this theory is typically mentioned when introducing the Theory of the Firm, i.e. why do companies exist at all? why not everyone just freelances, and when you need a marketing/finance/legal/coding person, you just contract one on the free market? the idea is that there are always frictions when doing business with someone new (is this person good? trust worthy? how do i find out?), and how much friction there is determines how big the firm will grow (to incorporate functions in-house, or expand to other industries).

in a perfect frictionless economy, it could indeed be true that business transactions all happen at the smallest unit, i.e. the individual. at the other extreme, there would just be one firm that coordinates all economic activities. all real world economies sit some where in between.

On the application front though, it feels that the advancements from a couple of years ago are just beginning to trickle down to product space. I used to do some video editing as a hobby. Recently I picked it up again, and was blown away by how much AI has chipped away the repetitive stuff, and even made attempts at the more creative aspects of production, with mixed but promising results.

for a short video version of this history, see https://www.youtube.com/watch?v=zaXKQ70q4KQ from Veritasium.

reasons for recommendation: - it's an example in the biological science, to complement the heavy representation of examples from computer science and entrepreneurship in this thread - the main character, Kary Mullis, is colorful and controversial. Not a likable individual, but nevertheless had traits (mostly an unreasonable obsession) that enabled him to make such a discovery - the discovery of high temperature tolerant enzymes predated Mullis' insight by some two decades, and it played a key role in making PCR practical and widely applicable. this is a pattern I have seen often in major inventions, which were made possible by prior discoveries (often decades old) which lay dormant until someone put everything together. This process of re-discovering the pieces and making connections is also where I think machine learning could be particularly helpful. In fact this is my main motivation for picking up this book (by online reviews, not a particularly well-written one).