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RE: LeoThread 2025-05-01 19:47

in LeoFinance5 months ago

Evolvability of AI Firms

The ability of AI firms to replicate themselves, their culture, institutional knowledge, and operational excellence, sets them apart from human firms, which are limited by the constraints of human replication, and enables AI firms to scale and adapt at an unprecedented pace.

Limitations of Human Replication

Human firms, like those led by Elon Musk, are limited by the fact that they cannot simply clone their leaders, culture, or expertise, and are constrained by the physical and cognitive limitations of human beings, making it impossible to replicate a single individual, like Elon, to tackle multiple tasks or industries simultaneously.

AI-Driven Replication and Scaling

In contrast, AI firms can create multiple copies of their AI leaders, like AI Elon, and deploy them across various tasks, industries, or verticals, allowing them to scale and adapt with unprecedented speed and flexibility, and enabling them to create new descendant organizations with optimized templates for success.

Revolutionary Implications

The evolvability of AI firms has revolutionary implications for the way businesses are structured, managed, and scaled, and will likely lead to a new era of innovation, entrepreneurship, and growth, as AI firms are able to replicate and adapt at an unprecedented pace, and create new opportunities for value creation and capture.

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So then the question becomes: If you can create Mr. Meeseeks for any task you need, why would you ever pay some markup for another firm, when you can just replicate them internally instead? Why would there even be other firms? Would the first firm that can figure out how to automate everything will just form a conglomerate that takes over the entire economy?

Ronald Coase’s theory of the firm tells us that companies exist to reduce transaction costs (so that you don’t have to go rehire all your employees and rent a new office every morning on the free market). His theory states that the lower the intra-firm transaction costs, the larger the firms will grow. Five hundred years ago, it was practically impossible to coordinate knowledge work across thousands of people and dozens of offices. So you didn’t get very big firms. Now you can spin up an arbitrarily large Slack channel or HR database, so firms can get much bigger.

AI firms will lower transaction costs so much relative to human firms. It’s hard to beat shooting lossless latent representations to an exact copy of you for communication efficiency! So firms probably will become much larger than they are now.

But it’s not inevitable that this ends with one gigafirm which consumes the entire economy. As Gwern explains in his essay, any internal planning system needs to be grounded in some kind of outer "loss function" - a ground truth measure of success. In a market economy, this comes from profits and losses.

Internal planning can be much more efficient than market competition in the short run, but it needs to be constrained by some slower but unbiased outer feedback loop. A company that grows too large risks having its internal optimization diverge from market realities.

That said, the balance may shift as AI systems improve. As corporations become more "software-like" - with perfect replication of successful components and faster feedback loops - we may see much larger and more efficient firms than were previously possible.

The market continues to serve as the grounding outer loop. How does the firm convert trillions of tokens of data from customers, markets, news, etc every day into future plans, new products, and the like? Does the board make all the decisions politburo-style and use $10 billion dollars of inference to run Monte Carlo tree search on different one-year plans? Or do you run some kind of evolutionary process on different departments, giving them more capital, and compute/labor based on their performance?

What is Coase theorem? How does it apply to this discussion?