For people who are in AI companies or have heard their pitches: What's the typical response to "What makes your AI special that can't be replicated by a dozen competitors?"
Contrast that how quickly Adobe rolled out Generative Fill, a product that will keep people subscribed to Photoshop. (e.g. it changed my photography practice in that now I can quickly remove power lines, draw an extra row of bricks, etc. I don't do "AI art" but I now have a buddy that helps retouch photos while keeping it real)
If they went and screwed around with some startup they'd add six months to a project like that unless it was absolutely in the place where they needed to be.
(2) If you were like Pinecone and working on this stuff before it was cool you might be a somebody but if you just got into A.I. because it was hot, or if you pivoted from "blockchain" or if you've ever said both of those things in one sentence I am sorry but you are a nobody, you are somebody behind the curve not ahead of the curve.
(3) I've worked for startups and done business development in this area years before it was cool and I can say it is tough.
Somebody who needs a system built for their business right now gains very little talking to them.
If a startup is a year or two post funding it might really have something to offer, but the huge crop of A.I. startups funded in the last six months have missed the bus.
Big co's can frequently move very fast when there is a lot on the line.
It's just software, there's little "secret sauce" in the engineering, it's the knowledge of the customer problem that's the differentiator.
That is, a lot of people are thinking at the level of "let's build a model" but for a business you will need to build a model and then update it repeatedly with new data as the world changes and your requirement changes.
There would be a lot to say for a solution that includes tools for managing training sets, foundation models, training and evaluation, packages stuff up for inference in a repeatable way, etc.
One trouble though is that you have to make about 20 decisions or so about how you do those things and developing that kind of framework people get some of them wrong and it will drive you crazy because other people will make different wrong decisions than you will. (To take an example, look at the model selection tools in scikit-learn and huggingface. Both of these are pretty good for certain things but they don't work together and both have serious flaws... And don't get me started with all the people who are hung up on F1 when they really should be using AUC...)
So given the choice of (a) building out something half baked vs (b) fighting with various deficiencies in a packaged system, you can't blame people for picking (a) and "Just doing it". (Funny enough I always told people at that startup that we'd get bought by one of our customers, I thought it was going to be a big four accounting firm, a big telecom, or an international aerospace firm but... it turned out to be a famous shoe and clothing brand.)
2. data. Can't do anything custom without good training data! How to get this varies widely across industry. Partnerships with established non-tech companies are a common path, which tend to rely on the network and background of founders.
Even with both those things it's not easy to outcompete a large, motivated company in the same space, like a FAANG. They have the researchers, they have the data and partnerships, so the way to beat them is to move quickly and hope their A- and B-teams are working on something else.
They were L7/L6 ML researchers/eng at FAANG, I'd bet there are quite a few people like that lurking here.
The Mistral folks have impeccable timing, but are leaving FAANG somewhat late compared to their peers.
If you know how to run a Python script, you can fine-tune a LLama model:
As an example, the startup-employed AI researchers I know had already PEFT'd llama2 within a day or two of the weights being out, determined that wasn't good enough for their needs, and began a deeper fine tuning effort. That's not something I can do, nor can most people, and it's a serious competitive advantage for those who can. It's a rather different interpretation of "can adequately fine-tune" than "can follow a tutorial".
When I think "AI startup", I think of the places where these people work. I don't think there's many of those people, and I think their presence is a big competitive advantage for their employers.
I've also heard some companies that build the LLMs say that those LLMs are their moat, the time, money, and research that goes into them is high
If one can scrape the data from the web, I can't imagine having much of a moat or selling point.
Just like forms over SQL, there seems to be a never ending demand.
All the usual things.
First mover
Features
Integrations
Platform synergies
As you can see with all the responses here, they have failed to realize that this is a trick question.
The real answer is that none are special and can be replicated by tons of competitors.
So all these AI startup companies depending on cloud AI services or even open source models have no moat.
Only the same big tech incumbents.