No Priors · 3 min instead of 1 h 10 m · Oct 9, 2026
Enterprises go closed first, then move to open models once costs bite
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 expects enterprise tokens to come from customized systems around open models, not fine-tuned ones. Companies only consider open models after ramping closed workloads, often around $100 million a year in spend.
From “Beam: The Great American Open Model with ReflectionAI Co-Founder and CEO Misha Laskin”, Oct 9, 2026. Watch the full episode
“I have not seen a straight shot sprint to an ownership market, but you have to go through the rental stage to become an owner.”
Takeaways
- Enterprises customize the harness first and fine-tune later
- Nobody sprints straight to ownership; they rent first
- About $100M a year in closed spend triggers a look at open models
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 bite2:47
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 28:03
I think that the majority of enterprise token consumption is going to come not from customized models but from customized systems, meaning you took an open model, you didn't actually fine tune it yet, you just customized a system around it, like your agentic harness around it to make it work for some KYC flow or something like that. then once you're sophisticated enough, you might start fine-tuning and moving to that.
- 28:29
But I actually think that in enterprise, it will be kind of reversed in the sense that the journey that AI natives went through, which was start the closed model, then move to an open model, then customize it. They went through it very quickly because they set up native products that are data collectors. Enterprises are mostly architecting this around their existing products and existing tools. And I think that there are a lot of frictions there to just do a straight shot to fine tuning. So an enterprise, I suspect it'll be a bit different.
- 29:02
And do you think they'll still start with state-of-the-art models or the closed models and then they'll move over, but the harness will be the mechanism by which they customize the open model? That has been our observation that the point at which an enterprise is considering open models and most of the enterprise we talk to are considering now is by the time they've ramped up some significant workloads with closed models. I have not seen a straight shot sprint to an ownership market, but you have to go through the rental stage to become an owner.
- 29:36
It has to get a big enough cost driver for your enterprise, and then you say, how do I cut costs? And this is valuable enough for me to keep it. And so then I switch over. Yeah. If you're spending, let's say, $100 million plus, which is not uncommon at all on closed models a year, then you start thinking about, well, maybe I should figure out a more optimal way to do this. And I think there's an interesting resurgence of on-prem in the sense of also because there's a compute shortage. And so it sometimes becomes, if you're an enterprise and you're consuming from your favorite hyperscaler, very often there's just not enough compute. It's hard to get good rates there. But you're spending so much money. So you might go with an infrastructure provider like Dell and say, I just want to set up the bare metal, but I want to serve stuff into
- 30:29
my enterprise. And then, well, what happens then? Who helps them with that layer between the bare metal and actually making them successful? And you view that as your role in the open model world?