I know nothing about AI, but when DALLE was released, I was under the impression that the leap of tech here is so crazy that no one is going to beat OpenAI at it. We have a bunch now: Stable Diffusion, MidJourney, lots of parallel projects that are similar.
Is it because OpenAI was sharing their secret sauce? Or is it that the sauce isn’t that special?
If it wasn't for patents you'd never get a moat from technology. Google, Facebook, Apple and all have a moat because of two sided markets: advertisers go where the audience is, app makers go where the users are.
(There's another kind of "tech" company that is wrongly lumped in with the others, this is an overcapitalized company that looks like it has a moat because it is overcapitalized and able to lose money to win market share. This includes Amazon, Uber and Netflix.)
Most modern tech companies are software companies. To them, the means of production are a commodity server in a rack. It might be an expensive server, but that's actually dependent on scale. It might even be a personal computer on a desk, or a smartphone in a pocket. Further, while creating software is highly technical, duplicating it is probably the most trivial computing operation that exists. Not that distribution is trivial (although it certainly can be) just that if you have one copy of software or data, you have enough software or data for 8 billion people.
Google's Transformer patent isn't relevant to GPT at all. https://patents.google.com/patent/US10452978B2/en
They patented the original Transformer encoder-decoder architecture. But most modern models are built either only out of encoders (the BERT family) or only out of decoders (the GPT family).
Even if they wanted to enforce their patent, they couldn't. It's a classic problem with patenting things that every lawyer warns you about "what if someone could make a change to circumvent your patent".
Once you know that OpenAI gets a certain set of results with roughly technology X, it's much easier to recreate that work than to do it in the first place.
This is true of most technology. Inventing the telephone is something, but if you told a competent engineer the basic idea, they'd be able to do it 50 years earlier no problem.
Same with flight. There are some really tricky problems with counter-intuitive answers (like how stalls work and how turning should work; which still mess up new pilots today). The space of possible answers is huge, and even the questions themselves are very unclear. It took the Wright brothers years of experiments to understand that they were stalling their wing. But once you have the basic questions and their rough answers, any amateur can build a plane today in their shed.
The sauce is special, but the recipe is already known. Most of the stuff things like LLMs are based on comes from published research, so in principle coming up with the architecture that can do something very close, is doable to everyone with the skills to understand the research material.
The problems start with a) taking the architecture to a finished and fine tuned model and b) running that model. Because now we are talking about non-trivial amounts of compute, storage and bandwidth, so quite simple resources suddenly become a very real problem.
Right now the magical demo is being paraded around, exploiting the same "worse is better" that toppled previous ivory towers of computing. It's helpful while the real product development happens elsewhere, since it keeps investors hyped about something.
The new verticals seem smaller than all of AI/ML. One company dominating ML is about as likely as a single source owning the living room or the smartphones or the web. That's a platitude for companies to woo their shareholders and for regulators to point at while doing their job. ML dominating the living room or smartphones or the web or education or professional work is equally unrealistic.
Most likely this.
But the counter for the high moat would be the atomic bomb -- the soviets were able to build it for a fraction of what it cost the US because the hard parts were leaked to them.
GPT-3 afik is an easier picking because they used a bigger model than necessary, but afterwards there appeared guidelines about model size vs. training data, so GPT-4 probably won't be as easily trimmed down.
