A prominent topic of discussion at the workshop in Silicon Valley was the differences among technological fields. Frequently discussed points are highlighted in Box 3-1.
Differences across Sectors and Technologies
• Innovation can be understood better when in its particular context. Very few generalizations can be made that apply equally to software, computer hardware, medical devices, pharmaceuticals, energy production equipment, etc.
• The time cycle of bringing a product to market benefits some areas of innovation and impairs others.
• One common weakness of universities is a reluctance to make hard decisions about shutting down unproductive projects.
• There is existing and increasing concern about the biomedical sector.
• Regulation has a significant influence on innovation.
• Venture capital undergoes dramatic fluctuations by field.
• Open research – particularly at the precompetitive level – has been valuable and generally preferable for some sectors.
Innovators in information technology, biotechnology, and energy technologies, which were the three sectors discussed in detail at the California workshop, face very different circumstances with regard to such factors as financial demands, time to product, and intellectual property protection. These differences call for nuanced policy approaches, as described in the final chapter of this workshop summary.
David Hodges, professor of electrical engineering and computer sciences at the University of California, Berkeley, described two university-based information technology projects as examples of innovations that have produced large returns in the past. The first involves a program called SPICE, for Simulation Program with Integrated Circuit Emphasis, which was inaugurated by Hodges’ doctoral adviser Donald Pederson in the 1970s. SPICE is an electronic circuit simulator that is used to design circuits and examine their behavior. “It was a huge success and had a high impact,” said Hodges, and 40 years later it is still the preferred tool for its purpose.
The project was based on complete openness, said Hodges. It had no patents or non-disclosure agreements, and its copyright agreement said that others were free to use the program so long as they acknowledged its source. As a result of the project’s openness, industry initially was not much interested in SPICE, but the program was adopted by Hewlett-Packard and Tektronix for use in the design of chips for their own instruments. Faculty members, students, interns, and company personnel participated in the project, and they brought their experience with the program to subsequent endeavors in academia, industry, and government.
The second technology Hodges described was Reduced Instruction Set Computing (RISC), a term coined by David Patterson at Berkeley, who was one of the developers of the technology. This technology, too, was developed on an open basis, with the developers at Berkeley inviting representatives of computer companies to come talk about the technology so long as the conversations were not secret or proprietary. According to Hodges, participants at those meetings would say, “Wow, I learned so much from those other guys’ questions.” Today, cell phones and many other devices have RISC chips, and many successful companies have emerged from the development of the technology.
“I’m advocating for the model where you say, ‘Let’s have the maximum free exchange of ideas,’” Hodges concluded. “There are lots of ways for innovation to occur, and universities are in a unique position to create [an open] environment. . . .You would have a far inferior research environment if you shut down the free exchange of ideas.”
BIOTECHNOLOGY AND MEDICAL TECHNOLOGIES
In contrast to the information technology sector, which remains a hotbed of innovation, the innovation model in the life sciences is “deeply broken,” according to Hennessy. Information technologies are generally well along the path to commercialization once a company is spun off to develop that technology. The question then becomes whether the company can build a team to get the technology to market while the window is open for that technology. (Another question is whether, as Hennessy put it, the “dogs will eat the dogfood”—in other words, will the technology be used by the people at whom it
is aimed?) With the life sciences, however, many innovations have not yet been developed to the point that they work reliably when startup companies are formed. Both Edward Penhoet, co-founder and former Chief Executive Officer of Chiron, and William Rutter, co-founder of Chiron and professor emeritus at the University of California, San Francisco, pointed out that as a result, the development those companies are doing is too often at a fundamental level. Startup companies need to focus on getting a product out the door so they can get a second product out the door, Hennessy said. They cannot afford to devote their resources to research.
Development also tends to be much more expensive and take longer in the life sciences than in other fields. The money needed to fund a startup information technology company can be barely enough to rent laboratory space for a biotechnology company. And the development of a product can take longer than venture capitalists or other sources of support may be willing to wait.
Several participants pointed out that because of these obstacles to innovation, the biotechnology and medical technology fields have been emphasizing short-term incremental improvements in products rather than major innovations. Yet major innovations are needed to support the investments required to develop products in these field. In addition, medical technologies suffer because the markets for such products are smaller than for pharmaceuticals but the costs and time needed to bring such products to market also are increasing.
Great diversity also exists within individual fields of technology, and this diversity sometimes can be leveraged to foster innovation. For example, Rutter described his involvement in Synergenics, which essentially consists of eight different, linked biotechnology companies that are pursuing a variety of long-term and short-term projects. Each of the companies is independent and has its own intellectual property, but they also engage in a great deal of interchange so that they can benefit from each other. Facilities and even personnel can be shared while executive overhead is minimized. Having more than one company within an overarching structure also makes it easier both to start a new company and to shut down an existing one.
A technological sector with different attributes on many of these measures than either information technologies or biotechnologies is energy technology—and specifically battery technology was discussed at the workshop.
When Yi Cui began doing research on batteries in 2005, interest in the topic was at a low ebb. But Cui perceived a need for better batteries, and he believed that he could bring his expertise in nanomaterials to problems in the battery field. For his first few years at Stanford, he had trouble interesting others in battery technology and getting grants for research on energy storage. But starting in 2008, attitudes began to change, and batteries became a hot
technology. Though his university colleagues advised Cui to get tenure first, he became interested in commercializing the technologies he was researching. He began talking with venture capitalists and eventually co-founded the company Amprius, which has been working to build batteries that use nanowires to yield greatly expanded capacity and recharging capabilities.
Battery technologies face some of the same problems as technologies in the life sciences, Cui observed. For example, most venture capitalists would like to see a return within four years or less, if possible. But many battery technologies take substantially longer than that amount of time to develop, which increases the difficulty of securing venture capital or other sources of support.
Battery technologies also can be expensive to develop, which can lead innovators to look for ways to stretch available funding. For example, Cui pointed out that the money that it takes to support a startup company in the United States for two days can support a comparable company in China for a month. He also pointed out that refusing to make these investments can be immensely shortsighted because of the huge returns some innovations can produce. As an example, he pointed to a study released in 2012 estimating that companies formed by Stanford entrepreneurs generate world revenues of $2.7 trillion annually and have created 5.4 million jobs since the 1930s.4
IS INNOVATION GETTING HARDER?
Workshop participants also discussed the intriguing debate currently under way about whether innovation is becoming more difficult. Mowery pointed out that a body of primarily economic research contends that it is. Inventors are producing their most important contributions to knowledge at an advancing age across different fields of research, which points to the increasing need to amass a large body of knowledge and experience to make a significant innovative contribution. The number of authors on research papers and contributors to patents also is growing, suggesting that innovation requires larger and more complex undertakings than in the past. Meanwhile, key indicators, such as the number of new drugs or new chemical entities being produced, are trending downward. Even Moore’s law—that the power of computer chips doubles approximately every eighteen months—is running up against physical limitations, and the pharmaceutical industry is beginning to speak of Eroom’s law (Moore spelled backwards), given that the absolute number of innovations and return on investments in that industry appear to be declining over time.
4 Charles E. Eesley and William F. Miller. 2012. Stanford University’s Economic Impact via Innovation and Entrepreneurship. Available at http://engineering.stanford.edu/sites/default/files/Stanford_Alumni_Innovation_Survey_Report_102 412_1.pdf.
One important aspect of innovation is that economists and policy makers cannot measure the output, Mowery said. They can only measure the inputs—in terms of funding or investigators, for example—and try to infer an output. But influential economists have been arguing that a given set of inputs will produce fewer outputs in the future (though Mowery said he had “no idea how they know this”). This issue might be a good topic for COSEPUP or some other group at the National Academies to investigate, he suggested.
Workshop participants also discussed other fundamental and long-term impacts of technology. One possibility is that new technologies are displacing employment in the United States, although this concern also has surfaced periodically in the past (about every 25 years, Mowery noted). Treating unemployment at an aggregate level also glosses over the underlying dynamics. For example, Mowery pointed out that additive manufacturing—or as it is sometimes called, 3D printing—could create a demand for workers with skills that U.S. schools are not producing. In addition, private sector employment has recovered from the recession that started in 2008 at about the same pace as after previous recessions, but public sector employment has lagged.
Technology also may be having an impact on the growing economic inequality in industrialized countries, Mowery said. Again, a workshop on the links between innovation, unemployment, and inequality could explore how these factors are conceptualized, measured, and interconnected.