As Dan said, I see the purpose of this session as being the connective tissue that takes this general discussion about institutions and links it to economic development. And so what are the institutional designs that create knowledge that can then be translated into economic activity and jobs? So there's a lot of discussion about job creating in the political environment right now. The question is, what are the organizational structures? What are the policy decisions that must precede that job creation, particularly when we're talking about innovation and entrepreneurship? And so to do that, we're going to have the this session structured in the following way. We'll have two presentations, and then we'll have a group discussion. So the first presentation I want to introduce Michael Lind, who is not only one of the co-founders of this New America foundation, but also the policy director of the Economic Growth Program here. He's written on numerous topics, but his most recent book gives him a great perspective to launch us into this discussion entitled Land of Promise, an Economic History of the United States. I've never known Michael to take on small questions in his writing, and he's true to form in this one. So we'll give Michael 15 minutes, and then I'll introduce our next presenter following. Also, I've been requested to remind people that if you were tweeting, don't forget to use the hashtag Save Research. Well, I was going to deliver this as a tweet, but I guess I have to use the whole 15 minutes now. Alfred North Whitehead, the philosopher, said that the great discovery of the 19th century was the invention of invention. And it seems kind of obvious, but the link between coming up with new ideas and new products as fairly recent development in history took place largely in Europe, not the United States, and particularly in the middle and late 19th century. If you look at the first industrial revolution based on the steam engine, that was the product of incremental work over generations. There was some input from the academic sector, for example, with James Watt. But the real link between science and technology on the one hand and innovation in engineering and commerce occurs with what historians call the second industrial revolution of the middle and the late 19th century based on, among other technologies, electric motors and electric batteries and the internal combustion engine. And there you see the beginning of a kind of circular flow between laboratories, universities, and groups of mechanics. So you get the beginning of the sort of innovation ecosystem. It's developed in something like its modern form, first in imperial Germany, which was an authoritarian state, but it was fairly enlightened in terms of research in higher education. Very few people have heard of Friedrich Althoff, who was Prussia's minister for education and who de facto made education policy for the Kaisers Germany in the late 19th, early 20th century. But a lot of our post-war American ideas about research universities, government-funded research institutes, were actually pioneered in imperial Germany through Kaiser Wilhelm Institutes and through research universities. If you look at the Anglo-American world, and at Britain in particular, well into the 20th century there was this arms length between the polytechnics, the red brick schools, which dealt with the actual real world of industry, and theoretical physics, theoretical chemistry. So the Germans sort of broke that barrier down. They inspired the conversion or formation of universities like MIT and Stanford into German-style research universities, self-consciously modeled on them. At the same time, within the United States, even before we had large-scale government-backed R&D, you had corporate research labs, where companies, monopolies and oligopolies that could take advantage of economies of scale, like AT&T, which was a legal monopoly, and later on Xerox and IBM, used their, frankly their monopoly rents to plow back some of their profits into R&D in Bell Labs and X-Park and some of these others. What we think of as the modern system of American R&D owes a lot to the thinking of Vannevar Bush, who was the former MIT dean, he was an engineer by background, early computer scientist, headed America's strategic research effort during World War II, and then prevailed with his vision of what post-war R&D should look like, and it was kind of a continuation of the World War II system, where you have government grants, particularly as it turned out in the Cold War from the Defense Department, going to major research universities. Very hierarchical, very elitist sort of approach based on basic research. There was an alternative at the time, championed by populists like the West Virginia representative Harley Kilgore, which wanted a different model of relationship between R&D and industry, similar to America's successful attempt to industrialize and upgrade agriculture. Going back to the 19th century, there were government-funded agricultural labs, as well as A&Ms, which agriculture and mechanical colleges funded by federal land grants. And this was a much more decentralized, kind of populist approach to innovation, which never really took off in the US, although in the last decades of the 20th century with technology extension programs of the federal government, something like this was created, but on a small scale. And just interestingly, the Germans looked at both of these options and took them. So on the one hand, the Germans in the late 40s and 50s established a kind of research infrastructure where they have basic research through the Max Planck Institutes and through the German Federal Research Council. At the same time, they have Fraunhofer societies, which play the role, they're 60 separate Fraunhofer institutes in Germany that in particular industries and fields, which do research for small and medium-sized businesses which can't afford to do it. So ironically, we Americans claim we're the great champions of small business and entrepreneurs, but actually this post-war German model allowed small businesses to enjoy some of the kind of R&D benefits, which in this country were largely limited to a very big businesses with big profits and big in-house development. So this kind of brings us up to the last generation where what some people call the Silicon Valley model of economic development based on technology is kind of the stereotype in the back of our minds and like all these stereotypes, it needs to be questioned a bit. And if you look at the history of information technology, there's a very clear kind of linear progression from government basic R&D in computer science, much of it funded by the Defense Department during the Cold War, to commercialization of hardware and software by Bill Gates and Steve Jobs and others. And now to apps, which is sort of what you do with the hardware and the software with things like Facebook. And that model worked brilliantly in IT. You can look at other technologies and I'll get into this in a minute, other industries where it was always inapplicable and it will be inapplicable in the future. So I just wanna warn you about the danger of extrapolating from a successful model in one particular sector to all the kinds of IT in all sectors and I'll return to that. So as long as we're on the present before we get to the future, here are the 2012 sources of R&D funding, 280 billion from industry, 126 billion from government and 31 billion from the academic non-profit sector. Now if you look at the actual performance of R&D who's doing it, you get a somewhat different proportion. 311 billion industry, only 29 billion government, 61 billion academy and then on top of that there's another 35 billion from non-profit, non-academic researchers. So what you see there is that the government's role in funding R&D is much more important than it is in actually performing it outside of a relatively limited number of government labs. You see another division of labor among government industry and the academy when it comes to a basic research, applied research and development. Government spends a 7%, this is of all total R&D funding. Government spends 7% of total R&D funding on basic. It's responsible for 8% of applied, mostly through tax credits and through development, 6%. Industries, you would expect, as overwhelmingly supplies its resources to applied R&D, 72% and development, 91% with only 20% going to basic. In the academy, it's pretty much the reverse of industry. Basic research is about 60%, much of that funded by the federal government as I pointed out. Applied research, a mere 13% of the total in development, a minuscule 1.5%. And that's not necessarily a bad thing. There's a certain logic to this division of labor where universities and government can do a lot of basic research and it sort of makes sense for government to do applied research. So sometimes, if it ain't broke, don't fix it. Now what are the challenges to this system we've inherited from the 20th century? Well, to begin with, there's the challenges to the Silicon Valley model. We simply don't want to over extrapolate from that in order to think that all industries are going to follow Moore's law in terms of getting more and more productive. There are areas like energy where that's not necessarily the case and people have fallen into the trap of thinking that you will have the same kind of curve in, say, solar panels that you had in transistors. It's just not the case.  Let me read you something from Michael Schellenberger and Ted Nordhaus from the Washington Post recently entitled, A Boomin Shale Gas Credit the Feds. What they point out is that the federal government had a much larger role in the funding of the shale gas technology than people realize. George Mitchell, the oil man who's associated with the breakthroughs, learned of shale's potential from the Eastern Gas Shales Project, a partnership beginning in 1976 between the energy department and dozens of companies and universities. His success depended on a revolution in monitoring and mapping technologies driven largely by government labs. The third critical technology was horizontal drilling and well installation and in addition, government tax credits for the oil and natural gas industry allowed him to finance a lot of this. So the shale industry is very interesting because everything economists will tell you about what will not work from picking winners, from subsidizing particular technologies, from having tax credits for big oil companies actually was responsible for the greatest, most transformative innovation of the last couple of years. Well, so just to wrap up real quickly, here are the challenges that this existing model faces. And we'll just begin with corporate R&D. I wouldn't write off these big monopolistic, oligopolistic companies too soon. Google, for example, manages to fund all sorts of innovative technologies including robot cars. But in general, it's the case that in more competitive markets, both nationally and globally, you're not going to have the kind of monopoly rents that say, belt AT&T had in the 20th century. So corporations are going to be forced to a crowdsource innovation, to have alliances. And a lot of it simply, the basic research may become even more and more and more of a responsibility of the nonprofit sector and of the government. The nonprofit sector could expand considerably in my opinion. In the United States, nonprofits are largely oriented towards a charity and towards a particular philanthropic endeavors. Their role in funding basic R&D and also in technology transfer conceivably could increase particularly since we're going to see an enormous transfer of intergenerational wealth from this generation's 1% to the next generation's 1%. And so some of that could go into R&D and not simply in terms of traditional philanthropic projects. Now when it comes to government, here's where there's room for the most creative thinking I believe. All of the industrial countries and many of the developing countries as a result of the aftermath of the Great Recession are going to be extremely constrained in terms of their budgets and discretionary spending which includes most spending for R&D is the first to go. That's the bad news. The good news is there's enormous amounts of capital, both private capital and sovereign wealth funds particularly from Petro States but in some cases from manufacturing export powers looking for safe long-term investments. Creative use of the bond market, it seems to me it's possible to tap into these enormous oceans of private capital and sovereign wealth capital in order to fund R&D to a greater extent to make up for the shortfall in a discretionary spending out of ordinary tax revenues. And this can be done in different ways. It can be done by municipal bonds. For example, in Maine they have R&D bonds that have been approved by initiatives. There was a stem cell research initiative in California. It can be done at the state level. One proposal that my colleagues and I have made in a background called public purpose finance. We look at the use of tapping bond funding for various purposes. If you have a national infrastructure bank, why not have a national R&D bank? It's essentially the same logic of tapping private capital for what really is like infrastructure, something that pays long-term dividends but the short-term costs are worth borrowing. So that's just the thought I want to leave you with. If we began in the 19th century where the relationship between innovation and products and advances in scientific technology and scientific knowledge was sort of an accident, it slowly become institutionalized. Maybe in the future we need to push that institutionalization one step further so that we think of innovation as simply infrastructure in the same way that we think about roads and electricity and water. Thank you. Thank you, Michael. So one of the, sorry, he did the numbers too quickly for me to do the multiplication, but if you took Michael's aggregate investment in research and then took the proportions that are going into applied R&D versus more basic science, it doesn't take a genius, admittedly somebody beyond my level, to realize that the amount of money that's gonna go into the basic research is gonna be very small relative to the amount that's gonna go into the applied R&D. And so then the question is, A, is that a problem? That is to say, what are the consequences of that distribution of investment? And B, if you wanted to change that equation, what would be the model? And Michael gave us some different historical patterns and some different contemporary patterns. What would be the model that would be the most effective mechanism? So one model that people are familiar with, we'll come back to the Bell Labs model with a panelist who's terrifically poised to address that, but one model is the government laboratory, which has proven effective and is in some sense one of the governmental mechanisms to get at this basic research question. And so we're lucky to have as our next speaker, Eric Isaacs, who's director of the Argonne National Laboratory, which focuses particularly on energy. Dr. Isaacs is also a professor of physics at the University of Chicago, and appropriately enough was previously with Bell Labs, where he was a leader on research at that institution, which will be a topic when we get to the conversation. So I'll turn the microphone over to Dr. Isaacs and get some insight into what exactly these national labs do. Thank you, thank you. Thanks. Thank you. So we're here today to discuss a couple of very important questions. How can we save America's knowledge enterprise from tight budgets, primitive myths, we've heard some primitive myths, and the shadow of Albert Einstein, and I'll get back to that in a minute. Is our knowledge enterprise complex capable of seeding a healthy and safe jobs creating industrial revolution of tomorrow? And I would say that I don't think this is an overstatement to say that the future of American economy rests on the answers to these questions. So it's very important that we discuss them, and I very much appreciate the foundation here for asking them today. Today our economic strength is powered by past discoveries. Some estimates 25 to 50% of the GDP today is based in technological discoveries over the last 50 years. We'll talk more about those. Our cutting edge technologies have roots and they do stretch back to before even World War II when the industrial revolution sort of hit this country in a big way. Today I would say also that the landscape in the U.S. has dramatically changed from what it was even 10 or 15 years ago. Most industrial labs, such as Bell Labs, such as IBM, have really by and large either totally deceased or they're dramatically reducing their investment in basic research. So funding from both private and public sector has decreased in the fundamental part of the research. We heard some figures already. I'll just give some really focusing more on the R part of the R&D. The AAAS shows that the federal R&D funding as a percentage of GDP has slipped from 1.3% in 76 to 1% today in spite of the flat overall relative to the budget, relative to the GDP, it's slipped. So that's our investment in the future. Between 95 and 2001 it's dropped even below 1%. So there are decreases in the R part of R&D which are critical. So in both public and private sectors there is a reduced willingness one could say. We can ask the question why in projects with long time horizons, the horizons that ultimately impact technologies that we utilize today. So these failure funding have resulted in some very large and troubling gaps in the innovation pipeline if you can call it pipeline. And it raises serious concerns about the future of American innovation. So to solve these problems we need to build a public support. And part of this discussion today is about thinking about how to develop not just the new technologies but really more about how do we get public support investment in basic research which is really the seed corn of future innovation. To do this we have to attack a few myths. And we've heard about those myths and I'm gonna go in a little bit more in depth here about them. The mythology of innovation. What really creates innovation? We just heard a little bit of a discussion about this sort of Silicon Valley model. The notion that somehow a brilliant inventor doesn't need much more than a garage, a sturdy workbench and a dream to change the world. So the myth that innovation genius burns brightest in isolation has a real impact on the way the nation views the importance of knowledge enterprise, of our enterprise in particular. The scientific infrastructure, for example, that supports it is very expensive. It's very engaging, it's very complex. And so the idea of a single inventor in a laboratory is a great idea but it doesn't necessarily hold true to the ultimate innovation enterprise. So the critical need is for all involved in this knowledge enterprises. We're calling it here today to work together to demonstrate the value of scientific innovation, scientific facilities and the infrastructure that we heard about. We heard a little bit this morning about the articulation of scientists for themselves and I'll get into that as to how we've articulated for ourselves in a way which may not be as strong as it could be. And this won't be easy because our notion of the inventor of the innovator today from Thomas Edison to the Iron Man in movie this past weekend, our inventor heroes have been popularly viewed as single combat warriors working feverishly in the threadbare den of solitude. In the Iron Man's case it wasn't quite so threadbare but partly that mythology is due to public relations efforts of ourselves, scientists.  One is our favorite which is Thomas Edison. So let me give you a few examples of his innovation, the light bulb. In 14 month quest to develop commercially practical light bulb, he wrote in quote, I tested fewer than 6,000 vegetable, no fewer than 6,000 vegetable growths and ransacked the world for the most suitable filament material. It really is awe-inspiring to think about this guy Edison sitting alone at his work bench in Menlo Park going through thousands and thousands of fibers to find that one fiber that ultimately resulted in what changed the world, providing electrical light in your house. But I should also say that it's patently untrue. So it was self-promoting but it was untrue. And in fact Edison was really leading, a lot like myself, one of the world's first large scale research labs. He was highly organized and at the time of the bulb he actually had about 40 or 50 scientists and engineers working with him in Menlo Park and they worked hard and furiously for a single mission-driven goal. So after the light bulb proved successful, Edison went on to build an even larger invention factory called the Invention Factory in West Orange and it goes on, the complex included up to 200 scientists. So Edison's self myth was not entirely true. And I wanted to read a couple quotes from 1931 from the New York Times, which once he passed away, which mourned him and said, no figure so completely satisfied the popular conception of what an inventor should be. Here was a solitary genius revolutionizing the world, a genius that conquered conservatism, garland cities in light and created wonders that transcended the predictions of utopian poets. With him passes perhaps the last of the heroic inventors in the greatest of the line. The future probably belonged to the corporate research laboratory with trained engineers directed by a scientific captain. So it's a great irony because away from reporters of course, Thomas Edison was nothing more than a director of a great laboratory. He basically put together a large group of world-class people, engineers and scientists to make something happen, something important that transformed the way we live. So more modern example of course is Hewlett-Packard. I'll do this quickly. And they of course, William Hewlett and David Packard worked together in 1938 in their garage. Now that garage is memorialized. You can go visit it. I've seen it in Palo Alto declaring the birthplace of Silicon Valley. We just heard about Silicon Valley. It is true that Hewlett-Packard were alone inventors of what they first did, which was a radio oscillator. No need to understand the details inside this garage, but the prototype was built not in their garage. It was built at a very nice, high quality Stanford laboratory. And then prototypes were built later on with an engineer and entrepreneur named Charles Litton. So again, these two great inventors, singular or two great people, ended up having to invent in an environment which was much bigger than the one that they started in their individual garage. So I find it interesting but sometimes troubling that people considered an attack or insult to say, and my colleagues at the university, for example, often find it that these great inventors worked in a well-quipped labs. They worked in much bigger environments with much bigger ecosystems. Their inventors were built on research and ideas of other great scientists, not just their own. So clearly as director myself of a multi-purpose lab, I may be biased on this issue, but I really can't understand why people would not think that this is the right way to do invention. And so that's sort of the theme of what I'm gonna tell you a little bit more about. So this kind of myth, if you will, of the single inventor in a garage is doing everything is really a myth and it can't in the end really make a difference. Today, just to change the subject a little, to me, American scientists and engineers are facing a number of the biggest, most, I'd say even exciting questions that have a lasting impact on our economy and will, on our economy and environment for many years to come. For example, how can we create a solar cell that costs five cents per kilowatt hour, no subsidies to compete with what we have now when we plug into the wall and produce power from coal? How can we reduce the cost of a car battery to one cent per mile, about a factor of 100 away? How can we cost effectively capture the excess carbon and atmosphere and sequester it for thousands of years to come? So I would say that we don't know the answers to these questions, but the answers will come from scientists not working in isolation but working in these types of groups. So the most promising way, as I said, to find those answers is by putting world-class researchers in world-class labs, not just my lab, but many labs across the nation. They inspire them with a sense of urgent mission, so that's a little different between a laboratory and a university laboratory in a mission-driven focus like at a Department of Energy lab like my own. Organize them into dream teams where they can communicate routinely and combine their ideas, both engineers, scientists, and even venture capitalists, to come up with the smartest, most practical solutions to these challenges. So in past years, as I've already mentioned, these types of mission-focused teams of experts could be found in America's renowned corporate laboratories, IBM, Xerox PARC, AT&T Bell Laboratories, organizations that were really designed to turn great scientific discoveries into commercially viable inventions that take technologies like the transistor. As you already heard, I began my career in 1988 at Bell Laboratories, so I am biased, no question. I was drawn there by the reputation not just for great science and churning out Nobel Prizes, but from the fact that that place had an environment, an ecosystem where you could take science if you had a great idea and find out if it might work embedded in a communication system, find out if it might have a bigger impact on society. I'm sure we'll hear more in a few minutes from John Gertner who is an expert, probably more than myself, even though I spent 15 years there on Bell Labs, having written the book on Bell Labs, about how this whole place became a magnet for the best and brightest minds in the world and how that was a mission-driven environment to drive this stuff. So I won't spend much time, I just wanna mention a few things that I was very proud of when I was there, the things that happened there, just to give you an example of why that place was so great. The laser is one example. The laser was invented in a lab by Shaloh in towns, towns at Columbia, Shaloh at Bell Laboratories. You can't tell me that that hasn't had a huge impact. The transistor, of course, the famous three, Bardin, Britann, and Shockley invented the transistor there. The transistor was invented only out of basic knowledge of condensed matter physics, but it was there to solve a very simple problem, that of the tube, a terrible thing if you had to run a radio on a navel vessel in 1947. It was not even in 1943. So they had a very, very focused mission, but they also did amazing, amazing science. I could go on and on. Information theory was essentially invented at Bell Labs by Claude Shannon, C-language by Kernighan and Richie, many of you have probably seen their book in on and on. But the point was that you put great scientists in great facilities next to other great people, a place like Bell Labs, a place like Oregon, a place like even Stanford today can spur great innovation that can create the jobs that we're talking about today. The problem is that corporations are not willing to do this anymore. Corporations have just not, because of we heard the quarterly reports, they're just not willing to invest in the long term. They'll invest in short term and they should be focused on short term, but they're not willing to invest in the long term. So here, ultimately, is the trillion dollar question. Who, if the industry can't do it, who is gonna focus? Who in the United States in particular will provide the investment in world-class researchers, the facilities that make the future of fundamental discoveries that I've talked about, turn them into something which ultimately spurs new industries and new jobs? Okay, that's happening in Silicon Valley today in digital media, but it is not happening the same way in areas like the transistor, what I call hardware and energy and on and on and on. So it's an enormous responsibility for the government if the government has to do it and I guess we'll have a chance to talk about that. And it requires a lot of things to happen. We've heard some proposals already as to how to garner some of the money, but let me just say about more than the money, it's really a little bit more than the money, it's a lot more than the money, it's an ecosystem that we have to build and it's not just investing money, it's putting it in the right places to spur an ecosystem. It requires armies of highly intelligent, highly educated, and I know this is even under debate today, with people with deep curiosity, strong work ethics and a real unflagging persistence and a drive to try to make something change the world. So that's very important. It's a little bit different than maybe an academic researcher. Requires a new open approach to collaboration teamwork, but not only teamwork, we hear teamwork all the time. When you start putting entities like government labs, universities and industry together, you have to solve and divide as new R&D model because it really hasn't worked yet in depth. You also need a new IP model and we haven't figured that one out yet. We're just starting to figure the IP model. Requires a critical mass of state of the art laboratories and instruments because as I've already said, a lot of the stuff all the way back from the original HP oscillators were not possible in a garage. You needed state of the art equipment that was available elsewhere. And requires getting back to the funding issue. You can't do this without the funding and part of it is funding to put the researchers, we heard a little bit about getting research to do the right thing to move toward the cats, if you will, herding cats. You need to put the cat food in the right corner to make sure the researchers are following the mission, the clever mission that we think we can devise. But most importantly, it requires the reality-based understanding of most breakthrough innovations don't occur by single individual investigators. Now, individual investigators are critical, very important for solving this problem, but taking that up since we're talking about the innovation component of this, you really need to have these kinds of teams. So as a scientist myself, I've already said this, I strongly believe in the transformative idea, the first idea that came from Albert Einstein. Today we use gravitation to determine, for example, the amount of ice in the ice caps by using basically by using the forces of gravity and very sensitive detectors. We had to understand that from one person, but to actually turn that into a usable technology takes a much bigger group. So the lone inventor burning midnight oil cannot have the global impact without a multidisciplinary team, without brilliant experts working to develop the idea that came out of that original person's brain, with a system designed to maximize discovery and access to the best tool on earth. And the tools I talk a lot about and when I speak are supercomputers, which help accelerate discovery dramatically, synchrotrons, which help you discover new materials, proteins and drugs that the pharmaceutical industry uses, and other dazzling types of technologies that we see in the universities. We see them at government labs, but we also see them in industry. So the romantic myths that about creative loaners can't be allowed to overshadow what we know to be the truth. It's a big enterprise. It's a big collective coherent enterprise that we're talking about. And one that I will say isn't fully established now in this country, we need to establish it. Our knowledge enterprise depends on work of these very robust teams. And it is true that we need to find new ways of approaching the urgent questions of energy, environment and security based around the scientific discoveries. So I just want to finish by saying the work we do in the national labs, I'm director of a national lab promises to dramatically accelerate discovery by bringing together the new materials, the technologies and the processes, but also the people, the key people ultimately that will power the creation of the next generation, new industries and the expansion of the American economy. Thank you. So what we're going to do now, we're going to use those two excellent table setting talks to pivot to a discussion. And I want to bring into the discussion the third member of the panel that was just alluded to, John Gertner is an editor of Fast Company magazine and a contributing writer at the New York Times Magazine and recently the author of the Idea Factory, Bell Labs and the Great Age of American Innovation. Interesting, I think that this book has captured so much attention, I think Michael, has captured so much attention because I think it speaks to, A, I think it speaks to this desire or interest in heroes as Dr. Isaacs was talking about and indeed the book talks about Bell Labs but it also talks about some of the heroic, the sort of heroic individuals and innovators that come out of Bell Labs, but it also begs the question of what do we need to do to create such an institutional framework? I just want to call out one theme that came up in both the previous remarks because I think it's ultimately critical, which is what is the return on investment in scientific research? And so what do we mean by that and to whom is that return going? So when we talk about the return on investment to corporate R&D, I think that the question is how does this affect the bottom line? One of the reasons that that investment has changed is different expectations about when that return is realized. The investment of government dollars in R&D speaks to our belief that we collectively benefit from this investment and so it's a different sort of return but I think if we think about the shifting landscape of investment in research, it's really about different expectations of return on investment and where it should be coming from. I think it's noteworthy and this is brought up in John Gertner's book and I think it's well known that for all of those innovations, most of the profits were not realized by Bell, right? They were realized elsewhere, right? The transistor made a lot of money for a lot of people but Bell was not for the most part the primary beneficiary of that. That was permissible in part because as Michael said, they were investing these monopoly rents to create this public good, not unlike a national laboratory model. Do we have an analogous, do we have an analogous sort of expectation for that translation as well? One point I wanted to also, just before I turn it over to John, I think to get your reflections on these remarks. I kept thinking of reading this terrific book which talks about some of these amazing individuals and the work they did. I kept thinking about a great piece by Tom Wolf about Robert Noyce where he makes I think very poetically the same point which is Robert Noyce only existed because of this other work that had been done on the transistor and these threads of development just happened to come together that he happened to have a connection to Bardeen and it was sort of a quirky thing that he was in Iowa and so on. So I think that there is this undercurrent of understanding even as we have this sort of hero worship in scientific innovation. But anyway, I wanna turn it over to you John Gertner to sort of reflect on where you see the Bell Labs story and the lessons fitting into this conversation. Sure, is this working? No, it sounds like it, okay there. A couple of thoughts, I didn't prepare any set remarks so I thought I'd jump in based on questions but the theme of sort of interdisciplinary work or ecosystems obviously kept coming up again and again and just a couple of points. I mean in writing about Bell Labs there is understandably a lot of focus on research those sort of breakthrough moments of the transistor, the laser, Claude Shannon and digital communications who somewhat uniquely was a bit of a loner but as I sort of spent all these years researching this book I gained a deeper and deeper appreciation for the ecosystem and for the development process for just how many years it took for failure upon failure in creating and in the manufacturability of these products and understanding how to make them in mass, how to make them economical. They didn't have to be too economical for the phone system if being a monopoly they could make certain concessions that were not necessarily because they were not facing other kinds of competition. But as much as we rightly focus on those great research innovators there are for instance other archetypes in my book for instance Claude Shannon often had breakfast with a guy named Harry Nyquist and this is interesting. I mean there are people in that ecosystem who were I guess what I'd call instigators in some ways and I think when you have this sort of larger group of people you might have expert managers, you might have the brilliant physicists and then you have people like Nyquist. Now Nyquist was a communications engineer. In the 50s the legal department of Bell Labs wanted to understand why are some people at Bell Labs more productive than others? Why do some have more patents than others? And the only thread they could actually discern was that the people with the most patents actually had breakfast or lunch with Nyquist. And Nyquist didn't give them ideas. He never told people what to do. He just asked questions and he didn't take credit. I mean he has a fair amount of fame if you look him up. I mean he was a very accomplished engineer in his own right and Shannon, Nyquist too. And these other people who were part of that ecosystem who were incredibly vital to the sort of functioning of that innovation machine that was Bell Labs, John Pierce is another person. Not somebody who necessarily stuck with a project.  But these were parts of that ecosystem and manufacturing was too, which I think was crucial too. I've said a number of times over the last couple months and I've done a fair amount of reporting for the times as well on say the lithium ion battery factories that they're trying to set up in Michigan now with DOE money. But the importance at least for Bell Labs, for manufacturing, I mean when you're making something and that was Western Electric, which was the manufacturing arm of the phone company in that era, but when you're making something you're always trying to think about how to make it better. And there were these sort of virtuous feedbacks I think that filtered back to the engineers and sometimes the applied scientists at Bell Labs. And I think that was a very sort of fruitful exchange and Bell Labs in fact had small laboratories that they set up at Western Electric factories for the technology transfer. And when we talked about that old model of basic research to applied research to development, to manufacturing, in some ways I think Bell Labs has been misunderstood as sort of hewing close to that model in truth to think if you look at what actually happened there, there were by design exchanges of knowledge going in all different directions. And I think that's part of the answer would why it works so well. Can I comment on that? So I'm living proof of what you just said since I was at Bell Labs for 15 years, I didn't invent the transistor nor did I win a Nobel Prize, but the thing about Bell Labs, which I'd just like to emphasize, manufacturing was important, but the key I think that made it so successful was that it had a mission. So they didn't actually manufacture just anything, they had a focus on telecommunications. And what did that mean? That meant that an invention, even if it were fundamental physics destined for a Nobel Prize, would ultimately be decided and pared down and essentially compared to something that would have the big impact. So you had a group of people, and this gets back to kind of what I was saying. Before we had a group of people, it had to be a large critical mass, as you said. There had to be leaders, there had to be people who weren't necessarily the Phil Anderson's and the Claude Shannon's, but they had a focus in the end. And so that made a huge difference because then people, and the great example I like to give is the charge couple device, which was invented by two guys in a lab. It's now changed the world. Everybody in this room and probably listening to us today has one or two or three or four charge couple devices in a camera. It changed the world. It changed the way we see the world. We can do telecommunications and teleconferencing because of it. It also won a Nobel Prize. It won a Nobel Prize in 2009, but it started with two guys working in a laboratory trying to digitize photons, which ultimately is the way we communicate today. You needed that device. So it was mission driven. It was great science. And so I'd get back to that point is that mission is all, I think, very important. It doesn't obviate winning Nobel Prizes, but it also enables a more focused way of thinking about things. Sure. But it's like the mission was never, let's create jobs. And so I wonder, even when we have that in the title of the session, it's about job creation. So that wasn't the mission. The mission was to solve problems, do something about mobile telephones. It does ultimately lead to jobs. Is it self-defeating to start off the enterprise by saying, today we're gonna create jobs? Do you have to have a different pitch, Michael? Well, the purpose of innovation is to increase productivity. Productivity creates jobs in new sectors while destroying them in old sectors. And the sectors in which the jobs are destroyed are often the ones in which the greatest technologically driven productivity is taking place, not always, but that's frequently the case. So as agriculture becomes more productive, you can grow more food with fewer people. So labor market shifts to manufacturing. Manufacturing in the US, I think, is artificially low largely because of mercantilist policies of foreign rivals. It could actually be, we could have higher manufacturing employment. But nevertheless, in the long run, you're going to shed labor at least at upfront production jobs. And then you'll get more employment in upstream inputs, producer service inputs, R&D, business services going into manufacturing. But also a lot of labor will simply go into, say, personal services, right? So I think that you don't want to say, we're going to create green jobs by funding green energy. Or we're going to create more highway jobs by coming up with robo cars and smart highways. You're going to create a much more efficient economy and reduce costs, but the actual ultimate mix, you know, you'll be better off, but not necessarily in that particular sector. But wouldn't you say that the different sectors are different? I mean, in the energy sector, it's quite different from the telecommunication sector. That's right. Because the energy sector doesn't have necessarily a natural customer for, so you could say just let government do the basic science and then let, you know, industry pick up the great innovations. But in fact, industry in the energy sector is not really there at this moment to pick up all those. Well, that's why I picked the shale gas example as an alternative to the silk. And that may be one place where you could. But in most places in the energy sector, you don't have the natural, you know, the natural embedded technology sector that's ready to pick up on these things. So what's the, so because I think we all want to say, well, what should we do? I think that's our natural inclination. What's the decision rule that then can guide us to say, and this comes back to the sort of investment. So if I'm the public sector or I'm the nonprofit sector saying, where do we steer our investment in this research where private sector simply won't do it itself, right? Or the landscape or the ecosystem isn't right, right? Because I think in some sense we want to figure out what's the sort of best collective return on investment we can get. How do I translate that observation about one sector or another into a decision rule about where we make the investment in research, where we drive institutional change to get the kind of outcomes we want? Before I jump into that, I just maybe should add that historically if you look at all these people in my book and then sort of the transistor and the laser and these inventions we're talking about on communication satellites, I mean it's really never even on the minds of these people of creating jobs or even industries at the time. So I mean in terms of purpose-driven innovation at Bell Labs, I think it was, or not specific purpose, it was mission-driven I think to use Derek's term that that was far more fruitful as long as they were sort of operating within the bounds of this might have some fundamental knowledge that will be useful for human communications. The mission was broad but significant and it informed the work they did but it did not inform the sort of purpose and particular experiments. I may defer to these guys on the policy questions of today. I'll avoid policy questions but maybe say a few things that seem to be starting to work and I do think that it is show me the money because it's the cat food argument that scientists are great, they'll follow their own nose unless they're guided a little bit in the way they want to go and it's not to say we shouldn't have basic science, we need to have a lot of basic science, it is the seed corn but one opportunity is to be a little more strategic about where we place our money from a federal point of view. The DOE is doing a little of this with these energy innovation hubs saying here's a big problem, they're not saying here's how you're gonna solve it, here's a big problem. How do we convert sunlight, CO2 and H2O to fuels, to sugars, to ethanol, how do you do that? You can't, I can't answer that question today. How do you create the 1 cent per mile battery? Those big questions or we know they have to be answered, we just don't know how to answer them. If you put the seed corn in the right places to encourage great fundamental thinkers to work side by side with engineers to work side by side even with the businesses that are ultimately gonna adopt these things, that's a good idea. It is a public-private partnership and I think the more, if you look at China, you can say they're being strategic, you can say they're not. A lot of strategy can be corrected by just throwing lots of money at it and China they're throwing a lot of money at the R&D and the D and the D, the deployment part across the board. It's hard to distinguish even the universities from industry sometimes but they are putting money in very clear areas. They're saying we're gonna invest in solar energy, we've heard this, our country is now complaining about unfair trade practices. You can probably speak more clearly to this but nonetheless they're throwing a lot of money and I see my colleagues in universities getting a lot of that money but at least it has a focus. It's money with a focus. In this country, we tend not to do that as much. We're starting to do it a little more and NIH actually has done it by picking diseases and throwing money at diseases. In the technology sector we haven't because we've been carried by big companies like IBM and Dow and DuPont. We're no longer getting carried by those companies so in my opinion the way to do it is in a public-private partnership where you set up. I would also go back, one more point I'd like to make and then I'm happy to turn the mic over is this idea of telecollaboration which I think is great. It's very contemporary, pushing apps and getting out there and talking to your friends is great. I still think there's infinite value in having people sit down together over a cup of coffee because most of the ideas that you talk about in your book, anyone talks about occur because a couple of people, one person sitting over here who's a physicist and a person over here who's an engineer, take Edison's lab, it had both kinds of people are sitting in the same room, not even knowing what to talk about and they come up with some of these hair-brained ideas that make a difference. So I do love telecollaborating, I'm a big fan of it but I also think you really have to put people under one roof. I think it's very important. So how do you do that? I mean the question is, right? So at universities you have to create more ability to put these types of people together. Universities have everybody, they've got everyone from policy to engineers to scientists but we don't yet put the money yet in a place where we encourage faculty to go after these things. At government labs we're just starting to really think in a big way about solving, take a big problem and just put some money in it. So I think that's the part of it. Well I think, I agree with this, term ecosystem keeps coming up and it's a cliche but that's because it's such a useful concept. In the 1990s there was this orthodoxy that the United States could invent things and then they would all be produced somewhere else. And I think there's growing recognition that the R&D ecosystem, if not identical with the manufacturing ecosystem at least is interrelated with it and there's exchange and in a world where we actually are not a borderless world where there's real home country bias that makes a difference. We had an interesting, real historical experiment. It's very seldom in social science that you can do these experiments between the United States in the first half of the 20th century and Britain in the first half of the 20th century. Britain, of course, led the world in the industrial revolution, made many scientific breakthroughs including a lot of the early work on computers, nuclear energy, television, radio, radar. They did not have the infrastructure of the mechanics, like the Yankee mechanics that we had here in the United States. As I said, they had a very snobbish distinction between their red brick polytechnic universities and their elite universities. The United States up until World War II was largely an importer of technology but we had all of these mechanics in Detroit which started off making bicycles and then they made automobiles because the bicycle skills could be transferred and these guys read scientific papers and they published them in journals and they reverse engineered things. And we found it was much easier for the United States to grow an R&D sector where you had this pre-existing industrial commons other than it has been for a deindustrialized Britain to maintain its lead in a lot of this cutting edge research. Scale has something to do with it but a lot of it has to do with this interaction between production and R&D. Let me push back a little on the university point and then I want to open the session up for other folks to join in. You mentioned, you said in some cases it's hard to distinguish universities from industry because of that collaboration in China you were talking about how they're so interwoven and suggested that you could have more of the same here. There are many people who are alarmed already at the commercial sponsorship of research on campuses precisely because it makes it more focused, it makes it more D less R to use your differentiation. If the university we think of as being the place where you can do the basic research, the stuff that undergirds the application later on and we sort of invite industry to direct that research in very sort of specific ways, isn't there a danger that you thin further the pool of people who are doing the basic research which does make possible the subsequent applications both at the high end and at that sort of tinkerer level, the person who takes a transistor and says, oh, what can I do with that? I mean, isn't there a danger that that trend is exactly what's wrong with the R&D ecosystem today? I agree with that. You have to still maintain the long-term 2030-50 even unfettered blue sky research because that is the seed corn. A lot of the things we invented 50 years ago we didn't know would get into our pocket in a camera, understanding a silicon who cared about that right on the 30s and 40s. So I think it's very, to your point, very important but I would turn it around the other way which is because industry isn't doing it, who will do it? If you don't have the brightest minds at some level, and by the way, if you go to a university professor today, the best university professors whether University of Chicago, Berkeley, Stanford are interpreters of their own. Even you take the basic scientists, they're doing basic science, but I'll bet they have one or two projects on this side. They're working with Hewlett-Packard. They're getting money from Dow. You talked to the best scientist, so it's happening. People can do that. People can do that kind of parsing. But is the myth of the research scientists who's just there exploring the properties of Germanium, is that as much a myth? No, I mean, there's still people who need to be there doing that. I'm not saying they don't and that's purely federally funded. And I would say on the other side, there's stuff that has to be purely industrially funded because it has no interest to the basic science. But there needs to be more of minds coming together where you do have, otherwise, so today the model is, if you go to Stanford, Stanford less so, but in other places, the model is you get an idea, you patent it, and you may throw it over the fence and hope that a company picks it up, maybe Cisco will buy it, but even before that happens, it'll be a small startup and then the professor will make a little money for a while. And that's really a nice model. But that model doesn't offer the feedback. I mean, I think the attempt frequency, it's much less efficient, I would say. So I actually have that inventor invent something in IP and then talk to the company and work with the company is a much more effective way to do it. I don't think that in any way denigrates the basic science that that individual or somebody else in that same department can pursue at all. So I think you can have both and I think you need to have both. I wanna give other people an opportunity. There's a couple hands. Oh, Steve Wenders, local researcher. I went to a talk by the chief scientist of Intel last year. I mean, he was discussing how Intel is putting serious money into this attempt to nurture a culture of innovation in various parts of the country. Now they're working with the idea of trying to develop university consortias in various parts. But I asked him, I said, don't we already have a model here with DARPA when you think of so many things that came out of DARPA? And he said, actually, I think DARPA lost the ball. He said, I think there was a golden age of DARPA back 20 years ago and they just lost their way. So one question is, even if you do have the model that it works, how do you keep it working?  And then the second comment I wanted to make is in terms of the national labs, I have read this argument, for example, that if you take something like the Santa Fe Institute, relatively small thing, but they benefit greatly from people starting getting in their cars and coming over from the national labs and sitting around in a more atmosphere where they can talk about general issues and that that's where you're really getting something like this Nyquist effect where freed from their particular goal-oriented daytime work and with their collared down, they come up with some really fundamental ideas that have come out of that area. So could you comment on that? I'm not sure which one to mention first, but I do think that, yeah, certainly unfettered thinking is important, but even in a place like Santa Fe where Los Alamos, certainly Los Alamos has a mission, right? And that mission is still, they do basic science in the service of the mission. So when I say mission, it doesn't preclude you from thinking 20, 30 years out. So at Los Alamos, you may be thinking about national security and weapons stewardship and thinking about the materials in a bomb and thinking about, but you could also then start thinking about defects and solids and start, that's a fundamental problem that impacts semiconductor industry as well as, so I think having that ability to have unfettered thinking is very important.  Bell Labs had a lot of time to do those kinds of things and having industrial research in universities and having universities working in industry doesn't preclude that kind of thing from happening at all. And I want to make that very clear, but it gets back to this comment, it's an ecosystem, right? And so you need to have this whole, you need to have the full ecosystem, I think to make it work. You need to have the person with that individual idea, but you still need to have that unfettered thinking as well as focused research.  This was the lesson of DARPA because they changed it, I think, in the 80s and 90s to ARPA, the Advanced Research Project Agency, and the conservatives in Congress tried to kill it. So then they made it, they put defense back and then it was okay. A few years ago, there was a member of the house who was trying to cut funding for a DARPA's research into bionic implants that would allow paraplegics to move things on screens, cursors, with their thought waves. And this was a classic example of Jeffersonian populism. Isn't this just a waste of taxpayers' money? I'm from Texas myself. We had a governor, Papio Daniel, who got elected around World War I by claiming that the University of Texas professors were trying to grow hair on the backs of armadillos. So there's this anti-intellectual strain and the DARPA representative, in the case of this particular, and they were doing this on monkeys, right? So why are we paying to have monkey brains and things like that? And he said, well, a lot of our soldiers in Afghanistan have lost arms and legs and this will help them recover as paraplegics. And so they won that particular battle. But it is an irony, the United States, the anti-status tradition is so strong that the only thing that overcomes it is military danger. And we're competing with societies in Europe and Asia, which historically are extremely militaristic up until World War II, in the case of Japan and Germany. But they're much more tolerant of civilian support, both for R&D but also for applied research. So it's just, it's one of those political culture things we have to think about. Just one quick other point, if I could. DARPA also, DARPA is a very aggressive and I think still a successful model. They've had big hits, as you know. There's now ARPA-E within the Department of Energy. So the Department of Energy thought it was such a great idea that they wanted to do the same thing. I will say that DARPA isn't the very, very fundamental R. It's more the D, not just the D that defense, but also the D on the development side. It really does aggressively say, in three years you gotta go from A to B. But there's one point I wanna make that having this ecosystem argument about DARPA, having DARPA, having fundamental science, all that in the same place. We always think of the pipeline that's been mentioned a lot today. There's research, there's a great invention. So you've got the great discovery of the transistor. Then there's this long linear pipeline and eventually it comes out in a telephone or a computer. And that pipeline is great, but there's the other side which works really well too. I like my fundamental scientists to actually be talking to people who are worried about systems analysis because they come up with great ideas. There's many examples that even at Bell Labs where people have won, for example, the high electron mobility transistor, it's called a hemp, and it's in every telecommunication station today. It's gallium arsenide. It was very important to get the right kind of silicon, I'm sorry, gallium arsenide to make that happen. It turned out that the purity of gallium arsenide then reflected back into a guy named Horst Sturmer who came up and discovered an entirely new state of matter called the fractional quantum hall effect at one of the Nobel Prize a few years ago. So it goes both ways. I mean, great inventions can go out in effect and change the economy, but then those great developments come right back and affect scientists. So scientists don't always, especially those that haven't grown up in an environment where that is just the way of thinking, it works both ways. So I think it's very important that the RDD and D is not just a pipeline in one direction, it cuts both ways. Makes our science better. Just to chime in a little bit on that too, I mean, the inventions at Bell Labs, a lot of things that we never talk about were process inventions and they were not product inventions. So I mean, photolithography, for instance, and those kinds of huge advances that actually made chips manufacturable. You talked about the Fraunhofer Institute in Germany. I mean, they study, if I understand correctly as well, they do improvements in process manufacturing. That's their mission. Yeah, exactly. Mission. Which we don't have an analog to that anymore. And the way the Fraunhofer Institutes work, a smaller, medium-sized business can have a problem. You know, on the process side, something can be breaking and they don't know why, and then they can bring it to these government-funded labs and they'll have a contract, you know. And it's a great advantage, particularly to what they call the Mittelstand, that is the smaller and medium-sized businesses. Huge corporations with deep pockets in the US can do this, but we don't have quite an equivalent system. I think they were other hands. Hi, I'm Polly McLean-Pont. I would like to hear the panel a little bit, speak a little bit more critically about the initial hypothesis, shall I say it, that there is something wrong with our current science infrastructure or science network, because I hear you saying two things. Inside notes, each of yous say that, well, obviously the government is funding basic research, and yes, we all need the basic research, and then we have the other side of the spectrum, and this is what you're talking about is in the middle. However, all your stories, the more elaborate arguments you're making don't support the natural being of this, so I would like to hear you make a more explicit argument for why, what this fundamental basic research, how this is crucial in the way it is done now in an isolated fashion for the innovative enterprises you're talking about, or maybe it's not as much as we tend to see. I mean, it's a great question, so you're asking what's the value of basic science and how come, so the first thing I'll say in defense, yeah, in defense of I think what all of us are saying is that, and it gets back to the discussion we had earlier today about our scientists articulating their impact, and so if I stood up here and told you the impact of Oregon or the impact of Bell Labs was 1500 science, nature, fissure of letters a year, all new knowledge, and by the way we had several mobile prizes, you'd probably yawn and say ho hum, so the story that scientists need to tell is the story that resonates in the public that makes the public feel connected, I think, that connected, and frankly not every scientist can tell that story, so part of it is that I think you're hearing, and when you talk about Bell Labs, we don't talk about all the basic science that went on, right, there's a lot of fundamental science that was just pure unadulterated, great mission, not even mission driven, sort of blue sky science, so we didn't talk a lot about it, but you're asking what the value of? No, but it's in the lab. Yeah, it's there. So the white lab. Yeah. But there's a big part of research being performed in isolated institutes where all this interaction is not going on. Is there any positive relationship between that or not? I'll just say one thing and then pass it on. There is interaction always going on, I mean any individual scientist still publishes, and every publication is out there in the literature as our scientists all over know, so there's no such thing as really isolated, I mean you're always publishing and putting stuff out there, and very often you don't know what's published, what will have an impact in the future, so there is a literature out there, and that's a very positive thing, right, so most of the literature tells you what you shouldn't be doing, not what you should be doing, but it's out there and it's saying things, so I'd say there is always connection. I think we're talking more about the innovation piece, so if I can be really simplistic, the research development, we hear R and D, development demonstration deployment, so there's actually three D's in an R, we're talking more about the middle two D's and then maybe the final D, which is actually what impacts the economy, it's not the first R which directly, other than scientists being employed at universities, but I mean I would turn the question right, it's a very hard question to answer, which I think you started off asking, or you started off asking is, what is the value of R in society and can you quantify it? What's the metric you're gonna use to tell me that the research you're doing is a value, and that's been a challenge for hundreds of years for societies to answer. I mean I intuitively know it is the right thing to do because I can trace back to the transistor and understanding silicon and the theory behind silicon, the band theory that came out in the 30s and 40s, the Shockley was one of them, all these guys coming out with basic theory, if we didn't know that as a foundation, we never would have created a transistor, it just never would have happened. So the problem when you're talking 20, 30 years out or 40 or 50 years out, it may or may not have an impact. How do you tell that story? And I think that was the discussion and the educational discussion is how do you tell that story? You're asking how come we're not telling that story? It's just a harder one to tell. Jack Dishchandra, George Washington University. I think I want to follow up this because you are somewhat defensive in terms of the basic research. I think there are, for example, all the things that you are talking about, there is no mention of what's foundational work, let's say in mathematics, which is kind of affected everything that you are talking about, but it is very hard to talk about. I agree. So I think sometimes it helps not to be defensive and say that these things have long impact. Sometimes they are not visible, they're sometimes they're not very sexy to talk about, but without that kind of work, you cannot expect advances in information technology, physics, anything else. Point well taken, I spend too much time in DC. But I want to get back to where you started because you both sort of issued a little bit of a call that says we're going in all the wrong directions. And yet the conversation has been, I think, mostly, yeah, okay, there's stuff going on. Do we have a problem or do we not have a problem? That is to say, do we need to be mobilizing publicly? You said we have to mobilize public opinion, we have to show people the value, we have to get people to appreciate this.  I think there are inherent limits to what you can fund if you depend on discretionary appropriations by short-term elected legislators. I'm just more skeptical about whether you can sell the really important upstream science and math to that. That's one of the reasons why I have suggested that we take this model of a national infrastructure bank and apply it to R&D because what you do there is you not only tap into potentially much greater volumes of funding of private capital and sovereign wealth capital, then you're going to get squeezed out of Congress, particularly in stressed times. But you would also get an arms-length distance in deciding which projects to fund, presumably. There would still be overall accountability, but you would not have to be persuading every single member of Congress who by seniority or partisanship happens to be on that committee. So I guess I confess I'm a bit more skeptical as to the potential for selling people on the importance of basic R&D. So you just have to take it out of the public realm in order to happen? The quasi-public, delegated, yeah. I just add very quickly, I think there's an educational issue too, the way we've come to understand innovation. This was discussed a little bit when we talked about Hewlett-Packard. In my book I write about this a little bit. We sort of lump it all into one category now, and I think the very, we're risk averse, but at the same time we understand huge companies grow out of technological advances in Silicon Valley. We might think of Facebook or Apple, all great companies, innovative companies as the apex of innovation, and it's just really one very small part of it. So then what do we do when we think of these incredibly high-risk experiments that may or may not pay off, and if they do pay off, we don't know when they'll pay off, and we don't know how they'll pay off, and in that sense, I find it's a fantastically large conversation that sometimes we're talking about different things when we're talking about innovation because it's just this enormous category that we've created, and there are different kinds. I mean, we sort of succeed by doing R, D, and D and dividing things up, but even then it can get very confusing because those lines are indistinct and they're permeable sometimes. Wait, we're done. Okay, well I wanted to thank our panelists for an interesting discussion, and thank you for your attention. Thank you. Thank you.