My (brand new) product solves a regulatory compliance problem using GPT.<p>How would you defend it against competition?<p>* Competition includes customers building the thing themselves and third party competitors.<p>* Don't worry about product market fit, distribution channels, audience targeting, MVP features, or any other very valid concerns for early-stage startups. This post only concerns moat.
Data moats?
Data not on the web, both for the first pass of training [1], and for fine tuning / RLHF [2].<p>Maybe compute would be a moat...can you run it in inference cheaper? Conditional computation, very quantized models. Probably training compute is not a moat however.<p>Not sure what SOTA is on callouts to oracles (e.g. calling out to Mathematica or some database) during inference, but maybe if you could use.<p>I would look at BloombergGPT for inspiration on a SOTA vertical GPT [3].<p>Probably speed to deployment & integration & sales would be a moat too, as once you have customers using your stuff your competitors will have more trouble selling to the same folks.<p>[1] In the recent interview on Lex, Sam Altman referred to "data from partnerships".
[2] Probably very reviewed by human raters.
[3] <a href="https://arxiv.org/abs/2303.17564" rel="nofollow">https://arxiv.org/abs/2303.17564</a>