Decagon's moat is the ability to make models deployable within enterprises: the software, governance, testing, collaboration, and integration infrastructure built around them that labs and raw models don't provide.

34:52you've talked about like why there's room to have like much more specific use cases and specific companies but you know you you you've also got had like oh moments where you're like these labs are getting so much better and the models are getting so much better. Um and so long term let's say like we we hit AGI and the LA the models can do all sorts of things we can't even imagine

35:11today. What's decagon's moat at the end of the day after all of that and like why does decagon like 10 years from now still have a right to exist? I think in the short term actually it is the ability to work with enterprise resources and what I mean by this is that the capability of models today is far greater than they are being used for within the enterprise right by which you

35:37can't just take a model and say I'm just going to give this model access to everything within the enterprise and I'll it'll just figure everything out right like that's practically not how these things So to make models like this deployable within the enterprise, right? Like let's assume that every model is like just perfect and makes no mistakes. But to make something like this deployable within the enterprise, you

35:59need to say, okay, I need a way to be able to tell the model what it can and cannot do and make sure that it cannot do anything like catastrophically wrong. Then I need a way to make sure that, you know, hundreds of people within the enterprise can collaborate to make sure that the agent is behaving as expected in the use cases in which they are experts. Then I need a way to be able to

36:18test this model and make sure that it doesn't cross any like regulatory lines that I have and test it, make sure it works well. Then I need a way to, you know, look over the, you know, millions of conversations that happen for me to extract insights for the rest of my teams, right? Right. So there's a lot of just infrastructure and software that you need to build around these models to

36:39make them deployable within an enterprise to make them be uh work with all the legacy systems that these companies have. And so I think for the next few years that's probably going to be a you know the primary thing that these models need to be able to work. uh now once that gets commoditized because the agents can build that on the fly that I don't know and we'll figure out

37:01in three years from now >> by the way I think just listening to you guys it's very clear that you guys deeply understand how to sell AI to the enterprise and I don't just mean you know mid-market newly IPOed companies I'm talking about some of the largest companies in the world um and I think this is particularly interesting because you you both are very very technical but

37:22also go to market animals Um, so I want to talk a little bit about that. Um, but you know, it just feels like you've turned what maybe started as feeling more like a David and Goliath with multiple Goliaths. Um, uh, market dynamic to really a two- horse race between you and Sierra. Um, so share a little bit more about why some of the largest enterprises in the world are