This is a weird time to be alive.

I grew up on Asimov and Clarke, watching Star Trek and dreaming of intelligent machines. My dad’s library was full of books on computers. I spent camping trips reading about perceptrons and symbolic reasoning. I never imagined that the Turing test would fall within my lifetime. Nor did I imagine that I would feel so disheartened by it.

Around 2019 I attended a talk by one of the hyperscalers about their new cloud hardware for training Large Language Models (LLMs). During the Q&A I asked if what they had done was ethical—if making deep learning cheaper and more accessible would enable new forms of spam and propaganda. Since then, friends have been asking me what I make of all this “AI stuff”. I’ve been turning over the outline for this piece for years, but never sat down to complete it; I wanted to be well-read, precise, and thoroughly sourced. A half-decade later I’ve realized that the perfect essay will never happen, and I might as well get something out there.

This is bullshit about bullshit machines, and I mean it. It is neither balanced nor complete: others have covered ecological and intellectual property issues better than I could, and there is no shortage of boosterism online. Instead, I am trying to fill in the negative spaces in the discourse. “AI” is also a fractal territory; there are many places where I flatten complex stories in service of pithy polemic. I am not trying to make nuanced, accurate predictions, but to trace the potential risks and benefits at play.

Some of these ideas felt prescient in the 2010s and are now obvious. Others may be more novel, or not yet widely-heard. Some predictions will pan out, but others are wild speculation. I hope that regardless of your background or feelings on the current generation of ML systems, you find something interesting to think about.