Well other than the new wave of trying to mine useful saleable insights using new AI tools, there has always been a demand for insider trading signal sources. If you can find data that predicts stock market movements consistently, even if those movements are slow, then you can beat the bond rates and obtain consistent returns (barring any stock market crash).
A friend of mine owns a few home health care businesses in the bay area. He said he's approached weekly and offered anywhere from $500k-2M for his data.
Interesting. I understand it’s for forecasting and emulating the same business too. Just didn’t think HHC would be there as Medicare dispursement is the biggest payer and that’s public record so hmmm
There are a lot of specific workflows where you can't exactly verify whether something works due to the number of interactions between devices, systems, manufacturing & marketing specs, etc. So if you ask an LLM normally, it might say The ACF 2000 or whatever will do Y with the XYZ 3e, but that's not how either of those things behave together. Hidden in a lot of firm's operating data is the actual answer. So if you could, say, get all that data for a single vertical and join that into a normalized dataset, I imagine that would greatly improved accuracy for both LLMs and any model you might build for that vertical.
They're looking for like, plumbers' email and quickbooks which tells you what kind of plumbing jobs they do and how much they get paid, so they know what kind of things to make robots for.
Here’s how I think about it: only organic data from real scenarios will provide realistic edge cases. The world is much harder to accurately simulate than people sometimes assume.