00:10:43 · John Daley
[00:10:43] S1: Yeah. And I think that's a good segue to talk about the work we're doing with other larger enterprises in financial services, in health care, in government, and in industrial technology. Because if you think about the range of things Jack just mentioned there, covering internal risk and compliance work, a third party risk management, and then increasingly also agent governance and insurance work. There are point tools that look at sort of parts of those individually. I think there's not really anyone bringing to bear agent capabilities to have those all managed and observable in a single platform. Right? So if you are thinking about the risks and compliance implications of a particular other party, well, we intelligently and and dynamically link that to your internal requirements, the controls that you've implemented, how those are implemented, and and that is all being updated on an ongoing basis. And so when you, as a user, come into our platform, you're benefiting from the massive amounts of work the agents have done on your behalf on an ongoing basis, and it's bringing you much closer to that point of decision making and action, which is really what the value add as a person using our platform is. Right? It's like, what are you actually... What decisions are you taking that change the way your your enterprise operates to be more aligned with the security and compliance posture that you want? Or are you continuing, in fact, in the same way with more conviction on the basis that... Of the work that agents have done? I think that's really quite different from a legacy tools in this space that in the failure mode devolving to just checklists divorced from operational decision making.
00:12:27 · Sebastian Johnson
[00:12:27] S0: And now you've raised this 7,300,000 round. Is this gonna be on back further building out the product, adding new capabilities, or is this gonna be about, alright, we've got something that works, we've got something that we know can scale, it's just about go to market and expansion?
00:12:41 · John Daley, Sebastian Johnson
[00:12:41] S1: Yeah. So I think there's a bit of both. We we got to about a million dollar run rate with a three person team in nine months from launching the platform, which, you know, was only possible because we leverage AI for everything across the business, from software dev to to go to market to... Of running our daily internal sign ups. Right? But with the ambition we have, we know we need to bring on more MFN people, although not the size of company that we would have had to build, say, two... Even two, three years ago to get to the same outcomes. So basically, three three priorities of of this round. People. So we're now 10 people. We brought on a bunch of great engineers and the foundation of a great go to market team. Products, there's a ton of customer demand we have to extend the capabilities we currently have, and then we have big ambitions up to the on the agentic assurance and and governance side, which we can talk more about to build out over the next six to twelve months. And then, yeah, go to market. So to date, it's just been Jack and I time to pave into a counter with sales, but now we're actually beginning to put a real go to market motion in place. And, you know, we think we're going into a big replacement cycle in the next few years where there's gonna be tens of billions of dollars of of of enterprise spend changing over, and we need to be in as many of those conversations as as we possibly can. [00:14:02] S0: And that replacement is really interesting. Why do you think that's gonna happen? Why are the incumbents not well prepped for this new world?
00:14:11 · John Daley
[00:14:11] S1: Yeah. So I think, as Jack mentioned earlier, our our... A graph database Atlas, and I think it really comes down to, have you architected your platform assuming it is agents doing most of the work? Right? And I think, like, really foundationally, you need to have that assumption that we are building for a world where it is agents talking to other agents and people to both collect data, do assessment, and then take action. And that's been our foundational belief since we began building the platform, you know, eighteen months ago. I think the... A legacy of platforms who bolted on AI chatbots, sort of... There's a veneer of AI capability, but they're really not providing agents that sort of... That context and that capability in the background to do as much as effectively and as accurately as as what we built. And then there are other AI native platforms out there, but I think they haven't necessarily internalized some of the costs around that kind of data lot of building, which we work on very early in our engagements with customers, and this is informed both by Jack's research and also my time at Palantir where we were very early to see that. That data model of your enterprise is actually, like, the key differentiating thing in many ways. And so we internalize that, and when agents are carrying their tasks, they have the ability to understand like an analyst or a person would. Right? Why am I doing this? Right? Well, it's because I'm in this part of the org. This is the type of concern we have. Here's the data I'm looking at. Right? And I think that's a that's a core thesis we have.
00:15:48 · Sebastian Johnson, John Daley
[00:15:48] S0: And this has become a a particularly hot topic, I think, at the moment in the last few months given some of the incidents that we've seen at OpenAI and Anthropic. You must live and breathe incidents like this. You must be really into the weeds and have a sort of an insider view of what's going on and what's happening. Can you share your views, I guess, on incidents like this, maybe a specific one, and what it means for the broader industry? [00:16:11] S1: Yeah. Yeah. Mean, if you want to go?
00:16:12 · Jack Miller
[00:16:12] S2: So as I I think, you know, anybody that reads, the the report on on the OpenAI incident, is... I don't think you can read that report and not update your views on the world. It's just such an an insane thing to have happened. And and, you know, there's there's obviously the report, then WeShed internally was mandated listening for for everybody at the company, the the three Dwarkesh episodes about about this. And so, obviously, Ryan Greenblatt one was not after the report, but but covered the same topics. So that was mandated listening for everybody. Even even the salespeople, they've gotta they've gotta have a listen to it. And I think what it shows is is a couple of I think number one, it shows that we do not have a good handle on what these models can do. It is remarkable to me that they were able to, you know, do the sorts of things they were doing with this kind of secret message board with the coordination. It it is just incredible that that occurred. I mean, using the models day to day, they still make quite silly mistakes. And then seeing that this occurred internally is just... Is actually just a remarkable fact about the world that you have this kind of spikiness and capabilities that's that's hard to wrap your head around. I think the second thing is I think the cybersecurity side is really interesting because in some sense, the cybersecurity capabilities are pretty pretty good. They're they're pretty remarkable that they were able to do this, but I think it's actually more of a reflection on the incentives that exist in the industry today. And those incentives are towards, getting the best model out that you can and not necessarily observing what's going on within, your organization. So, you know, if you read the report, you can see that actually they use AI models to discover what has happened. Right? So, in in a post post hoc analysis, they use AI models to basically go through the logs, look at the traces, determine what's happened. Now they could have easily been doing that during the time. Right? If you had a monitor looking at the way that these models were doing the evaluation, you could have seen that that this was occurring. Right? And so that tells me that it's not necessarily a technology problem. It's... It is a technology problem in some sense, but it's also a, incentive problem.