Transcript: From Napkin Sketches to Precision Frames: How Robots Are Transforming Home Building

[00:00:00] Start

[00:00:00] Audrow Nash: Hi everyone, Audrow here. Housing is expensive. A big and growing part of that expense is the cost of labor. People are expensive, and the laborforce is shrinking as more people retire than join. A big way to drive the cost down is to automate parts of the process that are labor intensive. But robotics in its current state is only good at certain things. Therefore there's an art in picking things to automate. If the task is too obvious, you have tons of competitors and it becomes a race to the bottom. If you pick a task that's too hard, you run out of money trying to solve the problem and the company fails. Today I'm talking to Barrett Ames, who's a cofounder of Botbuilt. I think Botbuilt has found that sweet spot. They solve a few really thorny problems and it lets them automate build wood frames for houses and other buildings. This conversation will be interesting to - any aspiring entreprenur as a great example of how different challenges can be overcome - people curious about robotics construction - and any investors looking to learn more about a strong robotics company with what seems to me to be a good moat Before we dive in, a small secret since I'm making an announcement in a couple of days, but I'm excited to that I've create Audrow Nash Podcast Jobs board. The board grew out of a problem I kept seeing: robotics companies struggling to find talent, and talented people struggling to find the right robotics opportunities. I hope this jobs board can help here. Several great robotics companies are already posting positions, including this episode's guest BotBuilt, along with Formic, Luxonis, and Rerun. BotBuilt is still finalizing their hiring plans and should have more engineering roles posted in the coming weeks. If you're looking for a job or maybe thinking about making a change, you can sign up for free email updates to be the first to know about new positions. If you're at robotics company that's looking to hire new people, consider posting on the Audrow Nash Podcast Jobs board to gain visibility with our great community of robotics professionals. Job postings are free. You can learn more at jobs.audrownashpodcast.com. I'll have a link in the show notes. Alright, grab a snack, get comfortable, and here's my conversation with Barrett Ames. Hey, Barrett. Would you introduce yourself?

[00:02:22] Overview

[00:02:22] Barrett Ames: Yeah. My name is Barrett Ames. I'm one of the co-founders of BotBuilt. At BotBuilt we build homes with robots.

[00:02:30] Audrow Nash: Tell me more about that. What are you doing to help build homes with robots?

[00:02:33] Barrett Ames: Yeah. So we do a bunch of construction in a warehouse. Right? So, somebody sends us a floor plan that's just like a PDF drawing of the kind of house that they want. In fact, it doesn't even have to be a PDF anymore. We just literally did a napkin drawing, like somebody drew out on a piece of paper. Like, this is how I want my house to be. And we were able to design that from the piece of paper. But anyways, however you have it, you give us your floor, floor plan design, your house design. We ingest that, design out the 3D aspects of it. So where does every piece of lumber, where does every nail go in order to build this house? That gets transitioned over to our robots. And we figure out first kind of a high level plan, how do we go and do each of these pieces? So where does where, you know, do we put this piece of lumber in before that one? Do we put these nails in before those nails. We do that and then we execute it on the robots. So that's, you know, dealing with all of the gnarly ness that is lumber, right? It'll be Bo. It'll be twisted all sorts of, bad lumber. And we do all of that in a warehouse, in big chunks. Build your house over a period of time in the warehouse, stack it up on the back of a truck, ship it out to the jobsite. And the big win there is. It lets the framers go. The the rough carpenters that are on site lets them go from a 14 to 21 day build cycle down to a four hours. Wow. So there's a big labor savings, in particular on the jobsite, right? Which is just like, not job sites are, like, not a great place to be, right? Like, you're out there in the cold, you're out there in the sun. You're out there in the heat. We just got our site video back from our last, build, and, one of our panels didn't make it because they tore it up and burned it to stay warm, right? Oh, my gosh. Like, that's,

[00:04:40] Audrow Nash: That's hilarious. Oh, man. Yeah. So you don't want to be out there. And this is specifically for the framing part, which is the walls that go up. Correct?

[00:04:49] Barrett Ames: Correct, correct. Yeah. So you can think about it as just like the, the bones of the house.

[00:04:54] Audrow Nash: Okay. And so how is this done now and then. I'd love to hear about how you guys are doing it. And then the challenges you come into by doing it with robots.

[00:05:03] Barrett Ames: Yeah. So right now it's all done by hand, right. It's just, if you go out to the job site, there will be like one really tattered piece of paper on every job site. And that's the floor plan. Every framer is going and looking at it, drawing on it, spilling their, their beer or their monster on it. Right. Like, and they're just they're just picking up pieces a two by four, cutting them down to size and nailing them together. And it's, it's, it's surprisingly dangerous, right? Like, you don't, you know, you kind of think about it abstractly and you're like, oh, yeah, you're just putting two by fours together. Yeah, whatever. But in order to build the house up. Right, like, you really have to start doing some pretty, pretty, interesting gymnastics pretty early. Right? So, like, on that site video that I was, just referencing, we could see guys on other jobs standing on top of the first floor, walking around on two by fours. Right. That's all. No safety equipment. So it's just like, there's saws everywhere, nail guns, and and it's just, it's just generally not a safe place to be.

[00:06:16] Audrow Nash: There. Yeah, but the accident rates are pretty high. For this and especially once you get into multi storeys. Right. Because I mean first floor it's like well you have to stand up these things and you have to cut and you have nail guns and things like this I'm sure accidents happen just on the first floor. But yeah I'm sure with more than one floor it becomes very dicey very quickly. Yeah probably.

[00:06:36] Barrett Ames: Yep yep. And so it's yeah. The way that they do it right now is, pretty rudimentary, right. It's just, like, you get the plan, you build it. And then somebody, somebody else, comes by and checks it, and that's, that's kind of the end of the story. And you gotta you gotta do it. And whatever, whether there is.

[00:06:59] Challenges

[00:06:59] Audrow Nash: Yeah, that's sinks a cot or cold or anything. Right. But so I think there are some challenges because we talked before. And so I remember some of the things there are some challenges that sound very interesting with this one. The floor plan is very low resolution, like it's it's just basically this is where the walls are going to go. They're not saying the like the distance between the studs, how much wood they need, all sorts of thing. So I think there's like one there's a lot of ambiguity in this. And especially for like custom homes or things like this, a lot of variation and then probably a lot of waste because of this. Tell me a bit about this.

[00:07:36] Barrett Ames: Yeah. So every floor plan, when you look at it first you, you think like, oh, this is a technical document, right? Like it's got lots of lines and there's like a scale, right? Like there's all these dimensions drawn everywhere. And what we've found is, it's not a technical document at all. It's actually just providing designer intent. Right? Like we want a window, and the window is here. The door is there. Right? And and we understand it to be intent because, like, if you shift a door an eighth of an inch left or right, no one cares. No one cares, right? They do care if all of the doors, are lined up in a hallway or something, right? Like, so there there are kind of these design guidelines, and so the first thing that we have to do is, is really bring all of that intent in and then go and design every little bit. And because we do that, we get a really accurate accounting. Right? Like we have to know where every nail goes in order for the robots to come in, do that. And when they're building a house right now, they're literally just looking at a piece of paper and going, oh yeah, I need to do that over there. And they walk over there and do it right. There's not a lot of like, yeah, there's there's not a lot of planning necessary. And so they're they don't do a lot of planning. And so when we go through and do every single stud in every single nail ahead of time, we've get this, this really accurate picture of the house well before it gets built. And because of that we can order material more accurately, drastically reducing waste. Right. So on the houses that we built, we've had our our contract partners say we usually have 3 to 5 dumpster dumpsters of waste, but with you we only have half a dumpster of waste.

[00:09:32] Audrow Nash: Right. So that's a big reduction.

[00:09:33] Barrett Ames: Big Reduction in waste. And then on top of that, the way that they actually currently order materials is really interesting. They'll take the floor plan and they'll go to a lumber yard and they'll say like, hey, lumber yard. How much material do you want or do we want? And, that.

[00:09:50] Audrow Nash: Sounds cooked.

[00:09:51] Barrett Ames: Right. Yeah. And so we've we've found 20 of all of it. Yeah, yeah, yeah. So we find, we find, 20 to 40% saving in material, just by, by kind of solving this asymmetry in it in, in in incentives.

[00:10:05] Audrow Nash: Okay. That's very interesting. What about so another problem that I see that's kind of difficult is the variation in wood quality. So like you buy a two by four it's slightly twisted or a little longer or the angles are weird. Or tell me a bit about that too, because that that probably is a challenge. Also.

[00:10:22] Barrett Ames: Yeah, it's a massive challenge. And it's it's a key challenge, right. There are a ton of grades of lumber out there. You can go all the way up to LSL, which is this, this beautiful engineered lumber, and it will be straight and perfect, but it cost ten times as much as what a normal house is built with.

[00:10:46] Audrow Nash: You're paying for that precision, right? Right. Or this kind of thing, because it's two spec, right?

[00:10:49] Barrett Ames: Okay. Right. And so we're early on at BotBuilt we had this decision put before us, like, do we decide to engage with the lumber that is on the job site, or do we pick this much better engineered lumber? Right. And it would have made the engineering problem much, much easier. But we decided that the the fundamental thing is that we need to build more houses in America, and that means cheaper, you know, cheaper materials. So we went with what homes are built with today. And if you look at what homes are built with today, it's just, it's just the two by fours that you can see at Home Depot, right? Like it's just going to be twisted and borrowed. There's this thing called waning, which is where big chunks of the corners are missing. Right. There's going to be knots. There's a big pine beetle problem right now. So there there will be holes in some of the lumber. And that's okay. Right. Like there there's some.

[00:11:50] Audrow Nash: Amount that doesn't compromise the structural integrity that much. Right?

[00:11:53] Barrett Ames: Okay. Right, right. And so there's there's just a ton of variation within one species of lumber. Right. Like we in the southeast build with, what's called spruce pine fir. But if you go to the Pacific Northwest, they're going to be more Douglas fir. And so you'll get variations in, in all sorts of the texture, like the visual texture, but then also the, the geometry of the two by four, that are species dependent, location dependent, harvest dependent. Time. Like, how old was this tree when they chopped it down? All of that leads to just an immense amount of variation, I bet.

[00:12:33] Audrow Nash: Okay, so how do your, So, like, I feel like I have a good grasp of the problem now. How do your systems work? And what have been kind of the hard problems on the way? I can imagine a few and we've talked earlier, but tell me about kind of like end to end. What are you guys doing? I know you're building the frames, but what does it look like in your operation? And then I'd love to just quickly, as you're describing it, talk about the, hard problems and then we'll dive more into each of them.

[00:13:04] Barrett Ames: Yeah. From a operational standpoint. Right. Like working with lumber, starts at, like a really material handling specific level. Right? A lot of a lot of approaches. Don't think about just the logistics of moving a two by four around. And so what we do is we just stack all of our lumber right next to the robots. And then the robots look over, grab the piece of lumber out of the out of. It's called a bunk, which is just a big old thing, a lumber freight. Just think about all the lumber that you see stacked up in Home Depot. That's a bunk. We grab a piece out of that, we put it in in, a measurement device at the back of our system that allows us to position it precisely to a 32nd of an inch. Then we pick up a saw, cut the lumber down to size, and then pick it up out of our our. We call it the input table, but basically our measurement device and put it on the, put it on the work table. You do that a couple thousand times and you've built a house.

[00:14:08] Audrow Nash: Right? Yeah. Okay. So you process the lumber, you put it in a big bin, the bunk, you grab it out, you cut it, you place it where you need it. How do you. I guess so the main challenges that I see with this one, getting an accurate 3D model of all the wood and then doing something appropriate with it to cut it, and things like this. Like, tell me a little bit about that. I suspect that's a good challenge.

[00:14:36] Barrett Ames: Yeah. Yeah. So I actually like even before we characterize the lumber, just the, so the, the bunk actually comes to us just strapped. Right. So there's okay, there's like, this nice 14 by 14, grid of lumber. And when we, cut the straps, it's it's a bunch of lumber all recently cut from the same tree, all laid out next to each other. So I actually like t first problem is individual weighting or simulating that piece of lumber. Yeah. Visually. Right. And so, so there's actually, a really interesting challenge there, that we solve with a deep convolutional neural network, and a whole bunch of data that only we have. Right? Like, there's not a whole lot of people out there that are collecting massive amounts of two by four data, but we we're one of them. Yeah. And so we that's fine. Yeah. We we simulate each of them. Just using a stereo pair, and then, pick it up, we pick it up using vacuum grippers. Wow.

[00:15:43] Audrow Nash: Yeah. I'm impressed. It's vacuum grippers for that. That must be a big arm and big vacuum grippers. Then how heavy is each of these pieces of wood?

[00:15:49] Barrett Ames: Two by four. It's actually only 10 pounds, so they're actually pretty light. And.

[00:15:53] Audrow Nash: Okay. Just awkward and long.

[00:15:54] Barrett Ames: Yeah, yeah, it's like a motion planning nightmare, right? Like, just imagine you don't whack stuff, right? Yeah. Just imagine everything's a bug trap problem, because that's what everything turns into when you have this. What long piece.

[00:16:07] Audrow Nash: What is a bug trap?

[00:16:08] Barrett Ames: Oh, yeah. Yeah. Just like, you know, the, the, it comes from the, the bug traps that you'd use to catch, like, fruit flies. Right? Like you take a cup, you put some, vinegar and some sugar in the bottom of it, and then you put some Saran wrap over it, and you poke hole in it. Right? Okay. And the fruit fly flies into it, but it's the they're too dumb to get out. Like there's just one very narrow passage that can get through. Yeah. That's that's what in motion planning. Like that's, that's how we describe kind of these narrow passages. Right. Where relatively easy to get in one direction, but like very specific to get out the other direction.

[00:16:48] Audrow Nash: To pull it out. Right. Of the bunk. Right. For this kind of thing.

[00:16:52] Barrett Ames: Right. Okay. And so yeah. So just moving with lumber in general kind of turns everything into these narrow passage problems. Yeah. For sure. Right. Because you're you're.

[00:17:02] Audrow Nash: Because you have to, like, put it this way and bring it that way. Right? Like backing up a car, kind of like turn the wood. Right. This kind of thing. Right? Okay. So, yeah, you have to move all that. That sounds painful or sounds like a challenging motion planning problem. And segmenting it from all the other wood that looks similar with, 3D sensor. Yeah, that sounds hard. But so you have a data set for that that you've collected through your own. Means. And that lets you do it. Okay. So that's kind of like problem one is the segmenting it gets at. Problem two is moving it. Then you move it to your saw and your position for measuring it right. Everything.

[00:17:41] Sketch to Structure

[00:17:41] Barrett Ames: Right. And that's that's a really tricky problem as well because, gripping on to lumber. Lumber is actually pretty soft. Right. And so you have to worry about deforming it while you're moving it. And how that impacts your measurement. And so there are, there's a bunch of how do we move it really quickly because we need to move it really quickly in order to, to process it in, in an economical way, and get it down to a 32nd of an inch. Right. So we have to measure very precisely, to, to really, get the, get the tolerances we need. Right. Those tolerances are basically driven from like, how the lumber stacks up in, in a building. And so there's like a really interesting controls problem there, that we tackle with, with some in-house, kind of combination of, of controls, sensor fusion and some custom, mechanisms.

[00:18:45] Audrow Nash: Gotcha. So once you're grabbing the wood and then you're placing it, cutting, measuring it, cutting it, this kind of thing. Now there's still the difficulty in my head of going from the napkin sketch to the full plan.

[00:19:01] Barrett Ames: Yeah. Yeah.

[00:19:02] Audrow Nash: That has like windows and doors and all the features that one might expect and matches code and things like this. Tell me a bit about that because that sounds hard and interesting also. Yeah.

[00:19:12] Barrett Ames: Yeah. So that very difficult problem. And just, just absolutely key to getting everything working, in the right direction. Right. And so the, what, what we do is, is kind of this combination of, mixed integer programing and generative AI. Right? So, we need to take in all of these constraints and generate, the true constraint. Right. And so we we use a generative model.

[00:19:42] Audrow Nash: What is your true constraint. What do you mean.

[00:19:43] Barrett Ames: Well, like we have to interpret all of these as an intent as opposed to like, mathematically true dicta.

[00:19:51] Audrow Nash: Right.

[00:19:51] Barrett Ames: Yeah. And so that's kind of that's that's the translation that I'm talking about here.

[00:19:56] Audrow Nash: I see. Yeah. So there will be a true constraint. And that is something that matches all of the specifications dictated in the. Okay. So you have to interpret that into something that's like right, complete right. Right.

[00:20:08] Barrett Ames: And so okay, in order to build that model, we actually have just just over 2,000,000ft² of floor plans that we've now processed. And that lets us, train, I think we've got three, no, we've got four generative models now that are built off of that, just picking out all of these different constraints and synthesizing them together to give us something, that can go into the optimization.

[00:20:37] Audrow Nash: Right. Okay. Why? For models, what are they doing differently?

[00:20:40] Barrett Ames: Well, we kind of segmented it up into different problems. Like finding windows and doors, as opposed to dimensions and like, there there's just some nice, areas to segment it up.

[00:20:54] Audrow Nash: I see, yeah. Okay. Tell me more about that because that's very interesting. So you trained on a bunch of floor plan data. What did that data look like? And where were your sources? Yeah.

[00:21:05] Barrett Ames: For that. Yeah. Our our sources, mostly come from builders that we've been able to talk to. And it's just, you know, anyone and everyone that we can get to give us, floor plan. What? We'll we'll use the data to help train our models.

[00:21:26] Audrow Nash: Okay, okay. And then so do you have to annotate it? Yeah. Initially. Or how did how did all that work?

[00:21:32] Barrett Ames: Yeah. So we've got, a labeling team that goes and annotate, really difficult to outsource that, because I bet it's not. It's not your standard, like, labeling problem. Right? It's not just a classification problem. It's it's I mean, in some parts it is, but, you know, there's just a lot of.

[00:21:52] Audrow Nash: Code you need to be sort of subject experts, right? Some some, like you need some training. Right? So I'm sure you can't like, or Amazon Turk or whatever it is, that thing where you have, like, random people do your task for a few cents, right? Or whatever it might be. Right?

[00:22:05] Barrett Ames: Yeah. So we spent six months training up a labeling team, to be able to read construction plans and go and label everything. And they're like, they're very key to the beginning of the whole process. Right? Like, without them, we wouldn't have been able to get to this point where now they're more supervisors, right? They're doing a lot of validation. And, the models are doing most of the work. But early on they were providing all of the annotations for.

[00:22:36] Audrow Nash: That's cool. Okay. So you have a bunch, you have a bunch of floor plan data. Yep. You use that to learn how to generate plans that your robots can make. You've had a bunch of people bootstrap this by adding the data in and then you learn from that. What? It just sounds hard to me like I wouldn't naively know that that would be, a tractable problem in a sense, or that the data wouldn't be just, like, total garbage. The output wouldn't be total garbage. Yeah. For this kind of thing. Like, how do you, like, it seems like an optimistic bet to me. And I'm glad that it seems like it's working. But tell me a bit more about the challenges around there.

[00:23:22] Barrett Ames: Yeah, I, I think, first of all, you're not alone, right? Basically every engineer that I've talked to about this problem has been like, well, why why did you even solve this problem? Right? Like, can't we just make, make architects build it in CAD, right.

[00:23:40] Audrow Nash: And expensive and slow.

[00:23:41] Barrett Ames: Right, right. And, the fact of the matter is that most PDFs are or most homes are, are coming out of designs that are already exist, and 99% of them exist in paper. So that that really provided the impetus for going and building this thing. And it's, it's really this combination, of our, like, ripping out all the information or extracting all of the information from the PDF with our optimization. That that makes it all work. Right? Because, there's this, this, feedback cycle of, you know, here, here's everything that we've learned from the plan. All right, run it through the, the more procedural optimization constrained approach. Right. And, the way that we built that optimization is really important, because it can, well, we we knew upfront that builders were going to have different ways of building things. We knew that, every state you go to is going to have different building codes. Definitely. So we had to build that optimization in like a very, very nice, way.

[00:24:52] Audrow Nash: To, to include those features. Right. Or like labels or something so that you can use them. It's like so your model understands the differences that it may be saying, do this with the constraint of 16in between stud. Right. I'm making it up. But like something like that, where one place might be 18in, something like that, ten foot walls versus eight foot walls versus whatever.

[00:25:12] Barrett Ames: Right. And that's why we went with like a more classical optimization approach there, because we can just swap in and out the constraints and those, those give us like these, these hard, hard guarantees that we're going to satisfy the building code. Even though we're, we're shoving in a whole bunch of stuff up front. And if, you know, if, if the labels are wrong or the annotations wrong, which at this point, like, they're like 99%, which is probably pretty good, right? Yeah. It's great. Oh, totally. Then.

[00:25:45] Audrow Nash: And then hopefully if they're 99%, that 1% is just like a little tweaking or you run it again or something.

[00:25:51] Barrett Ames: Right, right, right. So like the first time we built a plan, I went through, we didn't we didn't know that we needed the software. Right. I went through the plan and designed every single panel by. Right. Yeah. And like, that took me a week. Wow. And then I was like, this is nuts. I should find somebody else to do this. So I went and asked a civil engineering firm like, hey, will you guys design panels for us? They gave me a quote for $20,000. Wow. I was like, all right, so we need to build this software. And and now we're down to, just under two hours with validation. Right. And the dam, the goal is 30s. I want to be able to put a plan in, get a code out while you're on the phone with someone.

[00:26:41] Audrow Nash: Oh, that would be so sick. Hell, yeah. What is it? Do you have any idea about the cost reduction in that? So if they quoted you at $20,000, this probably if you don't include all the R&D cost and time developing it, your your run cost is probably like super cheap. Yeah. I run a.

[00:26:58] Barrett Ames: Dollar or a few dollars. Yeah I've run it. Our run cost with validation is about 20 bucks now.

[00:27:04] Audrow Nash: It's amazing. Yeah. So a thousand reduction.

[00:27:09] Barrett Ames: Yep.

[00:27:10] Audrow Nash: That's bonkers. That's so fun. Yeah. That's that's, Beautiful. Yeah. Oh, yeah. Yeah, I love it.

[00:27:15] Barrett Ames: Yeah. It's it's it's, it's it's like one of the key driving things here, right?

[00:27:20] Generative Solutions

[00:27:20] Audrow Nash: Yeah. Big differentiator I imagine too, because it's a, it sounds like a thorny problem and it sounds like. So when you say generative, solutions, you're not talking like ChatGPT, you know, generative AI, you're talking more like classical optimization or. Yeah, constraints or. No. Like, what is it.

[00:27:40] Barrett Ames: Like like.

[00:27:41] Audrow Nash: Evolutionary strategy?

[00:27:42] Barrett Ames: No. You know what? What kind of thing? When I mean generative, I really do mean generative. Like there is. Like like there there is an underlying distribution and we're learning that underlying distribution. And, and you know, you can think about new things coming in, and then sampling from that distribution. So you can kind of condition your distribution based on information that you've brought in. I've seen and then sampling from that to give you an idea of confidence in something. Right. So if you make a bunch of samples and they're all the same thing, you should have pretty high confidence, like, oh, yeah, this. Totally. Right. So it's, that I, when I, when I say generative, I do mean like, literally generative.

[00:28:25] Audrow Nash: You're making a distribution, you're sampling from it. Right. I see with, with that, what kinds of decisions is the model making? Like what what all is it deciding.

[00:28:37] Barrett Ames: It's it's, you know.

[00:28:40] Audrow Nash: It's like stud placement or like, configuration of wood when you're putting, like, an actor somewhere or like all these things. Like what what what all is it deciding. Yeah, it's.

[00:28:50] Barrett Ames: It's really synthesizing all of the various constraints. Right. So it's it's like, what's the, you know, given given window placement here and dimensions here. Like what what where do we really want that window? And so that's the it's kind of, it's taking all of that,

[00:29:11] Audrow Nash: Hell yeah. And then how do you. So you're getting a higher fidelity model with all the studs and things like this, in their position. How do you go from there to what your robots are building?

[00:29:25] Barrett Ames: Yeah, yeah, yeah. The great question. It's, yeah, deeply technical question. But at a high level, like, we're just thinking about the geometry of the robot, the geometry of the problem, putting those two together and saying, all right, we need this two by four to go here. How does that impact what the robot can do? And vice versa. It it's the the way that we've implemented it very much falls in line with, with, an area in robotics called task in motion planning. And specifically, like a lot of the details are just stuff that come directly from my PhD. So I did a lot of work on how you do this kind of high level task planning, but it's parameterized by some behavior that the robot has. And so that that,

[00:30:24] Audrow Nash: What do you mean? I don't quite understand. Parameterize. So task I think of. So you have something to do that requires many steps. And then parameterized with by the behavior that the robot has, I don't quite. Yeah.

[00:30:36] Structuring Build

[00:30:36] Barrett Ames: And so I kind of got my words flip there. The behaviors are oh no are parameterized in the sense that like, you know, if, if, you want to put a two by four on the table, there's a whole bunch of spots on the table, you could put it down, or you need to figure out what the where, where on the table is it going to go.

[00:30:54] Audrow Nash: Yep. So your parameters are dictating what to do next. So you have a bunch of simple behaviors like place the thing here, but then you have the parameters that specify the what should go in this location. And then another piece should go in that location. Right for this kind of thing. So you break down, but so that the process of breaking down your output from the, the the blueprint to like PDF to, a more detailed frame design, that part's very interesting. And maybe that's very hard to talk about for this kind of thing. Yeah. But, I'm curious about that trends, because I imagine you have to figure out where all the nails go. You have to figure out what order tasks must be done. Yeah. Like, there's a lot there that I imagine is very hard. And the way that I would naively suspect you implemented it is a bunch of heuristics. Have been specified, and then you kind of run it over this, like basically make sure you have something placed so you can attach something to it. Nails go every this often this kind of thing. But tell me how you've done that.

[00:31:59] Barrett Ames: Yeah. So at at a design point, right. Like at the end of the, floor plan to design stage, we end up with, just a CAD of the house, right?

[00:32:12] Audrow Nash: Yeah. That's so cool.

[00:32:13] Barrett Ames: It has every two by four, every nail. And so from that, we can we can take a panel out, right? Like, take our Lego block of the house out of the house and say, all right, we want to build this chunk. And, and building that chunk is really just a planning problem. Right. We know what our end goal is. And we know a bunch of initial conditions which are the we know we have lumber and we know where nails come from. Right. Yeah. They they.

[00:32:47] Audrow Nash: I like your framing.

[00:32:47] Barrett Ames: Of this. Right. Yeah. And then we just we have, you say go, right. We have a classical planner, that interacts with the geometry of the robot and searches through that space to find, you know, given our set of behaviors, and the parameters for those behaviors, how do we get from pile of lumber and the ability to magically make nails appear to end panel?

[00:33:13] Audrow Nash: That's awesome. And do you, do you do you kind of go through it in simulation first?

[00:33:18] Barrett Ames: Yeah. Yeah.

[00:33:19] Audrow Nash: So how does all that work?

[00:33:20] Barrett Ames: Yeah, we do it all first in simulation, to figure out like what's the feasible path, right? Yeah.

[00:33:26] Audrow Nash: Because that sounds very hard.

[00:33:27] Barrett Ames: Right. And a feasible path. We get it in about like a minute and a half to two minutes. And then we spend, like another 25 minutes optimizing. And then once we have that optimized path, it's giving us like high level behaviors and, and and their, their parameters. Right. So it's, it's put down this two by four, which for us is almost always the top plate goes in first top plates. The two by four that goes to the top of the wall. And then you put down a bunch of studs and then you put down the bottom plate, and how you put the order in which you put all of those studs down is very important because the geometry of our end effector like.

[00:34:09] Audrow Nash: Can't get everywhere, right.

[00:34:10] Barrett Ames: Unless you put it in just the right order. And so we figure out that order, just by simulating everything. Right.

[00:34:18] Audrow Nash: I would imagine. Okay. That's really cool. And you basically is it like you're trying to simulate it forward and then it's like blocked. And then you throw out that one and you generate again and you do that a few times. Or how do you I or it's an optimization problem where you keep moving. I just I'm curious how you frame the optimization problem to get the targets I suppose. Or like the the path that you're going to end up doing. Yeah. For everything.

[00:34:45] Barrett Ames: Yeah. So we because we know what we want at the end and we know everything that we have, both material and behavior wise. Right. You can in parallel look at a whole bunch of different configurations that we might run into along the way and decide whether or not they're valid. And that's a really fast simulation shot.

[00:35:11] Audrow Nash: Oh, that's cool.

[00:35:11] Barrett Ames: And so it just shunts off whole branches, of of the planning problem really quickly.

[00:35:18] Audrow Nash: Yeah. So it's a big branching thing and you keep going. Oh, this one doesn't work. Right. Cut that off and then keep proceeding down other ones. And so you'll have this big explored space of possible solutions. And then from there maybe that's where you're like 25 minute optimizing. Yeah. Yeah. You're like trying them scoring them whatever it is.

[00:35:36] Barrett Ames: Right. Yeah. Because the first the first plan through. Well it'll like pick up and put down the tool like 25 times or something. So it's free because it's just doing like depth first search through this massive, you know.

[00:35:49] When Things Go Wrong

[00:35:49] Audrow Nash: Huge space. Right. That's wild. What a cool thing. Okay. Hell, yeah. So then you assemble your pallet and you load it on a truck and then ship it out, and then builders assemble it. That's awesome. Okay, so how do you handle exceptions with this? Like, I'm sure that things don't go right all the time. Yeah, yeah. Like, maybe sometimes you drop a piece of wood or a nail doesn't go in the correct play. Probably go in the correct spot, but like, say it chips the wood or something like I'm sure exceptions happen.

[00:36:26] Barrett Ames: Oh, yeah. What.

[00:36:27] Audrow Nash: What are some of the exceptions and how do you deal with that. Yeah.

[00:36:30] Barrett Ames: Yeah, yeah. So the exceptions are, they are exceptional, but not as exceptional as I would like. Right. Of course, there's, you know, if you think about a house being a thousand pieces of lumber and 2000 joints, there's going to be like, we have to be really, really good in order for there to be no errors. Yeah. And so what we do is actually pretty interesting. And and I've never, I've never seen anybody in robotics do this. I hope I'm wrong. And there are just smarter people out there. But but we use this, thing called the good Turing estimator. So. Okay. So Ian good was one of Alan Turing's, colleagues while they were building the Enigma machine. And he developed this estimator that just allows you to estimate the number of unseen bugs. Right. Okay. Really useful. Interesting tool. And it's actually how we quantify our exceptions, even though we haven't seen all of them. So we just, write them down, as we see them, there's a few kind of key hotspots, and then there's a long tail of stuff. And that's one of the really, really key things to construction is that there's not one big like, hairy problem. It's.

[00:37:51] Audrow Nash: A bunch of little one.

[00:37:52] Barrett Ames: Thousand little ones. Yeah. Totally. And so I actually early on, I was really lucky to find this guy who I consider to be the oracle for the problem that BotBuilt is solving. He he spent the first part of his career as a Seabee, in the Navy. So he is building stuff. And then when he left the Navy, he went from a laborer to a superintendent of construction. And then just, you know, around that he was like 40, 45 and decided like, I don't want to do this anymore. Robots are cool. I'm going to go build robots. And I did all the way up through the DARPA Grand Challenge. Right. So he has a vehicle entered in the DARPA Grand Challenge. Like, just awesome. Just like how how do you find people like this? I don't know, I just got really lucky and found this one, dude. Yeah. And so I invited worlds. Amazing, right? I invited Mark to BotBuilt, and I said, like, look, how do we die? And and his his his, advice was exactly what I just said. The construction. There's not one big problem that kills you. It's not doing the thousand things well. And so that's how we are. We're always trying to figure out what are the thousand things, and then how do we how do we prioritize those thousand things to do? Well, because, you know, we're we're, we're constrained, right? Like, we can't solve them all at once. So, like, the big things for us, over the last couple of couple of months have been taking out those really high frequency things like the computer vision, simulating all of the lumber, and doing a, a high enough of every, degree. Those, those kinds of errors are actually pretty nice in that, when we do have a problem in labeling, we just have an image, and we just need it labeled, so that we can actually just ship out, get it labeled, comes back, robots keep going. And now we have more labeled data. That's that's like, super nice problem. For.

[00:39:56] Audrow Nash: Sure. Yeah. Computer vision problems tend to be nice, right? A lot of times if you get them. Right. Okay.

[00:40:01] Barrett Ames: But other like other classes of problem are like, we, we have this unobservable problem that happens, about like once every 5 or 6 homes, a nail will go into a piece of lumber and there's a knot, hidden beneath the face of the lumber. And so the nail will actually hit the knot, curve down around it, because it's not more dense. Right? It's harder. Yeah. And it'll actually go down into our work table.

[00:40:35] Audrow Nash: Then it gets stuck.

[00:40:35] Barrett Ames: And then it's stuck on your work, and we have no way, like the first time it happened. We're, like, trying to pull this panel up off the. What happens. Right? And then we figured it out. So that's just like, an exception that we have to, to, to sense, right? Like, oh, we're trying to pull this thing up, and it's not, it's not coming up. But we really don't have a great way of solving that one yet.

[00:41:01] Audrow Nash: Can you put everything on, like, a not hard, surface? Like, I'm thinking, like, a spongy thing. But you may also want the rigidity for your framing. So that's actually probably pretty hard. Yeah.

[00:41:14] Barrett Ames: Yeah, it's it's definitely an interesting problem. I think, we haven't even really tried to tackle it yet because it's so low frequency, but, if I had to guess, you know, we'd use the force torque sensors in the wrist and, and we'd notice, like.

[00:41:29] Audrow Nash: Oh, hey, we.

[00:41:30] Barrett Ames: Can tell we're or we can't pull this up. But there is like, when I pull it up, you can feel that there's, like, this direction that it wants to go. Yeah. And if you just allowed it to kind of follow along that path,

[00:41:44] Audrow Nash: It might be able to pull it up that way, but interesting. What a funny thing, that the notches are harder. Yeah. And then, you get the nail deforming for all of this. What a crazy thing. That's so funny. You're like, why the hell is this nailed to the table? Like, yeah, yeah, it's wild. Okay, any other classes of different exceptions? You've kind of run across? Yeah.

[00:42:06] Barrett Ames: We see, constrained motion planning problems a lot, right? Like, that's just, fundamentally difficult field still. Yeah. Right. Like you're, you're trying to, to sample from this manifold in, seven dimensional space, but it's, it's, zero volume manifold from the samplers standpoint. So, like, yeah, that's just difficult.

[00:42:30] Audrow Nash: Super hard.

[00:42:31] Traditional vs. Automated

[00:42:31] Barrett Ames: And we we've made some improvements there. And it's, it's all driven around, the fact that we have to move these big pieces of lumber in and, you know, very specific ways sometimes.

[00:42:44] Audrow Nash: Okay. Very interesting. So for the end result, how does this compare? Like how are, I guess, how many houses have you done so far and how does it compare to the traditional building method of like the people on site building it.

[00:43:01] Barrett Ames: Yeah, yeah. So, we're just finishing up 27, so.

[00:43:08] Audrow Nash: 27 houses or.

[00:43:10] Barrett Ames: 27 houses. That's awesome. Yeah. And, there are just people living in them, right? Like, That's wild.

[00:43:19] Audrow Nash: What, what I bet that feels.

[00:43:20] Barrett Ames: Yeah, yeah. It's great. I mean, there's nothing like writing code. And then somebody lives inside your code, right? Like. Oh, so definitely, definitely a lot more like substance to that. It's it's pretty cool. And they don't know that they're robot built, right? Like it. It's just another home.

[00:43:38] Audrow Nash: Yeah, they're just home. Right? It's great. So how does it compare to. So you have 27 homes. That's super exciting. How does it compare in like efficiency or cost or things to, when you're building it on site?

[00:43:54] Barrett Ames: Yeah, yeah. So, the robots are slower right now. But more cost effective because one person can monitor a whole bunch of them. Right. And I actually like the grocery.

[00:44:09] Audrow Nash: Store right thing where you have one checkout person manning ten kiosks or something like that.

[00:44:14] Barrett Ames: Exactly, exactly. So our kiosk checker, his name is Tim. Tim used to work at, chick fil A. He's just one of the guys his sons. He's 16. And he.

[00:44:27] Audrow Nash: It's so good. Hell, yeah.

[00:44:28] Barrett Ames: He can operate all of the robots, right? And what a.

[00:44:31] Audrow Nash: Cool job for a 16 year old.

[00:44:32] Barrett Ames: Yeah, yeah, it's it's he loves it. And we love having him because he. Yeah, he pushes it way harder and faster than I will. Right. Like I know too much 60. And of course, like he'll just go for it. Yeah.

[00:44:44] Audrow Nash: And so it's like any good 16 year old right. So that's awesome.

[00:44:47] Barrett Ames: Exactly, exactly. So we we Yeah, he can he can run all the robots. And that's where we get a lot of our efficiency is just scaling out, Tim's ability to build a whole bunch of panels at once.

[00:45:04] Audrow Nash: Nice. Yeah. And that's awesome to Tim being 16. So I, I suppose, like, maybe he has some construction background. But, like, that's exciting that it's someone who's, like, relatively new to the workforce. Right. And, it's a good model for that kind of thing that probably is very favorable for you guys. Scaling eventually. Yeah, that Tim can be right. Manning all these machines. Right?

[00:45:28] Barrett Ames: Yeah. All right. My co-founder, Brant was, in, in the Army, and he said that I had to make the robots army proof. Right? So, like, you just have to be able to have anyone, any old infantry, be able to run it, and, that's, that's that's what we've tried to do.

[00:45:47] Audrow Nash: That's hilarious. Yeah. One of my good friends is in the Navy, and he says the same thing. But Navy brew, right? For this kind of thing. Right? Hilarious. Okay. Hell, yeah. Do you have any idea? So with this parallelization, do you have any idea in terms of like time or cost, how it compares?

[00:46:04] Barrett Ames: Yeah, we're still getting, better numbers on all of those.

[00:46:09] Audrow Nash: And maybe the trajectory of your, improvements would be interesting to mention.

[00:46:14] Barrett Ames: Yeah, the trajectory has been pretty awesome. Year ago to now, we've improved our throughput by five x. So that's yeah, big, big improvements there. The.

[00:46:29] Audrow Nash: Throughput, is that correct? Yeah. Yep. Okay. Yep.

[00:46:32] Barrett Ames: Yep. And we're about to drop a big feature that's going to double our speed. So that that'll be awesome as well.

[00:46:41] Audrow Nash: And so be ten times better than it was a year ago. Roughly. Yeah. Yeah, yeah, I like that. Yeah. That growth curve.

[00:46:47] Barrett Ames: That's awesome. Right? Right. Yeah. You don't you that's that's the kind of growth curve you need for a venture backed company. Right. Yeah. So and then the kind of the other big wins that we see are that our panels are our, like, frankly beautiful, in that they're, they're very precisely made, and put together. We, we were just walking one of them recently with, an inspector here in Durham. And he said, wow, this is like, the best framing that I have seen in Durham. Right? Which, like, feels really good. But it also just means that at the end of the day, the consumer is getting a better product. And it actually takes significantly less time to inspect. Right. So, we've seen inspection times drop from two and a half to three hours, down to 30 to 45 minutes.

[00:47:44] Audrow Nash: That's awesome, because.

[00:47:45] Barrett Ames: The inspectors just gain a lot more trust, a lot more.

[00:47:48] Audrow Nash: Quickly. Now, not to the, like, I'm sure that's true, but to play devil's advocate. Yeah. With, like, ChatGPT and things, they can seem very sophisticated. And it's kind of easy to trust for a lot of things. But there is definitely cases where they, like, we'll just hallucinate random stuff. And so the trust can occasionally be betrayed in some sense, or just like it wasn't well founded. Right. This sounds a lot more structured than that. But how would you think about that kind of thing if there is kind of a downside to decreasing the, increasing the trust so much that they just let you go on past, or on uncosted if something is a little bit wrong because they were just like, yeah, everything looks so good because they assume a high level of competence for the builders that are the machines. But you still can do something wonky somewhere and they just may not see it. How would you think about that kind of thing?

[00:48:47] Barrett Ames: Yeah, that's an interesting question.

[00:48:54] Audrow Nash: And obviously it's like you're on your way to doing very good work. Right? So in the path of doing good work, you'll come like you're on the right path for sure. Right? But this may be a thing that,

[00:49:07] Barrett Ames: Yeah. What could occur. Yeah. So one, one thing, you know, like, there are they're just pattern matching, right? Like they're when they look through a house, they're just kind of looking for things that are odd and out of place. And our stuff tends to be extremely regular, orderly. Right.

[00:49:25] Audrow Nash: And so that so they're looking for exceptions. Right. And you don't have many exceptions. Right. That's exciting right. Oh yeah.

[00:49:30] Barrett Ames: Right. And so that that gives them the confidence. But it is it is a human inspection process. Right. From, from from the building inspector standpoint, we actually do QA internally as well. Right. So because we we have this 3D model of what we expect to build, we can.

[00:49:49] Audrow Nash: And you've added against that.

[00:49:50] Barrett Ames: Right. We can just look at that. And so that gives us, higher confidence and I think I'm not positive yet, but I think it will also help, with insurance matters down the road. Oh, there's.

[00:50:04] Audrow Nash: That's super cool.

[00:50:04] Barrett Ames: Like, building insurance is kind of this really messy place, like, you know. Oh, yeah, a wall falls down and all the contractors just go, not me. And then the judge just gives everybody an equal share. And because we capture so much data on every joint and every piece of lumber will be it will be able to very clearly say, like, look, we did our part. And if we messed up, like, yeah, we'll, we'll also be able to say, yeah, of course, clearly like, yeah, that's our bad.

[00:50:35] Audrow Nash: Yeah. Because you have like a 3D camera and you have like a camera image. Right. And probably a 3D scan of everything. And so they could be like, this wasn't done right. And it's like, well, leaving our facility, here's the thing. It was perfect. Right. Which yeah, I view that as a big win for insurance. Yep. For sure. Yep. It's so interesting to me that a lot of these automation, a lot of the output or a result of increasing automation is better monitoring and that it's very interesting to me that like insurance seems to be a big winner. Yeah. Through this because and just like business tracking right. And things like you mentioned like inventory tracking and things very interesting.

[00:51:15] Barrett Ames: Yeah. I think so I've been, I've been thinking about this like, framework or paradigm for like how to build robots. Right. And I call it teleoperation driven development. And the idea is basically like first you have to design the mechanism, then you have to run the mechanism, but you want to run it with teleoperation. And that helps you get this, this idea.

[00:51:38] Audrow Nash: Problem fit.

[00:51:39] Barrett Ames: Right like problem fit, but also like you have to have really good perception in order to operate it. Right. And so you get this like, observability from a control standpoint, very early in the process. And because we're building control algorithms that are subhuman, they always seem to need more perception than humans, right? Like they can't keep this in. What do.

[00:52:04] Audrow Nash: You mean? Subhuman?

[00:52:05] Barrett Ames: Just like like below, below human care.

[00:52:08] Audrow Nash: But they're not as sophisticated as us, I guess. Yeah, yeah. Or they may be in other ways, but for the most part, they're less sophisticated than us, right? Right. In sensing. Say.

[00:52:16] Barrett Ames: Right. Right. Well, I'm kind of like keeping an internal model, right? Like where we kind of sub out a lot of our perception for like, this really rich internal model, at least, I think. I don't know, I'm not a neuroscientist, but I.

[00:52:28] Audrow Nash: It sounds right to me. Yeah.

[00:52:31] Barrett Ames: And then, you know, once you've done the teleoperation stuff, you're, you're you've now guaranteed kind of two things. One, that you have enough perception and two, that, you have enough actuation to get the job done, then you can go and build your own. Yeah, build your control algorithm.

[00:52:46] Audrow Nash: I like that, yeah. I've been thinking of, like, one thing through all these interviews that I've seen as a big pattern and something that I've often said, I suppose, is like a human in the loop. Development is very good for startups, for the reasons you described. But also, like, you can gradually automate parts of it. You're getting data you're checking for, unknown unknowns because you're actually getting out there and solving it. Yeah. And using Tim or similar people where it's like you can slowly scale the number of robots one person is able to manage, then eventually you can get to full autonomy. Right? And at the same time, you're proving your market right, which is really effective, so that you can go get investment and, you can go start working with and pleasing customers. Yeah, for this kind of thing. So it's they seem very similar, these two ideas. And I really like your perspective on it, where you have enough sensing and then you can confirm that the actuation can do it. So now you can start automating right for this. Right.

[00:53:49] Barrett Ames: Right. Yeah. Yeah. That's your your thought process there I think exactly. Mirrors kind of how I think about teleoperation driven development.

[00:54:00] Audrow Nash: Yeah. Yeah. And it's so interesting to me because a lot of the like hardcore roboticists don't like this approach. But it is I think a very fast way to find value. Right. Like where you can add value. Yeah. And to me that's more interesting. Yeah. You know to make something really work.

[00:54:16] Barrett Ames: Well it also really.

[00:54:17] Audrow Nash: And solve something. Yeah. Yeah. Go ahead.

[00:54:19] Barrett Ames: I was just gonna say it also really helps you quantify the problems to solve, right. Like totally the good Turing test that we do just really helps us figure out, like, okay, these are the things with high frequency and high severity. Those are the things that are worth solving.

[00:54:34] Audrow Nash: Yeah. Right. It's a good way of weighting the problems to solve for sure. Right. Yeah. Because frequency time severity is the urgency. Right, right. I don't know that thing but I'm gonna look at Turing and good. That seems like a wonderful.

[00:54:47] Barrett Ames: Yeah it's, it's a model. It's a pretty interesting way to think about how robust your system should be. The other thing that I've done kind of, kind of with it is I've applied it to scheduling, which is pretty, pretty interesting as well. We.

[00:55:04] Audrow Nash: What do you mean by schedule like.

[00:55:05] Barrett Ames: Like engineering scheduling. So, so if you have, if you've got, some, some big engineering task that you want to do, you get a whole bunch of engineers together, you all kind of lay out the path that you would want to do. And the areas where like.

[00:55:22] Audrow Nash: Highest technical risk, right, kind of thing. Well.

[00:55:25] Barrett Ames: Those will show up as, like a whole bunch of different ways of doing the same thing, and where people are in agreement. It kind of collapses down to like, one thing. And the, the cool thing about is the.

[00:55:38] Audrow Nash: Frequency, right? Dimension is the consensus. Right. Interesting.

[00:55:42] Barrett Ames: And so the, the cool thing that you get out of the good Turing estimation is you get now instead of just saying, like, hey, to the schedule, you get to scale your schedule estimate based off of uncertainty. Oh, I love that. And so the, the, the awesome thing that we were able to do with it was I took a six month project. We laid it out with this, and we were within a week.

[00:56:07] Audrow Nash: Damn. That's awesome. Yeah, yeah, it's like how a GPS will take into it. Like, when you're taking directions on a map, it'll take into account all the uncertainty. But by the distributions of how traffic progresses and whatever, on average, you'll arrive pretty close to the time you want it, right? That sounds super cool. If you could do that with engineering effort, because that's not like everything is always happening later than it was expected. And things like this. And it's interesting if you can codify your problem in this way and then get a really good estimate, that takes into account a lot of the uncertainty. Yep. Super cool.

[00:56:43] Barrett Ames: Yeah, it's been fun. I'm still still experimenting with it, but at least the early tests I've done with it. It seems really helpful.

[00:56:52] Time & Cost

[00:56:52] Audrow Nash: I would absolutely love if you make like a blog post or a video or something once you have it more fleshed out. Yeah, yeah. Or just hell, we can do another podcast interview. Yeah. And, I would love to learn more about that because that that's a hard problem. Yes. And, yeah, I mean, people do or companies do scrum and stuff like this and yeah, not that satisfying in my experience. And this, this kind of uncertainty because I think that's always funny with scrum is it's like, well, I don't know the task. So therefore it's hard to do it for this kind of thing. Right. And so this is like it takes into account the uncertainty. Right. Which is quite cool. Right. Yep. Very cool. Do you have any idea of the potential cost? I know I keep asking around the same kind of things, but yeah, I'm wondering like, do you think it'll be a good bit faster because like one of the things that comes to my mind through most of my interviews is that there are massive labor shortages, and I think construction is one of those areas particularly strongly affected. Yeah. And so I wonder how, like just getting the task done at all is fantastic. But if it's done really efficiently and you make a lot of gains in terms of speed and it allows people to use it for hours instead of 20 days or whatever. You said at the beginning. Yeah. I maybe that's the good metric to assess. I'm curious. Kind of like an apples to apples in terms of like, you build a house this way and you build a house the traditional way. I'm curious if there's like a cost estimate and then a person time estimate, maybe 20 weeks to the other one. But can you talk a little bit to this? And it sounds like the trajectory is improving rapidly. Right. But I'd be interested to hear where you are and where you think it's going.

[00:58:39] Barrett Ames: Yeah, we're we're right at parity right now. Like it depends a little bit on the complexity of the job. But we're like up and down right around parity with humans.

[00:58:48] Audrow Nash: That's exciting.

[00:58:50] Barrett Ames: And so I think, you know, we will be we still haven't turned the dial up on the actual industrial arms, right. Like they're still set at 20% speed. So we can we can certainly get a lot more speed out there. And the big the, the big thing that we're, we're releasing shortly will be what will help improve speed even more.

[00:59:19] Audrow Nash: Can you speak to that at all or is that, secret thing that will be announced eventually.

[00:59:25] Barrett Ames: Well, just, stay tuned. It'll it'll be it'll be a pretty cool video. And I'll, Oh, hell yeah. So we'll we'll, we'll show it, for sure. But, yeah, it's there, there's a lot, efficiency still to be squeezed out. And we're, you know, we're we're making good progress on that every day.

[00:59:48] Audrow Nash: Yep. So cost is about the same. And so if you make it faster, that'll make it cost less. Yeah. I suppose with all of this and I mean, you guys from my. So you guys are approaching series A. Yep. Correct. Yeah.

[01:00:02] Barrett Ames: Yeah yeah. We're just about to do our series A fundraise.

[01:00:06] Audrow Nash: Hell yeah. Want to share any details with that or.

[01:00:11] Barrett Ames: Yeah. You know, we're, we're, always looking for investors who are interested or experienced in robotics. There's, you know, I think an ever growing group of people out there who.

[01:00:23] Audrow Nash: It is growing.

[01:00:24] Barrett Ames: Yeah. Who who understand? I call it the return profile. But, like, it's, you know, the moats are much bigger in robotics. Totally. And so I think the, the long term returns will be higher. But it does take it does take longer. Right. Like there there's. I can't ask ChatGPT to build me, robot code yet. Well, you can. Yeah, yeah yeah yeah yeah, yeah. But it's not a it's it's certainly not as easy as building a web app now.

[01:00:58] Audrow Nash: Definitely. Yeah. Yeah. It's, there's a new breed of investor that's, I'm seeing that's coming out, that's thinking long term for robotics and it's very exciting to see, and they understand a lot of the grievances that robotics companies have had in the last ten years working with investors that try to turn everything into software as a service.

[01:01:19] Barrett Ames: Right.

[01:01:20] Audrow Nash: With subscription.

[01:01:21] Barrett Ames: Models and stuff. And I think as a community, we're also just doing a better job of getting to value faster, right? Like totally doing doing this teleoperation driven development type approach, like, let's, provide and verify value, much, much earlier. Whereas, you know, like, I totally used to think this way that like, yeah, we just got to automate the whole thing. But that's, that's, that's clearly the wrong way to go about building a robotics startup. Right. Like, yeah.

[01:01:52] Audrow Nash: Not very capital efficient. Right. All right.

[01:01:54] Barrett Ames: For this. Right. So I think, you know, they're they're the kind of investor community pairing I think is really close to being at, I kind of think about it as like getting ever closer to, like 20. Well, like 2008, 2009, like web dev or SAS, right? Where you're like, yeah, Ruby on Rails is just starting to come out. People are like, oh, yeah, I can build all these cool things now. And yeah, I think, I think the category as a whole is about to have an inflection point.

[01:02:33] Audrow Nash: I think that's true. And I think that is going to be brought on even more by labor shortages and like, aging demographics causing labor shortages. Yeah. Where things are going to need robots to continue. Yeah.

[01:02:46] Barrett Ames: Yeah. Construction for sure has that problem, right. Where like the. Oh, yeah. The crazy the crazy anecdotes that I have. And data for construction here are just like 40% of contractors are over the age of 50.

[01:03:00] Audrow Nash: I know, right? Oh, my God. And new ones are not being trained.

[01:03:03] Barrett Ames: Right. So it is.

[01:03:04] Audrow Nash: Everyone went to college and didn't want to go to blue collar job, right? Which now we need so many more blue collar people, right?

[01:03:11] Barrett Ames: For every day in North Carolina, there are three contractors who can do electrical work that are licensed under the age of 30.

[01:03:19] Audrow Nash: Wow. That's the whole state. There's a.

[01:03:20] Barrett Ames: Whole state. Holy cow. Yeah. That's wild. Yeah. So there there's they're there has to be automation. Right. Like. Yeah. And and one way to think about it is that automation is just taking this old construction problem and making it sexy. Because if you, if you do it with a robot, it's hot. And like, doing it by hand. Well, let's say it's not.

[01:03:49] Audrow Nash: Yeah. Well, I mean, but it lets you there's so many exciting things about doing it with robots because one, the robots get better and better. It's like you can become a bit more of like a technician. Yeah. For the robots. Right. I don't know. I think the jobs will be changing. Definitely. For all of the stuff, I mean.

[01:04:07] Barrett Ames: Yeah. And then, you know, there there are labor shortages on the job site as well. Like, on a recently I saw somebody post they expected 27, framers to show up for a job and only three showed up. And like, that's, that's part like that's just the way contractors are, right? Like they're a very mercurial bunch. But then also like the political climate is also not great for the contractor. Yeah, right. Like, the, the, official numbers are that a 30 to 40% of contractors are undocumented. Our experience is more like 90%. Holy cow. So there's there is kind of this, you know, very interesting political aspect to it, that that will, will, you know, I think demand more more automation, more robotics.

[01:05:04] Audrow Nash: Yeah. That's just what I'm saying. Yeah. Because it's the solution. We can automate ourselves into a better situation. Right. I do think there will be, I suspect, a wave of blue collar workers. Yeah. Because plumbers are making 300 K in the Bay area. So yeah, it's like they're making more than some software engineers. Yep. And I think we're seeing that kind of everywhere. Where are a lot of places anyways that it's like a lot of these jobs, it's so scarce, that you have a competent person that they can command a wonderful wage. And so that's driving people back into it. Which will start to correct, I expect, in the US. Yeah. Specifically because we're relatively young demographically. Right.

[01:05:49] Barrett Ames: Right, right. And I think still will be tough. I think you're right there. It'll be a little bit slower in bigger cities because they tend to be more unionized and have a lot more requirements. You know, you gotta apprentice for 7 to 10 years or something like that, right? Yeah. And so that'll, that'll slow things down. But on the flip side, unions are also very good at adopting technology quickly. Because, they, they have to be able to like, demonstrate, like, hey, are higher wages, provide more value, right. And so we actually we're really concerned about unions at first, but what we found is that they're actually, pretty interested in adopting technology.

[01:06:32] Audrow Nash: That's cool. Yeah. One of the companies that I talked with a while ago was canvas, and they're doing robots for drywall. Yep. And they work very closely with unions in California. Yeah. Last I talked to them, and that was very interesting to hear. Are you guys doing any similar work with unions or how are you guys working with or thinking about unions?

[01:06:55] Barrett Ames: Yeah, unions aren't a big thing. In the southeast in general. So not terribly important.

[01:07:01] Audrow Nash: And you're in North Carolina. Yeah, yeah. As you're saying. Durham. Yep yep.

[01:07:04] Barrett Ames: Yep. So we're we're not terribly concerned. Like we they just don't exist here. In, in other places, you know, it's definitely going to have to be a conversation, but I think, like Kevin from canvas, has demonstrated, like, the right way to approach, unions and automation and just, like, show that, like, yeah, this, this, this is a tool. And just like all tools, craftsmen should use them.

[01:07:36] Long-Term Vision

[01:07:36] Audrow Nash: Yeah. Totally. Especially, like for canvas with drywall work. One the labor shortages are intense. There. And to the people who do it get like unbelievable repetitive stress shoulder injuries because they're just holding a sander. Yep. And it's like finishing the wall and it's just super, super difficult on their body. So they retire quickly from that kind of job. Yeah. So that makes sense for the union to really want to help them. And I would bet that it's similar for a lot of jobs, like you were saying at the beginning that, the injury rate is quite high or accident rate because it's like sores and stepping on two by fours, one floor up or more, this kind of thing. Yeah. So I would bet that this would be a big benefit. So you guys have 27 homes. You're going for your series A so you're looking at scaling more. Yep. Well what do you think the future is going to look like for you guys. Like kind of draw it out for me because I think like my impression is the last, like getting to 27 homes now or getting to 27 homes has been kind of like a proof of concept. You've gotten it working. You're now making efficiency gains. Tell me how we go forward from here. And what do you think the timing and what do you think the bottlenecks are? Yeah. Everything.

[01:09:00] Barrett Ames: Yeah, yeah. So forward, is is kind of along I think three fronts. First is, continuing to push on efficiency, of robots. Second is adding more value to panels. Right. So if you if you think about the what, what can be prefab, the biggest piece of that pie, is what we're already doing. That's about 20% of the cost of the home, right. It.

[01:09:35] Audrow Nash: Is that factoring and labor? Yeah. 20%. Yep. Okay, so if you remove labor, if you just look at. Oh, but I guess okay, that's the fair comparison for this okay. So that's 20%. Yeah I guess finishing takes a lot of it. Like the finished it parts or I.

[01:09:50] Barrett Ames: It's I don't know. So like a roughly 20% of it is lumber. Sorry framing. And then the next two biggest chunks are actually plumbing and electrical. And yeah. And so we have, we have the ability to help them a lot there. In that we can rule, plumbing and electrical.

[01:10:14] Audrow Nash: Oh, I like that a lot.

[01:10:16] Barrett Ames: We can save a lot on on copper. Copper is expensive. And then actually plumbing in electrical are the number one causes for framing failures, when it comes to inspection, because when you're, when you're if you're drilling a hole through two by four to run a, pipe, the hole saw, if it walks a little bit one way or the other,

[01:10:37] Audrow Nash: Kinda compromises the integrity of the structure.

[01:10:39] Barrett Ames: Right. Right. And so being able to a root it and B cut the holes very precisely makes their job easier. And then you know, there I think there is kind of an interesting, I don't know, 18 to 24 month problem of like do we start to wire it in in the warehouse. Right.

[01:11:01] Audrow Nash: Like that would be so great.

[01:11:02] Barrett Ames: And there there's, there's some interesting things there.

[01:11:06] Audrow Nash: Sounds really hard. I imagine a lot of hard problems around it because are deformable, right? Materials. I know they're pretty rigid. Right. But, still.

[01:11:13] Barrett Ames: Right, right. And then that motion planning problem.

[01:11:17] Audrow Nash: Right. That'd be much harder, I bet.

[01:11:18] Barrett Ames: Yeah. Right. Right, right. And then, the kind of like third area that I think is really important, for us is we'll be pushing a lot on learning from demonstration, because, as you expand into more and more of the house, things get, more fabric, like, or more squishy, and there's a ton of experience locked in craftsmen. That if we can get them to demonstrate it, we can get the robots to do it.

[01:11:50] Audrow Nash: Oh, that's really cool. That's a nice long term perspective, right?

[01:11:54] Barrett Ames: Right. So, like, a really early, thing for us to tackle is, is actually insulation, right? Insulation is this kind of fabric, right? When it's when it's made from fiberglass. It it feels kind of just like a big blanket, being able to put that in, it is not a straightforward, like just a motion.

[01:12:15] Audrow Nash: Planning because it's, it's a very difficult compressible surface. Right. You have to do it just right and not have it overlap and stuff and whatever else. Right. Okay. Yeah. A lot of cool a lot of very cool, problems and a lot of high potential use cases that sound like they leverage what you already have pretty. Well. Yeah.

[01:12:38] Barrett Ames: Yeah, yeah. I think one of, one of the, the key things for us is that by tackling framing first and doing that in a warehouse, we really set ourselves up because everything else builds off of framing. Yeah. Right. And so we so we can leave ourselves kind of crooked cookie crumbs where, where necessary to help future problems.

[01:13:01] Audrow Nash: Yeah. Super cool I would say like the super low hanging fruit to me that adds a lot of value is just cutting those holes for pipes and wires. Yeah. Right. As you're doing it that's like I can't imagine that's that much harder because you could just drill it while you're, you grab the piece of wood you want, you drill the hole through it. Yeah. And then you go place it where you want that holes there now. Yeah. That that to me seems super, super exciting. And then all the other things are very, very cool. As you guys grow as a company. Yep.

[01:13:30] Barrett Ames: Yep. Yeah. Though the holes are like our number one requested feature right now. We don't get a lot of requests from from, the job site. They tend to usually just say everything's great. But that that's one, that's one that that we've gotten.

[01:13:47] Audrow Nash: Oh, yeah. Okay. Very cool. So tell me a bit more about the construction space in general. Because a lot of people I did a poll on X a while ago, and like, it was bonkers. It was like 90% of people that are in the audience are interested in starting a robotics company at some point, and like, 25% already had or something like bonkers. Yep. So tell me about the construction space. From the perspective of an entrepreneur, what are like low hanging fruit? Lots of opportunity. Yeah, yeah. How do you think of it? Yeah, it's an exciting space. And I don't know too many robotics startups that are going in. And I think there's a lot of low hanging fruit. Probably. Yes, but what do you think? Yeah.

[01:14:35] Construction Opportunities

[01:14:35] Barrett Ames: The way that I think about it is threefold. And it, it really ties into like, well, why is it awesome to work at BotBuilt every day. A that for, for me as a CTO there are, there are there's an endless number of problems right. Like and for me that's really exciting. For some people that's overwhelming. Super fun. But like, you know, I'll solve this problem there will be another one. Yeah. And it's because for the last 70 or 80 years, there's been basically no innovation in construction. So you're 100% right. There's a ton of low hanging fruit. Where where strides have been made that they're typically made in high end commercial where when you fail, failure means losses in the hundreds of millions of dollars. We haven't seen a lot of innovation in homes. Because if your home goes up in, you know, ten, $15,000 in price or whatever your mortgage goes up by, you know, ten, 15 to 50 bucks a month or something like that. Right? And so you don't really like the kind of American consumer has been boiled like a frog, as the prices keep going up, because there's not really anything pushing it down. So the result is that the US is short homes, about tons, four and a half to 5 million homes. And we're not building them fast enough to catch up. That problem is getting worse.

[01:16:06] Audrow Nash: I wonder, I wonder how you work with, baby boomers moving into retirement. Yeah. For everything. And if they'll move into, like, condos and free up their homes or pass away and free up their homes or things like this. But yeah, there's a huge home shortage and so many people are just moving into apartments. Right. Which is really interesting. And like if you live in a many big cities, it's just absolutely prohibitive to buy a home, right, for this kind of thing. So you're taking a bite out of that huge problem? Well, I guess the four and a half to four or 4 to 5 million. Yeah. Is that does that count people that are in apartments or is that just how do they get that number? I don't know if you know.

[01:16:47] Barrett Ames: Yeah, I there's like 3 or 4 different people that that work on this problem. We know Stephen Kim who's economist at Evercore, if I remember correctly, he's just down the road here in Raleigh. Cool. And there's a bunch of ways that they get at this number. And I don't remember any of them off the top of my head.

[01:17:09] Audrow Nash: So that's okay.

[01:17:11] Barrett Ames: But the.

[01:17:12] Audrow Nash: Cool it's a huge growth.

[01:17:13] Barrett Ames: Rate. The cool thing is that he he actually releases a report every year on the state of housing, and just looks at, oh, that's also like latent demand, all sorts of, you know, various technical aspects. He's doing it from, like, an investor standpoint. Yeah.

[01:17:31] Audrow Nash: And so with this huge shortage of homes that pushes up the price of all the other homes, making it even harder for people to buy homes, right then, so you guys are trying to increase supply? Yep. Which will make it so that costs come down. More people can be in homes, right? I really like the mission. I think like I've been a homeowner now for almost three years and it is glorious. I absolutely love it. Like the best thing is sitting in the backyard, right. And everything. Yeah. And but, it's a yeah. Go ahead.

[01:18:02] Barrett Ames: It's actually pretty interesting because, I bring this up because not a lot of people believe this. More supply will drop the price down. And, and and there there's a number of people who don't believe this, but, Austin is a great example of a city that I.

[01:18:19] Audrow Nash: Was going to say if you didn't.

[01:18:20] Barrett Ames: Yeah, that that has massively increased its housing supply and has seen, rents and mortgages drop, by 15%.

[01:18:31] Audrow Nash: Totally. Yeah. We bought a house right around that time, and we're in San Antonio and it went through San Antonio is like an hour away from Austin. We saw the same thing. And then it's just amazing. It's like market forces working because you see the prices go rise up because they can't build as fast as the demand is growing, because everyone has the internet and everyone decided to move there all at the same time. Right. And then, they build and build and build and then years later, it's like the demand or the, the, the demand matches the supply. And then you see prices come right back in line.

[01:19:04] Barrett Ames: And it was just remarkable.

[01:19:05] Audrow Nash: Yeah. Like I oh wonderful I absolutely love that.

[01:19:08] Barrett Ames: Yeah. So so there's, there's, you know big technical problems. There's a big societal problem. Right. Like if we don't solve the housing problem there's going to be a lot of unrest. Right. Like people that the American dream kind of really, really affects how a lot of people think about whether or not they're successful in life. Right. And so you could have this big societal unrest if, if you have a whole generation that can't get into get into homes, totally. And then the the last bit is it's a massive market, right? Like it's $1.7 trillion in the US. And I want to drive a fancy car and have a nice house. So you, like, you put all of those together?

[01:19:51] Audrow Nash: I think it's forces aligned.

[01:19:52] Barrett Ames: I think it's a great place to build a robotics company. And there's so much, so much to do. I would love to have more friends come and build and construction.

[01:20:04] Audrow Nash: Hell, yeah. Yeah, that would be awesome. Oh. One thought with the you mentioned unrest from lack of housing. Yeah. One other thing is that if people don't have houses, houses, or like space, it's harder to have kids because you guys are all on top of each other. And, like, we need a big replacement generation coming up. Yeah, for this kind of thing. Yeah. To just keep life going and make it so we have a good, strong consumption economy, right? Yeah. 30 years from now. Yeah. Or this kind of thing. Yeah. Because a lot of advanced societies, you see, they just move, industrialize and then they stop having kids and the birthrate plummets like a lot of Europe. Right? So.

[01:20:40] Barrett Ames: Right. Right. Yeah. Yeah. When we when we moved to Durham, we, we had, a tiny little two bedroom, two bath, got four kids. Now, I now have a four bed, four bath. Definitely need lots of space for kids. Can confirm.

[01:20:55] Audrow Nash: Oh, totally can confirm. Yeah, yeah. That's great. Let's see. So tell me about North Carolina because I don't know too many. Like. So I lived in Charlotte for a while. I really loved it. But tell me a bit about Charlotte or, North Carolina as a place for you guys as a robotics company. Yeah. And being in the southeast in general.

[01:21:21] Barrett Ames: Yeah. The as a robotics company, where we're located is actually, I think deeply underappreciated. The they call it the Research Triangle. Right. Basically, Durham is Chapel Hill and Raleigh, in that triangle, we actually have the second highest number of computer science pitches. We're only be by the Bay area. Oh. And like, people are like, no, that can't be true. That is that. But RedHat, IBM, Cisco, Lenovo, Epic Games, there's all that's a whole bunch of smaller companies, that have spun out of that. And then there's, there's a bunch of biotech. And so we see.

[01:22:04] Audrow Nash: Oh, yeah, that's what the area is known for, right.

[01:22:06] Barrett Ames: For sure. Right. But we also see a lot of surgical robots because of that as well. And then you add Duke and NC state. I have wonderful, engineering programs. I went to Duke for my PhD, so, like, I guess I'll begrudgingly say uncW is also a good school. We actually just made our first UMC hire, and he is absolutely crushing it. So. Hell, yeah. It's it's it's great to, to be in kind of this, this area where, I can have, a big house, lots of kids, and start a robotics company. And, you know, like, that's all very affordable, right? Like it's not, I'm not I'm not getting crushed by rent in San Francisco. Yeah.

[01:22:56] Audrow Nash: Yeah. It's crazy. My my house when we bought in, like, the housing peak when everyone was doing it, the mortgage on my house is the same as my studio apartment in San Francisco. Yeah, just before moving here. Right. And that price has increased since that was, like, Covid discount for this kind of thing. Yeah. So bonkers.

[01:23:15] Barrett Ames: Right. And then the the southeast as a whole. That's right. Like the Sunbelt, is growing and everybody's building houses down here. So it's a wonderful place for a construction robotics company. And then there's just this really weird happenstance, where Durham, where we're located, has become kind of a construction tech hub. There are two other two other construction tech companies, both doing really cool things. Planted, is down the road. They're building, plywood sheets, from grass. And the cool thing there is that it? It's, a very.

[01:23:50] Audrow Nash: Don't know what that means at all.

[01:23:51] Lessons Learned

[01:23:51] Barrett Ames: It's like a very particular type of grass, but what it what it boils down to is it's not as, susceptible to mold growth, that it's a it's a couple of space guys that rolled out of space and decided to build up, a create a greener, safer, cheaper, building material. And then hammock is, is also down the road and they're doing like, they want to make it. So that way you can design your, your dream home from your house. Right. Like, it's just, building out kind of the video game aspect of, like, designing your house. So cool. The the three of us, you know, we it's it's been really cool to just be surrounded by other people who are. We're thinking about how to change construction.

[01:24:40] Audrow Nash: Hell, yeah. And also makers and doing the good problems and everything. Yeah. That's great. Yep. Hell, yeah. What do you. So if you had to start over or, say, advice to a young founder, what do you think? Like what would be some really good things to communicate to a younger you.

[01:25:02] Barrett Ames: Yeah. Yeah. Younger me or earlier. Yeah. Yeah, yeah, yeah. I think, like, we came to the, teleoperation driven development, part way iteratively. Yeah. And I like that would be the biggest thing is, is start with that as your, your formative paradigm. And then, honestly, like, just don't talk to SAS investors. You're just wasting your time, right? Like, a lot of them. A lot of them will totally. Just like they'll say they're cool and they're like, oh yeah, we do hard tech, right? Like that's a cool thing to say. But like, if they haven't made robotics investment, like, just don't expect them to, right. And then, I would, I would also like update. KVK right. So like we have a lot of, we have a lot of strategic that have helped us. What is CV. Oh sorry. What is a corporate venture capital? Oh yeah. Totally. And and at least in construction, it's been it's been massive for really understanding more of the nuance of, of all the, the particulars in this field.

[01:26:22] Audrow Nash: Totally. And you're finding good partners and they're opening up a lot of doors, I expect. Yeah.

[01:26:27] Barrett Ames: Yeah. So that those are kind of the, the big areas that I would think about, it's, and, and the last thing, the last thing I'll say, founder market fit is ridiculously important. And maybe, maybe it's just, under underappreciated from my perspective. But I don't hear a lot of people talking about it anymore.

[01:26:51] Audrow Nash: What is it? Yeah. What do you mean, cylinder market? Yeah.

[01:26:53] Barrett Ames: So, like, if it's like.

[01:26:55] Audrow Nash: Finding markets, it.

[01:26:56] Barrett Ames: No no no no no. Like like, you as a founder, do you fit with the market that you're trying to sell into?

[01:27:05] Audrow Nash: And so I, I don't hear this about ever basically. Yeah. So that that's a great point. Okay. Yeah.

[01:27:10] Barrett Ames: Tell me more. Yeah. So like the, the idea but one, one way that has been described by outsiders is that we're white collar workers, with a blue collar attitude. Right. And, and so that lets us engage in construction. Right. Like this is probably the longest I've gone without cursing, for several days. Right. And you didn't have to do that. All right, I know, I know, but, like, I don't know, the kids, my watch. So.

[01:27:45] Audrow Nash: Yeah. So funny.

[01:27:47] Barrett Ames: But, like, you know, there's there's superficial stuff like that, but there's also like, like, do you share values with your customers? Right. And if you don't, maybe you don't want them as customers. Right? Because it can be really hard for you to have any kind of customer empathy. If you're not like if there's not some kind of value alignment there.

[01:28:14] Audrow Nash: How would you know about that? I guess looking at your values. But I do think it's also very easy to delude yourself into something that seems to be lucrative, where you're like, oh, I could be interested in that, or I'm sure I'm interested in this altruistic purpose. Even though it doesn't align with who you are and what you're actually interested in. Like, how would you find that? Because that seems like, a good introspective. Yeah. Challenge that, I think a lot of people will struggle with.

[01:28:44] Barrett Ames: Yeah, that's a good question. I mean, I it's just what I gravitate towards, right. And so, like, I enjoy hanging out with construction people. So, that's kind of how.

[01:29:05] Audrow Nash: How I the simple answer that I think is kind of it.

[01:29:07] Barrett Ames: Right. Yeah. Yeah. Like, you know, it's it's just, Yeah. There, there, there's just something about the, the market that you want to go into that you find interesting or fascinating, because at the end of the day, like, it's going to be deeply frustrating, right? Like, totally there there are so many things that are going to go wrong and so many people that, that, you know, that, that I'll butt heads with. Brant, our CEO is amazing at being friends with everyone. And so like, for, as a founder, like, you just have to, like, what's going to get you through whatever that frustration is.

[01:29:55] Audrow Nash: Okay. I like that a lot. I have not heard about founder Market Fit. I think that's a really good idea to pick customer or pick some group to work with that you really like anyways. Because yeah, I say you, you hate the values of one group like you find it's silly or something, right. And go and try to work with them. And now it's like your startup's going to take ten years. And so now you're interfacing with this group the whole time. It's a really easy way to get burned out, right. And not do a good job. Whereas if you like, really like the values of them, then you're going to enjoy hanging out with them. You're going to enjoy trying to understand their problems. And, you guys will probably both be more open to working with each other. Like they'll try your thing and they'll let you in on good feedback and not sabotage your device or something like this, which might occur. Right? If they really dislike. Right?

[01:30:47] Barrett Ames: Yeah, 100%.

[01:30:50] Audrow Nash: Hell yeah. So looking forward, I know that it's hard. And you pointed to three things that look really exciting. In the future, a botbuilt, but like, say, 2 to 4 years from now, where do you expect you guys will be? Yeah.

[01:31:07] Barrett Ames: We'll be on site 100% like. All right. Oh, yeah. I, I think for us to capture ever more value and to provide ever more value, we have to get out of the warehouse. And we like very specifically chose to begin in the warehouse because I knew just enough about lumber to know that, like, it was going to be painful to learn how to do it. And I wanted to do that in a controlled environment. But to really make an impact, to, to fundamentally change, how construction is, we need to get onto the job site.

[01:31:47] Audrow Nash: That's awesome. Hell, yeah. Any thoughts with, like, size of the company, number of houses deployed? And these can be like, because obviously none of us know the future. But like, your current rate of growth, growth seems amazing. And I suppose if a fraction of that is capped over the years, it will be quite impressive where you guys get. But, looking out in the future, any ideas around that?

[01:32:13] Barrett Ames: Yeah. So right now we have, three robot teams, that are producing all of our houses. The robot team is just a pair of industrial arms that work together. I think in three years, we'll have call it 150 to 200 teams.

[01:32:32] Audrow Nash: Holy cow, that'd be amazing.

[01:32:34] Barrett Ames: And we'll we'll have deployed with a whole bunch of franchise partners, right? Like, we can't we can't own the US just by ourselves. Particularly in construction, where, so much of the sales is like, well, my dad knew his dad, right? But it's a very relationship based business. But we can make them more efficient, and let them focus on what they like doing, which is sell me. And so, we're actually doing our first deployment, this this summer to Florida.

[01:33:06] Audrow Nash: Oh, so out of North Carolina, you're saying? Yeah.

[01:33:08] Barrett Ames: So we'll have a bunch of teams set up, down in Florida. And that'll that'll be kind of, the beginning of this franchise opportunity.

[01:33:20] Audrow Nash: Hell, yeah. And the start of, like, remote scaling, which is its own hard problem. Right. And that will be amazing because. Yeah, once you nail that, then you can go everywhere, right? Come to Texas, go everywhere else. Right?

[01:33:32] Barrett Ames: Right.

[01:33:32] Final Thoughts

[01:33:32] Audrow Nash: Hell, yeah. Okay. Wrapping up, what do you hope our watchers and listeners take away from this interview?

[01:33:42] Barrett Ames: You know, I, I think there's two things. One is that, the future of construction is automated. And second, that, working on robots that work in construction, it is a really interesting problem, that can help a lot of people, right? Like you can you can do really good things for people and work on really interesting technical problems.

[01:34:12] Audrow Nash: Yeah. I think it's a super exciting space. All right. Hell yeah. Thank you Barrett. Yeah.

[01:34:16] Barrett Ames: Thank you. Great to be here.

[01:34:19] Audrow Nash: All right. Bye everyone.