Venture Europe: Entrepreneurship | Technology | Venture Capital | Eu/Acc
00:19:04 · Casper Henningsen, Calin Fabri
[00:19:04] S0: And and take for granted that it has been a very focused initiative with certain amount of resources invested in it. And for me, it gives so much meaning to start thinking about this at higher velocity levels, more democratized motions, because if this is the result of being focused for a period of eighteen months which these numbers are alluding to. What would happen if we went to all categories? More functions. More investment. What type of enterprise would you end up having? I think you're going to have an enterprise that are fully in connection, in deep resonance with the markets they're trying to win. [00:19:42] S1: And let's say so the old world, it's a report that is done by one of the big like Nielsen Ipsos and then the New World is a company that has across categories this infrastructure layer where they get all the insight from the customer. They get the why. Where do you think we are now?
00:19:42 · Calin Fabri, Casper Henningsen
[00:19:42] S1: And let's say so the old world, it's a report that is done by one of the big like Nielsen Ipsos and then the New World is a company that has across categories this infrastructure layer where they get all the insight from the customer. They get the why. Where do you think we are now? [00:20:02] S0: I think we are at a I would zoom a bit out Calin. I would start with the recognition that the enterprise that essentially are the one that drives growth are changing. Right. The enterprise is going agenda and the core OS of that enterprise is going to look very different than what we know. Essentially every service function enterprise, whether that's marketing, research, finance, HR, whatnot, are going to be left with a simple question. How are you going to make yourself relevant for that enterprise? I focus much more on that than I focus on anything else. I'm obsessed with that modality of the agenda enterprise and how insights and understanding at scale can be embedded. In texture of the why, in formats that fits the models, in applications that actually drive significant performance in their growth ambitions. I stay focused on that. Good luck if you still run a modality of six weeks of work to come with a PowerPoint or PDF in the era of the agenda enterprise, you're gonna be run over.
00:21:18 · Calin Fabri
[00:21:18] S1: How much money though? Like like I I remember you telling me like this is a ticket of 160,000,000,000 that still runs the majority of it on reporting. Yeah. That's that's crazy. And how do you so I like the idea of the agentic enterprise, and I see it absolutely all the departments are just going to be, you know, agentic. And then I probably have like a data warehouse or like a data lake and all the information sits there and you can kind of query. Like, how do we see the inside? The inside will come into the data lake, will be like a layer in between the data warehouse, the company data, and then like all the decisions that are being made by the departments? A 100%. I think if you accumulate and look at what's actually going on in the data models that they are compounding and building and taking decision on, you will find that there's a lot of transactional what data in it. So social listening, category performance, transactional, whatnot. A lot of what I would call quantitative matters or analytics. Scraping
00:22:25 · Casper Henningsen, Calin Fabri
[00:22:25] S0: also communities as LLMS has in certain areas of the internet. The problem with it is there's no context, there's no real human depth. In the best examples I've been looking into, approximately 3% of the stack had what I consider qualitative understanding and context. What you end up with now is very interesting. Back in the days, it was like a research gap or a research liability. That turns into, when running the agenda enterprise, a commercial existential risk. Decisions are going to speed up, it's going to accelerate. And if your stack of data are not representative and contextual relevant for the markets you're building towards, you're going to end up in a very hard situation. So exactly what you're saying. I'm imagining a world of a human layer. I have that on my LinkedIn. It's between two to ten years in front of its time. But trust me, it will come now. Every model that is born and built to serve a commercial application needs a layer of human understanding as a competitive vehicle for that application's performance. That is my belief. [00:23:47] S1: Absolutely. And where do you see the synthetic data? Synthetic
00:23:47 · Calin Fabri, Casper Henningsen
[00:23:47] S1: Absolutely. And where do you see the synthetic data? Synthetic [00:23:53] S0: is a huge opportunity. I'm still very cautious of how to apply but I think synthetic is to be front end in a lot of the agents that we're going to feed into the enterprise from understanding. The difference though that the front end and the synthetic layer, because you need that to actually interact with the models, needs to be built on grounded interviews and real truth data. So imagine this idea of we run AI moderated as I said before real human beings being interviewed for a long time really on layering a lot of different topics to build replicates on that as a digital twin or a synthetic simulation will be the future as the front end of the application but you still need verified human to sort of like be vibrant and constantly updated in the back end of that model. If that makes sense. [00:24:49] S1: Absolutely. Now, I've watched yesterday like the latest All In podcast and like they were talking about composable models. I almost see that also here you will have composable models where, let's say at the forefront, you have the synthetic data which can take and answer some questions, but in the back there always need to be humans. Maybe you do have some data with a certain degree of certainty on some specific questions, but then in the back you would always have, you know, the humans that kind of back everything up.
00:24:49 · Calin Fabri, Casper Henningsen
[00:24:49] S1: Absolutely. Now, I've watched yesterday like the latest All In podcast and like they were talking about composable models. I almost see that also here you will have composable models where, let's say at the forefront, you have the synthetic data which can take and answer some questions, but in the back there always need to be humans. Maybe you do have some data with a certain degree of certainty on some specific questions, but then in the back you would always have, you know, the humans that kind of back everything up. [00:25:18] S0: A 100%. When we talk about synthetic as a front end and as a modality that starts to engage with the agentic enterprise, it needs to do two things. Today, have a very reactive way of looking at models, which I'm prompting and it brings back something. So like all modalities across is kind of like still the majority is like that. The modality that I think we will be leaning ourselves into are autonomous loops between models. The true agentic modality and here you would find that writing code would interact with customer needs autonomously. Creative development will run autonomously with understanding of consumers. And when we run simulations in pilots now in our models, you can easily run thousands of loops over durations of hours. And you can follow kind of like the code or you can follow the creative just completely being reshaped by the amount of loops you're running.