No Priors · 3 min instead of 1 h 10 m · Oct 9, 2026
Enterprises rent intelligence first, then want to own it
Misha Laskin
Co-Founder & CEO, ReflectionAI
Sarah GuoHost · Founder, Conviction
Ex-Greylock partner; early investor in Cognition, Harvey and Mistral.
Elad GilHost · CEO, Gil & Co
Investor in Airbnb, Stripe, Coinbase and Anthropic; co-founded Color.
About this segment
Misha frames open versus closed as renting versus owning inference. Open weights still need cluster software, inference software and a harness, which is where services and a paid inference business come in.
From “Beam: The Great American Open Model with ReflectionAI Co-Founder and CEO Misha Laskin”, Oct 9, 2026. Watch the full episode
“I liken it to we start typically off renting our apartments, but then as you grow up, you want to own a house for various reasons, even if it's a bit of a headache.”
Takeaways
- A closed token rents the whole stack: harness, model, software and GPUs
- Open weights alone are not enough; enterprises need the surrounding tooling
- Services act as a demand driver for the inference business
In this episode
Full episode- 8:50Frontier training costs rise 4x per generation, and AI speeds the work4:09
22:29Enterprises rent intelligence first, then want to own it2:40
- 27:57Enterprises go closed first, then move to open models once costs bite2:47Up next
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 22:36
And from a monetization perspective, there's different ways to commercialize both open source and open-weat models. And Mistral and others were early in terms of different approaches to doing so. Is there a specific direction that you all have been heading and that you can share? Yeah, I think that commercialization of, let's say, open and closed models is in some sense pretty similar. What you're really trying to do is you're trying to maximize inference.
- 23:02
That's really what you're trying to do. And the difference is that it's basically like rental versus ownership inference. So an example is that when you're buying a token, you're renting a piece of a whole stack, which is the harness. if there's an agentic harness in there, the model, the inference software, the cluster management software, the GPUs that it's running on, and all of that is sort of amortized into a token, and you're renting a piece of that whole thing.
- 23:32
When you want to own your intelligence for a number of reasons, and I liken it to we start typically off renting our apartments, but then as you grow up, you want to own a house for various reasons, even if it's a bit of a headache. right like you yeah like it is i mean uh it can be yeah uh but you know as you get to a point where you know you're financially mature enough and you have a family and so forth you want to you want to own it and uh ai has matured as a commercial like in a commercial market to the point where enterprises are spending a lot of money on renting their intelligence and then want to start owning it for various various you know reasons around control and so going back to monetization like what's the difference between a closed and open model? Well, the open model is permissive,
- 24:18
but in order for a customer to make use of it, they need all the other stuff that went around it. Like the stuff that you kind of take for granted from a closed model, you need the cluster management software, you need the inference software, you need the harness and so forth. And so we provide to large enterprises, sovereigns, all the tools that they need to make kind of open model deployment it's successful. And even when we think about services is because a lot of enterprises, most enterprises need some handholding on this. We think about services is how do we go in, unlock really valuable use cases that drive a lot of compute demand, right? Inference demand. And in a sense, services and open model is like a demand driver for an inference business.