What "AI Companies" Really Are: Of 2,716 Posts, Only 16.1% Had Work Flowing Between AI Workers

"I run my company with 30 AI employees." Lately I often see posts like this on note and Threads in Japan. Below I write "AI employee" and "AI company" (AI社員 / AI会社 in Japanese).

When I first heard "AI company," I pictured something like Paperclip. Paperclip is open-source software that gives AI agents an org chart, assigned work, budgets and approvals. Work drops into an inbox, the worker in charge picks it up and handles it, hands the next piece to another worker, and moves on to the next item. As someone who runs a systems development company, that is what I imagined a company made of AI would look like.

But when I read the posts, many of them were something else. An instruction file for an AI, given a name, was called an "AI employee," and a set of those files was called an "AI company." I did not expect that to be called an "AI employee."

So I collected 2,716 posts about "AI employees" and "AI companies" and sorted them by what they actually describe. I also included posts that did not use the word "AI employee" itself but wrote things like "AI secretary" or "an organization of agents." Posts where work flows from one worker to the next, the "company shape" I had in mind, were 438 of them, or 16.1%.

This is not an article saying the other posts are wrong. For people who use AI for personal tasks, or who do not usually use IT much, even one chat with a role name can be a big change. What I want to show is that even if you are happy with your setup now, there is a more convenient stage beyond it.

The word "AI employee" refers to different things depending on who uses it. Some people have prepared 30 files of instructions for an AI. Others keep 30 programs running 24 hours a day. Even with the same "30," you cannot tell from the name whether you could reproduce it in your own work.

So when you read a post and try to judge "can I use this in my own work?", you first have to figure out what is actually inside. This article sorts the things being called "AI employees" by three axes: what runs, whether there is a real company, and what the human does.

I collected 2,716 posts. The breakdown by platform is as follows.

I had an LLM (the kind of AI model behind ChatGPT and Claude) read the body of each post (up to 8,000 characters for note, and including threads and replies for social media) and classify it on three axes. For posts I cite as examples in this article, I opened the original and checked that the classification and summary were correct.

These numbers have the following limits.

The first axis is what is inside the thing called an "AI employee," that is, what runs. Ordered from the most human effort to the least, they split into the following 7 types, plus ones where the content is unclear.

The remaining 483 posts (17.8%) either do not use the employee framing, or their content is unclear because they are ads or behind a paywall. There was even an article where "AI CEO" referred not to an AI but to a human CEO who relies entirely on AI.

The "company shape" I described at the start is Multi-agent: 438 posts, or 16.1%. Instruction file and Role prompt together, where a set of instructions with a name is called an "employee," come to 540 posts, or 19.9%.

Here is one table that shows how the 7 types differ.

When I was unsure at a boundary, I used these questions.

This is the part you can least tell from the name. Here are the numbers written in posts next to what each number actually counts.

Under the same "N AI employees," there is a person who lined up 63 names on an org chart, and a person who set up 32 AI employees, each in charge of one job, and has each one check for work in its area every minute. This is not about which one is right. Because they count different things, the numbers cannot be compared with each other.

Besides how people count, the meaning of "running" is not consistent either. The authors themselves wrote about this when looking back.

All of these are records the authors wrote about their own failures. "How many AI employees there are" and "whether those AI employees did any work today" are different numbers.

The second axis is whether there is a real company. For each of the 2,716 collected posts, I sorted what kind of "company" it was talking about by whether there is a legal entity, whether there are human employees, and whether there are paying customers.

When narrowed to the 129 posts that used the word "AI company" itself, the most common were introductions of software that sells the "company" shape (53 posts), followed by a company as a framing (45 posts).

The same word "AI company" refers to everything from a single folder on a computer, to a real company with 3 employees, to the name of an OSS project.

The third axis is what triggers it to run, and what the human does.

The most common was "Not described," with 967 posts (35.6%). More than 1 in 3 posts say "we have AI employees" but do not say what triggers them or what the human does. On top of that, 777 (80.4%) of these 967 posts also had no specifics such as numbers or time periods.

Combined with the first axis, you can see a tendency in what the human does for each type.

For Role prompt and Scheduled job, what the human does is almost fixed (both over 80%). On the other hand, Always-on single agent and Multi-agent stay at 50–60%, and 205 Always-on single agent posts and 126 Multi-agent posts did not say what the human does. The less a system needs a human to step in each time, like a single agent that stays running or multiple agents handing work to each other, the more the description of the human's role varies.

And the people who write "zero humans" or "humans only approve" also write themselves about the work that remains for humans.

Even when it says "humans only approve," there are 2 kinds of approval.

Under the same "humans only approve," how firmly things stop is completely different. When comparing, you need to look at which kind of approval it is.

What these results show is that "AI employee" is not the name of a system. It is the name of a way of treating one. The name "employee" can be attached to any system. The name only shows how it is treated, and it does not guarantee what is inside.

On the other hand, some writers are trying to define what an "employee" is with clear conditions. One person defines it by these conditions: it has its own ID, minimal permissions and company memory, and important actions are approved by a human. This is written as a plan to start next year ( note, in Japanese ).

This view, that "an employee is really a bundle of ID, permissions, memory and tools," also matches failure reports. One report says that when running 2 agents with OpenClaw, the conversation histories got mixed and one agent called itself the other ( Facebook, in Japanese ). The person who reported it looks back that they needed to fix the ID, environment, permissions and constraints on the system side. Just giving it a name does not decide who that "employee" is.

So when comparing "AI employees" and "AI companies," I think it is better to write the following three things instead of the name.

So far I have written that many "AI employees" are not the company shape I pictured. That does not mean they have no value.

More than half of the posts I collected (56.4%) were not about a company. They were about help with personal life or reports on adopting a tool. Many people write that giving a chat a role name, or keeping one agent running on a Mac mini, changed how they spend their day.

From the view of a development company, or of someone who runs a company, a set of instructions with a name is a tool, not an employee. But for people who use AI for personal tasks, or who do not usually use IT much, it is different. A chat that answers as an "editor," or an assistant on a Mac mini that replies when you message it on Slack, is a big change in itself. I understand that, for them, this alone can feel overwhelming.

So if you are happy with your setup now, is that the end? I do not think so. Leaving out Mindset only and Product name, the other 5 types line up as stages, and at each stage, what the human does changes. Here they are with the most common human role for each type:

With each step up, one more thing leaves the human's hands: the role is kept in a file, the start is set by the clock, you no longer have to open or start it, and finally the workers hand work to each other. In exchange, you need more systems around it. As the records above show, more running does not mean more work done: 16 departments with only 2 used, a video job that stopped for 37 days without anyone noticing, 4 agents on Paperclip nearly using up the usage limit in one night. At the company shape stage, you also need a way to count whether work was actually done, and approvals that the system enforces, not just ones asked for in a prompt.

Which stage fits depends on what you want to stop doing yourself. But whatever stage you are at now, there is a next one.

Finally, let me apply the three axes to my own company. EarthLink Network is a legal entity founded in 2003 and an organization with human employees. Inside it, several systems of different types run in layers. The company shape I described at the start is what we actually run.

So how many AI employees does my company have? My secretary has 7 workers, but those are not the company's AI employees. The company's work flows from the top layer to my secretary or to Local Commander, much like outsourcing. The answer changes with which layer and what you count, so I cannot answer with one number. That is why I try to write not "how many" but "what runs, and what the human does." I wrote about the big picture of running all of a company's work with AI in this article.

In the next article, I will sort the tools themselves: Claude Code, ChatGPT (including dots), other agents you keep running, software such as Paperclip, OpenClaw, and running them on a Mac mini. For each one, I will write what it can and cannot do, and which kind of person it suits and does not suit.