Agents Aren't Taking Your Jobs. They're Creating More Work Instead.

Look, we were told agents would take our stuff off our plate. Some of us were told agents would take our jobs. It

turns out agents aren't going to take our jobs because agents are generating so much work for humans. They're giving

us more work to do. Everybody buying an agent is doing the same math. They're

thinking the agent does the work, I need fewer people doing the work. They're all

wrong. The volume numbers on open router don't lie. Agent token usage is up

don't lie. Agent token usage is up 14-fold between February and August.

Agents now burn more than five tokens for every single one a human burns. And

OpenAI is on the same page. It says its heaviest Codex users generate more than 60 hours of agent activity a day.

Nobody's watching 60 hours of work. This

video is about what happens after that.

What happens after we discover the agents give us so much work. How does

that affect us? How does that affect us at various company scales? How do we go from individual to small medium business to enterprise? We're going to cover all

to enterprise? We're going to cover all of it in this video. Yes, with specific examples. We create agents to take work

examples. We create agents to take work away from us, and yet the evidence so far shows that agents create more work for humans. This video is about how

for humans. This video is about how agents are actually showing up in the workplace. The surprises we found so far

workplace. The surprises we found so far and what that suggests for workers, what that suggests for small businesses, and what it suggests for enterprises. Look,

right now it seems like most of us are saying agents handle more of the execution. I've heard that a lot. I've

execution. I've heard that a lot. I've

said it a lot. I've heard it back from leaders as well. Uh and that means that people are deciding what runs, people are giving the agent what it needs, people are checking the results, and people are taking over when it fails.

The question is, does that add up to more work for all of us? Who does that work? How does that work involve as

work? How does that work involve as agents get better? Let's talk about that, too. It at the personal level this

that, too. It at the personal level this is simple, right? One person can manage one agent for themselves. That's the

whole open claw phenomenon, and we'll we'll talk about that. A small business needs multiple agents and often pays a vendor to manage it, and we're going to talk about specific examples there, too.

An enterprise can hire engineers and deployment teams to build all of that agent management into the company and customize workflows, and we'll talk about what that looks like and why

that's a different case than small businesses and why agents show up so differently in larger companies. Let's

talk about small businesses first. Small

businesses are getting mixed results from agents. You see that in the

from agents. You see that in the conversations I have privately with small business leaders, but you also see it in publicly reported results, which we'll talk about here as well.

Enterprise leaders I talk to, meanwhile, are talking about better returns, and we'll get to that a little bit later in the video and why that is. Let's start

with small businesses and legal. Legal

is an interesting case. Most law firms are small businesses, but of course, law is a very small part of the SMB category overall. Why is legal use growing

overall. Why is legal use growing quickly? Honestly, it's because legal is

quickly? Honestly, it's because legal is a verifiable domain. A verifiable domain is a domain where you can prove

something is right or wrong, incorrect or correct. It's why coding has done so

or correct. It's why coding has done so well. Well, law is like almost a version

well. Well, law is like almost a version of coding for human letters and language, right? We are either correct

language, right? We are either correct and in line with the law or not. And

sure, there's interpretation and lawyers have arguments back and forth, but it's a much more verifiable domain than a lot of the other ones out there. Perhaps

that's why legal agentic AI use, as measured by usage of Codex, is up roughly 108 X since January. What I find

practically is kind of the opposite.

When I talk to SMB owners, they're typically cash-strapped and they're time-poor, and they're trying to use the agents to buy time back. And as they get their agents to do more, they're

spending more time on agent management.

And it's not just legal, on open router agent token usage has grown about 14-fold between February and August and now exceeds human token use by more than

five to one. Agents are using so many tokens these days and they are going to need to be managed by somebody. OpenAI

meanwhile says its heaviest Codex users regularly generate more than 60 hours of agent activity every single day by running several agents at once. Now, no

one is watching 60 hours of work step by step, you can't. The person is leveling up above the loop and that's one of the trends I see in individuals, it's kind of leaking into small businesses as

well. The person is choosing jobs, the

well. The person is choosing jobs, the person is starting runs, the person is checking what comes back and the person is deciding what needs attention. Now,

as agents get better, any given agent may require less supervision even though the number of agents and the amount of work continues to grow. And this is over

the last 6 months, I think a classic example of what we would call the Jevons effect. The idea that making something

effect. The idea that making something more efficient can increase total usage.

It's why the human role doesn't simply shrink as the agent improves. Anthropic

saw a similar pattern when they studied about 400,000 Claude code sessions and the division of labor is visible there as well. In a typical session, the human

as well. In a typical session, the human made about 70% of the planning decisions while the agent made almost all of the execution decisions. So, the person

execution decisions. So, the person chose what ought to be built and then supplied the context and the agent ended up doing the work of like finding the files and writing code and changing things and running tests. This

absolutely matches how I use Claude, right? And it matches how most of the

right? And it matches how most of the people I talk to use Claude. The more

experienced users did something very interesting. They approved more actions

interesting. They approved more actions automatically, but they also interrupted the agent more often when it went in in wrong direction. On about 9% of

wrong direction. On about 9% of conversational turns, experienced users interrupted the agent compared with only 5% for newer users who seemed more deferential to Claude. In other words,

more experienced users watched fewer small steps and got better at noticing when the whole run was going kind of off the rails. Domain knowledge mattered

the rails. Domain knowledge mattered here, too, and that makes me think of law again. Sessions led by experts

law again. Sessions led by experts averaged about 12 agent actions for every single instruction compared with only five for novices. In other words,

knowing the problem mattered more than knowing how to code because the expert could describe the job really specifically. There's that verifiable

specifically. There's that verifiable domain again. Recognize a plausible

domain again. Recognize a plausible mistake and tell whether result was usable. And this is what is beginning to

usable. And this is what is beginning to define the human job, especially for individuals and for small businesses.

People are picking work the agent can finish, verifiable domains. They're

giving it the files and permissions it needs to do that work, and they're telling it what a good result looks like up front and in advance. Then, they know when to let it go because they have the

domain knowledge versus when to stop it and steer it. They know how to check the answer against whatever they want to check it against, right? Maybe it's the law code for lawyers, maybe it's a test

of some sort, and they know what to change when the same mistake keeps happening. One person can carry all of

happening. One person can carry all of that in these tiny businesses because the same person holds the goal and the context, the permissions. They hold all of that domain knowledge, right? If the

agent ends up writing something poor, then the user can just say, "You This is crap. I'm not going to use it." If it

crap. I'm not going to use it." If it takes research in the wrong direction, the the law firm partner can say, "Hey, this is not where I want to go." And the

failure then is also constrained to that person's work. And that can be not true

person's work. And that can be not true for a business when the business needs to depend on the result. And this is where there begins to be a divergence.

Failure stays at one-person scale when we use AI individually. Failure can get much larger even at the SMB scale when we use this in a business context. Part

of what accelerates the failure case is when agents are used in ways that are not for verifiable domains. There's

many, many small business owners in plumbing, in electrical, in in tax.

Regardless of where you're picking, there's lots of small business categories that are not legal that you can go after here. They don't want a new job managing agents. They already have a

job. The The business owner wants

job. The The business owner wants work done, wants outcomes, right? They

want calls answered after hours. They

want quotes sent while the customer is still interested. They want invoices

still interested. They want invoices collected. They want appointments booked

collected. They want appointments booked correctly. All of these things that

correctly. All of these things that would help their business. They don't

want the job to be managing agents because that would keep them from actually managing the business and helping it grow. Those kinds of outcomes are not outcomes that small businesses can purchase for 20 or 40 bucks a month.

And that's the amount they're paying. JP

Morgan looked at explicit AI service payments across 4.6 million small businesses and found that almost 2/3 of them are paying for AI at a rate of

about 40 bucks a month. So, maybe two seats, not even pro on Claude or Open AI. And when you pay that amount, you're

AI. And when you pay that amount, you're not going to be expecting phenomenal agent results that take care of all of your bookings for you. You're just not going to get that. What you're going to

get is a glorified chatbot assistant.

Goldman Sachs surveyed 1,256 owners in its 10,000 small businesses program and only 14% of those owners said AI was fully integrated into their core operations,

whatever that means. While 73%

said they needed more training and resources to implement and evaluate it.

That absolutely matches anecdotally what I hear from small business owners. They

don't have time, they don't have capital, they don't have resources. They

need help to actually get to the outcomes I just described, to more pipeline, to more booked appointments, to the to the results they're looking for that don't require them to have the job of managing agents. A $40

subscription is just not going to handle appointment booking. It's just not going

appointment booking. It's just not going to handle figuring out that something broke 3 days ago and proactively fixing it. That's not what those chatbots are

it. That's not what those chatbots are designed to do. And the problem is that precisely because SMB owners are strapped for time, the person who understands the domain is also the one

who has to hire and who has to sell and check the finances and who has to deal with whatever else broke that day and book the next day's work. So, frequently

in these cases, what I see and what others see is a pattern of vendors ending up picking up AI management for SMB owners. And so, if you are hiring

SMB owners. And so, if you are hiring some to do AI transformation as an SMB owner, the vendor takes over the AI management job. The vendor chooses the

management job. The vendor chooses the workflow. The vendor connects the

workflow. The vendor connects the software. The vendor checks results and

software. The vendor checks results and changes rules and shows where the customer is making or saving money.

Which kind of makes sense if you're strapped for time, but also limits the impact of what AI can do for you.

Because for for for a second, like in a vendor pitch, this sounds good, right?

The owner gets the result. The owner

gets the appointments booked. The vendor

handles everything around the agent.

Except that the vendor has now taken responsibility for a core part of the business. Pocket OS, this is an example,

business. Pocket OS, this is an example, is a small software company whose customers run car rental businesses. Its

founder asked Cursor to handle a routine task in a test environment. Now, you're

probably going to guess and yes, you're right. The agent hit a credential

right. The agent hit a credential problem, found an account-wide railway token in another file. Account-wide

tokens are dangerous. And decided to delete an entire storage volume. Nine

seconds later, the live database and all of the ordinary backups were gone.

Rental operators could no longer find reservations, they could no longer assign cars to customers arriving at the desk. Everything was offline.

desk. Everything was offline.

Now, in this case, Railway eventually was able to recover the data from an off-site disaster backup, but the poor founder of Pocket OS spent the next 30

hours working with every single client pen to keep them operating. The agent

did that much damage in 9 seconds. And

the human recovery, of course, took much longer. It took 30 hours, and that

longer. It took 30 hours, and that doesn't include all the pain and suffering and lack of trust that that wrecked up. And so,

wrecked up. And so, any vendor who is running an agent for a small business has to price in that kind of risk because of the fragility of the

systems they are managing.

Someone has to check the workflow.

Someone has to limit what the agent is able to read and write to. Someone has

to notice those kinds of failures before they happen. Someone has to have a

they happen. Someone has to have a disaster plan that helps with recovery.

I think stories like Pocket OS is part of why we see such a dramatic mixture of results for small and medium businesses.

Legal may be off the charts in token usage. Legal is the exception. Most

usage. Legal is the exception. Most

small businesses have less verifiable domains. They have less resources. They

domains. They have less resources. They

have less time. And they struggle with getting agents to work well, especially when vendors outside are managing agents.

The vendor managing agents can work. You

have to have a good vendor, strong alignment with what the business is going after. And in the meantime, the

going after. And in the meantime, the customer, the the the the small business may end up buying a cheap agent, but getting a useful product out of that

cheap agent, getting useful work done, still often depends on the human that is wrapping around that agent. And so in that situation, the value is still often

coming from the small business itself, not from the agent, because the agent isn't sufficiently embedded in the small business cuz the vendor doesn't know how to do that. So in that case, if the SMB

can't get that value out, the vendor often charges enough to cover the value of what they got. The vendor walks away seeing a success story. The vendor said we did our part and they part ways, but

there's not a lot of value transfer.

There's not an agent left that actually adds to the SMB experience. And again,

why is that? Because the SMB didn't have the capital to invest to get this right by deeply integrating it, because they don't have the time to invest, because when they're outsourcing to a vendor, the vendor's making choices that favor

the vendor even if inadvertently, and because not having a verifiable domain means a ton of work. A verifiable

domain, by the way, that is just can I prove that the agent did it well or not relatively easily. And that's

relatively easily. And that's surprisingly hard to do in a lot of business domains outside coding. It's

why legal took off. It's why I think a lot of other small business domains struggle. And if you're struggling,

struggle. And if you're struggling, you'll need capital to make that domain verifiable and SMBs are classically short of capital. It's not easy to be an

SMB and to have to invest in something like making a net new note domain verifiable. Okay.

verifiable. Okay.

That's the rough news for SMBs. That's

what I'm actually seeing in the field.

That's what the research is showing.

Let's move to enterprises and see how the story looks a little different. An

enterprise, of course, faces at least the same management cost if not more.

And the difference is an enterprise is better capitalized and it has more people to keep that work inside the company. So when I talk to enterprise

company. So when I talk to enterprise leaders who are putting agents into production, I hear consistently much better returns than I hear from small businesses. And I want to be very clear

businesses. And I want to be very clear about why that is. It's not that there's a public study comparing return on investment between the two groups. This

is part of the advantage of talking with a lot of folks as you start to see patterns emerge. What we do see in

patterns emerge. What we do see in public backs this up though. OpenAI has

compared its heaviest enterprise users with typical users. And in January, the heaviest enterprise users generated about 2.6 times as many output tokens

per person as other users. By June, that difference had actually grown up to 8.3 times. In other words, enterprises that

times. In other words, enterprises that were firmly adopting AI were accelerating away from the group. Those

firms were twice as likely to use plugins, six times as likely to be using skills, meaning they'd connected the models to more company systems and built more repeatable ways to use them.

Classic patterns of enterprise adoption and interaction there. Now, when you look at that from inside the firm, which I've done, you can hide a lot in an

enterprise demo, just like you can hide a lot in SMB demo. But when you are inside the enterprise itself, you have to do a lot of work just to light

something up and do it well. So you have to be able to get files from a really wide variety of places. You have to grant access across a wide variety of tools and do so within infosec policies.

You have to monitor and log all of your agent runs. You have to consistently

agent runs. You have to consistently have a bar for deciding whether the agent run was any good. In an SMB, all of that might be one person's job. But

in an enterprise, that's a manager's job, it's a security team's job, it's a quality control person's job, it's a product manager's job. There's just a whole team that's involved in setting

this up and getting it to work. And so I think that although it leads to slower progress initially, ultimately it leads to deeper

integration. Now, OpenAI and Anthropic

integration. Now, OpenAI and Anthropic have both realized that deeper enterprise integration takes time and takes investment. That's why I've talked

takes investment. That's why I've talked about them both creating FDE deployment companies, right? OpenAI has one that

companies, right? OpenAI has one that has hundreds of engineers, deployment specialists, they've committed billions of dollars against it. Anthropic has a similar initiative. The job of those

similar initiative. The job of those engineers, and I've met them, is to sit with leaders, sit with operators, sit with frontline staff, choose the workflows, connect up the data and

tools, set up the controls, test the systems, and then really ultimately ensure that work gets done differently,

ensure impact. That's a ton of human

ensure impact. That's a ton of human work. And it is necessary to get agents

work. And it is necessary to get agents to be productive. And an enterprise can afford to pay the bill, frankly, because the cost is spread across thousands of employees, the cost is spread across a

large amount of revenue, and the upside is correspondingly big if they get it right. Whereas a small business would

right. Whereas a small business would either ask the vendor to absorb that work or live with a much thinner implementation that has correspondingly

less upside. That may be why I see

less upside. That may be why I see enterprise leaders reporting better returns to me consistently from agents than SMB leaders. The base model is not the difference. They both have access to

the difference. They both have access to frontier intelligence. What changes is

frontier intelligence. What changes is how much work the company has done around the agent. So, why is legal different? And can legal point the way

different? And can legal point the way for small business owners? I've talked

about verifiable domains. I think that's important. And And even though we know

important. And And even though we know that Codex users in legal drew like a 108-fold, we also should be aware that Codex is not the only one reporting this

data. This is a widespread pattern that

data. This is a widespread pattern that shows us widespread AI adoption in legal firms found that 71% of solo lawyers and 75% of small firms are already using AI

in the legal field. Codex has their results. And those are statistics, but

results. And those are statistics, but we also have actual stories from legal firms that we can learn from as well.

Traubly and Singer is a small personal injury firm in Washington, D.C. It uses

Even Up to review medical records and prepare the first draft of demand packages. The firm says that one

packages. The firm says that one workflow saves about 40 staff hours a month and lets it avoid another hire.

The lawyers are not having to invent a new way to judge whether what the agent does is right. All they're doing is comparing the package with the records in the case, which they would have to do

anyway. It's not more work to that. It's

anyway. It's not more work to that. It's

not that the agents are always perfect.

Uh a recent benchmark gave systems 1,300 excerpts from legal briefs and asked them to find citation errors, and even the best systems missed subtle problems and too many steps. So, lawyers still

have to verify all of that work, but the difference is they have a way to verify it. They have an existing process. They

it. They have an existing process. They

were doing it anyway. Why not do it with an agent? So, if we look at where

an agent? So, if we look at where lawyers are using AI, I think we get lessons for how SMBs can start to use AI more productively. Start with what

more productively. Start with what you're already doing. Lawyers are not changing what they're doing when they review a demand package. They're doing

what they would do anyway. The agent

just created the work. Where are there areas inside SMBs where small medium businesses are already doing the work?

Maybe they're preparing a quote for the client, and and the manager has to review the quote either way. Maybe the

agent can step in there. When you are trying to find areas for agents to yield value, and you don't have a ton of capital, I think there's a great lesson

there. Find places where you would do

there. Find places where you would do the work anyway, and let the agent do the preparatory step. This also has implications for vendors. Vendors still

need like Steve Jobs level product stubbornness, especially if they're working with SMBs. They cannot expect to drop a general agent into a company and

hope that the owner magically finds value. Instead, you should be looking at

value. Instead, you should be looking at where the owner is spending their time anyway and finding places to make the inputs easier for the owner so they can

do the checks they'd be doing, not waste additional time, and magically get time back on the inputs and get value back that way. If you're not thinking that

that way. If you're not thinking that way, you're probably accidentally creating human work for managing agents when you're selling saving time for humans. And that does not add up over

humans. And that does not add up over time. That does not work. This gets more

time. That does not work. This gets more complicated when we talk about teams. And teams and teams and AI usage is something that is a consistent pattern across SMB and enterprise, and I think

it's useful to understand how teams work with AI. In a field experiment with 776

with AI. In a field experiment with 776 Procter & Gamble employees. So, this is enterprise. One person using AI produced

enterprise. One person using AI produced work that was about as good as a two-person team without it. That sounds

great. But, among the best 10% of answers, only teams using AI were able to improve. So, solo workers using AI

to improve. So, solo workers using AI did not. In other words, the experiment

did not. In other words, the experiment showed that you still need teams of humans working with AI to iterate and

get the very best answers to a problem versus just having individuals use AI.

But, that yields a bunch of management questions, doesn't it? Who manages the agent when it's a team? Who keeps track of what the agent is doing? Who

prioritizes what the agent does? Who is

managing the agent's runs overnight?

These are real questions that startups are grappling with right now that I talked to because you want to take advantage of those overnight runs. Your

agent can run all the time. You want to have that value running overnight. how

do you make sure that you're using that time well? To be very honest with you,

time well? To be very honest with you, most of the way that's answered in small businesses is that the owner steps in and decides where the agent will be allocated. And most of the time where

allocated. And most of the time where the agent is allocated is very specifically against stuff that is verifiable and that is against the revenue line. Which kind of makes sense

revenue line. Which kind of makes sense if you're the owner. We are going to have to come up with more sophisticated answers as agents continue to scale at companies. Because what we're going to

companies. Because what we're going to find is that it's not just one or two agents, it's not just a few agents.

We're going to find more and more and more agents and agents scaling faster and faster at a company until even small medium businesses have to deal with agents at a relatively significant

scale. Maybe 10 or 20 agents for a team

scale. Maybe 10 or 20 agents for a team of 10. Well, now one owner can't define

of 10. Well, now one owner can't define all of that. You need to think through the coordination problems. This is going to impose a new agent management tax and

a new set of startups that will be responsible for effectively helping you manage your agents. If you want to find a trend for 2027, there it is. That's

it. But if we step back, let's step back from legal for a minute, let's step back from small medium business, let's step back from enterprise. Is there a larger lesson we can all learn here? Agents are

doing more and more work. Agents are

generating more work as they do more work. The work of management of agents

work. The work of management of agents is real. And so people are having to

is real. And so people are having to change their jobs to manage agents.

And they are doing so by moving what we would call above the loop. You know how we talk about humans in the loop for AI?

Increasingly in 2026, people are above the loop. They're deciding what the

the loop. They're deciding what the agent is going to do, the way I described it as small business owner deciding it. They're deciding who may

deciding it. They're deciding who may act with approval, which failures matter, what happens next. And if you're wondering from an enterprise perspective how this looks, if if you, you know, your CEO is not defining how everybody

at Procter & Gamble's using the agents.

No, they're not. This is done at the managerial level. And the managerial

managerial level. And the managerial level, like L7 roughly at Amazon, right?

Like that is a role that is becoming more and more, as I talk to these folks, an agent management role, an agent allocation role, as much as a human

management role. And these teams, these

management role. And these teams, these managers, were never trained for that.

And they are running into, in microcosm form, the same issues that small medium businesses are running into. And I've

heard it over and over again. They talk

about the problem of figuring out how to allocate agent runtimes against cheap token hours on the clock. How do you make sure that you are taking advantage of the fact that cloud is less used

overnight in the US? How do you make sure that you are using your agents against spots that have the heart highest ROI, so you can show that you're using them that way when your VP comes

calling? They have a lot of the same

calling? They have a lot of the same concerns, but they're doing it within a matrix organization. And because they

matrix organization. And because they have a lot of the same concerns, the human above the loop issues remain for both small medium businesses and managers in enterprise context. They

both need enough knowledge of the work to tell when the result is wrong. They

need enough authority to change the system if it fails. They need to be confident that what they are doing is going to yield a better result for their

business, whether it's a business unit they're managing in a larger company or the overall business as a small business owner. I'd like to close by laddering

owner. I'd like to close by laddering out a few of my takeaways. I talked to a bunch of small business owners for this video. I've talked enterprise managers.

video. I've talked enterprise managers.

I've talked to enterprise leaders. What

are we taking away here that we can all learn from as we head into the rest of 2026 and think about agents differently?

Where is the puck going here? Agents are

creating more work for humans, not less.

And I think that's a misperception that we've generated when we've said agents sort of take work away from humans. No,

the examples we see empirically at all scales are that agents generate more work for humans. Two, we don't have good established norms around what managing

agents look like and the places where the norms are forming the fastest are in verifiable domains. I mentioned legal, I

verifiable domains. I mentioned legal, I mentioned coding. You can look at

mentioned coding. You can look at healthcare as a verifiable domain as well. Where you have verifiable domains,

well. Where you have verifiable domains, where it's easy to see what's what's good and right and what's bad and wrong.

You have faster progress on agents and agent management norms because it's easy to see when the agent did something right. Where you don't is where things

right. Where you don't is where things get sticky and where you start to see divergence between small medium businesses and enterprises. Enterprises

have the capital and the resources to put agents into sticky, hard to get value situations and if there's strong management, strong will to win, they're able to deeply integrate agents and get

value in those situations. Good examples

are figuring out pricing recommendations, figuring out item recommendations in carts, figuring out how to write better product requirement documents that allow agents

to develop against that work more effectively but still demonstrably meet customer requests and customer requirements. Another one that's sticky

requirements. Another one that's sticky and difficult to validate that enterprises are good at is getting really good at deck creation. That's

notoriously non-verifiable domain, right? What how good are PowerPoints?

right? What how good are PowerPoints?

How do you know? Enterprises have the money to go after those kinds of problems for their specific house style, for their specific insights. This is why you see reports on PowerPoint and insight creation from financial firms,

from McKinsey, from others because they have the capital to go after that.

Another lesson that I would call out is that regardless of scale, you should assume that agents are going to have more and more of an agentic management layer over

time. We are going to start to see

time. We are going to start to see agentic management harnesses, just as we see agentic work harnesses today, but even there, because of Jeeves' paradox,

we're still going to have plenty of work to do because what we're going to find is that the management harnesses allow us to abstract up a level, make it a little bit easier to manage agent execution runs, and that allows us to

manage more agents, and then in turn, we will be expected to do that. The

implications for all of us are profound.

We have to develop the skillset of being able to manage agents. If we are vendors, we have to develop the skillset of recommending where agents should be placed with small

medium businesses with accuracy, so we're not generating more work for them.

If we are entrepreneurs, we have to learn how to build agents for specific use cases that are verifiable, that regardless of scale, businesses are just

going to eat up and say, "I I can get that. I can get behind it. I can get

that. I can get behind it. I can get value out of it." And if we are simply working in a big company, and we're expected to somehow use AI, what this video should remind you is one,

these things aren't taking your job.

Two, you will have to learn how to manage agents to get promoted. And

three, if you are managing agents, the problems you're facing as a manager in a large business are extremely analogous to the problems in a small medium business. In a sense, you can think of

business. In a sense, you can think of it as a fractal problem. Enterprises

have many small medium business units that they're all managing at once, and although you may have more resources than an SMB owner, you still have the same strapped for time, got to show

value pressures that an SMB owner has.

And so, there's things we can learn across different scales. I hope this has been helpful to you. If you want to dive farther, if you want to understand better how agents are actually showing

up, I have a full article on that on the Substack. I also have a guide to

Substack. I also have a guide to managing agents day-to-day, which I think everybody should pay attention to.

So, if you want to go deeper on what I think about managing agents and how to get into the application from today's video, that's where you can head. I'll

see you next time. I hope this has been helpful. I hope the specifics have been

helpful. I hope the specifics have been helpful. I love telling these individual

helpful. I love telling these individual stories of companies. I love talking about what I hear from leaders. I hope

this has given you insight today.

Cheers.