00:29:52 · Josh Gruenstein
[00:29:52] S0: Yeah, it's a lot of things. One is that's most of the market. The vast majority of US businesses in manufacturing logistics are small businesses or mid market businesses. So if you wanna serve the market, that's what it takes to serve them. We also started this company at a time where we're seeing kind of all of these very academic supply chain oriented robotic startups kind of get started and then not really succeed. And we had the opportunity of being like, okay, what went wrong here? And one of the big things I think that robotics companies get wrong is they're not actually really product companies. They're like, okay, some really big company has a very specific problem. They're willing to write us a shiny piece of paper that says they're gonna expand with it, we're gonna spend two years building a very custom system to solve their problem, we'll deploy it. Chances are it won't work, because anybody who's built products knows that you can't just one and done a product. Doing product requires iteration. Then even if it does work, who's to say that customer still has that problem? So boom, the company's done. I feel like I saw this happen five to 10 times in a row to the point of, my god, if this happened to us, that would just be embarrassing. And so I think working with a diverse range of customers is this beautiful forcing function for building the right shape of product, which to us is a general robot. Right? We don't wanna build a Rube Goldberg machine that works really well for big industrial customer A or B. We wanna build a platform that is generally capable, robot bodies running generally capable robot models, that can immediately get to work. And if you can, you know, sign a contract in two weeks with a mid market customer and have a robot running immediately after that, and that's a totally different type of product and business than your other customers, you know you're being successful.
00:31:36 · Martin Casado, Josh Gruenstein
[00:31:36] S3: How do you think in the limit what the industry looks like, assuming you continue to be successful? Is it just that this work gets cheaper faster, or does somehow quality go up, or can they do more things, or What is the economic convergence or the productive convergence? [00:31:56] S0: Yeah. I think everything gets better. So there's a whole set of goods that if they are cheaper, we will buy more of them, and if you believe those goods are valuable, that's good for everybody. There are also a bunch of discontinuities in the world. I was talking with somebody the other day who worked on the line in a housing factory in, I think, Wisconsin. They were describing how the houses that they're building are about 5% more expensive to build them in a factory than to do traditional construction on-site. But if these houses were even just 1% cheaper than the equivalent quality of house manufactured or constructed on-site in the traditional way, all of the demand from developers would immediately go to that other shape of building these homes in a factory. And the same thing could be said about reclaiming materials and landfills and a whole bunch of other markets. So I think our hope is if Tutor's really successful, we can provide this robot substrate, this labor force that other businesses can use to transform the physical economy and make the world ultimately a cheaper and better place to live in. [00:33:02] S3: Will you ever go in the hub? [00:33:05] S0: Yeah. We're we're not opinionated.
00:33:05 · Josh Gruenstein, Martin Casado, Raghu Raghuram
[00:33:05] S0: Yeah. We're we're not opinionated. [00:33:06] S3: We'll go anywhere. Yep. But I I would imagine that would be kind of a much tougher technical problem just from, like, I don't know, complexity of task and safety and [00:33:16] S0: And the economics of it. Right? Like, many... What is the economic value a Tutor Robot delivers in a factory, and how many consumers are willing to pay for that in their home? And it doesn't quite work out. But, again, that that comes back to our our overall philosophy is take trade offs across as many layers of the stack as possible to get a foothold, start building the platform, and as you build the platform, you have to take fewer and fewer trade offs over time. [00:33:41] S1: How does product innovation work in robotics? I mean, most companies are just from v one, whereas you've got two versions of the robot and you continue to iterate really fast. How is it different than digital product innovation or other categories? [00:33:59] S0: We're trying to make it as similar as possible. So we're manufacturing our own robots. This gives us the ability to own that manufacturing process and figure out how do we push changes to the manufacturing process every two weeks. We are shipping code to robots either every day or major releases every two weeks, and have built a huge amount of infrastructure to support that level of iteration. We've built, I think, a really unique shape of product function where our product function in our organization actually sits right next to our customer success and implementation functions
00:33:59 · Josh Gruenstein, Raghu Raghuram, Martin Casado
[00:33:59] S0: We're trying to make it as similar as possible. So we're manufacturing our own robots. This gives us the ability to own that manufacturing process and figure out how do we push changes to the manufacturing process every two weeks. We are shipping code to robots either every day or major releases every two weeks, and have built a huge amount of infrastructure to support that level of iteration. We've built, I think, a really unique shape of product function where our product function in our organization actually sits right next to our customer success and implementation functions [00:34:29] S1: Mhmm. [00:34:29] S0: And operate actually as one big team. So they're as close to the customer as humanly possible, which I think has been really successful for us. And then we also have a research team which is able to be both, on the one hand, deeply embedded in kind of the customer problem space, and we're able to pull all sorts of levers to get specific types of data from customers, but also able to take these moonshots. And, you know, if we have a breakthrough on our modeling side that lets robots do a whole new shape of thing, we can kind of co design a product around that in a that I think most companies couldn't and get that out into the field much quicker. [00:35:06] S3: If I'm listening to this and I have a warehouse Yes. And I want to get a robot, do I just go to the website and put in my email? Does this work?
00:35:06 · Martin Casado, Josh Gruenstein
[00:35:06] S3: If I'm listening to this and I have a warehouse Yes. And I want to get a robot, do I just go to the website and put in my email? Does this work? [00:35:17] S0: You can go to our website, you can put an email at you. We actually have cool pricing calculators where you can put in some operational data about how your warehouse or factory runs today. And you can get an estimate of, hey, with an initial deployment of Tutor robots, we could be saving a $100,000 a year, a million dollars a year, a $100,000,000 a year. And that's not like, okay, this is a road map over five years of custom engineering. It's like, no, you could save that a million dollars a year two months from today without putting capex down. [00:35:45] S3: And have you found that there is demand for... I mean, it's just such a sci fi thing. Is there demand out there? [00:35:52] S0: There is so much demand. And it's not even demand for robots, because again, people don't know that the shape of a robot exists, that it works, or that it So could do this it's ultimately, you know, we have this framework of the value of a robot is you take how much work a robot can do, and you divide it by its cost. And that's it. Everything else is commentary. And so, if we're able to compete in these highly liquid labor markets that exist across American physical economy, and we can offer something that delivers greater equivalent capability to lower cost, the market for that is the whole market. Right? It's all physical work done.
00:35:52 · Josh Gruenstein, Martin Casado
[00:35:52] S0: There is so much demand. And it's not even demand for robots, because again, people don't know that the shape of a robot exists, that it works, or that it So could do this it's ultimately, you know, we have this framework of the value of a robot is you take how much work a robot can do, and you divide it by its cost. And that's it. Everything else is commentary. And so, if we're able to compete in these highly liquid labor markets that exist across American physical economy, and we can offer something that delivers greater equivalent capability to lower cost, the market for that is the whole market. Right? It's all physical work done. [00:36:29] S3: And just one quick wonky question on this, which is how much when you say cost do you mean amortizing the upfront cost versus the ongoing cost to operate it? [00:36:38] S0: It's both, which is very interesting and different. Traditionally, we've thought about robotics as ROI. Big CapEx expense and then ten years to make it back. And now it's actually like no, more and more of the cost is going into ongoing inference teleoperation. [00:36:54] S3: But does that also include maintenance and break fix and all of that stuff? And then what is the dominant cost, or are they roughly equal? [00:37:02] S0: We drive down all of them over time. [00:37:04] S3: No. No. I'm just saying, from from your standpoint, when you, like, pencil out your COGS, how much of is it the upfront cost versus the ongoing? [00:37:11] S0: Yeah. It is more ongoing at this point, and that's surprising Interesting. To