The link: Causal Discovery in Nonlinear Dynamical Systems using Koopman Operators (arXiv)
What it says: It describes an interesting data-driven method to measure causal influence between two components of a nonlinear dynamic system through induced linear dynamics in function spaces of observables.
Er...? It's more technical than the usual thing I link to (although it's a very clearly written paper, so if you feel comfortable with Hilbert spaces you can get a lot out of it) and it certainly doesn't have the contemporary cachet of work on LLM-based AIs, but think of this and parallel lines of research (this is an interesting approach, but far from the first or only) as the equivalent of the mathematical work on large-scale network training that opened the door to word2vec and thus all the way to OpenAI. The sort of AI behind the next revolutions in things like biology or complex engineering will look much more like this than like a LLM, no matter how big - because biology, engineering, physics, etc, are about figuring out and taking advantage of causal structures in complex nonlinear dynamical systems, not so much about summarizing and extending texts.
So, should you read it? If you enjoy math or data modeling for their own sake, definitely. But even if not, one of my main bets during the next few years is that we'll (finally!) see an increased demand for superhuman analysis capabilities driven by the hype from existing AI models, coupled with an increasingly clear inability from those same AI models to provide it. Large scale implementation of this sort of mathematics, as user-unfriendly and hard to implement as they are in comparison with the prompt-driven paradigm, will fill that role, and might well become the main strategic differentiator between companies and economies. Might as well start getting familiar with it.