Latest / Automated with Brian Heater / Eric Danziger on the Reality Gap in Robotics and AI

Transcript

Eric Danziger on the Reality Gap in Robotics and AI · Automated with Brian Heater

• 0:00
  SPEAKER_01: Do people who work in the industry also tend to overestimate what systems are currently capable of doing?
• 0:07
  SPEAKER_00: If you were to get someone in the industry out to dinner and and not be recording, uh they would have a probably fairly realistic estimate of where things are and kind of what's going on.
• 0:20
  SPEAKER_00: I do think that a lot of people's business does depend on, let's say, an inflated expectation, right?
• 0:25
  SPEAKER_00: That we are very close and that this is, you know, all I need is another couple hundred million bucks and we'll get there.
• 0:31
  SPEAKER_00: We're close, we're close.
• 0:32
  SPEAKER_00: We just need this like next tranche of investment.
• 0:34
  SPEAKER_00: We're gonna hit all of our milestones, right?
• 0:36
  SPEAKER_00: And don't worry about the fact that we haven't hit any of our previous milestones.
• 0:45
  SPEAKER_01: Hello, I'm Brian Heater, the managing editor of A3.
• 0:49
  SPEAKER_01: I am back.
• 0:50
  SPEAKER_01: We are back with another episode of Automated.
• 0:53
  SPEAKER_01: This week we are going to be speaking with Eric Desinger, the CEO of Invisible AI.
• 0:59
  SPEAKER_01: If you're enjoying the show, don't forget to like and subscribe and check out the newsletter over at automated.fm.
• 1:05
  SPEAKER_01: Here's Eric and also me uh talking to Eric.
• 1:09
  SPEAKER_01: Enjoy.
• 1:11
  SPEAKER_01: So you were in the army for what, about eight years?
• 1:15
  SPEAKER_01: Is that right?
• 1:15
  SPEAKER_01: Seven and change, I guess, yeah.
• 1:18
  SPEAKER_01: So you didn't do the right out of high school thing, right?
• 1:20
  SPEAKER_01: I mean, you went to college and then went into the army.
• 1:23
  SPEAKER_00: Yeah, I went into college and then went into the army, but uh, but I still enlisted um instead of uh going the officer route.
• 1:32
  SPEAKER_00: Um and it was a kind of a funny story actually.
• 1:34
  SPEAKER_00: While I was in uh infantry basic trading, um my drill sergeants pulled me aside and they were like, dude, they try to show me the pay table.
• 1:42
  SPEAKER_00: So like, look at this, look at the difference in like what could happen if you just went to officer school.
• 1:46
  SPEAKER_00: And uh they tried to convince me to go be uh go be an officer.
• 1:50
  SPEAKER_00: But um uh no, I don't know.
• 1:53
  SPEAKER_00: It's just it felt a little bit too much, I think like a real job, if that makes sense, you know, you know work up behind a desk and do PowerPoints and um kind of you know, you the rest of your life to do that stuff, right?
• 2:05
  SPEAKER_01: So is it I mean, is that in a sense, is that why you joined the military?
• 2:10
  SPEAKER_01: Was to kind of like delay the inevitable?
• 2:12
  SPEAKER_00: There's a few different like kind of uh archetypes, maybe for people that join the military, right?
• 2:16
  SPEAKER_00: Um, some people find it uh a kind of a calling, a career, right?
• 2:20
  SPEAKER_00: They're gonna spend their whole life um doing it.
• 2:23
  SPEAKER_00: Uh that that was never exactly uh me.
• 2:26
  SPEAKER_00: You know, I'm I'm very interested in technology.
• 2:28
  SPEAKER_00: I knew that I would wind up kind of here eventually, right?
• 2:31
  SPEAKER_00: Working in tech and working in um kind of advanced intelligence systems and that kind of thing.
• 2:36
  SPEAKER_00: So really it was more on what do you want uh from your life?
• 2:40
  SPEAKER_00: I don't know.
• 2:42
  SPEAKER_00: Without making it sound way too deep, right?
• 2:44
  SPEAKER_00: I think there is a lot um when you're younger, right?
• 2:46
  SPEAKER_00: That you just, you know, yeah, you want to have a sense of purpose, right?
• 2:49
  SPEAKER_00: You want to feel like you're doing something important.
• 2:51
  SPEAKER_00: Um, and I feel like, you know, if you have to choose between serving in the kind of global war on terror or going to be a consultant, it wasn't a hard choice, right, for me at that age.
• 3:01
  SPEAKER_00: But I do think that that um yeah, I don't know.
• 3:05
  SPEAKER_00: It's tough, right?
• 3:06
  SPEAKER_00: I think that like it's a it's a it's a good thing to do.
• 3:09
  SPEAKER_01: But but it, you know, there was there was something that kept you there.
• 3:12
  SPEAKER_01: I mean, seven, seven to eight years is not an insignificant insignificant amount of time.
• 3:18
  SPEAKER_00: One of the things I think that was kind of the most striking for me was uh on the limited amounts of leave that I did get going to visit friends of mine that were working and living in San Francisco or New York, having a great time, right?
• 3:28
  SPEAKER_00: They were living, I guess, the traditional kind of post-college life, right?
• 3:32
  SPEAKER_00: Um, but at the end of the day, you know, I I would get to wake up and you know, go just be outside for 12 hours, and that was like my work day, you know.
• 3:42
  SPEAKER_00: Um, and I think there's something very kind of very different about that.
• 3:47
  SPEAKER_00: Um, I think, you know, one of the things that that people need more than an occupation, um uh is is that kind of, I don't know, sense of purpose, sense of kind of doing something larger than yourself, right?
• 4:01
  SPEAKER_00: Um, and it's funny, I mean, this is this is honestly one of the reasons that uh I think I'm so drawn to to things like manufacturing, right?
• 4:10
  SPEAKER_00: Is that uh manufacturing is not a job that people typically get for you know for the pay, although they can't pay better than other jobs, right?
• 4:18
  SPEAKER_00: Um a lot of times the the amount of work and the amount of effort that people are putting in is way beyond what they would do at another job.
• 4:26
  SPEAKER_00: Um, but it is, you know, it does feel very different to feel like you're a part of making something, right?
• 4:32
  SPEAKER_00: Or doing something, um, and not just making lattes or, you know, kind of being a security guard at a mall or something, right?
• 4:41
  SPEAKER_00: So I think there's something to be said for having that kind of collective sense of purpose.
• 4:45
  SPEAKER_00: I find that to be, I don't know, I think very important.
• 4:48
  SPEAKER_00: Um, and certainly when I was in my my twenties, it was something that I that I needed.
• 4:52
  SPEAKER_00: Yeah.
• 4:53
  SPEAKER_01: So you were kind of like scoping out Silicon Valley at that point.
• 4:56
  SPEAKER_01: You had an expectation that you were going to probably work for a startup at some point in the future, but after the military.
• 5:07
  SPEAKER_00: To be honest, I wanted to work in uh space robotics.
• 5:10
  SPEAKER_00: That's what I decided.
• 5:11
  SPEAKER_01: Uh yeah, Whomst Among Us, right?
• 5:15
  SPEAKER_00: It seemed like really cool.
• 5:17
  SPEAKER_00: Uh there was some uh asteroid mining company.
• 5:19
  SPEAKER_00: I tried to like send them an email and they totally ignored me.
• 5:22
  SPEAKER_00: Um but uh watched Armageddon once and you're like this.
• 5:26
  SPEAKER_00: That's what I want to do.
• 5:27
  SPEAKER_01: I really want to I'm really I mean that's that is a way to make a difference, right?
• 5:30
  SPEAKER_01: Is to mine an asteroid.
• 5:32
  SPEAKER_00: Yeah, I watched Top Gun and then I joined the military, and then I watched Armageddon, and I was like, okay, this is my next career.
• 5:37
  SPEAKER_00: Um yeah, exactly.
• 5:39
  SPEAKER_00: So so I think look at the end of the day, uh I I figured, okay, I guess you know, this is gonna be the next thing that I work on.
• 5:47
  SPEAKER_00: Um I looked up, you know, where can I go to graduate school uh to work on robotics?
• 5:52
  SPEAKER_00: Um and uh funny enough, you know, I don't think I've told this to anybody at Carnegie Mellon, but I've never even heard of Carnegie Mellon at that point.
• 5:59
  SPEAKER_00: Uh growing up in California, it's just everything's about California schools, right?
• 6:04
  SPEAKER_00: Um and that's just kind of the world you live in.
• 6:06
  SPEAKER_00: Yeah, exactly.
• 6:08
  SPEAKER_00: But I I found out about Carnegie Mellon um and I applied to a robotics program.
• 6:13
  SPEAKER_00: Um actually, this was kind of in my third deployment to Afghanistan.
• 6:17
  SPEAKER_00: I was in Afghanistan when I applied and then got in, came back in I think uh April or March and was in in grad school in August.
• 6:26
  SPEAKER_00: So um it was pretty, pretty quick kind of transition uh out from from the military.
• 6:33
  SPEAKER_00: Um, and it was a that was an uh an absolutely crazy, crazy time.
• 6:39
  SPEAKER_00: Having not been in school for almost a decade uh with a bunch of people that were more or less all coming directly from undergrad.
• 6:45
  SPEAKER_00: Uh that first semester was incredibly, incredibly tough.
• 6:49
  SPEAKER_00: Uh, but Coney Ground's a great school.
• 6:51
  SPEAKER_00: Um, and I think they did a great job to kind of help us uh prepare to work in uh whatever you want to call it, automation robotics.
• 7:00
  SPEAKER_00: Um the big thing at that time was self-driving.
• 7:03
  SPEAKER_00: Uh, a lot of my friends went to go work in all of the kind of major self-driving programs.
• 7:07
  SPEAKER_00: Meanwhile, I kind of was looking around trying to understand um what I could do in computer vision and and in manufacturing.
• 7:15
  SPEAKER_00: I actually visited in Pittsburgh a ton of different factories trying to understand some of their problems and trying to kind of understand what could be done.
• 7:23
  SPEAKER_00: Um, and then I moved to Silicon Valley um and uh joined a startup and and kind of you know went working on uh a whole bunch of other problems that had nothing to do with manufacturing.
• 7:37
  SPEAKER_01: Yeah, I mean obviously like as you get older, you know, six, seven years isn't isn't a huge amount of time, but uh I'm I'm guessing like you still you're pretty self-conscious at that point in your life that you hadn't been in school for seven years, and all of a sudden you're the guy like coming in and you're with all these like recent, recent graduates.
• 7:56
  SPEAKER_00: I don't know.
• 7:57
  SPEAKER_00: It's yeah, it was just it was just different, you know.
• 7:59
  SPEAKER_00: I think it's good in the sense you have a little perspective, right?
• 8:02
  SPEAKER_00: Um I think I think going to school from from kind of undergrad to graduate school, you just you take a lot of it for granted.
• 8:09
  SPEAKER_00: Um, and I certainly did not do that.
• 8:11
  SPEAKER_00: Uh I was I was very uh kind of grateful to be there.
• 8:16
  SPEAKER_00: It was a very different experience than being in the military, especially being enlisted, um, in the sense that you know, I didn't have to tell anybody where I was, I didn't have to make any plans for the weekend, I could just literally do whatever I wanted.
• 8:28
  SPEAKER_00: The school itself is very difficult, it requires a lot of effort.
• 8:31
  SPEAKER_00: Um, but it was, you know, something that I was kind of well used to, I guess, at that point, right?
• 8:37
  SPEAKER_00: So it was um, you know, I I just found that I could kind of work through a lot of the problems uh just through kind of force of will, I guess.
• 8:49
  SPEAKER_01: Yeah, yeah, brute force, yeah.
• 8:51
  SPEAKER_01: Uh so obviously there's a lot of uh tech companies that are you know contracting that are that are working with the military, but as far as actually like being in the military and working on technology in a meaningful way, it sounds like that's not as much of a straight shot.
• 9:08
  SPEAKER_01: Is that fair to say?
• 9:10
  SPEAKER_00: Yeah, absolutely.
• 9:11
  SPEAKER_00: The kind of interview process, for example, when I was um leaving the military was very, very interesting because I had uh a fair bit of kind of job experience, if you will, right?
• 9:25
  SPEAKER_00: But it was in a field that no one really understood um and that they they knew that they kind of cared about, right, in this maybe more abstract way, uh, but they just didn't have a way to kind of map it to something that they they did, right?
• 9:39
  SPEAKER_00: So I basically got kind of two categories of job offer.
• 9:43
  SPEAKER_00: One was to be kind of a a leader, but in some of the like kind of blue-collar areas of tech, right?
• 9:52
  SPEAKER_00: So in self-driving, for example, it's working with the vehicles themselves, right?
• 9:57
  SPEAKER_00: So there's maintenance teams, there's drivers, there's a whole bunch of people that have to make that operation happen, right?
• 10:05
  SPEAKER_00: Um, or I could be an entry-level engineer, right?
• 10:08
  SPEAKER_00: And those are the two options.
• 10:10
  SPEAKER_00: And so very different, right?
• 10:11
  SPEAKER_00: Very different level of seniority.
• 10:13
  SPEAKER_00: Um, and there was no way for them to kind of map those two things together.
• 10:19
  SPEAKER_00: Um, and I think that's, you know, part of honestly, ultimately what kind of drove me to um uh entrepreneurship and and startups as well.
• 10:28
  SPEAKER_01: This is really interesting.
• 10:29
  SPEAKER_01: It really struck me the first time I I visited Pittsburgh, is that obviously there's this like great history of manufacturing there.
• 10:37
  SPEAKER_01: And that and it and it is something it is a history that the town really takes pride in.
• 10:42
  SPEAKER_01: Um and this is less and less the case as there are more and more robotics startups, but is it but it is interesting that there does seem to be or there was kind of a disconnect as far as like maybe there not being a ton of manufacturing robotic startups or that not being the focus in the same way that self-driving was out of CMU.
• 11:02
  SPEAKER_00: It's tough, right?
• 11:03
  SPEAKER_00: I think that manufacturing is a very, very tough industry, right?
• 11:08
  SPEAKER_00: And I think that self-driving's advantage was that um it was a lot of it was was really about technology demonstration, right?
• 11:15
  SPEAKER_00: Um there wasn't a kind of customer per se building up kind of large technology demonstrations, it's really just about being a very good technologist, and that's it.
• 11:26
  SPEAKER_00: Uh, and I think Congy Mellon kind of excelled in a lot of ways.
• 11:29
  SPEAKER_01: That's interesting.
• 11:30
  SPEAKER_01: So so, like in a sense, it it almost benefited by how like unrealistic the timeline was, right?
• 11:36
  SPEAKER_01: That you couldn't do this overnight.
• 11:38
  SPEAKER_00: Kind of, right?
• 11:39
  SPEAKER_00: I think that the challenge, right?
• 11:40
  SPEAKER_00: So being in the Bay Area, right, and and being right next to Stanford and kind of knowing uh a lot of what um Carnegie Mellon, you know, um is trying to do, right?
• 11:51
  SPEAKER_00: They have a lot of great entrepreneurship programs, right?
• 11:53
  SPEAKER_00: And they're they're really trying.
• 11:54
  SPEAKER_00: The state of Pennsylvania is trying very hard, right?
• 11:56
  SPEAKER_00: Everyone has been trying to foster an environment there.
• 12:00
  SPEAKER_00: There's just a huge amount of natural gravity here in the Bay Area, right?
• 12:06
  SPEAKER_00: Which makes it very, very easy for you to learn about startups, to kind of, you know, interact with either new startups or venture capital or whatever it may be, right?
• 12:17
  SPEAKER_00: Um it's much, much harder, I think, in um in Pittsburgh.
• 12:23
  SPEAKER_00: And again, they're they're doing some incredible work to make it easier, right?
• 12:27
  SPEAKER_00: This is something that they are very, very focused on.
• 12:29
  SPEAKER_00: They understand is very important.
• 12:31
  SPEAKER_00: But I think that, you know, in an area where it's really just about building kind of, if you will, the best robot, right, then they can compete with anyone, right?
• 12:41
  SPEAKER_00: They are the best robotics program in the world.
• 12:44
  SPEAKER_00: What you need to do is kind of build and understand commercialization and all that kind of stuff, right?
• 12:50
  SPEAKER_00: I think it's very tough.
• 12:52
  SPEAKER_00: And I don't think it's uh and that part, to be honest, I don't think is a fault necessarily of quantity valid.
• 12:58
  SPEAKER_00: I think just robotics in general is very tough, right?
• 13:00
  SPEAKER_00: And I think manufacturing especially is very tough.
• 13:03
  SPEAKER_00: Uh, and I think that, you know, any kind of uh a group of people that I've talked to in manufacturing technology, something that we all kind of have in common, right?
• 13:14
  SPEAKER_00: Is that this takes longer, this is harder than if you just start a simple software startup, right?
• 13:20
  SPEAKER_00: And so uh anything you want to do in the physical world, anything you want to do with robotics, it's it's very, very difficult, right?
• 13:26
  SPEAKER_00: And so um I think you know, there will be a time in the very near future uh where those companies are very prevalent, right?
• 13:36
  SPEAKER_00: I think we all feel the reason that we work in robotics, the reason we study robotics is because we feel that this is very important, right?
• 13:43
  SPEAKER_00: That this is one of the most important areas of of anything that that may come out of the AI kind of uh uh boom that we're in.
• 13:52
  SPEAKER_00: But um yeah, it's it's really, really tough, right?
• 13:56
  SPEAKER_00: So I think for it to take over uh the city, for it to really kind of replace what what steel was in its in its psyche, so to speak, um, we need many of these companies to succeed and we need them to become very, very large.
• 14:14
  SPEAKER_00: I think the the the only kind of example I guess that I can see of that um are things like um like Dell and Intuit, maybe in Austin, right?
• 14:23
  SPEAKER_00: And Amazon and Microsoft and Seattle, right?
• 14:26
  SPEAKER_00: Amazon and Microsoft obviously much, much larger and created a much bigger kind of tech ecosystem, I think, naturally, just by virtue of their size.
• 14:35
  SPEAKER_00: Uh, but I think without kind of breakouts like that, it's very difficult for you to kind of create that, if that makes sense, right?
• 14:43
  SPEAKER_00: And then of course in the big area you have whatever, probably three dozen companies like that, right?
• 14:49
  SPEAKER_00: And so it's you know uh it's different.
• 14:52
  SPEAKER_00: The the natural gravity is very different, you know.
• 14:54
  SPEAKER_00: And I think that it's tough when you kind of have to consider where you want to start a company, where you want to live, right?
• 15:02
  SPEAKER_00: You uh all these factors kind of come into play.
• 15:04
  SPEAKER_00: Uh and so you really are working from behind as as any anyone who isn't the Bay Area, basically.
• 15:12
  SPEAKER_01: Yeah, I and as far as the kind of the the big self-driving boom, I was talking to somebody about this recently, and I don't want to like put too fine a point on this, the parallel between what we're seeing now with humanoids and what we were seeing then with self-driving cars.
• 15:29
  SPEAKER_01: But I mean, you you you go where the investment is, right?
• 15:32
  SPEAKER_01: You know, you go a lot of a lot of people do where the money's going and where the interests is, like knowing full well that that that's where the innovation is going to start to happen.
• 15:41
  SPEAKER_01: It may not manifest in exactly the way that you expect it to, but it, you know, that's where a lot of these companies ultimately end up being built out of.
• 15:53
  SPEAKER_00: It's tough, right?
• 15:54
  SPEAKER_00: I think that if you look at self-driving, for example, Waymo and Tesla being two major players even back then in this space, are still major players in this space because they have a bigger business basically that supports them, right?
• 16:10
  SPEAKER_00: Um, and without that, no one would have been able to put the amount of money that they put into those programs for that amount of time.
• 16:18
  SPEAKER_00: And I think that fundamentally is the is the challenge is that robotics, uh, I mean, the the reason that I studied robotics, the reason that anybody studies robotics is because you believe that it's possible, right?
• 16:28
  SPEAKER_00: Like you don't go into a field thinking ass will never work.
• 16:31
  SPEAKER_00: I think that the challenge is that, you know, where we are today, uh we have you know some amazing examples of of locomotion, right?
• 16:42
  SPEAKER_00: Of moving around in the world.
• 16:44
  SPEAKER_00: Um but uh you know, I think if we can't rely on this this kind of new form of AI that's become prevalent in the last you know three to five years to write documents correctly for us or to you know guess what we want from code correctly, right?
• 17:03
  SPEAKER_00: Both of which are much more constrained problems in some ways than than robotics, right?
• 17:08
  SPEAKER_00: Um it's hard to imagine that you'll have a fully working, fully capable, dispatchable robot to go do a job, right?
• 17:16
  SPEAKER_00: Um, and it's tough.
• 17:17
  SPEAKER_00: I think that you know, everyone in robotics knows that demos are easy, right?
• 17:20
  SPEAKER_00: Demos are the lifeblood of robotics in many ways.
• 17:24
  SPEAKER_00: Um, that's how funding happens, that's how everything happens, right?
• 17:27
  SPEAKER_00: Uh, and yet, you know, it's really hard to kind of bring it to the real world.
• 17:31
  SPEAKER_00: Um, one of the examples that I that I that really stuck with me is uh from Boston Dynamics, right?
• 17:37
  SPEAKER_00: And and again, Boston Dynamics, they're a team that's been working on this for a very long time, but a lot of it wound up after the kind of DOD and government funding by being a part of another business, right?
• 17:48
  SPEAKER_00: Being a part of Google, being part of Hyundai, right?
• 17:50
  SPEAKER_00: They have the funding to kind of continue to work on this stuff, even though it's very hard and it takes a long time, and it will be a while until they can pay their own way.
• 17:58
  SPEAKER_00: But there was a great article about uh one of their uh big dogs, right?
• 18:02
  SPEAKER_00: It uh was doing a mock patrol with a bunch of Marines.
• 18:06
  SPEAKER_00: They put a bunch of equipment on it, and of course, you know, halfway through it like nicked a hydraulic line or something and fell over, right?
• 18:13
  SPEAKER_00: So this thing's like bleeding out, it's hydraulic fluid, all of the weight of this machine plus all of the weight of the stuff that they had put on the machine is now the responsibility of these Marines, in addition to the fact that this thing probably costs millions of dollars and I can't just leave it, right?
• 18:27
  SPEAKER_00: Um, and and that is kind of the problem in robotics, and in my mind, in a nutshell, right?
• 18:32
  SPEAKER_00: It's like this is not uh, you know, a demonstration is great, right?
• 18:36
  SPEAKER_00: But anytime you want to actually get something out into the world, right?
• 18:39
  SPEAKER_00: We see this with electric vehicles, right?
• 18:41
  SPEAKER_00: Electric vehicles work.
• 18:43
  SPEAKER_00: There's no disputing whether or not they work, right?
• 18:45
  SPEAKER_00: I've owned them.
• 18:46
  SPEAKER_00: Um the the challenge is that you know, we have a new infrastructure problem now that has to be solved, right?
• 18:53
  SPEAKER_00: And it is being solved, but that's a relatively simple infrastructure problem, right?
• 18:57
  SPEAKER_00: It's literally just like where can I charge this car and how far can I go and all this kind of stuff.
• 19:02
  SPEAKER_00: Uh, if you imagine kind of what it would take for a business to switch over to using robots from people, right?
• 19:07
  SPEAKER_00: That's a whole different infrastructure problem that you have to be able to solve at the same time as the robotics problem, right?
• 19:14
  SPEAKER_00: And and there's a whole kind of ecosystem of things that need to come together basically for this to be useful.
• 19:19
  SPEAKER_00: We don't know, quite frankly, what that is.
• 19:21
  SPEAKER_00: We have some limited deployments of robots.
• 19:24
  SPEAKER_00: Um there are companies like Locust that have done an incredible job, right, of deploying robots in warehouses.
• 19:29
  SPEAKER_00: Um, and you know, there there are robots that that obviously do work every single day and have for probably 60, 70 years in factories all around the world, right?
• 19:39
  SPEAKER_00: So there's we we know some of how to do these deployments.
• 19:42
  SPEAKER_00: Um, but the idea that you're gonna have a fully packaged kind of person replacement that just drops in and does useful work.
• 19:51
  SPEAKER_00: Um I think people kind of get caught up in the demo and they forget how hard it is, right?
• 19:56
  SPEAKER_00: Just like self-driving.
• 19:57
  SPEAKER_00: You can go on the highway, you can take people around a kind of neighborhood that you've mapped a hundred times.
• 20:02
  SPEAKER_00: Um, but I haven't heard of any plans for anyone to go to Toronto, for example, right?
• 20:08
  SPEAKER_00: Like that's not on the roadmap yet.
• 20:10
  SPEAKER_00: We're still trying to get, you know, the three or four cities that everybody's kind of figured out to work.
• 20:15
  SPEAKER_00: One of the most interesting, I think, things about this technology is that we can both see it so clearly.
• 20:20
  SPEAKER_00: Like self-driving cars or robots.
• 20:22
  SPEAKER_00: I mean, these are things that we've talked about for a hundred years, right?
• 20:24
  SPEAKER_00: Uh, probably longer, I guess.
• 20:26
  SPEAKER_00: But we know like what we want to do.
• 20:29
  SPEAKER_00: It's just really hard and it's gonna take longer than than we expect, basically.
• 20:34
  SPEAKER_01: Yeah, and and it's always it's always like the last few millimeters that and that proves to be the most difficult part of it.
• 20:40
  SPEAKER_01: And it's a a lot of it, I think, ends up being um weighing the consequences, right?
• 20:46
  SPEAKER_01: I mean, if ChatGPT makes an error in your school essay, not a big deal.
• 20:51
  SPEAKER_01: If you know a Waymo hits a pedestrian, that's a much bigger problem.
• 20:56
  SPEAKER_00: 100%.
• 20:57
  SPEAKER_00: Way mows are incredibly safe, right?
• 20:59
  SPEAKER_00: You feel very safe being in one.
• 21:00
  SPEAKER_00: Yeah, it is a big deal, right?
• 21:01
  SPEAKER_00: And it is taking those things into account uh is is very, very tough.
• 21:06
  SPEAKER_00: Again, there's you know a lot of work to be done, right?
• 21:09
  SPEAKER_00: I think I guess one way to to to kind of continue the analogy in my mind is is you know, trying to imagine at what point the majority, let's say, of the United States will be able to hire a robotaxi, right?
• 21:25
  SPEAKER_00: Um, or at least like maybe the all the places you can easily get an Uber today, right?
• 21:29
  SPEAKER_00: Could you get a completely autonomous vehicle?
• 21:33
  SPEAKER_00: Like what year do we think that would happen?
• 21:35
  SPEAKER_00: Remembering that we got really excited about this in like 2016, right?
• 21:39
  SPEAKER_00: And we had demonstrations of this probably from the 80s, right?
• 21:43
  SPEAKER_00: So uh we could we could do this for quite a while.
• 21:47
  SPEAKER_00: Um, and I think if you kind of take that and then extend that to this humanoids project, right, we can decide if we think it's harder or easier because it's not going at 80 miles an hour, right?
• 21:57
  SPEAKER_00: And so there might be less risk.
• 21:59
  SPEAKER_00: Um But if we think that, you know, humanoids today are kind of where self-driving was in 2016, and we think it's going to take another decade, two decades or something for this to get fully rolled out in self-driving, right?
• 22:10
  SPEAKER_00: I think that's, in my mind, a pretty solid analogy of when we think humanoids will become capable enough to do a lot a lot of work.
• 22:19
  SPEAKER_01: Having kind of, sorry, um pun partially intended, but having a front row seat specifically to the world of self-driving, both through CMU and you actually spent some time at a startup as well.
• 22:29
  SPEAKER_01: It sounds like that made you much more of a pragmatist because you've been through one of these big hype cycles.
• 22:35
  SPEAKER_00: Honestly, going back to the military too, right?
• 22:37
  SPEAKER_00: There's a dozen examples that we had in every single deployment of kind of really cool whiz bank technology that then stopped working at maybe the most important time for it to work, right?
• 22:50
  SPEAKER_00: And the like thing that you have to do instead of that whiz bank technology is actually more dangerous if you didn't have it at all, right?
• 22:55
  SPEAKER_00: And you had planned from the beginning to not have it.
• 22:57
  SPEAKER_00: The reality is like anytime that the stakes are high, you really do wind up being very conservative, I think, in your kind of application of technology.
• 23:05
  SPEAKER_00: I do feel like self-driving, you know, it felt it, I think it felt for a brief moment very, very easy until you start to to really kind of weigh the risks and and you know try and solve the problem completely and not just you know to the 80% level.
• 23:23
  SPEAKER_00: Uh I think that that did fundamentally make me want to do something just kind of one step behind the actuation, right?
• 23:33
  SPEAKER_00: I actually do find what we do to be very similar in a lot of ways to robotics.
• 23:38
  SPEAKER_00: And we we take a very robotics-y approach at this company.
• 23:41
  SPEAKER_00: Um, we do things in uh kind of a global frame, we do a lot of frame transformations, right?
• 23:47
  SPEAKER_00: We kind of combine data from all these different sources in 3D to make it a little bit more easier to work with, I should say, right?
• 23:53
  SPEAKER_00: Whereas a lot of computer vision companies operate purely in the kind of frame of the image and try and do everything with deep learning, right?
• 24:00
  SPEAKER_00: So I think that the we kind of imagine our devices as really, in a lot of ways, the the kind of head and and vision system of a robot.
• 24:11
  SPEAKER_00: Uh, it just doesn't actuate, just doesn't move.
• 24:14
  SPEAKER_00: That part of it is the part that in my mind we we need to figure out first, right?
• 24:19
  SPEAKER_00: And if we can get the kind of vision system and the understanding of the world to the point where uh we we can do it flawlessly, we can do it fast, right?
• 24:29
  SPEAKER_00: We can have everything that we need kind of there at the edge, ready to kind of pass off to a planner or something that's going to make decisions on behalf of the robot, um, then uh I think we'll we'll you know be able to do significantly better.
• 24:45
  SPEAKER_00: Right.
• 24:45
  SPEAKER_00: But um the reality is computer vision itself is quite a bit behind uh the language systems that we have, right?
• 24:54
  SPEAKER_00: And that a lot of computer vision research is now becoming kind of language research, right?
• 24:58
  SPEAKER_00: And they're just trying to tack language models onto image uh uh models and kind of get some understanding and interact with everything in text and all these kinds of things.
• 25:07
  SPEAKER_00: So um we'll see kind of where this winds up going, but ultimately we still have, I think, quite a bit of work on the vision side to figure out and understand what's happening in the world, right?
• 25:17
  SPEAKER_00: A video that you or I could kind of describe effortlessly uh still trips up a lot of these systems, right?
• 25:23
  SPEAKER_00: And so we need to get there.
• 25:25
  SPEAKER_00: And then I think once we get there, uh that in my mind is the gate to start talking about the robot.
• 25:31
  SPEAKER_01: That's really interesting.
• 25:32
  SPEAKER_01: So um, I mean, obviously, you're building the company is building this product that can like currently like be deployed in a work setting, but you're really at the same time, you feel like you're also building the infrastructure for physical AI at the same time.
• 25:50
  SPEAKER_00: It's funny that you say that because I think yes, we are also building the infrastructure, right?
• 25:54
  SPEAKER_00: But I think it's it's um infrastructure to me is more the devices themselves, right?
• 26:00
  SPEAKER_00: And the fact that we can kind of completely map in 3D in real time uh a factory.
• 26:07
  SPEAKER_00: Uh kind of the way that we encourage our customers to use our systems, right?
• 26:10
  SPEAKER_00: They're very dense, they're throughout their entire uh assembly area, right?
• 26:14
  SPEAKER_00: And and that means that we can kind of help them uh actually understand holistically what happened, and for us to be able to kind of combine that with uh some amount of robotics would actually be fairly easy, right?
• 26:26
  SPEAKER_00: Um, and I think that all the things that would be incredibly hard to imagine kind of a collaborative robot doing, um, understanding who they're supposed to work with and what part of the work they're in and what they're supposed to do next, right?
• 26:38
  SPEAKER_00: All those things are things that we could feed to a robot.
• 26:40
  SPEAKER_00: So I think from an infrastructure perspective, I think we are setting that up pretty well.
• 26:45
  SPEAKER_00: But I do think that, but I I guess part of for me, what this is is the future kind of understanding components, right?
• 26:53
  SPEAKER_00: So the actual work that we're doing on our devices to understand the world, I think that is also setting the stage for this next generation of robotics.
• 27:02
  SPEAKER_00: It's going to be more important, I guess I should say, right?
• 27:05
  SPEAKER_00: Is that the end, you know, if someone's gonna build a robot body, they're gonna have their motors and a battery, right?
• 27:13
  SPEAKER_00: They might have some kind of additional components that are in there, right?
• 27:18
  SPEAKER_00: Some some sort of special end effectors, right?
• 27:21
  SPEAKER_00: Or they'll make some special hands.
• 27:23
  SPEAKER_00: Um, but ultimately there's something that's gonna be controlling all of this, governing all of this, that understands the environment that it's in, right?
• 27:29
  SPEAKER_00: What's happening, what's gonna happen next.
• 27:31
  SPEAKER_00: Uh, and I think that's the piece that that more than anyone else will be set up to build, right?
• 27:36
  SPEAKER_00: Just because this is at that point going to be an environment that we've operated in for quite a long time, right?
• 27:41
  SPEAKER_00: Having seen factories, having seen tons and tons of different places.
• 27:46
  SPEAKER_00: Um, and you know, one assembly shop in in one automotive facility is something like uh 2% of the video that YouTube uploads every hour, right?
• 27:55
  SPEAKER_00: That's a statistic that I learned recently, and I absolutely love.
• 27:58
  SPEAKER_00: And that means if we get you know 50 of these put together, right, we will have of just engineering uh and and uh manufacturing video, right?
• 28:08
  SPEAKER_00: As much data created every hour as as YouTube creates.
• 28:13
  SPEAKER_00: And so, you know, having a data set like that, having a kind of a unique ability to see the real world uh in a domain that actually matters, right?
• 28:22
  SPEAKER_00: Not Instagram videos or TikTok videos, right?
• 28:25
  SPEAKER_00: Uh is I think very, very important.
• 28:27
  SPEAKER_00: So um I think in both ways, right, I see this really as a kind of a stepping stone to this world where we are eventually able to do real robotics um and and have it actually take on a chunk of work.
• 28:43
  SPEAKER_01: What what do you mean when you say real robotics?
• 28:46
  SPEAKER_00: I don't know.
• 28:47
  SPEAKER_00: That's yeah, that might get me in trouble, I guess.
• 28:49
  SPEAKER_00: But I think uh robotics is one of those funny things, right?
• 28:53
  SPEAKER_00: I think the old joke is like it's only a robot if it doesn't work.
• 28:55
  SPEAKER_00: Yeah.
• 28:56
  SPEAKER_00: There's obviously systems, mechatronic systems that operate everywhere, every day, right?
• 29:02
  SPEAKER_00: They're running conveyors, they're you know, junting packages between different, you know, different belts.
• 29:09
  SPEAKER_01: You're talking about like autonomy, is that kind of what you're getting at?
• 29:12
  SPEAKER_00: I guess uh I think the idea that we all have uh of this endpoint, right, is you have, you know, you're kind of manufacturing C3PO, right?
• 29:23
  SPEAKER_00: Where you can like just tell it what to do and it goes over and it does something.
• 29:27
  SPEAKER_00: You have the ability to not have to kind of babysit the system, right?
• 29:31
  SPEAKER_00: You don't do a ton of extra work to kind of set up everything specifically so that you can do it, right?
• 29:37
  SPEAKER_00: The story of kind of a lot of the robotic systems today is that the majority of the work actually is the surroundings of the robot, right?
• 29:45
  SPEAKER_00: It's like how do you install the robot so that it can touch everything?
• 29:48
  SPEAKER_00: What end effector does it get, right?
• 29:50
  SPEAKER_00: How are you programming the robot, right?
• 29:52
  SPEAKER_00: How do you program all the other systems to interact with the robot, right?
• 29:56
  SPEAKER_00: The actual robot itself is just a kind of an object that you install and you feed the program, right?
• 30:02
  SPEAKER_00: Um, and again, it's a huge amount of work, right?
• 30:04
  SPEAKER_00: There's there's companies that that make those systems, obviously they're very difficult, or there'd be a lot more companies that make those, but but I think that you know, when you think about what it would take to install a robot in a specific place, um, it's everything around it that that takes all this time, right?
• 30:19
  SPEAKER_00: And so if you could just direct a robot to go do something without having to change anything about the environment, um, or even have it change its own environment, right?
• 30:28
  SPEAKER_00: Like one of the first steps you're gonna want to do is kind of go through the the uh lean consulting kind of, you know, how do I change this process to be more efficient so that the robot can do the more efficient process and not be stuck doing the less efficient process, right?
• 30:41
  SPEAKER_00: But if the robot itself can go and kind of look at the layout and say, actually, these two machines should be closer to each other, and I'm gonna move this thing here, and then it starts work.
• 30:49
  SPEAKER_00: I mean, that would be the kind of holy grail, right?
• 30:52
  SPEAKER_00: It's kind of almost a self-organizing factory at that point.
• 30:55
  SPEAKER_00: And uh all we have to do is say, like, here's the widget, just help me figure out how to make this, and then make like 10,000 of them and make sure the quality's high.
• 31:02
  SPEAKER_00: And then the system goes off and does it.
• 31:04
  SPEAKER_00: Right.
• 31:05
  SPEAKER_00: I think that's kind of, you know, this is where we want to get to.
• 31:08
  SPEAKER_00: Um, I think it's just, yeah, there's a lot of steps between here and there.
• 31:12
  SPEAKER_01: Yeah, I I I I mean, I think that most people would probably uh most people who know anything about the industry would agree that like the average person probably overestimates what AI and robotics is capable of, right?
• 31:27
  SPEAKER_01: I mean, you know, you see the it write a story or um, you know, you see these humanoid robots walk and all of a sudden like you you project a lot of things on them.
• 31:35
  SPEAKER_01: But I I to a certain extent, and I ask this like in the context of you saying um that uh computer vision is behind um like obviously computer vision is at a point where like you know some navigation is able to happen, you know, you mentioned locusts, like robots can hang out in a warehouse, navigate around.
• 31:56
  SPEAKER_01: Do people who work in the industry do they also tend to overestimate what systems are currently capable of doing?
• 32:03
  SPEAKER_00: If you were to get someone in the industry out to dinner and and not be recording, uh they would have a probably fairly realistic estimate of where things are and kind of what's going on.
• 32:15
  SPEAKER_00: I do think that a lot of people's business does depend on uh, let's say an inflated expectation, right?
• 32:22
  SPEAKER_00: That we are very close and that this is, you know, all I need is another couple hundred million bucks and we'll get there, right?
• 32:28
  SPEAKER_00: Um and again, self-driving operated in very much the same way, right?
• 32:31
  SPEAKER_00: Which is like we're close, we're close.
• 32:32
  SPEAKER_00: We just need this like next tranche of investment, we're gonna hit all of our milestones, right?
• 32:36
  SPEAKER_00: And don't worry about the fact that we haven't hit any of our previous milestones.
• 32:40
  SPEAKER_00: Um, and so I think it's tough, right?
• 32:42
  SPEAKER_00: I think that that at the end of the day, uh, I think that for the most part, everybody has a pretty realistic estimate.
• 32:49
  SPEAKER_00: Who works in the industry, I think who understands everything, right?
• 32:52
  SPEAKER_00: They have a pretty realistic estimate of where we are, how much is left, what is possible today.
• 32:58
  SPEAKER_00: A lot of the kind of robotics use cases that I've heard uh are frankly robots walking around, right?
• 33:07
  SPEAKER_00: Because that is what they can do today, right?
• 33:10
  SPEAKER_00: Locomotion and kind of working walking on very terrain.
• 33:13
  SPEAKER_00: Uh, it's a difficult problem, right?
• 33:14
  SPEAKER_00: It's not trivial that they've solved it, but that is kind of the extent of of what kind of they're they're gonna be trying to do with these systems outside of like very carefully calibrated demos.
• 33:26
  SPEAKER_00: And I think the biggest problem and the thing that drives all of this, right, is that there's always that just fraction of a chance that we're all wrong, right?
• 33:35
  SPEAKER_00: And that like we are very close, actually, right?
• 33:38
  SPEAKER_00: And that it did only take this one little tweak and all of a sudden everything works.
• 33:42
  SPEAKER_00: Like you kind of know that it's not true, right?
• 33:44
  SPEAKER_00: Like you're sure like the sun is gonna come up tomorrow, like all of these things, you know, we we know with pretty high probability that this is we have a ton more work ahead of us.
• 33:53
  SPEAKER_00: Um, but I think it's it's tough for people to kind of, you know, like go with that idea that maybe it's very close.
• 34:01
  SPEAKER_01: Yeah, I I I think maybe also what you're getting at, and and you know, you're you know, you're from California.
• 34:06
  SPEAKER_01: I'm from the Bay Area myself, so like I've experienced this to a certain extent through much of my life.
• 34:13
  SPEAKER_01: I worked at TechCrunch for nine years.
• 34:15
  SPEAKER_01: Like, I get it.
• 34:16
  SPEAKER_01: You've raised several several rounds.
• 34:19
  SPEAKER_01: Uh you raised a series A, I think, in in 2022, that obviously you're selling yourself to investors, right?
• 34:28
  SPEAKER_01: I mean, that's that's what the pitch deck is.
• 34:30
  SPEAKER_01: You're selling yourself um not just what you are now, but what you're what you're gonna become, what your what your potential is.
• 34:36
  SPEAKER_01: So how do you how do you square that and how do you walk that line between being exciting enough to draw in investors without overpromising?
• 34:51
  SPEAKER_00: Yeah, um, you know, I think I think that is uh something that we have definitely been trying to do.
• 34:59
  SPEAKER_00: Uh I think that, you know, one of the things that's kind of interesting about all businesses, right?
• 35:05
  SPEAKER_00: Self-dropping went through this, um, and and you know, we we are going through it is that at some point it's not about the technology anymore, right?
• 35:14
  SPEAKER_00: And it is about your kind of business milestones, right?
• 35:18
  SPEAKER_00: And we're fortunate that we work with some very, very large companies um who are pushing pretty aggressively to kind of expand and install this technology.
• 35:26
  SPEAKER_00: Uh, but it's you know, that's this is another thing that's very, very difficult about manufacturing technology companies in general, um, and robotics and kind of advanced technology companies in particular.
• 35:36
  SPEAKER_00: We've seen some really amazing counterexamples in kind of AI coding and some of the like language-based systems that are out there, right?
• 35:43
  SPEAKER_00: They've grown incredibly fast.
• 35:45
  SPEAKER_00: Um, but for the most part, people that are trying to make these really complicated systems work in the real world, um, it's just, you know, it takes a lot of work.
• 35:54
  SPEAKER_00: Uh, and it takes a lot of work for you to get this generalizable enough for kind of your variety of customers, right, to hit the accuracy benchmarks, whatever benchmarks your customers want, right?
• 36:06
  SPEAKER_00: Uh, I think robotics companies see this a lot, right?
• 36:08
  SPEAKER_00: Where, you know, they need to hit a certain, let's say, jobs per hour before they can, you know, really expand with the customer because they're competing against, you know, what their customer status quo is, right?
• 36:19
  SPEAKER_00: And if they can't hit that jobs per hour, no, they're not going to expand, right?
• 36:22
  SPEAKER_00: And so they kind of wind up gated by all of these benchmarks, right?
• 36:26
  SPEAKER_00: So um at some point that is kind of what you are judged against.
• 36:31
  SPEAKER_00: And uh it's it's good and bad, right?
• 36:34
  SPEAKER_00: I think if you can kind of get into an area that expands very quickly, which is what we think we're in, um, then you are able to kind of match kind of traditional startup targets, right?
• 36:45
  SPEAKER_00: But I think this is the the, yeah, again, the biggest challenge I've seen in manufacturing technology and in robotics companies, is that you, from a software perspective, a software company's growth is supposed to be two, three, five, whatever, some large increase over the last year.
• 37:01
  SPEAKER_00: And if you're a manufacturing technology company that's growing 20% year over year, that actually might be for that market fantastic.
• 37:08
  SPEAKER_00: Uh, but it's not as good of an investment as a company that's growing 5x year over year, right?
• 37:13
  SPEAKER_00: And so uh that's kind of I think the biggest, uh, the biggest challenge.
• 37:17
  SPEAKER_00: So you have to kind of you know make sure that you're representing the technology and the business milestones in a realistic way.
• 37:26
  SPEAKER_00: Yeah.
• 37:26
  SPEAKER_00: And I think that, you know, people that are still in the technology fundraising portion have an easier time promising a new demo, right?
• 37:35
  SPEAKER_00: Uh, and I think that was the kind of early years of self-driving, right?
• 37:39
  SPEAKER_00: And then at some point, investors are like, wait, how long have we been doing this?
• 37:42
  SPEAKER_00: When are you guys gonna actually start getting paid customers?
• 37:45
  SPEAKER_00: And they're like, oh and that's when kind of the whole thing wound up drying up, right?
• 37:50
  SPEAKER_00: And it it kind of flipped.
• 37:52
  SPEAKER_00: So um it's tough.
• 37:54
  SPEAKER_00: I think that that you know, that that kind of uh experience, I think in in my mind, the kind of shorthand I give it is is like five years, right?
• 38:02
  SPEAKER_00: So uh the current robotics startups, if they started two or three years ago, right, they have about two or three years left.
• 38:09
  SPEAKER_00: And then investors are gonna start saying, Okay, but we put all this money into it, like where's the business coming value and and when is this coming back?
• 38:18
  SPEAKER_00: Um, so I think until that point, we can we can sell technology and we can sell really well polished demonstration videos.
• 38:25
  SPEAKER_00: I think a friend of mine in robotics calls some of these companies uh more of a movie industry, right?
• 38:30
  SPEAKER_00: Because they spend more of their time making uh films than they do working kind of robotic systems.
• 38:36
  SPEAKER_00: But uh that to me is kind of gonna be the big shift, right?
• 38:40
  SPEAKER_00: So whatever it is, 27, 28, 29, right?
• 38:43
  SPEAKER_00: That's the point where everything has to be working and and kind of generating revenue, um, or it's gonna get kind of starting to get choked off a little bit and uh and we'll be in the same place we are with self-driving, right?
• 38:59
  SPEAKER_00: Where the people that have another business that can kind of keep that thing going will survive.
• 39:04
  SPEAKER_00: You know, I would guess Boston Dynamics and Hyundai will still be there two or three years.
• 39:08
  SPEAKER_00: I would guess that uh anyone else that can kind of attach themselves to a very, very large cash-producing business will still be there.
• 39:16
  SPEAKER_00: Um and everyone else is gonna be where the self-dragon companies are.
• 39:20
  SPEAKER_01: Invisible AI was founded in 2018.
• 39:23
  SPEAKER_01: Do I have that right?
• 39:24
  SPEAKER_00: Late 2018, really early 2019, but yeah.
• 39:27
  SPEAKER_01: How much did the pandemic speed up your own timeline for you know, in terms of like manufacturing, but also obviously like the supply chain shortages?
• 39:37
  SPEAKER_00: I don't know that the pandemic necessarily sped anything up, but it did, I think, show everybody the value maybe of having of kind of interacting over video, right?
• 39:46
  SPEAKER_00: Of using video, right?
• 39:48
  SPEAKER_00: I think people got more comfortable with these tools, uh, made it easier in some ways for us to kind of interact with our customers because everyone is well conditioned now to teams meetings and all this kind of stuff instead of having to fly out every single time and do a meeting.
• 40:01
  SPEAKER_00: The reality is that we are in a pretty dynamic environment, I would say overall, right?
• 40:07
  SPEAKER_00: I think the the kind of geopolitical environment that we're in now has its own pluses and minuses, right?
• 40:13
  SPEAKER_00: The the kind of instance of the city.
• 40:14
  SPEAKER_01: Dynamic is a really nice and diplomatic way of putting it.
• 40:18
  SPEAKER_00: It changes, right?
• 40:19
  SPEAKER_00: A lot.
• 40:19
  SPEAKER_00: Yeah.
• 40:20
  SPEAKER_00: So so I think uh, you know, I think there's a lot of advantage, right, in being flexible and helping reduce cost, right?
• 40:26
  SPEAKER_00: I think that's kind of a big focus for us.
• 40:28
  SPEAKER_00: And so um at the end of the day, that we are uh in a really good place.
• 40:34
  SPEAKER_00: We're something that people need, right?
• 40:36
  SPEAKER_00: This is a uh a time when you want to have really, really strong visibility into how your processes work, into how to reduce costs, into um how to kind of improve efficiency across the board, right?
• 40:48
  SPEAKER_00: COVID was kind of in a lot of ways a big shock and kind of woke up the system.
• 40:53
  SPEAKER_00: But I think that the other forces that are out there kind of reshaping global trade and reshaping where we manufacture things are ultimately going to be bigger, right?
• 41:02
  SPEAKER_00: But uh it's yeah, I mean, this is this is kind of one of the most interesting times probably to work in manufacturing and manufacturing technology.
• 41:11
  SPEAKER_00: And uh, you know, we have AI on one side, right?
• 41:15
  SPEAKER_00: We have kind of redrawing kind of our entire kind of idea of global trade and what it means and where things should be built and how things should be built.
• 41:24
  SPEAKER_00: And um, you know, we have this kind of looming specter of of changes and in climate, changes in how we power things, right?
• 41:32
  SPEAKER_00: We have just a million different things, all that are out there, all that are trying to reshape kind of how we how we do the basics of building stuff.
• 41:42
  SPEAKER_00: So um I don't know, it's gonna be wild.
• 41:44
  SPEAKER_00: It's gonna be very interesting 10, 20 years trying to figure out kind of what the right mix of of anything is.
• 41:52
  SPEAKER_00: Um, and then of course, you know, in that entire time, we'll just be getting better and better robotics and better and better automation and and then who knows, right?
• 42:01
  SPEAKER_00: It just doesn't seem like things are gonna uh become less kind of dynamic anytime soon, I guess, is what I'd say.
• 42:12
  SPEAKER_01: Yeah, so so I'll I'll put you on the spot to end things because we had this like whole big conversation about um you know, like about predictions and self-driving cars and humanoids and everything else.
• 42:23
  SPEAKER_01: But so let's say you said 10 to 5 10 to 20.
• 42:27
  SPEAKER_01: So let's say 15 years from now, do you think that manufacturing will be much more meaningfully decentralized versus where it is right now?
• 42:37
  SPEAKER_00: Decentralized meaning um like you can build things closer to where they're they're consumed.
• 42:42
  SPEAKER_00: Yeah.
• 42:43
  SPEAKER_00: I think one of the key things you have to understand about manufacturing is is uh how long it takes to build things.
• 42:50
  SPEAKER_00: Right?
• 42:51
  SPEAKER_00: Not the not the like widgets, but the factories, right?
• 42:53
  SPEAKER_01: And the to build the things that build the things, yeah.
• 42:56
  SPEAKER_00: Yeah, and so in 10 or 15 years, um we'll probably have kind of like one round of changes in where things are built, right?
• 43:06
  SPEAKER_00: And so it will really depend over the next 10 or 15 years uh what the posture geopolitically of all our various countries are, right?
• 43:16
  SPEAKER_00: If uh we have a uh you know a complete change in how we view things here in the United States, or a country like China has a complete change in how they view things, things go back to maybe what was before the status quo, then you know, we'll have built one round of factories, we'll have made things way more expensive, they won't be able to compete because we've changed back to building things the way it was before and they'll all get shut down or something, right?
• 43:41
  SPEAKER_00: And so, and so this is kind of the challenge is that like really, you know, this is a uh a very, very gradual game that requires sustained effort and pressure over many, many years, right?
• 43:52
  SPEAKER_00: And so in 10 or 15 years, they'll be, you know, having built their first factories, maybe working on the second one, maybe, you know, planning the third one, right?
• 44:01
  SPEAKER_00: And all of that work will depend on uh on on what happens um with with our politics, with our consumer, right, with everything.
• 44:11
  SPEAKER_00: And and you know, same can be said for electric vehicles, right?
• 44:14
  SPEAKER_00: Same can be said for anything else.
• 44:16
  SPEAKER_00: I think Intel was supposed to build a fab that was uh in Ohio or something, and it was going to start next year, right?
• 44:22
  SPEAKER_00: It's now planned to start in like 2030 because their balance sheet is bad, they need to make a bunch of considerations.
• 44:28
  SPEAKER_00: A lot of the money the government was promising didn't quite come through and didn't come through it for the right, you know, for the same reasons and stuff.
• 44:34
  SPEAKER_00: So uh at the end of the day, they just push that back four years.
• 44:38
  SPEAKER_00: Like that's nothing, that's just like a planning, a planning thing, right?
• 44:41
  SPEAKER_00: And so I think that you know, 15 years just isn't long enough, in my opinion, uh, for us to see that much of a change unless everything stays at the same um maybe the same tenor or the same tone that it is today, right?
• 44:57
  SPEAKER_00: Um, but if you know, God willing, the the uh the war in Ukraine gets gets figured out, right?
• 45:04
  SPEAKER_00: And we have all you know a series of kind of geopolitical realignments that happen, temperature comes down across the world, everything starts to slow down, um, then then the world might just look very much kind of the way that it does now.
• 45:15
  SPEAKER_00: Um so yeah, it's hard to say.
• 45:17
  SPEAKER_00: I think 15 years is just it's it's it's funny.
• 45:19
  SPEAKER_00: It feels like a really long time.
• 45:21
  SPEAKER_00: Uh 15 years ago was a very long time uh for us as human beings, but I think in in the in the realm of of this of technology and of this specific area, it's it's just not very long.
• 45:32
  SPEAKER_00: Um and I think that you know that's kind of the paradox, right?
• 45:36
  SPEAKER_00: Is that you know, I I believe that we will get to uh crazy automation and and huge amounts of of the economy, but it just might be 2100 and not 2030, you know.
• 45:48
  SPEAKER_01: And that's yeah, I think things will be things will be incredibly different, but also exactly the same.
• 45:53
  SPEAKER_00: That's that's exactly yeah, that's a great way to sum it all up.
• 45:57
  SPEAKER_01: Eric, well, thank you so much for taking the time.
• 45:59
  SPEAKER_00: Yeah, this was great.
• 46:01
  SPEAKER_00: Uh anytime.
• 46:04
  SPEAKER_01: Thank you so much to Eric for taking the time to speak with us.
• 46:07
  SPEAKER_01: Really enjoyed that conversation.
• 46:09
  SPEAKER_01: Thanks to you, the viewer andor listener for viewing andor listening to this episode.
• 46:16
  SPEAKER_01: If you like the show, please like and subscribe.
• 46:20
  SPEAKER_01: And don't forget to check out our sister sibling newsletter uh over at automated.fm.
• 46:27
  SPEAKER_01: And we will catch you next week for another episode of Automated.