Your --number one-- most valuable resource is time. I know exactly how LLMs, and ML in general works. I am under no illusions of "this is the best thing ever", or "we're all gonna die to skynet".

LLMs, and ML in general, have made it so that the average employee no longer spends most of their time doing mundane bureaucracy and documentation. This is objectively a good thing. Instead of spending 2-3 hours drafting and writing a report, you can feed the raw data into an LLM, have it spit out a report according to a pre-prepared boilerplate, and then review it. This task takes Claude on the free tier, maybe a minute; for the super data-heavy stuff. Its' accuracy is also ~99.8% correct, and the human review fixes that ~0.2% in maybe a few minutes after that. We are down from 2-3hours of tedium, to maybe 4 minutes of double-checking. That's a massive time-save.

Modern open-source models can be run on anyone's hardware (at varying levels of speed), making the only benefit of datacenters, as mentioned above, time. If I can offload a task that would take 5 hours to run locally, onto a datacenter rack that completes it in mere seconds, that is a very good trade.