People laughed at Gas Town, and at that "Fences, not Sandboxes" article yesterday.[1] They're both AIs set up as an organization, not an emulated human. They're both expensive to operate, because the AI members of the organization spend a lot of time talking with each other. They're both applied to frivolous problems.
Now we have the same concept, applied to a more useful problem. It's time to pay attention. Something important is happening here.
Everybody has been talking about "human-level AI", or "superhuman-level AI". This is a different kind of AI - corporate-level AI. It works and thinks like a company's management. It watches a wide range of data sources and responds to some of them with action, and some with internal discussion within the AIs. It can potentially pay attention to far more inputs than any human. That overcomes a basic problem of corporate organization.
A classic management failure is not paying enough attention to something. This is usually not a problem for computer systems. Humans are bandwidth-limited. Teams of AIs are far less bandwidth-limited. They may soon be outperforming humans for that reason alone.
Think about this for a while. These team AI systems may be very useful.
You're probably totally innocent, but this comes across as being AI-written, especially in the context of an AI problem and when your response is pro-AI. For the next 3-12 months (maybe more, we'll see), I suspect you'll have a lot more success if you shy away from those speech patterns.
No single human could design a rocket to go to the moon or keep all Wallmart stores supplied on a daily basis. Moreover when something like the CEO or US president changes, the policies and actions change a bit but the majority actually stays the same. This is collective intelligence embodied in the organization and its institutional memory. This is also what i believe is wrong with Searle's Chinese Room argument - the algorithm is the intelligence.
Our cells and neurons act based on local chemical gradients and electrical signals and we wouldn't call any single one of those particularly intelligent. Yet collectively they produce human intelligence and even what we call consciousness!
That's a feature, not a bug. CEOs should be focusing on the key goals of the business at a high level.
Having an inexhaustible AI CEO that can pay attention to every little thing in the company could easily turn into a micromanagement nightmare.
"I noticed your key pressed were down 27% last week and your agents sat idle 3 nights in a row. And honestly? The thing to hold on to is that it's not a small thing, it's a load-bearing reduction in your output. Let's try to minimize the blast radius with some belt-and-suspenders productivity tips from the bullet point list below:"
But it turns out it's better to do it the other way around. A good plan is easy, implementing it is not.
So the responses will be even more incoherent than Opus 5?
I hope legislation is never manipulated enough to allow an AI to form a corporation or become it's legal director. With whom would accountability rest? Who would you throw in prison if it acts egregiously enough to warrant such a measure? If it's bots all the way down.
If nothing this is a useful demo in real world if used that will show that there are good reasons why hierarchy forms anyways and how power law is at the center of it. Too much democracy may not work in some instances but there may be many cases where the values can hopefully be aligned as well as be lean enough to adapt
Prompts like: you are an activist in XYZ, you wrote this book. Not tell us if this decision is ethical, and if you approve it.
Or running entire HR department in AI...
> A classic management failure is not paying enough attention to something. This is usually not a problem for computer systems.
This is actually my #1 problem with frontier LLMs. They are plenty smart, but they have no true long term memory, and their context window is not nearly enough for a complex system like a corporation. When my project gets big enough and I'm using a lot of /compact, the LLMs always have the same failure point: they fail to pay attention to something.
When they figure this out, I think it's going to be a very, very big moment.