Inspired with Alexa von Tobel
00:24:40 · Alexa von Tobel, Daniel First
[00:24:40] S2: That's so interesting. What you're basically saying is right now, if you're manufacturing company, your... All of your brain effort focus goes to the top 10%, wherever the biggest headache, pain point, etcetera. And because of the proactive nature of what Axion is doing for your customers, they're actually gonna have a lot more surface area. So... Sorry. Is the punchline of that just like far better products for all of us in every SKU, every sort of on every dimension? [00:25:10] S0: Yeah. Absolutely. I think it's gonna be much better for customers. I think innovation is gonna happen much faster. I think also that the humans who work at, who work at these manufacturers, instead of spending all day reading line by line through data, you know, AI is gonna take care of all that. AI agents are gonna be learning about customer needs, proposing iterations to products. And I think it's gonna be much more exciting to work at a manufacturer, which, you know, today, it is for some people, but it may not be for others because they have to spend all day slaving away at the data. I think in the future, humans will be presented with complex decisions about customer needs. And I think that... And and and how... They're gonna decide how to react to that. Think it's gonna be a much more exciting future to work at a manufacturer. [00:25:50] S2: I'm pumped about when my my stroller no longer breaks and I have to deal with trying to figure out how to get a wheel replaced. That sounds like a beautiful feature for people here.
00:25:50 · Alexa von Tobel, Cameron
[00:25:50] S2: I'm pumped about when my my stroller no longer breaks and I have to deal with trying to figure out how to get a wheel replaced. That sounds like a beautiful feature for people here. [00:26:00] S3: So, Daniel, there's there's studies out there that show that the vast majority of enterprise AI pilots fail, yet Axion has been quite successful in converting real contracts in industries that have historically been very slow to adopt technology. What do you think people are missing or misunderstanding most about AI? [00:26:19] S2: Yeah. How did you... Tell us more. Like, what did you get right there?
00:26:23 · Daniel First
[00:26:23] S0: I joined McKinsey in 2016. And, you know, it's really funny reading this this study that came out, you know, this year about the ninety five percent of AI pilots failing. I mean, I didn't need the paper. I, you know, I I watched myself and my friends at, you know, companies like c three or BCG Gamma that were building all these enterprise AI pilots. We watched them fail in real time. And it was very easy to make a flashy demo, but then when it came to actually bringing things to production, it often wouldn't make it. And it was really, it was really soul crushing, you know, bringing all these really brilliant colleagues to enterprises, having this big flashy project, and then seeing time after time, it didn't make it to production. The the projects that failed most consistently were ones that, you know, I think of as cross functional intelligence workflows, which is bit of a mouthful. But, basically, people would would hire, McKinsey or QuantumBlack pretty much always with the same problem formulation. It's a very unique one. It's, hey. We have a lot of different groups with different data across the enterprise. They... You know, everybody... If they could just link up all their datasets into this omniscient pool, then suddenly everybody would be able to do their work much better. So I worked with a professional sports team that had twenty twenty six different datasets. They're trying to make decisions about who to play and game strategy and injury. Could we link up the 26 datasets? I worked with a clinical trials study. Can we link up all the data about clinical trial audits? Ultimately, the reason that these projects were failing, it wasn't because the AI didn't work. It was because there were these very big gaps between the prototypes that were created and, and what you actually needed. So there was a a knowledge gap, which is that when you showed the results of the machine learning to experts, they would say, you know, my expert knowledge is not incorporated. There's a process gap, which is that people had very legacy processes. And so, you know, they they they... There was no way for them to really fit the AI into their... Into a new process. And number three, you know, you might have AI models, but humans would need to dig through and find insights and somebody would need to take action.
00:28:28 · Daniel First, Alexa von Tobel
So Axion was really born to close these three gaps and do it in a way that scaled, as a product. So really just seeing all these enterprise AI projects, fail to make it to production is really what gave me the the chip on my shoulder to to found the company. [00:28:43] S2: I do love that you sort of describe your own chip on your shoulder. We've very much seen kind of your ambition and hustle and it's very real. Where do you think Axion's future intersects with robotics? Just trying to like get a sense of, you know, you're at the forefront of manufacturing. I very clearly understand that you're going to save literally trillions of dollars. You're going to save injuries and problems. So that's just an incredible human benefit. You're going to be able to speed up these response times and cycle times of catching problems to allow people to now spend significantly more time on building better products across most SKUs. So that's awesome. Where does that intersect with robotics in your mind?
00:29:28 · Daniel First, Alexa von Tobel
[00:29:28] S0: You know, people get very excited about new technologies, whether it's electric vehicles or robotics or software rich strollers, which are coming. And, actually, the more innovative the technology, the more quality issues there are. And that's why you see recalls happening about electric vehicles all the time in the news. So I think that, you know, robots already and will continue to have tons of quality issues because of how difficult hardware software, interaction is in engineering and how new a lot of the technology is. And I think that the faster that we can learn about what those issues are, the faster we're able to build the next generation of the robot that can overcome it. So I think that, you know, robots will will be an increasing... We we have some customers who are using Axion for robots, but I think as that market grows, we'll... It'll become more and more of a focus. [00:30:14] S2: What what else do you worry about these days? I... Like, what can you see that maybe we should just all be more aware of?
00:30:14 · Alexa von Tobel, Daniel First
[00:30:14] S2: What what else do you worry about these days? I... Like, what can you see that maybe we should just all be more aware of? [00:30:23] S0: I think that right now, there's a tremendous amount of of investment in data centers and in the entire supply chain, all with the expectation that this is eventually gonna show up in GDP. But if the enterprise AI pilots keep failing and we don't learn from that and figure out how to do enterprise AI in a way that actually works and makes it to production, then you could see, you could see that, you know, the investment may not lead to as much of the GDP lift off that people expect, and that could lead to a big pullback in investment in in data centers and associated, you know, things in the supply chain. So, you know, I'm quite hopeful that we don't just keep on trying over and over again to, you know, chuck AI models over the fence and and hope that they stick. [00:31:11] S2: You're currently one of the founders who's kind of deepest in what I'm gonna call incumbent corporate America, right? These huge, massive companies that have been around for, in some cases, even hundreds of years. What from the inside out, how do they think about AI? Meaning how forward leaning are they? Are you know, you've been wildly successful in getting very quick adoption, which has been so impressive. But give us a sense from what you hear and see, given your vantage point over the last three years.