> Every tech company minus the few doing core research have been doing this for at least half a year

I'm assuming you mean all those new 'AI wrapper' startups popping up.? I wouldn't say "every tech company". But yeh it seems incredibly easy, definitely an easy win and leaders get to feel ahead of the curve on AI.

I agree, in fact I would wager the opposite, that most who claim to be a tech company are simply using OpenAI or another vendor with a turnkey API for things launched recently. Over time I expect more use of fine-tuned models, but fine-tuning is not easy, especially if your goal is GPT parity (or better).

The thing I don't understand about this strategy is that it itself shows that there really is no money to be made here. I mean it's a pretty obvious giveaway that:

1. they don't have the resources to build their own technology and probably never will

2. even if they did have, the best they could do is come up with something very similar to OpenAI's GPT, i.e. a (somewhat) generic AI model. This means that OpenAI can also easily compete with them.

All these companies are doing (if anything) is that they test the market for OpenAI (or Google, MS) for free.

The flaw in your assumption is that perfect tech or tech powerhouses win. I mean, sure when they do, they win big; but the endgame for b2b SaaS is mostly M&A, powered by sales, which is mostly down to c-suite relationships and perception of being one among the market leaders ("nobody ever got fired for buying IBM").

If you can move fast, deliver, expand, and raise money, there's a good chance the AI wrapper lands a nice exit and/or morphs into a tech behemoth. Those outcomes (among others), even if mutually exclusive, are equally possible.

So, if I understand you correctly, the business strategy for an AI wrapper company would be that they acquire customers quickly from a specific niche, build a name, while having very little custom technology and then get acquired by some of the larger players who do have the actual AI tech in-house. And, for them, it would be worth it for the brand/market/existing client base.

Assuming that the advance made in the meanwhile in AI doesn't eradicate the whole thing. I mean say some company builds a personal assistant for managers to supplant secretaries, they become the go-to name and then Google buys them in 2-3-5 years. Unless Google's AI becomes so good in the meantime that you can just instruct it in 1-2 sentences to do this for you.

> get acquired by some of the larger players who do have the actual AI tech in-house. And, for them, it would be worth it for the brand/market/existing client base.

The key is, if the incumbents truly feel they can't breach whatever moat, M&A is the safer bet over agonizing what if (I am thinking "git wrapper" startups that saw plenty competition from BigTech; remember Microsoft CodePlex, Google Code, AWS CodeCommit?). Given Meta's push and other prolific upstarts (OpenAI, Mistral), I don't believe access to SoTA AI itself (in the short term) will be an hindrance for product-based utility AI businesses (aka wrappers).

No, as far as I have seen, the "AI wrapper" companies have been clinging to GPT-4 a lot faster than other tech companies. Many bigger companies deploy GPT-4 very sparingly if at all.

I have a question about this—-isn’t it against the OpenAI Terms of Service to do this?

Have the same question - I mean, for training an open source model with no monetization attached, not much Open AI can do besides ban the user, but they can make another account. For a company doing this with the intent to sell it as a capability... seems risky.

Yes, it's explicitly against their TOS.

> What You Cannot Do. [...]

> Use Output to develop models that compete with OpenAI.

Which is ironic given the fact that scraping was likely against the ToS for many of the sites which ended up in OpenAI's training corpus.

But didn’t X do that with their ML model Grok?

Burning bridges and getting sued isn't uncharted territory for Elon Musk.

What's good for the goose...

It will be interesting if the same court cases proving their use of everyone else's data make it fair use to use their machine output as training data. They're definitely in their rights to ban whomever but who knows if they have recourse beyond that?

If you're not selling/ putting your model out there as a generic competitor to OpenAi then you're not competing with them

That's a moving target :)

Yes but I doubt anyone is going to get the Aaron Swartz treatment over it, especially when OpenAI's own models are no doubt generated by playing fast and lose with ToS. E.g. at least as early as 2018, StackOverflow's ToS said:

"Any other downloading, copying, or storing of any public Network Content (other than Subscriber Content or content made available via the Stack Overflow API) for other than personal, noncommercial use is expressly prohibited without prior written permission from Stack Overflow or from the copyright holder identified in the copyright notice per the Creative Commons License"

Ahhhh, yes. OpenAI's good old ToS... Where it's OK to break a ToS / copyright if you're OpenAI for the input to generate the output that, you (customer) don't own and can't cache. Because that would impact their revenue model, be more efficient (power and cost) and still leave them holding the bag after ingesting loads of content they never had a right to in the first place but have staked their claim that it's OK because there's a lot riding on their success.

And, oh by the way, they'll just change their ToS as it suits them for more revenue opportunities even when they stated they wouldn't do business with - oh you know nation state militaries. But - JK! Now we will because <enter some 1%er excuse here>.

It took me 30 seconds to read their TOS and confirm you're just making most of that up.

> As between you and OpenAI, and to the extent permitted by applicable law, you (a) retain your ownership rights in Input and (b) own the Output. We hereby assign to you all our right, title, and interest, if any, in and to Output.

It follows that your claim about caching violating OAI's terms is nonsense.

I think you missed my point. "Caching" output by training a more efficient / cheaper model with that output is in fact against their ToS. In my simple brain that is a form of caching, and I stand by my original post.

I've not made anything up. Your claim that I have is nonsense.

OpenAI changing their ToS for the military on a whim: https://archive.is/GILKl - for your enjoyment.

OpenAI ToS: "What You Cannot Do. You may not use our Services for any illegal, harmful, or abusive activity. For example, you may not:

* Use Output to develop models that compete with OpenAI."

> I think you missed my point. "Caching" output by training a more efficient / cheaper model with that output is in fact against their ToS. In my simple brain that is a form of caching.

If that was your point, I'm pretty sure everyone missed it. No one is training models as a form of caching their previous responses. They want to improve the quality of responses they haven't generated yet. That's not caching.

> I've not made anything up.

You said customers don't own the output; they do. I said you made most of it up, and you did. Including your apparent retconning of your original point.

> If that was your point, I'm pretty sure everyone missed it. No one is training models as a form of caching their previous responses. They want to improve the quality of responses they haven't generated yet. That's not caching.

So... You didn't read the article of which you're commenting in?

> You said customers don't own the output; they do. I said you made most of it up, and you did.

You don't own it. If I own something, I can do whatever I want with it. This is just like your iPhone. You don't actually own it, because you can only do with it what Apple allows you to do.

> Including your apparent retconning of your original point.

Wow, enjoy your day. Your misunderstanding is, apparently, my "retconning". Maybe read the original piece you're responding to within the thread.

Did I read the article? You mean the tweet? If you're saying it supports your claim that fine-tuning a model is equivalent to caching, you are mistaken.

> If I own something, I can do whatever I want with it

BRB digitizing my entire media collection and uploading it to the public internet.

Did you know that removing the tag from a mattress is illegal too? according to the tag.

If you read them they say it's illegal only if you don't own the mattress.

And even this analysis is optimistic as it doesn’t factor in the $$$$ it costs to hire a data scientist to fine tune the models. Just use off the shelf models with RAG until you really need custom models

You've a point. Doesn't look like RAG (w/ in-context learning) or fine-tuning add domain knowledge to the LLM, so some claim they're equivalent: https://twitter.com/Shahules786/status/1748059074556760421 / https://archive.is/iRN5j

Proxy-tuning (https://twitter.com/rasbt/status/1748021765790376385 / https://archive.is/oQs0m) and other such merged models(https://twitter.com/osanseviero/status/1745121420353454219 / https://archive.is/hFYbh) are proving to be interesting area of study, too

I'm curious if you can actually get better than 3.5 though considering how meh it is at most applications. I'd be nice to know whether I could actually get a better model without the effort of trying this.