Smith-Eppsteiner: AI companies face 40% to 70% gross margins from inference costs
Sam Smith-Eppsteiner · Specialized AI brains for physical industry · Apr 4, 2025 · at 33:43
Sam Smith-Eppsteiner discusses business models, unit economics, and margin compression in industrial AI startups with host Shail Khan.
0:00 / 0:14exact quote · 14.9s
720p mp4 · rendered on demand · StarZero watermark
“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.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Sam Smith-Eppsteiner
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.”
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.”
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.”
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.”
Smith-Eppsteiner: DeepSeek proved low-cost, small models are viable for startups
“Like DeepSeek and others have shown, they're Is a path to sort of lower cost, smaller models that are viable for startups to build.”
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…”