Reflecting on this nine months later, it feels a lot of people misread the pace of innovation, and where inertia actually stays. A couple of aspects I thought of when I heard about Jasper layoffs.<p>1. A lot of value was supposed to come from selling to enterprises. The narrative was that they would move slowly and hence nimble startups could sell to them and generate quick revenue.
The assumptions are really tested on this one. First, the virality and popularity meant any Engg leader working on AI related projects got social capital and prestige (and a promotion) inside the company, making it preferable for companies to build than buy. An API form factor helped immensely in getting to a POC within a day. Second, for those buying, many startups (in LLMOps) ended up selling the same thing, so they slowed down to evaluate. Third, the data privacy issues meant no enterprise was willing to go for cloud solutions.<p>2. A lot of startups never picked up the tougher problems. Eg: Training an open source model, or finetuning as a service, the core aspects to change the underlying behavior of a model was picked up in open source, but most startups never picked that part up. Partly to do with things that got hype. An LLM wrapper would show off a cool demo, gets shared widely, thus encouraging others to build something similar, rather than go deep.
A very clear indication of this was how Open AI and then Anthropic stopped offering finetuning services on newer models electing to just enable zero/few shot learning and bigger context windows. Easy for them, but tough for consumers who really wanted a customized solution.<p>There are still very cool moonshots out there, and probably unlock the value not captured by incumbents. At this point, my working assumption is that for an AI startup to capture value, they would have to go deeper into the stack, and offer a service their competitors would take effort to do (and by extension enterprises would take time to do). Eg: Ability/Training a open source model locally for search and summarization based on proprietary data. I know BCG[1] did it pretty well and got spectacular results.<p>[1]<a href="https://bcg.com/press/10may2023-intel-bcg-announce-collaboration-enterprise-grade-secure-generative-ai" rel="nofollow noreferrer">https://bcg.com/press/10may2023-intel-bcg-announce-collabora...</a>