The Information's TITV
01:01:22 · Nicole Su
[01:01:22] S3: Yeah. I can certainly talk this in a broader general terms for Frontier Labs. Me personally, right, if the first number I would focus on is very much gross margin, but I also understand, I need to understand what does gross margin really mean for these companies because the definition could be very different. I think we had this huge debate around like the difference between two Frontier Labs and how they define revenue, right, that obviously would translate into gross margin. But then I think more importantly, would just go straight to, if I want to cut out all the noise and differences, I would go straight down to free cash flow, right? How much cash are these guys burning? And it because if they are bringing less cash, is it because they underinvested previously, or is it because truly they have a more efficient model when it comes to... Business model and comes to finance their growth? So it's very much like, Hey, let's look at gross margin, how healthy it is, but obviously be very clear on differences in definition, at same time also looking at cash flow and see, you know, what's the burn and what's not just the today, but the future and then the likelihood of, you know, getting to profitability in a more sustainable way. Totally. And, you know, we've read
01:02:30 · Stephanie Palazzolo, Nicole Su, Jessica Lessin
[01:02:30] S0: a lot about the different definitions for gross margins here, both in terms of, you know, most of the time they don't count the compute use for training, but even Anthropic versus OpenAI, there's differences in how they account for, you know, the revenue they're getting from cloud partners. It can get very confusing. Totally get why you, at the end of the day, just kind of look at what's happening with, with cash. I mean, how much do you think is riding on the performance of these big model developer IPOs coming in the upcoming months? If they go badly, is the IPO market closed? Like, what's what's gonna happen? [01:03:01] S3: Well, I don't have a crystal ball. I... [01:03:03] S1: As a banker, I surely hope it all goes well. [01:03:07] S3: And then we obviously use a lot of high quality companies in the pipeline looking to go public post some mega IPOs. So my hope is they will go well [01:03:16] S0: and then we'll see surging AI IPOs. Yeah. And the AI labs have also been, you know, quite aggressive when it comes to acquiring startups as well. What types of companies are the labs interested in? Where do you kind of expect to see more M and A activity when it comes to these smaller AI startups?
01:03:16 · Stephanie Palazzolo, Nicole Su
[01:03:16] S0: and then we'll see surging AI IPOs. Yeah. And the AI labs have also been, you know, quite aggressive when it comes to acquiring startups as well. What types of companies are the labs interested in? Where do you kind of expect to see more M and A activity when it comes to these smaller AI startups? [01:03:35] S3: Yeah. The way I think about the lab's M and A strategy is very much aligned with capturing economic value of AI because obviously they all have approved the capability of the AI, but we are at this point where we want to make sure AI diffusion is happening, but when it's happening, the labs obviously would want to capture a piece of that. So the way you can do it is very much going up and down the tech stack, I think many of them made effort in terms of acquisitions in specific verticals and acquisitions in application layers. At the same time, they also would go down the supply chain of AI, right, getting down to the compute layer, the chip layer. So I think you will see this more verticalized approach for the labs to be able to capture more economic value, at the same time also like acquiring some application layer companies or deployment companies to really make sure that they actually capture the end customers as well and be part of the AI diffusion, not just the creation of intelligence. Mhmm.
01:03:35 · Nicole Su, Stephanie Palazzolo, Brittany Greenfield
[01:03:35] S3: Yeah. The way I think about the lab's M and A strategy is very much aligned with capturing economic value of AI because obviously they all have approved the capability of the AI, but we are at this point where we want to make sure AI diffusion is happening, but when it's happening, the labs obviously would want to capture a piece of that. So the way you can do it is very much going up and down the tech stack, I think many of them made effort in terms of acquisitions in specific verticals and acquisitions in application layers. At the same time, they also would go down the supply chain of AI, right, getting down to the compute layer, the chip layer. So I think you will see this more verticalized approach for the labs to be able to capture more economic value, at the same time also like acquiring some application layer companies or deployment companies to really make sure that they actually capture the end customers as well and be part of the AI diffusion, not just the creation of intelligence. Mhmm. [01:04:34] S0: Great. Okay. Well, thank you so much. We'll have to have you back on after Anthropic is public to talk about how things go. Sounds great. That was Nicole Su, UBS' head of AI investment banking in The Americas here on TITV. Brittany Greenfield is the founder and CEO of Wabbi, a cybersecurity startup focused on helping companies build secure software. Brittany, welcome to the show. [01:05:08] S4: Thanks so much for having me, Stephanie.
01:05:08 · Brittany Greenfield, Stephanie Palazzolo
[01:05:08] S4: Thanks so much for having me, Stephanie. [01:05:09] S0: Of course. For those who might not have heard about Wabbi before, tell me a bit about your company and how its approach differs from what people might think when they think AI cybersecurity startup. Yeah. [01:05:21] S4: I'm going to give you a boring answer. We're a DevSecOps orchestration platform that's always helped companies develop and deploy software at the speed that enterprises need. So, some sense, we're not doing anything new in the age of AI. The volume is just faster, louder. [01:05:39] S0: And, you know, as you mentioned, it does feel like in the past year or so, the big difference has just been the scale and the amount of code that is getting pushed all the time. [01:05:47] S4: Correct. Right. Wherever you're looking at the metrics, whether it's, you know, pull requests tripling or quadrupling, actual lines of code increasing by 30%, there is no doubt that the biggest impact we are having in AI is in software development today. [01:06:03] S0: And so, the kind of amount of code that's being written has increased. But in terms of the quality of that code itself, is it more or less secure compared to human written No. Code in the
01:06:03 · Stephanie Palazzolo, Brittany Greenfield
[01:06:03] S0: And so, the kind of amount of code that's being written has increased. But in terms of the quality of that code itself, is it more or less secure compared to human written No. Code in the [01:06:14] S4: This is the problem that the software development industry is having today. While the amount of code is going up exponentially, the flaw rate is exactly the same. And so now, we are shifting from not just a finding problem to really being a fixing problem, which is why I say Wabbi's almost boring in this space in the best possible way. The fix has always been the issue for organizations, and now it's come front and center for every software development organization. [01:06:42] S0: And where do you think is kind of the best place to insert yourself in the kind of software development cycle? Is it while they're writing the code? Is it after they've written it? During the checking process?