Investor Sam Smith-Eppsteiner explains how startup architectures are evolving to build bespoke AI domain models economically.
Prediction Not checkable as stated
Smith-Eppsteiner: AI companies face 40% to 70% gross margins from inference costs
“Like, inference cost is non-trivial. I think for a bunch of the AI companies, both in our portfolio and that we've seen you know, this is, like, not going to be a 90% margin SaaS. Like, we may be talking about 40 to 70% gross margin based on inference costs.”
Assertion Supported
Smith-Eppsteiner: Some law firms use Anthropic directly over Harvey
“Like I know law firms that are using Anthropic instead of Harvey or legal AI tools, so there's definitely anecdote on both sides.”
Assertion Partly supported
Smith-Eppsteiner: Last multi-billion hardware engineering software started in 1990s
“If you look at, I think we were looking at once you know, companies that have built sort of multi-billion category defining products selling to hardware engineers, and the last one was started in like the nineties. Maybe even the eighties.”
Opinion
Smith-Eppsteiner: Foundation AI models struggle with blueprints and technical diagrams
“I think the reality is these models are just not that good today at sort of understanding you know, a very technical diagram, a blueprint, because they haven't seen enough of them.”
Insight
Smith-Eppsteiner: Prompt engineering reaches 70% to 90% of performance
“In talking to, you know, founders who are actually building, it seems like prompt engineering can get you quite far, right? So like, it's widely variable, but let's say you can get to like, 70, 90% of where you need to be from a performance perspective.”
Insight
Smith-Eppsteiner: Agentic AI works best on annoying, error-prone, or seasonal tasks
“What's a good fit is anything where the work is annoying, like, where the person who's doing it actually doesn't like doing it, finds it frustrating for whatever reason Is where the work is already error-ridden. I think that's very common where you have to ref…”