Do AI companies work?

Programming is codified applied mathematics and involves numbers in all but the most trivial programs.

> LLMs are pretty human-like in their mental abilities.

LLM's are algorithms. Algorithms do not have "mental abilities", people do. Anthropomorphizing algorithms only serves to impair objective analysis of when they are, or are not, applicable.

> In the products we've built, there is an AI feature that generates aggregations of spreadsheet data. We have a dual unittest & aggregator loop to generate correct values.

> The first step is to generate some unittests. And in order to generate correct numerical data for unittests, we ask it to write some code with math expressions first. We interpret the expressions, and paste it back into the unittest generator - which then writes the unittests with the correct inputs / outputs.

> Then the aggregation generator then generates code until the generated unittests pass completely. Then we have the code for the aggregator function that we can run against the spreadsheet.

How is this not a classic definition of overfitting[0]?

Or is the generated code intentionally specific to, and only applicable for, a single spreadsheet?

0 - https://en.wikipedia.org/wiki/Overfitting

I don’t think most people looking to build an AI company want to build an LLM and call it a company.

The business model for those is to produce amazing LLM models that are hard to re-create unless you have similar resources and then make money providing access, licensing, etc.

What are those resources? Compute, data, and time. And money. You can compensate for lack of time by throwing compute at the problem or less/more data. Which is a different way of saying: spend more money. So, it's no surprise that this space is dominated by trillion dollar companies with near infinite budgets and a small set of silicon valley VC backed companies that are getting multi billion dollar investments.

So the real question is whether these companies have enough of a moat to defend their multi billion dollar investments. The answer seems to be no. For three reasons: hardware keeps getting cheaper, software keeps getting better, and using the models is a lot cheaper than creating them.

Creating GPT-3 was astronomically expensive a few years ago and now it is a lot cheaper by a few orders of magnitude. GPT-3 is of course obsolete now. But I'm running Llama 3.2 on my laptop and it's not that bad in comparison. That only took 2 years.

Large scale language model creation is becoming a race to the bottom. The software is mostly open source and shared by the community. There is a lot of experimentation happening but mostly the successful algorithms, strategies, and designs are quickly copied by others. To the point where most of these companies don't even try to keep this a secret anymore.

So that means new, really expensive LLMs have a short shelf life where competitors struggle to replicate the success and then the hardware gets cheaper and others run better algorithms against whatever data they have. Combine that with freely distributed models and the ability to run them on cheap infrastructure and you end up with a moat that isn't that hard to cross.

IMHO all of the value is in what people do with these models. Not necessarily in the models. They are enablers. Very expensive ones. Perhaps a good analogy is the value of Intel vs. that of Microsoft. Microsoft made software that ran on Intel chips. Intel just made the chips. And then other chip manufacturers came along. Chips are a commodity now. Intel is worth a lot less than MS. And MS is but a tiny portion of the software economy. All the value is in software. And a lot of that software is OSS. Even MS uses Linux now.

Actually, in this case, comparing how we as humans think to how LLM's work is in fact useful. It's hard for us to eyeball a word and say how many consonants are in it, we need to count. I wouldn't ask a human to eyeball a tax return and tell me what the totals are reliably without giving them the tools to add things up. LLM's are the same way.

It's true that anthropomorphizing in general can be a trap, but when working with LLM's it can be a useful guide in pointing the way towards workable solutions.

I don't see it this way. In plumbing I could have chosen to use 4" pipe throughout my house. I chose 3". Heck, I could have purchased commercial pipe that's 12", or even 36". It would have changed a lot of the design of my foundation.

Just because there is something much bigger and can handle a lot more poop, doesn't mean it's going to be useful for everyone.