Software engineer and engineering manager, 31 years in. Building Personetta (github.com/EdwardAF-IT/Personetta) — one YAML persona rendered natively for Cursor, Copilot, Claude Code, and Cline. I write about keeping AI agents on-spec at truing-dev.github.io. Oklahoma, .NET/Python/Azure.
A very interesting article, suzyahyah. I especially appreciated your definition of stationarity, a concept with which I struggled in my own time series class. If I understand correctly, it sounds like the basic premise is that a fundamentally statistical methodology (LLMs) can't realistically predict a non-stationary data generation, which makes sense.
Separately, I've wondered for some time if there might be some reliable way to predict non-stationary data. While I don't have the answer, it occurs to me that it will possibly be a non-statistical method due to the fundamental incompatibilities. However, it also occurs to me that, given enough information, every data-generating process actually could be predicted. For instance, in the stock example, if you could model every single input into the system of a single company's stock, including every variable affecting every human that might conduct a transaction of it (daunting and unrealistic as that might be, but this is a thought experiment), then I believe the problem of prediction stops being non-stationary and in fact becomes completely deterministic, if complex. In such a scenario, wouldn't you be able to accurately make your prediction? I believe that perhaps chaos theory could present us with some solutions here where pure statistics (or, rather, simple statistics) cannot.
At the end of the day, ours is an idea business. The more ideas you can gather, and the more diverse they are, the more competitive you will be. Beyond issues of right and wrong, it just makes good business sense.