An AI capitalism primer
Claire Anderson (hi Claire!) asked me to break down the economics of AI. How is it going to make money, and for whom?
In this post I’m not going to talk too much about how the technology works, and the claims of its vendors vs the actual limitations of the products. Baljur Bjarnason has written extensively about that, while Simon Willison writes about building tools with AI and I recommend both of their posts.
The important thing is that when we talk about AI today, we are mostly talking about generative AI. These are products that are capable of generating content: this could be text (for example, ChatGPT), images (eg Midjourney), music, video, and so on.
Usually they do so in response to a simple text prompt. For example, in response to the prompt Write a short limerick about Ben Werdmuller asking ChatGPT to write a short limerick about Ben Werdmuller, ChatGPT instantly produced:
Ben Werdmuller pondered with glee,
“What would ChatGPT write about me?”
So he posed the request,
In a jest quite obsessed,
And chuckled at layers, level three!
Honestly, it’s pretty clever.
While a limerick isn’t particularly economically useful, you can ask these technologies to write code for you, find hidden patterns in data, highlight potential mistakes in boilerplate legal documents, and so on. (I’m personally aware of companies using it to do each of these things.)
Each of these AI products is powered by a large foundation model: deep learning neural networks that are trained on vast amounts of data. In essence, the neural network is a piece of software that ingests a huge amount of source material and finds patterns in it. Based on those patterns and the sheer amount of data involved, it can statistically decide what the outcome of a prompt should be. Each word of the limerick above is what the model has decided is the most probably likely next piece of the output in response to my prompt.
The models are what have been called stochastic parrots: their output is entirely probabilistic. This kind of AI isn’t intelligence and these models have no understanding of what they’re saying. It’s a bit like a magic trick that’s really…