You have a fair point. It's interesting because I was literally just going through government programs that provide startup funding, where the application burde

You have a fair point. It's interesting because I was literally just going through government programs that provide startup funding, where the application burden is high, the value is pretty low, and the format overall skews hard towards acting like the funding org has all the power and applicants should be thankful that someone deigned to review their application. It's actually similar to the job market. There is government, where only the most desperate, process oriented folks bother to go through the silly bureaucratic process, there's "traditional" application-interview-offer where the employer has the power, and there's a pull model for top people where they get hunted for jobs. The more power and brand you actually have, the more you can skew traditional / bureaucratic. If you're an unknown startup, you can't just post a job as and expect to get good candidates (I've tried that) - but you get a lot of mediocre ones. And if you're an unknown investor, I'd expect the same. These investors either have done similar enough things before that they have a sense of the candidates / companies they'll get and are cool with that level, or will learn this time about the volume and quality this kind of solicitation generates. ( Products, not papers. Tinkering, not training. Apps, not Arxiv Personly I think we're still at a stage where real research is important if you want ML to be an actual differentiator, and just e.g. cramming GPT into something is going to give you something very shallow, but let's see. I've been thinking about AI products a lot recently because I have an AI startup, and what I've concluded is that for AI to actually be a core differentiator and not a peripheral feature requires fundamental ML work specific to the product. But as I say, let's see what people can do)