Am I in another world? (See my response above.) Most of the ‘hundreds of LLM apps’ I see are, well, not very fancy and struggling to keep up on accuracy in comparison to the meatspace solutions they promised to massively outperform.
I agree with your assessment that the IT risk barrier is very high in big corp so that entry might be hard for Trellis. Plus a continuous push afterwards to go back to traditional cloud once their offerings catch up.
I totally agree, and it's useful to play out the shrinking quality gap over time:
- Today: Financial companies are willing to pay cloud providers for DB, LLM, & AI services, and want to paper over the rest with internal teams + OSS, and maybe some already-trusted contractors for stopgaps. Institutional immune system largely rejects innovators not in the above categories.
- Next 6-18mo: Projects continue, and they hit today's typical quality issues. It's easiest to continue to solve these with the current team, maybe pull on a consultant or neighboring team due to good-money-after-bad, and likely, the cloud/AI provider solves more and more for them (GPT5, ..., new Google Vertex APIs, ..)
- Next year or year after: Either the above solved it, or they make a new decision around contractors + new software vendors. But how much is still needed here?
It's a scary question for non-vertical startups to still make sense with the assumption that horizontal data incumbents and core AI infra providers don't continue to eat into the territory here. Data extraction, vector indexing, RAG as a service, data quality, talk to your data, etc. Throw in the pressure of VC funding and even more fun. I think there's opportunity here, but when I think about who is positioned wrt distribution & engineering resources to get at that... I do not envy founders without those advantages.
I agree with your assessment that the IT risk barrier is very high in big corp so that entry might be hard for Trellis. Plus a continuous push afterwards to go back to traditional cloud once their offerings catch up.