1 00:00:00,000 --> 00:00:08,180 Kevin Mako: Hello, product innovators. Today we learn from a robotics community leader on what modern robotics innovation startups are doing to succeed in the consumer product segment. 2 00:00:11,660 --> 00:00:20,660 Narrator: You're listening to the Product Startup Podcast, the show that helps bring your product idea to life by chatting with successful inventors, 3 00:00:21,000 --> 00:00:29,320 Narrator: product developers, manufacturers, and hardware industry professionals. Our goal here is to get to the bottom of what makes a product successful, from initial 4 00:00:29,320 --> 00:00:38,380 Narrator: idea to getting your product on store shelves. We're taking you step by step to build a functional product and scale your product business. 5 00:00:38,640 --> 00:00:47,300 Narrator: Hosted by Kevin Mako, one of North America's leading experts on hardware development for small product businesses. Now, on to the show. 6 00:00:47,660 --> 00:00:51,260 Kevin Mako: Welcome back, everyone. Today I'm very excited to introduce Andra Keay to the show. 7 00:00:51,720 --> 00:00:57,920 Kevin Mako: Andrew is the managing director of Silicon Valley Robotics, which supports the innovation and commercialization of robotics technologies. 8 00:00:58,340 --> 00:01:06,960 Kevin Mako: She is headed up Venture Capital Rounds as a fellow lecturer and an XPRIZE. judge. Her office is also just down the hall from our Mako Design California office at circuit 9 00:01:06,960 --> 00:01:11,440 Kevin Mako: launch. Today, Andra is going to share some valuable knowledge in how inventors, startups, small 10 00:01:11,440 --> 00:01:15,240 Kevin Mako: manufacturers can, today Andrew is going to share some valuable knowledge in how inventors, 11 00:01:15,500 --> 00:01:23,900 Kevin Mako: startups, and small manufacturers can understand how robotics is becoming a mainstream part of new electronic mechanical products, also the future of the consumer 12 00:01:23,900 --> 00:01:32,920 Kevin Mako: robotics industry and tips for anyone thinking about robotic products or even incorporating basic robotics into their new hardware product. Now, on to the episode. 13 00:01:34,170 --> 00:01:35,390 Andra Keay: Hi, Andra. Welcome to the show. 14 00:01:35,750 --> 00:01:42,270 Kevin Mako: Thanks, Kevin. It's great to be here. We're excited to have you on. And of course, your office is right across the hall from our office at 15 00:01:42,270 --> 00:01:49,250 Kevin Mako: CircuitLaunch at our San Francisco facilities, an amazing facility. And you've been there for a while now. What can you say about that place? 16 00:01:49,310 --> 00:01:58,290 Andra Keay: Because I'm a huge fan of CircuitLaunch. I love it. And, you know, I'm distracted by the robots that go past my window quite frequently. But it has been 17 00:01:58,290 --> 00:02:07,550 Andra Keay: great to be there as perhaps one of the second tenants there to see that the space is completely full now. And that's in spite of the pandemic. 18 00:02:08,190 --> 00:02:17,210 Andra Keay: It's such a range of interesting startups there. What can I say? I really love being there. And I've seen the benefits for the startups that are there 19 00:02:17,210 --> 00:02:24,250 Andra Keay: as well because they operate as a community where one person's answer can solve another person's 20 00:02:24,250 --> 00:02:31,130 Andra Keay: problem. And we also see people cycling through the community as well in one role at one company, 21 00:02:31,670 --> 00:02:36,950 Andra Keay: then moving to another role at another company, which I think is wonderful because one of the 22 00:02:36,950 --> 00:02:45,810 Andra Keay: hardest things to do as an early stage company is to hire and to have expert answers for some of your important questions. 23 00:02:45,810 --> 00:02:47,630 Andra Keay: It's a community of knowledge. 24 00:02:48,150 --> 00:02:55,690 Kevin Mako: And that is one of the most useful things that startups can have. I love that you mentioned the community element. We had Alex Dantas, 25 00:02:55,870 --> 00:03:01,370 Kevin Mako: as a CEO of CircuitLaunch on the show, actually a few episodes ago. And that was one of the biggest things that we were talking about is how incredible it is. 26 00:03:01,470 --> 00:03:07,330 Kevin Mako: And now us, you know, Mako Design is kind of the design firm of record there. It's great to see all the startups. So obviously, we have clients 27 00:03:07,330 --> 00:03:13,590 Kevin Mako: in the space and see how they emerge and grow. But it's such a powerful community of all the different 28 00:03:13,590 --> 00:03:19,350 Kevin Mako: types of connections that you may need either in one degree or two degrees of separation. Definitely 29 00:03:19,350 --> 00:03:28,970 Kevin Mako: in three degrees, you're getting huge access, especially in the hardware space. Because hardware, obviously, in the whole Bay Area, software always, and really still does 30 00:03:28,970 --> 00:03:34,510 Kevin Mako: prevail, is kind of taking the limelight. And that's what's so amazing about a facility that's focused so exclusively 31 00:03:34,510 --> 00:03:44,270 Kevin Mako: on hardware and hardware companies and helping them grow and scale to be, you know, big things that eventually graduate out of the space and get their own or evolve from 32 00:03:44,270 --> 00:03:50,690 Kevin Mako: there. So it's really amazing to see the growth of that facility and see how popular it's been, even despite COVID, as you mentioned going along. 33 00:03:50,850 --> 00:03:54,310 Kevin Mako: So give us a bit of a background on yourself. You're an incredibly well-connected 34 00:03:54,310 --> 00:04:01,090 Andra Keay: person in the industry. Give us the Coles notes. How did you get here to where you are today? I came over from Australia in 2010. 35 00:04:01,510 --> 00:04:10,690 Andra Keay: I was finishing up my master's in human-robot interaction or human-robot culture. What fascinated me most was the commercialization of robotics. 36 00:04:11,190 --> 00:04:16,090 Andra Keay: And so I wanted to come to Silicon Valley, which is ground zero for robotics. 37 00:04:16,900 --> 00:04:25,200 Andra Keay: Now, most people don't realize that. Because Silicon Valley is the internet giant. 38 00:04:26,300 --> 00:04:35,340 Andra Keay: And indeed, in Silicon Valley robotics is a small fish in a very big pond and is frequently overlooked in the same way that you said hardware is 39 00:04:35,340 --> 00:04:44,760 Andra Keay: overlooked because real estate and talent are expensive in the valley because of the top tier companies that are software 40 00:04:44,760 --> 00:04:54,010 Andra Keay: based. And yet we have twice to three times. the amount of robotics innovation as Boston and Pittsburgh put 41 00:04:54,010 --> 00:04:59,180 Andra Keay: together. We're the leading center in the world for robotics innovation. 42 00:05:00,170 --> 00:05:05,410 Andra Keay: Although China has tried very hard to catch up and they're creating a lot of robotics businesses, 43 00:05:06,630 --> 00:05:16,070 Andra Keay: the innovation factor is still perhaps strongest in the United States. There's certainly also some great robotics coming out of other countries 44 00:05:16,070 --> 00:05:18,750 Andra Keay: and the grant funding in Europe is good, 45 00:05:19,550 --> 00:05:28,310 Andra Keay: but on the heels of the defence industries, there has been a lot of R&D funding that has gone into robotics over the last 20 years. 46 00:05:28,590 --> 00:05:34,430 Andra Keay: And that has come to fruit. This is what I saw when I came over in 2010, 47 00:05:35,270 --> 00:05:42,560 Andra Keay: and I stepped into an association that was forming to meet that needs, Silicon Valley Robotics. 48 00:05:42,940 --> 00:05:45,820 Andra Keay: And this is a coalition of robotics companies in the Bay Area, 49 00:05:47,040 --> 00:05:52,320 Andra Keay: pioneers like SRI International, Willow Garage, while it still existed, 50 00:05:52,920 --> 00:06:00,300 Andra Keay: Bosch and Adept Technologies, which was America's only traditional robotics manufacturer, 51 00:06:00,900 --> 00:06:08,900 Andra Keay: and they were trying to pioneer autonomous mobile robots at the time. They were perhaps just a little bit early as a publicly traded company. 52 00:06:09,100 --> 00:06:16,120 Andra Keay: They were affected by the stock market crash. And what we see is that the autonomous mobile robots, 53 00:06:16,140 --> 00:06:25,490 Andra Keay: companies that they were working on then are now very successful and they've all been acquired by other companies, of course, global companies. 54 00:06:26,450 --> 00:06:36,270 Andra Keay: But we saw the start of the wave of commercialisation of what I call a new form of robotics. I call it Robotics 2.0 because it is just as different 55 00:06:36,270 --> 00:06:43,760 Andra Keay: as the different iterations of the web or internet technologies. The original robotics industry, 56 00:06:43,760 --> 00:06:47,820 Andra Keay: 50% of it is employed in automotive welding. 57 00:06:48,600 --> 00:06:54,640 Andra Keay: Now, that is highly susceptible to the ups and downs of a single 58 00:06:56,370 --> 00:07:00,450 Andra Keay: industry, the automobile industry or automobile manufacturing. 59 00:07:01,840 --> 00:07:10,340 Andra Keay: And the other 50% is split across a range of things from agriculture to primarily dangerous materials, handling, mining, 60 00:07:10,920 --> 00:07:13,860 Andra Keay: paintwork, chemicals, that kind of thing. 61 00:07:14,620 --> 00:07:23,720 Andra Keay: And these are the stupid robots. These are the robots that are expensive and that needs safety cages around them. By and large, that's robotics 1.0. 62 00:07:25,070 --> 00:07:30,910 Andra Keay: In 2004 and 2005, DARPA ran a grand challenge for self-driving cars. 63 00:07:31,690 --> 00:07:34,930 Andra Keay: And the first challenge, nobody crossed the finish line. 64 00:07:35,920 --> 00:07:40,420 Andra Keay: The second challenge, almost all the teams crossed the finish line, and that was only one year later. 65 00:07:41,140 --> 00:07:50,040 Andra Keay: The third challenge, they moved from the desert to a six. simulated suburban environment, although it was at an Air Force base. 66 00:07:51,480 --> 00:07:57,250 Andra Keay: And there were three different tiers of companies competing in that, or teams competing 67 00:07:57,250 --> 00:08:04,050 Andra Keay: in that, and Google was in the background buying them up, as were some of the other vehicle manufacturers after that. 68 00:08:04,490 --> 00:08:13,590 Andra Keay: So we saw the rise in interest in robotics then. And then in 2012, Amazon acquired Kiva systems, and 69 00:08:13,590 --> 00:08:21,050 Andra Keay: that was one of those early mobile robots and more connected, 70 00:08:22,800 --> 00:08:31,960 Andra Keay: that made everybody sit up and go, look, if Google's investing in robots, if Amazon's investing in robots, this technology might be becoming interesting. 71 00:08:32,620 --> 00:08:39,640 Andra Keay: Now, those companies could afford to take a kind of long bet on it, but Amazon hasn't slowed 72 00:08:39,640 --> 00:08:48,740 Andra Keay: down their interests in robotics, even if individual robotics companies have been slow to bear promise. 73 00:08:49,940 --> 00:08:55,600 Andra Keay: And I think if there's one thing that I can say to make sense of this, 74 00:08:55,860 --> 00:09:04,600 Andra Keay: because the internet is flooded with things like Boston Dynamics videos, and people go, well, that's not a very useful robot, is it? 75 00:09:05,570 --> 00:09:13,970 Andra Keay: And it's not supposed to be. Really, Boston Dynamics strength is their research on the leading edge of robotics. 76 00:09:14,530 --> 00:09:22,250 Andra Keay: And when you acquire the Boston Dynamics, team, you really shouldn't be thinking about a product that looks like something they're making. 77 00:09:22,710 --> 00:09:29,770 Andra Keay: You should be thinking about how useful are the developments that they pioneered in making something 78 00:09:29,770 --> 00:09:38,530 Andra Keay: that's smaller, more robust, more affordable, and useful to the consumer. They're a leading edge firm. They're not a product company. 79 00:09:39,450 --> 00:09:46,770 Andra Keay: And yet the most popular videos show robots that are really not useful and not likely to commercialize. 80 00:09:47,470 --> 00:09:57,250 Andra Keay: In a sense, you know when a robot is done right, then you don't even think about it anymore. It just becomes an appliance or a tool 81 00:09:57,250 --> 00:09:59,610 Andra Keay: or something that's getting a job done. 82 00:10:00,590 --> 00:10:07,430 Andra Keay: And, you know, this is a joke amongst robotists. How do you know what a robot is? It's something that doesn't work in a demo. 83 00:10:08,560 --> 00:10:16,560 Andra Keay: And that kind of captures Is it? Robotics is quite a moving field. If you looked at the current definition today of what a robot is and applied it to 84 00:10:16,560 --> 00:10:23,880 Andra Keay: the robotics industry for the last 50 years, they're too stupid to count as robots. 85 00:10:25,060 --> 00:10:31,580 Andra Keay: Nonetheless, that is the robotics industry. And that's why I say there's robotics 1.0, which is a major 86 00:10:31,580 --> 00:10:36,320 Andra Keay: industry. It's, you know, maybe worth $42 billion a year in 87 00:10:36,320 --> 00:10:40,220 Andra Keay: sales. And then there's robotics 2.0. And this is a major industry. And this is a major industry. It's, you know, maybe worth $42 billion a year in sales. And this is, you know, 88 00:10:40,240 --> 00:10:48,380 Andra Keay: is the new wave of robotics that has enabled on this self-driving technology. It's the technology of navigation. 89 00:10:49,060 --> 00:10:54,600 Andra Keay: It is the connection of sensor input and affordable sensors at that that can 90 00:10:54,600 --> 00:11:03,190 Andra Keay: operate rapidly enough over a long enough distance into a computer onboard a robot that can 91 00:11:03,190 --> 00:11:09,030 Andra Keay: make decisions in real time based on that environmental data. 92 00:11:10,000 --> 00:11:15,020 Kevin Mako: Now, Shaky, the mobile robot that SRI produced in 1974, 93 00:11:15,840 --> 00:11:25,580 Kevin Mako: was able to make environmental decisions based on sensor input. But it was tethered through giant cables to mainframe computers in the room behind. 94 00:11:26,600 --> 00:11:33,580 Kevin Mako: What we're talking about now is the ability for even small delivery robots, even small robot vacuum cleaners, 95 00:11:34,460 --> 00:11:40,400 Kevin Mako: to make the use of sensor data, real time, in the real environment. 96 00:11:40,400 --> 00:11:46,380 Kevin Mako: and that's what makes a robot arm collaborative rather than an industrial robot arm. 97 00:11:46,660 --> 00:11:55,500 Kevin Mako: It's able to assess if there's somebody in the planned path and then renavigate a path 98 00:11:55,500 --> 00:11:59,540 Kevin Mako: around the obstacle, whether it's a person or another obstacle. 99 00:12:00,560 --> 00:12:09,420 Kevin Mako: And that's exactly what a mobile robot is doing. It's able to remap a pathway when there's an obstacle in the environment. 100 00:12:10,370 --> 00:12:19,250 Kevin Mako: The amazing thing about this robot 2.0, and I appreciate you bring that up, is that it's really looking at how robot technology is being incorporated 101 00:12:19,250 --> 00:12:22,890 Kevin Mako: into everyday consumer interactions. 102 00:12:23,550 --> 00:12:27,790 Kevin Mako: And a lot of people, when they think of robotics, of course, I think of these complicated things, but I can tell you, as a consumer 103 00:12:27,790 --> 00:12:33,050 Andra Keay: product design firm, we're designing numerous inventions that have these elements of robotics, interactivity. 104 00:12:33,210 --> 00:12:39,690 Andra Keay: I mean, you look at anything in IoT or wearable tech, elements of robotics or entire robotics in themselves are being put into. 105 00:12:39,710 --> 00:12:42,110 Andra Keay: these things, even if it's just small and simple arrangements. 106 00:12:42,910 --> 00:12:49,170 Andra Keay: And that's why I find it so fascinating when you really look at this robot 2.0 that you're talking about and mention the word 107 00:12:49,730 --> 00:12:53,730 Andra Keay: commercialization. And I want you to dig into that a bit, especially looking at the fact of 108 00:12:54,250 --> 00:12:59,130 Andra Keay: robots becoming consumerized and especially to robot inventors or people that are coming up 109 00:12:59,130 --> 00:13:05,070 Andra Keay: with new devices that use sensors that have something in one way or another interacts with the environment around us. 110 00:13:05,730 --> 00:13:14,110 Andra Keay: How is that, you know, looking at that definition is very different. And that's why I think this is a very important episode, because robotics is so integrated with so much 111 00:13:14,110 --> 00:13:16,730 Andra Keay: electronics technology that's coming out at the consumer level. 112 00:13:17,630 --> 00:13:24,850 Andra Keay: So just give us a bit of a highlight around kind of how that definition has changed, why this robot 2 .0 is so important 113 00:13:24,850 --> 00:13:33,790 Andra Keay: to hardware invaders, especially in the electronics sphere these days. By 2030, your household robot 114 00:13:33,790 --> 00:13:35,370 Andra Keay: will be your house. 115 00:13:36,760 --> 00:13:44,640 Andra Keay: Exactly as you said, it's robotics technologies and it's spread, it doesn't have to be all inside the box. 116 00:13:45,240 --> 00:13:49,220 Andra Keay: And I think that's the key thing for robotics is to think outside the box. 117 00:13:49,880 --> 00:13:59,160 Andra Keay: In our minds, we anthropomorphize everything. And so we tend to think of a robot like a pseudo-humanoid operating in 118 00:13:59,160 --> 00:14:08,540 Andra Keay: that fashion. And that is the least interesting way to think about robots. Robots can be split into components and spread around. 119 00:14:08,540 --> 00:14:11,960 Andra Keay: Think outside the box when you think about robotics. 120 00:14:12,840 --> 00:14:22,680 Andra Keay: And, you know, I prefer to use the term robotics technologies a lot of the time because it takes us away from that immediate anthropomorphization of saying a robot, 121 00:14:22,900 --> 00:14:28,140 Andra Keay: that's a box over there, and it will therefore behave in certain fashions. 122 00:14:30,280 --> 00:14:34,940 Andra Keay: And indeed, talking about robotics 2.0 and consumer products, 123 00:14:35,840 --> 00:14:42,400 Andra Keay: iRobot was one of the leading companies in robotics, and they've gone through this transition. 124 00:14:42,920 --> 00:14:46,520 Andra Keay: They were the first company to commercialize a consumer robot. 125 00:14:47,180 --> 00:14:55,150 Andra Keay: And that was the robot vacuum cleaner. And the first robot vacuum cleaners from iRobot were beautifully simple. 126 00:14:55,910 --> 00:14:59,510 Andra Keay: They just bounced off the walls until they'd covered the room. 127 00:15:00,170 --> 00:15:02,390 Andra Keay: And I loved, back in Australia, we had some. 128 00:15:03,050 --> 00:15:07,760 Andra Keay: And people were in love with watching them, 129 00:15:08,180 --> 00:15:15,980 Kevin Mako: but they would insist on a school. describing intelligence to them that wasn't there, they would say, well, it must know where it's 130 00:15:15,980 --> 00:15:21,780 Kevin Mako: been. Or how does it know to do this room after that room? And I'm kind of like, the only thing 131 00:15:21,780 --> 00:15:26,980 Kevin Mako: that we do with this robot is we have a little infrared wall to stop it falling down the stairs. 132 00:15:27,620 --> 00:15:35,320 Kevin Mako: And it just bounces off the walls until it's finished covering the floors pretty much. And then it runs out of charge and it goes back to base. 133 00:15:36,120 --> 00:15:40,360 Kevin Mako: Okay, the most advanced thing that that robot did was go back to base to charge. 134 00:15:41,780 --> 00:15:50,400 Kevin Mako: Now, that's very different of robot vacuum cleaners today because the new Wave Robotics 2.0 that really started in 2010 135 00:15:50,400 --> 00:15:59,280 Kevin Mako: has reached those technologies. And in fact, NETO was one of the first to bring in a LIDA onto their robot vacuum cleaners. 136 00:15:59,800 --> 00:16:09,220 Kevin Mako: And there's been a lot of change in who owns the different robotics companies. But firstly, one of the things, every major vacuum cleaner company, 137 00:16:09,240 --> 00:16:13,060 Kevin Mako: now has a robot vacuum cleaner in their product lineup. 138 00:16:13,740 --> 00:16:19,020 Kevin Mako: And robot vacuum cleaners are 28% of the market share for vacuum cleaners. It's probably more these days. 139 00:16:19,720 --> 00:16:27,480 Andra Keay: So it's taken a while, but you can see from the leading edge of technology to being 140 00:16:27,480 --> 00:16:36,220 Andra Keay: an established part of the market is possible for robot technologies. And it's only growing. It's only growing like crazy, right? 141 00:16:36,260 --> 00:16:43,140 Andra Keay: It's almost like we're at the tip of the iceberg. I've said it before on prior podcast. that I believe if you look around the room in 20 to 30 years from now, 142 00:16:43,500 --> 00:16:50,840 Andra Keay: everything you touch and see is going to have a chip in it one way or another. It's going to have some sort of connected technology or, you know, QR code or scanning device 143 00:16:50,840 --> 00:17:00,380 Andra Keay: or whatever it might be to say that something needs to be replaced or that, you know, this is the part that you're looking at or some other information that it's relaying 144 00:17:00,380 --> 00:17:01,760 Andra Keay: that's important to the user at the time. 145 00:17:01,860 --> 00:17:09,760 Kevin Mako: It doesn't have to be like our desks are actually robotic, but it might be saying that, you know, our sensor does notice that it's not, you know, there's certain back. 146 00:17:11,000 --> 00:17:16,040 Kevin Mako: bacterial contaminants on your surface, and you might want to give it a clean. You know, there's lots of things that are going to happen. 147 00:17:16,260 --> 00:17:24,000 Kevin Mako: But with all this sensor technology and everything that's happening, we are just at the tip of the iceberg of connecting all these devices together. But also, 148 00:17:24,400 --> 00:17:28,600 Kevin Mako: I really like what you said, is simplifying them. So not looking at a robot like an arm that welds 149 00:17:28,600 --> 00:17:35,980 Kevin Mako: parts to a car, but looking at a robot, like maybe a tiny little piece that has a little sensor that does something in your desk to tell you when it's not clean. 150 00:17:36,000 --> 00:17:43,120 Kevin Mako: It can be very simple and stripped down as the technology evolves, as it simplifies, as it becomes easier for developers 151 00:17:43,120 --> 00:17:48,640 Andra Keay: like us to actually design the products to commercialize. And all that's happening right now. 152 00:17:48,960 --> 00:17:54,500 Andra Keay: Absolutely. And I think the distinction between passive and active sensing is shifting. 153 00:17:55,020 --> 00:18:04,310 Andra Keay: As we introduce more things like temperature sensitive materials and new 154 00:18:05,980 --> 00:18:14,540 Andra Keay: chemicals, new, you know, the world of packaging materials is quite fascinating when you look at what's happening, 155 00:18:14,700 --> 00:18:21,400 Andra Keay: what's coming up in the latest advances there. And so I think we will start to see responsive 156 00:18:21,400 --> 00:18:29,280 Andra Keay: products, perhaps as a first step. And as you said, that might not even have computation 157 00:18:29,280 --> 00:18:36,140 Andra Keay: involved so much as being responsive. You know, it's an umbrella that opens up when the sun hits 158 00:18:39,450 --> 00:18:45,580 Andra Keay: Oh, I lost my train of thought just there. 159 00:18:46,880 --> 00:18:53,260 Andra Keay: Something I wanted to ask you, first of all, we can cut that piece out, so don't worry about it. It will just flow through, so I'll just jump into it. 160 00:18:54,820 --> 00:19:03,840 Andra Keay: So if we're looking at all this technology, for people who are creating an invention idea that has incorporated either entire robotics technology, 161 00:19:03,980 --> 00:19:11,520 Andra Keay: even as per the modern definition or components of it, what have you seen? You've dealt with hundreds of hardware startups in the robotic space. 162 00:19:11,580 --> 00:19:17,100 Andra Keay: both on the funding side and the commercialization side and on the production side, 163 00:19:17,560 --> 00:19:26,500 Andra Keay: what do you see are some of the best practices for how to succeed as your newer hardware startup emerging into the space of 164 00:19:26,500 --> 00:19:28,000 Andra Keay: commercializing robotics? 165 00:19:29,760 --> 00:19:35,040 Andra Keay: That's a great question. And, you know, I think I've seen thousands of robotic startups. 166 00:19:35,540 --> 00:19:45,220 Andra Keay: Sometimes one of the most important things that I can offer as advice to founders is that I may have seen a half a dozen similar companies 167 00:19:45,220 --> 00:19:48,500 Andra Keay: that have failed. And they've disappeared without trace. 168 00:19:49,510 --> 00:19:50,550 Andra Keay: But I do remember them. 169 00:19:51,330 --> 00:19:58,970 Andra Keay: And founders will say, well, we've done our research. Nobody else has done what we're doing. And I'm like, well, actually they have and it didn't work. 170 00:19:59,830 --> 00:20:07,870 Andra Keay: And I can remember these companies. And this was what they were trying to do, just like you are. And this was why it didn't work. 171 00:20:08,230 --> 00:20:12,850 Andra Keay: And that can help speed up the pathway. You've got to look on it as a race. 172 00:20:13,610 --> 00:20:20,340 Andra Keay: And some of the most successful entrepreneurs are entrepreneurs who've done this several times before 173 00:20:20,340 --> 00:20:30,100 Andra Keay: because you're expected to have such a lot of knowledge as a startup founder. And it can be very, very difficult acquiring that in the get-go. 174 00:20:31,900 --> 00:20:39,070 Andra Keay: Robotics founders were slow to attempt to commercialize difficult technologies, 175 00:20:39,950 --> 00:20:49,690 Andra Keay: primarily, I think, because they're so used to researching and they wanted to research how to grow a company without falling into these pitfalls. 176 00:20:50,150 --> 00:20:53,730 Andra Keay: So I find often they're some of the best prepared founders out there. 177 00:20:54,530 --> 00:21:02,690 Andra Keay: But nonetheless, there's such a lot that you need to know. And I think we talk briefly about the three schools of the process. 178 00:21:03,210 --> 00:21:08,750 Andra Keay: And in fact, with Silicon Valley Robotics, which is the Roboaks Industry Association in the Bay Area, 179 00:21:09,070 --> 00:21:18,490 Andra Keay: Our first five-year plan was focusing entirely on increasing investment for early-stage robotics startups because there was next to no investment. 180 00:21:18,630 --> 00:21:20,830 Andra Keay: It was down in the hundreds of thousands of dollars. 181 00:21:21,710 --> 00:21:27,670 Andra Keay: And now we have around $30 billion a year invested into robotics technologies. 182 00:21:27,670 --> 00:21:37,170 Andra Keay: It's been an exponential growth in the last 10 years. Wow. But it took the first five years before traditional investors stopped laughing 183 00:21:37,170 --> 00:21:44,230 Andra Keay: when I told them robotics was ready and they should pay attention and some of the leading firms were starting 184 00:21:44,230 --> 00:21:52,080 Andra Keay: to pay attention and you can see that they have some fairly good portfolios and then the 185 00:21:52,080 --> 00:21:58,400 Andra Keay: followers have joined and we've seen a real increase in the number of accelerators and firms 186 00:21:58,400 --> 00:22:04,720 Andra Keay: that are now offering to be a boutique investor for X, Y or Z. 187 00:22:05,340 --> 00:22:13,440 Andra Keay: So I believed in 2015 we when one of the top-tier VCs cold-called me instead of me cold-calling them, 188 00:22:14,260 --> 00:22:21,920 Andra Keay: that we'd succeeded in our strategy of getting investment into robotics. So I looked for the next challenge. 189 00:22:22,540 --> 00:22:27,700 Andra Keay: And the next challenge, this is what I'm getting from the robotics founders themselves, 190 00:22:28,720 --> 00:22:30,300 Andra Keay: was finding customers. 191 00:22:31,300 --> 00:22:37,220 Andra Keay: And in the early stages, it's finding your first pilots. It's finding product market fit. 192 00:22:37,220 --> 00:22:44,380 Andra Keay: and then being able to adjust around that general area 193 00:22:44,380 --> 00:22:49,720 Andra Keay: until you've really mailed down the exact right thing or have ruled it out completely. 194 00:22:50,500 --> 00:22:59,240 Andra Keay: And this is also what the National Science Foundation is now rolling out in the ICOR program. 195 00:22:59,500 --> 00:23:09,240 Andra Keay: And they started with medical and biotech, science-based start-ups. Now they roll it out to every science-based or deep, tech startup founder. 196 00:23:09,760 --> 00:23:18,440 Andra Keay: The ICOR program funds you to go out and do customer development methodology not to build anything, not to test, not to prototype, not 197 00:23:18,440 --> 00:23:20,960 Andra Keay: to build, but to spend six weeks 198 00:23:21,840 --> 00:23:28,780 Kevin Mako: asking customers if they need what you're doing and what their pain points are, what problems they're trying to solve. 199 00:23:29,280 --> 00:23:38,550 Kevin Mako: And that is the most valuable use for your time once you've isolated the technology, that is your technology advantage. 200 00:23:39,010 --> 00:23:44,650 Kevin Mako: If you're really doing an innovative product, then you have both a technology risk and a market risk. 201 00:23:45,560 --> 00:23:47,360 Kevin Mako: And from an investor's perspective, 202 00:23:48,640 --> 00:23:56,540 Kevin Mako: they really want you to have ruled out as much of the risk as possible before they want to invest in you. 203 00:23:56,620 --> 00:24:03,600 Kevin Mako: It is that horse and part where founders say, but I need to have funding before I can get 204 00:24:03,600 --> 00:24:09,200 Kevin Mako: the next pilots because they need to have three robots that I can't afford to build, etc. 205 00:24:10,260 --> 00:24:20,160 Kevin Mako: But sometimes you have to be very fast, very creative and often privately funded 206 00:24:20,160 --> 00:24:28,020 Kevin Mako: or angel funded or look for non-diluted grants. It's very difficult to get investment that's worthwhile. 207 00:24:28,920 --> 00:24:34,460 Kevin Mako: I do see too many startup founders throw away their companies through accelerators. 208 00:24:34,880 --> 00:24:42,960 Kevin Mako: And accelerators can take an incredible amount of your equity for, comparatively speaking, small checks and small amounts of time. 209 00:24:44,000 --> 00:24:52,670 Kevin Mako: Silicon Valley Robotics, for example, we act at the level above that, like a meta-accelerator. We don't bring you into a cohort. We don't give you funding. 210 00:24:53,680 --> 00:24:59,800 Kevin Mako: But we try to provide advice wherever you are, at whatever stage you are, at whatever point you need it. 211 00:25:00,280 --> 00:25:10,160 Kevin Mako: So finding sources of assistance like that are far more useful. than surfing from accelerator to accelerator and 212 00:25:10,320 --> 00:25:11,700 Kevin Mako: exhausting your equity. 213 00:25:12,730 --> 00:25:20,610 Kevin Mako: You know, you've become uninvestable at that point. But non-delusive funding and all revenue and all friends of family and angels 214 00:25:20,610 --> 00:25:30,120 Kevin Mako: and a strategic investor that is coming from the industry. I've seen some great robotic startups or hardware startups getting funded through things like 215 00:25:30,120 --> 00:25:35,440 Kevin Mako: agricultural associations, for example, or pension associations. 216 00:25:35,440 --> 00:25:42,670 Kevin Mako: for particular healthcare -related things. 217 00:25:42,790 --> 00:25:49,230 Kevin Mako: In fact, it was a group of orthodontists that funded a robot for dental surgery. 218 00:25:50,780 --> 00:25:56,180 Kevin Mako: And that is brilliant because from the very start, you can go, okay, we know what our product 219 00:25:56,180 --> 00:26:02,070 Andra Keay: market fit is because our market is funding the development of this product. Well, it's amazing. 220 00:26:02,250 --> 00:26:07,310 Andra Keay: You know, you've got these two things and we could go into the manufacturing, which is the third pillar. 221 00:26:08,210 --> 00:26:14,970 Andra Keay: But looking at the funding and the product market fit, those things somewhat go together. And I love how you mentioned the funding in terms of private, because that's a big 222 00:26:14,970 --> 00:26:24,530 Andra Keay: misconception in any consumer product. The key is that a serious investor who's going to give you a fair price, they want to see some traction, but even just early traction. 223 00:26:25,130 --> 00:26:31,250 Andra Keay: So it's getting some of that customer engagement. And if, you know, maybe even selling some, or trying out 224 00:26:31,250 --> 00:26:40,830 Andra Keay: some light prototype units or something along those lines to prove that the market, it, A, is willing to buy it, and B, that they like it once they actually do buy it, 225 00:26:41,110 --> 00:26:50,510 Andra Keay: or getting some of that feedback from potential customers, whether it's a letter of intent or some other thing that really helps you as a startup say, look, I've validated and you 226 00:26:50,510 --> 00:26:57,610 Andra Keay: mentioned it the technology because an investor really wants to know, well, okay, it's a great idea. And I understand the use that you're going to solve. 227 00:26:57,990 --> 00:27:04,990 Andra Keay: And sure, somebody's going to buy it if you make it, but can you even make it? And of course, that's where understanding the technology, getting the designs, maybe even 228 00:27:04,990 --> 00:27:09,110 Andra Keay: light prototyping, whatever it is to say, okay, no, look, here's the technology. I built it. 229 00:27:09,450 --> 00:27:12,570 Andra Keay: I know how it works. I just don't have the money to now commercialize it. I need to, like you 230 00:27:12,570 --> 00:27:17,550 Andra Keay: say, build three of these things to give to three customers to then get, you know, more formal, 231 00:27:18,070 --> 00:27:22,530 Andra Keay: maybe quotes for manufacturing, to get price points ironed out or whatever the next steps might be. 232 00:27:22,650 --> 00:27:32,020 Andra Keay: But the big key that, you know, when you tie in this customer to funding is the fact that the further you push it privately, either yourself, if you can, you know, if 233 00:27:32,020 --> 00:27:37,840 Andra Keay: you're an engineer yourself or whether you're, you know, friends and family funding or even angel funding to get you 234 00:27:37,840 --> 00:27:45,920 Andra Keay: further along the chain, the further you go in both the technology and in the customer interest, the exponentially more valuable your company becomes. 235 00:27:45,980 --> 00:27:55,680 Andra Keay: You'll get much more money and give up much less equity the further you push it. And absolutely, I see it as well, especially in early, super early stage startups 236 00:27:55,680 --> 00:28:05,360 Andra Keay: because they think, well, you know what, it's worth nothing today. So, yeah, I don't mind giving up half the business because I'd I prefer to have something to nothing. 237 00:28:05,880 --> 00:28:11,580 Andra Keay: That's easy to say looking forward. I can tell you, everybody who's done that when you actually succeed in the product, 238 00:28:12,120 --> 00:28:16,480 Andra Keay: looking backwards, regrets it enormously because it's easy to say looking forward. 239 00:28:16,800 --> 00:28:25,140 Kevin Mako: But when you've got a multi-million dollar product down the road and you've got interest and you're scaling and you've given up 90% of your company for peanuts on what you could 240 00:28:25,140 --> 00:28:32,860 Kevin Mako: have done if you had just self-funded or at least just push it forward through private funding, that is when you're going to start regretting it because like you said, Andra, 241 00:28:32,860 --> 00:28:38,340 Kevin Mako: that can actually stifle your ability to scale. It may plateau you because you have no more equity to give. 242 00:28:39,350 --> 00:28:46,310 Kevin Mako: So it's brilliant how you pull those two together. I appreciate that. Or as we see in many cases, you go bankrupt and a year later, 243 00:28:46,890 --> 00:28:54,350 Kevin Mako: all of your assets and IP are acquired by one of the big competitors and that allows them to integrate your product ideas. 244 00:28:56,250 --> 00:29:00,450 Kevin Mako: So sometimes you've got to realize as well that it isn't, 245 00:29:01,730 --> 00:29:10,220 Kevin Mako: how do you say it can be a competitive world as well and so you 246 00:29:10,220 --> 00:29:16,580 Kevin Mako: can fall into a couple of pitfalls i really one of the pitfalls is getting too 247 00:29:16,580 --> 00:29:25,580 Kevin Mako: many expressions of interest from the research arms of companies as opposed to the operational arms of companies so 248 00:29:25,580 --> 00:29:35,000 Kevin Mako: the KPIs for the research and innovation group is to go out and find as many interesting startups as they can. They will write you letters of intent till the cows come home. 249 00:29:35,480 --> 00:29:44,610 Kevin Mako: The people you're actually going to sell to once you go past that unit of one are your operations people. And that's another thing that investors look at. 250 00:29:44,950 --> 00:29:53,410 Kevin Mako: Like, is this going to translate into larger demand? Have you really understood the problems and the sales cycles of 251 00:29:53,410 --> 00:30:02,820 Kevin Mako: your final market correctly? And as you said, the third leg of the stool, which is so important, is if you've navigated 252 00:30:02,820 --> 00:30:09,400 Kevin Mako: your technology risk, you've got an innovative product and you've got an IP strategy and you're first in the field. 253 00:30:10,000 --> 00:30:19,040 Andra Keay: And then you've navigated your market risk so that you've found the perfect product market fit, for example, you've got orthodontists funding you for a dental 254 00:30:19,040 --> 00:30:27,880 Andra Keay: robot. Then the next thing is going from the unit of one to the unit of many. And that is such a stumbling block. 255 00:30:28,520 --> 00:30:31,440 Andra Keay: So that's the third leg of the stool, prototype to product. 256 00:30:32,320 --> 00:30:40,250 Andra Keay: And avoiding, I know so many startups that build what they can with the materials that are 257 00:30:40,250 --> 00:30:46,830 Andra Keay: to hand and technologies that they can use. So they'll cobble together something on Raspberry Pi 258 00:30:46,830 --> 00:30:54,850 Andra Keay: or Arduino or, you know, one form of prototyping because it's accessible for them and they can do 259 00:30:54,850 --> 00:31:01,090 Andra Keay: it without having the money to pay for that next kind of level up. But it can lead them in a dead 260 00:31:01,090 --> 00:31:09,000 Andra Keay: end when it comes to manufacturability because they've relied on components that are not 261 00:31:09,000 --> 00:31:15,360 Andra Keay: accessible, that maybe no longer are easy to get. Certainly, we find with sensor technologies 262 00:31:16,100 --> 00:31:20,100 Andra Keay: that can be hard to get your hands on the sensors that you need at the moment. 263 00:31:21,180 --> 00:31:29,260 Andra Keay: That's quite common. We do a ton of projects, which people come to us with their adreno boards and say, okay, we've got this prototype roughed out. It works. The market likes it. 264 00:31:29,580 --> 00:31:38,660 Kevin Mako: This is great. But we know we need to now redesign it from the ground up. But generally, it's from a cost perspective, because those boards to produce, you 265 00:31:38,660 --> 00:31:46,880 Kevin Mako: know, in the thousands of units, especially, it's very expensive. You're buying essentially a whole computer when all you need is a small PCB with maybe a few inputs and outputs. 266 00:31:47,400 --> 00:31:54,780 Kevin Mako: So that's quite common. I still think, you know, there's a ton of value in getting to that point, but just understand that that last pillar is 267 00:31:54,780 --> 00:31:57,780 Kevin Mako: important. You do need to figure out now how to manufacture the thing. 268 00:31:58,460 --> 00:32:03,500 Kevin Mako: And don't think that once you've got to the end of prototyping and you've got market fit that your job is done. Because that's 269 00:32:03,500 --> 00:32:13,220 Kevin Mako: where you really need to either raise your next round, a much more significant round, to then do all the hardcore detail engineering to make a manufacturable product, something 270 00:32:13,220 --> 00:32:18,300 Kevin Mako: that both can be made in volume. Like you said, a lot of parts, especially right now with global supply change shortages, 271 00:32:18,800 --> 00:32:23,740 Kevin Mako: you have to be very cognizant of how you're designing something based on what you think you'll be able to order in volume. 272 00:32:24,600 --> 00:32:31,400 Kevin Mako: And then as well, making sure that the cost is reasonable. So you're not overcommitting to a product. You're not having a full computer in a product when you only need a 273 00:32:31,400 --> 00:32:38,680 Kevin Mako: small PCB, a $2 part as opposed to a $35 part. It's going to be very difficult. That's your entire margin baked into that product potentially, right? 274 00:32:38,820 --> 00:32:46,440 Kevin Mako: So definitely designing a Ford, but keep in mind that if you've done a good job on the first phases, you've designed it well, you've also got the funding 275 00:32:46,440 --> 00:32:51,720 Kevin Mako: and you've got some market fit, that should lead you to a very nice position to raise a very 276 00:32:52,360 --> 00:32:58,260 Kevin Mako: fair and proper funding round at that point to get you through that to, you know, through that 277 00:32:58,260 --> 00:33:07,020 Kevin Mako: final engineering design for manufacturing phase and your production, which is very expensive to set up, tool up, get moving smoothly, let 278 00:33:07,020 --> 00:33:09,140 Kevin Mako: alone making sure it's working well, defects, 279 00:33:10,340 --> 00:33:14,300 Kevin Mako: refunds, glitches, all that sort of stuff. All that has to be ironed through. All that costs time and 280 00:33:14,300 --> 00:33:20,600 Kevin Mako: money. But that's a really good position to then, you know, get it to market if you've done the first two legs of the stool well. 281 00:33:21,720 --> 00:33:30,040 Kevin Mako: Exactly. And there are so many traps within that commercialization stage, as you talked about, returns, for example. 282 00:33:30,660 --> 00:33:38,260 Andra Keay: You know, how often is that something that you factor in? And when you're doing something for the first time, how do you know how long it's going 283 00:33:38,260 --> 00:33:47,560 Andra Keay: to last, what its robustness is, what standards it's going to meet? I've seen some early stage companies, you know, again, primarily in robotics, 284 00:33:47,760 --> 00:33:55,960 Andra Keay: but I'm sure this happens everywhere, where they're still at the product market. stage but at the same time they have a unit on a treadmill or in 285 00:33:55,960 --> 00:34:02,570 Andra Keay: some other testing type facility and they're working on finding out where 286 00:34:02,570 --> 00:34:11,010 Andra Keay: the boundaries are for the technologies that are essentially part of what they're doing the other thing i will say is that we 287 00:34:11,010 --> 00:34:19,450 Kevin Mako: for quite a long time the theory has been that china is where you go to manufacture and it's 10 288 00:34:19,450 --> 00:34:27,110 Kevin Mako: times cheaper but in many cases that cases, swings and roundabouts, it actually 289 00:34:27,110 --> 00:34:36,550 Kevin Mako: costs you a lot more in terms of staff time away and the need for constantly being in attendance in those processes. 290 00:34:37,270 --> 00:34:46,870 Kevin Mako: And it's like your actual manufacturing cost might be lower, but the cost that is on your 291 00:34:46,870 --> 00:34:53,070 Kevin Mako: shoulders to deal with that and to deal with something that perhaps hasn't reached 292 00:34:53,090 --> 00:34:57,030 Kevin Mako: the quality standards that you needed has been mis-machined. 293 00:34:59,350 --> 00:35:09,230 Kevin Mako: Well, there's only three costs, I would say there's three costs in production, and people only look at one of them. One, there's your direct cost that's foreseen. 294 00:35:09,910 --> 00:35:17,440 Kevin Mako: Two, there's your direct cost that's unforeseen. And then three, there's your, there's your, or let's 295 00:35:17,440 --> 00:35:23,420 Kevin Mako: say three is really your unforeseen costs or your indirect costs that aren't. 296 00:35:23,440 --> 00:35:29,480 Kevin Mako: aren't exactly obvious in a direct, you know, dollars and cents basis. And between those three, 297 00:35:29,800 --> 00:35:33,940 Kevin Mako: people only look at the first, but you really need to factor in what is your overall cost of 298 00:35:33,940 --> 00:35:38,300 Kevin Mako: these three things combined when you're weighing your decision on manufacturing locally, 299 00:35:39,140 --> 00:35:42,420 Kevin Mako: but not just that, the types of manufacturing, the volumes you're looking at, 300 00:35:43,180 --> 00:35:49,540 Kevin Mako: international versus local versus different types of production methods. And then where you're actually trying to go with your business has a lot to do with it. 301 00:35:49,540 --> 00:35:57,420 Kevin Mako: Are you looking to generate interest to get an equity round, you might not need to sell many units. Or are you really trying to prove that you can build volume quickly? 302 00:35:57,840 --> 00:36:06,420 Kevin Mako: Well, then maybe it's a cash flow play. Depending on what you're trying to do with your business is going to very much make the manufacturing decisions more 303 00:36:06,420 --> 00:36:11,300 Kevin Mako: complicated. Now, what I'll say to kind of wrap it in a bow, the major silver line is all this 304 00:36:11,300 --> 00:36:19,880 Kevin Mako: and something psychologically you have to think about when developing a product is, yes, it's a bit of a marathon. But your comfort should be in the fact that if you go 305 00:36:19,880 --> 00:36:25,960 Kevin Mako: through all these things, and you figure out, you get your funding in order, you build the technology, you get the 306 00:36:25,960 --> 00:36:35,460 Kevin Mako: product market fit, you comb through it, and you fight through it, and you get your manufacturing going. Your company now is exponentially more valuable than it was before. 307 00:36:35,580 --> 00:36:41,940 Kevin Mako: You have really created something legendary, and I'm sure, Andrew, you're seeing it with a lot of companies, the valuations 308 00:36:41,940 --> 00:36:51,000 Kevin Mako: that they're getting bought out for, the big companies that are looking at these new emerging technologies, those valuations are only going up and up and up. And that is, 309 00:36:51,020 --> 00:36:57,280 Kevin Mako: really, you know, the end goal or potentially. I mean, you could build a business out of it or you could sell it outright. 310 00:36:57,780 --> 00:37:05,580 Kevin Mako: But the reality is, if you actually fight the fight to get through all these things, it can be a tremendous, it could be a lifelong value to you as an individual. 311 00:37:06,980 --> 00:37:14,860 Kevin Mako: Absolutely. And we are seeing the investment into these areas is on the increase. The valuation is on the increase. 312 00:37:15,500 --> 00:37:24,760 Kevin Mako: And I would argue that most deep tech companies, most hardware companies, most robotics companies, are still undervalued at the, 313 00:37:25,040 --> 00:37:27,880 Kevin Mako: say, series C and beyond round. 314 00:37:28,840 --> 00:37:36,360 Kevin Mako: People underestimate the power of spreading physical product in the world. 315 00:37:37,100 --> 00:37:47,060 Kevin Mako: We've become used to a kind of software valuation, which is a little bit more easy spread, but then also easily changed, whereas I think 316 00:37:47,060 --> 00:37:49,920 Kevin Mako: you create a more inherently lasting value. 317 00:37:50,540 --> 00:37:54,340 Narrator: Thanks for tuning in to this episode of the Product Startup Podcast, 318 00:37:55,020 --> 00:38:01,360 Narrator: the show that teaches you what it really takes to bring your product to market and turn it into a big success. 319 00:38:01,860 --> 00:38:10,420 Narrator: This podcast series is brought to you by Mako Design + Invent, the original and leading firm in North America to provide global caliber 320 00:38:10,420 --> 00:38:20,120 Narrator: in-to-in physical consumer product development to startups, inventors, and small product business clients. If you're looking for product development help on your invention, 321 00:38:20,120 --> 00:38:29,080 Narrator: head over to MakoDesign.com. That's M-A-K-O Design dot com for a free consultation from one of Mako-designs 322 00:38:29,080 --> 00:38:34,320 Narrator: for design studios from coast to coast. Thanks for listening and see you next time.