Exactly, that's the problem with the current state of things with open models, the players that keep their secret sauce keep an edge over the people doing things in open while benefiting from all of their work without contributing back.
At the end of the day, if their profit margins aren’t good, it doesn’t matter whether their competition is open source or not (which is often where OSS wins). I think we are seeing that AI isn’t the slam dunk for increasing productivity or we would see companies like UIPath being profitable. I don’t think we’ve seen anyone net a profit on AI software and the only company that has been investing since at least 2017, Apple, gets zero credit for their contributions and commercialization of the tech. I think about how Amazon abandoned the AI-powered, checkout-free tech because the margin of error stayed stubbornly high for too long. The clock is ticking on the industry and some players, like Apple, already have found it isn’t profitable (well to their standards of 60% return on investment).
The project from the thread would take me an impossible amount of time without GPT. Even the page itself would take me twice as long to generate - the charts were done by pasting source data to GPT and GPT writing plotlib code for me to chart them, and the equations were originally written by GPT as well, because I wasn't familiar with MathJAX.
Ditto with the code - a bunch of it was written by GPT originally, since this is my first Swift/Metal project. I kept telling it what I want to do in Python, and it kept rewriting it in Swift/Metal until I learned the latter.
The name "effort" was also invented by GPT. Originally, internally, I was using "quant" but that would be confused with quantization. I considered "perc" from percentage - but that's ugly. I described the project to GPT, and it suggested "effort" as a metric.
As for self-checkout - in Poland we have Żabka Nano which is still going on, and seems more solid than Amazon, but of course the time will tell :)
The biggest companies in the world are running closed-source software for profit that uses open source foundation while barely contributing back, so it's really not the counter-argument you think it is. And that's no wonder we're seeing open source companies going for source-available licenses now (Redis, HashiCorp) or other kinds of restrictions (RedHat), because they were helpless regarding the parasitic behavior of the big bad wolfs.
In these fast moving early times of LLMs, they can maintain this advantage with simple things like proprietary datasets and greater compute.
The difference in quality between the best model that can be made with such proprietary utilities and without is likely to decrease over time as open datasets of greater quality are published and the field matures.
The difference in quality and number of competitors ultimately pays the bills and the harsher the competition is, the less money there will be, for each individual company, to maintain their possibly dwindling proprietary edge.
The greater access to compute is an edge companies will likely hold for a while. It will be interesting to see how much open models will be able to catch up and how great of an edge proprietary models will maintain.
