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?
Wouldnt AI remove the information asymmetries? We could take this focus back into a single company, where the AI have full knowledge of every department, task, and transaction.
Can you elaborate on that?