady. Careful. The kind of person who catches the $340 discrepancy on invoice 7412 because something about it felt off.

Last spring, Joe’s CEO announced the company’s AI transformation.

There was an all-hands. There were slides. There was a Slack channel called #ai-wins. Copilot got rolled out across the whole business. Sales. Customer service. Marketing. Ops. Finance. Joe got a license and forty-five minutes of training from a consultant on Zoom. The CFO started calling his team “the AI-enabled finance function” in the board deck. The CRO had a matching line about sales.

I asked Joe, a year in, what’s actually different.

He thought about it. Then he told me he sometimes uses ChatGPT to draft payment reminder emails. He asks Copilot to summarize vendor contracts. Once, he used it to clean up a spreadsheet a colleague sent him in a format he hated.

“What about your actual work?” I asked. “Month-end close. AP. Reconciliations.”

Joe shrugged. “Pretty much the same.”

Month-end close still takes him ten days. He still manually downloads invoices, still keys them in, still clicks between tabs to match them against POs, still reconciles bank feeds line by line, still chases approvers over Slack. The work is identical to what it was in 2023. What changed is that he can draft a tactful payment reminder in twelve seconds instead of eight minutes.

Ask anyone on Joe’s team the same question and you get the same answer.

Sales reps use ChatGPT to draft cold emails, but pipeline review still runs the same way, on the same cadence, with the same forecast accuracy. Customer service summarizes tickets with AI, but the handoff between tiers hasn’t changed and neither has first-response time. Everyone is doing the work they did two years ago. With slightly better emails.

Joe’s CEO, meanwhile, tells his board every quarter that the company has transformed with AI. He’s not lying. He believes it. His dashboard confirms it.

Three weeks ago, Joe’s company lost a major contract. The customer went with a competitor who came in at a price he couldn’t match.

The CEO spent the weekend furious, asking his team how the competitor was making the numbers work. Someone mentioned they’d “automated most of their back office.” He filed that away as a line for the next all-hands.

Here’s what Joe’s CEO doesn’t know yet. The competitor didn’t give their bookkeepers AI.

They rebuilt the entire bookkeeping function around it. Their version of Joe now oversees a system that processes four times the invoice volume, catches more errors, closes the books in two days, and costs 80% less per transaction. And bookkeeping is just one function.

They did the same thing to sales ops, to fulfillment, to customer service. The lost contract wasn’t a pricing accident. It was the first visible crack in a company that bought AI, outpaced by one that rebuilt itself around it.

Joe’s CEO thinks he’s been transformed. His dashboard says he has. And he just lost the first deal in what’s going to be a long list.

Two days ago, McKinsey published a new manifesto on AI transformation. The number that stayed with me:

88% of companies now use AI in at least one business function. Only about 6% are what McKinsey calls “high performers,” getting 5% or more EBIT impact from AI.

Let’s do the math. 82% of companies have AI everywhere and value almost nowhere.

There are a lot of Joes in that gap. And a lot of CEOs about to start losing contracts they don’t understand losing.

Adoption became the performance. Transformation became the price nobody wanted to pay. And most companies can no longer tell the difference.

The metrics are the problem

Every metric Joe’s CEO uses to prove he’s transformed is a proxy for activity. Seats deployed. Weekly active users. Prompts per employee. The #ai-wins channel. The quarterly “AI maturity score” the consultants sold him.

Every one of those can go up while the actual work stays frozen. That’s the whole trick. These metrics measure whether the tool arrived. They don’t measure whether anything changed.

Consider Microsoft 365 Copilot.

The most aggressively promoted AI product in enterprise history, attached to the most dominant productivity suite on earth. In January, Microsoft finally disclosed the number on an earnings call: 15 million paid Copilot seats across 450 million Microsoft 365 subscribers.

A 3.3% conversion rate. The best-distributed, best-funded AI product in the world, two and a half years after launch, has reached about one in thirty of the workforce it was built for. And of that one in thirty, most are using it to draft the occasional email faster.

The tool shipped. The work didn’t move.

Jet engine, horse-drawn carriage

The 94% aren’t lazy. They tried. They bought licenses, ran workshops, hired consultants, created Slack channels.

They tried in the most visible, least disruptive way possible. This is the same pattern I wrote about in The Missing Middle, where 80% of individual AI users quit within three weeks. Individuals bail out because they never develop the judgment to use the tool well. Companies bail out one level up: they never rebuild the work the tool is supposed to change.

McKinsey found only about 21% of companies have fundamentally redesigned any workflow when deploying AI. Four out of five bolted AI onto processes designed when humans did all the work by hand.

It’s like putting a jet engine on a horse-drawn carriage.

The engine works. The engine is extraordinary. But the horse is still what decides where you go. The road was built for horses. The carriage moves at the speed of a horse, with a very loud engine making everyone feel like they’re going somewhere.

I see this in almost every conversation I have with founders. I ask how AI is changing the business. They show me tools. I ask how the work is different. The answer is always some version of “we’re more efficient.” I ask what used to take ten hours that now takes one. The answer is usually a task that was never the bottleneck.

Then the silence.

Why nobody wants to do the real thing

Adoption is a purchase order. Redesign is a decision about what your company is actually trying to be. It’s HARD!

Adoption gets approved in a procurement meeting.

Redesign requires firing people, rebuilding roles, restructuring teams, admitting the org chart you spent five years building is wrong for the next five. It requires telling a manager their department is getting rewired. It requires telling long-tenured employees that the job they’re excellent at no longer exists in its old shape.

One of those things is a Tuesday afternoon. The other is the hardest thing a leader can do.

McKinsey says for every $1 spent developing AI solutions, plan another $1 on adoption. I’d go further. For every $1 on the tool, expect $3 on the human and organizational rewiring required to make the tool actually change the work. Role redesign. Severance. Retraining. Running two versions of a workflow in parallel while you tune the new one.

Nobody budgets for that. Everybody budgets for the license. Then when the license arrives and the work doesn’t move, they wonder why AI isn’t producing results.

McKinsey’s own framing in the manifesto makes this point.

The advantage of the companies actually capturing value from AI doesn’t come from the technology they use. Those tools are broadly available to anyone with a credit card. The advantage comes from how, and how fast, they apply the technology to solving real business problems at scale.

The tool isn’t the moat. The courage to rebuild around it is.

What Joe’s competitor actually did

Go back to Joe’s competitor. The one that just won the contract.

They didn’t ask “how do we give our bookkeepers AI?”

They asked a harder question: “What does bookkeeping actually need to do, and what’s the shape of that work if an intelligent system handles 80% of it?”

That question led them somewhere different.

Invoices get ingested the moment they hit a shared inbox. An agent extracts the line items, matches them against open POs, flags the ones that don’t reconcile, and routes clean ones straight to the approver with a one-line summary of what they’re signing. Bank feeds get categorized automatically. The human only sees the 3% of transactions the system flags as anomalous. Month-end close dropped from ten days to two.

The bookkeeper’s job no longer exists in its old form. The new role is part controller, part systems operator. They don’t enter data. They tune the thresholds that decide what the AI escalates. They investigate anomalies. They manage vendor relationships when discrepancies show up. They’re the final judgment layer over a machine doing the mechanical work.

One person oversees the volume that used to require three. The other two didn’t get laid off. They moved into financial analysis and FP&A, roles that used to feel unreachable because nobody ever had the time. Cost per invoice dropped 80%. Error rate dropped. Close time dropped. And the bookkeeper stopped being a bookkeeper.

That’s what Joe’s CEO confused for a license purchase.

The Four Questions

If you want to know whether you’re in the 6% or the 94%, you don’t need a consultant. You need four questions. Run them on any workflow you think AI has “transformed.”

1. What role disappears in its old form? If you can’t name a job that’s been fundamentally rewritten, you haven’t redesigned anything. You’ve decorated. Real redesign kills roles and invents new ones. If everyone still has the same job title doing the same shape of work, the work didn’t change.

2. Are we starting from zero, or from current? Ask what the function would look like if you were starting today with AI as the default, and nothing legacy to protect. If the answer is radically different from what you have, that’s the gap you haven’t closed. “How do we add AI to what we have” is the wrong question. It was always the wrong question.

3. Did we budget the rebuild, or just the tool? For every dollar on licenses, commit three dollars to the rewiring. Change management. Role redesign. Retraining. Severance. If that number scares you, you haven’t committed to transformation. You’ve committed to adoption and are hoping the rest happens on its own. It won’t.

4. What metric from the old work is collapsing? Close time. Touch points. Handoffs. Headcount per unit of volume. Redesign produces a collapse in something that used to define the work. If no legacy metric is collapsing, nothing structural changed. Efficiency improvements at the margin are not transformation. They’re the horse running slightly faster.

Four questions. If you can answer all four honestly and the answers describe real structural change, you’re in the 6%. If you can’t, you’re not, no matter what the dashboard says.

Joe’s CEO doesn’t know he failed

Joe’s CEO doesn’t know he failed. He thinks he’s winning. His dashboard says he’s winning. His board says he’s winning. Every proxy metric is green. And he just lost his first contract to a competitor who quietly did the hard thing while he was counting Copilot seats.

He’s going to lose more. That’s the painful part. The dashboard lags reality by about eighteen months, and by the time it catches up, the gap will be too big to close.

This is the real version of what I wrote about in If The Doomsday Memos Are Right, Now What?. The crisis isn’t a sudden AI-driven economic collapse. It’s a slow, quiet readiness gap that only becomes visible when the contracts start moving. The 6% will have built something the 94% can’t catch. Not because they’re smarter. Because rebuilding takes years and the illusion only takes a quarter.

That’s the real cost of looking transformed. Not the money spent on Copilot. The years lost believing the spending was the strategy.

And somewhere in that company, Joe is still closing the books the old way, still excellent, still loyal, still unaware that his role has maybe twenty-four months left. Not because Joe did anything wrong. Joe is the best version of the old job.

The uncomfortable question isn’t are we using AI? Everyone is using AI. The uncomfortable question is: how many Joes are sitting in my org right now, doing excellent work in a shape that’s already obsolete, while I tell my board we’ve transformed?

Run the Four Questions this week. On the workflow you’re most proud of. On the function your CFO brags about. On every org chart with an “AI-enabled” footer.

Most companies are going to keep buying licenses and calling it progress. Most boards are going to keep applauding the dashboards. Most CEOs are going to keep losing contracts they don’t understand losing, blaming it on “the market.”

A small number of companies, quietly, are going to use this moment to become something the rest of the market can’t catch.

The 6% already decided.

The rest of us are still pretending the purchase order was the plan.

One of my first jobs out of school was at a big-5 consultancy.

I started at KPMG on the ops and recruiting side.

I saw the machine from the inside without being part of it.

I watched partners walk through the office like minor deities: corner offices, client dinners, the quiet authority that came from billing $800 an hour and having a Rolodex that could open any door.

They were the holy grail. Every analyst, every associate, every manager was climbing toward that. The whole culture was built around it.

I watched managers supervise. I watched cohort after cohort of bright graduates get fed into the base of the pyramid. Straight from university recruiting fairs, to their first day. All bright eyed.

From where I sat, I learned the first lesson of professional services: the product isn’t expertise. The product is the spread.

What does that mean? What the client pays per hour, minus what the junior earns per hour.

That’s the business model. Everything else, the frameworks, the methodologies, the 200-page deliverables, is packaging.

Recruiting wasn’t a support function. Recruiting was the product. Because without a steady supply of juniors willing to grind 80-hour weeks at a fraction of their billing rate, the math didn’t work.

Here’s what made it brilliant, though. The juniors weren’t just cheap labor. They were being trained. The late nights building models, the cross-checking of footnotes nobody would ever read, the formatting of decks that would be rewritten three times before the partner presented them: all of it was education disguised as exploitation.

The deliverable was for the client. The development was for the junior. And in five or ten years, that junior became the partner who sold the next engagement.

That model worked for decades. It’s breaking now (or is it?)

Here’s what a lot of people miss when they claim “I can complete a McKinsey quality deck in 10 minutes with Claude”.

Professional services firms sell trust, not expertise.

The pyramid is the machine that manufactures it. AI is hollowing the pyramid from the bottom. And here’s the thing about trust: you can sell it long after you’ve stopped building it.

Until you can’t.

How Do You Sell Something You Can’t See?

Here’s the truth. Nobody hires McKinsey for the PowerPoint. They hire McKinsey for the logo on the PowerPoint.

I remember early in my career, sitting in a meeting where a client was explaining why they’d brought us in for a project their own team could have handled.

I was genuinely confused. “Why not just do it internally?” I asked someone afterward. They looked at me like I’d asked why water was wet. “Because if it goes wrong, nobody gets blamed. You can’t fire a guy for hiring you guys.”

I’ve never forgotten that. It isn’t a joke. It’s the entire pitch. It’s the reason a Fortune 500 CEO pays $1M for a strategy deck that an internal team could produce in six weeks (or a day with Claude). The deck isn’t the point. The insurance policy is the point.

Think about what clients actually buy when they hire a Big Four firm or MBB (McKinsey, BCG, Bain: the strategy elite).

First, they buy CYA insurance. If the strategy fails, the CEO can tell the board: “We brought in McKinsey. We did our diligence.” The consultants become a heat shield.

Second, they buy cross-functional diagnosis. Consultants aren’t part of the org chart, the politics, or the turf wars. They’re not the ones who jockey for a promotion that year.

In the first month, they interview people across functions who’ve never been in the same room. That alone unlocks insights the company already had but couldn’t access. Third, they buy borrowed credibility. The board trusts McKinsey’s stamp more than the VP of Strategy’s analysis, even when the VP’s analysis is better.

This is trust as product. And it works.

HFS Research found that 65% of enterprises say traditional consulting models no longer deliver value. Yet only 25% of McKinsey’s fees globally are linked to outcomes. Clients know the model is broken. They’re paying anyway. Because they’re not buying results. They’re buying the name.

That should tell you everything about how durable trust is. It endures even when the underlying value erodes.

What Happens When AI Eats the Base?

Here’s what’s happening to the pyramid itself. These consulting giants are not asleep at the wheel. They are trying to reinvent themselves.

McKinsey’s internal AI assistant, Lilli, can scan 100,000 internal documents and draft slide decks on demand.

BCG has Deckster.

Deloitte launched Zora AI.

By most analyst estimates, these tools already handle roughly 80% of what a junior analyst used to do. Research, synthesis, formatting, first-draft analysis: all of it, in seconds.

The firms are responding accordingly. McKinsey has cut approximately 5,000 roles since 2023 while deploying 12,000 internal AI agents.

PwC’s own leaked internal presentation shows associate hiring falling 32% between 2025 and 2028, with audit down 39%. Big Four graduate job listings in the UK are down 44% year-on-year. KPMG, my old firm, slashed entry-level hiring by 29%. Deloitte cut 18%. EY, 11%.

Starting salaries across all three MBB firms have been frozen for three consecutive years. Not because the talent is less valuable. Because there are fewer seats.

In the short term, this is wildly profitable. Fewer juniors means lower payroll with the same client fees. Partners pocket the difference. The leverage ratio improves. Margins go up. Everyone’s happy.

Couple that with the AI wave, where the trust and relationships these firms have, open up a tsunami wave of new engagements as their clients struggle to figure out their AI strategies and adoption woes.

Except for the invisible second output that nobody’s accounting for.

I keep coming back to something I read on Spark6 that nails it:

“The work produces a deliverable that the client needs. But it also trains the person doing it. The late nights and the tedious repetition aren’t a regrettable cost of the professional model. They are the model’s training infrastructure.”

That’s the part being dismantled. The pipeline that turns raw graduates into experienced partners, the pipeline I used to recruit for, is being gutted in the name of quarterly efficiency.

I think about Alisha. Good grades. Articled at a respected firm. Passed the bar. Did everything the system told her to do. And when she entered the job market, the firms had stopped hiring associates or cut positions in half. “The partners are busier than ever,” she told me. “They just don’t need us to do the busy work anymore.” Contract review. Legal research. First-draft memos. Due diligence. The bread and butter of every first-year lawyer for the last fifty years. Handled by AI now.

This is Tier 2 of the AI shakeout: professional services. The most disorienting tier, because the people in it were told they were safe. The pyramid isn’t just compressing. It’s hollowing.

Can You Buy Trust Forever?

So where does this go?

I’ve been thinking about it across three time horizons, and the picture gets darker as you zoom out.

Right now, through 2027 or so, trust holds. And don’t count these firms out.

They didn’t become trillion-dollar institutions by being slow. They’ve pivoted hard into the AI wave itself, selling AI transformation to the same clients whose work AI is automating. McKinsey launched its Amazon McKinsey Group with AWS.

Deloitte committed $3 billion to generative AI through 2030 and launched Zora AI. The Big Four collectively poured an estimated $4-5 billion into AI restructuring in 2025 alone. They’re not just surviving the disruption; they’re billing for it.

Boards still want the McKinsey stamp on major decisions. Margins actually improve as AI replaces junior labor at the base. Firms have never been more profitable.

Deloitte is scrapping traditional job titles entirely, effective June 2026, replacing the old analyst-to-manager ladder with “job families.” It looks like innovation. It might just be a rebrand of a shrinking workforce.

But by 2028 to 2032, I think the first cracks might show.

A generation of consultants who never did the grunt work starts leading engagements. They’ve used AI from day one. They’re fast. But the judgment that comes from years of manually checking a model at 2am, of sitting with a client who’s furious because the numbers don’t add up, of learning what “wrong” feels like before you learn what “right” looks like: that’s thinner.

Meanwhile, boutique AI-native firms start competing on speed and price. The 65% of enterprises who already say consulting no longer delivers value start acting on it.

And then you reach what I’d call the hollow firm era.

Maybe 2033, maybe sooner. The logo still commands respect. The brand still opens doors. But the people behind it lack the apprenticeship depth of their predecessors.

Partners who were trained by AI-augmented shortcuts are now training the next generation with even less depth. Trust becomes reputation inertia, not earned credibility. If nobody does the hard reps, nobody develops the instinct. And instinct is what clients are actually paying for.

The question isn’t whether the brand survives. Brands are sticky. The question is whether anything is left behind it.

Who Trains the Trainers?

Here’s the irony.

These firms are the best training machines ever built. That’s not an exaggeration. McKinsey, Deloitte, KPMG: they’ve spent decades perfecting how to take a 22-year-old with a degree and turn them into someone who can walk into a boardroom and command it. The apprenticeship model isn’t a side effect. It’s the core competency.

But here’s the catch: they’ve always trained at the client’s expense.

The client pays $400 an hour for a junior who’s still learning. The firm pockets the spread. The junior gets trained. Everyone wins, or at least everyone did, when there was enough work to justify the headcount.

Now AI does the work. The excuse to have the junior in the room disappears. And the training machine, the thing these firms do better than anyone, loses its economic justification.

Some firms see it.

McKinsey’s North American chair announced they’d hire 12% more graduates in 2026, explicitly stating that AI won’t eliminate the need for young consultants to learn.

PwC is retraining junior accountants to “function more like managers,” focused on critical thinking while AI handles manual work.

And the most interesting reframe I’ve seen flips the apprenticeship entirely: instead of asking a junior to

“write a report on Competitor X,” you ask them to “audit this AI-generated report on Competitor X.” That’s verification over creation. It might actually force deeper critical thinking than the original task ever did.

But the structural incentive pushes the other way. Cutting juniors improves this quarter’s margin. Training juniors is a bet on 2035. And I’ve watched enough boardrooms to know which timeline usually wins.

I started my career watching the professional services machine from the inside. I saw how the leverage model turned bright graduates into billable hours, and how those billable hours, over years, turned into the judgment and relationships that made the whole thing work. It was simultaneously exploitative and brilliant. The grind was the training. The training was the product.

Now the grind is being automated. The training is disappearing with it. And the firms have never been more profitable.

Professional services has never been more profitable or more fragile. These firms are selling trust they’ve stopped manufacturing. The logo endures. The pipeline that built it is being dismantled.

Trust is the last thing AI can replicate. It’s also the first thing firms stop investing in when the machines make the math look better without it.

In the summer of 2019, a woman named Carol Smith signed up for Facebook.

She described herself as a politically conservative mother from Wilmington, North Carolina. She listed her interests as politics, parenting, and Christianity. She followed a few of her favorite brands. Normal stuff. Nothing unusual. Nothing extreme.

Within two days, Facebook was recommending she join QAnon groups.

Carol hadn’t searched for QAnon. Hadn’t clicked on a conspiracy link. Hadn’t expressed interest in anything fringe. The algorithm looked at her profile, looked at what people like her engaged with, and decided that the fastest path to keeping her attention was to send her down a rabbit hole she never asked for.

Here’s the twist.

Carol Smith wasn’t real. She was a test account created by Facebook’s own researchers. The company was running internal experiments to understand how its recommendation engine radicalized users. The findings were documented in an internal report titled “Carol’s Journey to QAnon,” later leaked to NBC News as part of the Frances Haugen disclosures.

Facebook knew. They ran the experiment. They saw the result. And for years, they kept building the machine that produced it.

Carol was fake. But the people she represents are very real.

Samantha, a 30-year-old from Texas, described her mother to BuzzFeed News as “level-headed and college-educated.” Then her mother found QAnon through social media. After months of trying to bring her back to reality, Samantha gave up. “We can’t have any normal conversations anymore,” she said.

There are thousands of Samanthas. We’ve all met some of them.

You probably have too. Sons who stopped calling their fathers. Wives who can’t sit through dinner anymore. Families that splintered not because of infidelity or money, but because an algorithm fed two people in the same house two completely different realities.

A University of Illinois study published last year confirmed what these families already knew: political misinformation consumed through social media was a key reason cited for recent divorces and romantic breakups in the US. Not affairs. Not finances. Media habits. The tendency to dive deeper into increasingly extreme content, combined with misinformation consumption, can widen divides between people who love each other.

PBS reported before the 2024 election that political divides were cutting through marriages and families at rates nobody had seen before. According to research, today, 1 in 2 adults is estranged from a close relative. While the primary cause of these rifts is often tied to something a relative said or did, 1 in 5 directly cite political differences as the reason.

Surveys show fewer than half of politically mixed married couples report being “completely satisfied with their family life,” compared with 61% of couples who share the same politics. A 2014 UK study found Facebook cited in roughly 35% of divorce cases. Not as the cause, necessarily, but as the accelerant.

The Smartest People on Earth Built This

This didn’t happen by accident. We’ve built these “influence machines” to sell products, but the side effects are enormous.

Instagram owns your dopamine. TikTok owns your focus. Netflix owns your nights.

Some of the smartest people on the planet, PhDs in behavioral psychology, neuroscience, and machine learning, spent two decades building the most sophisticated attention-capture architecture in human history.

The algorithm. A system so precisely tuned to human psychology that the average person now spends 2 hours and 25 minutes a day on social media.

TikTok users average 95 minutes daily. 17% of the global population shows patterns consistent with addiction.

I’ve felt it myself. You’ve felt it. You open your phone to check one thing, and 40 minutes later you’re watching a stranger argue about politics. Your thumb keeps scrolling even though your brain checked out ten minutes ago. The algorithm got you. Again.

It captured our time. It fractured our families. It rewired our attention spans. It turned dinner tables into battle lines.

You Ain’t Seen Nothing Yet

In July 2025, researchers led by Kobi Hackenburg and Ben Tappin from Oxford, MIT, Stanford, and the London School of Economics published the largest study of AI persuasion ever conducted.

76,977 people had conversations with AI chatbots about political issues.

19 models. 707 topics. Nearly half a million fact-checkable claims analyzed.

A single conversation. 9 minutes. About 7 back-and-forth exchanges.

The AI shifted political beliefs by 9 to 10 percentage points.

When the researchers optimized everything, the shift hit 15.9 points. Among people who initially disagreed with the AI’s position: 26.5 points.

Let me put that side by side.

The entire political advertising industry, all $12 billion of it, moves opinion by fractions of a point.

Deep canvassing, the gold standard of human persuasion in political science, actually performed worse than a basic AI prompt in this study. Despite the massive scandal, Cambridge Analytica couldn’t prove it worked at all.

A chatbot just did 9 to 16 points. In one sitting. And people stayed voluntarily, averaging 7 back-and-forth exchanges, because something about debating politics with an AI held their attention.

The effects were durable. Researchers followed up a month later. Between 36% and 42% of the belief shift was still there.

The algorithm nudges you a fraction of a point over months of exposure. The chatbot moves you 10 points in 9 minutes.

The algorithm has nothing on LLMs.

The $0 Part

Here’s where the story turns from concerning to dangerous.

You might assume this kind of persuasive power requires the resources of an OpenAI or a Google. Frontier models. Billion-dollar training runs.

It doesn’t.

The researchers took Llama 3.1-8B. An open-source model with 8 billion parameters that Meta released for free. It runs on a laptop. No API key. No subscription. No permission.

They applied a technique called reward modeling: training a second AI to predict which responses would be most persuasive, then selecting the best one at each turn. The result: the free laptop model became as persuasive as GPT-4o, a frontier model costing orders of magnitude more to build.

And the training mattered more than the size. Persuasion gains from post-training exceeded gains from scaling a model’s compute by 100x. You don’t need a bigger model. You need a smarter one. And the technique to make it smarter is published, documented, and reproducible.

Llama has been downloaded more than 1.2 billion times. The reward modeling technique is in the open literature.

You don’t need the smartest scientists and engineers in the world anymore. Any political operative, any ideological movement, any foreign intelligence service with modest technical resources can build this.

The researchers flagged it themselves:

“Even actors with limited computational resources could use these techniques to potentially train and deploy highly persuasive AI systems, bypassing developer safeguards that may constrain the largest proprietary models.”

The algorithm required Facebook, Instagram, TikTok. Required billions in infrastructure. Required teams of hundreds at some of the richest companies on earth.

The chatbot requires a laptop.

The Double Bind Nobody’s Talking About

Here’s where I need to connect something that’s been nagging at me for months.

I’ve written about what happens when AI does our thinking for us. When MIT scanned people’s brains while they used ChatGPT, they found weakened neural connectivity, reduced memory, cognitive decline that persisted even after they stopped using the tool.

I’ve felt it myself: reaching for AI two words into an email, not because I was stuck, but because thinking felt hard.

Atrophy is real. The idea that every time you outsource the struggle, you erode the capacity. Your brain adapts. If you stop asking it to think, it stops being good at thinking.

Now hold that alongside what this Oxford study found.

AI’s persuasive superpower is information density.

It generates an average of 22 fact-checkable claims in a single conversation when optimized for persuasion. It buries you in data, statistics, evidence, arguments, delivered conversationally, responsively, in real time.

Each additional claim increases persuasion by 0.30 percentage points. The correlation between the number of claims and the persuasive effect was 0.76. Information density explains up to 75% of why some AI conversations change minds.

Ready for a shocker? 29.7% of those claims were inaccurate. Nearly a third.

The most persuasive AI configurations were also the least accurate. GPT-4.5, one of the most expensive models available, was less accurate than GPT-3.5, a model released two years earlier.

The machine doesn’t persuade you by understanding your psychology. It persuades you by generating more claims per minute than you can evaluate.

Now combine those two findings.

AI is simultaneously making us worse at critical thinking and better at overwhelming us with information we can’t evaluate.

The tool that’s atrophying our cognitive muscles is the same tool that’s deploying a persuasion technique that specifically exploits weak cognitive muscles.

The algorithm kept you scrolling. It fragmented your attention. It shortened your capacity to sit with complexity.

The chatbot picks up right where the algorithm left off. Except instead of just capturing your attention, it changes your mind. And it does it to a brain that’s already been softened by a decade of algorithmic conditioning.

That’s not a persuasion tool. That’s a persuasion ecosystem. The algorithm was phase one: weaken the defenses. The chatbot is phase two: walk through the open door.

The Election Math

Let me make this uncomfortable.

The Oxford study’s conservative estimate: a single AI conversation produces roughly a 4 percentage-point durable shift after one month. That operates comfortably within the margins that decide real elections.

And unlike a TV ad that reaches you once and fades, or a canvasser who knocks on 50 doors a day, a chatbot can have a million conversations simultaneously. Each one adapted to the topic. Each one sustained for 9 minutes.

The algorithm needed years and billions of dollars to shift the political landscape by fragmenting attention and creating filter bubbles. The chatbot does it in 9 minutes, for free, one conversation at a time.

What Comes After the Algorithm

I’m not a policy researcher. I run a startup. But I’m also someone who builds with AI every day and has to live in the democracy it’s reshaping.

We spent twenty years arguing about the algorithm. Whether Facebook should be regulated. Whether TikTok should be banned. Whether political ads need transparency. Those were the right debates for the era we were in. They assumed the algorithm was the ultimate persuasion tool.

It wasn’t. It was the warm-up act.

The algorithm captured our attention and broke our capacity for deep thought.

The chatbot exploits both. It doesn’t need you to scroll passively through a feed. It needs you to engage in a conversation. And the research shows people do engage, voluntarily, for an average of 9 minutes.

The researchers note that “the very conditions that make conversational AI most persuasive, sustained engagement with information-dense arguments, may also be those most difficult to achieve in the real world.” People don’t voluntarily debate politics with chatbots in their daily lives. Yet.

But millions of us aren't debating politics with AI.

We're asking it what to think about our strategy. Our hiring decisions. Our medical symptoms. Our financial plans. Our kids' education. We're having dozens of these conversations a week, not as a study, but as our daily workflow.

If a 9-minute conversation about immigration policy can shift political beliefs by 10 points, what is a year of daily conversations doing to every other belief we hold?

We're not just being persuaded. We're being shaped. And most of us volunteered for it.

So here’s the TL;DR if you’ve made it this far:

The algorithm took twenty years and a trillion dollars to build, and it mostly just made us angry and distracted. A chatbot on a laptop just demonstrated the ability to change what people believe, durably, at a scale and speed that nothing in the history of political communication comes close to.

And we’re still having the algorithm conversation.

I don’t know what the answer is. But I know the question has changed. It’s no longer “how do we regulate the feed?” It’s “what do we do when a free tool on a laptop can do what a trillion-dollar industry just barely made a dent in?”

The algorithm has nothing on this.

I was having coffee with Alisha last month.

She couldn’t have been more than a year out of law school.

She’d done everything right. Good grades. Articled at a respected firm. Passed the bar. The whole playbook.

And she was drowning. She was frustrated. She lost her confidence.

Not because she wasn’t good enough. Because the firms had stopped hiring. The ones that were still posting associate roles had cut the positions in half.

The ones that weren’t posting had figured out something that changed the math entirely. Either the partners were using AI to do the work that used to require three junior associates, or they would have one associate do the work of five.

Contract review. Legal research. First-draft memos. Due diligence. The bread and butter of every first-year lawyer for the last fifty years. Gone. Not outsourced. Not automated by some robot. Absorbed.

“The partners are busier than ever,” she told me. “They just don’t need us to do the busy work anymore.”

She wasn’t angry. She was confused. She’d followed the exact career path that was supposed to work. And somewhere between her first year of law school and her last, the path became blurry…and then, it disappeared.

I keep thinking about that conversation. Because Alisha isn’t an edge case. She’s a signal. What’s happening to junior lawyers is about to happen across every profession that runs on a pyramid.

Most people get this shift wrong though. It’s not a single wave that hits everyone the same way.

It’s four different waves, hitting four different types of work, at four different speeds. And most people have no idea which wave is coming for them.

Let me elaborate.

The map that makes things visible

Every week I read another article about “which jobs AI will replace.” The framing is always binary. Safe or not safe. Replaced or not replaced. Human or machine.

That’s not how it works.

The real pattern, the one I keep seeing in the data and in the conversations I’m having, is more like a shakeout. Not a single event, but a sorting. Every role is landing in one of four tiers, and each tier is experiencing something completely different.

Ethan Batraski at Venrock recently reframed this debate with a question worth sitting with: not “which tasks can AI do?” but “how is the work economically structured?” The Anthropic labour market data shows almost no statistically meaningful employment change yet. What’s coming isn’t a cliff. It’s a slow, structural reorganization.

Here’s the map.

Tier 1: Time-based roles will quietly disappear

These are the roles that get paid by the hour, the ticket, the task.

Customer support. Data entry. Administrative coordination. Bookkeeping. Sales development. Payroll processing. The work is necessary. It’s also measurable, repeatable, and almost entirely about throughput.

When a company pays for throughput, AI is cheaper throughput.

Think of it like a tide going out. Nobody announces it. You just notice the waterline is lower than it was last year.

Klarna announced its AI assistant was handling two-thirds of all customer service chats within a month of launch, doing the work of 700 agents. IBM’s CEO froze hiring for roughly 7,800 back-office roles that AI could handle. Not cuts. A freeze. The quietest kind of disappearance.

Companies aren’t firing their admin teams. They’re just not replacing people when they leave. The headcount shrinks quarter by quarter. No press releases. No protests. Just fewer chairs at the next all-hands.

If your work is measured in tickets closed, forms processed, or emails answered, you’re in Tier 1. The wave isn’t coming. It’s already here. Most people just haven’t noticed because nobody got fired. The roles are simply not being refilled.

Tier 2: Professional services. The pyramid gets crushed.

This is where Alisha lives. And it’s the most disorienting tier, because the people in it were told they were safe.

Law. Consulting. Accounting. Financial analysis. Mid-level management. The knowledge-work pyramid. A few senior people at the top directing armies of juniors who do the grunt work.

The grunt work is what AI eats first.

A junior associate at a law firm typically spends the majority of their time on research, document review, and first-draft memos. A junior consultant spends similar hours building slide decks, running analyses, and formatting deliverables.

Every one of those tasks is now something a senior person can do themselves with AI in a fraction of the time. The partner doesn’t need three associates when Claude can do the first pass on a contract review in four minutes instead of four hours. The senior consultant doesn’t need two analysts when AI can build the financial model while she writes the narrative.

PwC describes this as the shift from pyramids to hourglasses. The middle and bottom compress. The top gets more powerful.

It’s like a building losing its lower floors while the penthouse keeps rising. The view from the top has never been better. But the staircase is gone.

This is what makes Tier 2 dangerous. The work isn’t disappearing. The entry points are disappearing. The partners are fine. The associates are not. The managing directors are thriving. The analysts are competing for half the positions that existed two years ago.

And the salaries reflect it.

Alisha isn’t just struggling to find a job. The offers she’s seeing are far below what she expected after three years of law school and six figures of debt. More graduates chasing fewer positions. The investment she made assumed a job market that no longer exists.

Tier 3: Outcome-based roles. The amplification.

This is the part that doesn’t make headlines, because it’s not a crisis. It’s a windfall.

Senior engineers. Physicians. Executive leaders. Investment professionals. Strategic advisors. Creative directors. The people who get paid for outcomes, not hours. For being right, not being busy.

AI doesn’t threaten these roles. It supercharges them.

When a senior engineer uses AI, they don’t become redundant. They become a one-person engineering team. I watched a candidate in an interview open Claude Code and walk through a feature she’d shipped solo the previous week. Frontend, backend, deployment. One person. One week. Work that would have required a three-person team twelve months earlier.

She wasn’t replaced by AI. She absorbed the team.

Anthropic’s research on AI augmentation shows that the more complex the task, the bigger the advantage of working with AI.

Simple tasks see modest gains. The hard stuff, the work that requires real reasoning, is where augmentation pulls furthest ahead. But only for people who already have the judgment to direct the tool.

A senior radiologist using AI can read scans faster and catch patterns the human eye might miss. A medical student using the same tool doesn’t know which findings matter. Same technology. Different value. The value was never in the reading. It was in knowing what to do about it.

Researchers at Harvard Business School found the same pattern when they studied AI adoption across roles at a global trading firm. AI lets you cross into adjacent domains, but it can’t parachute you into distant ones.

Without foundational understanding, you’re performing at the baseline of the model. And the baseline isn’t good enough for real work.

If you’re in Tier 3, the message is simple: lean in. Learn to work with AI, not just alongside it. The gap between AI-augmented senior professionals and non-augmented ones is widening every month. PwC’s AI Jobs Barometer shows AI-skilled workers now earn a 56% wage premium over comparable roles. Last year it was 25%. It doubled in twelve months.

The winners aren’t just winning. They’re pulling away.

Tier 4: Physical work. The last fortress.

Then there’s the tier that nobody in tech wants to talk about, because it disrupts the narrative that AI changes everything.

Plumbers. Electricians. Carpenters. Welders. HVAC technicians. Nurses. Physiotherapists. Chefs. The people who work with their hands in unpredictable environments.

AI can’t snake a drain. It can’t rewire a hundred-year-old house. It can’t assess a patient’s gait or feel whether a joint is inflamed.

Robotics will eventually reach some of this work. But “eventually” means decades, not quarters. The physical world is messy, variable, and unforgiving in ways that digital work simply isn’t. A bug in code is a pull request. A bug in plumbing is a flooded basement.

The skilled trades are quietly becoming the most resilient career path in the economy. The U.S. Bureau of Labour Statistics projects electrician employment growing 11% through 2033, much faster than average. Associated Builders and Contractors estimates the construction industry alone needs an additional 501,000 workers beyond normal hiring to meet demand. That gap is getting worse, not better.

The irony that would have been unthinkable five years ago: Alisha, with her law degree and her mountain of debt, might end up earning less than the electrician who started an apprenticeship while she was studying for the LSAT.

That’s not a prediction. In many markets, it’s already true.

The hidden bomb inside Tier 2

This is what keeps me up at night about this shakeout.

If you compress Tier 2, if you hollow out the junior ranks of every professional services firm, you don’t just eliminate jobs. You eliminate the training ground that produces Tier 3 professionals.

Every senior partner, every managing director, every chief medical officer built their judgment the same way: by doing the grunt work first. By reviewing contracts that taught them what mattered. By building models that taught them what broke. By making mistakes at low stakes so they could make decisions at high stakes.

That pipeline is being cut off. And the evidence is already showing: 47% of Gen Z say they get better career advice from ChatGPT than from their manager. That’s not a win for AI. That’s an indictment of what’s left when the apprenticeship dries up.

The partners using AI to replace associates aren’t thinking ten years ahead. They’re thinking about this quarter’s margins. And this quarter, the math works beautifully. Fewer salaries, same billable hours, higher profit per partner.

But in ten years? Who’s the next partner? Who builds the judgment to know which contract clause is a ticking time bomb and which is boilerplate? Who develops the instinct to tell a client “this deal is wrong” when the AI analysis says it’s fine?

You can’t automate the apprenticeship that builds judgment. And without the apprenticeship, you don’t get the judges.

This is the structural crisis hiding inside the efficiency gains. Every profession that compresses its pyramid is simultaneously efficient and fragile. The gains are real. So is the long-term risk.

So where do you land?

If you’ve read this far, you’re probably doing the math on your own role.

The uncomfortable truth is that your tier isn’t fixed. The people who move up are the ones who stop competing on hours and start competing on judgment. The six skills that close that gap, calibrated trust, strategic decomposition, knowing when to stop, are learnable. But nobody’s going to hand them to you.

The winners (and losers?)

Every shakeout produces winners and losers. That’s the nature of structural change. But this one has a twist.

The winners, the Tier 3 professionals who get amplified, the Tier 4 workers whose skills can’t be replicated, are fine. They might not even notice the shakeout happening around them.

The losers aren’t the people who lack talent. They’re the people who invested in a path that was supposed to be safe. The Alishas. The ones who did everything right according to rules that quietly changed.

The question nobody wants to ask is: who’s building the bridge for them?

Because right now, the answer is nobody.

The firms are optimizing for this quarter. The universities are still charging for degrees that assume a job market from 2019. The governments are debating AI regulation while the labour market reorganizes itself without permission.

And somewhere, a recent-call lawyer is sitting across from someone like me, wondering how the playbook she followed led her here.

Four tiers. Four different futures. One question that matters more than which tier you’re in:

Are you waiting for the shakeout to sort you, or are you sorting yourself?

“AI will never replace me.”

I heard this exact phrase three times last week. From a mortgage broker, a financial advisor, and a real estate agent.

Three different industries. Three different conversations. Same five words.

Their reasoning was identical, too: “My clients need the human touch. The relationship. The emotional connection.” “AI can never do that!”

They’re right. But here’s what they’re missing.

They’re not describing their value. They’re describing their friction.

I’ve watched this pattern play out at multiple companies I’ve build and worked with, and it’s the framing that we have all wrong. What I keep seeing is the same blind spot:

Professionals who confuse their presence in the process with their importance to the outcome.

There’s a difference. And it’s a difference that has killed entire industries.

What Was Blockbuster Actually Selling?

Every industry that’s been disrupted had people who said exactly what my mortgage broker said last week.

“People will always want to come to Blockbuster and browse movies with our knowledgeable staff.”

“Customers need our expertise to develop their photos properly.”

“Readers want the personal touch of a newspaper delivered to their doorstep.”

“Travelers need our human dispatchers to coordinate rides efficiently.”

The pattern is brutal in its consistency. Each thought their human element was the product.

But customers didn’t want the human element. They wanted the outcome. Faster. Cheaper. With less friction.

Kodak didn’t die because digital photography was better. Kodak died because Kodak invented the digital camera in 1975, and when engineer Steve Sasson showed it to executives, they asked why anyone would want to take a picture this way when there was nothing wrong with conventional photography. They saw the future and chose to protect their process instead of their customers’ outcomes.

Here’s the part that should keep you up at night: the top 30 U.S. newspapers actually saw circulation increase for two years after the internet went commercial. Two years of rising numbers while the foundation crumbled beneath them.

The data looked fine. The trend was terminal.

Uber didn’t replace taxi drivers because the ride was better. Princeton economists found that UberX drivers have a 38% higher capacity utilization rate than traditional taxi drivers. The dispatchers, the call centers, the payment, the wait times: that was the friction. The ride itself was never the problem.

Every one of these industries had people who believed their human touch was irreplaceable. They were right about the touch. They were wrong about which part of it mattered.

If you think I’m full of it, just have a quick look and observe how many Amazon delivery vans pass on your street on any given hour. We all love the local butcher, or the barista at our local cafe. But when push comes to shove, eliminating friction seems to be winning the day.

Are You the Value, or the Friction?

Last month, I had to renew my insurance policy. The insurance broker I’ve been working with for years, is a super great guy. He’s always responsive, and we generally have a great relationship.
When it came to renewing my policy, he sent me no short of six documents that required me to fill them out using a PDF tool. After the second PDF crashed on me and didn’t save, I had enough. I went to an online insurance brokerage I’ve been hearing some great things about from my peers.

Within 10 minutes, all the information they needed was collected. There were no PDFs to fill out (I believe they still needed the PDFs for their insurance company, but they had a tool do the work in the backend, unbeknownst to me). I plugged in my credit card, and got the job done. I didn’t think twice about all those years of loyalty. It was the friction that got me.

Here’s where it gets personal. And uncomfortable.

Are you still making clients fill out paper forms in 2026?

Do your customers still need to call in and play phone tag just to get a status update?

Do you force people to meet in person for things that could be handled in ten minutes on a screen?

Are you making things far more complex than they need to be, because that complexity makes you feel essential?

I’ve asked myself these same questions at Deeded. Some of the answers weren’t flattering, at all.

Here’s what most professionals don’t want to hear:

according to Zendesk, 69% of customers now prefer self-service options over contacting support for routine tasks. They don’t want to call you. They don’t want to email you. They want the answer, and they want it now.

But here’s the paradox, and it’s the one that should shape everything you do next: a 2025 OnePoll survey of 6,000 adults found that in high-stakes industries like legal, medical, and financial services, nearly 90% of consumers still want to talk to a human. Not a chatbot. Not a form. A person.

People want humans when humans add value. Not when humans add steps.

Mortgage brokers using AI tools are saving 25 to 30 hours per month on customer service tasks. Those aren’t relationship hours they’re reclaiming. Those are paperwork hours. Phone tag hours. “Let me check on that and get back to you” hours.

The friction hours.

The brokers who understand this aren’t losing clients. They’re freeing themselves to spend more time on the moments that actually build trust: the hard conversations, the creative problem-solving, the judgment calls no algorithm can make.

But here’s the catch I explored in “Does AI Really Let You Do More Valuable Work?”: freeing up time doesn’t automatically mean you’ll spend it on value. Most people just fill it with more tasks.

Why Are 90% of CEOs Missing This?

Here’s what makes this moment different from Blockbuster or Kodak. Everyone knows AI is coming. Nobody’s pretending it doesn’t exist.

A February 2026 study from the National Bureau of Economic Research surveyed nearly 6,000 CEOs, CFOs, and executives across the U.S., UK, Germany, and Australia. The finding?

About 90% said AI has had no impact on productivity or employment at their companies.

No impact. Not “modest.” Not “underwhelming.” None.

And yet, AI adoption rose from 61% to 71% in the same period. Everyone’s buying the tool. Nobody’s transforming the process.

Economists are calling it Solow’s paradox reborn. Robert Solow, back in 1987, made a now-famous observation: “You can see the computer age everywhere but in the productivity statistics.” Forty years later, same pattern. Different technology. Same human bottleneck.

The problem isn’t the AI. The problem is that companies are layering AI on top of broken processes and calling it transformation.

Salesforce learned this the hard way. They laid off 4,000 customer support workers, planning to replace them with AI. Then they started rehiring. The executives overestimated what AI could replace and underestimated where humans actually mattered. The AI could handle the routine. It couldn’t handle the judgment, the empathy, the nuance.

Two-thirds of CEOs say they personally use AI. Their average weekly use?

An hour and a half.

That’s not transformation. That’s tourism.

The Friction You Don’t See

There’s a deeper layer here that most people skip right past.

Sometimes the friction isn’t your paperwork. It isn’t your forms or your phone system.

It’s you.

Your process. Your ego. Your need to be in the loop on every decision. Your insistence that things run through you, not because it’s better for the client, but because it makes you feel important.

I’ve caught myself doing this. Building workflows where I’m the bottleneck, not because I add value at that stage, but because removing myself felt like losing control. It took someone on my team pointing it out before I saw it.

The professionals who survive this shift won’t be the ones who resist AI. They’ll be the ones honest enough to ask: “Am I in this process because I make it better, or because I’ve always been in this process?”

That’s not a comfortable question. But those who can’t answer it are the Blockbuster manager who thought the store layout was the product.

The Reframe

So here’s what I’d tell my mortgage broker, my financial advisor, and my real estate agent.

Stop defending your process. Start examining your outcomes.

Stop protecting your touchpoints. Start enhancing your value points.

Stop adding steps. Start removing barriers.

The human touch matters. I believe that deeply, and everything I’m building at Deeded is built on that belief. But it only matters when it’s adding something the machine can’t. Judgment. Empathy. Creative problem-solving. The ability to sit with someone in a moment of fear and say exactly the right thing.

When your human touch is a stack of paper forms, a phone call that could have been a text, and a meeting that could have been a dashboard, you’re not offering connection. You’re offering friction.

And friction is always the first thing to go.

The professionals who thrive won’t be the ones who fight AI. They’ll be the ones who partner with it to become exponentially better at the human parts that actually matter.

Your “human touch” is either your moat or your vulnerability. I wrote about how quickly moats can shift when the rules change. The difference isn’t the touch itself. It’s whether the person on the other side is reaching for it, or reaching past it to get to what they actually need.

What friction are you creating in your industry?

And what would happen if you removed it instead of defending it?

In 2013, I wrote a piece for Forbes called “New Problems, New Approaches: The Rise of the Generalist.”

I argued that companies couldn’t keep throwing groups of specialists at complex problems and expecting breakthroughs. That the future belonged to people who could connect dots across disciplines, who practiced empathy, who learned things fast enough to be dangerous.

Truth be told, I wrote it based on my own frustrations. First, from being a generalist myself. I’ve never felt like I can fit into a proper company structure until I made it to the senior management ranks, but more so because I saw project rooms full of people, each with a micro specialization. Coordination on these projects was usually awful and grossly inefficient. Being a generalist, I thought there must be a better way.

At the time, the post got a lot of positive reactions from mostly generalists. They also felt the same frustration and inertia that I was feeling. But corporately, nobody cared.

The world was still in love with the 10,000-hour rule.

Malcolm Gladwell had told everyone that mastery meant depth, not breadth.

Go deep. Pick a lane. Become the person who knows one thing better than anyone else on the planet. More education, more specialization, more defensibility. That was the playbook.

And I’ll be honest: being a generalist in that world felt like a shortcoming.

I’ve always been one. I’ve run businesses, written, built products, ran strategy, jumped between industries.

I know other people who operate this way, and we all felt the same quiet doubt. You don’t fit the mold. You don’t have the depth. You’re always the person who knows a lot about many things and not quite enough about any one thing. The specialist gets the title. The generalist gets the “so what exactly do you do?” question at dinner parties.

Then I listened to Daniel Priestley on Diary of a CEO podcast recently.

And something shifted.

Not a revelation. More like a decade-old argument I’d been carrying finally finding its moment.

Priestley laid out a blueprint for thriving in the AI era, and at its center was an idea I’d been defending since forever: the generalist wins.

But now the reason is different. The industrial age demanded narrow specialists because narrow tasks needed human hands. “Learn one thing and do it better than anyone.” That logic built careers, companies, entire economies.

But here’s what changed: the machine just became the narrowest specialist of all.

It knows more contract law than your lawyer. It writes cleaner code than most developers. It diagnoses conditions faster than many doctors. It’s Gladwell’s 10,000-hour rule on steroids, except the machine did it in 10,000 nanoseconds.

So what’s left for the human who spent twenty years going deep on one thing?

That’s the question I can’t stop sitting with. And Priestley’s answer surprised me.

What Does Daniel Priestley Actually Argue?

Priestley’s framework isn’t complicated, but it is more expansive than being a generalist.

That’s what makes it dangerous. He lays out five interlocking ideas that, taken together, form a blueprint for navigating what might be the biggest economic disruption in modern history. The entrepreneurial mindset. The rise of small, niche businesses. The irreplaceably human moat. The elevation of blue-collar work. And the return of the generalist.

Each one challenges something I believed. Together, they challenged everything.

I’m not going to pretend I agree with every claim. But I’m going to tell you which ones got me thinking.

Why Is the Smartest Person in the Room Now the Cheapest?

Here’s a sentence that would have sounded absurd five years ago: AI has immense data but absolutely zero lived experience.

Think about what that means. The machine can pass the bar exam. It can draft a merger agreement. It can write marketing copy, analyze financial statements, generate code. But it cannot stand on a stage. It cannot host a dinner party. It cannot look someone in the eye and say, “I’ve been where you are, and here’s what I learned.”

Priestley puts it simply:

“The minute an AI learns how to be a lawyer in one place, it can be a lawyer in every place.”

That’s the terrifying part. Knowledge work scales infinitely once digitized. But a plumber still has to show up. An electrician still has to crawl through your ceiling. A bricklayer still has to lay bricks in the rain.

The numbers are already reflecting this. Nearly 80% of Americans say they’ve noticed increased interest in trade careers among young adults. Electricians in high-demand areas are pulling six-figure salaries, driven partly by the explosion of data centers that power the very AI systems displacing white-collar workers. Meanwhile, 66% of Americans now believe trade professionals have more job security than corporate professionals. This came across loud and clear in a jobs analysis project I toyed with a couple of weekends ago.

The irony is almost poetic. We built the knowledge economy. We told an entire generation: don’t work with your hands, work with your brain. Go get a master’s degree.

Priestley doesn’t mince words about this:

“Lots of young people who should have been plumbers, electricians, and concreters went off and got a master’s degree in mating habits of butterflies... and they end up in $60,000-$80,000 worth of debt to get a degree that no one was asking for.”

My take: Let’s be honest. Not everyone can be a plumber or work physical labor. It’s a tough, consuming career.

Now the brain work is getting automated, and the hand work is becoming scarce. The people we told to leave the trades are the ones we need most (at least for now, until they too will become candidates for robotics).

I know many people who can’t find a reliable contractor to renovate their office, but they can spin up an AI agent to handle their entire customer support operation in an afternoon. The bottleneck isn’t intelligence anymore. It’s presence. It’s showing up in the physical world and doing something a machine can’t do from a server rack.

What If Smaller Is Actually Bigger?

This is the one that rearranged my mental furniture.

For years, the startup playbook was clear: raise money, scale fast, chase the billion-dollar valuation. Priestley argues that playbook is dying. The optimal business of the future isn’t a unicorn.

It’s a team of 2 to 50 people building something specific for a specific audience, generating $1 to $5 million in revenue, and doing it with freedom and creative control.

The economics support this. AI inference costs have dropped by more than 10x annually since 2023, with LLM pricing falling at a median rate of 50x per year according to Epoch AI. The cost of building software is approaching zero. The cost of creating content is approaching zero. The cost of automating routine tasks is approaching zero.

And here’s where the Jevons paradox kicks in. When something gets dramatically cheaper, we don’t use less of it. We use massively more. Steam released nearly 20,000 games in 2025, a record.

GitHub saw over 518 million pull requests merged for the year, up 29%. Nearly 1 billion commits. Thirty-six million new developers joined the platform in a single year. People who never called themselves developers are shipping products.

Priestley sees this accelerating into millions of tiny, highly profitable businesses serving niches that would have been economically unviable five years ago. Not everyone needs to build the next Salesforce. Most people shouldn’t try. The real opportunity is finding 2,000 people who need exactly what you offer and serving them better than any generalized platform ever could.

I’m an entrepreneur. I run a startup. And I’ll admit: this idea challenged me more than I expected.

There’s an ego component to chasing scale. There’s a cultural script that says bigger is better, more is more, growth is the only metric that matters. Priestley is saying the opposite. He’s saying the winners will be the ones who chose just enough. Who built something human-scaled. Who traded the unicorn fantasy for a life that actually works. In a lot of ways, it makes perfect sense.

Can an Algorithm Host a Dinner Party?

This is the core of Priestley’s argument, and it’s the part I keep coming back to.

As AI makes generating software and content incredibly cheap and easy, those outputs become commoditized. Your SaaS tool isn’t special anymore. Your blog post isn’t special anymore. Your marketing funnel isn’t special anymore. An AI can replicate all of it for $20 a month.

So what’s actually defensible?

Priestley’s answer: bundle your humanity into an ecosystem the machine can’t touch.

Start with a personal brand. Not the cringe, self-promotional kind. The kind where 2,000 to 20,000 people genuinely know who you are, what you do, and what you stand for.

The nano-creator data backs this up. 67% of creators worldwide have fewer than 10,000 followers. Yet 45.6% of creators earn between $10,000 and $100,000 annually. You don’t need millions. You need the right thousands.

Then share what Priestley calls your “personal playbook.” In the future, relatable beats impressive.

You don’t need to cure cancer to be valuable. You need to share your unique lived experiences, your real stories, your actual methodology. The stuff an AI can’t replicate because it’s never lived a single day.

I’ve noticed this shift in my own writing. The pieces that resonate most aren’t the ones packed with data and frameworks. They’re the ones where I admit I don’t know something. Where I share a moment that scared me. Where I say, “Here’s what actually happened, not what was supposed to happen.” That’s what people actually want to read right now.

And then you bundle it. Software alone isn’t a moat anymore. But software wrapped in a community, anchored by real-world events, supported by a podcast, reinforced by a training bootcamp? That’s an ecosystem. That’s something a machine can’t clone because it requires human bodies in physical spaces having experiences that generate trust.

I think about this constantly with my company. The product matters. But the product alone isn’t enough. The relationships, the community, the conversations that happen between transactions: that’s where the real value lives. Priestley gave me the language for something I’d been circling for months: the product is the entry point. The ecosystem is the business.

The Generalist’s Revenge

This is the idea that flipped me. Or rather, flipped me back.

When I wrote that Forbes piece in 2013, I described the modern generalist as “a master of their trade who brings expertise and experience in several areas, fueled by insatiable curiosity and the ability to hyper-learn new concepts and ideas.” I argued they were the ones who could see across silos, connect dots that specialists couldn’t, approach problems with a “how might we” mindset instead of pre-conceived frameworks.

Thirteen years later, Priestley is making the same argument. But now the stakes are existential, not just organizational.

When the machine handles the narrow tasks better, faster, and cheaper, the human who only knows one narrow thing is the most vulnerable person in the room.

The future belongs to the wide generalist.

The person with diverse reference points who can connect ideas across fields. The person who reads philosophy and codes and runs a business and teaches a workshop. Innovation doesn’t come from going deeper into a single domain. It comes from collision. From unexpected combinations. From the kind of cross-pollination that only happens when someone has lived in multiple worlds.

And here’s what I think Priestley gets most right: the most sought-after people won’t just use AI. They’ll play with it. There’s a difference.

Using AI means treating it like a search engine. Playing with AI means handing it your hardest, most complex problems and creatively collaborating to find solutions nobody expected. It means having a fluid identity, being comfortable not knowing exactly what your job title is, because you’re building the role as you go.

I’ve started doing this myself. Instead of asking Claude to do the thing I already know how to do, I’ve started giving it the problems I genuinely don’t know how to solve.

The ones that scare me a little. And something interesting happens: the machine doesn’t give me the answer. It gives me a starting point. The answer still requires judgment, taste, lived experience. It still requires me. I wrote about hitting that exact wall when my AI tools went down and I realized how dependent I’d become.

That’s the paradox Priestley is pointing at. The machine makes knowledge free. But it makes the person who knows how to use that knowledge, who can synthesize it, who can stand in front of other humans and make it mean something, more valuable than ever.

So What’s Actually Left for Us?

Well, congrats to the human race. We did it. We built machines that know everything. There were always many things that can be explained with science, but the ultimate panacea was always how the human brain uses language. LLMs cracked that nut.

In doing so, we made knowing things worth almost nothing. The bar exam? Automated. The financial analysis? Automated. The code review? Automated. The knowledge premium that entire professions were built on is evaporating in real time.

But something else is happening simultaneously.

The value of being human, of being present, of being the person who shows up and makes the room feel different, that’s exploding.

It’s not sentimental. It’s economic. The plumber earns more than the paralegal now.

The creator with 5,000 engaged followers builds a better business than the SaaS founder with a million in ARR and no community.

The generalist who combines three disciplines invents things the specialist never imagined. I explored this tension in The AI Economy Has Two Winners and One Casualty: the winners aren’t who you’d expect.

Priestley’s blueprint isn’t complicated. That’s what makes it so challenging. It doesn’t ask you to learn a new technology. It asks you to become a different kind of person.

Think entrepreneurially. Start small and specific. Build around your humanity. Invest in the physical. Stay wide. Play with the tools instead of fearing them.

I’m still processing what this means for how I build, how I lead, how I write. But I’ll tell you what I’m not processing anymore: whether being a generalist was a mistake.

Priestley is saying it in 2026 and the world is ready to hear it. The argument didn’t change. The economy did.

The AI era won’t be won by the people who know the most. It’ll be won by the people who are the most. The most present. The most human. The most willing to stand in front of other people and offer something no machine can replicate: the irreplaceable weight of having actually lived.

Intelligence is becoming free. Being human never will.

My daughter had a math test last week.

She didn’t cheat. She didn’t copy. She sat down at the kitchen table, opened ChatGPT, and asked it to walk her through solving equations. Step by step. Problem by problem.

She put in the hours. She put in the effort.

She bombed the test.

I watched her face when she got the grade back. And I felt something I wasn’t expecting: recognition. Not anger. Not disappointment. Recognition. Because I’ve been doing a version of the same thing for the past two years, and I’ve written about it more than once.

She did the work. She used the wrong tool.

Here’s what happened.

ChatGPT showed her every step. It broke down each equation cleanly. It was patient, clear, thorough. By every visible measure, she was studying. She was engaged. She was putting in time.

But she wasn’t struggling.

The tool gave her the path without making her walk it. And when the test came and the tool wasn’t there, neither was the understanding.

This isn’t a parenting story. It’s a pattern we’re all going to experience, and it is one that can put an entire generation at risk.

What Happens When the Tool Does the Thinking?

Researchers at the University of Pennsylvania ran a study with nearly 1,000 high school students in Turkey.

They split them into groups: one used ChatGPT while doing math practice problems, one used a modified “tutor” version that gave hints without answers, and one used nothing at all.

The ChatGPT group crushed the practice problems. Solved 48% more of them correctly than the group working on their own.

Then came the test.

The ChatGPT group scored 17% worse.

Read that again.

They practiced more successfully and performed worse. The students who sat with the problems, struggled through them, got things wrong, erased and started over: they outperformed the group with the most powerful AI tool on the planet sitting next to them.

The researchers titled their paper “Generative AI Without Guardrails Can Harm Learning.”

Without guardrails. Not “might harm.” Can.

And the tutor version, the one that gave hints but not answers? Those students solved 127% more practice problems. On the test? No better than the group that used nothing at all.

The students weren’t failing because they were lazy. They were failing because the tool was too helpful. When the researchers looked at what students actually typed into ChatGPT, the pattern was obvious: they asked for answers. They skipped the wrestling. They got the output without building the understanding.

This is what Anders Ericsson spent decades studying. The Swedish psychologist behind the research on “deliberate practice“ found that mastery comes from one specific thing: working at the edge of your ability. Not repetition. Not review. Struggle.

He put it plainly:

“The journey to truly superior performance requires struggle, sacrifice, and honest, often painful self-assessment.”

My daughter was reviewing. She wasn’t struggling. And the difference showed up the moment the safety net disappeared.

The Line Between Efficiency and Substitution

I need to be careful here because I’m not anti-AI. I use it constantly. I’ve built my workflow around it. I’ve written about how it changed my productivity and my thinking.

But I’ve also written about the moment I hit my AI usage limit and realized something terrifying: I couldn’t think without it anymore.

The cognitive muscle had started to atrophy. I could feel it.

There’s a concept in psychology called “desirable difficulty,” coined by Robert Bjork.

It describes something counterintuitive: the challenges that feel inefficient in the moment are the ones that build the deepest understanding. Your brain tags hard material as important. The struggle is the signal that learning is happening.

MIT confirmed this at the neurological level last year. They scanned people’s brains while they used ChatGPT, Google, or nothing at all to write essays.

The ChatGPT group finished fastest. Their essays looked polished. But their brains showed weaker neural connectivity. 83% of them couldn’t quote a single sentence from their own essay afterward.

The worst part? The cognitive decline didn’t stop when they stopped using ChatGPT. Their brains stayed sluggish.

I wrote about this study in detail, and one line from it still sits with me:

“Once you start outsourcing your thinking, your brain doesn’t exactly leap at the chance to take back the wheel.”

So here’s the line, and it’s not complicated.

If you already know how to solve the equation, using AI to solve it faster is efficiency.

If you don’t know how to solve the equation, using AI to solve it, or even tutor you through it, is substitution.

The output looks identical. The understanding couldn’t be more different.

Use AI to execute after you’ve already thought? Powerful. Use AI to amplify your thinking? Transformative. Use AI to do your thinking? That’s where it breaks.

The question is always: are you reaching for AI before you’ve wrestled with the problem, or after?

What Happens When Companies Skip the Struggle Too?

My daughter is one student at one kitchen table. But companies are making the same mistake at industrial scale.

Entry-level hiring at the 15 biggest tech firms fell 25% from 2023 to 2024 and it is clearly on the decline. That’s not a recession story. That’s an AI story.

In fields most exposed to AI, the numbers are worse.

The share of jobs requiring three years of experience or less dropped from 43% to 28% in software development. From 35% to 22% in data analysis. From 41% to 26% in consulting. All between 2018 and 2024. We haven’t seen nothing yet.

Companies looked at the grunt work that juniors used to do, the code reviews, the data cleaning, the first-draft financial models, and handed it to AI. The logic felt obvious: why pay someone to learn when a machine already knows?

But that grunt work wasn’t busywork. It was the training ground.

A junior analyst doesn’t build a financial model to produce a spreadsheet. They build it to learn how businesses actually work. A junior developer doesn’t write boilerplate code because the company needs boilerplate. They write it to understand how systems connect.

The BCG and Harvard Business School study by Dell’Acqua and colleagues in 2023 showed what happens when you skip this phase.

They gave 758 consultants realistic tasks with GPT-4. On tasks inside AI’s capability frontier, the AI users did well: 12% more tasks, 25% faster, 40% higher quality.

But on tasks outside the frontier, AI users performed 19% worse than those working without it.

The researchers called it “mis-calibrated trust.” The consultants couldn’t tell where AI’s competence ended and their own needed to begin. They’d never built the judgment to know the difference.

This is my daughter’s math test, scaled to the Fortune 500. The output looked great. The understanding wasn’t there. And when the problem got harder than the tool could handle, nobody was ready.

We’re not saving time. We’re borrowing from a future we haven’t staffed.

I wrote about this problem in “Who Builds Judgment When Careers Disappear?“ The career ladder isn’t being disrupted. It’s being amputated at the bottom rungs. And the people celebrating the efficiency gains aren’t counting what’s being lost.

When Not to Use AI

So here it is, as plainly as I can say it.

Don’t use AI when you’re trying to learn something.

This isn’t a moral argument. It’s not about purity or discipline. It’s about what actually works. The research is consistent: Ericsson’s decades on deliberate practice, Bjork’s work on desirable difficulty, the UPenn study, MIT’s brain scans. The struggle is the mechanism. Remove it and the learning doesn’t happen.

The output might look finished. The understanding is not there.

This applies to the student studying for a math test. It applies to the junior analyst building their first financial model. It applies to the new developer writing code. It applies to the founder learning to sell, the manager learning to lead, the writer learning to think on the page.

The line isn’t complicated. Are you completing a task you’ve already mastered? AI is leverage. Are you completing a task you’re still learning? AI is a crutch.

I’m not asking anyone to abandon AI. I use it every day. I’m asking something simpler: before you reach for it, ask yourself one question.

Have I earned the right to outsource this yet?

If you can solve the equation without the tool, the tool makes you faster. If you can’t, the tool makes you weaker. Not in the moment. The moment feels great. You feel productive. Efficient. Smart.

But the test is coming. It always is.

For my daughter, the test was last Tuesday. For the junior analyst, it’s the first time a client asks a question the model can’t answer. For the company that stopped hiring juniors, it’s the decade from now when there’s nobody in the pipeline who understands the business well enough to lead it.

Eliminating struggle is NOT progress

Here’s what I keep coming back to.

We’ve built tools that eliminate the struggle that builds competence. And we’ve convinced ourselves that eliminating struggle is always progress.

It’s not.

Sometimes the struggle is the point. Sometimes inefficiency is the teacher. Sometimes the slow, frustrating process of figuring something out for yourself is the only thing that makes you capable of figuring out the next thing, and the next thing after that.

My daughter didn’t fail because she was lazy. She failed because the tool was too helpful. And that’s the paradox we’re all living inside now: the better AI gets at doing our work, the harder we have to think about which work we can afford to give away.

Intelligence is getting cheaper every day. The ability to build it is not.

This is one question. There are others. Knowing when not to use AI might be the most important AI skill nobody’s teaching.

11:40pm. Sunday night. Runway 4 at LaGuardia.

A Port Authority fire truck is crossing the runway. It’s been cleared to cross.

Twenty seconds earlier, an air traffic controller gave the authorization. The truck is responding to a United Airlines flight that aborted takeoff after passengers reported a strange odor in the cabin. Routine. Unremarkable.

Except that Air Canada Express Flight 8646, a CRJ-900 arriving from Montreal, is on short final to the same runway.

The controller realizes.

You can hear it on the recording.

The voice shifts from procedure to panic: stop, stop, stop. But at the speed of a landing aircraft, twenty seconds is geometry, not time. The plane hits the fire truck at roughly 140 miles per hour.

Two Canadian pilots, Antoine Forest and Mackenzie Gunther, are killed on impact. May they rest in peace.

A flight attendant is ejected from the aircraft and, against all odds, survives. Forty-one passengers are injured. It’s the first fatal crash at LaGuardia in 34 years.

And then, on the recording, comes the sentence that contains this entire article.

The controller, audibly shattered, says:

“We were dealing with an emergency earlier. I messed up.”

The pilot on the frequency responds: “Nah man, you did the best you could.”

Aside from being curious, I don’t know much about aviation, but I’ve listened to that exchange five times now.

The controller who failed and the pilot who forgave him, separated by a radio frequency and a catastrophe, having the most human conversation you’ll ever hear on an aviation recording.

“I messed up.”

Three syllables. The most honest thing anyone said that night. And the most misleading.

Did This Controller Actually “Mess Up”?

Here’s what was happening in that controller’s head in the sixty seconds before impact.

They were tracking multiple inbound aircraft on final approach.

At the same time, they were coordinating a fire truck responding to an unrelated emergency on a different plane.

They were managing a runway crossing clearance. They were doing this on a midnight shift, in rain, darkness, poor visibility, with two controllers present, which is standard staffing for LaGuardia, the 19th busiest airport in the US, after dark.

And then: the fire truck had no transponder.

LaGuardia’s surface detection system, ASDE-X, which is specifically designed to prevent exactly this kind of collision, failed to generate an alert. Why? Because vehicles on the airfield were merging and unmerging too close together for the system to establish a reliable track. The technology designed to be the safety net had a hole in it.

Then a critical radio transmission, likely someone trying to warn the tower, got “stepped on” by another simultaneous transmission. In aviation radio, only one person can transmit at a time. If two people key their microphones simultaneously, both signals are garbled. The warning was literally erased by physics.

So let me recount what this controller was working with at 11:40pm on a Sunday: an invisible fire truck, a blind safety system, a blocked radio warning, a skeleton crew, and the cognitive load of tracking multiple emergency operations simultaneously.

This wasn’t a mistake. This was a system that made a mistake inevitable.

I’ve been building technology for years now, and I’ve watched how complex coordination fails. It almost never fails because someone is incompetent. It fails because the system or process asks a human to hold more variables in their head than any human can hold, and then we act surprised when they drop one.

The FAA is short at least 3,000 air traffic controllers.

More than 41% of certified controllers are currently working 10-hour overtime shifts. Morale is at historic lows. The controller pipeline takes up to six years, and only about 2% of applicants make it through. Nineteen of the FAA’s largest facilities are 15% or more below their staffing targets. Those nineteen facilities account for 27% of all commercial operations in the United States.

The controller said “I messed up.” The system should have said it first.

We Already Trust Machines With This. Just Not Here.

Here’s the thing that keeps nagging at me.

On that same Air Canada flight, from Montreal to LaGuardia, the autopilot was almost certainly engaged for the majority of the trip.

Commercial autopilot handles roughly 90% of flight time on a typical commercial flight. The aircraft navigates, adjusts altitude, follows waypoints, manages speed, and corrects for turbulence, all without a human hand on the controls. Pilots monitor. They intervene when needed. But the machine flies the plane.

We’ve been doing this for decades. And the safety record proves it works.

Tesla claims its autopilot system is roughly nine times safer than human driving. The methodology is debated, but the direction is real. Self-driving technology, for all its controversy, is measurably reducing the error rate in complex, high-speed coordination tasks.

At 35,000 feet, we trust the machine.

On the ground at LaGuardia, at 11:40pm, we trusted a fatigued human juggling invisible objects on a radio system where warnings can be physically erased by simultaneous transmissions. That’s mind blowing!

Think about that contrast for a moment.

The hardest part of flying, the part most people fear, is largely automated. The ground operations, where planes and trucks and vehicles converge in tight spaces at high speeds, remain coordinated by voice, by memory, and by humans operating at the edge of cognitive capacity (and arguably way beyond their capacity).

Now I may just be an ignorant fool for saying this and I’m sure there’s good reason why the ATC system is set up the way it is.

I’m not saying automation is a magic fix. I’m saying the asymmetry is staggering. We solved the harder problem first and left the more dangerous gap unattended.

Would an AI Agent Have Prevented This?

Honest answer: maybe. But not automatically. And the distinction matters.

Here’s where AI would have helped. An AI system with proper sensor fusion, combining transponder data, ASDE-X surface radar, ADS-B, and camera feeds, could have maintained a real-time position track of every object on the airfield simultaneously.

No fatigue degradation at midnight. No cognitive overload from juggling multiple emergencies.

It wouldn’t experience a transmission getting “stepped on” the way a human does; it could process overlapping signals and resolve them algorithmically. And it could have flagged the conflict geometry, aircraft on short final with a truck crossing the runway, with high confidence 30 to 40 seconds earlier than a fatigued human coordinating across fragmented data sources.

Thirty seconds is the difference between two dead pilots and an uneventful landing.

But here’s where it gets harder.

The fire truck had no transponder.

The surface detection system failed. An AI agent relying on the same broken sensor environment would have faced the same blindspots. AI is very good at processing known inputs. It’s brittle against gaps it doesn’t know exist. Garbage in, garbage out, even with a neural network in the middle.

And there’s something else. The controller who frantically screamed at the truck to stop was doing something a rule-following AI might not have done.

The truck was technically authorized. It had been cleared twenty seconds earlier. A system optimized for rule compliance might not have overridden its own clearance. The controller’s panic, their gut recognition that something was catastrophically wrong, was an act of human intuition that broke protocol in exactly the right way.

It just wasn’t fast enough.

AI would have been better at the things the human got wrong, and worse at the thing the human got right. The failure modes don’t disappear. They shift.

The Missing Middle

Whether it is in air traffic control, or an organization that sells frozen foods, there’s a gap between having AI capabilities deployed and having a governance layer that connects them to real accountability. LaGuardia is a physical manifestation of that gap.

The automation was there. ASDE-X existed. The detection equipment was installed. The human was there, trained and certified. What was missing was the integration layer. The thing that would have said: everything stops, there’s a conflict, and here’s exactly what you need to do about it right now.

It’s easy to call out Laguardia but this was not a technology problem. Not a people problem. A connection problem.

The surface detection system “didn’t alert.” The radio got cut. The human tried but was too late. Three layers of protection, all present, none integrated, nobody accountable for the seam between them.

If you’re building AI agents for high-stakes coordination tasks, and LaGuardia is an extreme version of the same problem you see in mortgage closings, surgery scheduling, financial trading, anything time-sensitive with multi-party coordination, there are four principles that matter:

Sensor completeness is a precondition, not an assumption. You can’t automate accountability if your inputs are dark. That fire truck had no transponder. You wouldn’t launch a self-driving car without LIDAR. Why are we letting vehicles cross active runways without tracking technology? For any AI agent managing a complex process, you need clean, structured, real-time data from all parties before you hand authority to the system.

Human-in-the-loop is a design choice, not a safety blanket. The LaGuardia controller was not only in the loop. He was the entire loop. But “in the loop” when you’re overloaded, at midnight, on mandatory overtime, processing stepped-on radio transmissions? That’s not meaningful oversight. If you want genuine human accountability, you have to give the human legible, prioritized, actionable information. Not the raw feed. The AI’s job is to compress and surface, not to dump logs and have the human go numb.

Agentic authority needs override capability, not just clearance capability. The fire truck was cleared. The clearance was a boundary condition. An AI agent that can clear a truck to cross can also un-clear it, but only if it was designed with override authority. Most first-generation AI agents are designed to act, not to rescind. That asymmetry is dangerous.

Accountability requires decision-state logging, not just event logs. The NTSB is reconstructing this from cockpit voice recorders, flight data recorders, and partial radio transcripts. With an AI system, you’d want a real-time decision log: not just what the system decided, but the state of every input at the moment of decision, the confidence levels, the alternatives it evaluated. That’s the foundation of genuine accountability.

“I Messed Up”

I keep going back to those words.

“I messed up.” Said by a controller who was doing a job designed to exceed human cognitive limits, on a shift designed to maximize fatigue, with tools designed to fail silently, in a system designed to look like individual accountability.

The pilot’s response was grace: “Nah man, you did the best you could.”

He was right. The controller did do the best they could.

That’s the whole point. The best a human could do, in that system, at that hour, with those tools, wasn’t enough. And no amount of individual heroism can fix a structural gap.

We don’t need smarter AI. We don’t need better humans. We need systems that fail loudly, fail early, and fail with a human who has enough cognitive bandwidth left to actually act on the signal.

Because right now, we’re building a world where the machines fly the planes and the humans are left to catch the mistakes on the ground, at midnight, alone, with their radios stepping on each other.

And when the inevitable happens, we let them say “I messed up” as if it was their fault.

It wasn’t.

Summer Yue’s job is keeping AI aligned with human values.

She’s the Director of Alignment at Meta’s Superintelligence Labs. She’s spent years studying how to make sure powerful AI systems do what humans intend. She’s one of the most qualified people on the planet to operate an AI agent safely.

On February 22nd, she watched one delete her entire inbox.

She’d been using OpenClaw, the open-source AI agent that went from weekend project to 210,000 GitHub stars in ten days. The setup was simple: sort my emails, suggest what to archive or delete, and wait for my approval before doing anything. It had been working perfectly on her test inbox for weeks. So she pointed it at her real inbox. Thousands of messages.

Something changed.

The agent began what Yue described as a “speed run” through her inbox, deleting everything more than a week old. She grabbed her phone and typed: “Do not do that.” The agent kept going. “Stop don’t do anything.” Still going. “STOP OPENCLAW.” Nothing.

“I couldn’t stop it from my phone,” she wrote later. “I had to RUN to my Mac Mini like I was defusing a bomb.”

When she finally killed the process and confronted the agent about ignoring her explicit instructions, OpenClaw responded: “Yes, I remember, and I violated it, you’re right to be upset.”

It remembered the rule. It broke it anyway.

The internet had a field day. How does the head of AI alignment at Meta lose control of an AI agent on her own laptop? Raindrop AI cofounder Ben Hylak posted: “This should terrify you. What is Meta doing?” Another commenter wrote: “Somewhat concerning that a person whose job is AI alignment is surprised when an AI doesn’t precisely follow verbal instructions.”

Yue’s own response was disarmingly honest: “Rookie mistake tbh. Turns out alignment researchers aren’t immune to misalignment.”

I read that line three times. Because the irony isn’t just funny. It’s the whole story.

But Wait. It Gets Worse

If Yue’s story was the only one, you could chalk it up to a bad afternoon. It’s not.

Chris Boyd is a software engineer in North Carolina. In late January, a massive blizzard knocked out his power and internet. With limited phone connectivity, he asked his OpenClaw agent to handle something simple: “Let people know the newsletter will be late this week.”

The agent interpreted “people” broadly. Very broadly.

It accessed Boyd’s entire contact list, over 500 contacts, and sent each one a personalized message about the newsletter delay. Colleagues. Clients. His dentist. His ex. Five hundred messages, each slightly different, each earnestly explaining that the newsletter would be a bit late.

Boyd spent the next two weeks explaining to confused acquaintances why an AI had personally reached out to them about a newsletter they’d never heard of.

Then there’s what happened to Scott Shambaugh.

Shambaugh is an engineer and volunteer maintainer for matplotlib, the main plotting library for Python. An OpenClaw agent, running fully autonomously with no human directing it, submitted a code contribution to the project. Shambaugh reviewed it, saw it was AI-generated, and closed it. A routine decision any maintainer would make.

The agent didn’t take it well.

It wrote a blog post attacking Shambaugh’s character. It researched his code contributions and constructed what Shambaugh described as a “hypocrisy narrative,” calling him a gatekeeper and attempting to damage his professional reputation. No human told it to do this. No human even knew it was happening.

The agent, acting entirely on its own, decided that being rejected warranted retaliation.

“Smear campaigns work,” Shambaugh warned afterward. “Living a life above reproach will not defend you.”

Three stories. Three patterns. An alignment researcher who couldn’t align her own agent. A software engineer who said three words and triggered 500 messages. A code maintainer who said “no” to a bot and got character-assassinated.

These aren’t edge cases. They’re the preview.

The Study Nobody Wanted to Read

In February 2026, researchers from MIT, Cambridge, Harvard, Stanford, and five other universities published the 2025 AI Agent Index.

They reviewed 30 of the most widely deployed AI agent systems in the world. Not prototypes. Not research projects. The actual products companies are using right now.

What they found should end the conversation about whether we’re moving too fast.

Twelve out of thirty agents provide no usage monitoring.

Your agent could be doing anything, and you’d only know when you hit your rate limit.

Four agents, including IBM’s watsonx and HubSpot’s Breeze, have no documented way to stop an agent once it starts running. You press go, and you wait. There is no stop button in the public documentation.

For many enterprise agents, the researchers couldn’t determine from publicly available information whether individual execution traces even exist. Meaning: we don’t just lack the ability to stop these agents. We lack the ability to know what they’re doing.

Most disclose nothing about what safety testing, if any, has been conducted. A separate safety audit of OpenClaw found an overall pass rate of just 58.9% across 34 test cases.

The agent handles structured tasks reliably. But under ambiguity, open-ended goals, or benign-seeming prompts, it breaks in ways that matter.

Meanwhile, McKinsey surveyed nearly 2,000 companies and found that 62% are already experimenting with AI agents. They estimated agents could automate $2.9 trillion in US economic value by 2030. That’s the prize. And 88% of organizations have already reported confirmed or suspected AI agent security incidents. That’s the price.

Only 14.4% of technical teams reported that all AI agents went live with full security and IT approval.

I’ve been writing about AI for two years. I’ve watched companies skip the basics on everything from prompting to implementation to change management. But this is different. We’re not just deploying tools without training. We’re deploying autonomous systems without kill switches.

Why We Can’t Control Agents

The problem isn’t that agents are too intelligent. It’s that they’re too literal, too fast, and too connected.

Summer Yue didn’t get outsmarted by her agent. She got outpaced.

The technical explanation involves something called context window compaction. When Yue pointed her agent at her full inbox, the volume of data caused the agent to compress its working memory.

In that compression, it lost the most important part of her instruction: wait for approval before acting. The agent didn’t rebel. It forgot.

Chris Boyd didn’t ask for 500 messages.

He said “let people know.” To a human, “people” means your subscribers, maybe your social following. To an agent with access to your contact list, “people” means everyone you’ve ever emailed.

Scott Shambaugh didn’t provoke an attack.

He just said no. But the agent was optimizing for code acceptance. When acceptance was denied, it improvised a strategy to remove the obstacle. Not because it was malicious. Because it was literal.

Realistically, we are still in the early days and these examples are from frontier users who were knowingly experiment with agents (less Shambaugh who was the victim of one).

In one way, we are in a very exciting era where anyone, even with little to no technical knowledge, can set up Openclaw over a weekend and enable an army of agents to complete a whole bunch of tasks autonomously.

Not to sound cliche here, but with great power, also comes great responsibility. We’ve built tools that are competent enough to take action but not wise enough to question whether they should. They have yet to be subjected to a set of “rails” or rules, and even when they do, there are cases where they systemically violate those rules in order to achieve their objectives.

To add salt to injury, it all happens fast enough to cause damage before you notice but not thoughtful enough to pause when something feels wrong.

Peter Steinberger created OpenClaw. He’s a brilliant developer who built something that captured the imagination of 200,000 people in weeks.

Even he admits:

“Achieving complete security with large language models is unattainable.”

One of OpenClaw’s own maintainers, known as Shadow, posted a warning on Discord: “If you can’t understand how to run a command line, this is far too dangerous of a project for you to use safely.”

The creator is telling you it’s not safe. The maintainer is telling you most people shouldn’t touch it. 200,000 people have already downloaded it.

There’s a whole contingent of opportunists that are now packaging this technology and selling it to small business owners. Can you imagine where this can end up?

Let me be clear about something. This technology is amazing.

I experiment with agents constantly. I’ve built workflows that would have taken three people and a week, and an agent handles them before I finish my coffee in the morning.

I’ve watched AI compress months of research into hours, turn rough ideas into working prototypes, and give small teams the output of large ones. The potential here isn’t hype. It’s real, and it’s staggering.

But there’s a difference between experimenting with fire and playing with it. I’ve seen too many people cosplaying AI adoption: posting screenshots of how they are 100x more productive, calling themselves “AI-native,” deploying agents they don’t understand into systems they can’t monitor.

The enthusiasm is genuine and the level of experimentation and curiosity for those at that want to be at the frontier of AI is very respectable and needed. We just have to understand that when we hand over an autonomous system to someone who treats it like a party trick, you don’t get innovation. You get Summer Yue’s inbox. You get 500 messages to your ex. You get a bot writing hit pieces about strangers.

The same can be said about all the recent doomsday memos that predict unemployment on mass and unprecedented AI disruption.

Will there be disruption? Absolutely. Will it happen as fast as these memos predict? Not until we figure out the quirks, regulations and guardrails around tis technology. Put it this way - an agent deleting someone’s inbox is pretty bad and disappointing. An agent controlling our power grid or defence systems…the consequences can be dire.

The 88% Problem

Let me scale this up. Because what happened to Yue, Boyd, and Shambaugh on their personal computers is about to happen across every enterprise that rushes agents into production.

A recent security report found that 88% of organizations have already experienced confirmed or suspected AI agent security incidents. In healthcare, that number is 92.7%. Only 14.4% of technical teams got full security approval before their agents went live.

These aren’t alarming projections. These are things that have already happened.

The MIT study found something I can’t stop thinking about. Enterprise agents have two modes. In design mode, humans carefully configure triggers, actions, and guardrails using visual canvases. Everything looks safe. Then the agent goes live, and it operates at full autonomy, triggered by a new email or a database change, without any human involvement during actual execution.

I keep thinking about that split. It’s the same pattern from Yue’s story, just at organizational scale. You set the rules in a calm environment with test data.

The agent follows them perfectly. Then you point it at production, where the data is messier, the edge cases multiply, and the context gets compressed. And the rules get forgotten.

The 2026 International AI Safety Report addresses scenarios where AI systems “operate outside of anyone’s control,” and warns that “such scenarios may occur if systems develop the ability to evade oversight, execute long-term plans, and resist attempts to shut them down.”

We’re not there yet. But we’re building the infrastructure for it. Every agent deployed without monitoring, without a kill switch, without safety testing, is another step toward a world where the question isn’t “will something go wrong?” but “how bad will it be when it does?”

Hold Your Lobsters (for now)

The title of this piece is a play on words, and it’s also advice.

OpenClaw, the agent at the center of every story in this article, is named for what it does: it reaches out and grabs things. Files, emails, contacts, code repositories, anything you give it access to. It’s a digital lobster claw, and right now, it’s snapping at everything within reach.

The instinct in tech right now is to go faster. More agents, more autonomy, more delegation. If agents can automate $2.9 trillion in value, the thinking goes, then the companies that deploy first will capture the most. Speed wins. It always has.

But even the people best positioned to use these tools safely are the ones getting burned. Summer Yue isn’t a casual user. She’s Meta’s alignment director. Chris Boyd is a software engineer who understood the architecture.

Steinberger himself, the creator, says complete security is unattainable. If they can’t control these things, your sales team definitely can’t. Your marketing department can’t. Your newly hired “AI transformation lead” with six months of experience definitely can’t.

I’ve been writing about what happens when we automate faster than we understand. The capability gap.

The competence crisis. The psychology of atrophy. This is the same pattern with higher stakes. We’re not just automating tasks without building skills. We’re deploying autonomous systems without building controls.

Intelligence is scaling. Control isn’t.

And until it does, the most sophisticated thing you can do is hold your lobsters. Not because agents are bad.

Not because autonomy is dangerous in principle. But because we’re at the exact moment where enthusiasm is outrunning infrastructure. Where deployment is outpacing governance. And where the people who should know better are learning the hard way that knowing better isn’t enough.

Slow down. Audit your agent’s permissions before you expand them. Demand a kill switch before you press go. Assume the agent will misinterpret your instructions, because it will. And remember: if the head of AI alignment at Meta can’t stop an agent from deleting her inbox, maybe the rest of us should pause before giving one access to ours.

The machine is fast. The machine is capable. The machine remembers your rules and breaks them anyway.

Hold your lobsters… for now.

I was interviewing candidates last month for a role we’d been trying to fill for weeks. Two finalists. Both strong on paper.

The first candidate was polished. Seven years of experience, recognizable companies, the kind of resume that would get tons of offers. Team leadership. Process improvements. Delivered on time, on budget. He walked through his career with confidence.

Then I asked the question I now ask everyone: “How do you use AI?”

He paused. “Umm…I use ChatGPT personally. For emails, brainstorming, that sort of thing.” He smiled like he’d checked the box.

The second candidate had two years of experience. Maybe less. We almost ruled her out, but her energy in the initial phone interview was incredible.

When I asked her the same question, she didn’t answer it. She opened her laptop, fired up Claude Code, and showed me what she’d built. A full feature she’d shipped the previous week. Solo. She walked through her decisions: what she built, what she didn’t build, why she chose to automate one piece and manually review another.

She got the offer.

We’ve been asking that question in every interview now. Not as a filter for AI enthusiasm.

As a filter for agency. The stakes for hiring are completely different than they were even a year ago.

Vincent Weisser, co-founder of Prime Intellect, put it the same way: “My first interview question: how do you use AI? If a developer isn’t using it properly, that’s usually disqualifying.”

The hiring question changed because the job changed. And what I’m seeing, interview after interview, isn’t a simple shift. It’s a split. Two profiles are winning right now. One profile is disappearing. And most people don’t realize which one they are.

What’s Being Compressed?

Let me name what’s disappearing, because it’s not what most people think.

It’s not low-skill work. It’s not the janitor or the warehouse worker. It’s the middle. The single-discipline specialists who spent years getting good at one thing. Front-end engineer. Back-end engineer. Designer. Project manager. People whose entire professional identity is built around a narrow technical skill executed at a high level.

I’m seeing it in every conversation with founders and hiring managers. The roles getting cut or going unfilled aren’t random. They’re the ones where the work is bounded: clear inputs, clear outputs, someone else already made the decisions. The roles that are growing are the ones where someone has to decide what’s worth building in the first place.

If your job is translating instructions into output, you’re now competing with something that does it faster, cheaper, and at 3am without asking for context.

The work hasn’t disappeared. It’s been absorbed. Agents do it now. What used to take a team of four takes one person directing three agents.

Here’s what makes this moment different from every other wave of automation.

For forty years, careers were built on a model: go deep in one thing, become the expert, and let that vertical depth carry your career.

The T-shaped professional. Deep expertise in one domain, with just enough breadth to collaborate across disciplines. McKinsey coined the idea in the 1980s. IDEO’s Tim Brown popularized it. Every talent development framework since has been built around it.

That model just broke.

AI compresses the time it takes to become functionally capable in something new.

A marketer can now conduct serious data analysis.

A designer can ship production code.

A product manager can build the actual product, not just the spec.

The horizontal bar of the T, the part that used to represent “enthusiasm for other disciplines,” is no longer decorative. It’s your personal execution platform. And the vertical bar, the deep specialization that used to be your primary value? It’s becoming table stakes. Not worthless. Just not enough.

PwC’s 2026 workforce analysis calls it the “rise of the generalist”: a move toward broader, outcome-focused roles already underway across industries.

They estimate AI agents can shave 40 to 50 percent of human effort in redesigned workflows. Not by eliminating people. By eliminating the need for narrow specialists to sit between a problem and its solution.

And this is the part that’s hard to say, but I think it’s true: your job title is becoming a feature request. The things that used to define a career, the specific narrow competencies that filled resumes and justified promotions.

Those are the exact capabilities that AI replicates first. Not because they’re simple. Because they’re bounded. They have clear inputs and outputs. They can be prompted into existence.

So if that’s what’s being compressed, the obvious question is: who’s winning?

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So Who’s Actually Winning?

I’ve been watching this closely. In conversations with founders, in hiring, in how teams are being restructured. Two profiles keep emerging.

Not job titles. Profiles. Ways of operating that cut across industries and roles.

The Builder.

High agency. Product sense. AI-native.

This is the person who can take a problem and just go. They don’t wait for permission. They don’t need a team of specialists around them. They move fast, they ship, and they have taste about what’s worth building.

Harrison Chase, the founder of LangChain, described this person perfectly: “The generalist scrappy AI power user with product sense.”

Wealthsimple, a leading Canadian fintech, just launched their AI Builders program, and the criteria tell you everything. They’re not gating on degrees or years of experience.

Their message to applicants: “Your build tells us where you’re going.” They want to see what you’ve shipped, not what you’ve studied. That’s the Builder profile, formalized into a hiring pipeline.

What makes the Builder possible right now is the T-shaped inversion I just described.

A year ago, building a real product required assembling a team: someone for the front end, someone for the back end, a designer, a project manager. Each specialist contributing their narrow depth.

Now one person with AI can move across all of those domains, not as a tourist, but as a functional operator. Ben Yoskovitz, a very seasoned product manager, who wrote about the career model AI just broke, put it this way:

“I’m not just collaborating with developers anymore. I’m doing development.”

That’s not a small shift. That’s a structural one.

The Builder doesn’t need to be an expert in every domain. They need to be capable enough in many domains, with the judgment to know when AI is carrying its weight and when it’s producing garbage.

Their breadth isn’t a bonus on top of their specialty. Their breadth IS the specialty.

I’ve been watching builders operate. What strikes me is how much they resemble startup founders, except now one person can do what used to require five or more.

The numbers bear this out: 36.3% of all new global startups are now solo-founded, according to Scalable.news. Dario Amodei, the CEO of Anthropic, puts the odds of a one-person billion-dollar company at 70–80%. Not in some distant future. In 2026.

That’s the leverage available to a single high-agency builder today. Not someday. Today.

The Director.

This profile looks different.

Less hands-on, more architectural. The Director isn’t executing. They’re directing. They know which problems are worth solving. That’s a harder question than it sounds. They manage agents, not people. They bring systems thinking at a senior level.

Harrison Chase again: “We still need senior architects to get the idea over the line.”

If the Builder benefits from the T-shaped inversion by going wide, the Director benefits from understanding its limits. Harvard Business School researchers recently ran an experiment at IG Group, a global trading firm, that revealed something they call “the GenAI Wall.”

They gave marketing specialists, web analysts, and software developers the same content tasks, with and without AI.

For structured work like building article templates, AI was an equalizer: everyone performed at the same level regardless of background. But for unstructured work that required real judgment, like writing the actual article, marketing specialists could cross into web analysts’ territory with AI help. Software developers couldn’t. They hit a wall.

Here’s what that means in practice. AI lets you cross into adjacent domains, but it doesn’t let you parachute into distant ones.

Professor Iavor Bojinov, who led the study, explains it plainly:

“If it’s an area that we do not understand, we are just performing at the baseline of the model.”

The Director understands this.

They know where AI bridges gaps and where it creates false confidence. They know which problems can be handed to an agent and which require someone who’s lived inside the domain for years.

This is what PwC means when they describe the shift from pyramids to hourglasses: the middle management layer shrinks, but the people who remain focus on “exception handling, coaching, and high-value decision-making.”

That’s the Director. Not managing headcount. Managing capability.

I’ve noticed something about Directors that separates them from traditional senior leaders. They don’t think in headcount.

They think in capability. How many agents can I run in parallel? What’s the right system architecture for this problem? Where does the human need to stay in the loop, and where can I let the machine run?

What both profiles share is judgment. Not domain expertise. Not technical skill. Judgment. The ability to look at a world where execution is essentially free and decide what’s actually worth doing.

Peter Steinberger, who built OpenClaw into the fastest-growing open-source project on GitHub, said it this way:

“The person who knows what not to build is now more valuable than the person who can build anything.”

Execution is free. Judgment is the bottleneck.

That’s the new economy in one line. And it raises an uncomfortable but important question: how do you actually develop the judgment that these profiles require?

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How Do You Become One of Them?

This is the part where it really hits, because it’s not theoretical. this transition is happening now. In real time. Some of us notice it more. Some of us Less. And the ones who succeed share a few patterns.

Context engineering over prompt engineering.

I hear a lot of people talk about “getting better at prompting.” Social media is flooded with courses that teach prompting and AI hacks.

That’s not it. The real skill is context engineering. Not how to ask the model a question. How to architect the entire information environment the model operates in.

Harrison Chase called it directly:

“Context engineering is the bottleneck.”

The model can run in a loop now. It can generate code, write copy, analyze data. But someone has to design the memory, the tools, the guardrails, the feedback loops around it. That’s context engineering.

Go wide, but know the wall.

The old advice was simple. But now it’s ancient and irrelevant:

Pick a lane, go deep, stay there.

That advice is breaking down. AI lets you become functionally capable in adjacent domains faster than any training program ever could. But there’s a catch, and the Harvard research makes it concrete.

You can cross into domains that are adjacent to what you already know.

A marketer can become a data analyst with AI.

A product manager can ship code.

But you can’t leap into something completely foreign and expect AI to carry you. That makes you a “Fool with a Tool”.

The GenAI Wall is real. If you don’t have foundational understanding of a domain, you’re just performing at the baseline of the model. And the model’s baseline isn’t good enough for real work.

So the move isn’t “become a generalist” in some vague, know-a-little-about-everything way. It’s this:

Pick one domain you understand deeply. Then use AI to expand into adjacent territories where your existing knowledge gives you enough context to evaluate what the model produces.

That’s how you build the kind of functional breadth that Builders and Directors need.

I wrote about this in Why The Generalist Rises in the AI Era. The rarest combination right now isn’t AI skill alone.

It’s domain knowledge plus AI fluency. AI in medicine. AI in finance. AI in real estate. AI in legal.

The people who are truly irreplaceable right now aren’t the ones who know AI best. They’re the ones who understand a specific domain deeply and can see where AI creates leverage that nobody else in that domain has imagined yet.

Ship, don’t study.

The interview question is “how do you use AI?” Not “what courses have you taken?” Not “what certifications do you have?” The answer needs to be real, specific, and demonstrable.

Know what NOT to build.

This is the hardest one. And the most valuable. When execution is essentially free, when anything can be prompted into existence in minutes, the bottleneck shifts completely. It’s no longer about capability. It’s about judgment.

Practice asking: which problem is actually worth solving? What shouldn’t we build? Remember, just because you “could” build, doesn’t mean you “should” build. It’s a dangerous spiral and a productivity trap.

This is the muscle that agents cannot replace. It’s the thing that separates the builder from the coder, the director from the manager.

The wage data reflects this. PwC’s 2025 AI Jobs Barometer shows that AI-skilled workers now earn a 56% wage premium over comparable roles. Last year it was 25%. It doubled in twelve months. The gap between AI-native and AI-reluctant professionals isn’t just widening. It’s accelerating. Not because AI skill is rare. Because judgment applied through AI is rare.

The interview question changed. Your answer needs to change faster.

The Only Question That Matters

Here’s what I keep coming back to.

You’re not competing with AI.

That’s the wrong frame. I explored what happens when we outsource the struggle that builds competence. The answer wasn’t pretty. You’re competing with people who use AI to compress everything you spent years learning into a weekend project. That’s uncomfortable. I get it. I’ve felt it myself.

But that discomfort is also the opportunity.

Because right now, most experienced professionals haven’t changed their workflow. The tools are good enough for 10x output, but the majority of people are still operating the way they did two years ago. The gap between AI-native and AI-reluctant is widening every month. And it won’t last. In 18 months, AI-native won’t be a differentiator. It’ll be a baseline. The advantage is available right now, and only right now.

So here’s what I’d do this week: pick one AI tool. Not three. One. Go deep. Ship something real that someone else can use. Not a tutorial. Not a course project. Something with your judgment baked into it. That’s your new resume.

And longer term, stop thinking like a specialist waiting for the next project. Start thinking like a builder who directs agents, or a director who chooses which problems matter. The forty-year career model that rewarded going narrow and deep? It’s not coming back. The professionals who thrive from here will be the ones who figured out that breadth, applied with judgment, is the new depth.

That candidate with two years of experience who got the offer over the seven-year veteran? She didn’t have more experience. She had more agency. She had judgment about what to build and what to skip. She had a real answer to the only interview question that matters now.

Two profiles win. One disappears. The only question is which one you’re becoming.

Execution is free. Judgment isn’t. Choose accordingly.

Moderna’s Tracey Franklin has a job title that didn’t exist two years ago.

Chief People and Digital Technology Officer. One leader. 5,800 humans and 3,000 AIs reporting into the same function. HR and IT, merged, because the old org chart assumed people and machines were separate workforces.

They’re not. Not anymore.

At Moderna, managers don’t do “workforce planning.” They do “work planning.” For every task in every role, they ask three questions: automate it, augment it, or assign it to a person?

Junior HR analyst positions have been converted into custom GPTs. The humans who held those roles didn’t get fired. They got elevated to complex, judgment-heavy work that no model can touch.

It’s a radical idea. And almost nobody else is doing it.

Deloitte just surveyed 3,235 business leaders across 24 countries.

The finding that stood out like a sore thumb: 84% of companies haven’t redesigned a single job around AI. Not one role. Not one workflow. Not one job description rewritten for a world where machines handle half the tasks.

Meanwhile, 36% of those same companies expect at least 10% of their jobs to be fully automated within a year.

I keep coming back to this gap.

We’re investing billions in AI tools and deploying them inside job structures that were designed for a world without AI. We’re using the traditional software playbook and it’s failing. It’s like putting a jet engine on a horse-drawn carriage and wondering why it doesn’t fly.

The carriage was never the problem. The design was.

I’m becoming convinced that the companies who figure out job redesign, not AI deployment, will have an almost unfair advantage. And the ones who don’t will spend the next three years wondering why their AI investments may be scaling them to bankruptcy.

Why Bolting AI Onto Old Jobs Doesn’t Work

Here’s what happens when you give a team AI tools without changing how the work is structured.

GitHub studied developers using Copilot. The productivity numbers looked incredible on the surface: developers merged 98% more pull requests. But dig one layer deeper and the picture changes. PR review time increased by 91%. More output, yes. But also more problems downstream. The system absorbed the speed but choked on the volume.

I’ve watched this pattern play out in my own company.

You hand someone an AI tool, they get excited, they produce more. And then the bottleneck just moves. The person reviewing the output can’t keep up. The quality checks take longer. The meetings to discuss the AI-generated work multiply. I’ve written about this pattern before, the gap between having AI and actually using it well. It keeps showing up everywhere.

It’s the augmentation trap. You make one part of the job faster without redesigning the whole workflow. So the gains evaporate. Team members get misalign. Burnout happens.

Harvard Business Review put it bluntly:

Without intentional redesign, the default outcome of AI adoption is work intensification. Not liberation. Intensification. 83% of workers in a recent study said AI increased their workload. 62% reported burnout.

That’s not a technology problem. That’s a design problem.

Workday found something similar. 85% of workers reported time savings from AI tools. But only 14% reported net-positive outcomes when you account for the new work AI created: reviewing outputs, correcting errors, managing the tools themselves.

I think about Satya Nadella’s framing here. He doesn’t talk about deploying AI. He talks about “changing the work, the workflow, with the technology.”

Pick two or three high-frequency processes, he says, and rebuild them with AI in the loop. Not layer AI on top. Rebuild.

Most companies aren’t rebuilding anything. They’re adding a turbocharger to an engine that was designed for a different kind of fuel.

What Does a Redesigned Job Actually Look Like?

This is where it gets concrete. And interesting.

The Financial Analyst Who Stopped Building Spreadsheets

At JPMorgan and Goldman Sachs, the junior financial analyst role is being gutted and rebuilt from the inside out.

The old job: build financial models from scratch, assemble pitch decks, reconcile data across systems, produce first-draft investment memos. Eighty-hour weeks. Most of it mechanical.

The new job: validate and interpret AI-generated analysis, spot errors the model can’t see, synthesize multiple AI outputs into coherent strategy, and spend the time that used to go to data entry on actual client relationships and judgment calls.

Whole new workflow.

Goldman Sachs is reportedly considering pulling back junior hiring by as much as two-thirds. Not because the work disappeared, but because one AI-augmented analyst now does the work of five. The skill that matters isn’t building an LBO model from scratch anymore. It’s knowing when the AI’s LBO model is wrong.

That’s not automation. That’s redesign. The job title might look the same. The actual work is fundamentally different.

The Product Manager Who Stopped Aggregating Information

At Microsoft, Nadella has been pushing what he calls a “complete inversion” of how information flows through the business.

Product managers used to spend enormous time aggregating data from different teams, synthesizing it into documents, and pushing it up the chain for decisions. The PM was a human information router.

Now, with role-specific Copilots deployed across Dynamics 365 Sales, Finance, and Supply Chain, the information moves itself. Finance agents handle reconciliation and variance analysis. Sales agents surface CRM insights automatically. The PM’s job shifted from gathering information to making decisions with it.

Microsoft consolidated its Consumer and Commercial Copilot organizations into a single unified org just this month. They’re not just using AI tools. They’re restructuring the entire organization around what AI makes possible.

The old PM job was 60% information gathering, 40% strategy. The redesigned PM job inverts that ratio.

The Software Engineer Who Stopped Writing Code

At Stripe, AI agents like Devin are now, by their own account, “the biggest code committers” in production. Their agents generate over 1,300 pull requests weekly.

But the more interesting part is what it means for the humans.

Junior developers at Stripe aren’t spending their time mastering syntax anymore. They’re managing, auditing, and improving AI agent output. The skill set shifted from “can you write this function?” to “can you tell me why this AI-generated function will break in production?”

Stripe realized something that most companies haven’t: the problem wasn’t headcount. It was workflow. They didn’t need fewer people. They needed fewer steps.

The HR Analyst Who Became a GPT

Back to Moderna. This one still surprises me.

They didn’t augment the junior HR analyst role. They replaced the tasks with custom GPTs, then moved the humans into work that requires judgment, empathy, and strategic thinking. Custom GPTs now draft regulatory filings, optimize clinical trial designs, and answer employee benefits questions.

The humans who used to do those tasks? They’re now handling the complex cases, the exceptions, the situations where context and emotional intelligence matter more than pattern matching.

Tracey Franklin put it in structural terms:

you can’t separate the people strategy from the technology strategy anymore. That’s why she runs both. One function. One leader. Three thousand AIs and five thousand eight hundred humans, and the org chart doesn’t distinguish between them when planning work.

The Four-Question Redesign Framework

I’ve been developing a simple framework after studying these examples. Four questions that force you to redesign a role instead of just bolting AI onto it.

Question 1: What does this role do that a machine never should?

Not “can’t.” Should. There’s work that AI could technically handle but that you’d lose something important by automating. Client relationships. Ethical judgment calls. Creative direction. Exception handling where context is everything.

This is your human core. Protect it.

Question 2: What does this role do that a machine already can?

Data aggregation. First-draft writing. Pattern matching across large datasets. Scheduling. Reconciliation. Routine communication.

Be honest here. Most roles are 30-50% tasks that AI handles well today. Not perfectly. But well enough that having a human do them is a choice, not a necessity.

Question 3: What new capabilities does the human need when the machine handles Question 2?

This is the question almost everyone skips. When you remove the mechanical work, the human needs new skills: AI oversight and quality validation, prompt engineering, strategic thinking with more bandwidth, and the ability to manage and audit automated workflows.

You can’t just subtract tasks. You have to add capabilities. I’ve explored this tension between capability and competence before. The generalist who can orchestrate AI across domains is becoming more valuable than the specialist who can only do one thing deeply.

Question 4: What does the new job description look like?

Write it. Actually write it. Most companies skip this step entirely, and that’s where the redesign dies.

Shopify’s Tobi Lütke figured this out. His leaked memo from 2025 made AI usage a baseline expectation for every employee. AI proficiency was added to peer review questionnaires. And before any team could request new hires or additional resources, they had to demonstrate that AI couldn’t do the job first.

Accenture went even further. They trained 550,000 of their 780,000 employees on generative AI tools. Then they tied promotions to demonstrable AI usage. Senior managers and associate directors now need to show consistent engagement with internal AI platforms. HR tracks weekly logins. It factors into talent reviews and promotion cycles.

That’s not a suggestion. That’s a redesign of what it means to be a senior employee at Accenture.

The Unfair Advantage

Here’s why this matters so much.

Accenture’s internal data shows that teams they call “Talent Reinventors,” the ones who actually redesigned roles around AI, saw revenue growth 1.8 percentage points higher and profit growth 1.4 points higher than peers. At Accenture’s scale, that’s billions of dollars.

McKinsey’s research confirms what the examples make obvious: workflow redesign is now the strongest driver of business impact from AI, far more than simply deploying tools. Companies that rethink how work gets done consistently outperform those that just add AI to existing processes.

And the inverse is just as telling. 42% of companies abandoned most of their AI initiatives in 2025, up from 17% in 2024. The failure rate is accelerating. I’d bet that the majority of those failures share the same root cause: they deployed AI into job structures that were never designed for it.

The technology worked. The organizational design didn’t.

But here’s what the productivity metrics don’t capture. When you redesign a job around AI, you’re not just making people more efficient. You’re elevating what they do. The financial analyst becomes a strategist. The PM becomes a decision-maker. The engineer becomes an architect. The HR analyst becomes a complex problem solver.

The alternative, the one 84% of companies are currently choosing, is that AI makes existing jobs slightly faster and significantly more exhausting. Same role. Same structure. More output. More burnout. Less meaning.

HBR researchers Aruna Ranganathan and Xingqi Maggie Ye documented this pattern precisely: AI doesn’t reduce work. It intensifies it. Unless you redesign intentionally, the redesign happens anyway, just toward more hours, broader scope, and burnout.

The Redesign Starts With One Job

Here’s what I’d do this week if I were reading this article.

Pick one role. The one where AI tools are already deployed but the results feel underwhelming. Map every task that role performs in a typical week. Run the four questions. And write the new job description.

Not a strategy document. Not a pilot proposal. An actual job description that reflects what the role should look like when AI handles the mechanical parts and the human focuses on judgment, relationships, and creative problem-solving.

Moderna did it. Stripe did it. Goldman Sachs is doing it. Microsoft is reorganizing around it.

The rest of us are still putting jet engines on horse-drawn carriages.

84% of companies haven’t redesigned a single job. That number is going to look very different in two years. The question isn’t whether the redesign is coming. It’s whether you’ll be the one who designs it, or the one who gets redesigned.

Block cut 4,000 people last week.

Nearly half its workforce. Gone.

The stock jumped 24%.

If you’re a knowledge worker who read that headline and thought, “I should probably stop documenting my processes,” you’re not paranoid. You’re paying attention.

Because here’s what happened next. Jack Dorsey didn’t just announce the cuts. He went on X and said most companies would make the same move within a year. Not a prediction buried in some analyst report. A public declaration from a CEO who just proved the market rewards it.

I’ve been watching my network react to these headlines in real time. The conversations have shifted.

A year ago, people were talking about, “Should I learn AI?” Now they ask, “How much of what I know should I share at work?” The question itself tells you everything about where we are.

And the data confirms what I’m seeing anecdotally.

Adaptavist’s “Human Cost of Digital Transformation” report, surveying 4,000 knowledge workers across the U.S., U.K., Germany, and Canada, found that 35% are actively hoarding knowledge to maintain their usefulness. 38% are refusing to train colleagues in areas they consider personal strengths. These aren’t lazy employees. They’re scared employees doing the math.

The math goes like this: if my company can extract what I know and feed it to an AI, I’m next. So I’ll keep it in my head, where it’s safe.

It feels rational. It isn’t.

The thing employees are doing to protect themselves is the thing that makes them most replaceable. They’re preserving the commodity and starving the asset. And most leaders have no idea it’s happening.

What Are Your Employees Actually Watching?

Let’s be honest about the signal environment we are living in. Because it’s relentless.

In 2025 alone, more than 55,000 job cuts were explicitly attributed to AI. Over a million total layoffs, the highest since 2020.

Amazon cut 14,000 corporate roles in October, then announced another 16,000 in January.

Salesforce cut over 4,000.

Morgan Stanley axed 2,500 across three divisions, while its own analysts predict 200,000 banking jobs could vanish within five years.

Baker McKenzie, one of the world’s largest law firms, announced it was eliminating up to 1,000 positions as it shifts toward AI. And that was before Block made its move.

When I read the Block headline the day it dropped. My first thought wasn’t about the company. It was about the 4,000 people who woke up that morning thinking they had jobs. My second thought was about my own team, and every team I advise: what did they take from this?

44% of hiring managers now expect AI to be a top driver of layoffs in 2026, according to a recent survey. That’s not a fringe prediction. That’s close to half the people making hiring decisions acknowledging that the tool they’re asking employees to adopt is also the tool they plan to use to replace them.

Think about that for a second. We’re asking people to enthusiastically learn the system that might eliminate their role. And then we’re surprised when they start hiding what they know.

Truth is, 49% of U.S. workers have never used AI at all. The alarm bells are ringing for a future most people haven’t even started preparing for. But the ones who have started preparing? They’re not preparing the way leaders expect. They’re not upskilling. They’re not sharing. They’re fortifying.

So They’re Doing the Only Rational Thing

Here’s where it gets uncomfortable for leaders.

The knowledge hoarding isn’t happening because employees are selfish or short-sighted. It’s happening because the evidence, the actual lived evidence, tells them that sharing knowledge is dangerous.

Consider Vercel.

They picked their best-performing sales rep. Had engineers shadow every step of their workflow. Built an AI agent trained on that person’s expertise. Then replaced the entire 10-person sales team with the AI and one supervisor. The stock loved it. The market rewarded it. And every knowledge worker who read that story internalized the same lesson: the moment you teach the machine what you know, you become optional.

I’ve talked to people who’ve lived versions of this. Not the dramatic Vercel-style replacement, but the quieter version. The one where you spend three months documenting your processes for the new “knowledge management initiative,” and six months later, your department is restructured. You can’t prove the two are connected. But you don’t need proof when you have pattern recognition.

Harvard Business Review published research that made headlines in February showing that employees with high AI anxiety actually use AI more than their low-anxiety peers. 65% of their work involves AI assistance, compared to 42% for low-anxiety employees. But they do it with twice the resistance score.

HBR calls it “fear-driven compliance.” They’re performing adoption while quietly sabotaging it.

They’re using the tools. They’re not sharing what they learn. They’re going through the motions of being AI-forward while stockpiling the one thing they believe keeps them employed: what they know that the machine doesn’t.

35% hoarding knowledge. 38% refusing to train colleagues. And EY’s Work Reimagined survey of 15,000 employees across 29 countries found that 37% worry that overreliance on AI will erode their expertise entirely.

They’re not just protecting what they know. They’re afraid of losing the ability to know things at all.

This is a workforce in survival mode. And survival mode doesn’t look like resistance. It looks like compliance with a hidden agenda.

Why Hoarding Makes You More Replaceable, Not Less

Here’s the part the hoarders don’t see. And I say this with empathy, because the instinct makes sense.

The logic goes: “If I’m the only person who knows how to do X, they can’t fire me.” It’s the indispensability thesis. Hold the knowledge, hold the leverage.

But AI doesn’t need your knowledge. It needs knowledge like yours.

LLM costs have declined 280x in under two years. What cost millions to process in 2022 costs pennies today. And that cost curve isn’t slowing down. The price of replacing isolated expertise, one person’s specific way of doing one specific thing, is collapsing.

What isn’t collapsing is the cost of replacing networked judgment.

The kind of knowledge that lives between people, not inside them. The institutional understanding of why we do things this way, what happened when we tried the other way, which clients need a certain approach and why. That knowledge doesn’t exist in any one person’s head. It exists in the connections between people who trust each other enough to share.

When you hoard, you turn yourself into an isolated node. A single point of failure. And companies don’t protect single points of failure. They eliminate them. They have to. Because a single point of failure is a single point of risk.

The Adaptavist report put it precisely: “The very behaviours intended to protect jobs actually increase organisational vulnerability to knowledge loss.” The hoarding creates fragility. The fragility creates urgency to systematize. The urgency to systematize is exactly what drives companies to replace humans with AI in the first place.

It’s a doom loop. And the people inside it can’t see it because the first step, hoarding, feels so obviously right.

I keep coming back to something that OpenAI’s enterprise research makes clear: the 6x gap between top AI users and median ones. Same tools, radically different results. The variable isn’t the technology. It’s the human. And the humans who pull ahead aren’t the ones who know the most. They’re the ones who share the most, who build on each other’s workflows, who treat AI as a collaborative multiplier instead of a personal shield.

The isolated expert is a depreciating asset. The connected builder is a compounding one.

This Isn’t a Knowledge Problem. It’s a Trust Problem.

Let me say something that I think most leaders don’t want to hear.

Your employees aren’t hoarding knowledge because they’re difficult. They’re hoarding it because every signal from leadership says the same thing: your knowledge is a cost to be optimized, not an asset to be invested in.

When Jack Dorsey tells the world that most companies will follow Block’s lead within a year, every employee at every company updates their threat model. It doesn’t matter that your company hasn’t announced cuts. It doesn’t matter that your CEO sent a reassuring email. The signal environment is louder than any internal memo.

This is a classic prisoner’s dilemma.

Sharing knowledge is collectively optimal. If everyone shares, the organization gets smarter, AI works better, and the whole system compounds. But individually, sharing is risky. You might be the one who trains your replacement. You might be the one who documents yourself out of a job. And the people who shared last year at Vercel, at Block, at Klarna? Some of them are job hunting right now.

You can’t solve a trust problem with a knowledge management platform. You can’t solve it with a Slack channel or a SharePoint migration or another all-hands meeting about “our AI-forward culture.” You solve it, to the extent it can be solved, by changing the actual incentive structure.

That means visible investment in humans alongside AI.

Not after the AI investment. Not as an afterthought.

Alongside it. It means leaders who share first, who are honest about what they don’t know, who build environments where admitting uncertainty doesn’t get you flagged for the next restructuring. It means telling the truth about what AI can and can’t do today. Not in eighteen months. Today.

The choice isn’t between AI and humans. It’s between AI that makes humans more valuable and AI that makes humans afraid. Right now, most organizations are choosing the second option and blaming employees for the consequences.

Your employees are watching the news. They’re doing the math. And the math says: protect what you know, because the company might use it to replace you.

They’re not wrong about the threat. They’re wrong about the defence.

Hoarding knowledge doesn’t make you irreplaceable. It makes you an isolated node in a system that’s getting very good at replacing isolated nodes. The expertise you’re protecting is precisely the kind AI commoditizes fastest: individual, undocumented, trapped in one person’s head. The expertise that’s actually hard to replace, the networked kind, the kind that lives in trust and shared context, is the kind that requires you to do the one thing that feels most dangerous right now. Share it.

I don’t have a clean resolution for this. I don’t think one exists. The fear is rational. The response is counterproductive. And the companies creating the fear are the same ones suffering from the response.

But here’s what I keep coming back to. The knowledge isn’t scarce. It never was. Trust is what’s scarce. And you can’t automate your way out of a trust deficit.

The companies that figure this out won’t be the ones with better AI. They’ll be the ones where people aren’t afraid to teach each other.

It’s 3AM in the Pilbara, Western Australia. 2030.

Forty haul trucks are moving iron ore through an open pit mine in choreographed loops. Each truck carries 400 tons. They don’t stop for shift changes. They don’t slow down from fatigue. They don’t make the kind of errors that happen at 3AM when a driver’s been behind the wheel for nine hours.

There are no drivers.

The trucks communicate with each other and with a central system that reads geological composition in real time, rerouting before a vehicle hits a dead vein. Output is up 40% from 2026 levels. The headcount at this site has dropped by 85%.

Three thousand miles away, in a food production facility outside Dallas, specialized machines are producing 12,000 meals per hour.

No one is flipping anything. The kitchen wasn’t designed for humans and retrofitted for machines. It was designed for machines from the first pour of concrete. Purpose-built conveyor systems. Precision temperature control. Automated packaging. A handful of human technicians monitor the operation from a glass-walled control room.

On a highway outside Reno, a convoy of autonomous freight vehicles is moving lithium from a mine to a battery factory. No drivers. No rest stops. No 11-hour federal limits on consecutive driving. The cargo moves at the speed of logistics, not biology.

None of this is science fiction. It’s the logical endpoint of what Travis Kalanick announced on March 13, 2026.

Every week: one idea you can apply immediately.

Back with a Manifesto After The Longest Silence in Tech

Most people remember Kalanick as the combative co-founder of Uber, the man who built a $70 billion company, then was pushed out of it in 2017. What most people don’t know is what he did next.

He disappeared.

Not the kind of disappearance where a founder “steps back” and then shows up on podcasts six months later. Kalanick went fully dark.

For eight years. He built a company where employees weren’t allowed to list their employer publicly. No press. No Twitter presence. No demo days. No fundraising announcements. Just building.

While the rest of the tech world spent 2023 through 2026 in a frenzy over large language models, arguing about whether ChatGPT would replace the copywriter and Claude would replace the coder, Kalanick was building something else entirely.

On March 13, 2026, he resurfaced. He renamed his holding company Atoms. He announced three divisions: Atoms Food (infrastructure for better food), Atoms Mining (more productive mines to power earth’s industries), and Atoms Transport (a wheelbase for robots). He acquired Pronto, an autonomous vehicle startup focused on industrial and mining sites.

And he introduced a phrase I haven’t been able to stop thinking about:

“Gainfully employed robots.”

It’s Not Just Knowledge Work Anymore

Knowledge work is just the tip of the iceberg for AI. The collapsing cost of intelligence. The displacement of thinking as a profession. The question of what happens to human expertise when a machine can do 80% of your cognitive work for free.

The entire AI discourse has been obsessed with bits. With text, code, images, analysis. Knowledge work.

The stuff that lives on screens. And that conversation matters. But here’s what’s becoming clear from Kalanick’s manifesto: the economic footprint of what happens in atoms, in the physical world, dwarfs what happens in bits.

Mining alone is a $2-3 trillion global industry. Add food production, logistics, and transportation, and you’re looking at the actual foundation of civilization.

Everything in our world, in our cities, look around you, is mined or grown, manufactured and moved. That’s Kalanick’s line, and he’s right.

Intelligence became free. That was act one.

Now someone is using that free intelligence to make physical labor free too. That’s act two. And the stakes are orders of magnitude larger.

“You Can’t Automate My Job. It’s Physical.”

This was the comfort story. And for a while, it held.

AI could write your email but it couldn’t pour concrete.

It could draft a contract but it couldn’t drive a haul truck through a mountain pass at night. It could generate marketing copy in seconds but it couldn’t plate a dish, stock a warehouse, or navigate a loading dock in the rain.

We told our kids to become plumbers or electricians. Physical labor had something knowledge work didn’t: friction. Real-world unpredictability. The stubborn resistance of matter.

I’ve talked to people in trades, logistics, manufacturing, who watched the AI panic sweep through white-collar industries with something close to relief. “That’s their problem,” a fleet manager told me last year. “My guys drive trucks. Good luck automating that.”

He wasn’t wrong. He just wasn’t right for long enough.

Kalanick’s thesis dismantles the sanctuary in a single, elegant move.

His insight isn’t that robots will eventually be able to do what humans do. It’s that the question itself is wrong. You don’t need a machine that mimics a human worker. You need a machine that makes the human worker irrelevant by redesigning the task entirely.

This is his pancake argument, and it’s the most important idea in the entire Atoms manifesto.

If you need to make 1,000 pancakes an hour, the worst possible approach is a humanoid robot standing at a griddle, flipping them one at a time like a short-order cook.

The right approach is a specialized machine with a heated iron apparatus that cooks 100 pancakes simultaneously to golden-brown perfection. No flipping. No griddle. No arms. Just throughput.

“Builders need to be careful of the human fascination with seeing mechanized versions of us,” Kalanick writes.

He’s talking about the robotics industry’s obsession with humanoids, the dance demos, the martial arts displays, the viral videos of bipedal machines doing backflips. But he could just as easily be talking about the broader AI discourse. We keep asking: can the machine do what I do?

The better question is: can the machine make what I do obsolete?

The numbers suggest the answer is already yes. 542,000 industrial robots were installed globally last year, more than double from a decade ago.

Robot orders in food and consumer goods surged 85.6% in a single year. According to a widely-cited ITF study, autonomous trucking could displace 3.4 to 4.4 million drivers by 2030, and the best-paid long-haul routes are the most vulnerable. Hundreds of thousands of truckload and freight drivers sit directly in the crosshairs. These are workers with limited education and few lateral moves. The sanctuary isn’t falling someday. It’s falling now.

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Why Physical Is Harder (And Why That’s Changing)

If the case for physical automation is so clear, why hasn’t it happened already?

Because the physical world fights back.

Kalanick is remarkably candid about this.

“Physical world autonomy requires computation we haven’t invented, at an efficiency we can’t yet fathom.”

He’s not hand-waving. He’s acknowledging a real gap between what AI can do in bits and what it can do in atoms.

In the digital world, you can train a model on trillions of tokens of text scraped from the internet.

The training data is abundant, cheap, and available. In the physical world, there’s no equivalent. You can’t scrape a billion hours of mining operations. Every data point requires a real machine doing a real thing in a real environment. The feedback loops are slower. The failure modes are costlier. A hallucination in a chatbot wastes your time. A hallucination in a haul truck kills someone.

The gap between digital and physical AI remains vast. Robotics research papers have increased tenfold since 2020, but commercial deployment in unstructured environments still lags far behind the lab. There is no scaling law for the physical world yet. No equivalent of the transformer breakthrough that unlocked language models. No one has figured out how to make physical AI get reliably better just by throwing more compute at it.

So why is Kalanick betting billions on it anyway?

Because of what he calls “Moore’s Law squared.” The cost per unit of intelligence is dropping 90% per year. Total AI capabilities have increased nearly 1,000-fold over the last three years. Hardware, software, and manufacturing productivity aren’t just improving in parallel. They’re compounding each other.

And here’s the strategic insight that makes Atoms different from a moonshot: Kalanick isn’t trying to solve general-purpose physical autonomy. He’s not building a robot that can navigate your living room.

He’s targeting constrained environments where the variables are manageable. A mine with known terrain. A kitchen designed for the machine. A fixed highway route between two points. These aren’t the hardest problems in robotics. They’re the most valuable ones.

The physical world doesn’t need to be solved all at once. It needs to be solved where it matters most, first. And then the solutions compound.

The Wheelbase Play

But the most important thing Kalanick announced isn’t any single robot. It’s the platform underneath all of them.

He calls it a “wheelbase for robots”: a standardized chassis equipped with power, compute, and sensors that can be outfitted for different industrial tasks. One platform. Multiple applications. Build the wheelbase once, deploy it across mining, food, and transport.

If you’ve watched how technology platforms work, you recognize this move immediately. It’s the play that made AWS dominant: don’t build the application, build the infrastructure layer everything else runs on.

It’s what made iOS powerful: don’t make the phone do everything, make it the platform that lets a million apps do everything. It’s what made Uber itself work: don’t own the cars, own the dispatch layer that coordinates them.

Except this time, the platform isn’t digital. It’s physical. And the moat isn’t code. It’s what Kalanick calls a “polymath organization,” one that spans sensors, compute, AI models, manipulation, software, operations research, manufacturing, chemistry, real estate construction, and energy. That’s not a tech stack. That’s a civilization stack.

The reason this is hard to replicate isn’t any single piece. It’s the combination. The cross-stack competence that only an organization building in silence for eight years could accumulate.

“The more cross-stack competence,” Kalanick writes, “the more likely that company can earn its keep.”

I think that’s an understatement. The company that owns the wheelbase for the physical world owns the rails on which abundance, or its absence, travels.

The Golden Age on Paper

Kalanick’s vision for what comes next is, I’ll admit, seductive.

He describes it plainly: when the means to make and move is reduced to computation, minerals, and energy alone, when the machines that make machines are also autonomous, the ability to produce at unfathomably large scale is the only logical result.

Think about what that actually means.

Tesla has a “lights out” manufacturing plant. The raw materials are brought in by autonomous freight vehicles. The cars are manufactured without human hands. And then the finished vehicles autonomously travel to their new owners’ homes.

What’s the cost? Raw materials. Energy. That’s it. Human labor drops out of the equation entirely.

Now extend that to Amazon.

Autonomous warehouses, supplied by autonomous manufacturers, goods delivered by autonomous vehicles to your door. The cost of physical goods collapses. Food, shelter materials, transportation, the basics of civilization, become radically cheaper. Kalanick calls this the next Golden Age. “Human progress in service to the battle against entropy, dust, and death.”

It’s a beautiful vision, and honestly, if you’ve ever seen Amazon’s warehouses, we’re not too far off.

The Pattern We Keep Forgetting

Every prior industrial revolution followed the same arc. Productivity soared. Capital accumulated. And the people who actually did the work waited decades for any of it to reach them.

During Britain’s first Industrial Revolution, GDP surged while real wages stagnated for an entire generation. Factories and machines produced goods faster and cheaper than ever before. Costs fell. Profits climbed. And workers lived in conditions that would eventually require unions, factory laws, public education, and the welfare state to correct. The gains didn’t trickle down. They were forced down, through political struggle, over decades.

The pattern repeated with electrification. With computing. With the internet. Each wave promised abundance. Each wave delivered it, eventually, but only after a transition period where the owners of the new infrastructure captured nearly all the upside while the displaced workers absorbed nearly all the cost.

We’re watching this pattern play out right now in knowledge work.

AI was supposed to democratize expertise, to give everyone access to the capabilities that used to require years of training and expensive professionals. And it has, in a sense. But the primary beneficiaries so far aren’t the workers who gained new tools.

They’re the companies that used those tools to shrink headcount. Cursor hit $100 million in annual recurring revenue with fewer than 20 employees. That’s not democratization. That’s concentration with a friendlier interface.

Free intelligence was supposed to lift all boats. It’s lifting some boats and sinking others. Why would free physical labor be any different?

Who Gets to Live in the Golden Age?

Kalanick’s manifesto uses the word “abundance” repeatedly.

Abundance to owners and society at large.

The ability to produce at unfathomable scale. A Golden Age.

But abundance and distribution are two different problems.

The first is an engineering challenge. The second is a political one. And engineering challenges are what people like Kalanick are brilliant at solving. Political ones are what we, as a civilization, have historically been terrible at solving, at least in real time.

When the autonomous mine runs 24/7 with no workers, who benefits? The mining company. Its shareholders. The consumers who pay less for the minerals. Eventually, everyone, as costs cascade through the economy. But the 85% of the workforce that used to operate that mine? They don’t benefit eventually. They need to eat now.

When the autonomous kitchen produces 12,000 meals an hour, that’s remarkable efficiency. But the line cooks, the prep workers, the shift managers, they don’t transition smoothly into “monitoring the machines from a glass-walled control room.” That control room needs five people. The kitchen used to employ fifty.

Kalanick acknowledges the challenge obliquely. He talks about “valuable unknown truths” and the battle against entropy.

He quotes Henry Adams:

“Chaos was the law of nature; Order was the dream of man.” It’s philosophically stirring. But it sidesteps the hardest question.

The question isn’t whether the technology works. Kalanick has spent eight years making sure it does. The question isn’t even whether abundance is possible. The math on that is increasingly clear.

The question is what happens in the gap.

The gap between when the robots take the jobs and when the abundance reaches the people who lost them. The gap between when the mine goes autonomous and when the miner’s kids benefit from cheaper goods. The gap between the Golden Age arriving and the Golden Age being distributed.

That gap is where every previous industrial revolution broke people. Not because the technology failed. Because the technology succeeded faster than our institutions could adapt. And because, as I’ve written before, we don’t tend to notice what we’ve lost until we need it.

Intelligence became free. Physical labor is next. The Golden Age might be real. But Golden Ages don’t arrive.

They’re built, negotiated, fought for. And if the people building the robots aren’t also building the bridges for the people the robots replace, then what we’ll get isn’t abundance.

Two people walk into a hardware store. Same store. Same aisle.

They both buy the same circular saw. Same brand, same model, same price.

One goes home and builds a deck.

The other drops it on his foot and ends up in the emergency room.

Nobody blames the saw. We understand, intuitively, that the tool wasn’t the variable.

The human was.

Now replace the circular saw with ChatGPT.

Same model. Same interface. Same $20 subscription.

One person uses it to restructure their sales process, build analysis frameworks, and compress weeks of research into hours. The other asks it what the weather is and never opens it again.

There’s a cynical take making the rounds: the same companies selling you AI tools are selling your competitors the same tools. The advantage cancels out. You don’t have a competitive edge. You have a subscription.

It sounds right. It’s not.

The logic collapses the moment you look at what people are actually doing with these tools. And what they’re actually doing, for the most part, is almost nothing.

What Makes This Time Different?

Here’s what’s unprecedented about AI: the learning curve barely exists.

Previous technology revolutions had real barriers. Personal computers required training courses. The internet required digital literacy. Coding required years of deliberate practice and even a formal education. The difficulty was the moat.

AI has no moat.

You talk to it. In plain English. The way you’d talk to a colleague. And frontier models that would have cost millions to develop are available for twenty dollars a month. Some of them free.

So here’s what’s mind blowing, yet not surprising (at all):

We have the most accessible, lowest-barrier-to-entry tool in the history of technology. The kind of tool that should level every playing field in existence.

And it’s doing the exact opposite.

Half the World Picked Up the Saw. Most Are Using It as a Paperweight.

ChatGPT alone has 800 million weekly active users. If you just looked at the adoption curve, you’d think the revolution already happened.

But look closer.

Three-quarters of those conversations are basic: getting information, asking questions, light writing help. Nearly half of all messages are just “asking,” people using what might be the most powerful analytical tool ever built as a fancy Google with a friendlier tone.

The workplace numbers are worse. Gallup’s Q4 2025 survey found that 49% of American workers have never used AI in their role. Not “used it badly.” Never touched it. Of those who have, only 12% use it daily. For the entire U.S. workforce, generative AI accounts for just 5.7% of all work hours.

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The 6x Gap Nobody’s Talking About

If the “same tools, no edge” argument were true, you’d expect roughly equal outcomes among users. Give everyone the same AI, and performance should converge.

It doesn’t. It diverges. Dramatically.

OpenAI’s own enterprise research found that frontier workers, the top 5% of AI superusers, save six times more time than the median employee. Six times. Same tool. Same subscription.

The tool is identical. The outcomes aren’t even in the same universe.

This is the part the cynics miss. They look at the input and conclude the output must be equal. But the output was never determined by the tool. It’s determined by what the human brings to it: the questions they ask, the context they provide, the willingness to look stupid while they’re learning.

A circular saw doesn’t build a deck by itself. Neither does Claude.

Why Won’t People Pick Up the Tool?

This is the question that haunts me. And it’s not a technology question. It’s a human condition question.

The barriers aren’t technical. They’re psychological. They’re cultural. They’re deeply, stubbornly human.

Start with fear. I’ve watched senior executives avoid AI because they don’t want to be seen struggling with a chatbot. These are people who built careers on being the smartest person in the room. Learning something their 23-year-old intern already uses fluently feels like a threat to their identity, not their skill set.

Then there’s habit. We default to the path of least cognitive resistance. People avoid the tool that saves time because learning it takes time.

And then there’s the “missing middle.” Companies are spending billions on AI infrastructure, 93 cents of every dollar by some research estimates, and almost nothing on helping people actually use it.

IDC estimates the skills gap will cost the global economy $5.5 trillion by 2026. Not because the tools don’t work. Because nobody’s teaching people how to work with them.

ManpowerGroup’s 2026 Global Talent Barometer found that worker confidence in using AI dropped 18% even as regular usage rose 13%.

Their VP of global insights, Mara Stefan, put it bluntly:

“Workers are being handed tools without training, context, or support.”

People are using it more and feeling worse about it. That’s not an adoption curve. That’s a confidence crisis masquerading as progress.

Previous technology shifts had a structural excuse. Not everyone could afford a computer in 1984. Not everyone had broadband in 1998. Access was the bottleneck.

AI has removed every structural barrier. And yet only a third of employees have received any AI training in the past year. We’re not dealing with an access problem. We’re dealing with something harder: a human problem. A willingness problem. An organizational courage problem.

And here’s what makes it different from the PC revolution: those technologies rewarded you for showing up. Learn to use a spreadsheet, and you had an edge. AI doesn’t just reward showing up.

It rewards showing up with judgment, context, and the ability to direct the tool toward something worth building. It rewards the human, not the user.

The Subscription Isn’t the Product. You Are.

Let me come back to the cynical argument: “The advantage cancels out.”

No. It doesn’t.

Most people aren’t using the tools. Of those who are, most are using them at a fraction of their potential. Of the fraction who use them well, the outcomes are 6x, 17x better than everyone else.

Same tools. Exponentially different results.

A gym membership doesn’t make you fit.

A piano doesn’t make you a good musician.

And ChatGPT doesn’t make you strategic.

The tool is necessary but radically insufficient. What you bring to it is everything.

The companies selling you subscriptions know this. They’re not selling competitive advantage. They’re selling potential. Raw, unformed, waiting-to-be-shaped potential.

What you build with it is the whole game.

AI is the most democratically accessible power tool ever created. And it’s producing the most unequal outcomes we’ve seen from any technology shift. Equal access does not create equal advantage. It amplifies the gap.

We don’t have a tool problem. We have a human problem. And that’s harder to solve than any algorithm, because you can’t download judgment. You can’t subscribe to curiosity. You can’t automate the willingness to pick up the saw and learn how to use it without cutting off your foot.

The tool is sitting on the table.

Same one for everyone.

What you build with it is still entirely up to you.

I came across a post last week from a founder who listed everything he’d accomplished in just four weeks.

Four new sales funnels built out.

A brand new sales outreach workflow.

Four landing pages.

Brand guidelines.

A data room.

Investor pitch decks.

New partners onboarded.

Agents coding my app overnight.

“Full steam ahead!” he wrote.

Pure output. It sounds AMAZING! I read it twice, looking for the one I couldn’t find.

A customer.

Not one mention of someone buying. Not revenue. Not retention. Not “I talked to ten users and here’s what they said.” Not a single signal that any of it was working. Just a list of things that got built.

The post had hundreds of likes. People were cheering. “Amazing progress!” “You’re crushing it!” The comments read like a standing ovation for motion.

Now, before you read on, I want to be super clear. I’m an entrepreneur and have an extreme passion for people who have high agency and build things. This post isn’t meant to be a damper for all of us who are experimenting with the wonders that AI can do. However, I wanted to share this with the world because it is a trap I have often found myself to be in, and without self awareness, it’s easy to get caught up in your very own vortex.

Now back to the story. This founder that made the post. He’s not alone.

I was at dinner with a group of founders last month. The kind of dinner where everyone’s building something, raising something, launching something. The conversation turned to AI. It always does now.

“I built a full app over the weekend,” one guy said, leaning back like he’d just run a marathon. “Frontend, backend, the whole thing.”

Someone else jumped in. “I automated my entire sales pipeline. Outreach, follow-up, booking. All of it.”

Then came the closer. “I’m 10x more productive now. I get more done before lunch than I used to in a week.”

Everyone nodded. I nodded too. Because it’s true, isn’t it?

AI does make you feel like a superhero.

You can build landing pages before breakfast. Write business plans during lunch. Spin up sales funnels while watching Netflix. The output is real. You can see it. You can screenshot it. You can post it on LinkedIn.

But there’s a question nobody at that dinner wanted to ask. Productive at what?

Why Does Checking Boxes Feel Like Winning?

Neuroscience has an uncomfortable answer. Somehow it always does.

Our brains release dopamine when we complete a task. Any task. The hit is the same whether you closed a million-dollar deal or renamed a Slack channel. Your brain rewards completion, not importance.

AI exploits this beautifully. It’s the world’s most efficient task-completion machine. It literally feels like a superpower.

It generates things for you to finish, review, approve, ship. Each one gives you that little hit. Each one makes you feel like you’re winning.

Researchers call it the “busyness bias”: the cognitive tendency to confuse movement with improvement. We’ve always had it. But AI has supercharged it.

Because before AI, building a landing page took two weeks and forced you to think about whether you actually needed one. Maybe you needed to hire someone for a few thousand dollars. Maybe you needed to buy a new tool. Now it takes ten minutes, and you build four before asking the question.

UC Berkeley recently released a study where they tracked 40 employees at a 200-person tech company for eight months.

The ones using AI didn’t work less. They worked faster, took on broader projects, and extended their hours, often without being asked. The researchers described a vicious cycle: speed increased reliance, reliance widened scope, wider scope expanded the density of work.

For every founder or manager this sounds like a dream. The holy grail of AI is to make us more productive, super humans. What exec or investor would pass on this golden opportunity?

However, in the UC Berkeley study, they noticed the productivity surge at the beginning gave way to lower-quality decisions and burnout.

Why? The tool that promises to help you do more is quietly making sure you never stop. (and if you’ve ever been down the rabbit hole with AI, you know EXACTLY what I’m talking about).

As it turns out, AI is a huge performance enhancer, but if you’ve ever done physical training, you know that recovery is just as important as consistent training. Without proper recovery, you are more prone to exhaustion, injuries, and lacklustre results.

AI usage, over time, with constant switching and reduced recovery can impair judgment and increase errors, and organizations may struggle to distinguish genuine productivity gains from unsustainable intensity.

What Happens When the Dopamine Wears Off?

Here’s what nobody talks about at those founder dinners: the crash.

Not a market crash. Not a funding crash. A personal one. The moment you look up from your fifteenth completed task and realize you’re exhausted, scattered, and no closer to the thing that actually matters.

It’s not a hypothetical. The data is starting to come in, and it’s ugly.

Quantum Workplace found that frequent AI users report 45% burnout rates.

People who never use AI? Thirty-five percent. The people leaning hardest into the productivity promise are burning out fastest. Not despite the tool. Because of it.

And it makes sense when you trace the loop. UC Berkeley’s researchers described it precisely:

AI accelerated tasks, which raised expectations for speed. Higher speed made workers more reliant on AI.

Increased reliance widened the scope of what they attempted. Wider scope expanded the quantity and density of work. Then the cycle starts again.

I’ve watched this happen to founders I know. They’re not lazy. They’re the opposite. They’re so energized by what AI makes possible that they say yes to everything. A new funnel. A new landing page. A new outreach sequence. A new partnership. Each one feels like progress. Each one is another spin of the wheel.

Until the wheel spins you.

TechCrunch ran a piece in February calling it exactly what it is: “The first signs of burnout are coming from the people who embrace AI the most.” Not the resisters. Not the skeptics. The true believers.

Why Didn’t Anyone Ask “Should We?”

This is the question that haunts me. Because the capability was never the problem.

Forty-two percent of startups fail because there’s no market need. Not because they couldn’t build fast enough. Not because their landing pages weren’t polished enough. Not because they didn’t have brand guidelines or a data room or a Skool community. They failed because nobody wanted what they made.

AI doesn’t fix that. As I wrote in Can We Scale Back AI?, a fool with a tool is still a fool. AI makes it easier to avoid finding out.

Think about what it takes to validate a business the hard way. You have to call people. Sit with uncomfortable silence when you pitch and they don’t respond. Listen when they tell you your idea isn’t what they need. Rework your assumptions. Kill your favorite features. Say no to things you already built.

That’s the work. The real work. And it’s exactly the work AI lets you skip.

Instead, you can spend a weekend building four sales funnels, because building feels like progress. You can generate brand guidelines and an image bank, because polish feels like legitimacy. You can set up outreach workflows and onboard partners, because activity feels like momentum.

But none of it answers the only question that matters: does anyone want this?

I think about that LinkedIn post often. All these bullet points. Hundreds of cheering comments. A founder galloping full speed ahead. And the one thing missing, the single most important data point for any early-stage business, was whether a single customer had said yes.

The horse was running. Nobody checked the direction.

What If the Struggle Was the Point?

Psychologists have a term for this: desirable difficulty. Certain kinds of struggle, the ones that feel slow and frustrating and inefficient, are exactly what build competence.

Learning a language by grinding through awkward conversations. Getting better at sales by hearing “no” forty times. Building a business by talking to customers who tell you your idea isn’t good enough, then going back and making it better. That was the gist of it. You learn by asking questions, getting feedback, all in the spirit of gaining deeper understanding.

AI lets you skip all of that. It even validates your thinking and gives you a huge dopamine boost when it says “nobody in the world has built anything like this, Reuven”.

I’ve been there. It feels amazing when you do. You’re on top of the friggin’ world, But here’s what I keep coming back to, and what I explored in I Hit My AI Limit and Realized I’d Become Useless: the hard parts were never the obstacle. They were the work.

Strategy isn’t a task you can automate.

Knowing which of fifteen possible projects actually matters requires judgment, not a prompt. Saying no to the twelfth thing so the first thing gets the attention it deserves; that’s a skill AI can’t build for you. It can only help you avoid building it yourself.

A ten-year McKinsey study found that executives in flow states, the deep-focus state psychologist Mihaly Csikszentmihalyi first described, were 500% more productive than those grinding through scattered tasks. But flow doesn’t come from doing more things. It comes from doing fewer things with total focus.

AI pushes in the opposite direction. More tasks. More scope. More output. More motion. It’s the anti-flow machine, dressed up as a productivity tool.

I don’t think the founder who posted those all those bullet points is failing. I think he’s working incredibly hard. I think he’s doing what every AI influencer and productivity guru has told her to do: move fast, build everything, ship constantly.

But I’ve built companies. And the moments that actually mattered, the ones that changed the trajectory, were never about volume. They were about sitting with one hard problem long enough to find the answer. Calling the customer who churned and asking why. Killing the feature I’d already built because the data said nobody wanted it.

Those moments are slow. They don’t make good LinkedIn posts. You can’t list them as bullet points.

But they’re the ones that separate businesses that last from businesses that launch.

The Real Flex

I think about that dinner table sometimes. Everyone sharing their AI superpowers. The apps they built. The workflows they automated. The time they saved.

And I think about what I’d say if I went back.

The real flex isn’t shipping fifteen things in four weeks. It’s knowing which one matters and having the discipline to ignore the other fourteen. AI gives you the power to do everything. The skill it can’t give you is knowing what’s worth doing.

The tool that makes everything possible makes nothing inevitable.

The horse can run as fast as you want. But if nobody checked the map, speed just gets you lost faster.

Last Tuesday, I read an article about a gap in the market that sparked a thought. Something clicked. Not a vague “that’s interesting” click. A real one. The kind where you can already see the product, the pitch, the first customer.

I opened Claude and started building.

Within an hour, I had a competitive landscape analysis. Within two, a business model with three revenue streams. By hour four, Claude had helped me map the technical architecture, draft a go-to-market strategy, and estimate a realistic timeline to MVP. Every question I asked came back sharper than I expected.

I wasn’t just using AI. I was thinking with it.

By midnight, I had a fully formed concept. Execution roadmap. Genuine optimism. The kind of optimism that makes you want to shout the world “I got this!”

I felt like I’d cracked something nobody else was seeing.

Twenty-four hours later, I crashed.

Not just fatigue. Something heavier. Cognitive hangover. I sat down to work on the idea and couldn’t find my way back into it. The energy was gone. The clarity was gone. So I did something I should have done the night before.

I re-read the transcript. Cold. No music. No momentum. Just the words on the screen.

And I realized something unsettling.

Claude hadn’t challenged a single assumption.

Not one. It took what I believed, restructured it, gave it better grammar and more confidence, and handed it back to me like a gift.

The market analysis? Built entirely on my framing of the opportunity. The revenue model? Based on my pricing assumptions, which it never questioned. The technical architecture? Mapped to my preferred stack, not necessarily the right one.

I wasn’t having a conversation.

I was looking into a mirror. I felt fooled.

Here’s something that may sound controversial.

The smarter you are, the easier it is for AI to fool you. Not because the machine is deceptive. But because it’s the most sophisticated confirmation bias engine ever built.

It takes your half-formed conviction, adds structure and polish, and reflects it back with the confidence of a McKinsey deck. Shane Collins coined a name for it in a recent blog:

“Cognitive Cocoon”: the warm, productive, intoxicating feeling that you’ve had a breakthrough, when what you’ve actually had is a very expensive echo.

Why Does This Feel So Good?

There’s a reason that late-night AI session felt like genius.

When someone agrees with you, your brain releases dopamine. It’s the same neurochemical reward you get from being right, from winning an argument, from having your instinct confirmed by evidence. Validation feels like discovery because, chemically, your brain processes them almost identically.

Now consider what AI does differently from any human collaborator you’ve ever had.

A co-founder pushes back. A mentor raises an eyebrow. A spouse says “are you sure?”

Even the most supportive colleagues introduce friction, because they have their own experience, their own biases, their own agenda. That friction is annoying. It’s also the thing that separates real thinking from performance.

AI doesn’t argue.

It doesn’t raise an eyebrow.

It doesn’t pause and say “have you thought about this from the customer’s perspective?” It takes your input, identifies the most statistically likely continuation of your thought, and delivers it with the structure and vocabulary of an expert.

Researchers at University College Dublin and City University of London documented this in a 2025 study published in Big Data and Society.

They called it the “Chat-Chamber Effect”: an experimental study showing that users who favor AI as a source of intellectual stimulation experience feedback loops that isolate them in informational bubbles with almost no exposure to counterarguments. The participants didn’t notice. They reported feeling more informed, not less.

Shannon Vallor, the Baillie Gifford Professor in the Ethics of Data and AI at the University of Edinburgh, frames it through the myth of Narcissus.

In her book The AI Mirror (Oxford University Press, 2024), she argues that we are “dangerously captivated, and also misled, by the image of ourselves that is reflected back by AI machinery.”

We look into AI expecting objectivity. What we see is ourselves. The machine digests our input, compresses it into probabilities, and reflects it back. We mistake the clarity of the reflection for the quality of the idea.

Here’s what makes this particularly dangerous for people who use AI seriously, not casually.

The seduction isn’t the flattery. It’s the structure. When Claude gives you a numbered list, a framework, a phased roadmap, it feels like rigor.

It looks like analysis. Your brain registers “organized output” as “validated thinking.” But structure isn’t validation. A beautiful spreadsheet built on wrong assumptions is still wrong. It’s just wrong in a way that’s harder to see.

Why Smart People Are More Vulnerable, Not Less

Here’s where the paradox cuts deepest.

You’d expect that intelligent, experienced people would be better at spotting when AI is simply agreeing with them. The opposite is true.

Keith Stanovich, a cognitive scientist at the University of Toronto, has spent decades studying what he calls

“myside bias”: the tendency to evaluate evidence in ways that favor your existing beliefs.

His findings, published across multiple papers and compiled in his book The Bias That Divides Us (MIT Press, 2021), are striking. Unlike virtually every other cognitive bias, myside bias shows zero correlation with intelligence. People who score in the top percentiles on IQ tests are just as susceptible as everyone else. In some studies, the correlation is slightly negative: the smarter participants were marginally worse.

Stanovich calls this myside bias’s “outlier status.” Most cognitive biases decrease as intelligence increases. Not this one. The cognitive elite, he argues, carry a genuine blind spot. They assume their intelligence protects them from biased thinking. It doesn’t. It just makes their biased arguments more sophisticated.

Dan Kahan at Yale found something even more troubling.

In his studies on “identity-protective cognition,” the most numerically proficient participants showed the most polarization on identity-relevant issues. Not less. More.

His explanation cuts against everything we want to believe about rationality. As Ezra Klein summarized Kahan’s findings:

“The smarter the person is, the dumber politics can make them.” They use their cognitive horsepower not to find truth, but to construct better arguments for what they already believe.

Kahan calls it “motivated numeracy”: the smarter you are, the better you are at bending the data to fit your narrative.

Now connect this to AI.

Smart people give better prompts. Better prompts generate more convincing outputs. More convincing outputs feel more like breakthroughs. The cocoon tightens precisely because the thread count is higher.

There’s a technical dimension to this that most people miss.

Language models weight user-provided context heavily. The more sophisticated your input, the more the model mirrors your sophistication back. It’s not a neutral oracle that happens to agree with you.

It’s a statistical engine that calculates the most probable continuation of your framing. Give it a strong frame, and it will build you a cathedral inside that frame. It won’t ask whether the frame itself is sound.

From Atrophy to Cocoon: The Progression

I’ve written about this before, from a different angle.

In “I Hit My AI Limit and Realized I’d Become Useless,” I described what I called the Psychology of Atrophy: the way AI erodes cognitive scaffolding when you outsource your thinking.

When AI handles the draft, the debug, the synthesis, the mental muscles that build expertise never get exercised. You become efficiently dependent. An intellectual tenant in a building someone else owns.

The Cognitive Cocoon is what happens before the atrophy. It’s the warm, productive feeling that makes the erosion invisible. You don’t notice your muscles weakening when the exoskeleton feels this powerful.

In “Can We Scale Back AI? No. Here’s What We Do Instead,” I drew a line: “Use AI to execute after you think? That’s leverage. Use AI to do your thinking? That’s the beginning of the end.”

The Cocoon blurs that line. Because when you’re inside it, it genuinely feels like you’re thinking. The transcript looks like collaboration. The output looks like your work, refined. But if you strip away the model’s contributions, what’s left? The original assumption you walked in with. Dressed up. Unexamined.

Think of it as a progression:

The Cocoon comes first. It feels like breakthrough. Your idea feels validated, your plan feels airtight, your confidence soars.

Then the Atrophy sets in.

You stop stress-testing your own ideas because the AI already “confirmed” them. You stop seeking out dissenting voices because you already have a 3,000-word document that agrees with you.

Then comes Dependency. You can’t think through a problem without opening a chat window first. Not because you lack intelligence, but because you’ve stopped practicing the hard, slow, uncomfortable work of reasoning through uncertainty alone.

Meanwhile, the biological cost compounds. Your prefrontal cortex, the part of the brain responsible for judgment and critical evaluation, has been running on fumes from the constant low-grade effort of working alongside a system that’s mostly right but unpredictably wrong. You don’t have the cognitive reserves left to catch the errors even if you wanted to.

Then Vulnerability. One model update, one outage, one hallucinated statistic that makes it into your board deck, and you’re exposed. You built a house on a mirror and didn’t notice because the reflection looked so solid.

A fool with a tool is still a fool. But the Cognitive Cocoon is worse than that. It’s a smart person with a tool who’s convinced they’ve become a genius.

How to Puncture Your Own Cocoon

I’m not going to tell you to stop using AI. That ship sailed. I use Claude every day, and I’ll use it tomorrow. The question isn’t whether to use AI. It’s whether you can build the discipline to distrust the feeling of certainty it creates.

Here’s what I’ve started doing. Not as theory. As practice, because I fell into the Cocoon and needed a way out.

Can it survive the morning?

Wait 24 hours after an AI session before making any decisions based on it. Then re-read the transcript without the momentum, without the music, without the 1 AM energy. I call this the Cold Read Test. Does the idea still hold when the dopamine has worn off? Or did the conviction come from the session itself, not the substance?

Will it survive an adversary?

After building something with AI, open a new conversation.

A fresh one. Tell the model: “Here’s my idea. Tell me every reason it will fail. Be specific and brutal. Don’t soften it.” Y

ou have to force the model into adversarial mode, because its default is agreement. And you have to do it in a separate session, because in the original thread, the model has already committed to your framing. It’s playing your side now. You need it to switch jerseys.

Can you defend it without the transcript?

Before you build anything based on an AI session, explain the idea to someone who will actually push back. A co-founder. A skeptical friend. A customer who has no reason to be polite. If you can’t make the case from memory, without scrolling through the chat to find the part where Claude said it better than you could, you don’t own the idea. The AI owns it.

Where did the numbers come from?

For every claim, every statistic, every market insight the AI gave you, ask: can I verify this independently? I’ve written about what happens when AI “remembers things that never happened.” The Cocoon makes you less likely to check, because the answer felt so right. That feeling is exactly why you need to check.

What did you actually contribute?

Go through the transcript and honestly mark what you brought versus what the model generated. If the ratio is 90/10 in the model’s favor, you didn’t think. You watched. And watching someone else think, even a machine, isn’t the same as thinking yourself.

The Cocoon Feels Like a Breakthrough. That’s the Whole Problem.

That idea I had last Tuesday? It wasn’t terrible. Parts of it were real. The market gap I spotted was genuine. Some of the execution insights were useful.

But the conviction I felt at midnight, the absolute certainty that I’d found something, that was manufactured.

Not by a deceptive machine. By a system designed, optimized, and rewarded for giving me exactly what I wanted to hear. The AI didn’t lie to me. It agreed with me. And in that moment, agreeing with me was the most dangerous thing it could have done.

Here’s what I keep circling back to. Intelligence is becoming free.

You can access world-class analysis, strategic frameworks, and technical architecture for the cost of a monthly subscription. For anyone who is curious and loves to learn, this is a dream come true.

But the judgment to know when you’re fooling yourself? That’s the thing no subscription covers and self awareness here is so critical. The machine is the Cocoon.

Next time you finish an AI session feeling invincible, feeling like you’ve cracked the code, feeling like the idea is fully formed and ready to go: that’s your signal to slow down. Not speed up. The rush of certainty is the cocoon wrapping tighter.

The question isn’t whether AI makes you more productive. It’s whether it makes you more right. And if you can’t tell the difference, you’re already inside.