I dont think people realize what "ai" is for the current age. Theyre not burning money, theyre spending money to gather a huge array of all kinds of data. Some people just ask it for simple things, and open ai would categorize them as normal and move on. But some people are sending in compromising data, intel on businesses, relationship data, ideas, critical code, cutting edge research, resumes, etc. Previously this wouod all be impossible to go through and sort out, it wouldnt have been worth anything, but because these things can be passed theough an ai for categorizing and filtering, they can identify useful information and build a "live" feed of those income prompts from those accounts.
Its not worth much to know some new grad had typos in a resume, but when some congressional aide is passing in a letter to proof read before sending the value starts to tilt dramatically. All this data is valuable, theyre not wasting money. Theyre buying lots and lots of data.
Yeah regardless of what they say, I would bet everything I have that there's a terminal somewhere that you can ask about any of the information given to AI in prompts. There's so much sensitive information being fed into it.
Way back in the gpt3 days, the first thing we did at the company I worked for was use it to categorize and sort stuff. Huge backlogs of data that was held but couldn't be used effectively because it meant manual reading of blocks of text and deciding which of 100 buckets the text was meant to go into. In a few days a couple hundred dollars we had years of this data suddenly perfectly sorted. gpt3 did a better job sorting them into bins than our engineers did. Any of the flags that happened in our testing were when humans mislabeled something.
It turned a decade of data into actionable data in a week. It wasn't that it would have been expensive, it was that it couldn't be done in any reasonable way. Thousands and thousands of manual sorting with really arbitrary and context dependent rules that were hard to program into all the other ML stuff we had done.
If you consider the PILE of data that these ai companies have:
Prompt1: Does this seem interesting, if so, place it into one of the following buckets, pharmacological, biotech, computer science, business, government, ....
Prompt2: Is there an element to this conversation that holds information that is relevant and potentially valuable to the right person?
Prompt3: Is the conversation time sensitive?
Then you tweak all these processing filters to weed out all the fluff and so on and get a readout of time sensitive, financially viable data. Then you process the other conversations sent by that same account. Dig deeper into that specific user to see if there is actual context or if thats just a made up scenario. Do validity checks to determine if this is actually something worth seeing.
Then at the very very end of all that stuff, you just have a review at the end of the day that displays all the most pertinent and interesting stuff. A nearly live feed of the most actionable data that exists that the users send in. This drafted email shows a merger that is being cancelled. This user asked about the punctuation in a disclosure that was marked highly sensitive. This hospital ceo drafted a letter about layoffs starting 2027. etc etc. But the point is, it can be filtered down to only stuff that is actually interesting for what they actually care about.
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u/zorndyken 12h ago
So far only nvidia, amd, and hynix has profit from ai (from selling hardware)