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At some point AI will be able to deal with the repetitive and template based parts of accounting better than accounting.

Which is saying a lot.



I'm not sure that's true. LLMs seem too unreliable for accounting.


Have you implemented accounting systems before?

I ask because if u were to start speaking about the specifics, might we be able to discuss some specifics from that world..

Many of the transactions are a series of small actions, not big things.

Knowing what to trigger, in what order feels a little plausible.

Since most accounting systems have an audit trail, not much that can’t be worked forwards or corrected.


I'm always surprised at how many people in the tech sector don't think that things can and will change. In the next couple of years? Sure. 5+? Who knows. Necessity is the mother of invention.


I'm always surprised at how many people in the tech sector assume overcoming fundamental LLM weaknesses is always “just a matter of time,” and that time will arrive quickly enough to be relevant. If you haven’t seen any of the videos of people trying to get frontier LLMs to count consistently, you might think we’re further from LLM accountant than you realize. It’s not a data problem— I’m pretty sure they’ve encountered numeric progressions between 1 ABs l and 100 before.

And there is no necessity. We have accountants.


I'm not just talking about LLMs though. People thought the internet and email were just a fad that would never catch on. People thought that the idea of home computing was absurd. All because they only saw what was in front of them at the time.

Also, how many accountants do you know that are truly happy with their work? I'd like to think many of them would love to do something other than crunch numbers all day.


Ok, let’s cherry pick some more societal tech revolutions. People also thought Segways were not the revolution in transportation despite fans and industry proponents saying we’d need to redesign cities to accommodate them. People also thought blockchain ledgers were a niche, even if useful, technique despite fans and industry proponents saying they were going to change literally everything.(remember Web 3.0?) People thought NFTs were a fundamentally flawed solution in search of a problem despite fans and industry proponents asserting they’d revolutionize IP. People thought that the metaverse was kind of ridiculous despite fans and industry proponents saying we’d all live a significant portion of our lives there, and some were practically running victory laps at the beginning of the pandemic when everybody was forced online (but then still didn’t use the metaverse. Some thought immersive VR headsets were a niche technology with limited consumer appeal despite fans and industry proponents claiming they’d essentially be the default display for computing by now.


> People thought the internet and email were just a fad that would never catch on. People thought that the idea of home computing was absurd.

We're far removed from "we've sold all the computers the world can buy" (back from when computers were the size of a building).

This is a disingenuous argument. The web and email were basically instant hits and people realized it. Similar story for home computing once the computing power caught up.


Email was a hit well before the internet existed. The utility was immediately apparent. And while people had lots of concerns about the internet, like misinformation and consumer-level utility, it was never broadly considered to be as dubiously useful or ethically fraught as LLMs and diffusion models. You can definitely find articles criticizing the bubble-forming finances, and one or two written by dedicated serial naysayers, but it was not a common perspective. Anybody who lived through that era will remember a hell of a lot more pushback against mobile phone adoption than Internet adoption, and even that was culturally marginal.


> I'm always surprised at how many people in the tech sector assume overcoming fundamental LLM weaknesses is always “just a matter of time,” and that time will arrive quickly enough to be relevant.

Lots of techies are tech-optimists ("tech always improves quickly").

Lots of people also have dollar signs in their eyes (or related, such as increased visibility and scope).

Hard to tell which is which.


I think those are more like two facets in the crowd rather than two distinct crowds. Almost all idealist tech industry workers in column A that believe this is the future of the tech business would also fit into column B. Column B folks believing this business isn’t a fantastically expensive mass delusion event (with regard to the financial impact predictions) would essentially fit into column a. Most that wouldn’t would probably be Wall Street types that told Leopold Aschenbrenner to get bent rather than handing him billions of dollars on blind faith.


Things can easily change for the worse, such as a stock market crash. Who knows? Will we get AGI or will we see more homeless? History says... AGI for sure!


It's very difficult for this sort of thing to change for the worse. It's basically impossible on the software side. If next year's model is worse then keep using this year's model. Hardware technology can potentially go backwards, but things have to get really bad for that to happen.


LLMs are inherently unreliable. We do already work with unreliable technologies for many things but there's this engineering principle called "use the right tool for the job".

And it's a lot more likely that LLMs can't be changed to become unreliable, it's just how they work. So we would need more basic research, that doesn't grow on trees and for which the timelines are basically open ended. Maybe tomorrow, maybe right after cold fusion hits mass adoption.


They’ve become a lot more reliable. It would be strange if that progress stopped now. It’s possible. My main point is that they won’t get worse, as the other commenter suggested.


This isn’t a theoretical or technical problem; in fact, it’s not about the models at all: it’s about the available services getting worse. When they preview a new model and people are benchmarking it, they afford them a shitload of compute and they work great. Then, they get worse. That extra compute is surely in the billions of dollars OpenAI allotted to marketing, and especially in higher-volume periods, people talk about Anthropic‘s responses being lower quality all the time. The finances don’t indicate these companies’ current MOs are sustainable. Since self-hosted models don’t stand a chance of competing with frontier model capabilities, there is a very real chance that the available services will get much worse, and where the rubber hits the road, that’s really all that matters in the foreseeable future.




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