Inspired with Alexa von Tobel
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.
00:31:42 · Daniel First, Cameron
[00:31:42] S0: Leaders at American enterprises, on the one hand, they all feel like they need to be using AI. On the other hand, I think that they they they... This... The secret that they maybe don't say out loud is that they're worried that their organization may not be ready for AI or that the pilots are gonna fail and it's gonna cause people to to lose trust for innovation. And, the reality is that that's that's true. I mean, many many American enterprises, you know, even if we show them what the issues are, they might not even have healthy processes in place to go act on those issues. So, you know, in order for the AI to have impact, you really need to have healthy processes for it to be embedded into. And so, you know, one of the views that I have is that I think that, you know, process redesign, you know, so that you can take an enterprise and enable it to design processes that AI agents can work well in is gonna be a hugely important skill over the next couple of years that I think is very, very underappreciated. I think that's the anxiety that a lot of leaders have. [00:32:45] S3: Related to that, Daniel, you... Can you talk a bit about what you're seeing broadly with American manufacturing and the... Just all of the tailwinds currently?
00:32:45 · Cameron, Daniel First
[00:32:45] S3: Related to that, Daniel, you... Can you talk a bit about what you're seeing broadly with American manufacturing and the... Just all of the tailwinds currently? [00:32:57] S0: Oh, I think there's a bunch of tailwinds here. I mean, I think, you know, that that are related to some of this. I think, you know, number one, you know, there's obviously this huge build out of data centers, but that this is really causing many other related, you know, industries to spike, whether that's HVAC, you know, energy, electrical components. And I think that what we're seeing is that all of these manufacturers are being asked to to scale up, build, and deliver more innovative products than ever before. But as a result, what we're seeing is, you know, tons of product failures and, tons of of of shortages, which can also be because things are being scrapped on the line. And so I think that that's something that's underlying a lot of the, you know, a lot of the focus on on customer quality. I think another another big shift is obviously that tons of these companies have been shifting where their manufacturing is happening, what supplier they're using. And whenever you have new suppliers or you have new suppliers that have never worked with each other before, what you end up with is that, often their parts don't integrate. It's very hard to figure out why. Saying that there's a lot of trends that are happening that are making customer quality more top of mind than ever.