I didn’t and got a scathing reply from this person.

And you know what? He was right! I let AI push too much me out and put too much corporate mush in.

Would my first draft have worked? Maybe not. But it would have been a much cleaner representation of who I am and what I was asking for.

And wouldn’t that have been better for both of us?

Sameness is cheap and safe to produce. It’s expensive and annoying to receive.

It’s the same instinct behind benchmarks, playbooks & templates.

Why do we love these highly professional “cheat sheets” so much?

1. Not every piece of a business requires ingenuity. Getting too creative in some functions can even land us in a court of law.

2. We can also only make so many decisions in a day. Looking at what successful people have done and doing that can be quite expedient especially for functions outside of our own expertise.

3. Using them gives us a nice identity boost as a tribal species. Doing what Amazon does from an ops perspective makes you “like Amazon”. Managing the way Nvidia does makes you “like Nvidia”. That’s appealing. We want to identify as winners.

4. Doing what everyone else is doing (aka benchmarking, playbooks & templates) prevents us from being wildly wrong. And if somehow we end up in the wrong with them we can easily point to the source. And the source won’t be us!

These are all really good reasons.

The supply of what “everyone else is doing” is exploding.

Building in public is its own strategy now. Some from people with big name recognition. Some from people with a slice of expertise and a love of writing (*raises hand & acknowledges participation in this tsunami*). Some from people with ChatGPT Plus and a dream.

Now with AI we don’t even have to bother asking someone else how they’re doing it. The model gives you an amalgamation of everything it’s trained on so it’s essentially a decent idea of what everyone else is doing at the low to mid (in some cases even high) level.

So, doing what everyone else is doing has never been easier!

It has also never been riskier.

AI makes it easier for more people to produce competent work. Competent used to beat enough of the competition to stay alive. But when everyone can reach a similar level then being a little better than average matters a lot less.

This is just math.

Consider spelling. At one time a perfectly spelled cover letter was a real signal. Just having everything spelled correctly and typed indicated competence and education. Spelling well was valuable and could get you hired.

Then we all got spellcheck and PCs. People still submit things with spelling errors and it’s a bigger red flag than ever. But no one gets hired because they spelled everything correctly. The competence to spellcheck is table stakes and the misses are even worse.

That’s what AI capabilities do to most white collar tasks. They make average competence table stakes instead of enough to win. They make silly mistakes more noticeable.

When competent sameness is abundant then it’s not valuable anymore.

Value comes from rarity.

Lulu Cheng Meservey is one of the best communication experts in the world. She uses no templates for it:

“No two situations are the same. And by pretending that they are, we miss that texture. Alpha comes from the little cracks in reality that only you can see and that you can exploit.”

This is how we end up with a new Class System at work. Marquee talent are not 10% better at executing. They operate under different economics because their judgment leads to 100x outcomes in the spots it matters most.

They aren’t innovating in every single piece of their business either. That would be insane. They might even be looking at some benchmarks and playbooks too. What they know is when to put down the map, look at the weather and start hiking instead of swimming if need be.

Jensen Huang can have 60 direct reports who email him without meetings because he has the resources, clarity and market conditions to hire the best people in the world and get out of their way.

If you don’t have that, you can’t operate like that.

Blindly copying the best companies in the world is business theater not best practices. It’s too easy now to benchmark yourself into oblivion and even feel good about it along the way.

But every practice you copy is a bet that both your conditions match theirs and that average in that area will be enough to win.

The answer isn’t to build everything from scratch. But it is to get clear on where being average is just dandy and where being average should scare the shit out of you.

It should have scared the shit out of me on that cold email. Now it does.

It’s nearly impossible as a technologist watching models leap forward to not see a world changing technological revolution.

But it’s also nearly impossible to be a mid-level manager watching your coworker AI slop everyone and then comprehend the scale of a technological revolution.

AI is fast. AI model progression is fast. AI is the fastest adoption ever. And yet it looks like this in real life:

Tech leaders are brilliant at giving us the future as inevitable. It was inevitable to be living in the metaverse. Alexa was supposed to kill the screen. The track record is big hype and mixed realities. The internet really did revolutionize. Mobile changed the world. The metaverse feels like a fever dream.

The revolution erodes the old ways but it doesn’t create a clean break. The tech has to meet and use human nature to actually move from decks to desks.

It’s rarely big leaps forward. It’s tiny steps forward and back.

An HR manager of a thriving midsized manufacturer told me that Copilot is on for them. Her execs are “excited about AI”. She’s the only person on the entire HR team using it at all. She also gets AI novels from around the company every day. Literal 10 page proposals for multi year transformations written by AI and thrown over to intake and manage.

There’s a growing pragmatism to AI adoption (like, it should actually *be cheaper* and *improve outcomes*). We’re realizing you can’t just say “use AI” and suddenly the data is clean, the skills are built and the product mindset emerges.

Is that HR Manager faster in some ways? Yes. Is the org faster? Debatable.

It’s the coexistence of the old ways and the new.

That pattern isn’t new.

Hand to steam: Mills needed water power sites and brick factories. You couldn’t mechanize an existing cottage. Thread demand outran what the new mills could weave.

Steam to electric: Factory floors were physically designed around one central steam engine with belts and shafts running to every machine. Switching to electric meant gutting the building and redesigning the entire layout around independent motors.

Horse to car: Cars existed but roads, gas stations and consumer financing didn’t. The horse infrastructure was everywhere.

Mainframe to PC: Early PCs couldn’t talk to each other. Coexistence held up until networked software made the old workflow incompatible. (PS: 44 of the top 50 banks STILL run on mainframes! We’re still in it!)

We’re not moving out of AI. We’re moving into coexistence.

We’re not reworking physical workflows like demolishing a full factory (yet) but successful AI native work requires that level of workflow disruption.

That’s why it’s so easy to see it in software engineering. The nature of the work and the workforce involved lends itself to adoption. People with product mindsets, systems thinking and software based outputs are moved by a technology that directly cheapens code.

Other spaces require a dismantling of the process and the role. That’s easier to build from scratch in an AI native org than to move within an enterprise. The HR Manager can’t independently rearchitect their role. They exist within a structure knitted with habit and history. The nature of most work is set up to be augmented at best.

The headlines say we’re hiring again and chatter over the bubble bursting is rising. The hype collapses but the technology doesn’t.

The mid-level manager watching AI slop flood their inbox and the technologist watching models leap forward are both living in reality. If you’re only watching the failures you’ll miss the shift. If you’re only watching the tech you’ll miss the friction.

The curve doesn’t care about either story.

In the new class system of work, leverage matters more than title. Owners, Marquee & Ensembles win because of their unique leverage. Traditional and Out see leverage drain.

Orchestration sits at the point where work gets assembled. As long as that coordination stays hard to do, Orchestration holds their leverage.

Cogwork is what happens when a role gets broken into interchangeable units that can be metered, routed and swapped. Leverage in Cogwork is real in aggregate and faint per unit. It is industrialized white collar work.

Orchestration

A symphony orchestra is made up of world class musicians with the sheet music right in front of them.

And yet they need a conductor.

Because when you combine a bunch of people, a lot of instruments and many parts you create complexity. Without wrangling, complexity is mess.

Some Orchestration roles will look like managers. Setting goals, outlining responsibilities and ensuring information flow. Others will look like architects building out pipelines and agents. Most will combine both.

The defining features of Orchestration are that they take a pile of instruments, musicians and scores and turn it into beautiful music.

In Orchestration work, the tools, tasks and workflows are interconnected. An orchestrator understands that when you add a bass drum, you change the sound entirely. They know how to weigh the tradeoffs of that decision. They know how to balance improvisation with reliable repeatable and recognizable output. Over and over again.

Plenty of people and even AI can tell you if this thing happens then here’s what’s likely to happen. Exceptional orchestrators tell you more than that. They tell you the odds and then the third and even fourth order effects after that. Not perfectly but reliably.

They know that people can’t handle ten options. They know that agents aren’t flawless. They know that both people and agents require training and maintenance.

Orchestrators turn complication into ease.

As long as the work stays difficult, Orchestration holds their leverage. If Orchestration work becomes easy and standardized, the leverage slips from them to ownership.

Orchestration Test

• Are you counting the work that’s been done (reporting) or building workstreams that prioritize momentum over maintenance (orchestration)?

• What conditions would need to be true for technology to handle your Orchestration work? Are they plausible now? Someday?

• Do you decide what gets handed to AI?

Cogwork

Cogwork is easy to mistake for Traditional. Plenty of the day to day might look the same as it always has. Same job title. Same office dress code. Same water cooler chat.

Yet there’s a new feeling of pressure. The work industrialized and decomposed into interchangeable units to measure and swap.

Why now when other types of work have been industrialized for decades?

1. The braggable business metric changed from revenue growth to revenue per employee.

2. 99% of CEOs anticipate using less labor thanks to AI so the value of the remaining labor is in question.

3. Knowledge work used to be very hard to measure. AI makes it easier to measure. Anytime the ability to measure changes, so does the way we price the deal.

In Cogwork, workers are interchangeable. The sensation is that everyone is replaceable and the arrangement is transactional.

The sense of being optimized exceeds the sense of meaning.

This isn’t just gig work. Yes, Klarna moving to an “Uber” style model for customer service is one example. Another might be staying salaried but getting measured in new and ever more minutiae laden ways. Keyboard trackers are Cogwork.

The intensity of cogification depends on:

1. Good ole supply and demand. Are employers competing over that talent?

2. Discernment of leaders. What do leaders believe about work & relationships?

3. Collective Action. What legal or contractual protections do these workers have?

4. PR. What will society and customers expect and accept?

This evolution doesn’t happen because an exec team puts “make people feel like cogs” onto a slide.

It happens one optimization towards some other rational goal at a time. It starts out as “improve margin in XYZ department by 25%” not as “demoralize the team”. It doesn’t even mean they don’t care about the values on the wall. They might still care deeply. But when we change what and how we measure, we change the tenor of the deal we’re making.

Then everyone looks around and asks “what is everyone else doing?”. If the answer is cutting benefits, they’ll consider it too.

Employees respond in kind. They draw harder boundaries around what they’re willing to do. They start trends like Lazy Girl Jobs with an emphasis on boundaries over career progression.

Orchestration and Ownership pull the leverage from Cogwork leaving individuals with tiny portions.

In Cogwork one brick holds almost nothing. You could pull one out and the structure wouldn’t move. Individual leverage in Cogwork is small enough to feel like none.

But the wall holds the building up. The weight is real. It’s spread across every brick. The force registers in the aggregate. Pull one and nothing moves. Pull enough and the whole thing comes down.

Cogwork Test

• How much of your role is prescribed, preprocessed and measured?

• Could the person next to you be swapped in for you with a week of ramp up time?

• Are you drawing harder boundaries around your work? If so is it because you are protecting focus or because the arrangement stopped feeling worth any more than that?

Where the Pressure Lands

Orchestration and Cogwork both sit inside the work itself.

Cogwork is work chopped into interchangeable and quantified pieces.

Orchestration is the job of deciding how those small pieces fit together.

While coordination is tricky, leverage is pulled up from Cogwork into Orchestration. Once it turns automatic and routine, the leverage skips them and pools with ownership.

These classes are forming. They aren’t fixed yet.

Where there’s leverage, there’s winning. Owners, Marquee & Ensembles win because of their leverage. Others see leverage slip away.

You might already be here. The work still gets done. The clients still pay. Nothing broke. You just stopped gaining ground and it’s hard to say when that started.

People assume a technological revolution hits everyone at once and all the way. But that’s not how humans work. Some firms will keep running the way they always have. The change is that their pay and outcomes stop climbing at the same rate. These are Traditional.

For others, they’ll either want to change and not succeed or decide on another path. They’ll be Out altogether.

Traditional

Two roads lead to Traditional.

One is by choice. The option to “revolutionize” exists and ultimately the decision is that it’s not worth it in some way. That could be a craft, standard, relationship or way of working. This is the handmade artisan making the whole shoe instead of going to the factory for parts.

There’s a man running a one person fruit brokerage shop off of notepads and pens today. There’s a wildly profitable roofing company that still keeps employee files on paper. There are niche manufacturers who have been serving the same customers for generations in their families.

These positions aren’t guaranteed to be safe from competition. But they are real and they’re not automatically eliminated by AI. And these people like it that way.

When electric light arrived in the 1880s the gas companies didn’t die. They pivoted to heating and cooking and are still running more than a century later. Many became the stable utilities we still pay every month. What changed was not their survival. It was their position. They went from the center of the lighting economy to a durable secondary role. Competent and reliable with a capped upside.

That is Traditional. The work is real. The revenue can even hold. The company persists on methods that still function. What it gives up is the growth rate. Growth flattens and the strategic center of gravity shifts to whoever captured the new leverage.

Some good companies will operate here for decades and serve their customers well.

Others will take a wrong turn and wind up in Traditional. This is the group that tries mightily and cannot implement. They lack the capital, talent or cultural health to adapt. They try but remain in endless “pilots” or endlessly discuss maybe possibly eventually someday perhaps turning on AI features. Stuck. They might acknowledge the failure or it might go on to die in a graveyard of projects where “we tried that once, didn’t work”.

The defining characteristics of Traditional are that it produces real value through established methods that still work. The skill is genuine and the demand is durable but the methods stopped being where new leverage forms. Whether it is a company or a person, the work still commands its own rate and holds its own customer or client. The ceiling is lower than it used to be but the pricing power stays with the one doing the work.

The Traditional Test

• If nothing about your methods changed for five years, who would be paying you?

• If a competitor can move twice as fast at half the cost, what remains of your position?

• What do you hold onto on purpose even though it costs you speed or margin?

Out

We talk about work like it all lives on a laptop. The kind we can analyze and optimize and write essays about. It is the work we know best and the work we most like to admire ourselves for doing. (*Raises hand, guilty!*)

But it was never the only kind of work and laptop work was never the whole economy. There is work that happens with hands and bodies. Work that holds the world up while the rest of us type and sit in meetings.

Out is what happens when people either can’t get back into laptop work or choose to leave it.

Think about phone operators. The technology to automate them existed long before it reached them. The displacement still took twenty years. It did not arrive in a quarter. It dripped. A slow erosion that looked survivable right up until the role was gone. When the operators were absorbed back into the economy most landed in lower paid work and underemployed.

Out shows up as erosion at the system level and pain at the individual one.

The Out Test

• At what point does this path have diminishing returns for you vs. starting a pivot today?

• Are you still in laptop work because you want to be or because you think you “should”?

Leverage Draining

Traditional and Out both see leverage drain. Traditional loses it from future growth. Out loses it now.

Traditional is the sort you can’t see because it all looks the same. Out is the sort you can’t miss.

Next up Orchestration and Cogwork. One directs the work. The other gets industrialized by it.

As we sort into new classes at work, not everyone misses out. Some actually win big.

This is the deep dive on the three big winners:

Why this is a Venn and not a Ladder

A ladder says you climb from one rung to the next and leave the last behind. The Venn says these are positions a person or a team can hold one at a time or at the same time.

The boundaries overlap because all three have leverage in different forms. Some will hold multiple types of leverage at once.

Ownership holds leverage as an asset.
Marquee holds leverage as an individual brand and impact.
Ensembles hold leverage in the trust between people.

Windsurf got sorted in 72 hours.

This is the new classes of work in action:

• Marquee. Google paid roughly $2.4B to license Windsurf technology without exclusivity and hire Varun Mohan, Douglas Chen and a handful of senior researchers. The price was for who they were not what they owned.

• Ensemble. Google didn’t pick the senior team off one by one. They moved as a unit because the value was the trust between them vs a single hire.

• Ownership. Cognition bought what was left. The IP, the product, the brand, the customers. The asset that keeps producing.

• Cogwork. The remaining 200 employees were kept long enough to transfer the IP then thinned.

One company sorted into 4 classes in 72 hours.

Marquee

Start with a school talent show with 100 kids.

For years, the show ran on effort. Everyone practiced. Everyone got a slot and the prize money got split kind of evenly. The best singer made a little more than the worst one but not by much.

Then the school posts the show online.

Now one kid’s video gets 2 million views. The next gets 50k. The other 98 kids together get maybe 10k. The top kid earns more from that one night than the rest of the school combined. Same talent show. Same 100 kids. Totally different payout.

Nothing about the kids changed. What changed is the system stopped rewarding effort and started rewarding reach. Once your performance could travel without you, a tiny number of performers captured almost everything.

Being pretty good stopped paying. AI multiplies whoever uses it best, lifts some of the middle into the top and strands everyone who stays merely good.

The kids with 2 million views have new leverage. They’re hard to find and hard to get and so they are expensive. They are marquee talent.

The distinguishing feature of Marquee talent is not AI. It is impact outsized enough for their market to know them by name. That could come from AI, personal access, existing relationships or other valuable skills like growth. AI accelerates this trend but it didn’t create it.

For most of the industrial era, talent paid out like a bell curve, where the strong performer earned somewhat more than the average one and most people clustered near the middle. As marquee talent pulls out and ahead, it’s a power law.

The Marquee Test

• How many people that have never worked with you know your name and your work?

• How many people could do what you do at the level you do it?

• If you went quiet for a year, what would your market lose?

Ownership

Imagine a river town with a hundred ferrymen.

Crossing the river ran on labor. A hundred ferrymen rowed people across and each earned a fare for each trip. More crossings meant more rowing. A few ferrymen owned nicer boats and earned a bit more but the gap was bounded by how many trips a pair of arms could make in a day.

Then someone builds a bridge.

Now everyone crosses on the bridge. It cost a fortune to build and almost nothing to run. No rowing and no fares split a hundred ways. Every crossing pays a small toll and all of it flows to whoever owns the bridge. The ferrymen who used to earn a living one trip at a time are standing on the bank.

Nothing about crossing the river changed. What changed is that value stopped flowing to the labor and started flowing to the asset. Once one structure could carry what a hundred rowers used to, owning the bridge beat rowing the boat. And boat trips stopped paying.

The bridge owner has new leverage. The asset collects tolls whether they are awake or asleep and every efficiency gain lands with them instead of a payroll.

The distinguishing feature of the ownership class is that they hold the asset that the value is flowing to. AI sharpens this because the asset now does what a team of people used to. That surplus pools at the ownership level more than spreading down to wages. For most of the industrial era, owning and working sat close together because production needed people. Production used to need a lot of people. With fewer people the gap is widens between owning the asset and renting your labor to it.

As production needs fewer people then the gap between holding the asset and renting your labor to it widens.

Founding a company is only one road to ownership. Equity is an example of someone operating and owning at the same time so the person who still helps run the bridge and also holds a piece of every toll it collects. Taking on investment is an example of selling ownership for cash.

The Ownership Test

• What do you own that keeps producing without you?

• What is yours in a way that does not depend on permission?

Ensembles

A jazz quartet that has played together for ten years.

Any one of them is a good musician. Hire four good musicians who have never met and you get four people playing the same song at the same time. Hire this quartet and you get something none of them could play alone. They know where the others are going. They cover each other. 1+1+1+1=10

The collective is the asset. More than the talent of the four players. It’s the dynamic between them that can’t be easily replicated.

Now suddenly people can fake musical depth pretty well. Anyone can sound polished for one song so a great audition tells you less than it used to. A band that’s already played a hundred nights together becomes a strong signal. Four strangers could become that. But they might also fall apart.

The value of the Ensemble is the unit. The trust and the shared reps are the defining characteristics of the Ensemble. The rhythm doesn’t take months and years to build. You buy it.

An Ensemble can move inside a company as a squad applied to specific problems or move firm to firm. They might be an extraordinarily lean company with $MMM revenue per person. The core of the ensemble is that the bonds inside of the team strengthen and multiply value.

As noise and fraud and pressure rise then building a trusted team from scratch gets slower and riskier. A team that arrives already trusted brings speed and cohesion.

We do this with acquihiring today. We see it in the way that a new VP tends to bring in their old set of directors. Small creative agencies often move this way.

Trust isn’t a fluffy nice to have. It’s core to achievement. So as it becomes more rare and harder to build, companies will need to find more ways to buy it.

The Ensemble Test

• Does your market know you as a group not just as individuals?

• When you move, do you move together?

• What have you built that wouldn't exist without this exact group?

These three win big.

Ownership, Marquee and Ensembles are where the leverage pools.

Look at where you’re spending. Where is leverage pooling and where is it leaking?

The rest of the sort is who it pools away from.

That’s next in this series. Reply with your questions and I’ll be doing a Q&A roundup!

NEW Full Video Summary (scroll down to read instead):

The Breakdown for Readers (& links):

I. Work ≠ Jobs.

For a long time, the job has been the atomic unit of work. You hired a person, they held a job and the job contained the work. The company was organized around the jobs. So was society.

That was built on the premise that tenure would equal stability.

Now in laptop work the workflow is the core. The work gets broken down into pieces and the pieces get assigned to whatever can do them best. Sometimes that is a person. Sometimes it is an agent. Sometimes it is a contractor.

The work and the job can now be related instead of being synonymous.

II. The misconception.

The panic is that work is disappearing.

Work isn’t disappearing. It is being resorted into a new class system.

For many people that resorting does not feel like a transition. It feels like an identity being threatened. It feels like confusion between dystopian headlines and the lived experience of a clunky AI rollout. It feels like layoffs looming.

For leaders it feels like the pressure to maximize efficiency while the existing work piles up. It feels like the pressure to say the right things to the board while soothing fears in the team.

III. Not everyone will move.

The gravitational pull of “how we’ve always done it” is strong.

Not every company will restructure. Some will just not see the value. Some will see it and decline. Some will talk endlessly about AI and get stuck in eternal pilots. For those companies, the work and the job will largely stay the same. The people inside them keep doing what they have always done.

Some people will leave on their own. A FlexJobs survey found 6 in 10 white collar workers said they would take a trade job for better stability and pay. Some will go because they want to. Some will go because they have to.

The point is that the concentration is real and so is the persistence of what was already there. There is no “everyone all at once” in structural transition. Only cracks that grow into canyons.

IV. The industrialization of white collar work.

Fifty years ago companies industrialized physical work. Now they’re looking to do the same with laptop work.

Just a few years ago Amazon treasured a data scientist and squeezed a distribution center worker. The data scientist had leverage. Their expertise was hard to replace. The warehouse worker didn’t have that.

Now an Amazon can squeeze both. Code is cheaper. Work that used to take a team can be done by a smaller group running tools that did not exist three years ago. The data scientist in the middle is now closer to the warehouse worker than they used to be.

The exception is a data scientist who holds a key to the recommendation engine. Or the one using AI to outproduce a team. That person is worth more than ever.

V. The direction is more clear than the speed.

There are major variables still at play here:

• The cost of compute. Right now humans are cheaper in many cases and that’s with the major players burning investor cash. The tech will get more efficient but the investors will also want returns.

• Trust. People like the idea of selling with AI. They hate the idea of being sold to with it. That asymmetry slows everything. Sales cycles, hiring, customer experience and internal decisions. The trust has to catch up with buzz.

• Moral views on AI. Workforces, customers, boards and regulators all have a tolerance for what gets handed to a machine. People detest AI more than most leaders are pricing in.

• Collective action. Unions, walkouts or bargaining don’t reverse the concentration but they bend the angle of the slope.

• Data out of heads. Whether the institutional knowledge sitting in people and memory can actually be organized and available to AI systems.

The forces behind this resorting are about more than AI. Execs are being asked every day to cut faster, ship faster and find margin that was a secondary concern just a few years ago. AI gives a convenient narrative and builds on pressure that was already there.

VI. But my job title is the same.

No one is going to rename their org chart with these exact classifications. These are not job titles. These sort by leverage and scope. They are real but not conscious.

A VP of Engineering keeps being a VP of Engineering. What changes is the class of the deal underneath the title.

Leverage outside of title and tenure isn’t new. A VP of Sales who 4x’d revenue or comes from a $XXX Series B could push harder for a package than one who just hit their goal.

No one will say all of this out loud. They will do it in response to margin pressure, board questions and what they see other companies do. The sort happens as they get more aggressive about allocating resources. Saving money in CX was the goal first more than aiming to make workers feel like cogs.

Workers feel it before they have language for it. They already do.

VII. See the sort in action.

ClickUp CEO Zeb Evans cut 22% of his workforce and announced that the people who stayed could earn up to a million dollars a year.

The path to that pay band is narrow and specific. It is for people who build or run the AI systems that produce what he called 100x impact. The Marquee talent.

Remaining employees will orchestrate agents rather than write code.

Same company. Same titles. A different deal in action.

VIII. The meaning.

This all means that in concentration, companies will identify first with scope and outcomes. Then with the role or seat and the person in it.

Talent will do the same. They will orient to what’s within their control. The work itself and how to extract value from it. Not loyalty to the company.

THE NEW CLASSES OF LAPTOP WORK

Ownership

People who hold the asset the work runs on. Value that used to be spread across the org flows to them. Equity is an example here. More efficient teams create more value at the ownership level.

Marquee

Talent so exceptional that the market knows them by name. Their impact is outsized and traceable which makes them expensive to lose and to get. See:

• Andrej Karpathy joins Anthropic.

• Jony Ive to Open AI.

There’s another tier to this that’s just word of mouth in ecosystems. People notice where So&So goes because they’re so distinctly known for excellence. These aren’t only people who build AI or use it to 1000x. Someone’s relationships, sales ability or other outsized impact can make them marquee.

Ensembles

Groups that move together as a unit. The trust and the team is the asset. An ensemble could move internally like a squad assigned to different problems or externally from firm to firm. They could even be a powerhouse company of 10 focused on one product and intentionally lean. See:

• The Star meeting with 15 disgruntled post employees. “Take Em All”.

• Uptech Studios from the early Acorns team.

• Hyperliquid is a team of 11 pulled from the winners of elite math competitions. They live in the space where ensembles, marquee & ownership overlap. They use “barely any AI” and generated over $800M in revenue in 2025.

Orchestration

The coordination layer running the stack of humans and AI agents together. Where a manager used to direct a team of people, the orchestrator directs a team of people and a layer of agents. See: Genpac’s ‘horizontal and vertical’ Agentic AI Officer role.

Traditional

People doing the work the way it has always been done in firms that look the way firms always have. The company stayed traditional because it couldn’t justify the upfront AI investment, was opposed to the technology or never got past the pilot stage. See: Example from law firms banning AI.

Cogwork

Work broken into pieces and run on more industrial or “blue collar” logic. Workers are more interchangeable. The exchange is more bounded and transactional in both directions. This is the tenor whether it stays as full time employment or moves to contract work. See:

• Workers actively focused on Act your Wage.

• Deloitte splitting benefits across literal different classifications of skilled workers.

• Klarna’s journey with CX.

Out

People who leave laptop work entirely. This could be opting out, career changes or people who are unable to find work again in their field. See:

• Small business as a path

• People choosing to retire instead of staying in.

Throughout this series, I’ll be breaking down each of these classes, how to see them in your org and how to know which one you’re in.

Please reply with your questions!

The tech didn’t fail. The full cost of operating without humans ended up meaning that the humans in stores were worth it at their labor cost.

The price was right. The price is about capital. Capital doesn’t flow to importance. It flows to returns. Those have historically overlapped enough that we conflate them but they are not the same thing.

For the better part of a decade the most richly valued work in the American economy was software that helped midsized companies manage sales pipelines or sell ad attention at scale. Those weren’t necessarily civilization defining solutions. But recurring revenue and high gross margins produced returns that capital liked. SaaS founders got richer than energy founders not because software was more essential to human life but because software had better margin structure. The market was pricing the packaging, not the problem.

Right now capital is flowing to civilization scale problems and the money is real. Fusion, longevity, frontier energy, even AGI. But these problems didn’t suddenly become more meaningful. Their returns got more defensible.

The pricing of work has always been about returns too. In most knowledge work the returns were clear at the company level and less precise at the individual one.

AI is dismantling the pricing logic that protected certain kinds of work from direct measurement. The output of knowledge work was often the product of a team, relationships and complex chains of decisions. You couldn’t follow the work precisely from effort to outcome so you priced the person instead. The price was set by what it would cost to replace them and paid out against time like hourly rate or yearly salary. Rational but not strictly outcome driven.

AI makes knowledge work production costs visible. The path from input to output goes from obscure to traceable. When the input cost becomes transparent the question of what the output was worth gets much harder to avoid. You can see exactly what was produced and what it cost you to do so. And what 10x more would cost you. For the first time there’s a token price on a unit of knowledge work.

So the fully loaded cost of knowledge work becomes two line items. The person and the compute. An engineer’s cost now includes their compensation and their tokens. A marketer includes theirs and the models producing their creative. Unlike a software subscription seat, tokens scale with what gets produced and each call returns a discrete result you can evaluate on its own. Right now compute is cheap because investors are subsidizing adoption. The point isn’t the ultimate ratio. It’s that now the new math exists.

When the result is traceable and the cost is known the market finds a way to price the outcome instead of the person.

This logic is thousands of years older than the technology. Ancient Mesopotamia had brokers working on commission. Before 1980 truckers were on weekly union pay because what happened between terminals couldn’t be cheaply observed. Deregulation and electronic logs made miles, hours and safety events measurable so pay moved to outcomes in cents per mile. Real estate agents aren’t getting salaries any day now.

Outcomes based pricing already existed wherever the work could be easily measured. AI is widening what can be measured.

Supply and demand still runs within that logic. Top talent will still command top compensation and an attractive package will still include a big salary or other guaranteed pay. Where companies are competing for talent, leverage holds the old structure firmer in place.

The jobs most exposed to this shift aren’t vulnerable because they lack value. They’re vulnerable because the work can now be priced on output instead of replacement cost. Every compensation structure and headcount justification in knowledge work was built on the assumption that individual contribution couldn’t be cleanly isolated. Organizations didn’t choose ambiguity. They built on it because there was no alternative. Now they have an (imperfect) alternative.

The transition won’t be clean because no one is rebuilding compensation architecture from scratch while also running a company. The repricing will happen the way most structural shifts do. Unevenly and reactively. Spreading from one “how are you guys handling this?” to another. New measurable bonus targets will come before slashing salaries. Collective pushback will succeed in pockets.

Ultimately capital keeps chasing returns. Returns reward measurement. Knowledge work is getting newly measured.

Here's what's underneath the numbers and the calls operators need to make this quarter.

The Headline Sounds Good

115k jobs added in the April 2026 BLS report exceeded estimates of 55k-70k. March still held up including latest JOLTS report alignment and a slight upward revision.

We’re still relying heavily on healthcare (+37k), transportation and warehousing (+30k) and retail (+22k).

More of the Story

1. Part-time for economic reasons (AKA stuck at part time) rose 445k in a single month. Hours are the leading indicator. Headcount is the lagging one.

2. The share of total unemployed that are long term unemployed is sitting at 25.3%. For reference, in the great recession this number peaked at 45.5% and stayed above 40% from Dec 2009 to 2012. Shortly pre-pandemic it was 19% or so.

   That proportion has climbed from 20.4 to 25.3% in roughly 24 months. The absolute number held flat at 1.8M in April, but the share kept climbing. In a healthier market that pool drains as people find work. It’s not draining.

3. Year over year, Q1 layoffs outside of tech are down 50%. In tech they’re up 33%. AI was the top listed reason but still only part of the story.

Size Matters

ADP’s numbers directionally confirmed the BLS report but the most interesting breakdowns were by size:

Jobs Added by Size

• 1-19: +43k

• 20-49: +22k

• 50-249: +5k

• 250-499: -3k

• 500+*: +42k

Wage Growth by Size

• 1-19: 2.5%

• 20-49: 4.1%

• 50-249: 4.7%

• 250-499: 4.8%

• 500+: 4.8%

Pressure is showing up in the middle. Bigger firms have cash for AI and talent. Small firms stay nimble. Mid-market has neither edge. Their wage gains are nearly the same as enterprise but they’ve stopped adding much new headcount. That leaves many in a tough spot between not enough cash to absorb AI OpEx and not small enough to pivot fast. They’re often the most exposed to tariffs. So they freeze hiring and protect who they have.

*ADP groups everyone 500+ as Large which lumps some mid-market in with enterprise. The squeeze likely runs deeper into the ‘Large’ bucket than this shows.

AI is NOT the Only Story

• Tariffs and the Iran energy shock are stacked up together. Tariffs already pulled 2025 hiring down with import exposed sectors cutting first. Full court decisions implications and refunds remain unresolved. With the Iran War freight and utility costs are squeezing the same firms still absorbing tariff impacts. And the same worried consumers buying their goods. The full ramifications will take awhile to play out but they’re hitting companies already under margin pressure.

• Non-profits have historically been around 5.3% of our GDP with 12.7M employees. For comparison, consider that construction has been 4% with 8M workers. Retail is only slightly higher with 5.5% of GDP and 15.5M workers. A third of nonprofits reported experiencing federal, state or local government funding disruptions in early 2025. That has only accelerated with grant cuts. How many will run deficits or deepen cuts is a meaningful TBD.

• The federal government cut another 9k in jobs in April. Since its peak in October 2024 federal employment is down 11.5%. The scope of Federal workers is roughly the combined US workforces of Walmart and Target.

Labor Signals → Real Life Decisions

CEOs are pushing either all in on the same team and adding AI like Spotify (more output per person) or cutting headcount and adding AI like Block (more output per person). In either case the emphasis is on more, more, more.

Attempting both at half speed is usually the worst option.

Questions to ask:

1. Can I name the two or three functions in my business where output per person or value per labor dollar has actually moved this year? And the ones where it has not?

2. With 25.3% of the unemployed sitting long-term and tech layoffs up 33%, am I using this market to scoop up talent in those functions or just freezing in place?

3. When a seat opens is the default to post the role, “try AI” or to actually trace the workflow and outcomes end to end?

Most organizations are running on workflows that have never been documented, data that has never been cleaned and processes that exist because someone figured it out years ago and everyone else just followed.

A shocking number of companies still have a critical spreadsheet named after the person who started it. That is not a failure of intelligence. It is the logical output of a system that never required anyone to see their own work from a bird’s eye view.

Knowledge was distributed to minimize dependency and maximize execution. Companies did the knowing. People did the executing.

That worked until the execution became the thing you were building an agent to do. The previous model of working rarely demanded a structural account of the work itself. Using an LLM to craft an email is not the same thing as directing an agent to do your job. One is a simple tool. The other requires you to understand your own work well enough to architect, manage, replicate and quality control it. A decade of using search engines over our memory has trained us to locate rather than grasp. We readily conflate the locating with the knowing. Just like we confuse the execution of a task with the architecture of a workflow.

If it were only a capability gap that’s trainable with better prompts and better interfaces. The bigger hurdle is clarity. Clarity requires understanding contextual tradeoffs and third order effects. The ability to know not just what to build but why to build that instead of something else. Most people are waiting for more information before they decide. Clarity means they already know which information matters. That is not a workflow problem. That is a judgment problem.

Judgment of that kind is the accumulated residue of decisions made and consequences observed until it’s instinct. The more tacit knowledge someone has the easier they make the work seem. That makes it dangerously easy to underestimate and tough to replace. Time is the true teacher and that’s a luxury we’re actively cutting. Median tenure in the private sector has been just under four years and declining. People move on with pattern recognition but not consequence memory.

Even with sharp capability and clarity, people are not attached above all to outcomes. They are most attached to what is theirs. Better branding beats better products because identity trumps results. Over half of workers with AI options available still opted to do their work manually. Replacing tasks means giving up not just a habit but a form of power and expertise. Most organizations are not set up yet to reward that trade. They are only set up to say they reward that trade.

The same system that concentrated knowledge at the institutional level concentrated risk at the individual level. Buying has usually been safer than building. The enterprise software contract comes with a vendor to blame if it breaks. The McKinsey recommendation comes with a firm to point to if it fails. That calculus doesn’t evaporate because the technology got more powerful. The operations director who automated order fulfillment and broke it for days pays a steep reputational price even if the CEO is “excited about AI.” The activation energy required to build something even through natural language, own it when it breaks and defend it is still real.

That assumes people would choose to build even if they could. People buy pre-chopped strawberries for 5x the price. Convenience is not a failure of ambition. It is a rational response to finite time and attention and it runs through every level of AI adoption. The question is never only "can we build this." It is "can we build this and should this take time right now and can we defend it when we hit an error?" Not because the building is hard but because the defending is expensive. Building concentrates accountability. Buying diffuses it.

Those excited to build do so locally. At scale when agents proliferate function by function and person by person they encode the same siloed logic that predated the tech. The agent managing cash flow tightened payment terms last Tuesday. The agent managing supplier relationships promised flexibility to the same vendor on Wednesday. Without a shared context each becomes a local solution to a local problem. The undocumented institutional knowledge becomes a collision point. The inability to see end to end becomes a liability.

For decades the system was designed so that no single person had to understand the full picture. That was a feature. It allowed organizations to scale by making individuals interchangeable and keeping the institution as the ultimate holder of knowledge and system. Now the people who cannot see their own work from above have nothing to direct. And the people who can see and direct it clearly become disproportionately valuable.

This is what adoption actually offers and why most organizations will flinch. Radical visibility into how the work actually fits together. Who owns decisions that cross boundaries. Where institutional knowledge lives and whether it is consistent or contradictory. Which judgment calls were deliberate and which were just inherited habits. The difference was hard to measure when both looked like productivity.

Most leadership teams will say they want clarity. Fewer will tolerate what clarity actually reveals. That concentration of context and accountability is new and visceral.

What’s behind the numbers?

1. The labor force is shrinking. Retirements, negative net migration, and more people opting out entirely are pulling the supply side down. That lowers the breakeven number of new jobs needed to keep headline unemployment low. That breakeven number used to sit around 100-150k jobs/month. Estimates of the new breakeven number range from less than 10k jobs/month from the Federal Reserve Board to 15-87k jobs/month from St. Louis Fed and even as low as -3k jobs/month from the Dallas Fed report.

2. Employers have the leverage and they’re using it. Compensation is shifting toward performance-based and long-term incentives over salary especially at the exec level.

3. Energy price shocks aren’t reflected in the March numbers yet but they’re already in the conversation especially for industries where fuel and input costs hit margins directly. To name just a few: airlines, logistics, chemicals, agriculture, manufacturing.

These forces are connected. The budget focus has shifted from growth to margin protection and freeing up cash for AI operating costs. That doesn’t mean no hiring. But it does mean cautious hiring. Demand is falling faster than the supply is shrinking. Energy exposure only accelerates that pressure.

A shrinking labor force should give workers more bargaining power. Instead employers are using this window to restructure compensation and hire in lower cost global markets.

When we’re managing headcount down or flat it means overall economic growth relies on productivity gains. That’s where the AI bets live.

The numbers.

The headline gain is +178k jobs in March. That beat expectations. The gap between expectations and reporting is often more meaningful to markets than the actual numbers.

The context that deflates the party balloons:

• February was quickly revised down to a loss of -133k (bigger than originally reported) while January was revised up a bit bringing the 3 month average to 68k.

• The big Kaiser Permanente strikes that pulled down February healthcare numbers ended so our primary growth industry came back online. But not due to massive exciting growth. Just due to people getting back to jobs that already existed. Healthcare in general continues to grow because of demographic demand not economic booming.

• Warmer weather (and data centers) gave a +26k boost to construction roles.

• White collar payrolls have now contracted for 29 consecutive months.

U-3 unemployment (the rate we say in headlines because it’s the smaller group of people actively looking for work) stayed steady at 4.3% but only because more people gave up on looking for work or have not looked recently enough to count for the numbers. When the “actively looking” pool gets smaller, so does that number. U-6 ticked up to 8% where you see more disaffected workers counted.

We finally retired the term human capital right around the time everyone started being quietly evaluated like it again. Adding headcount was becoming a risk to justify rather than a goal to celebrate. In 2021 a leader who posted 20 open roles was praised for ambition. In 2022 that same leader would need to justify 40 to get approved for 30. Today they might get 5. Capital efficiency replaced growth at all costs on the OKRs. Layoffs became a prioritization tool not a cash distress signal. The math around talent was changing well before AI gave us the narrative.

The workforce was already in structural transition. Boomers are the largest generation in American history and they are retiring at scale. By 2030 every Boomer will be over 65. By 2034 older adults will outnumber children in the United States for the first time in history. The pipeline behind the Boomers was never going to fill that many shoes. A labor shortage was dangerously inevitable to maintain the pace of growth.

But we weren’t just missing workers. We were missing skills. In 2019, McKinsey surveyed executives and found that 87% were already experiencing skills gaps or expected them in the near term. The gap was clearly visible and less than half of them had a clear plan to address it.

AI arrived as a new kind of capability that could partially fill each of these gaps at once. More output per person. Roles that don’t need to be backfilled. Work that gets absorbed and scaled instead of hired. A 24/7 availability without PTO. A feature to add instead of a skill to teach. A coded historical record rather than a lived tribal knowledge.

These forces don’t act in isolation. Combined they have created a displacement that’s been buried under a simpler and catchier narrative of replacement.

Displacement doesn’t announce. It erodes.

A role gets devalued without a conversation. Someone leaves and the position disappears instead of a new hire coming in. A team whittles down and the remaining members absorb more and more scope. The goals stretch because “now we have AI”. The org chart updates by compressing. Middle layers get absorbed or eliminated entirely. A new agent gets added instead of a new hire.

That’s the version most people are living even if the big press releases say otherwise.

The largest and most visible layoffs get labeled as AI-driven because at enterprise scale that framing serves multiple audiences simultaneously. Investors hear efficiency. Employees hear inevitability. It presses adoption internally. The press has a story.

Offshoring is more real and less appealing to say than AI replacement. A company moves significant work to cheaper markets, holds headcount roughly flat and the press release doesn’t need to mention it. Amazon more than doubled the share of its job postings in lower cost countries between 2020 and 2025. Salesforce announced a 10% workforce reduction in January 2023 while simultaneously growing its international employee base. By 2025 total global headcount was back within 4% of its all-time peak. AI is a better headline. It implies high tech progress over anti-US workers.

The same math is happening at smaller companies too. If AI were truly the primary driver you’d expect the pattern to be consistent across company sizes. It isn’t. Small and midsize businesses are adopting AI at nearly the same rate as enterprise but only 3% have cut jobs because of it.

Anthropic’s own labor market research draws a distinction between theoretical AI capability and observed exposure. For the most AI-exposed occupations, the theoretical ceiling is nearly an entire role. The actual observed impact is a fraction of that. What is showing up clearly is a quiet slowing of hiring for junior workers entering those fields. Not replacement. Displacement.

The story is convenient because the other forces were already doing the work. We were already trying to do more with less. The math was already changing, the workforce was already thinning and the skill gaps were already open. AI didn’t create any of that. It gave us a story that points forward instead of at the decisions that got us here.

AI will replace and change jobs. That is true. But the replacement narrative keeps leaders looking forward at the theoretical while displacement is already reorganizing us. Replacement cuts work. Displacement compounds it. The team is thinner, the scope is larger and the AI efficiency that was supposed to close that gap is uneven.

The replacement narrative keeps leaders looking forward while the reality of displacement sews grief, pressure and misaligned expectations throughout their organizations. The leader who gets the diagnosis wrong isn’t underprepared for the future. They’re behind in the present.

As a culture, we don’t like to talk about grief. We think it needs to be justified and private. We pretend grief is reserved for heartbreak in our “real lives”. Even in death we try to package it up into neat three day bereavement leaves. We’re not set up for closure or ritual even in smaller endings. We prefer distractions to feelings.

But grief isn’t about rationality or logic. Grief is a sensation. It lives in the body as much as the mind. It is powerfully collective. We can maintain intellectual optimism and still feel the agitation of change. Grief doesn’t clock out because we clocked in as a professional.

Grief comes dressed up in fear, rage and ambivalence. It can show up in strange places. A sudden obsession with a trivial detail. Dissociating at your laptop just long enough to panic about your work piling up. The low buzz of unease that you can’t quite name. Until we notice it, it buzzes through systems and people alike.

Yet grief’s purpose is not to ruin us or suffer. It is to teach us.

This is why this moment at work feels so strange. It seems odd to grieve a world of work that is technically still all around us.

Professionalism still matters. Sales still matter. My inbox and yours are still full of people hoping their email finds us well and nudging their message back up to the top. We’re still in meetings and then more meetings.

That makes this a living loss. One that still contains more familiar remnants than futuristic days.

The notes are quite familiar but the rhythm has shifted.

This change is bittersweet. For many of us, myself included, this moment with AI feels like a once in a lifetime opportunity to build. More speed, more ideas and more possibilities. But it’s hard to celebrate that potential without acknowledging how poorly we’ve prepared the workforce for this particular shift.

We’re not naive enough to think that work was idyllic before. Office Space still feels apropos with good reason. Work has always included obligation, boredom and pressure. This grief isn’t nostalgia.

But the pressure around work is changing. Today we’re in the messy middle across real economic headwinds, global news cycles and AI implementation that feels more like rising pressure than day to day relief. Add a decade of training our brains to rely on quick hit dopamine scrolling.

That combination means expectations at work are both less clearly defined and higher. Fit is more subjective and more crucial. The bar for excellence is rising while the pace accelerates. We’re layering more ambiguity and more pressure onto minds that we’re exposing to a world’s worth of news and information thirty seconds at a time.

This stretch is more than a soft job market. It is the rumble of transformation.

We can both grieve and build. But we can only build well when we understand the message. This grief isn’t about normalcy or routine. It is not just resistance to change. Most leaders already know the nature and scope of their work is changing. Many are even energized by the scale of it.

This particular grief lives in the gap between training and terrain. The model of competence people were trained for no longer matches the work being asked of them.

For decades, competence followed a simple pattern: accumulate responsibility, grow the team, endure for tenure and demonstrate mastery. People built careers and identities around being good at that model. These were the building blocks of safety and stability.

Those signals are now weakening.

Knowledge can be generated instantly. Tools can perform tasks that once signaled mastery. Decisions are happening faster than the traditional chain of expertise can process them. Layoffs aren’t reserved for last resorts anymore.

When identity is built around the old signals of competence and the new ones aren’t quite defined yet, confidence cracks and judgment splinters. The ambiguity forces an identity level reckoning of value and enoughness that’s deeper than a new job description. The nervous system registers the gap before language quite explains it.

Grief is change made metabolic. Not a weakness. A signal.

A strong executive can hold multiple horizons all at once. They can handle today’s fires without losing tomorrow’s strategy. They see their part of the business as a machine with inputs, constraints and momentum. They make moves that change how the whole system performs instead of getting caught up in the theater of busy.

A contributor asks “What should I do next?”
An executive has already mapped the possible paths, the ripple effects and the tradeoffs. They know exactly how their work ladders up and impacts out. Executives choose the move that protects pace not just comfort.

A contributor says “I need to protect my team at all costs.”
An executive asks “What creates the conditions for my team to thrive over time?”
One is guarding. The other is shaping.

Executives operate at the level of consequence. Contributors operate at the level of tasks.

Contributors weigh effort and outcome equally. Executives are accountable for the outcomes they drive and the conditions they create.

The title does not make the shift.
The lens does.

That approach is brilliant if you have the clout and resources to recognize and attract truly exceptional people. Even with those in place, Cursor are putting in 10x effort to attract 100x people across the team.

Most companies are not in that position and unleashing even pretty good people without clarity is still a pretty good mess.

So most teams start with a job description and go find people to fit it. Some teams will start with a standout person and shape a job around them.

I don’t see the smallest unit for hiring as the job or the person.

It’s the future state that the person needs to create:

- In 12 or 18 months, how will we know we hired the right person?
- What will they have created?
- What will they have prevented?

Once we answer that clearly, the kind of person becomes obvious and the role shapes itself around the work.

Following instructions used to be enough to be considered good. But now, reliable execution is just the starting point.

Think of it like music:

The Player: Reads the sheet music and plays what’s written.
The Improviser: Stays in key but adjusts in real time.
The Composer: Creates the music others follow.

The difference isn’t just in skill. It’s in judgment.

To thrive in what’s next, we need to build teams that can create the music, not just play along.

Why?

Top employees are unlocking sweeping new productivity with technology and they have the business savvy to make great choices with it.

Lower performers are now those who are change resistant and that mindset only accelerates the decay of their skills.

Beyond that, we see more requests for candidates who have not great skills but great judgment and taste.

The ability to be just different enough to get noticed but still on trend to be accepted is a fine line in a world full of noise. The finesse it takes to gather and leverage attention is rare. These skills are much harder to teach.

As such, those at the top of their field will soon be not just 10x their peers but closer to 100x.

That creates enormous space between low and even average performers and top ones. It’s not too late to move to the top but the time to start is now.

• Empathy as a Default

• High Urgency Always

• Insatiably Engaged

• Change Always & All Ways (added in 2022)

These still seem pretty good, right? So why change one?

It turns out that my idea of “High Urgency Always” is thoughtful, fast and proactive. And it turns out that other people interpret “High Urgency Always” as please be as stressed out as possible because everything is urgent.

So, we reflected on what we really wanted to see: strategic and quick action. And we welcomed (RIP High Urgency Always) Purposeful Action to the team.

I’m proud that we push our values so hard that they’re impacting our team’s choices day to day. And even more proud that we’re willing to change when we need to.

In this traditional hierarchical structure, a team of executives sets direction and a bunch of junior people execute on that strategy with some layers of management and seniority between.

The width of this pyramid depends on the nature of the business with higher “leverage” teams being wider at the bottom with more junior level talent.

But what does AI's growing ability to execute do to that structure?

This is not to say AI won’t participate in strategy either. AI is absolutely capable of laying out plans and strategies whether from scratch or as a thought partner.

But this first round of change is largely repetitive task removal.

So, what does that mean for the future of work?

It means smaller and more targeted teams with a premium for employees who can build, train and engage effectively with their AI teammate.

Curious how my 2024 predictions stacked up? Check them out here: 2024 Predictions – How Did I Do?

Spoiler alert: There were some Bs, a few As, and—let’s be real—some total misses.

Want to be the first to hear my 2025 predictions? Reserve your spot now: 2025 Hiring Trends Event

I’d love to hear your predictions too! Hit reply here!

Was there a ropes course at camp? Honestly, yes. It was also totally optional and I personally didn’t even do it so trust falls are not where this advice is heading!

Instead, check out these quick (and free!) team building ideas that our Engagement Manager, Tim Rogers, planned and brought to life:

• Secret Colleague: pair teammates at random and send the following:

  • Have 5 things prepared about your secret colleague below that you appreciate, respect, or admire about them. Include a specific example that shows this in the workplace you can share. 

• Team time capsule. Write a letter to yourself with your upcoming goals to be opened on the following horizons.

  • The end of this quarter

  • The end of next year

• Individually write down the habits and mistakes that held us back in the last quarter. Share and discuss. Burn the written notes at bonfire.