Enterprise AI adoption will arrive in step changes

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 20:28

   at your thoughts and just like big picture, where are we when it comes to AI usage in enterprise? How do you see this evolving over the next few years? Yeah, it's funny, like enterprise were not cool. And now with AI, enterprise went back cool. Enterprise sales, enterprise go to market. It's, yeah, four years ago, it was all like PLG. And now it's like with AI, everybody's attacking on enterprise. I think because the real, and I touched your question, the real, I think the real that question like all these all companies growing very fast kind of buying and reselling inference to each other whatever would they actually be in business 10 years from now it's not clear but i do know that you know united will stay in business 10 years from now and and so and coca-cola will be in business 10 years from now right so that's why enterprise i think are becoming

2. 21:19

   first of all because they're the largest are in this span in the world but but but also you have a durable grow of helping them move to a genetic enterprise and stay in business. In this genetic enterprise, as I mentioned before, the scale of token usage is not linear because they're now going multi-models and open-width models to lower their token span with modern routing, intelligent routing, all of those things.

3. 21:47

   It's not linear, though, like you have startups where everything grow and the token spot growth. They're more like step functions. Like it might take six months to put an AI on a gentle workflow in productions. When it does, boom, it's just you have 50 million, I don't know, mobile app user or whatever, and it just spikes. So that's the interesting thing. As our AI gateway goes in production into we have a thousand plus enterprises, you can see very large, large step functions.

4. 22:15

   That's one. Like when they do figure it out, workflow, it really spikes. But it just takes time to move technology, new technology into, you know, large, long, multi-decades organizations. So that's one. Big token spend, what happens? Open weight, multi-model, still multi-cloud, that doesn't change. A huge need for registries and identities. Okay, I have all this agent, I have all this MCP, I have all this API, I have all these models.

5. 22:44

   I need registry to see as a system or record how it all fits. That's a new growing needs. And identities. The way you identify agents, authenticate an authorized agent is different than the workforce. That's why I think IDPs are very important, all these IDP companies, but they will be less important in a genetic enterprise as enterprises are now requiring things like tokens vaults. So you can extend that token to an agent and the agent will never see your API keys, will never see your credential.

6. 23:19

   You'll never see your billing cards because nobody want to give those to them because they could do some damage. So the way you do registry, cataloging and identity of all these AI assets, it's changing a lot. And IDPs is one of the vulnerable spots that I don't know how valuable it would be in the very long run. Security is changing with these tasks before. Yeah. And those are the things we're seeing.

7. 23:44

   Yeah, no, it's really interesting to get your perspective on that,