B (3:21)

Thank you. And there's certainly a lot there. It's can be a difficult industry. I've had some guests on here that talk quite a bit about this but just highlight a couple things you did mention. Customer adoption cycles are slow. Typically in this industry people are more risk averse. Right. Slower to kind of adopt that. And then of course you definitely need to have that capital strategy which I think is important. So I appreciate that. And Roland, Inventure has a unique model creating standalone operating companies from validated technologies inside multinational corporations. How does this approach differ from traditional venture capital or corporate venture models?

C (3:57)

Good question again Brian. So there's a couple of parts to that. What's our process? I think right at the front, how do we differ from venture models? Venture models are typically models whereby investors are investing on a diversified way across a number of entrepreneurial businesses. I don't. It's more of a game of probabilities. If you make a number of investments, you expect a certain percentage of them to be very successful, other ones not to be and you're sort of starving the losers and feeding the winners. So it's a little bit like spread betting. In our model we are the owners and operators of the businesses we create and we are not investing in first time entrepreneurs. As mentioned, what makes our model unique is that we're 100% focused on a systematic approach of working with multinational corporations to unlock technologies they've developed. And for us that's a multinational research. That's a 700 billion to trillion dollar a year or body where we can look for proven technologies that they've developed beyond a proof of concept and where they help us catalyze adoption by providing pathways to the market or themselves being an early customer or first customer. So our model is very very different. We're not investors in a number of other people's businesses. We create a small basket of companies that we manage carefully and expect to have high growth.

D (5:22)

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C (5:37)

Oatmeal.

D (5:38)

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C (5:41)

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C (5:52)

Corporate venture and the difference between corporate venture is a little bit different and it really has to do with some of the barriers that stand in the way of of corporate innovation. And I think we, we actually offer a solution because part of the problem with corporate venture is that they're spawned from within an organization that has more vertical structures and they're not necessarily attuned to innovation and entrepreneur entrepreneurship.

B (6:19)

Thank you. And I appreciate you unpacking that for our audience. We can get into the weeds sometimes, which is helpful to get us up to speed on, on what you're really doing. But I like the fact that we your model you talked about is very unique, typically from those venture models where investors are investing in that diversified portfolio with there's some probability, right? You're unique. Your model is unique because you're 100% focused on that systematic approach to those types of technologies in those multinational corporations. So I appreciate that. And Roland, many corporations struggle to turn R and D into real world commercial success. What systematic barriers do you see inside large organizations and how does inventure help or overcome them?

C (7:02)

That's a good follow on to the previous question in terms of how we differ from venture models. I mean, I think some of the barriers that exist within large corporations is that innovation doesn't necessarily align with core business models. Large corporations are very good at R and D that incrementally improves what they're doing, but it's not necessarily geared towards innovation. We also don't the risk tolerances are also very different within large organizations and they tend to be more focused on predictable roi, not on ventures that might have long lead times and adoption cycles. Again, they're much more focused on the risk tolerances are much more focused on how do we maintain certain growth rates and have a predictable roi. And as I mentioned earlier, the organizational structures of large corporations don't naturally align with innovation. You need, with innovation you need a lot of cross collaboration amongst an organization. So you really need horizontal structures, not vertical structures that are quite siloed. So really what we do, and that's the whole beauty of our model, is we think we are a bridge between corporate R and D and entrepreneurial execution to get disruptive technologies to market. So we really unlock commercialization pathways for major multinationals.

B (8:24)

Thank you, I appreciate that. And I like how you talked about those larger organizations, their risk tolerance.

C (8:31)

Right.

B (8:31)

It's a lot very different, of course, and their focus is on that predictable roi, which I thought is interesting. And I know, yeah, you need to be very prudent in these large corporations when you're trying to make decisions like that. And then of course you talked about some of that siloing and it's better to have that horizontal structure. So again, I appreciate that. And Roland, the last question of the day. What macro, economic or technical forces like AI, supply chain shifts, reshoring pressures, etc. Will most impact the industrial growth landscape over the next five to ten years?

C (9:06)

Yeah, I think. Well, you have a, you have. I think really there's two main sets of forces that you have to pay attention to. As you mentioned, macroeconomic forces. And really what you're looking at there is you're looking at things like long term growth rates, inflation, geopolitical factors and demography as well our demographics. We do definitely have an aging workforce in large parts of the industrial world. And so those can provide, I mean, today you could argue that there are some headwinds from growth and inflation, growth concerns, inflation concerns and geopolitical landscape. And again, demographics, you could say is a headwind due to the aging population of developed countries. But offsetting that, I really think that the number of ways in which you can increase global GDP growth from productivity are just enormous. I mean, you've heard the Term Industrial Revolution 4.0 or Industry 4.0, AI, industrial robotics are going to create incredible pathways to improve productivity. And, and that I think is a big tailwind. And that can more than offset macroeconomic headwinds. So the future is uncertain. You really can't predict. What you can do though, is understand where the fault lines are and take advantage of that in terms of how they, how they fall in one way or the other. But my own personal view is that the opportunity is very significant. Certainly North America has done a very good job of unlocking innovation and, and the trend towards nearshoring or reshoring is very real. And that's the whole part of the inventure model is that we are trying to systematically find disruptive or transformative technologies from major multinationals, find ways in which to create businesses onshore and create productivity enhancement in North America. And I really think that's. That future looks bright.

B (11:01)

Thank you, I appreciate that. You talked about Industry 4.0, right? It's pretty big thing we talk about a lot on the podcast here. But in your words, basically industry 4.0 should be a tailwind, like all this innovation and technological growth and automation to the macroeconomic headwinds you talked about, which is geopolitics, demographics, growth, inflation, et cetera. So I appreciate you sharing your insights with our audience today. And Roland, it was such a pleasure having you on today and I look forward to speaking with you real soon.

C (11:35)

Thank you very much Brian. It's been a pleasure being here today.

B (11:38)

Bye for now.

E (11:47)

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