00:23:57 · Ev Randle
[00:23:57] S0: Yeah, so Gumloop is an independent third party software platform that does a collaborative AI agent and AI automation canvas for enterprises. And what that basically means at like a very simple level is that all of, like we first saw it in coding and coding agents where developers, like with OPUS 4.5, coding agents got to a place where developers felt like they could do a vast majority of their day to day job by offloading the work, not just like tab to complete like we saw with Cursor, but like actually offloading a bulk of their work to coding agents in Cloud Code Codecs or Cognition or any of these other products. And the thesis was what we've seen in code is going to happen in most white collar job functions, and eventually probably most blue collar job functions as well, but the average white collar worker is going to be doing an immense amount to automate the rote redundant work in their day to day via agents and via AI automations. So that's what Gumloop provides. It's a platform for enterprises and every single employee within an organization, not just developers, not just salespeople, not just marketers, every single employee within an organization can create, can iterate on, and can collaborate on both just like very simple automations like you do with like a Zapier back in the day, but then also full on AI agents that they can build and use cross functionally across the organization to do all sorts of things, both triggered by someone talking to them in Slack or Microsoft Teams or running in the background without anyone having to trigger them at all. And obviously there's going to be, and there already are great products like Cloud Cowork and the news around OpenAI combining Codex and ChatGPT into a productivity product that are gonna have big businesses in this general category as well, but we think that there's an immense amount of, and this goes to, you talk about how routers are becoming a popular term as well in the agent space, we think it's really, really important that there's a third party independent vendor for two reasons. One is which the models are actually pretty jagged. The models at any given point are good at different things, and so Gemini is like the best multimodal model. Claude typically has the best coding model.
00:26:10 · Ev Randle, Molly O'Shea
Although now a lot of people think GPT 5.5 is actually better than the latest Opus model at coding. And then you have open source models which typically have immense value for the cost that you're paying for the inference of those models. And so there's great models for all these different tasks, and you do need to have a third party vendor that's sort of watching out for the customer and not just saying, know, in some of these enterprises, you have employees checking the weather with Opus four point eight. [00:26:40] S1: I know. I well, I was talking I've talked to a couple of companies about this already, and, like, how do you control spend on on your tokens and the token maxing debate and all that kind of stuff? Because, like, I know when I'm like prompting Claude or whatever I'm using, it's usually the dumbest, like it's not, it's a small fix. I could probably just go in myself, but look, the tool is magic. So I want to do it myself and make that small fix. But you do that over huge pieces of content or whatever [00:27:12] S0: And thousands of employees. [00:27:13] S1: And thousands of employees, and it clearly aggregates very large bills and great revenue sources for something. [00:27:18] S0: That's right. I'm like asking Opus four eight what I should have for lunch. You know, it's like, it's probably not that's probably not the best use of of our inference budget.
00:27:18 · Ev Randle, Molly O'Shea
[00:27:18] S0: That's right. I'm like asking Opus four eight what I should have for lunch. You know, it's like, it's probably not that's probably not the best use of of our inference budget. [00:27:25] S1: But it's like, it's interesting. So I know you guys are investors in Legora. Was at Harvey the other week, and and they were talking about the same thing. Was like, it's kinda similar to to how text messaging was before. You would pay per text. [00:27:37] S0: That's [00:27:38] S1: right. And like, so now it's happening within our chatbots, like within the different kind of prompts that we have. Like how are you seeing, like do you, how do you think the business model of that will play out, and like, will it get more efficient? [00:27:51] S0: Yeah, yeah, think it all depends on, it sort of depends on two different things. So there's, I have something that I call, and there's many other mom tests out there, and so it might even be trademarked, I don't know. [00:28:05] S1: A mom test?
00:28:06 · Ev Randle
[00:28:06] S0: So my personal AI mom test is, you know, not every mom is like this, but my mom is from, lives in this rural suburban town in Colorado, and she's not exactly an AI native, or she doesn't know how to reset her password on her iPhone, for example. She's not the most tech savvy person. And so the thing I always ask is like, okay for my mom, what is the amount of queries or the amount of things that she needs out of AI that can't be done by a really, really, really cost effective open source model? And you know, I started asking this two years ago, and there was actually like, oh, well I don't know, now it's like 100%. There's nothing that my mom actually asks of her AI products that needs to be done by the frontier or even a near frontier model, and you can start layering that out. It's like, well okay, what about a high school student? Or like what about this person or this person or this professional in this area? And depending on your use case and who you are and what you're trying to get out of AI, you either, you might need the frontier. Like again, like I think what we're seeing in the market right now is people are willing to pay a huge premium to access frontier intelligence to this day, but there's a growing portion of tasks in the economy and within these enterprises where you just don't need a frontier model, and oftentimes you don't even need anything close to a frontier model. And so I think what you're seeing, and Cognition published some work on this as well where they actually trained, don't know if they, I think they post trained an open source model on tasks that they saw done within Cognition where there was low task complexity, like it didn't eat frontier intelligence, but they were extremely popular things that were happening within Cognition. So it was like people were maybe just defaulting to using a frontier model for this thing, and they're like wow, you'd actually save an immense amount of money if you took it away from the frontier because it's a very simple kind of gated task that doesn't need frontier intelligence, and you're gonna save 95% on that action or that query. So I think you're seeing more and more as the models get better and better, there's a growing proportion of queries or actions or whatever you wanna call the use of inference that's going on that doesn't need the frontier.
00:30:17 · Ev Randle
That said, again, I think what's also growing is uses for frontier intelligence because again, I don't think we had unbelievable quality coding models until Opus four point five until last winter, which is why you saw Anthropix revenue and why you saw Cloud Code go so parabolic because there was a genuine breakthrough in the usability of those models, and so it's not that frontier intelligence isn't growing and the demand for that and the ability and willingness to pay a premium for that isn't growing, it's just that there's a lot of non frontier tasks as well, and the demand for those is also growing immensely. And so I think the, my partner Eric Visria had this really great tweet where a lot of people try to make this very zero something. It's like, okay, is open source gonna win, or are Anthropic and OpenAI going to win? And I forget all the things that he went down, but it was basically like on device inference, yes. Open source inference, yes. Proprietary models, yes. Like, it seems like there's a lot of demand for all of this stuff, and the demand is all going parabolic, and so it's not this zero sum game, at least yet, in terms of like, well, either it's all gonna be open source or all gonna be a frontier model from one of the three frontier providers, it turns out that there's use for all of it and it's all growing very, very quickly.
00:31:33 · Molly O'Shea
[00:31:33] S1: So I guess to push on that a little bit, so to your point earlier this year and even to today, OpenAI has crazy amounts of revenue. Anthropic was like last rumored and reported around 45,000,000,000. And now people are speculating around 60,000,000,000. Both are trying to go public with the same with SpaceX and XAI. What happens once those models become efficient? Like, what what do you think is gonna happen to the scale? Do you think it's gonna continue? Do you think what do you think is gonna happen?