this post was submitted on 27 Feb 2026
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Financial services, so probably transcribing people's shitty scribbled expense reports into actually usable structured data, and doing it flawlessly enough they don't get sued.
Edit: Apparently this is the parent company of Square. That's not something that should really need a huge staff to maintain.
Can we start placing bets on when we find out that the "AI tools" they're using are just sweat shop workers in Bangladesh processing invoices?
Eh, I know this is the anti-AI instance, but reading and interpreting things like that is something you can verifiably get AI to do 90% of the time.
Hell USPS has been using machine learning (yes a kind of AI but not the kind they are implying) for years to do that kind of thing.
Kind of
They've had several address resolution centers around the country, where reviewers look at mail and figure out it's address. They don't physically handle the mail, it's an image on a screen.
Iirc they've been doing it this way since the 70s
No? For everything they can they just use OCR and then send it on its way without a human having ever seen it sometimes. If the hand writing is bad enough that the machine can't figure it out that's where the human reviewers come in.
Because 90% accuracy is acceptable for financial institutions ...
I've got an idea. If 90% of AI's output is accurate, just have humans review the 10% that will be inaccurate.
(Yes I am an AI expert, how did you know)
Which outputs are accurate, and which ones are inaccurate? How could you tell? What steps did you take to verify accuracy? Was verifying it a manual process?
That's easy. You just get a second AI to ask the first AI if their responses were accurate or not
(/s)
This is unironically what I've seen people try to do, except they assume the second AI is correct.
Unrelated, but this is how GANs work to some extent. GANs train during the back-and-forth though, while LLMs do not.
That's also basically how thinking models work too, isn't it? And probably the new GPT-5 router, which everybody hates...
Not exactly. Thinking models just inflate the context window to point the model closer to your target. GANs have two models which compete against each other, both training each other, with the goal of one (or both) of those models being improved over time.
No, it's really not. Thus the 6000 remaining employees.
(Assuming this is a significant part of their business)
Until you realize those 10% errors missed the decimal point.
Again, that's what the 6000 remaining employees would be for.
They own Square which is a card processing service typically used by small vendors. If you've ever gone to a festival or concert merch table or farmers market and they accepted credit card, it was probably Square.
Ah, never mind. Weird they don't just call themselves Square.
They were called that for most of their history until Jack Dorsey wanted to change the name to hype some crypto.
Lol!
Okay, that's way too right now. Of course he did.
All this talk is too meta for me.
...Like the company Meta, after they named themselves after the Metaverse.
That worked out swimmingly for them.
That wasn't the reason. It's mostly because cash app became so big that it got confusing if square was the company or the product. Same way Facebook became meta once the had Instagram.
I'm pretty sure "Block" is derived from "Blockchain"
That was part of the name choice. It's also the 3D version of square. Adding another dimension.
Right. So, generating training data :-)