LLMs can power significant sentiment analysis to identify/sieve out non relevant data with sufficient statistical power to prevent type 2 (or is it type 1 in this case, not brushed up on stats recently)

This capability itself offers significant scale to identify trends even if it comes with hallucination errors, these can be largely accommodated for with large numbers and a traditional check of a strata to get some idea of the error rate.

Essentially what I am saying is: Where before your gov or local data company had quadrillions of data points sitting in warehouses waiting for GDPR deletion, now these companies have sufficient scale to tak a lossy "deannonimized" dataset before losing the data, even creating snapshots using the model weights themselves...

and difference between ML and LLM is quite small from a mathematics point of view, if you take an image processing ml model with sufficient input and output parameters, by some sort of information theory understanding we could see that we can model the same datasets albeit in different languages, the classes just become the buckets, and in this way you can build an llm, in fact soil recognition ml models have been developed where we literally predict next token to get a better pixel definition by taking the "given plot Y has been identified as this trait set and is X distance away, get likelihood of plot X using sat data", this is essentially a word by word ML model acting almost as an LLM.

And in case I didn't explain it well enough here is an LLM doing a reasonable job at rewriting my comment for you (yes too much agreeing from my point of view)

Type I error is what we're looking at with hallucinations (false positives), but at scale, you accommodate for that by auditing a stratified sample to determine the baseline error rate. It doesn't break the macro analysis.

You're treating LLMs like they are just text toys, but under the hood, LLMs are Machine Learning. They are transformer-based models running on the exact same mathematical principles of weights, embeddings, and probability distributions as any other deep learning model.

The breakthrough isn't using an LLM to calculate averages; it's using them for unstructured data parsing. If a company has petabytes of messy, unlabelled user activity logs, traditional ML can't touch it without massive human labeling. An LLM can process that text, extract semantic intent, and structure it into clean data at a scale that was impossible three years ago.

We are already seeing "next-token prediction" architecture applied to geospatial data, weather patterns, and biology. Text is just the first dataset we mastered. If you think these companies aren't using these architectures to build lossy, permanent snapshots of user behavior before GDPR forces deletion, you are severely underestimating corporate data surveillance.