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True. Microsoft's all in, Apple's all in, Nvidia is selling shovels, insurance companies are all in, police & military are all in, education is all in, office management is all in. Who is left to pump line up?

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no one is successfully using LLMs for anything other than customer service related things and text generation(coding, writing)

mere trillian dollar industries. so far.

Rubbish. I built a pipeline to handle document classification that successfully took care of ~70TB of mostly unstructured and unorganized data, by myself, in a couple weeks, with no data engineering background whatsoever. This was quite literally impossible a couple years ago. The amount of work that saved was massive and is going to save us a shit ton of money on storage costs. Decades worth of invoices and random PDFs are now siloed properly so we can organize and sort them. This was almost intractable a few years ago.

Could you describe your stack and how its much more effective than two years ago? I heard of printed-table OCR and doc classification years back.

But LLM is obviously able to organize documents and data much more intelligently than any ML algorithm from the past.

Tagging with in house metadata like division, job code, who was the project manager etc.

Very interesting. If I may ask: how are you handling the correctness issue? What's the workflow there if even able to spot a mishap?

Organising data even if it's not 100% perfect is much better than completely unorganized data.

We came up with different categories of tags. I should clarify, the AI didn't actually do the sorting, it did tagging so sorting was tractable. After the tagging it's just a matter of grouping, either by algorithm or human.

I mean considering I did document classification back in 2010 using tesseract, I wouldn't say it was impossible.

But obviously it would be far from accuracy that LLM would be able to do. E.g. generate search keywords, tags, other type of meta data for a certain document.

Yup that's exactly it. By being able to tag things with all sorts of in house meta data we were then able to search and group things extremely accurately. There was still a lot of human in the mix, but this made the whole task going from "idk if we can even consider doing this" to "great, we can break this down and chip away at it over the next few months/throw some interns at it".

Yeah, I don't know - hearing arguments that this was already done by ML algorithms is to me hearing like "moving from place A to B existed already before cars". But it seems like a common sentiment. So much that simple ML attempted to be doing required massive amount of training and training data specific to your domain before you could use it, and LLM can do it out of the box, and actually consider nuance.

I think organizing and structuring data from unorganized data from the past is a massive use case that seems heavily underrated by so many right now. People spend a lot of time on figuring out where to find some data, internally in companies, etc.

That's fantastic. Congratulations!

As far as I know, finance is not all in. I see Goldman Sachs doing experiments, for example, but it doesn't feel like they're convinced yet.

Finance is basically all of the reasons not to use (generative, LLM based) AI , all in one vertical. The poster child of determinism.

Could you please explain?

Finance is a big industry, and they are doing lots of different things.

Nobody wants to lose money (savings) or go bankrupt because of hallucinations.

That's one small part of finance. (And essentially solved mechanically with index funds.)

There's a lot more to finance outside of that.

At the end of the day, the hard limit in finance is defaulting. Everything outside that is financial poetry (or engineering :-p).

I know every segment of finance loves to pretend that's not the case, because their jobs (and high salaries) frequently rely on that not being true (see the subprime mortgage crisis).

> At the end of the day, the hard limit in finance is defaulting. Everything outside that is financial poetry (or engineering :-p).

You are forgetting all about regulations and taxation (and how to work with / around them). And how to cleverly read documents, and exploit loop holes in contracts.

There's so much more to finance.

(And for eg stocks or commodities, there's not even any notion of defaulting. Defaulting only really makes sense when you have fixed obligations. 'Fixed income' is only one part of finance.)

> (see the subprime mortgage crisis)

That's actually a more nuanced topic than you think. See eg https://kevinerdmann.substack.com/p/subprime-bank-runs-and-t... and other posts by Kevin Erdmann on the topic.

Sure, there’s lots of room for LLMs in helping to do clerical work, HR, that kind of thing. I was actually thinking of the direct management of funds and investments. So yeah, like probably all businesses, the ancillary functions can probably improve productivity using Generative AI with a minimal hit to quality.

You are right about the clerical work, but even pure finance is a lot more than 'direct management of funds and investments'. Have a look at Matt Levine's Money Stuff newsletter for a taste.

And I'm not quite sure why you mention determinism in the grandfather comment? Finance people have been using Monte Carlo simulations for ages. (And removing non-determinism from LLMs by fixing the seed of any pseudo-random number generator used wouldn't really change anything, would it?)

Finance is all in on reading 10-Ks and generating summaries. If you have decisions in mind, I’ll be referring to IBM 1979 slide until an HR LLM fires me.

That's not finance, that's just a generic application, and I think it's a terrible idea that won't last anyway.

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