Name: Invisible Technologies on future of AI adoption
Uploaded: 2025-11-19T07:53:01.857Z
Duration: 5 min 55 s
Description: Matthew Fitzpatrick, CEO of Invisible Technologies, discusses his vision of AI adoption going into 2026. He also discusses the companies outlook for business and growth after raising $100M for its growth funding, at a $2B valuation earlier this year.

Matthew Fitzpatrick, CEO of Invisible Technologies, discusses his vision of AI adoption going into 2026. He also discusses the companies outlook for business and growth after raising $100M for its growth funding, at a $2B valuation earlier this year.

Marina Bay

Invisible Technologies' executive discussed the current state of AI investment and adoption, noting that while technological progress in AI has accelerated, funding is concentrated among a small group of private companies. The company itself has raised significant capital, which it is investing in core technology and enterprise-focused AI model training and validation.

The discussion highlighted the disparity between consumer and enterprise AI adoption, with widespread consumer use but only a small percentage of enterprise projects reaching production due to data quality and integration challenges. The company sees growing opportunities for AI in sectors such as agriculture, consumer goods, and sports, and differentiates itself by combining AI model training with a broad, tech-centric enterprise platform.

Invisible Technologies on future of AI adoption

We've been talking about circular deals. There's concern about how investments have been plowing through the AI ecosystem. And talk of froth, a bubble, what are you seeing in terms of demand? Yeah, so it's interesting. I think if you ask the question of a bubble, you can think about it two ways, the technology itself or valuations. And I think they are separate questions. I think if you look at the technology itself, there's no doubt over the last four years we've seen an absolute step change in model performance, the usage of those models in a lot of different areas. So I think the technology paradigm shift is very real. You then get to the question of valuations. And I think that's actually a more complicated question. You can definitely say valuations are high, but I'll just give you a couple of stats that I think about a lot. You have basically the big tech companies whose earnings are very material. So it's hard to argue that in their cases it's a bubble given that performance. And then if you actually look at the AI space, it's something like three companies are 30% of the total funding. And about 100 companies are 70% of the total funding. So what you really have here is about 100 private companies with a lot more demand than capital access at the moment. And none of them really are public. And I think that is the interesting thing. This is not a retail dynamic. This is not a situation where, I mean, an interesting fact I saw the other day is that public software multiples today are one half what they were in 2021 today. And actually, if you look at the number of IPOs, it's still only about two thirds of what it was, actually a third of what it was in 2021. So it's not a public markets dynamic. I think what you have is capital scarcity around a smaller number of AI companies. Not for you. I mean, you've managed to raise, what, $100 million back in September. And now you're telling me more than $200 million? $130. $130, OK, OK, OK. After that, we had a lot more interest. And so we actually did end up upsizing around $200. What are you doing with that capital? We are investing all of it in our core technology software platforms. So I think if you think about the history of our business, we've trained all the large language models. We're involved in statistical testing and what's called reinforced learning human feedback, where you basically test and validate the large language models. What we found is that that cycle is where the enterprise is going over the next 10 years. So if you're a big enterprise company and you're looking to deploy a model, let's say, for credit memo generation. Let's say you have a bunch of credits and you want to generate 10-page documents to do reviews of those credits. You need to do all the same stuff that we've been doing at the model builders over the last couple of years, where you create a rubric for does the output look good, you score it, you test it, you train it. And so a lot of our capital now is going to enterprise-focused and training all these similar models. How difficult is that to have the adoption at the enterprise level? And how does this differ from consumers? It is difficult. And I think this is actually, you asked about the question of a bubble. If you look at actually the uptake so far. So KPMG had this report, interestingly, that 60% of people worldwide, I think it's across a panel of 80 countries, use Gen AI once a month. So you have enormous adoption on the consumer side. On the flip side, on the enterprise side, MIT released a study that 5% of AI projects make it to production right now. And it's hard. I think you need to have clean data. You need to have testing and validation. You need to know that. I always give the answer, if you had to generate 1,000 memos and you had to bet your annual bonus that those memos would be right, how would you know? How would you train that? How would you test it? How would you get comfortable that these memos are right? And I think that's really the journey of the enterprise is looking for extreme precision and kind of human equivalence in output. And I think that's harder than people expect. When it comes to data, I mean, this idea of rubbish in, rubbish out, right? So the cleanliness, to your point, is important. But what do you see in terms of the scale of the market in data labeling? I think some estimates put it at $2 billion last year, quadrupling by 2030. And how will your company play in that ecosystem? Yeah, I would say we're in the first inning of that. I think if you extrapolate what I just said to the enterprise, you actually have all of the big enterprise companies over the next 10 years as they start to label things like contact center conversations or video image data. Basically, the enterprise has barely started in the context of kind of data cleanliness. The other thing I'd say is we're talking about unstructured data in the context you just brought up. But a lot of the pain for the enterprise has actually been structured data as well. So if you think about, I have 12 ERP systems, a CRM system. I have this huge fragmented landscape of data that underpins my AI. And I've got to get that all organized before I can do any AI. My favorite joke is that when good AI meets bad data, the data usually wins. And I think that is the story. When it comes to enterprise segments, where do you see the most opportunity? It's an interesting question. So I think the investment so far in the enterprise has predominantly been in the sectors you'd expect, so financial services and health care. I mean, they tend to have the largest technology organizations, so they've put the most capital into it so far. But there are a lot of really interesting spaces where we are seeing a lot of opportunity. To give you a few examples, agriculture. So we think about, if you think about crop yields and herd safety and all the different dynamics of, there's a lot of things you can do with video and image data in agriculture, for example. I actually look at some of the areas we've played in. So Swiss Gear, like Swiss Army of the Luggages, we've done a lot of analytics for them around inventory forecasting. So most consumer goods companies, for example, are still pretty basic in the information they bring together. And we saw a lot of opportunity there. My favorite one is sports. So I actually think everyone loves sports. If you think about the potential to look at the spatial movement patterns of players and who you might want to draft, we did that for the Charlotte Hornets. That's been another big area. Super quickly, how do you differentiate with competitors, scales, or AI? Yeah, I think, look, there's kind of three to four of us or so that have done this for the last five years in the AI training space with model builders. I think the difference for us is that's really only half or a third of our business. A lot of what we're doing also is on the enterprise side. I think we are a tech-centric platform, and I think on the enterprise side, where we serve, I think, eight different sectors now, I think that's a pretty different part of our business than the rest of those players.

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