00:18:13 · Guillaume Moubeche
[00:18:13] S1: So do you feel like to build up on what you were saying with, you know, the the initial prediction of having the the existing blockbuster of the world, like, take all the market share. We can compare this to some of the existing giants today. Do you feel like the fact that they have distribution, if you take Google, for example, even though Google is working, you know, like on AI and they're shipping a lot of things, I think they've been a bit more quiet compared to like what OpenAI and Anthropic, etcetera have done. Do you think they still have a chance like to to get like tons of market share or do you feel like it's a lot harder per your points because they haven't been built as AI company first?
00:18:59 · Matthew Fitzpatrick, Guillaume Moubeche
[00:18:59] S0: Oh, I think they absolutely were built as an AI company. I think they're the original and perhaps, you know, greatest AI company anyway. So I... What I'll... Without coming on the tech landscape, because I think that's a I think that's a different dynamic. That's a bunch of companies all investing heavily in tech. I think what I meant by that comment is more to take, like... Let's take banking, for example. Like, you're... If you take all the neobanks like a Revolut, I think the question is the adoption rate of AI in a business like that versus traditional banks, how does that change the paradigm? And I think you can extrapolate that to almost every sector, which is for companies that become hyper adopters of AI, their product theory should increase materially. And so how many new businesses like that do you see that spring up by sector? And contact center is another good example that we've already seen some new entries grow incredibly quickly. [00:19:50] S1: Okay. And you were mentioning, I think, that the the SaaS playbook is kind of like dead. So how exactly do you think it's gonna it's gonna evolve from where it is right now?
00:20:02 · Matthew Fitzpatrick
[00:20:02] S0: Yeah. Let me clarify that, Kyle. So so I think the repricing in SaaS companies is not... What the market, I don't think at least, is is not assuming that they will all decline in revenue. Like, they're not assuming something even gets ripped out. I mean, I think the rough multiples on SaaS was... In 2021, SaaS... The average public SaaS mobile was, like, 20 x revenue. Recently, it's been on the order of 10 x. Right? And maybe... So in many cases, 15 x revenue. And so what... The repricing I think you've seen is a view that they should be trading, you know, much lower than that as a multiple because in the future, their growth will be slower and the terminal value will be less. And so the repricing is not a view that they're gonna contract. It's just a view that they will grow less slowly because of a competition from AI companies, and as a result of that, pricing competition as well. And and I think that is a fair assumption in many ways. Like, I think that the way that people buy software and technology is gonna change. Like, I think in the last ten years, you kind of... Simplistically, the way you bought software was you bought a box, whatever that box is. So, like, you bought something that did, like, an InsurTech that did claims, for example. And then you have an SI come in and customize that thing incredibly for exactly what you needed. And so what you ended up with after, you know, two years of customizations was like a kind of an ill fitting thing that sort of fit what you needed, but was probably very different than the core box that you started with. And that's that's been why I think enterprise software has been so frustrating for so many is that a lot of the tech was really not fit for purpose. And now with AI, I think the question a lot of folks are asking is, rather than doing it that way, shall I take, you know, a variety of different tools that now exist to me and build something or use something that is pretty customized exactly what I want? And I do think that will be a change. I think that'll be a direction a lot of folks will go in. That's kind of effectively what Invisible does. We use a series of foundational infrastructure to deliver hyper personalized software. And I do think that's a much better user experience than buying a box that is kind of ineffectively customized.
00:22:12 · Guillaume Moubeche
[00:22:12] S1: Makes a lot of sense. And do do you feel like... Because you you touched a little bit on this about the fact that usage was gonna change, and I feel right now when I look at most AI software, they... I mean, a lot of people are talking about AI agent and things like that, but actually it's more or less the same capabilities, you know, as a a normal software or a workflow that you can do. In some cases, they add intelligence, but in most cases, it's like what I would call, like, just custom software or, like, just normal software. So in in what way do you think interfaces are gonna change to kind of, like, match the new needs of consumers or enterprise?
00:22:56 · Matthew Fitzpatrick
[00:22:56] S0: Yeah. It's an it's an interesting question. So I think what you're asking in some ways is everything is called an agent at the moment, but a lot of it looks still like traditional software. I I think that is accurate, by the way. I think there's... I've seen a bunch of interesting data on what percentage of, quote, unquote, agents are actually agents, and you see that a huge chunk, like, third share just basic, like, scripts, for example. So so so I think that's been one of the other reasons that adoption has been slower than estimated is actually the the prices of building, like, a task based agent that is actually autonomous is is not something that a lot of folks, like, what I would call, like, a multi agent system where you have an agent that orchestrates numerous single task based agents. A lot of people talk about that. Very few companies I've seen have been able to implement that effectively. And so I think that is is a hard thing to do, and I think that's been one part of it. But I think when this is all... Wait. Let's take five years from now for further along the curve. You will realize that the answer for everything... There there are numerous tools that you might consider. Deterministic software, machine learning, agents, a probabilistic LM, any of those. And and, actually, a lot of solutions are gonna be a mix of those four things, not just one thing. And so you've gotta be very directive about, like, which of those best solves a particular problem you're looking at. There are many cases where an agent might be worse than a software product. And so in that case, you might just wanna build software faster that solves a need. You wanna be purely deterministic. There are many other cases. Like, I think contact centers is probably the cleanest one where an agent is absolutely where you wanna go because actually deterministic flow of, like, an IVR, which is how you do routing in a contact center, is just very bad. And so you're much better off with an agent that can make judgments and respond. But there are other cases where if you want, like, much more, like, deterministic routing, then traditional software is better. And Yeah. I like there is the reason SaaS has come down...
00:24:57 · Matthew Fitzpatrick, Guillaume Moubeche
I I think software is gonna be more and more popular in the next ten years, but it won't be out of the box, like, kind of one one function software like much of the SaaS world has been. It will be... You know, it will allow for a lot more customization. That is the the benefit of many of the cool... New coding tools. [00:25:15] S1: And do you believe, like, customization can be done by AI down the line? Meaning that you would have, like, software that kind of, like, fork themselves into a unique version for each person using it? Or do you feel like this is a... This... There must be, like, a human working and a human in the loop to actually create what fits more of a group of people rather than an individual?