Latest / Automated with Brian Heater / Rick Faulk on Locus Robotics, Warehouse Automation, and Physical AI

Transcript

Rick Faulk on Locus Robotics, Warehouse Automation, and Physical AI · Automated with Brian Heater

• 0:00
  SPEAKER_00: Having a dedicated physical AI solution that works in a warehouse is much more compelling for operators and a much more scalable business model than what a lot of these other businesses are doing.
• 0:10
  SPEAKER_00: The form factor that a lot of them are looking at, which is the buy pedal feet, is probably a recipe that's not going to work in warehouses.
• 0:17
  SPEAKER_00: Most companies in the robotic space that I talk to, a lot of them fail.
• 0:21
  SPEAKER_00: And they fail because they don't understand the client's problem.
• 0:24
  SPEAKER_00: Big difference between a robot looking for a problem and a problem looking for a robot.
• 0:27
  SPEAKER_01: One of the conversations I keep having over and over again in the physical AI space is this lack of real-world data.
• 0:33
  SPEAKER_01: You have an incredible number of robots out in the fields.
• 0:36
  SPEAKER_01: What can be done with the actual data that you're actually out there processing and collecting right now?
• 0:43
  SPEAKER_00: We've done over seven billion picks.
• 0:45
  SPEAKER_00: As we speak right now, we're probably doing 150 picks a second right now.
• 0:50
  SPEAKER_00: A second.
• 0:50
  SPEAKER_00: Each pick we do, we learn something that we can make it more productive for the client.
• 0:56
  SPEAKER_01: A lot of these companies decided, hey, we're gonna leapfrog from AMRs to humanoids and skip anything that might be in between.
• 1:04
  SPEAKER_01: To a certain extent, the array strikes me as uh that in-between space.
• 1:09
  SPEAKER_00: There's two ways to automate a building, you could argue today.
• 1:11
  SPEAKER_00: One is with humanoids that are designed to do a hundred different things, none of them particularly well, or something like Array that's dedicated to do one thing and do it unbelievably well.
• 1:21
  SPEAKER_00: But having said that, we don't have our head in the sand.
• 1:23
  SPEAKER_00: I think eventually humanoids may get there.
• 1:25
  SPEAKER_00: I think over the next five years there's going to be a big challenge.
• 1:45
  SPEAKER_01: Here's a great talk with somebody that we've been looking to get on the show for a minute.
• 1:51
  SPEAKER_01: Rick Falk is the longtime CEO of Locust Robotics, and he knows more about scaling and deploying industrial robots than just about anyone else in the world.
• 2:03
  SPEAKER_01: It's a unique bit of insight, and I certainly learned a lot from the conversation.
• 2:07
  SPEAKER_01: If you're enjoying the show and also learning a lot from these conversations, don't forget to like and subscribe.
• 2:12
  SPEAKER_01: Check out the newsletter over at automated.fm.
• 2:16
  SPEAKER_01: And with that, here is Rick Falk of Locust Robotics.
• 2:19
  SPEAKER_01: One of the things Jasmine told me in the lead up to this conversation was that uh this is actually the third headquarters for Locus.
• 2:28
  SPEAKER_01: Not only is this the third headquarters, but everything was generally in this vicinity.
• 2:32
  SPEAKER_01: Is that right?
• 2:34
  SPEAKER_00: Yeah, it is the third, Brian.
• 2:35
  SPEAKER_00: Uh we started out in uh, well, I actually it's really the fourth.
• 2:38
  SPEAKER_00: We started out in uh building in Devins, Massachusetts, which was quite quiet logistics, because Locust spun out of that.
• 2:44
  SPEAKER_00: And then we moved to a smaller facility about two miles from here.
• 2:48
  SPEAKER_00: It was basically an office.
• 2:50
  SPEAKER_00: Smaller than this, not smaller than the first facility?
• 2:52
  SPEAKER_00: Um actually much smaller than the first facility because the first facility was Quiet Logistics, which was a big, a big warehousing operation, and Locust was in the back of that.
• 3:01
  SPEAKER_00: And then we moved to uh a smaller building um right down the street.
• 3:05
  SPEAKER_00: Uh that was only probably three or four thousand feet, no manufacturing, uh no operations.
• 3:10
  SPEAKER_00: It was a really small building.
• 3:12
  SPEAKER_00: Uh and now we are in three buildings in the area.
• 3:15
  SPEAKER_00: Um we're in uh one in Nashville, New Hampshire, which is our RD facility, one in Tewkesbury, which is primarily a storage and shipping facility, and then here at Locust Park, which is where we're at now.
• 3:26
  SPEAKER_00: And we do uh everything here from uh manufacturing to refurb.
• 3:30
  SPEAKER_00: We have most of our team here.
• 3:32
  SPEAKER_00: We have foot folks that work around the world, but uh a lot of our mechanical engineers here, a lot of our software folks and the like.
• 3:38
  SPEAKER_00: So yeah, we're in three buildings.
• 3:40
  SPEAKER_01: Initially, what was the relationship between Quiet Logistics and Locust?
• 3:44
  SPEAKER_01: It started as something like a like a side project, would you say a spin-off?
• 3:49
  SPEAKER_00: Well, I wouldn't say it was a side project or spin-off.
• 3:52
  SPEAKER_00: It was a major problem.
• 3:54
  SPEAKER_00: And I believe great businesses are billed out of major problems, and and Locus was.
• 4:00
  SPEAKER_00: So what happened?
• 4:01
  SPEAKER_00: Um, we were running a 3PL, Guy Logistics, and we decided the best way to run that business was to automate it.
• 4:07
  SPEAKER_00: And we did.
• 4:08
  SPEAKER_00: We automated it with a company called Kiva, which ironically is located about three miles that way, which is now Amazon Robotics.
• 4:15
  SPEAKER_00: And we were one of the largest Kiva customers in the world.
• 4:18
  SPEAKER_00: Uh we had 250 robots uh solving all the problems in logistics of getting volume out the door and doing it at the lowest cost.
• 4:27
  SPEAKER_00: Uh and then Amazon came along in 2012 and decided they wanted to buy Kiva, and they did.
• 4:32
  SPEAKER_00: Uh, Amazon actually toured our building before they actually bought them.
• 4:36
  SPEAKER_00: Of course they did, and then they decided that uh robotics was so strategic to them that they took the product off the market.
• 4:43
  SPEAKER_00: Um, so we had a big problem.
• 4:45
  SPEAKER_00: And the problem was either to go back to a manual operation that seemed uh not the best idea in the world.
• 4:51
  SPEAKER_00: Uh, we actually uh scoured the world to find a robot to buy, couldn't find one, and then decided that the best path was actually to build a robot.
• 4:58
  SPEAKER_00: Uh so we did.
• 4:59
  SPEAKER_00: So we actually incubated inside that building.
• 5:01
  SPEAKER_00: So uh we started in literally the back corner of Quiet Logistics.
• 5:06
  SPEAKER_00: But the great thing about that is, first of all, we understood the 3PL business, understood distribution, uh, but really understood the client's problem.
• 5:13
  SPEAKER_00: And frankly, being in the building, we could actually develop solutions during the day, test them at night, make sure they worked, and then iterate the next day.
• 5:21
  SPEAKER_00: So um, yeah, it was actually a great uh a great way to uh uh build and iterate a great product.
• 5:27
  SPEAKER_00: And you know, in some cases, we give Amazon credit for founding Locust.
• 5:31
  SPEAKER_01: Yeah, I that that's an element of the story that I haven't hadn't heard before that Amazon actually came by and toured the facility.
• 5:36
  SPEAKER_01: Were you or was quite aware of the time that they might be like scouting Kiva?
• 5:41
  SPEAKER_00: Well, they were, they were very upfront about it.
• 5:42
  SPEAKER_00: Okay.
• 5:43
  SPEAKER_00: Uh and they were, uh I wasn't there there at the time, but as I understand it, um, they they wanted to tour facilities to get um a a good read on the tech and uh efficiency gains and that sort of thing.
• 5:54
  SPEAKER_00: And again, it wasn't there, so I don't know the exact context, but uh it was clear uh short shortly after the visit that they wanted to buy Kiva, and of course they did, and and uh uh like I say, it worked out great for Locust because that was the stimulus behind behind uh forming the business.
• 6:10
  SPEAKER_01: Yeah, but it it sounds like even then, so you know, 2011, 2012, um Kiva having robotics automating those systems was already showing an ROI for the company.
• 6:20
  SPEAKER_00: Yeah, it was.
• 6:22
  SPEAKER_00: And and um it was an ROI that uh was measurable.
• 6:26
  SPEAKER_00: We could pass those costs on to our clients uh in a good way by reducing their cost.
• 6:31
  SPEAKER_00: It also allowed us to get volume out the door uh and meet SLAs.
• 6:35
  SPEAKER_00: Uh one of the challenges with that solution, though, um it had to be deployed in basically a new building.
• 6:42
  SPEAKER_00: Uh you have to go down to bare concrete to actually deploy that solution.
• 6:45
  SPEAKER_00: Greenfield.
• 6:46
  SPEAKER_00: Exactly.
• 6:47
  SPEAKER_00: Sort of a greenfield building.
• 6:48
  SPEAKER_00: Uh we actually had to get that building to deploy it, which at the time was the right, the right thing to do.
• 6:54
  SPEAKER_00: Um but the problem is it, you know, with fixed automation, you're fixed in what you can ship every single day.
• 7:00
  SPEAKER_00: It's like a it's like a big vending machine, if you will.
• 7:03
  SPEAKER_00: Uh where and you know, we learn from all that.
• 7:05
  SPEAKER_00: And the nice thing about Locus, uh, we can scale up and scale down based on demand.
• 7:09
  SPEAKER_00: With fixed automation, you have to buy, you know, we call it sort of for Easter Sunday.
• 7:14
  SPEAKER_00: You have to build a church for Easter Sunday.
• 7:15
  SPEAKER_00: So you've got to build the capacity, and with fixed automation, you've got to build that capacity to handle peaks.
• 7:21
  SPEAKER_00: Um, and that's one lesson we learned with that is is operators really want to scale up and scale down their solutions.
• 7:28
  SPEAKER_00: And uh that was uh a big learning uh behind behind building locusts and the other thing we learned was uh most operators, and we didn't want to do it at the time, but you really have to gut a building to actually deploy that.
• 7:40
  SPEAKER_00: And uh most operators today don't want to do that.
• 7:43
  SPEAKER_00: Uh there are 130,000, 40,000 buildings around the globe that are actually existing buildings that have to be automated.
• 7:51
  SPEAKER_00: And you know, these are operators that don't want to gut that building to actually deploy automation.
• 7:55
  SPEAKER_00: So uh yeah, the great thing about that, we learned a bunch about uh how to operate a building and how to build an automation solution that could scale around the world.
• 8:05
  SPEAKER_01: I I one of the things I learned talking uh from companies like you is so seasonality is a big part of those uh ups and downs.
• 8:10
  SPEAKER_01: So obviously, you know, like Christmas rolls around and all of a sudden there's a huge demand and then that that ebbs.
• 8:15
  SPEAKER_01: Um, but also just uh throughout the year, there are peaks and valleys for these companies.
• 8:20
  SPEAKER_00: Yeah, we have a lot of accounts.
• 8:22
  SPEAKER_00: Um, obviously around the holidays, uh, there's a huge peak, and we ship literally around 3,000 bots out of here right behind us for peak, and that allowed accounts to scale up, and uh then we get them back after peak.
• 8:35
  SPEAKER_00: Um, probably 15, 20% of them actually stick because clients realize they need them.
• 8:39
  SPEAKER_00: Once we get them back, we refurbish them downstairs.
• 8:42
  SPEAKER_00: Um but to your point, there are other peaks that exist.
• 8:45
  SPEAKER_00: We have accounts, uh, apparel companies as an example, back to school companies.
• 8:50
  SPEAKER_00: I can't mention the names, but they have to scale up and scale down.
• 8:54
  SPEAKER_00: Um, I can mention one Staples as an example.
• 8:56
  SPEAKER_00: Uh, they have back to school, and that implies a lot more volume that ships in uh, you know, certain times of the year to deal with that in in August and uh September.
• 9:07
  SPEAKER_00: We have other accounts that run uh big Mother Day sales.
• 9:10
  SPEAKER_00: Uh we have bots literally being shipped today uh to handle um other seasonal peaks.
• 9:16
  SPEAKER_00: So we get a lot of that.
• 9:16
  SPEAKER_00: And the nice thing about it is, you know, it isn't just one peak.
• 9:19
  SPEAKER_00: We have accounts that scale up and scale down throughout the year.
• 9:22
  SPEAKER_00: And this is the biggest strategic problem they have, uh, other than labor, is to scale up and scale down to meet those needs.
• 9:28
  SPEAKER_00: And uh we allow them to do it.
• 9:31
  SPEAKER_01: Yeah, so uh again, getting back to the formation of the company, um, you know, easier said than that, hey, let's start a robotics company.
• 9:39
  SPEAKER_01: You know, we have this problem right now.
• 9:41
  SPEAKER_01: Um, we we we we we know what the solution is.
• 9:44
  SPEAKER_01: What are the first steps to that process, you know, in terms of actually turning from a logistics company to, as you said, sort of like a backroom robotics company and starting that that early RD?
• 9:56
  SPEAKER_00: Most companies in the robotics space that I talk to, um, a lot of them fail.
• 10:02
  SPEAKER_00: And they fail because they don't understand the client's problem.
• 10:05
  SPEAKER_00: And the nice thing about being incubated inside a warehouse and having the DNA of understanding that problem, you avoid that problem.
• 10:13
  SPEAKER_00: Uh, there are a lot of uh, well, you know, frankly, the Boston landscape is littered with with robotics companies and hundreds of millions of dollars that uh went into technologists who really understood robotics, really got robotics, but didn't know the client problem.
• 10:28
  SPEAKER_00: They were a robot, I say, a robot looking for a problem, as opposed to a problem looking for a robot.
• 10:33
  SPEAKER_00: And uh Locus was just the opposite.
• 10:36
  SPEAKER_00: Um, and that's you know, I think a big part of our success.
• 10:39
  SPEAKER_00: We understand the problem, we can talk the clients, client's language when we go in there, allows us to develop features and functionality and and solutions that really cater to their problems.
• 10:49
  SPEAKER_00: So uh big difference between a robot looking for a problem and a problem looking for a robot.
• 10:53
  SPEAKER_01: But in terms of the actual building of the robot, you know, again, you know, Jasmine was talking about some of the early days about you know being in there with two robots and they, you know, obviously kind of like bumping into each other, like there was a lot of learning that had to go on, it sounds like really quickly in order for you to not not scale up, but to actually start deploying.
• 11:11
  SPEAKER_00: Well, there was.
• 11:11
  SPEAKER_00: I can remember um you know, I didn't find found locus.
• 11:16
  SPEAKER_00: I came on board uh very early before we had one robot in the field.
• 11:20
  SPEAKER_00: But when I was uh interviewing, I of course wanted to demo the solution and uh I got it, and there were three robots running at the time.
• 11:28
  SPEAKER_00: And during my interview, I of course wanted to play with the solution, that sort of thing.
• 11:32
  SPEAKER_00: And uh the three robots kept on colliding into one another.
• 11:35
  SPEAKER_00: This is just three robots, and uh one of them actually collided into me, as a matter of fact.
• 11:40
  SPEAKER_00: And um, you know, of course, the founders at the time talked about, you know, those are natural problems in building a business, and they were.
• 11:47
  SPEAKER_00: Um, so those were the early day problems of of getting just three or four or five robots running um so that they actually navigate around a building.
• 11:57
  SPEAKER_00: Well, if you fast forward uh a number of years, we're 10 years into it now, we had uh clients at Peak that would run 700 robots, 700, I mean hundreds of hundreds of robots in in a million square feet.
• 12:10
  SPEAKER_00: And the robots didn't collide, they knew where to go, uh operated efficiently, charged automatically, and it's just a whole different environment now.
• 12:18
  SPEAKER_00: But that was the problem in scaling.
• 12:20
  SPEAKER_00: And we have one of our uh account accounts, DHL, who talks about you know, innovation isn't innovation unless it can scale.
• 12:28
  SPEAKER_00: And we sort of took that to hard day one, and those three robots that we had running early, early on, we understood very, very quickly we had to get to 20 and then to 50 and then to 100 and then to 500, and now up to a thousand.
• 12:42
  SPEAKER_00: Um, and there's a lot of complexity that goes into that, a lot of complexity.
• 12:46
  SPEAKER_00: And uh we're able to do it, and we're uh I'm a little biased here, but we have the best engineers in the world that allow us to scale at those levels and uh for accounts to deploy around the globe, they really have to be able to do that.
• 12:58
  SPEAKER_01: I'm talking to companies in you know 2026 that are telling me how difficult it is to source parts and and to scale, to manufacture, you know, backing up well over 10 years, it I assume it must have been that much more difficult.
• 13:15
  SPEAKER_01: You know, in early days was I assume a fair share of assembly was happening on site.
• 13:20
  SPEAKER_01: Like, how are we actually building these things, putting them together and delivering them to customers?
• 13:25
  SPEAKER_00: Yeah, good question.
• 13:26
  SPEAKER_00: So initially, we built everything in-house, everything.
• 13:30
  SPEAKER_00: Uh there was a method to our madness because we wanted to learn the challenges and problems and costs and everything else.
• 13:36
  SPEAKER_00: So we built everything initially.
• 13:38
  SPEAKER_00: We we we we literally hand-built probably our first um 5,000 robots or so, uh, right down the street.
• 13:46
  SPEAKER_00: And we learned very quickly that we couldn't build them all ourselves and scale.
• 13:50
  SPEAKER_00: So we went to contract manufacturers.
• 13:53
  SPEAKER_00: We actually have two, one in Vermont, one in Massachusetts, who build the raw components.
• 13:58
  SPEAKER_00: So uh we have the CMs that manufacture the base and the mass that goes on the robots, uh, all the guts on the inside.
• 14:05
  SPEAKER_00: We design them all and then give them the specs.
• 14:08
  SPEAKER_00: And then we do final assembly and test downstairs, right behind us.
• 14:12
  SPEAKER_00: So uh we'll get those two major components in.
• 14:16
  SPEAKER_00: We'll knit them together, and it takes about two hours of labor to actually do that, and then we'll brand them here.
• 14:22
  SPEAKER_00: So we have a branding room downstairs where we'll do wraps and that sort of thing.
• 14:26
  SPEAKER_00: Um, and we do all that internally.
• 14:28
  SPEAKER_00: So we do the way we think about it, we do the final mile.
• 14:31
  SPEAKER_00: We have the contract manufacturers do the first five miles, if you will, uh, the detailed assembly, and they're very good at it.
• 14:39
  SPEAKER_00: It allows us to scale up and scale down.
• 14:42
  SPEAKER_00: So you don't want to build a manufacturing facility uh to produce thousands and thousands of bots when our peak throughout the year goes like this.
• 14:50
  SPEAKER_00: Uh but CMs can deal with that.
• 14:51
  SPEAKER_00: So we design it, we spec it, give those plans to CMs, they build out those core component parts, and then we do f final assembly and and and and test and then shipping from here.
• 15:03
  SPEAKER_01: Yeah, this is something that's just sort of occurring to me uh as I'm saying this, but if there's a little bit of, I don't know if irony is the right word, but there's in building what are these sort of specialty machines, a lot of it does have to happen with human labor and does have to happen by hand.
• 15:17
  SPEAKER_00: Well, it does.
• 15:19
  SPEAKER_00: Um we minimize the human labor component of it, the way we design it.
• 15:24
  SPEAKER_00: Uh, for example, we design wiring harnesses.
• 15:27
  SPEAKER_00: It sounds like a simple thing, but instead of having um Oh, everyone says whenever they talk about automotive manufacturing, it's always the wiring is always the wiring is the hard part.
• 15:35
  SPEAKER_00: So instead of having 10 wires that have to be connected in different spots, which takes manual labor to do that, we have one wiring harness as an example.
• 15:43
  SPEAKER_00: So uh it allows us to minimize the labor that goes into it.
• 15:46
  SPEAKER_00: And we've standardized that that over time.
• 15:49
  SPEAKER_00: And uh, you know, literally in a robot downstairs, only be about two hours of labor that goes into a robot.
• 15:55
  SPEAKER_00: So uh we we've dramatically simplified the process, which allows us to scale.
• 16:00
  SPEAKER_00: So the CMs, it takes them a few more hours than that to actually assemble the robot.
• 16:04
  SPEAKER_00: But once we get in here, it's only a couple hours of labor to actually do that final assembly, configuration, and branding.
• 16:11
  SPEAKER_00: So, yeah, we've got it down to uh yeah, I like to think somewhat of science.
• 16:15
  SPEAKER_01: Yeah, it's interesting because you know, I I I come from a bit of a uh consumer electronics background.
• 16:20
  SPEAKER_01: So um, you know, I I look a lot towards uh towards a smartphone, right?
• 16:26
  SPEAKER_01: I mean, all the innovations there, and that led to um the ability to manufacture a lot of other different devices.
• 16:31
  SPEAKER_01: And I don't know if there's a parallel that can be drawn to robotics, especially, you know, not here.
• 16:35
  SPEAKER_01: You're working with those contract manufacturers, but just because they're set up to um, you know, build the first five miles, as you said, of your machine doesn't mean that another company can necessarily come in and utilize the work that you've done.
• 16:48
  SPEAKER_00: Yeah, CMs specialize in building lots of different things in volume.
• 16:53
  SPEAKER_00: And there are a bunch of different CMs that specialize in different different devices.
• 16:56
  SPEAKER_00: Uh, we're fortunate to have two in New England who were really good at robotics and manufacture other robots in addition to ours.
• 17:03
  SPEAKER_00: So, but again, whether you're manufacturing a robot or a health care device and they do a lot of that, uh or a security of the machine, they're really good at doing that.
• 17:10
  SPEAKER_00: Um the the key with CMs is you have to have your bomb cost down and it's got to be stable.
• 17:17
  SPEAKER_00: What CMs don't like is they don't like part changes all along.
• 17:22
  SPEAKER_00: They like one consistent design that they can run with, build a line, and build it out for a year or two.
• 17:28
  SPEAKER_00: So uh we're at the point with Origin and Vector where the designs are stable.
• 17:32
  SPEAKER_00: The contact manufacturers can actually order the parts and buy the parts with confidence that they know those are going to be consistent for the next for the next few years.
• 17:40
  SPEAKER_00: So um great business for them, allows us to scale.
• 17:43
  SPEAKER_00: Uh, we get shipments in here every week from CMs, uh, the you know, the base units and the mass, and then, you know, again, we do that final assembly and test.
• 17:51
  SPEAKER_00: And it works out really, really well.
• 17:53
  SPEAKER_01: It it it strikes me too that using or or relying to a certain extent on off-the-shelf components is also pretty useful when it comes to actually building these robots.
• 18:01
  SPEAKER_00: No, it is.
• 18:02
  SPEAKER_00: Uh, we have a very large team that does software, and that's all developed and and it's one platform that runs those robots, as well as Origin of Vector.
• 18:12
  SPEAKER_00: It's it's basically, for the most part, standard parts other than the plastic and metal that of course uh are custom for us, um, that allows us to actually build build those bots, program, and run them.
• 18:25
  SPEAKER_00: I think the you know the key thing is having, again, that stable design and also a very few number of robots that you can configure different ways.
• 18:35
  SPEAKER_00: I like to say we have um a thousand different robots, but we only have two.
• 18:40
  SPEAKER_00: And I say that because we can configure our robots different ways.
• 18:44
  SPEAKER_00: We don't customize, we configure.
• 18:46
  SPEAKER_00: So on our origin and vector, for example, we have many, many different accessories that go on that robot.
• 18:52
  SPEAKER_00: Same core core robot that gets manufactured, the CM.
• 18:56
  SPEAKER_00: We effectively have different shelf types and printers and that sort of thing that go on that base device to allow for uh configuration at that account versus customization.
• 19:07
  SPEAKER_00: And I again I talk to a lot of robot robotics businesses.
• 19:10
  SPEAKER_00: They'll say, well, we'll do anything for the client.
• 19:12
  SPEAKER_00: Uh we'll design this and design that make these changes.
• 19:15
  SPEAKER_00: And it's a very difficult business to scale when you do that.
• 19:19
  SPEAKER_00: So we allow for all that, but we've done it with a strategy that says have two core form factors, our origin, our vector, and soon to be be array, but configure those in lots of different ways.
• 19:31
  SPEAKER_00: Uh so we have literally hundreds and hundreds of different shelf types that go on that printers and that sort of thing.
• 19:37
  SPEAKER_00: So it makes us easier for us to scale and gives the client exactly what they want.
• 19:41
  SPEAKER_01: I talked to a lot of humanoid companies who t who pride themselves in building full stack, and and part of building full stack for them is all of these custom components, custom actuators, custom screws, and that is gonna be incredibly difficult to scale.
• 19:56
  SPEAKER_00: Well, you hit the nail on the head.
• 19:58
  SPEAKER_00: And the screw.
• 19:59
  SPEAKER_00: Well, yeah, the nail and the screw and the hammer and the wrench.
• 20:02
  SPEAKER_00: Yeah.
• 20:02
  SPEAKER_00: Uh very, very difficult cult to scale.
• 20:04
  SPEAKER_00: Um, they're doing, I think, some exciting things.
• 20:07
  SPEAKER_00: I think it's going to be a ways off before there's use cases inside a warehouse.
• 20:11
  SPEAKER_00: They may eventually may get there, but very, very difficult cult to scale.
• 20:15
  SPEAKER_00: And the form factor that a lot of them are looking at, which is the buy pedal feet, is probably a recipe that's not going to work in warehouses for all the reasons that um you might imagine.
• 20:25
  SPEAKER_00: But the key thing, you're exactly right, very hard to scale.
• 20:28
  SPEAKER_00: And they're also trying to develop software that is generic in nature, so it can do lots of different things, can fold laundry, can make a burger, and can work in a warehouse.
• 20:38
  SPEAKER_00: Interesting at some level, and they're they're they're they're gaining a lot of investor attraction.
• 20:44
  SPEAKER_00: But the reality is having a dedicated physical AI solution that works in a warehouse is much more compelling for operators and a much more scalable business model than what a lot of these other businesses are doing.
• 20:55
  SPEAKER_00: That's how we think about it.
• 20:57
  SPEAKER_00: Let's go in with a dedicated physical AI product that does one thing and does it unbelievably well, as opposed to worrying about burgers and laundry and and uh you know delivering goods.
• 21:08
  SPEAKER_01: Yeah, I'm sure that over the years investors came to you and said, hey, by, you know, can this can this move over to this space and and can you um stick an arm on it and do these things?
• 21:19
  SPEAKER_00: Well, we got that question all the time in the early days, and we still get it.
• 21:23
  SPEAKER_00: You know, why why can't you go into hospitality?
• 21:25
  SPEAKER_00: Why can't you go inside hotels?
• 21:27
  SPEAKER_00: Why can't you go in restaurants?
• 21:29
  SPEAKER_00: Why can't you do why can't you do all these things?
• 21:31
  SPEAKER_00: The answer is we could do all those things.
• 21:34
  SPEAKER_00: But from my perspective, it's a recipe for failure.
• 21:37
  SPEAKER_00: In this business, if your focus do one thing unbelievably well in a massive market, which is what logistics is, as I mentioned, there's you know, 130, 40,000 buildings around the globe all need to be automated.
• 21:51
  SPEAKER_00: Why do we need to worry about restaurants and and all these other places that uh we could go into?
• 21:56
  SPEAKER_00: It's a whole different sales sales, it's different sales process, different value prop.
• 22:00
  SPEAKER_00: Different integration with systems, and it's a recipe for a big challenge in growing a business.
• 22:05
  SPEAKER_00: So our strategy is really simple.
• 22:06
  SPEAKER_00: Let's stay focused at one thing, a massive market, and do it unbelievably well and be the best in the world at doing it.
• 22:12
  SPEAKER_01: One of the conversations I keep having over and over again in the physical AI space is this lack of real world data.
• 22:18
  SPEAKER_01: You have a lot of real world data.
• 22:20
  SPEAKER_01: I mean, you have an incredible number of robots out in the field.
• 22:23
  SPEAKER_01: Um, you're out there collecting it.
• 22:26
  SPEAKER_01: What can be done with the actual data that you're actually out there processing and collecting right now?
• 22:34
  SPEAKER_00: Well, that's a really good question.
• 22:35
  SPEAKER_00: I love to talk about it because that is the thing that makes Locust different than a lot of other businesses in the world right now.
• 22:40
  SPEAKER_00: As we speak right now, we're probably doing, if you look at the counter in the other room, we're probably doing 150 picks a second right now, a second.
• 22:48
  SPEAKER_00: And that counter that we're looking at, uh, or maybe you saw, runs 24 hours a day, seven days a week.
• 22:53
  SPEAKER_00: It'll scale up and scale down.
• 22:54
  SPEAKER_00: Every single one of those picks, every single one of those picks, we're learning something that no one else is learning.
• 23:00
  SPEAKER_00: We've done over seven billion picks, seven billion picks.
• 23:04
  SPEAKER_00: Uh, DHL just made an announcement uh a few weeks ago.
• 23:07
  SPEAKER_00: DHL has done a billion picks with locus robots, and it is just an insane amount of data that we're collecting.
• 23:13
  SPEAKER_00: So what are we doing with it?
• 23:16
  SPEAKER_00: Think about this as the data flywheel.
• 23:18
  SPEAKER_00: That each pick we do, we learn something that we can make make it more productive for the client.
• 23:24
  SPEAKER_00: Uh, as an example, on our robots right now, we have agents that are running.
• 23:29
  SPEAKER_00: One agent that runs is an agent that's looking out through the cameras, looking at what it sees.
• 23:35
  SPEAKER_00: If it sees a forklift, for example, uh you could see a forklift, could see a human, could see a cart or whatever.
• 23:42
  SPEAKER_00: Uh, if it sees a forklift as an example, it'll it'll take that data that it's learned that this is now a forklift and it will take action and do a certain thing.
• 23:51
  SPEAKER_00: For example, if a robot runs through a forklift, it's going to lose, okay, and probably get get destroyed.
• 23:57
  SPEAKER_00: And we have some good experience of that before we launch this agent.
• 24:00
  SPEAKER_00: So when a robot, a locust robot right now sees a forklift, it will actually take action and do something.
• 24:06
  SPEAKER_00: So the do something could be turn around, go the other way.
• 24:09
  SPEAKER_00: It could be sit there, depending on how long the forklift is there.
• 24:13
  SPEAKER_00: It could be take action and go to another pick.
• 24:16
  SPEAKER_00: So it's an example of using that data that we know what a forklift looks like from the billions of picks we've done and the AI engine behind all this, and we'll take action.
• 24:26
  SPEAKER_00: Another example is uh what we call system-directed labor.
• 24:32
  SPEAKER_00: So, what's happening in that warehouse right now, we we have robots traveling around, we have humans traveling around, and we know the most efficient path you as a human have to take based on your past work and based on the work in the system.
• 24:46
  SPEAKER_00: We know your most efficient path to get to your next pick based on the data that we're gathering around the system right now.
• 24:56
  SPEAKER_00: We know where the humans are, we know where the picks are, we know the robots are traveling from and to, and we tell you as a worker what to do.
• 25:03
  SPEAKER_00: We have an agent over your shoulder telling you what to do using an AI algorithm behind that to maximize your efficiency as a human.
• 25:11
  SPEAKER_00: So those are just two examples.
• 25:13
  SPEAKER_00: Let me give you a third.
• 25:14
  SPEAKER_00: So with array that we're launching, uh we're collecting data on every single pick.
• 25:19
  SPEAKER_00: So if Array is doing a pick and it has to pick a roll of tape as an example, array will learn from all the data it's collecting that you don't want to pick a roll of tape in the center, because it's not gonna pick it, right?
• 25:31
  SPEAKER_00: So over time, through data, through AI, we learn that we've if you have to pick a roll of tape, you pick the roll of tape on the side, not in the center.
• 25:40
  SPEAKER_00: Simple example, but it's just what one of millions of picks that will do array to do with array to understand where to pick that based on the data to maximize success of the pick.
• 25:52
  SPEAKER_00: So we're using lots of other ways, but those are just several examples of um of how we're using data to be more effective.
• 25:59
  SPEAKER_01: So you mentioned array.
• 26:00
  SPEAKER_01: Uh, I was really interested.
• 26:01
  SPEAKER_01: You know, you and I spoke um uh a couple months back about the system.
• 26:06
  SPEAKER_01: Um I'm surprised it didn't get more coverage.
• 26:08
  SPEAKER_01: And I think a lot of that, again, is because of humanoids are sucking a lot of the oxygen out of the room.
• 26:13
  SPEAKER_01: A lot of these companies decided, hey, we're gonna leapfrog from AMRs to humanoids and skip anything that might be in between.
• 26:22
  SPEAKER_01: Um, this to a certain extent, um, the array strikes me as uh that in-between space, right?
• 26:30
  SPEAKER_01: I mean, it it has wheels, it has manipulator, it it is picking.
• 26:35
  SPEAKER_01: So it is something that um that that does that picking, but also the difference is like this thing is actually going to ship, right?
• 26:42
  SPEAKER_01: In the near future.
• 26:43
  SPEAKER_00: Right.
• 26:44
  SPEAKER_00: Um intentionally, we didn't want, did not want to get a lot of coverage over the last several months because we haven't really launched it yet.
• 26:51
  SPEAKER_01: Well, I I apologize for writing about it.
• 26:54
  SPEAKER_00: Not a problem.
• 26:55
  SPEAKER_00: Um so we actually launch it uh April 13th at Modex, and we have a big, a big launch plan, that sort of thing.
• 27:01
  SPEAKER_00: So uh we expect a lot of coverage, but you're exactly right.
• 27:05
  SPEAKER_00: Um there's two ways to automate a building, you could argue today.
• 27:09
  SPEAKER_00: One is with humanoids that are designed to do a hundred different things, none of them particularly well, or something like a ray that's dedicated to do one thing and do it unbelievably well.
• 27:20
  SPEAKER_00: And that's the difference.
• 27:21
  SPEAKER_00: Um also humanoids have all the problems that you talked about, cost and ROI.
• 27:26
  SPEAKER_00: Uh you know, if you believe most of the cost out there for humanoids, uh you're the other majored in in hundreds of thousands of dollars, and the ROIs really aren't there for humanoids today.
• 27:35
  SPEAKER_00: That cost will come down over time and be more competitive.
• 27:39
  SPEAKER_00: But at the end of the day, the whole secret is doing one thing and doing it really well versus lots of different things.
• 27:45
  SPEAKER_00: The other thing about a humanoid in a warehouse, if a humanoid was go to pick uh you went to pick something out of a bin, it's got to do something with it.
• 27:53
  SPEAKER_00: Just because it picks something, you know, doesn't mean that that's the end of the task.
• 27:58
  SPEAKER_00: It's got to do something with it.
• 27:59
  SPEAKER_00: Is it gonna walk, you know, 50,000 feet to drop it in uh a tote or a box?
• 28:05
  SPEAKER_00: Probably not.
• 28:06
  SPEAKER_00: So um again, it's an example of thinking through the workflows what's best based on the use case.
• 28:14
  SPEAKER_00: And uh it's it's really hard to find a use case for humanoids in a building right now that's really, really effective versus array.
• 28:21
  SPEAKER_00: And again, array will do everything autonomously.
• 28:24
  SPEAKER_00: When I say everything, I mean picking, replenishment, slotting, counting, sorting, do all that automatically with one form factor designed to do that one thing in a massive market.
• 28:35
  SPEAKER_00: Again, with a humanoid, maybe over time it would work, but you've got to deal with all the problems of cost, complexity, and all the things that present challenges for warehouse operators.
• 28:47
  SPEAKER_00: So um, but having said that, we don't have our head in the sand.
• 28:50
  SPEAKER_00: I I think it's you know, at you know at some point humanoids may play a role doing certain things.
• 28:54
  SPEAKER_00: Uh the question is what?
• 28:56
  SPEAKER_00: And how can how you can get an ROI for clients?
• 28:59
  SPEAKER_00: Because that's what they're interested in.
• 29:00
  SPEAKER_00: They're interested in, hey, does it work?
• 29:02
  SPEAKER_00: Is it safe?
• 29:04
  SPEAKER_00: And can I get an ROI with that product?
• 29:06
  SPEAKER_00: And um, you know, eventually humanoids may get there.
• 29:09
  SPEAKER_00: I think over the next five years is going to be a big challenge.
• 29:12
  SPEAKER_01: Yeah, I wanted to break down a little bit because you know, we we talk about ROI, and I'm kind of curious when you mention ROI, like what does that mean in bare terms?
• 29:20
  SPEAKER_01: Is it is a minutes saved?
• 29:23
  SPEAKER_00: It it is minutes, but it translates to dollars.
• 29:26
  SPEAKER_00: So the you know, the nice thing about Locus, unlike a lot of other fixed automation solutions, we have a very low upfront capital cost.
• 29:35
  SPEAKER_00: Basically, we have a RAS model, as you know, robots as a service, you pay a monthly fee, and your only upfront cost is the integration that's got to happen between the WMS and and and Locust thing one.
• 29:49
  SPEAKER_00: And we do charge a small installation fee up front.
• 29:52
  SPEAKER_00: So most of our clients get an ROI of less than 12 months on those costs.
• 29:57
  SPEAKER_00: And after that, everything is OpEx savings, everything.
• 30:00
  SPEAKER_00: And with array, basically it's the same way.
• 30:02
  SPEAKER_00: And and array is you know, for us, going to be a very expensive solution to actually deploy.
• 30:08
  SPEAKER_00: But we believe our clients want the RAFs model.
• 30:10
  SPEAKER_00: We've educated the market that the RAS model is is is the way to go in deploying automation.
• 30:17
  SPEAKER_00: So there is a large upfront capital cost uh that we have as a business to actually deploy that.
• 30:23
  SPEAKER_00: But we're going to do it because we think that's the best solution.
• 30:26
  SPEAKER_01: Array is obviously a pretty dramatic uh departure in terms of form factor from the other systems.
• 30:32
  SPEAKER_01: Is that why it's going to be so expensive for you to deploy it?
• 30:35
  SPEAKER_00: It is.
• 30:36
  SPEAKER_00: Uh it's a much bigger robot, much more complicated.
• 30:38
  SPEAKER_00: There are over a thousand parts in array.
• 30:40
  SPEAKER_00: I'm not sure what origin be origin is, I forget, but uh about array, it's over a thousand.
• 30:45
  SPEAKER_00: Um, you know, it's 10 feet tall, weighs a thousand pounds.
• 30:48
  SPEAKER_00: Origin weighs 80 pounds.
• 30:50
  SPEAKER_00: Actually, I think it might be 100 pounds, but uh much lighter, so many more parts, much more uh uh you know, industrial strength for that use case.
• 30:59
  SPEAKER_00: And so it is going to be more expensive.
• 31:02
  SPEAKER_00: And you know, we're gonna charge more for it because we're taking out all the labor, but um, you know, the value prop for clients will still be uh at the origin ROI or even better based on our initial um our initial data.
• 31:16
  SPEAKER_01: How difficult of a problem was the manipulation on a machine like that?
• 31:21
  SPEAKER_00: It was honestly really, really hard.
• 31:23
  SPEAKER_00: Yeah.
• 31:24
  SPEAKER_00: And it was hard because we didn't invent arms, we didn't invent vacuum and suction.
• 31:29
  SPEAKER_00: But most of those deployments right now in logistics or even in industrial uh deployments, you have a fixed station, you have building air, you have building power, and that makes it really easy to do vacuum-based picks.
• 31:47
  SPEAKER_00: But with Origin, it's a mobile device.
• 31:50
  SPEAKER_00: Things are moving.
• 31:51
  SPEAKER_00: You don't have the power, you don't have the vacuum to be able to do the things you can do with fix station.
• 31:57
  SPEAKER_00: So it was really, really hard.
• 31:58
  SPEAKER_00: And uh we we've been developing array for over three years, and a big part of it was solving that mobile manipulation problem in an environment that, like I said, is moving, it's mobile.
• 32:09
  SPEAKER_00: You don't have the vacuum power that you have with uh with building air.
• 32:15
  SPEAKER_00: Uh, you know, means you don't have the suction to get the grasp.
• 32:19
  SPEAKER_00: And it was just a much bigger challenge.
• 32:21
  SPEAKER_00: And also do it at a cost where you could deliver an ROI for the client to have a good business model for Locus.
• 32:27
  SPEAKER_00: So between cost and functionality and form factor, it was a big, big challenge to do that.
• 32:32
  SPEAKER_00: And and you know, like I say, it's taken us three years to do it, but we've got it figured out and we'll be able to pick.
• 32:38
  SPEAKER_00: We we believe uh come this fall when we roll this out at scale, 70 to 80 percent of the SKUs being shipped in e-com today, we can pick with Locus Array.
• 32:48
  SPEAKER_00: So we can do most all that volume.
• 32:49
  SPEAKER_00: And with origin of vector, we can go to a client and say, we can do 100% of your SKUs between pick and pass between uh arrays and vectors in origin, we can do 100% of your SKUs, and we'll do each one of those SKUs in the most effective, cost-effective way based on can we pick with array or not?
• 33:08
  SPEAKER_00: The first choice will be array.
• 33:10
  SPEAKER_00: If we can do it do with array and do it with no labor, that'll be the first choice.
• 33:14
  SPEAKER_00: If we can't, we'll pass it or origin or vector.
• 33:17
  SPEAKER_01: Yeah, I'm curious what for a system like this and for a company like Locust, what a pilot looks like.
• 33:22
  SPEAKER_01: Because again, I'm talking to companies, humanoid companies about pilots.
• 33:27
  SPEAKER_01: And then oftentimes, you know, it's two, three robots, they're set aside.
• 33:31
  SPEAKER_01: Obviously, uh a lot of logistics companies, certainly manufacturing companies, don't want to stop the line in order to experiment with these systems.
• 33:40
  SPEAKER_01: Um, how fully are you actually able to integrate these systems into existing programs?
• 33:46
  SPEAKER_00: Well, I I advise robotics businesses that um eliminate the word pilot from your vocabulary.
• 33:53
  SPEAKER_00: Uh, the reason being it's a recipe for a pilot.
• 33:56
  SPEAKER_00: And what you should want to do is full-scale deployments.
• 33:59
  SPEAKER_00: And that's what we want to do.
• 34:00
  SPEAKER_00: Now, having said that, there'll be initial deployments with array.
• 34:03
  SPEAKER_00: We have one going on right now with DHL in uh Columbus, have another one rolling out uh in several months.
• 34:10
  SPEAKER_00: And they're not rolling out with hundreds of arrays to start with.
• 34:14
  SPEAKER_00: It'll be a few numbers, uh, but we'll we'll get it down, make any design changes we have to make, learn the math of it so we can go to a client with confidence, tell them that this is what you can expect with array.
• 34:25
  SPEAKER_00: And once we develop that layer of confidence that we know what the UPH rates are, um, you know, know some of the issues and challenges of the form factors and that sort of thing, we can roll it out at scale.
• 34:35
  SPEAKER_00: So we don't generally like to do pilots because it's a recipe for, you know, being added for months and months and months with uh sort of no commitment on either side.
• 34:45
  SPEAKER_00: So we like up front to go in with confidence where we can say, we're gonna do this deployment, we know how many robots you need, we know what the ROI is going to be.
• 34:54
  SPEAKER_00: And if it doesn't work, we'll adjust it.
• 34:56
  SPEAKER_00: And we do that with our RAS model.
• 34:57
  SPEAKER_00: So the nice thing about the RAS model is we can scale up and scale down, where if we went to a client and said, hey, you've got to buy 40 arrays and let's hope they're gonna work, it's a much easier way going with a RAS model because again, we can we can make make some mistakes up front around BOF count and that sort of thing.
• 35:14
  SPEAKER_00: And we have a lot to learn, but we can scale up and scale down and make adjustments.
• 35:18
  SPEAKER_00: So um, yeah, it's gonna be a very interesting deployment model for us.
• 35:21
  SPEAKER_01: Yeah, one of the unspoken things when uh when we talk about again, humanoid uh pilots that we don't see in the videos or that we don't notice is that there aren't really humans around.
• 35:30
  SPEAKER_01: And that that that OAS has big one been one of the big differentiators for Locust, right?
• 35:35
  SPEAKER_01: These are uh designed to work alongside people.
• 35:38
  SPEAKER_01: Um, you know, in terms of the array, in terms of the early testing, in terms of safety and getting them out there.
• 35:44
  SPEAKER_01: How do you test them?
• 35:46
  SPEAKER_00: Yeah, well, to your point, array is designed to be used without humans at all.
• 35:52
  SPEAKER_00: Um, I think in the initial deployments uh, you know, for first deployment, we do have a fence around it, not that we needed it, but it felt like the right, the right, right thing to do uh because we want to learn and not and not not make make mistakes.
• 36:05
  SPEAKER_00: But array is designed with maximum safety involved.
• 36:09
  SPEAKER_00: We have a number of safety sensors on it, uh, a number of a number of special braking units on it, because there's a heavier robot, and um the safety zone around array is larger than our origin.
• 36:22
  SPEAKER_00: With origins, you can walk, and you literally can dance dance on origin.
• 36:25
  SPEAKER_00: Uh they're amazingly nimble.
• 36:26
  SPEAKER_00: Uh Ray won't be as quite as nimble as origin, what was never designed to be, doesn't have to be, but we have a very large safety zone around it.
• 36:34
  SPEAKER_00: Uh, and with LIDAR and cameras, uh, we're able to control that.
• 36:38
  SPEAKER_00: We're using the same safety stack that we use with um origin and vector.
• 36:42
  SPEAKER_00: And we have literally seven billions of picks of data.
• 36:45
  SPEAKER_00: So we know how to navigate uh around humans.
• 36:48
  SPEAKER_00: And the other the other thing is is is with array, humans don't have to be with array.
• 36:52
  SPEAKER_00: So uh we really don't expect any any problems or challenges at all.
• 36:55
  SPEAKER_01: Yeah, I mean the the bar must be really high, especially because you're you're already selling in Europe, you're already selling in Asia, and there's different standards in different places across the world.
• 37:05
  SPEAKER_00: There are.
• 37:06
  SPEAKER_00: Um in you know, let's talk talk Europe.
• 37:09
  SPEAKER_00: Um, they do have, I think honestly, higher safety standards than than the US.
• 37:14
  SPEAKER_00: And uh we built a Ray, designed it, so we were um compatible with those safety standards, and when we ship, we will be.
• 37:23
  SPEAKER_00: Uh our our Europe launch will be a little delayed because we have some testing we have to do.
• 37:28
  SPEAKER_00: But uh yeah, we've designed a day one to be compatible with um with all those requirements.
• 37:34
  SPEAKER_01: So you you you alluded to this a bit, but um array being designed to be fully autonomous, which means there are there won't be, at least when it's fully deployed, humans in the loop.
• 37:43
  SPEAKER_01: I mean, what does that ultimately mean for people in the warehouse setting?
• 37:49
  SPEAKER_00: These jobs and warehouses right now um are very hard to fill for clients, incredibly hard to fill.
• 37:56
  SPEAKER_00: Turnover is measured in double digits of some accounts, double digits a week.
• 38:00
  SPEAKER_00: Any worker that we replace with array will probably find another job within that warehouse.
• 38:06
  SPEAKER_00: It could be in the packing function, receiving function, that sort of thing.
• 38:10
  SPEAKER_00: So although we will be reducing uh labor with array, for the most part, most every single one of those workers will find another job within the building.
• 38:20
  SPEAKER_00: Um, and that's that's what we're saying, frankly, with Origin right now is we do take uh a good bite out of that labor problem.
• 38:27
  SPEAKER_00: Array will be a bigger bite.
• 38:28
  SPEAKER_00: But generally, those are jobs that uh A have gone unfilled, or B, will end up another sp spot in that building.
• 38:36
  SPEAKER_00: So um, and that's generally what happens for the most part with automation today.
• 38:40
  SPEAKER_01: Yeah.
• 38:40
  SPEAKER_01: And and are are are you ultimately creating different, more roles in the field when it comes to actually uh deploying and interacting with the robots?
• 38:50
  SPEAKER_00: Yeah, no, we are.
• 38:51
  SPEAKER_00: We are good point.
• 38:52
  SPEAKER_00: Um there are more higher value jobs than the repetitive jobs of doing picks that we are creating.
• 38:58
  SPEAKER_00: One is simply managing locus within buildings.
• 39:01
  SPEAKER_00: Um, I was in a building several months ago, and uh a gentleman came up to me and was so proud of the fact that he was uh the orchestrator and the manager of the Locus system in that building.
• 39:11
  SPEAKER_00: And his former job was being an associate doing picks, and now he's managing the whole Locus deployment in that building.
• 39:17
  SPEAKER_00: And he was he was so proud of himself that he got elevated up to that, and he's making more money as a result of it.
• 39:22
  SPEAKER_00: So there'll be more and better jobs because of automation, and not just with Locus, but I think automation in general.
• 39:28
  SPEAKER_00: Uh so it's exciting times for anyone deploying automation.
• 39:31
  SPEAKER_00: And a part of it is retraining those workers.
• 39:34
  SPEAKER_00: And you know, we have a whole program where we actually train them uh on skill sets and that sort of thing to be able to manage our system.
• 39:40
  SPEAKER_00: So you know, it really is exciting.
• 39:41
  SPEAKER_01: That's interesting.
• 39:42
  SPEAKER_01: Uh, how how does uh how does a worker actually get involved with one of your programs?
• 39:49
  SPEAKER_00: Typically, it happens within the building where they identify folks that are champions for Locus and we'll take them under our wing, uh, provide training, our CS staff provides training.
• 40:00
  SPEAKER_01: So they nominate somebody effectively?
• 40:03
  SPEAKER_00: Yeah, yeah.
• 40:03
  SPEAKER_00: We have Locus University.
• 40:05
  SPEAKER_00: We have a number of online courses, and then we actually certify them for being Locus capable.
• 40:11
  SPEAKER_00: Um we have um a whole course around, for example, maintenance and repairs that we train workers how to do that.
• 40:18
  SPEAKER_00: We train them how to manage uh the actual deployment, trait to train them how to manage induction and the workforce on the dashboards and all that stuff.
• 40:27
  SPEAKER_00: And again, we do that through through Locust University and our on-site CS organization.
• 40:31
  SPEAKER_00: So yeah, it's something we take, we take very very seriously.
• 40:34
  SPEAKER_00: And it's our best interest to have folks that really understand our solution on site.
• 40:38
  SPEAKER_01: One thing I'm I've always been curious about, you know, we talked about uh one of the big uh selling points of your system being that it it is a brownfield solution, that you can just sort of plop this in the middle of an existing warehouse.
• 40:50
  SPEAKER_01: But have you noticed that in spite of that fact that uh warehouses are still starting to maybe slowly build to accommodate more robots?
• 41:00
  SPEAKER_00: We we see it both ways.
• 41:02
  SPEAKER_00: It's very hard to automate a building that was built 10, 15, 20, even 50 years ago.
• 41:07
  SPEAKER_00: Really, really hard to do it.
• 41:08
  SPEAKER_00: There's infrastructure there, there are things in the way, warehouses are messy places, as you know.
• 41:14
  SPEAKER_00: So very hard to automate existing brownfield buildings.
• 41:17
  SPEAKER_00: And objectively, there are only several companies in the world that can do it.
• 41:21
  SPEAKER_00: I'm a little biased, but I think we're the best in the best in the world at doing it.
• 41:24
  SPEAKER_00: We've sort of proven that.
• 41:25
  SPEAKER_00: Uh, but increasingly we're finding also now that uh about 25% of our business is in greenfield buildings.
• 41:32
  SPEAKER_00: So we have accounts, uh, I can't mention any exact names, but are actually they actually started in brownfield buildings and are now building greenfield buildings specifically for locusts.
• 41:40
  SPEAKER_00: And the nice thing about it is we can do both.
• 41:43
  SPEAKER_00: And we can take a ray into a brownfield building.
• 41:45
  SPEAKER_00: We'll have to clear out a little area to put dedicated racking in, but we can actually stick a ray also in brownfield buildings and have a ray, origin, and vector run in total across that building.
• 41:55
  SPEAKER_00: So um, yeah, it's gonna be cool.
• 41:58
  SPEAKER_01: At the beginning, we talked about uh Amazon effectively being responsible for um the company existing.
• 42:04
  SPEAKER_01: Uh, you know, and it strikes me that Amazon sort of over the years has continued to be a big motivator for people adopting these, right?
• 42:11
  SPEAKER_01: I mean, because Amazon continues to have uh shaped people's expectations as far as shipping and logistics.
• 42:18
  SPEAKER_00: That's exactly what they've done.
• 42:20
  SPEAKER_00: Amazon has set the bar on expectations around um safety in the building, which they're very, very good at it.
• 42:27
  SPEAKER_00: Uh SLAs, delivery time, and also cost.
• 42:31
  SPEAKER_00: They've set the bar and they've done a great job with it.
• 42:34
  SPEAKER_00: Others have to model after that.
• 42:36
  SPEAKER_00: And we have other accounts, uh, you know, like DHL and a host of others, who are also setting the bar, setting the bar really, really high.
• 42:42
  SPEAKER_00: And others have to replicate that.
• 42:44
  SPEAKER_00: If you're a 3PL today and you're and you're bidding out a new account, going after that that account to win the business, and you don't do it with an automated solution, I guarantee your costs are going to be higher and your SLAs are gonna be slower and it's gonna be very hard for you to win that business.
• 43:02
  SPEAKER_00: So uh that's why, frankly, right now, almost 50% of our business is with three PLs, because they know that they have to automate.
• 43:08
  SPEAKER_00: And if you're building a new building today, and there'll be literally billions of new buildings, you know, square footage of buildings built over the over the next three or four years.
• 43:19
  SPEAKER_00: If you're not building a solution today that's automated, a new building, you're gonna be behind the curve.
• 43:26
  SPEAKER_00: And so all those new greenfield buildings have to be automated, and all these brownfield buildings have to be automated to compete, to meet the SLAs, to meet the cost, they have to be automated and and they will be over time.
• 43:38
  SPEAKER_00: It's gonna take, it's not gonna happen next week, next month, or even in the next next three or four years.
• 43:43
  SPEAKER_00: But over the next decade, all these buildings are gonna have to be automated.
• 43:46
  SPEAKER_00: And again, any new building going up that's that's not being built for automation, I think it's insanity.
• 43:52
  SPEAKER_01: So you you feel like it is still, in spite of everything, possible to be competitive with Amazon as a logistics company?
• 44:00
  SPEAKER_00: With automation, you can be competitive.
• 44:02
  SPEAKER_00: Yeah.
• 44:02
  SPEAKER_00: Amazon, again, Amazon has set the bar and they're very, very good at it, very good at it.
• 44:06
  SPEAKER_00: But with the solution we we provide, you know, we know the basic math of all these buildings.
• 44:10
  SPEAKER_00: With Locust, you can get an Amazon-like cost structure and Amazon-like SLAs with Origin and Vector and with Array, you can even beat it.
• 44:19
  SPEAKER_00: Well, Rick, thank you so much for taking the time.
• 44:21
  SPEAKER_00: You're welcome.
• 44:22
  SPEAKER_00: Thank you, Brian.
• 44:23
  SPEAKER_01: Thank you to Rick.
• 44:25
  SPEAKER_01: Thanks to Jasmine and the rest of Locust for accommodating us for uh several hours on a Monday at your headquarters.
• 44:33
  SPEAKER_01: Great conversation.
• 44:34
  SPEAKER_01: Uh, thanks to you as always for watching.
• 44:36
  SPEAKER_01: If you've been enjoying the show, please like and subscribe to it and our newsletter of the same name.
• 44:42
  SPEAKER_01: You'll find all of that over at automated.fm.
• 44:45
  SPEAKER_01: And we will see you next week with another episode of Automated.