Why the U.S. Innovation Ecosystem Is Slowing Down
Is American innovation sputtering? The data suggests so: Productivity growth in the United States, which is powered by innovation, has been decelerating. Total factor productivity grew substantially in the middle of the 20th century, but started slowing in 1970. This slow growth continues today, with productivity lower than it was more than 100 years ago.
This slowdown has occurred despite increased investment in scientific research. Data from the National Science Foundation (NSF) indicate that U.S. investment in science has steadily increased between 1970 and 2010, as measured by dollars spent (which has gone up 5X), number of PhDs trained (2X) and articles published (7X). Why is there little productivity growth to show for this?
One explanation is that today’s science is simply not as groundbreaking as before. Some dispute this, however, pointing to advances in quantum physics (quantum computing), plasma physics (thermionic conversion), and molecular biology (CRISPR Cas-9). Another explanation, which we explore, is that today’s science is not being translated into applications — in other words, something is keeping scientific discoveries from fueling productive innovation.
Our research finds that the U.S. innovation ecosystem has splintered since the 1970s, with corporate and academic science pulling apart and making application of basic scientific discoveries more difficult. Our analysis also shows that Venture Capital (VC)-backed scientific entrepreneurship has helped to bridge this gap between corporate science and academia — but only in a couple of sectors. These findings suggest that if we want to see greater productivity growth, we need to explore alternative ways to translate science into invention.
Large firms withdraw from science
Up until the 1970s, some large American corporations invested in scientific research to such an extent that corporate science resembled — and sometimes exceeded — university research in caliber. DuPont’s central R&D unit, a pioneer in polymer chemistry, published more articles in the Journal of the American Chemical Society than MIT and Caltech combined in the 1960s. AT&T’s Bell Labs, home to the transistor and information theory, boasted 14 Nobel Prize winners and five Turing Award recipients among its alumni.
By the 1980s, a combination of shareholder pressure, heightened competition, and public failures led firms to cut back investments in science. Corporations instead began to look to universities and small start-ups for new ideas. Bell Labs was separated from its parent company, AT&T, and placed under Lucent in 1996. Xerox PARC was spun off into a separate company in 2002. IBM under Louis Gerstner redirected research toward more commercial applications in the mid-90s. DuPont closed its Central Research & Development Lab in 2016.
Large firms’ withdrawal from science can be gleaned from the above chart, which shows that the share of research (both basic and applied) in total business R&D in the U.S. fell from about 30 percent in 1985 to below 20 percent in 2015. While the amount of basic research (the “R” of R&D) stagnated over the 20-year period between 1990 to 2010, total industry spending and patenting on development activities (the “D”) have grown steadily.
This decline in research appears in data on scientific publications as well. One study of 4,608 American listed firms that perform R&D found that the number of publications per firm fell at a rate of 20% per decade from 1980 to 2006. The trend also appears in data on scientific awards: Another study found that Fortune 500 firms won 41% of innovation awards in 1971, but only 6% in 2006.
This marks a significant shift in the U.S. innovation ecosystem. We’ve moved from an economy where big firms did both scientific research and development toward one with a starker division of labor, where corporations specialize in development, and universities specialize in research.
In contrast to the corporate sector, universities have continued to expand research, a trend which began in earnest after World War II. NSF data indicates that university spending on research has increased more than fourfold, from $15 billion to $62 billion between 1980 and 2015. Even in recent years, peer-reviewed scientific publications increased by 37% between 2003 and 2016 for universities, while those for firms have dropped by 12%.
Although specialization means universities and firms can become better at producing research and developing products respectively, this division of innovative labor has made it more challenging for innovative research to turn into useful products. University science is qualitatively different from corporate science. Firms have access to specialized resources that universities often cannot emulate easily. For example, Bell’s invention of the Holmdel Horn Antenna, Google’s invention of the Tensor Processing Unit (TPU), Pfizer’s use of High Throughput Screening (HTS) processes are feats that universities or small firms would have found difficult to accomplish. These inventions required both scale and scope that the individual investigator model in universities would find difficult to reproduce.
Furthermore, corporate and university researchers have different incentives, which may affect how readily their research can be translated. University researchers are rewarded for precedence (“who comes first”), while corporate researchers are rewarded for their usefulness in invention (“does it work”). Therefore, university research is more likely to be new, but less likely to function as intended by businesses. Inventors seem to be aware of this problem, as a recent study has found that a discovery published by a university research team is 23% less likely to be cited in patents than the same discovery, published by a corporate research team.
Even when universities produce knowledge that is relevant and applicable, firms may not be able to find it. As firms reduce spending on basic research, they have fewer researchers following and participating in the wider academic community. This implies that firms will increasingly lose the knowledge of where to look for relevant research and apply it.
VCs are bridges in an increasingly splintered ecosystem
Venture capital (VC) and startups have been solutions to the problem of connecting upstream university research with downstream commercial applications. Nascent technologies within molecular biology, biochemistry, integrated circuits, and personal computing were initially shunned by large companies until VC-backed startups developed working products, such as microprocessors (Intel), synthetic insulin (Genentech) and the Macintosh (Apple).
VC-backed startups connect the chasm between university science and corporate invention. VC managers often hold advanced degrees in the subject matter they invest in, and accumulate commercialization experience over their careers. Startups also do not have established business models, calcified over time, that make established firms resistant to disruptive ideas. Research from Xerox PARC, for instance, was commercialized successfully by Xerox only when it connected to the core business of the firm (e.g. laser printers), but not when it diverged from it (e.g. GUI, ethernet).
Yet, VC has not been a perfect solution, as investments have mostly been concentrated in the life sciences and digital innovation. According to data from PwC Moneytree, information and communication technologies (ICT) and life-science startups received around 83% of all VC investments between 1995 and 2019. This means that very little funding is going to innovation in other sectors vital to advanced economies, such as energy, which relies on fundamental advances in material sciences and nanotechnology to deliver more efficient grids and safer power generation.
The challenges of translating science outside of ICT and the life sciences
Why is it so difficult for science-based entrepreneurs to obtain funding outside of these two areas? We speculate that it’s due to both technical and commercial uncertainty.
Technical uncertainty is, put simply, whether a given technical problem can be solved using a proposed approach. Commercial risk refers to the challenges of accurately assessing demand for a proposed product and the likely costs of scaling up and servicing the market. Most software projects have limited technical risks: the key question there is what should be done, rather than how it is to be accomplished. Commercial risk is managed by setting out commercial milestones (such as number of users, or cost of customer acquisition). As the startup passes a milestone, it gets additional investment to move forward toward the next milestone. The life sciences face significant technical uncertainty, but market uncertainty is very low, since the need for new medical treatments and devices is relatively stable. Projects in life sciences can also be mapped to specific milestones and managed accordingly. As a project successfully achieves a milestone, investors can estimate the gain in value based on the likely size of the market.
Juggling both types of risks may prove prohibitive, which may explain why startups in the physical sciences have received limited private-sector funding. The energy sector offers a case in point: Thermionic energy generation is a method that directly converts heat to electricity and promises significant improvements on mechanical heat engines. Originally explored in the 1960s for powering satellites, the technology was neglected by investors until only recently because of technical challenges – the microfabrication tools required to create prototypes were not easily available.
Inventions in the energy sector also face significant market risk, as adoption generally requires changing existing technical infrastructure, consumer behavior, and government regulation. Clean-energy innovations in wind and solar, for instance, depend on the development of grid-energy storage technologies. But advances in these technologies, such as batteries, depend on downstream market demand. Owing to these risks, VC funding in battery technology startups started in earnest only by the 2000s, after the automotive sector began adopting hybrid and fully electric vehicles.
Large corporations can often manage these commercial and technical uncertainties better than startups because they have experience moving products from labs to markets, and because they, or their partners, can be a source of demand. Large firms can also better coordinate the changes required in other parts of the value chain, and are more experienced at dealing with regulatory challenges.
Where to go from here
What can be done to bridge science and application in neglected sectors? One solution is for the public sector to step in and finance promising startups in the physical sciences while they focus on solving technical problems, in the hope that private capital can then step in to help with the commercial challenges. The SBIR grant program at the Department of Energy, for instance, funds renewables startups and has been found to increase patenting, revenue, and successful exit rates by reducing financing constraints. More capital will help resolve technical uncertainty by allowing the construction of capital-intensive prototypes across long development cycles and validate performance for prospective investors. Mission-oriented organizations such DARPA and ARPA-E, for instance, continue to fund fundamental innovations like those that have led to the Internet, automated voice recognition, language translation and Global Positioning System receivers.
A parallel solution is to nurture scientific entrepreneurial talent. Cyclotron Road at Berkeley Lab and Runway at Cornell Tech both provide research fellowships for post-doctoral scientists and engineers to focus on the transition from discovery to application. The fellows leverage national lab and academic research infrastructure to advance technologies based on their research and explore commercial viability. Other research institutions in the U.S., Canada, and Germany are following their lead. The mentorships and equipment scientists receive in these programs allows them to more effectively manage commercial uncertainty by becoming more attuned to consumer needs and constructing viable business models.
Although it is tempting, there is little point in hankering for the past, golden or otherwise. The new innovation ecosystem has a great deal of promise. What we need is a better way to harness today’s scientific advances and technical breakthroughs to accelerate productivity growth.