In this episode, we chat with Henry Chesbrough, who coined the term “open innovation.” He is the educational director of the Garwood Center for Corporate Innovation at Berkeley Haas. He earned his BA in economics from Yale University, an MBA from Stanford, and a Ph.D. in business ministry from Berkeley Haas. His research focuses on technology management and innovation strategy.
Henry talks a little bit about his background from Michigan to Yale, then Harvard to Haas.
He then explains the term “open innovation,” a distributed innovation process that involves flows of knowledge into and out of organizations. He shares the three cycles that can lead open innovation to closed innovation and its risks and limitations.
“Open innovation is an entry point into the domain of corporate innovation. The idea is that not all smart people work for you. In fact, most smart people work somewhere else. No matter how big you are, no matter how good you are, you can’t do it all alone. It’s better to be open, to collaborate, to share. It can involve bringing in knowledge from the outside for your innovation activities, the outside in, or allowing things you’re not using to go outside for others to use in their innovation. And that would be the inside out.”
“Corporate venture capital can be a very effective tool to innovate. You’ve got to still have a culture inside your own organization because once you find and acquire these things to bring them in, you got to keep the people, and you got to get them to do the new things that extend beyond what they’ve done before. And without that, you get the form but not the substance.”
“Open innovation can be a mechanism to allow you to be more agile, to move more quickly. And you don’t have to do it all yourself, or rather, you seek out collaborations with startups, universities, and other sources. As long as you can move relatively fast, you could get an edge in the marketplace.”
- Faculty Profile
- Garwood Center for Corporate Innovation
- Open Innovation Results: Going Beyond the Hype and Getting Down to Business
(Transcripts may contain a few typographical errors due to audio quality during the podcast recording.)
Sean: Welcome to the OneHaas alumni podcast. I’m your host, Sean Li. And today we’re joined by Dr. Henry Chesbrough who coined the term Open Innovation. He is the educational director of the Garwood Center for Corporate Innovation at Berkeley Haas. His research focuses on technology management and innovation strategy.
[00:00:27] He earned his BA in economics from Yale University, an MBA from Stanford, and a Ph.D. in Business Administration from our very own Berkeley Haas. Welcome to the podcast.
[00:00:38] Henry: Sean, thank you very much.
[00:00:40] Sean: Henry, if you don’t mind me calling you that.
[00:00:42] Henry: No, by all means.
[00:00:44] Sean: My dad’s a doctorate professor and just get ingrained in me when I meet his colleagues, especially being Chinese, to always call people by their honorific titles.
[00:00:55] Henry: Yes. Yes.
[00:00:57] Sean: But here at Haas, there’s so many doctorates and nobody wants to be called a doctor.
[00:01:02] Henry: It’s true that we do have so many on our faculty that it isn’t much of a distinction. And in Germany, there’s also a tradition of doctor, professor, you list all of the degrees before the person’s name. So, different cultures recognize higher education in different ways.
[00:01:24] Sean: Can you give us a bit of your background, you know, your origin story.
[00:01:29] Henry: Sure. Well, I guess I would say that I have spent most of my life in college towns. I grew up in Ann Arbor, Michigan where the University of Michigan is located, even has the same color scheme as Cal. And so, also an excellent public university. And college towns have been a wonderful place for me because they combine the intimacy of a smaller town and accessibility and ability to get around without too much traffic, too much hassle, with the intellectual stimulation of much larger locations. So, Ann Arbor growing up, Berkeley today, things from all over the world come through. And so, you can really see a lot and experience a lot. That’s, I guess, a good starting point for me.
[00:02:25] Then I went to college on the East coast at Yale, and I met my then-girlfriend, now wife at Yale, but she was from California. So, I followed her out to California and got an MBA from Stanford, worked for about 10 years in the computer industry, and spent most of those years at a hard disc drive company called Quantum.
[00:02:52] And I was initially in product marketing and eventually became the vice president of marketing for a subsidiary company of Quantum. And then, in the early nineties, the disc drive industry became a less exciting industry to be in. And so, I went back to get a Ph.D. from Cal and went to Harvard Business School for six years where I taught there, and then came back to Cal in 2003. And I’ve been here ever since.
[00:03:25] Sean: That’s amazing. You’ve really been bi-coastal.
[00:03:29] Henry: Well, started in the middle in Michigan and then went by coastal, as you say.
[00:03:33] Sean: Do you still consider yourself a Michigander? That’s my question.
[00:03:37] Henry: Very much so. My family is from there. I still have one of my three siblings living there. I go back at least once a year. So, that you can take the boy out of Michigan but you can’t take Michigan out of the boy.
[00:03:51] Sean: So, your wife is from California, which I think you made the wise decision to move here with her. What made you go back out East to Harvard?
[00:04:00] Henry: Let’s see, partly it was a professor from Stanford named Steven Wheelwright who I knew from Stanford, but he had, by this time, had gone back to Harvard. That was one factor. The second was, it was Harvard. They have an amazing set up there. They have lots of resources and it was a chance they call themselves the West Point of capitalism.
[00:04:32] Which is a little bit of hyperbole, but what it does capture is they take it very seriously. And so, it was a chance to learn teaching and case method teaching from the very best and to work with some wonderful colleagues, not least to them, Clay Christensen who died this past year, but was a colleague of mine for the six years I was there. And for two of those years, I taught Clay’s class with him. So, I got to know him and his work very well.
[00:05:07] Sean: Listeners who may have heard of Clay’s name but can’t remember what the book is. It’s the Innovator’s Dilemma. At least that’s the book I know.
[00:05:15] Henry: Yes, he’s had many but that is absolutely the one that stands out.
[00:05:20] Sean: Yeah. Do you mind me asking you what was your PhD study?
[00:05:24] Henry: Oh, yeah, sure. This is one I rarely get asked. So, it’s a pleasure to have a chance to talk about it. I had mentioned I had worked in the hard disk drive industry for a number of years before my Ph.D. And in the Innovator’s Dilemma, Clay Christiansen talks about disruptive technology in the hard disk drive industry. I knew that my own industry had become the subject of some academic study. So, when I read this, I thought this is really good stuff, but I had also, through Quantum, been working with a number of Japanese and European disk drive companies. And I knew that the situation there was different. And so, my dissertation looked at the displacement of incumbent firms in the US in the disc drive industry, but not in Japan. So, the industry went through all the same transitions that the US industry did, all the disruptive technologies, but the incumbent response and the displacement was completely different. So, that’s what my dissertation was about.
[00:06:36] Sean: After Harvard, what brought you back to Berkeley Haas?
[00:06:40] Henry: Two things. One was, I got kicked out of Harvard. I was not promoted to tenure. And so, it’s an up or out system. And if you don’t get promoted, you have to leave.
[00:06:55] Sean: Wow.
[00:06:55] Henry: So that was a motivator. I’ve gone to Cal and had good colleagues here, both academically and professionally. And through a colleague named David Teece, I was able to find a position that allowed me to come back to Cal.
[00:07:12] Sean: That’s amazing. Did you start working on open innovation, the term that you coined at Cal or before Cal?
[00:07:20] Henry: So, this worked out beautifully from Cal’s perspective. I did all the research while I was at Harvard and this involved a lot of fieldwork, going out every summer for typically a month and a half or two months to Silicon Valley. Spent a lot of time at the Palo Alto Research Center of Xerox PARC.
[00:07:42] And so, I did all this fieldwork in Harvard, paid for all of that. But when I was forced to leave, the book was published in 2003, the year that I made the move from Harvard to Cal. So, Cal got the glory, and Harvard got stuck with the bill.
[00:08:01] Sean: Can you talk to us about open innovation? Actually, before that, can you describe for our listeners what is the Garwood Center for Corporate Innovation at Berkeley Haas?
[00:08:24] Henry: Absolutely. So, Berkeley Haas has amazing programs through the Berkeley Haas Entrepreneurship Program. It used to be called the Lester Center, but now it’s been upgraded since then. But once these startups age out, they get older and they scale up and get bigger, they kind of outgrow the entrepreneurship part of our offerings. Last year in 2019, there was more than a hundred billion dollars invested in venture capital in startup companies in the US. But in that same year, there was nearly $400 billion spent on R&D by established companies in the US. So, the Garwood Center is for that second set of activities.
[00:09:19] Once you get older, once you get bigger, once you grow up, corporations have to innovate as well. And how they innovate and what works better or worse is the stuff that we look at.
[00:09:32] Sean: So, let’s jump right into it. What is the term open innovation? How do you define that?
[00:09:39] Henry: So open innovation is an entry point for us into the domain of corporate innovation. It’s not the only approach to corporate innovation but it’s, I think, a surprisingly popular one. The idea of open innovation is that not all the smart people work for you. In fact, most smart people work somewhere else.
[00:10:04] So, no matter how big you are, no matter how good you are, you can’t do it all alone. It’s better to be open, to collaborate, to share. And this can involve both bringing in knowledge from the outside for your innovation activities, the outside in. Or allowing things that you’re not using to go outside for others to use in their innovation.
[00:10:33] And that would be the inside out. And a more formal definition of open innovation would be a distributed innovation process that involves these flows of knowledge into and out of organizations. So, this is what we mean by open innovation.
[00:10:52] Sean: You had mentioned before that historically most innovation was done in a vertically integrated way. How does that differ from open innovation?
[00:11:03] Henry: Sure. So right now, as we talk, just today, the UK government has approved the first vaccines for COVID-19, which we’re all excited about.
[00:11:16] Henry: And the pharmaceutical industry is a classic example of an industry that has really undergone a transformation from closed to open. If we were looking at Pfizer’s pipeline of compounds 20 years ago, 9 out of 10 of those compounds would have been compounds that started in their laboratories, underwent the preclinical testing, the modeling, then animal tests, and then early human trials, and then later larger clinical trials, and then finally FDA approval and into the market. All of those steps would have been performed by Pfizer or all of the other big pharma companies inside their own four walls. Essentially it would be vertical integration from the lab all the way through to the market. Or if you like metaphors, it would be like running a marathon because it takes years to go through these steps and you do it all yourself.
[00:12:17] With the approval of the Pfizer vaccine that was done with a company called Moderna, and the pharma compounds of Pfizer and all of the other big pharma companies today, most of them are compounds that originated outside of their laboratories.
[00:12:36] Usually from an academic medical center, often passing through a specialty pharma company or a startup that one of the university professors at the academic medical center helped to start. And then it’s a licensed on to one of these big pharmas to go to market, like with Moderna and Pfizer. So, the marathon has been replaced by a relay race.
[00:13:03] And the baton is now passed from one runner who starts the race to another runner who maybe continues the race and advances it further. And then a further handoff to the big pharma company who takes it into the market manages the regulatory process and the clinical trials and collects the big money at the end.
[00:13:26] Sean: It’s interesting. You give us these metaphors because when I think about the cycles of industries, we actually had this conversation on a panel that I was a part of last week on media and entertainment, we commonly see a consolidation of industries and then 10 or 20 years later, they break up, right.
[00:13:50] We hear similar stories in technology as well. Internets, all these things came out of educational institutions, and government-backed research. That then that baton was passed on to private companies to take that technology further. However, in the past, I would say 10, maybe 20, or decades, things have consolidated to these behemoths, right. And then shapes, yeah, the FAANGs, right? It makes me wonder what point are we in the curve in terms of the direction of where innovation is?
[00:14:26] Are we in a state of business where there’s a lot of open innovation right now or have we’ve been stuck in a place without open innovation?
[00:14:35] Henry: So, Sean, I love your question. And it actually was a motivation for me to write a new book that just came out in January of this year, 2020. It’s called Open Innovation Results: Going Beyond the Hype and Getting Down to Business. And the book opens in chapter one with something I call the exponential paradox.
[00:15:03] And your question touches really well on that chapter because the exponential paradox starts with the observation that technology is advancing at an accelerating rate, whether it’s Moore’s law or Metcalf’s law, or these new social media dynamics, things are spreading further and faster than ever before.
[00:15:27] The length of time a company spends on the Fortune 500 is getting shorter and shorter. The lifespan of CEOs is getting shorter and shorter. Things seem to be more disruptive, more tumultuous. So, all this is this idea of exponential technology. And if you think about it, that should suggest that we would have really a bounty of useful technologies all around us.
[00:15:56] Therefore, we become more and more productive, more and more efficient and effective. And here’s where the paradox comes in. When I look at data on economic productivity and in particular, the growth of productivity in the economy, whether it’s in the US or the other G7 countries, or most of the 40 countries in the OECD, we don’t see an acceleration of productivity improvement. If anything, we see a slowing down of productivity growth, which is the opposite of what you would expect with exponential technology. So that’s actually the whole focus of the first chapter, very much along the lines of what you’re talking about.
[00:16:45] And so we’re having multiple ways you could explain this and I try to unpack this and try to come up with an explanation. It’s hard to design a test to fully identify and solve this. So, I don’t have what would be considered the smoking gun that would conclusively show this but there is a lot of evidence that I put in the chapter that shows a couple of things.
[00:17:14] One is that we are not investing as much in the underlying science as we used to at the federal government level, in the basic natural research system that we have. So, a lot of the productivity gains that we made after the second world war were fueled by sustained support for public scientific investment in research that’s slowing down now. We’re not doing as much of that.
[00:17:46] The second thing that’s happening is we’re seeing a growing gap between the best and the rest. So, you were talking about the behemoths, the FAANGs, and whether it’s artificial intelligence or some of these other data-intensive companies, there seemed to be the best are getting bigger and bigger, faster and faster.
[00:18:13] They perhaps are keeping up with exponential technology. But in the process, they’re pulling away from the rest. So, when we look at productivity data, we’re looking across the whole economy. We’re not simply looking at Apple or Amazon or Google. We’re looking at everybody, all the retailers, all the IT organizations everywhere.
[00:18:37] And on average, because of that growing gap, things are not keeping pace. So, I think these are the things that are driving this paradox that I’m seeing.
[00:18:50] Sean: As you’re talking about all this, I do see these trends, especially in the venture capital world, right, with more and more corporate VCs. And I imagine this is one way they’re practicing open innovation where they’re investing in much smaller ideas and companies outside of their business to see where they can gain inspiration and then ultimately swallow these businesses.
[00:19:17] I don’t think that’s the right word for it, but I guess, do you feel like the FAANGs are still innovating openly? Are they still practicing good open innovation? And I guess maybe with everything, is there such a thing as good open innovation and overdoing it?
[00:19:36] Henry: Yeah. Let’s start there then come back to the FAANGs. As I mentioned with the financial crisis, the one we had before in 2008, 2009, bad open innovation would be simply outsourcing, cutting all of your internal activity, and just going out and buying what you need when you need it. Now it’s not bad in a moral sense but it’s in a sense that you’re not investing in nurturing the innovation capabilities of your own organization and your internal technical staff if you’re always going out and making acquisitions, and really, cashing out those people, making them very wealthy. They usually don’t stay. So, you get the assets and you get the technology they’ve already developed but what you want is the next thing they’re going to do, the new ideas. And if you don’t have a culture that nurtures and sustains that, you can get the form but not the substance of the innovation when you do the M&A. So, I think this is something that companies really rightly pay attention to. Openness is a way of nurturing such a culture because it says that all the smart people work for you. So, there’s a certain humility toward the world and a certain curiosity toward the world. What is going on out there? What am I not seeing that I should be seeing? And what new things should we be paying attention to? What experiments should we be running? What experiments should we be observing that others are running?
[00:21:24] So, now when we come to corporate venture capital, you’re absolutely right that it’s becoming a larger portion of all of the venture capital and not just in the US. I’m told that in China, it’s even a higher percentage.
[00:21:34] So, corporate venture capital can be a very effective tool to innovate. With all the things I was saying before, you’ve got to still have a culture inside your own organization because once you find and acquire these things to bring them in, you got to keep the people and you got to get them to do the new things that extend beyond what they’ve done before. And without that, you get the form but not the substance.
[00:22:00] Sean: That makes a lot of sense. Yeah. You can’t just buy your way through life. That’s the most apt phrase for it. So, it’s so funny you bring up ’08, ’09, you know, I just recently read an article on Apple’s new chip.
[00:22:35] Henry: Oh, yeah. And Apple calls the chip the M1.
[00:22:38] Sean: And all the reviews are coming out and they’re saying, you know, this chip is, for the first time, it’s surpassing Intel’s processing capabilities at a fraction of the energy consumption, and all this innovation happened because Intel back in ’08 refused to put any money towards R&D for the iPhone. And they’re saying this was the biggest mistake that the CEO had made at the time because then Apple was forced to have to build their own chip, right, using ARM technology because with that little battery, you had to use ARM because ARM was so much more efficient but it wasn’t that powerful yet.
[00:23:25] And now over the span of a little over a decade, you have this chip that is more powerful and it’s amazing. But the same time, it’s not surprising how Intel somehow lost its way with innovation.
[00:23:38] Henry: We could add a couple of elements to it. One is the rise of TSMC, Taiwan Semiconductor Manufacturing Corporation, which has been a manufacturing rival to Intel for 20 years now. But in the last 10 years, Intel had a big lead in process technology and had a lot more capital.
[00:24:04] But over the last 10 years, TSMC who’s been providing semiconductor manufacturing to other companies for their designs. So again, a more open innovation process. Intel would only build Intel designs in their foundries. TSMC would build any design in their foundries. And in fact, did not make their own designs but only manufactured other people’s designs. In the last 10 years, because of the shift to mobile, the emphasis on longer battery life and lower heat, less power consumption.
[00:24:45] Intel’s chips were optimized for performance. But usually for a system that’s running off of AC power that’s running all the time. And Intel’s chips did a great job in that environment. But so much of the growth came in these other environments and Intel who was led at the time by Paul Otellini, who was a Cal alum, now deceased.
[00:25:08] But he was the CEO at the time who fumbled Intel’s future. They already had a relationship with Apple in the Mac products, but they missed the boat on the iPhone and left the door open for this massive result. And so, now we have these ARM architectures with companies like Qualcomm designing chips.
[00:25:31] We have Apple’s own internal chip group, designing chips, really for very targeted applications for things like the iPhone. And now they can really engineer the power they want, the performance they need and use the chip real estate on the chip to its maximum advantage. Whereas for Intel, they’re still designing their x86 chips primarily for the high-end servers.
[00:25:58] They’re still running Microsoft-based operating systems and Linux-based operating systems. And that’s also a very big, valuable market, but it’s not growing the way this mobile market is.
[00:26:10] Sean: As we’re having this conversation, going back to the whole cyclical patterns of industries, it makes me wonder, does open innovation ultimately lead to closed innovation to vertically integrated innovation?
[00:26:26] Henry: So, I think there are cycles. And there are times when I think if you’re a company you want to be more open, but there are other times where maybe you don’t. And if I look at a company like Google, most people think of Google as a very open company and they’ve done a lot of things to put a lot of code into the open domain. Hadoop would be a very good example of a program for very large dataset processing. Android was built off of open-source code as well. So, they’ve done a lot in the open-source world and they’re viewed as a good citizen in the open-source community for the most part. But if you look at some of the Google apps that they sell now, those apps all started as open-source projects.
[00:27:21] And then when they got to a certain level of development, Google forked those projects, abandoned the open versions, and focused only on their own forked proprietary versions. Now, why did they do this? I don’t think they were trying to shaft the open-source community. I think they were trying to keep up with the user experience that Apple provides. And there are times when if you’re doing development for Android, there are so many versions of the software running on so many different kinds of devices that as a developer, you don’t know what your users going to be seeing when they’re running your code.
[00:28:05] So it’s hard to develop a really good tight user experience in such an environment. You end up having to design for the lowest common denominator. Apple, by contrast, can actually force its users to upgrade. And when you upgrade, you’ll find the hardware that is like two, three, four years old, doesn’t work very well anymore.
[00:28:29] So, you get motivated to buy the new stuff. Which of course makes more money for Apple. And in return, they give you a much more tightly integrated user experience. And this, I think, has been one of the things driving Google to fork some of these projects and enclose them, so that they too can be a better user experience for their customers.
[00:28:50] So, it isn’t the case that being more open all the time always wins. But on the other hand, Android is selling a lot more units than iOS for Apple. And then we’re going to see something in China that will be neither Android nor iOS and Samsung has tried in South Korea to create its own operating system called Tizen.
[00:29:16] And Tizen hasn’t done so well so far, but the difference between Samsung and a Chinese version is China has, you know, 1.3 billion users in its market. In South Korea, I think is on the order of 60 million. So, maybe 5% of what’s available in China. So, that critical mass, I think, will support a third version of something and it probably won’t run either Android or iOS.
[00:29:43] Sean: So, in your research, I’m wondering if you’ve come across this where for companies that are now stuck in this tightly integrated, ultimate user experience mode where they think they know what’s best, but at a certain point what’s working is not going to work anymore.
[00:30:03] Henry: So, I think the good news for someone like Apple is that when you start to lose track of the customer, you can get yourself in a lot of trouble. And these, the FAANGs, large companies in general, what makes them large is they usually have done a very good job of serving one or more customer needs very well. And then they scale up.
[00:30:26] And in the process of scaling, they become more efficient. They also become less flexible, less adaptable. And when customer needs begin to evolve and change, these now scaled up, but more brittle processes that you’ve put in place may not flex in the right way with your customers. And I think this is one of the root causes for when companies like GE that were exemplars to us of good management practice for many years.
[00:31:01] And now, they’re being sold off for piece parts. I think part of the problem is they got very big, very successful, but lost the thread on what customers really needed and the processes that they scaled up to serve customers’ set of demands. When those demands change, these processes couldn’t shift with them.
[00:31:22] Sean: This is a really fun conversation.
[00:31:35] Henry: Great, Sean, I’m enjoying it too.
[00:31:37] Sean: I feel like I’m learning so much. The last question I had, prior to this interview talked a little bit about some of the risks and limitations. And even in this conversation we had, you know, you had mentioned some of the risks and limitations of open innovation. Can you share a little bit more about just where open innovation may not be most beneficial?
[00:31:58] Henry: Yeah, I’m going to come at this from two or three different angles. One angle is what does open innovation mean for smaller companies? And here, I think there’s good news and bad news. The good news is as a smaller company, hopefully, you’re more flexible. You’re more adaptable. And if you are paying attention to what’s going on outside of your organization, you may be able to see things that other companies don’t see, and or you may be able to act on that before these other larger companies are able to act on it. So open innovation can be a mechanism to allow you to be more agile, to move more quickly, and you don’t have to do it all yourself, or rather you seek out collaborations with startups, with universities, and other sources, and as long as you’re able to move relatively fast, you could really get an edge in the marketplace.
[00:33:00] But as a smaller company, you don’t have as much room for error. And you also run the risk that in managing these collaborations, you don’t have enough people or you don’t have the right people to manage these collaborations. Because these people are not your employees. They work with you, but they don’t work for you.
[00:33:23] And so you need a certain mindset, very much a win-win approach to it. And if you sense that your partner is not performing or worse is maybe saying one thing to you but doing another and you don’t trust them, then you’re really in a difficult spot. And in this respect, open innovation can go badly wrong. And by opening up and collaborating, you may have set yourself up for a very difficult experience. So, that’s one way things can go sideways.
[00:33:56] Another way it can go sideways, and now I’m back to a bigger company, is open innovation isn’t only about these inflows and outflows of knowledge across organizational boundaries. It also requires more sharing of knowledge within an organization. Because if you’re collaborating with a startup company in some new area that startup often needs a number of things from you for their activities and that draws from different parts of your organization.
[00:34:32] And if you have internal silos that you’ve created around functional groups so that the marketing people don’t talk to the salespeople and the engineering people don’t talk to the manufacturing people, these silos can really surface and become friction points when the external startup is trying to do things and get things done, and they need things from all of these parts of the company.
[00:34:58] But in these large companies, they don’t talk to each other very much and things don’t move very fast. So ironically, you can be too closed inside your organization, and that can get in the way of responding rapidly to the needs of your collaborating partners. So that’s the second way that things can go badly wrong.
[00:35:18] A third way things can go wrong is that your business model and my business model don’t really align with each other. We have a memorandum of understanding and we’ve agreed we’re going to do this project together. But if, what in order for your business model to work well for you that comes at the expense of my business model or vice versa.
[00:35:43] If that misalignment is there, that’s also likely to end in tears. An example from my own disc drive days, geez, the example itself is 35 years old, but I think it’s completely relevant to today. We were looking for a manufacturing partner in Japan for a new class of drives we had come up with, we were very excited about it, and they were really a step ahead and we knew that we couldn’t do the manufacturing at the scale we needed internally.
[00:36:12] So, we needed a strong manufacturing partner. And we went to Japan and had conversations with a number of companies, but in particular, focused on Matsushita and Kyocera. Matsushita today is called Panasonic. And both Kyocera and Matsushita had really strong manufacturing skills, but when we looked at their business models, Matsushita had been making video cassette recorders for other companies on a private label basis or an OEM basis for many years. And these relationships had endured for a long time. When we looked at Kyocera, their major businesses were all done under the Kyocera brand. They didn’t really have an internal OEM business that had lasted for any period of time.
[00:37:00] And that’s when we realized for Kyocera to win, they needed to be selling disk drive technologies under Kyocera brand. And that was going to come at our expense. So, we ended up picking Matsushita instead and that became a 15-year collaboration that worked quite well for both sides.
[00:37:20] Sean: This is just giving me flashbacks. For some reason, it reminds me of zip drives. The Iomega Zip Drives and whatever happened to that.
[00:37:30] But another area that you had mentioned before pertains to the costs of failure. Can you talk a little bit about that and how the cost of failure has this limitation on open innovation?
[00:37:42] Henry: In many of the parts of our lives, if something glitches, we have to reboot and it’s annoying. It takes a few minutes but then you’re back up and running and you really haven’t lost cost much at all. So, the cost of failure is some annoyance and not a lot more. A low cost of failure allows you to try a lot of things and experiment with a lot of things. By contrast, we began our conversation talking about vaccines. The cost of failure with a bad vaccine are people’s lives. And so, the cost of failure there is very high. And so, this is why we have the FDA to look at all the clinical data.
[00:38:29] Look at the specific cases where people got sick, especially if they got really sick. And try to understand what happened and why it happened. Are there any things that could be done to reduce or eliminate that from happening in the future? Because the cost of failure is high. So, before we shoot it out to the world, we’re going to go through a pretty careful process and that slows down the rate of innovation. But given the cost is much higher, I think socially it’s the right trade-off.
[00:39:01] Sean: Right.
[00:39:01] Henry: I think there’s an airport, I think it’s Heathrow in the UK, that has a shuttle that is autonomously operated already. But it’s in a confined area without general traffic. The rate of speed is fairly slow and because it’s a contained controlled environment and the speed is not so fast; the cost of failure is moderate.
[00:39:25] So, I think my prediction is we’re going to see autonomous vehicles in those situations first where you can manage and contain the cost and risk of failure. And that will, I think, penetrate early. And then from there, we’ll get better and the code will get better and it will gradually become more widespread. As it becomes more widespread, the use cases are going to be more diverse. There will be more surprises. There will be some failures. And the other thing that I think hasn’t really been sorted out yet is if an autonomous vehicle crashes and kills somebody, who do I sue? If I sue the driver, we have that today. We have auto insurance and there’s a whole process for that. And you don’t get a lot of money because the insurance company covers it, but they only covered it to a certain point. But if it’s Google’s Waymo technology and some enterprising attorney can show that there was a bug in the code itself that caused this, and worse, if the attorney can show that some managers in Google knew that there was this bug, but made the judgment that it wasn’t a big deal so they let it go forward. Now I can sue mother Google. I can come after all of that balance sheet. And so, I think that’s something else that’s going to impact autonomous vehicles.
[00:40:58] Not so much in the cases where the cost of failure is low, but when they do get into more widespread use, I think this will also be something that has to be kind of sorted out.
[00:41:09] Sean: It makes sense. Well, Dr. Chesbrough, that’s been a real pleasure. We will definitely include a link to your latest book in the description of this episode. I just want to thank you again for taking the time to come on the podcast with us today.
[00:41:21] Henry: Sean, it’s a pleasure to talk with you. And let me just conclude by saying, go bears.
[00:41:30] Sean: Thank you for tuning in to this episode of the OneHaas podcast. If you enjoyed our show today, please remember to hit that subscribe or follow button on your favorite podcast player. We’d also really appreciate it if you could give us a five-star rating and review. You can also check out more of our content on our website at Haaspodcasts.org where you can subscribe to our monthly newsletter and check out some of our other Berkeley Haas podcasts until next time. Go Bears.