The reorganization of the American innovation ecosystem
and the challenge of translating science
Ashish Arora	Sharon Belenzon Andrea Patacconi	Jungkyu Suh 
August 26, 2025
Abstract
In this paper, we focus on lack of translational research as a potential explanation for the recent slowdown in productivity growth, as opposed to a slowdown in science or the decline in the novelty of science. We provide evidence that the translation of science (as measured through citations by patents) has dropped for novel science. This drop is correlated with the withdrawal of large firms from science (especially the physical sciences), as measured through their publications. Fields where VC-backed startups have entered have experienced an increase in the translation of science, but their entry has largely occurred in the life sciences and ICT. Sectors such as chemicals and materials science have instead been neglected, both by large firms and startups. Our results point to the role of large firms and their labs in facilitating the translation and commercialization of science. A growing division of innovative labor may have delivered gains from specialization in the production and use of science in some sectors, but appears to have left a void in others.
The greatest invention of the nineteenth century was the invention of the method of invention... It is a mistake to think that the bare scientific idea is the required invention only to be picked up and used. An intense period of imaginative design lies between. One element in the new method is just the discovery of how to set about bridging the gap between the scientific ideas, and the ultimate product. It is a process of a disciplined attack upon one difficulty after another.
- Alfred North Whitehead (1925)
1		Introduction
A fundamental feature of modern economic growth is the application of science to production. In many ways, the greatest triumphs of science-based innovation are so complete that we are often oblivious to them. From Jonas Salk’s polio vaccine to Fritz Haber’s synthetic fertilizers, the application of science has allowed humanity to meet the Malthusian challenges of the previous century.
Yet, there is growing skepticism over whether science can continue to meet the challenges of this century. Even with the consistent expansion of scientific knowledge, productivity growth in many advanced economies has plateaued, especially compared to the mid-twentieth century’s “golden age” (Gordon, 2012). With over $100 billion devoted to federally funded R&D annually and four times that amount spent by businesses, a host of recent work has tried to understand why American innovation had less to show for it.
Some argue that the plateau in productivity growth directly reflects a stagnation in science. Bibliometric research appears to show a decline in scientific progress (Park et al., 2023). With more science being produced every year, a “burden of knowledge” is also said to have lengthened the time and effort taken to reach the technological frontier (and thereby shortened the time spent expanding it). Others point out that innovation (measured in terms of life expectancy, crop yield, and transistor density) now requires more resources (Bloom et al., 2020).
However, as discussed more fully in Section 2 below, these explanations are at best incomplete. A higher percentage of patents cited scientific articles in 2020, around 13%, compared to just over 4% in 1980, casting doubt on the idea that scientific knowledge is less useful for inventive activity. The last few years have undoubtedly witnessed massive scientific and technological progress in fields such as artificial intelligence, structural biology, quantum computing, and battery technology. It is more plausible that the commercialization of science might have been uneven, with a significant share of scientific advances, especially in the physical sciences, still failing to cross the "valley of death" between discovery and product commercialization.
In this paper, we focus on the challenge of translating science, rather than a slowdown in scientific progress itself, as a potential explanation for the slowdown in productivity growth. We show that the average decline in scientific novelty has been principally driven by the increase in incremental scientific work, rather than a decrease in the supply of novel scientific findings. By contrast, the translation of science (as measured through citations by patents) has dropped for novel science, while it has climbed somewhat for incremental science. Next, we focus on the reorganization of the American innovation ecosystem as a possible explanation for the drop in translation of novel science. We show that the transition from a centralized system of corporate research labs to one featuring a division of innovative labor between universities, startups, and incumbent firms may have affected translation and commercialization in key segments of science. We document that while large firms exited many scientific fields, VC-backed startups have selected into biotech and software-related fields, shunning materials science and physics, which form the bulk of “deep-tech” or “hard-tech” sectors. Thus, a "translational gap" may have emerged in these sectors.
We argue that a translation gap reflects not just greater appropriability problems in deeptech, but also a smaller share of the private value accruing to startups. We develop an analytical model that explores how gains from specialization and coordination costs (including value-capture problems) interact. The model yields three regions: one where incumbent firms integrate R&D and commercialization, another characterized by a division of innovative labor, and finally, one where neither incumbents nor startups innovate. The no-innovation region is not socially desirable. It arises from a combination of insufficient appropriability, inefficiency in the incumbent’s R&D, and the inability of the startup to capture sufficient value. The integrated innovation region can also be inefficient if the incumbent preempts the more efficient startup and invests to capture more value.
Interestingly, corresponding to the deep-tech situation, a division of innovative labor may also be inefficient when appropriability is low but the market for technology is also not efficient. In this situation, integrated R&D would be better than division of innovative labor, because integration saves on coordination costs. However, low appropriability leads incumbent firms to put less weight on the inefficiency of the market for technology than is socially optimal: They prefer to let startups innovate rather than invest themselves. Deep-tech sectors may also have more externalities, implying that the innovator can appropriate only a fraction of the total social value of the innovation. Our model thus complements earlier work by highlighting the complex tradeoff between gains from specialization, frictions in the market for technology, and appropriability.
The rest of the paper proceeds as follows. We begin by explaining stagnant innovation by insufficient translation of science, rather than a decline in scientific progress or in its novelty. Section 3 delves deeper into what translation of science entails. Section 4 presents an analytical model and supporting data to investigate where the locus of translational research is located in an innovation ecosystem. We contrast large integrated firms that conduct research in their labs with a division of innovative labor (DOIL) between VC-backed startups and large firms. We show that lack of investment (a translational gap) can emerge in some sectors of the economy. Section 5 concludes.
2		Can a slowdown in science explain technological stagnation?
Scientific knowledge forms the bedrock on which new techniques and methods of manipulating nature are invented. Firms then develop and deploy these technologies as products and services to the consumer. “Basic research,” in Vannevar Bush’s words, is the “pacemaker of technological progress” (Bush, 1945). Many have pointed to a slowdown in the supply of science as being principally responsible for the stagnation in technological progress that Gordon (2012) has identified. One proposed reason is that the low-hanging fruits of science have already been picked (Bloom et al., 2020). Poorly functioning institutions for funding research or misaligned incentives for researchers are also thought to be responsible. Another proposed reason is the growing burden of knowledge: New generations of researchers have ever-growing amounts of already discovered knowledge to assimilate before they can create new knowledge of their own, leading researchers to specialize narrowly, with ever-diminishing impact (Jones, 2009).
We have two points of disagreement with these claims. First, the evidentiary basis for the claim of a secular decline in scientific progress is shaky. Second, even if inputs into inventive activity have become more expensive in a number of fields, the growing private investment in inventive activity points in a different direction, namely towards an increase in the private value of invention. The growing private returns to invention have, in turn, increased the demand for inputs into invention, namely science. The failure of technical progress to accelerate therefore requires a different explanation. We argue that bottlenecks in translating science in some sectors, rather than a decline in the supply of scientific knowledge or a deterioration in its quality, are a more compelling explanation for technological stagnation.
The most direct evidence in support of the proposition that scientific progress is slowing down is offered by Park et al. (2023), who calculate a “disruptiveness” index that measures how different a paper is from its predecessors relative to how similar subsequent papers are to it. They find that overall, papers are less disruptive in every field of science and technology. The average scientific publication may well have grown more incremental, but technological progress hinges on deploying the best ideas, not the average one. Park, Leahey, and Funk’s own analysis shows that, although the share of “disruptive” papers and patents has decreased, their absolute numbers have not declined; as the authors themselves note, “there is little change in the number of highly disruptive papers and patents.”(Park et al., 2023, p.141) On the other hand, Macher et al. (2024) find that disruptive patents have increased, not declined. They argue that the analysis by Park et al. (2023) suffers from a measurement problem: backward citations to patents published before 1976 are missing. As a result, patents issued in 1980 are mechanically more disruptive than an otherwise identical patent issued later. After adjusting for these factors and changes in U.S. patent law that allow for citations to patent applications in addition to patent grants, and updating the analysis to 2016, Macher and coauthors find that the number of highly disruptive patents has increased since 1980, particularly since 2008. Furthermore, not all disruptive papers are influential; many are themselves disrupted by future papers. It is the persistently disruptive papers that propel science. Deng and Zeng (2023) and Deng et al. (2025) conclude that these persistently disruptive papers have not declined. If anything, papers in recent years are more likely to show persistent disruption.
The history of science also cautions us against claims about the completeness of current scientific knowledge. Nor is the burden of knowledge a new roadblock. Indeed, the growth of science itself reduces this burden. A part of the growth of knowledge is reflected in ever more sophisticated tools, freeing up humans for more creative endeavors. The release of the open-source version of Microsoft Kinect meant that academic robotics projects were less likely to feature computer-vision specialists; much of their knowledge was now embedded in Kinect (Teodoridis, 2018). The growing applications of machine learning to scientific discovery must surely ease the burden of knowledge as well. Vast amounts of data and information can be stored and assimilated, augmenting human intuition and creativity (Chen et al., 2024). Conceptually, the growing specialization of researchers is a natural response to the growing number of researchers. Specialization is a way of reducing duplication.
Even if scientific progress is not slowing down, perhaps it is less useful for invention? Bloom et al. (2020), using evidence from the information, agricultural, and life sciences, present evidence suggesting that the relationship between investments in research and productivity has weakened. For instance, maintaining the exponential growth in semiconductor performance (a benchmark set by “Moore’s Law”) in 2014 required around 18 times the number of researchers it used to take in 1971. Although growth rates for yields per acre for corn, soybeans, cotton, and wheat have averaged around 1.5%, the number of researchers in the agricultural sector has grown by a factor between 3 (wheat) and 25 (soybeans), a decline in research productivity of about 4% to 6% per year. Alston and Pardey (2022), however, find that the number of new wheat varieties introduced divided by the stock of R&D devoted to wheat research has not declined between 1970 and 2018. If anything, it has tended to increase since 1990. Fort et al. (2025) separately analyze the relationship between R&D and inventive output (patenting), and between patenting and firm performance. They conclude that there is no evidence of a decline in patent productivity, even after adjusting for patent quality. Furthermore, the link between patents and growth is stable over time. They do find strong evidence of secular decreases in output unrelated to patenting, suggesting an important role for other factors.
Moreover, trends in business R&D further undermine the claim that R&D productivity has fallen, whether due to a decline in scientific progress or some other factor. When the productivity of inputs into an activity (in this case, invention) falls relative to their price, fewer resources would be devoted to that activity. That is, if science were less productive in stimulating technical advance, we should see less, not more, investment in R&D in the economy. Instead we see increasing investment, especially private investment, in both upstream research and downstream development. Figure 1 plots data from the National Science Foundation (NSF) that shows U.S. total expenditure on scientific research has risen around four times in real terms, while corporate research expenditures have risen five times (National Center for Science and Engineering Statistics (NCSES), 2024). The business share in research has also risen from 36% to 46% of the national total over the four decades since 1980. In fact, research as a share of GDP has increased by 45.5% during this period (from 0.79% in 1980 to 1.15% in 2020), with privately funded research increasing even faster.
[Figure 1 Here ]
The increased investment in science, including private investment, can be explained by the growing value of innovation. This has led to higher demand for scientific research. Economic growth, driven by international integration, has enriched countries, thereby increasing demand for new and improved products. Growing demand for invention, with diminishing returns, would predictably result in lower average productivity of inputs such as science (Cohen & Klepper, 1996). Kim (2024) shows that as markets expand and rivalry intensifies, firms become both larger and more narrowly specialized. Larger firms have greater incentives for incremental innovations (Arora, Cohen, et al., 2023). A complementary explanation is that rivalry for larger markets raises the private returns to R&D but also shortens the life of each payoff, as competitors imitate or leapfrog one another (Ando et al., 2025). Faster obsolescence, in turn, pushes companies toward narrower, quick-hit projects over long-horizon research. The result is the same compositional drift we observe in the data: more “D,” less “R.”
[Figure 2 Here]
To explain this slowdown, we argue for shifting our focus from scientific progress to the translation of science into inventions. Indeed, we find evidence of slowing translation of science in Figure 2, which plots the share of high-quality scientific papers (i.e., papers in the top decile in terms of being cited by other papers) being cited by a patent, by varying degrees of Park and coauthors’ Consolidation-Disruption (CD) measure. Throughout the sample period, we find that the most disruptive high-quality papers tend to be cited more often by patents, with those in the top quartile (with 12% of papers cited by patents) 60% more likely to be cited than those in the first quartile (7% cited by patents). Notably, the probability of a high-quality disruptive paper (within the top quartile of CD scores) being cited by a patent has dropped substantially since the mid-1990s. At its peak in the early 1990s (1990-95), 19% of disruptive papers were cited by patents, while in the 2010s, they drop down to 6%. In contrast, the least disruptive papers (in the bottom quartile) show a patent citation rate of 7% in the 2010s as well. The data are therefore consistent with our view that the slowdown in technological progress can be explained by a slower translation of science in some fields. We argue that this slowdown is related to the decline in industrial research in those fields, with universities and startups unable to fill the gap. The result is that the inventions produced are more incremental and less capable of fueling higher rates of productivity growth.
The following section explains what the translation of science entails and how changes in the American innovation ecosystem may have weakened the link between investments in science and productivity growth, at least in some sectors.
3		Translational research: A crucial nexus between science and commercial applications
In the “Dynamo and the Computer,” Paul David explored why the IT revolution was slow to translate to productivity growth. He pointed to the slow impact of IT on productivity by highlighting how the effective and widespread application an earlier general purpose technology — electrification — was hindered by various technical and economic roadblocks. Paul David highlighted organizational barriers such as user co-invention, as firms had to learn how best to maximize the potential of electricity. He pointed to the slow phaseout of the group drive system in favor of the unit drive system in factories, the delays in the re-design of factories and the layout of machinery to take advantage of modular unit drives, and delays in increasing the quality of products produced with electricity as a power source. He and others also pointed to lags in the development of complementary inventions, such as better motors and transformers to convert direct current into alternating current.
The productivity paradox discussed by David resembles the disconnect between scientific investments and productivity we have highlighted. As Nate Rosenberg also noted, unlocking the full potential of a scientific discovery requires a focus on scale and scope that is qualitatively different from developing a prototype that validates the initial concept. Penicillin was discovered by Alexander Fleming in 1928, but the consumer benefits did not transpire until scientists at the USDA’s Northern Laboratory in Illinois made three key advances. Instead of shallow trays, they adapted a submerged fermentation process used by Pfizer for gluconic acid. They also developed improved nutrient systems (milk sugar added to corn steep liquor), and discovered a more effective strain of Penicillium. These steps increased the output rates more than a hundredfold (U.S. Department of Agriculture, Agricultural Research Service, 2023). These, and other refinements, allowed antibiotics to be grown not in petri dishes but in deep tanks that fermented penicillin at scale (Elder, 1970).
The point is that the translation of science into productivity growth requires innovators to i) explore practical applications of scientific ideas and to ii) clear out technical obstacles to widespread application (Woolf, 2008). Even when practical applications are clear (as in the case of vaccine development), critical technical roadblocks must be cleared. Katalin Karikó and Andrew Weissman’s Nobel Prize winning research, for instance, was key in removing a fundamental roadblock to the development of mRNA vaccines: preventing unwanted inflammatory reactions to mRNA.
Advances in solid-state electronics encountered similar obstacles. The invention of the laser in 1960 opened up the possibility for using them in communication, as optical signals travel on denser frequencies of the visual range, beyond the limits of the radio and microwave ranges. However, the deployment of laser communication networks did not occur until three technical problems were solved. First, lasers needed to be smaller; the semiconductor diode lasers invented at General Electric by Nick Holonyak in 1962 reduced the size of lasers to a semiconductor chip. Early semiconductor lasers also suffered from short lifetimes (typically seconds to minutes) and overheated. Follow-on work at MIT Lincoln Labs, Texas Instruments, and RCA Labs solved this problem by refining processes for growing crystals and finding better architectures to lower the threshold current at which lasing occurred. Even with improved lasers, optical communications also could not occur over air, as unlike radio or microwaves, they were vulnerable to weather conditions. The development of the low-loss optical fiber at Corning Glass Works, building on earlier work by Charles Kao at ITT, solved this problem in the early 1990s.
Importantly, removing these obstacles sometimes raises problems of deep scientific interest. Simply put, the large-scale economic application of a scientific advance itself can involve considerable additional research and the creation of new scientific knowledge. Thus translational research cuts across the conventional divide between basic and applied research (Bush, 1945). Kao (2009, Physics) and Karikó and Weissman (2023, Medicine) all received Nobel Prizes for their contributions. Translational work therefore sits squarely within “Pasteur’s Quadrant” which is simultaneously inspired by a “quest for fundamental understanding” and “considerations of use” (Stokes (1997)). The prototypical example of use-inspired basic research mentioned by Stokes is Pasteur’s work on vaccines and microbial fermentation. Other examples mentioned by Stokes include the fundamental research on quantum physics at the Manhattan project and Irving Langmuir’s investigations into surface chemistry. In many of these examples, large corporations and their labs played a fundamental role. In the following section, we show that such research functions of large firms have atrophied over the past four decades and relate this to a decline in translation.
3.1		Industrial research in the American innovation ecosystem
Science-based innovation in America was driven chiefly by large firms operating centralized research laboratories such as GE Research Labs, AT&T Bell Labs, DuPont Central Research, IBM’s Watson laboratories, GM Research Laboratory, and Xerox PARC. Industrial research arose around the interwar period when large integrated firms in the electrical and chemical industries reached the technological frontier. As academic research in fields such as polymer chemistry, photo-chemistry, and radio engineering was inadequate at the time, firm such as AT&T, Kodak, and DuPont started hiring American faculty members with doctoral training (initially from Europe). The number of firms operating research labs rose almost eightfold (from 526 to 4,145) between 1920 and 1960 (National Research Council, 1980). While many firms operated labs for routine troubleshooting and applied work, a minority of firms, often supported by generous federal funding at the onset of the Cold War, also produced cuttingedge scientific research. Work performed at AT&T Bell Labs, for instance, won the firm 10 Nobel Prizes and 5 Turing Awards.
Industrial research was aimed at understanding more deeply the phenomena, material, and instruments relevant to the firm. The key feature of this system was that the entire innovation “supply chain” from basic research down to product development was housed within the boundaries of one firm. For instance, the basic research for the nylon, by Wallace Carother’s team at DuPont’s Experimental Station, while that for the transistor was done by AT&T Bell Lab’s Solid State Physics Group.
The hegemony of centralized corporate research laboratories lasted through most of the Cold War. Starting from the 1980s, however, firms gradually closed (DuPont’s Experimental Station), spun off (PARC from Xerox, Bell Labs from AT&T), or redirected (IBM) basic research functions, ceding this role to universities (Arora et al., 2018). In the modern pharmaceutical industry, for instance, startups typically build on university research to introduce new drugs which incumbent acquire or license to push through clinical trials and mass production (also dubbed the “Search and Development” strategy by Ceccagnoli et al. (2014)).
Multiple data sources are consistent with this pattern: for instance, the dashed line graph from Figure 1 shows the share of research out of Business R&D expenditures dropping from a high of 31% in 1986 down to 21% in 2020. Arora et al. (2018, 2021) also show that scientific publications (normalized by sales) by U.S. public firms has fallen by half between 1980 and 2015, signaling a decline in “intramural” scientific research at firms. In addition to reducing the share of upstream research in favor of development, firms are dividing research functions with universities. The bar graph in Figure 3 shows that the share of corporate publications coauthored with university coauthors rises from 24% in 1980 to 66% in 2020. Furthermore, the dashed line in the same figure shows that universities perform a slightly greater share of scientific research funded by firms (from around 3% in 1980 to above 4% in the 2010s). The data collectively point to large firms forgoing research functions and relying more heavily on university-industry cooperation to source new knowledge to commercialize.
[Figure 3 Here]
NSF data indicate that “mega” companies with more than 10,000 employees accounted for 73 percent of non-federally funded R&D in 1985. By 1998, this share had dropped to 54 percent, and to 51 percent by 2008 (Mowery, 2009). In terms of patenting, in the late 1970s, mega firms accounted for 58 percent of patents. By the 2010s, their share of patents had fallen to 48 percent (Fort et al., 2025). By contrast, the shares of the smallest firms—those with fewer than 20 employees—have grown steadily over time, from 7 percent in the 1970s to 10 percent in the 2010s. Smallness, however, is not the defining feature of startups; youth is. Young firms (0 to 10 years old) have a disproportionate share of high-impact patents, as measured by citations (Fort et al., 2025, Figure 4). In the 1990s, 2000s, and 2010s, young firms account for 15, 14, and 13 percent of patents and 19, 18, and 17 percent of cites. In contrast, the oldest firms produce an increasingly lower share of high-impact patents. In the 1990s, they accounted for 74 percent of granted patents, 67 percent of citations, and 68 percent of breakthrough patents. By the 2010s, the share of granted patents was 55 percent, 49 percent of citations, and 48 percent of breakthroughs.
A final indicator of the reduced contribution of large firms to innovation is the sharp drop in the share of Fortune 500 companies in the R&D 100 awards winners. While 41 percent of the awards went to Fortune 500 companies in 1971, only 6 percent went to them in 2006 (Block & Keller, 2009). These findings are especially remarkable considering the growing size advantage of the largest American firms. The share of employment in mega firms, for instance, grew from 25 percent in the 1970s to 28 percent in the 2010s (Fort et al., 2025, Figure 5). The share of the smallest firms decreased from 22 in the 1970s to 18 percent in the 2010s. Overall, the evidence strongly suggests that, over time, small firms and startups became a more potent source of technological invention.
This division of innovative labor can enhance efficiency as firms specialize based on comparative advantage. However, dividing labor also introduces coordination costs, as the entities applying scientific knowledge are frequently distinct from those applying it. Misalignment between scientific advancements and industry needs, along with the high costs of transferring knowledge from universities to firms, can impede the swift application of innovations. Indeed, recent evidence shows that scientific papers from universities do not affect corporate R&D, while university inventions that embody this knowledge substitutes corporate research (as measured through papers and employment of scientists) (Arora, Belenzon, Cioaca, Sheer, & Zhang, 2023). This indicates that scientific advances needs to be translated and their potential commercial applications clarified, and firms need to invest in absorbing this knowledge (Cohen & Levinthal, 1989). The failure to invest in absorptive capacity may lead to a translation gap, especially in “deep tech” sectors.
Put slightly differently, translating science requires knowledge of the potential applications and the likely technical and scientific roadblocks. Some of this knowledge is not readily available to universities and university spinoffs. By contrast, researchers in corporate labs, interacting with colleagues in product and process development, and manufacturing and sales, would have a better understanding of the translation process. Conversely corporate inventors may not be able to keep up with scientific advances in universities, and may fail to recognize their potential commercial application. This gap is wider in some sectors, such as materials and energy, than others, such as life-sciences. It may partially account for why less science is translated, even as the production of novel, high quality, science continues at the same rate as before.
[Figure 4 Here]
Figure 4 relates the decline of corporate science to translation of science. Translation of science is measured by the share of highly cited papers in a scientific field and year that are cited in a U.S. patent document; corporate science is measured through the share of such papers authored by corporate scientists. We find that fields where corporations reduce their investment in scientific research also exhibit lower use (citations) by patents. Halving corporate paper shares year on year is associated with a 4% decrease in patent citations. Thus, we find evidence that the slower translation of science shown in Figure 2 from the previous section may be linked to the withdrawal of large corporations from scientific research.
[Figure 5 Here]
Next, Figure 5 shows how translation of science has evolved over time (square-dotted line), together with the contribution to science by large firms (circle-dotted line) and translation of science by VC-backed startups (diamond-dotted line). Consistent with Figure 2, translation of science rose more than threefold until the mid-1990s, before declining thereafter. This pattern appears to be driven by startups, whose contributions rose more than fourfold until the early 2000s and then stabilized. By contrast, the contribution of large firms to science declined almost monotonically over the entire sample period.
[Figure 6 Here]
Figure 5 hides significant heterogeneity across scientific fields. Figure 6 explores this variation, paying special attention as to whether startups entered scientific fields that large firms exited. The hypothesis we want to explore is whether startups compensated for the withdrawal of large firms from science (by entering fields that large firms exited), or whether instead both large firms and startups withdrew from the same fields (and concentrated in others).
Figure 6 shows the results, split by changes between decades. Scientific fields where large firms reduce publications are represented in the second (top left) and third (bottom left) quadrants. Fields where startups substitute for the exit of large firms by translating more science are shown in the second quadrant. Fields where both types of firms withdraw from science (by publishing or translating less) are represented in the third quadrant.
We find that between the 1980s and 1990s (left panel), large firms increased their presence in the life sciences (biology and medicine) while reducing their publication share in the physical sciences (physics, engineering, and materials science). Startups mostly compensated for this difference by increasing translation in these fields. However, between the 1990s and 2000s (middle panel), startups reduced their entry into the physical sciences, and a “void” was created in fields such as chemistry, where the exit of large firms and stagnant entry of small firms coincided with a 36% drop in the share of papers cited by patents. Between the 2000s and 2010s (right panel), startups joined the exit of large firms from fields such as materials science. Except for life sciences and computer science, most fields are located in the third quadrant. Thus, the evidence from Figure 6 is inconsistent with the idea that VC-backed startups compensated for the exit of large firms. Starting from the 1990s, both large firms and startups withdrew from the same fields (the physical sciences).
[Tables 1 and 2 Here]
Tables 1 and 2 list the top users of science in each field by decade (where use is defined as the number of paper-patent citation pairs identified for each patent assignee). The life sciences (biology and medicine) show high prevalence of VC-backed firms. All top 5 users of clinical medicine in the 2010s, for instance, are VC-backed startups. Similarly, mathematics and computer science (which the ICT sector draws on) also feature VC-backed firms as prominent users of science. VC-backed firms are far less prevalent in materials sciences, with Nantero and Monolithic 3D Inc each taking second and fourth spot in the 2000s and 2010s respectively. Of these, Nantero (founded in 2001) stands out as a “deep-tech” company that aimed to apply carbon nanotube technology to design a new type of memory (named Nano- RAM). While the company succeeded in manufacturing a prototype in partnership with LSI corporation, the technology is yet to see widespread adoption in the semiconductor indus- try. Another notable trend is the entry of academic institutions into the life sciences. The University of California system, Harvard, and the Broad Institute all entered the top 5 spots after 2000 in biology. Interestingly, their entry coincided with a precipitous decline in patent citations made to this field (from 25% down to 10%). Whether this is an aberration or reflects a deeper development is unclear. To summarize, the source science for deep-tech continues to be produced by universities but remains under-commercialized, associated with a decline in industrial research.
4		The locus of translational research
Between 1995 and 2019, 83% of all United States venture capital went to digital and biopharma startups. The remaining 17% was spread among startups in fields such as in capital-intensive fields such as batteries, fusion, and quantum hardware (Lerner & Nanda, 2020). Arora, Fosfuri, and Rønde (2024) and Dalla Fontana and Nanda (2023) offer different but complementary explanations for why.
Dalla Fontana and Nanda (2023) note that startups are typically funded by VCs, whose investments are staged to match the resolution of uncertainty. Thus sectors where experiments to resolve uncertainty can be done more cheaply are more attractive to VCs. Other sectors, in particular, deep-tech sectors, are characterized by costly experimentation and thus are less attractive to investors. Though stated in terms of VC investment, the argument is more general. Sectors where experimentation costs are lower are not just privately more attractive, they are also socially more attractive. If so, there is no reason to subsidize deep-tech per se, because higher experimentation cost implies lower social value. In essence, Dalla Fontana and Nanda (2023) argue that deep-tech innovation has slowed because innovation in digital and life-sciences has higher social value (net of the cost of innovation). A common policy response is to try to increase the supply of venture financing for the neglected sectors.
Arora, Fosfuri, and Rønde (2024) focus on the division of value to explain the problem of deep-tech. They develop a model where innovation consists of upstream R&D and downstream commercialization. They assume that startups have an advantage upstream and incumbents downstream. Thus a division of innovative labor is socially more efficient than integrated innovation. However, uncertainty prevents startups and incumbents from negotiating till the startup successfully completes the upstream R&D. At this point, though, the startup’s investments are sunk. Unless the startup has an alternative route to the market, the incumbent may be able to capture a significant share of the overall value created. Arora, Fosfuri, and Rønde (2024) argue that a defining feature of deep-tech innovation is the importance of both technical (upstream) and commercial (downstream) challenges. They characterize deep-tech as sectors with intermediate values of the ratio of upstream (R&D) and downstream (commercialization) investments. They show that the startup’s expected payoffs first decrease and then increase with the ratio between the upstream and downstream investments. That is, that deep-tech innovations are more likely to be neglected because the startup is unable to capture enough value.
Roger Masclans’s research provides empirical support (Masclans, 2025). Analyzing 5,823 venture capital–backed startups acquired by public firms in the U.S. stock market between 1990 and 2024, Masclans finds that science-based startups generate 27% more joint surplus, defined as the sum of the surplus accruing to the buyer and the startup. However, sciencebased startups capture a smaller share of that joint surplus, only 46 cents per dollar of surplus generated, compared to 70 cents for non-science-based startups. In advanced materials, aerospace, computer hardware, and energy, science-based startups captured 23% less value compared to startups not based in science in the same industry. Masclans suggests that a lack of potential buyers and the lack of an alternative route to the product market for the startup may contribute to lower value capture by science-based startups.
Biotechnology is an outlier because there are virtually no startups that are not sciencebased and because contract development and manufacturing organizations (CDMOs) offer startups an alternative path to technology commercialization (Moreira et al., 2023). Between 1995 and 2015, CDMOs grew over five fold. An immediate implication is that biotechs have an outside option when dealing with potential licensing partners. Indeed, Moreira et al., 2023 show that the growth of CDMOs coincides with a decline in out-licensing deals, particularly by smaller biotechs. Between 2006 and 2015, the number of out-licensing deals by the nearly 800 firms in the study sample, declined by 62.01%, even as the R&D expenditure by the sample firms increased by more than 31.52%.
4.1		A model of technology commercialization
We develop an analytical model to study the interaction between appropriability, gains from specialization, and coordination costs (including value-capture problems). We consider an innovation ecosystem consisting of a large firm and a startup. The startup can invest in translational research (henceforth also R&D) but must then partner with the large firm to commercialize the invention. The large firm can also do the translational research, albeit at a higher cost. The private value of a commercialized invention is αV . Here V is the social value and α ≤ 1 is the share appropriated by the innovator.
Startups, especially university spinoffs, often have an edge in translational research due to their close connection to academia. However, translational research alone is not enough; commercialization of new technologies requires significant additional work, such as development, testing, manufacturing, and distribution. Incumbent firms are generally more efficient at carrying out these later stages than startups, so smaller companies often partner with larger firms to bring their innovations to market. Yet, uncertainty and market frictions can make matching startups with established firms difficult. This is the second key component of our model. We model the efficiency of the technology market as the inverse of coordination costs: the greater the matching frictions, the less efficiently startups can connect with the right partners to commercialize their innovations. The model has three periods.
Period 1: Large firm’s investment. In period 1, the large firm chooses whether to invest in R&D. The cost of this investment is C . One interpretation is that the large firm establishes a corporate lab to develop inventions internally. We label this the integrated outcome.
Period 2: Startup’s investment. In period 2, the startup chooses whether to invest in R&D. The cost of this investment is λC, where λ ≤ 1. Thus, the startup is more efficient at undertaking R&D than the large firm.
Period 3: Implementation and profits accrue. In period 3, the large firm chooses whether to implement its own invention (if it invested in R&D) or partner with the startup.
If the startup invested in period 2, there is an exogenous probability θ that a match between the large firm and the startup can be formed. We refer to θ as the efficiency of the market for technology. Factors that could affect θ include (i) the strength of patent laws, (ii) the availability of technology portals for open innovation (e.g., P&G’s Connect and Develop), or (iii) the ability of venture capital firms or university technology transfer offices to establish connections with larger firms.
If a partnership is formed, the large firms obtains a share ψ of the returns αV of the invention, and the startup obtains the remaining share 1 - ψ . The parameter ψ ≤ 1 reflects the net effect of the bargaining power of the large firm and the outside options available to the startup as in Arora, Fosfuri, and Rønde (2024). For simplicity, we assume that a startup would not invest in R&D if the large firm invests.
4.1.1		Solving the model
If the large firm invests in R&D, the startup will not invest, because the startup needs the large firm to commercialize its idea. For the large firm, the payoff from investing in R&D is πLInt = αV - C . For the startup, conditional on partnering with the large firm, the payoff from investing is πSDOIL = θ(1 - ψ)αV - λC. For the large firm, the payoff from partnering, conditional on the startup having invested in R&D, is πLDOIL = θψαV . Proposition 1 provides boundary conditions for the three equilibrium outcomes of the model.
Proposition 1.
1.		Internal R&D. If VC < α(1 - ψθ) or (1-λψ)α θ < VC ≤ α, then the large firm invests in R&D and commercializes its invention.
2.		Division of innovative labor (DOIL). If VC ∈ [α(1-ψθ), (1-λψ)α θ], then the startup invests in R&D and the startup invention is commercialized in partnership with the large firm.
3.		No innovation. If VC > max{α, (1-λψ)α θ} , then neither the large firm nor the startup invests in R&D.
Figure 7 illustrates the regions in the (θ, VC)-space where the different scenarios can occur.
[Figure 7 Here]
For the large firm, investing in R&D is profitable only if VC ≤ α. For the startup, investing in R&D is profitable only if VC ≤ (1-λψ)αθ. When neither of these conditions hold, there is no innovation. The economy finds itself in the trapezoidal region (diagonally hatched) in the upper part of Figure 7, where VC > α and VC > (1-λψ)α θ (and also θ ≤ 1 and VC ≤ 1). This no-innovation region is characterized by large R&D costs relative to the social value of the invention (high VC ) and inefficient markets for technology (θ low). This region shrinks as the returns from invention can be better appropriated (i.e., as α grows). Greater efficiency of startups relative to large firms (i.e., smaller λ) also decreases the size of the no-innovation region.
For the large firm, partnering with the startup is more profitable than investing in R&D internally if VC ≥ α(1 - ψθ). This condition, together with the requirement that the startup actually invests, VC ≤ (1-λψ)α θ, characterizes the division of innovative labor region (the light gray triangular area in Figure 7).
Finally, the integration region is the non-convex polygon (in dark gray) described by the pair of conditions: (i) VC ≤ α if VC > (1-λψ)α θ; (ii) VC < α(1 - ψθ) if VC ≤ (1-λψ)α θ. Condition (i) describes when it is optimal for the large firm to invest when the startup does not invest. Condition (ii) describes when it is optimal for the large firm to invest, when it would potentially also be profitable for the startup to invest.
Greater bargaining power for the startup (vis-à-vis the large firm), as captured by lower ψ, reduces the size of the no-innovation region. Lower ψ increases the startup’s incentives to invest. A countervailing effect is that it also reduces the large firm’s incentives to enter a partnership. However, even if an invention is not commercialized by an alliance, it is commercialized by a large firm through internal R&D. Thus, the no-innovation region always shrinks with the bargaining power of the startup. Lemma 1 summarizes some of the discussion above.
Lemma 1: Size of the no-investment region. The no-innovation outcome is more likely when: (i) the efficiency of the market for technology (θ) is low; (ii) the R&D cost advantage of startups is low (high λ); (iii) large firm has higher bargaining power (high ψ).

4.1.2		Social welfare
Investment is socially efficient when either V - C ≥ 0 or θV - λC ≥ 0. For simplicity, we assume that V - C ≥ 0 so that investing in R&D is always socially efficient. But social welfare also depends on how innovation is organized. Division of innovative labor is more efficient than internal R&D whenever θV - λC ≥ V - C . The trade-off for society here is that a division
of innovative labor has an R&D cost advantage compared to internal R&D, but also a cost due

to frictions in the market for technology.

C Figure 8 depicts the critical efficiency condition ≥
V

1-θ
1-λ

using the same parameter

values as in Figure 7. Above this line, the social planner wants the R&D to be carried out by the startup. Below this line, the social planner wants the R&D to be integrated in the large firm. To illustrate when the market equilibrium is efficient (or not), Figure 8 also includes the boundaries of the three equilibrium regions, as shown in Proposition 1.
[Figure 8 Here]
C
Because ≤ 1, the entire no-innovation region is inefficient. There are two other regions V,
where the market equilibrium is inefficient. In the bottom-right triangle in Figure 8, the equilibrium exhibits integrated R&D but a division of innovative labor would be more efficient. The inefficiency here arises the large firm preempts the startup and invests to avoid sharing rents with the startup. This maximizes the large firm’s profits but not social welfare.
A second region exists where there is division of innovative labor in equilibrium, but integrated R&D is socially efficient. Here the inefficiency occurs because, as a result of limited appropriability (α ≤ 1), the large firm is more willing than the social planner to risk coordination failure (i.e., that V may not be realized), by relying on startup invention. This region, with an inefficient division of innovative labor, tends to vanish in the limit as appropriability α approaches unity. (The proof of this second statement is in the appendix.) Lemma 2 summarizes this discussion.
Lemma 2: Efficiency of equilibrium. The market equilibrium exhibits three types of inefficiency. (i) Some regions have insufficient investment in R&D. Other regions feature either (ii) division of innovative labor when integrated R&D would be more efficient, or (iii) integrated R&D when division of innovative labor would be more efficient. The region in (ii), which exhibits an inefficient division of innovative labor vanishes as the appropriability becomes complete.
The model highlights some of the key forces that determine the rate of innovation as well as C how innovation is organized. Projects with very high ratio of private benefit to cost (low ) αV
tend to be organized efficiently as integrated R&D. Privately less attractive projects are implemented through a division of innovative labor, when the cost advantage of startups outweighs the inefficiency of the market for technology. For more marginal projects, there is insufficient innovation, and the innovation that does occur may be inefficiently organized. Reduction in appropriability creates inefficiency by reducing private incentives to invest. For projects that do go forward, lower appropriability may also distort the organization of R&D.
Insofar as deep-tech sectors are characterized by lower appropriability, one would expect lower rates of private innovation. Our model also suggests that if these sectors exhibit division of innovative labor, such division may be inefficient. Large labs may have a comparative advantage in these sectors.
4.2		Gains from specialization
It is an article of faith in economic thought that the division of labor results in higher productivity. Specialization deepens as the size and scope of a market increases, and as the costs to transact and deliver goods and services drop (Smith, 1778; Stigler, 1951). Indeed, Arora et al. (2022) show that patent classes with a higher proportion of traded patents are more likely to cite a scientific article. Figure 9 plots commercialization (the share of papers cited by patents) against the share of startups, large firms, and universities that cite papers in their patents. Echoing Figure 4, the participation of large firms is weakly positively correlated with commercialization (r=0.001), but it is dwarfed by universities (r=0.59) and VC-backed firms (r=0.74) which show far stronger correlation with the share of commercialized papers. In other words, areas of science with a deeper division of labor between small entities (closer to science) and large entities (closer to market) also seem to show a greater translation of science.
[Figure 9 Here]
The development of mRNA vaccines illustrates the power of the division of innovative labor. Decades of mRNA research, mainly funded by governments, had shown that many viruses, including the one responsible for the COVID-19 pandemic, use a spike protein to bind to cells in the body. Instead of using dead or attenuated viruses, vaccine designers could use mRNA to instruct the body’s own cells to produce the spike protein. The immune system of the vaccinated individuals would then produce antibodies against this spike protein, enabling it to combat the virus if the individual were to become infected.
However, there were two significant challenges in the application of the science of mRNA. First, injecting mRNA into the bloodstream caused an inflammatory response from the body, which would also destroy the mRNA before it could get the body to produce the spike protein. Katalin Karikó discovered a way to minimize immune detection and subsequent degradation of synthetic mRNA by incorporating pseudouridine instead of uridine, an nucleic acid that is a constituent of mRNA. Drew Weissman validated that this modified mRNA could efficiently produce the intended proteins without undesired immune reactions. Subsequently, Karikó joined BioNTech to help commercialize the research. In the life sciences, markets for technology are quite efficient (they exhibit high θ, in the language of our model). Thus, startups, often founded to commercialize university translational research (such as that of Karikó and Weissman), have a very important role to play here, as predicted by our model.
The second challenge to the application of mRNA science was that mRNA molecules are fragile and easily degraded. Lipid nanoparticles (LNPs) provided a solution by encapsulating mRNA and protecting it from premature breakdown. This critical development also contributed to the creation of today’s mRNA vaccines, including those developed by Moderna and BioNTech during the COVID-19 pandemic. The LNP technology itself was developed by startups such as Acuitas, Arbutus Biopharma, and Genevant, who licensed this technology to Moderna and BioNTech. This provides another illustration of the importance of exchange and collaboration in an innovation ecosystem.
BioNTech and Pfizer partnered to develop the BNT162b2 mRNA vaccine, combining expertise to stabilize mRNA within LNP and ensure effective cellular delivery. BioNTech had high bargaining power (low ψ in terms of our model) because it had an outside option. Moderna, another mRNA vaccine developer, leveraged suppliers and CDMOs: Lonza for production and McKesson and thermal shippers for distribution.
4.3		Limits of a division of innovative labor
A startup-based division of innovative labor may under-perform when i) the science is less mature and carries multiple types of uncertainty; ii) appropriability is low; and iii) R&D costs are significant relative to the value of the innovation.
Uncertainty — Uncertainty is intrinsic to innovation. VCs are said to favor projects where uncertainty can be resolved in stages. For instance, Ewens et al. (2018) argue that VCs favored digital technologies because advances in cloud computing greatly reduced the cost of resolving uncertainty about demand. However, a reduction in the cost of resolving uncertainty would also encourage investment by established firms, unless the latter are unable to commit to discontinuing unpromising projects. On the other hand, Guedj and Scharfstein (2004) shows that pharmaceutical companies are quicker to cull unpromising projects compared to VC- funded biotech startups. As a result, when biotech startups fail, they fail later than projects by established pharmaceutical firms.
In the model in subsection 4.1, uncertainty is captured partially by θ. A startup may not innovate if it anticipates that it may not be able to find a partner to commercialize (a low θ) or, if it does find a partner, the latter may capture too large a share of value (a low (1 - ψ)). Exante contracts could resolve these problems, but uncertainty regarding whether the science can successfully be translated for commercial application, and uncertainty about the most promising applications, make ex-ante contracts difficult. In particular, uncertainty would also make it difficult to identify the relevant stakeholders when the possible commercial applications of a scientific advance remain uncertain Arora, Fosfuri, and Rønde, 2024. An incumbent would have to identify a promising startup, assess the likely value of a successfully commercialized application, and specify what would happen should the most promising commercial application lie outside its areas of interest. This seems impractical, to put it mildly.
Appropriability and spillovers — Startups are disadvantaged when appropriability is low and the market for technology is inefficient. As Proposition 1 notes, there is a region where division of labor is not feasible but where the large firm can nonetheless profitably innovate,
C	(1 - ψ)θ given by 1 >	>
αV λ
The imperfect appropriability of the fruits of corporate research is illustrated by Google’s seminal paper “Attention Is All You Need” (Vaswani et al., 2023). The paper introduced the Transformer model, a groundbreaking architecture that revolutionized natural language processing (NLP) and significantly advanced the field of artificial intelligence (AI). All but one of the eight co-authors left Google for AI startups. This exodus of key scientists is an example of low appropriability from research.
Although we assume that the appropriability parameter is common to large and small firms, large firms, by virtue of their strong market positions, are better equipped than startups to capture sufficient private returns from their scientific research. In spite of the rise of competitors such as OpenAI, Google itself continues to reap significant benefits from its AI research, which has led to many of its AI-driven products and services, such as Google Translate and BERT (Bidirectional Encoder Representations from Transformers). Reputational benefits are also important, and reputation is more valuable to large firms than to startups. Consider for instance Google’s AlphaFold, for which Demis Hassabis and John Jumper received the Nobel Prize, the Breakthrough Prize in Life Sciences, and the Albert Lasker Award for Basic Medical Research. AlphaFold greatly enhanced Google’s scientific standing, but its impact goes beyond basic research. Isomorphic Labs, a DeepMind spinoff, is currently working to commercialize AlphaFold’s technology, for instance in collaboration with pharmaceutical companies such as Novartis and Eli Lilly.
The significant external benefits of corporate research are not a new phenomenon. Xerox’s Palo Alto Research Center (PARC), founded in 1970 to spearhead advanced research and innovation, has significantly contributed to the American economy through groundbreaking inventions such as the mouse-controlled graphical user interface, Ethernet for local area networks, and the laser printer. While the laser printer was a major commercial success for Xerox, the company captured only a fraction of the benefits from many other PARC inventions, particularly those in industries beyond its core business. Companies such as Apple and Microsoft notably capitalized on PARC’s research to advance their own products. For instance, Apple’s Macintosh computer drew heavily from PARC’s graphical user interface, and Microsoft incorporated elements of PARC’s research into its Windows operating system.
In our model, however, when the market for technology is efficient, it is startups that invest in projects where appropriability is low and spillovers are high. To a large extent, this is because of their R&D cost advantage. If the startup’ R&D costs are sufficiently low and the market for technology is efficient enough, even projects with very high external spillovers can be accomplished through a startup-based division of innovative labor. The COVID-19 mRNA vaccines, with their extremely high external benefits, may be a case in point. Here there were research spillovers, but also broader societal benefits that did not accrue to the innovating firms.
Startups lack the scale for cutting-edge science —Our model emphasizes the R&D cost advantage of startups, relative to large firms. This is a natural assumption in the context of the commercialization of university discoveries. Moderna, for instance, was founded in 2010 to commercialize pioneering mRNA research by Derrick Rossi, a professor at Harvard Medical School.
There are limitations of a division of innovative labor, however, which are not captured by our model. In particular, startups may instead have a disadvantage in translational research. Modern science often requires upfront investments in expensive hardware. The price of photolithography steppers used in semiconductor manufacturing, for instance, has jumped at least 10 times between the 1990s and 2010s as transistor feature sizes have shrunk exponentially. Research in lasers and light-emitting diodes also requires the use of chemical vapor deposition or molecular beam epitaxy devices, which can cost millions of dollars.
AI research illustrates the point. The share of corporate publications in top AI journals, including the International Conference on Machine Learning (ICML), has tripled between 2004 and 2016 (Hartmann & Henkel, 2019). Universities and startups were not technically deficient at the beginning of the AI “race” in the early 2010s. In fact, the first public demonstration of the potential of neural networks occurred with AlexNet by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton at the University of Toronto, which won the ImageNet competition in 2012. However, startups did not have access to the quantities of data and computing power that large firms can have. Training neural networks requires extensive resources. Ahmed et al. (2023) show that machine learning models trained by industry use around two orders of magnitude larger compute resources compared to academia.
Whether a division of innovative labor can outperform a centralized system of corporate labs depends on the balance between gains from specialization and coordination costs. There is wide variation in this balance across scientific fields, with the biggest gains of startups found in the life sciences and the biggest losses found in the material sciences. We argue that some emerging technologies often impose steep upfront investments in equipment and personnel and involve unclear paths to commercialization that startups and their investors find particularly problematic. In short, an innovation ecosystem that divides the task of commercializing new science increases efficiency in some cases but also leaves blind spots that an ecosystem with large corporate labs is better suited to tackle.
5		Conclusion and discussion
Anaemic economic growth in America has led to speculations that we have run out of good science to commercialize. We find little support for this claim and instead argue that the slower translation of the available science due to the decline of industrial research and the accompanying division of innovative labor may be the problem.
A division of innovative labor yields gains from specialization, but requires an efficient market for technology and adequate value capture by startups. However, the market for technology is less efficient in deep-tech fields, due to uncertainty surrounding the technology and the market potential. This uncertainty discourages startups. Moreover, deep-tech research may have more spillovers and lower appropriability. For instance, some of the benefits of cleantech innovations may be inherently difficult for firms to capture. The decline of industrial research in deep-tech sectors may also reduce the exit options for startups, reducing their bargaining power. These factors lead to unevenness in the translation of science in the economy.
If so, a two-pronged policy approach is required. Policy makers must support corporations that are making large-scale investments in upstream research. These firms typically enjoy significant market power in their core markets. In addition to directly investing in scientific research, these firms also represent an important exit option for innovative startups, as Google did for DeepMind. Anti-trust policy must be sensitive to the societal benefits created by these firms. A standard one-size-fits-all anti-trust policy may entail significant costs.
Most large American firms however, show no signs of resurrecting industrial research. Universities and venture-backed startups and the markets for technology that deliver their inventions to incumbent firms will likely remain mainstays of the new system. It is vital to improve the working of this new system, by funding university research and encouraging startups, especially in deep-tech sectors, and improving the efficiency of the market for technology by strengthening intellectual property rights.

(Millions of 2017$)

Figure 1: U.S. Basic and Applied Research Expenditures

Notes: Bar graph for “Total U.S. Research” plots the expenditure on applied and basic research by U.S. academia, businesses, non profit organizations and federally funded research and development centers (FFRDCs). “Research Funded and Performed by Business” plots the subset of this expenditure that was funded and performed by U.S. businesses. The dashed line divides dollars expended by businesses on research (applied and basic) by their expenditures on research and development. All dollar figures are deflated to 2017. Data is sourced from the National Patterns of R&D Resources tables from the National Center for Science and Engineering Statistics (NCSES) (2024).

Figure 2: Share of High Quality Papers Cited by Patents, by Scientific Disruption Quartiles

QuartilesofPaperDisruptiveness
1Q(LeastDisruptive)	2Q
3Q	4Q(MostDisruptive)
Notes: The binned scatterplots show the share of papers cited by patents within 5 years of paper publication, separated by quartiles of the papers’ disruptiveness indices (CD-5) from Holst et al. (2024). Sample is limited to papers that are in the top 10% of forward paper citations for their field and year and with nonnegative CD-5 scores.

Figure 3: Cooperative Research Arrangements between Corporations and Academia

Notes: Bar graph plots the share of corporate papers coauthored with an academic institution. Corporate scientific papers are sourced from the DISCERN project (Arora, Belenzon, Cioaca, Sheer, Shin, & Shvadron, 2024) which matches scientific publications in OpenAlex to U.S. headquartered public firms and their subsidiaries. Academic institutions are identified through OpenAlex. The dashed line divides the applied and basic research funded by businesses and performed by universities by the total business funding for research. Data is sourced from the NCSES.
Figure 4: Commercialization and Large Firm Share By Scientific Field

Notes: This graph presents a binned scatterplot at the scientific field-year level. The Y-axis measures the changes in the share of papers cited by patents within 5 years of the publication date of the paper. The x-axis measures the share of large firm publications taken from the DISCERN database (Arora, Belenzon, Cioaca, Sheer, et al., 2024). Sample is limited to papers that are in the top 10% of forward paper citations for the field and year. Scientific publication data is sourced from the SciSciNet database (Lin et al., 2023).
Figure 5: Translation of Science and Corporate Participation by Firm Type

LargeFirmPaperShare	ShareofVCCitations
ShareCitedbyPatents
Notes: Binned scatterplots summarize scientific field-year level data. “Large Firm Paper Share” divides the number of papers authored by publicly listed U.S. firms by all scientific publications (Arora, Belenzon, Cioaca, Sheer, et al., 2024). “Share of VC citations” divides the number of papers cited by at least one VC-backed startup patent by number of papers cited by any patent in the field-year (Ewens & Marx, 2024). “Share Cited by Patents” divides the number of papers cited by patents by all scientific publications in the field-year. Paper sample is limited to those in the top 10% by forward paper citations in their field-year. We count patent citations of papers that occur up to five years after the focal paper’s publication. Scientific publication data is sourced from the SciSciNet database (Lin et al., 2023).
Figure 6: Large vs VC-backed Firm Participation in Science

1980s vs 1990s

1990s vs 2000s

2000s vs 2010s

ο Phy!
oEn Ο
Mate ο , Ο I	ο Chemistr^edicine° lies
gineering
Computer science
o Biology
Mathematics	

—I		I	I	I—
-.5	0	.5	1	1.5
Δ Large Firm Paper Share

Material
ςoEr S
Tiatics ο( Ο 0 Ο	ο Medicine
; science gineering Computer science Biology	
Chemistry		

-.5	0	.5	1	1.5

Δ Large Firm Paper Share

o Matl Co 0 blogy	lematics nputer science
o Medicine	
	η Cho 8 Phy ο Engine o Material	mistry	
sics
ering
s science	

I	I	I	I
-.5 0	.5	1	1.5
Δ Large Firm Paper Share

Notes: Scatterplots are at tlie scientific field level. "Share of VC citations" divides the number of papers cited by at least one VC-backed startup patent by number of papers cited by any patent in the field Ewens and Marx (2024). "Large Firm Paper Share" divides the number of papers autliored by publicly listed U.S. firms by all scientific publications (Arora, Belenzon, Cioaca, Sheer, et al., 2024). Tire left panel plots change in average VC citation share between tlie 1990s and 1980s against tlie change in average large firm paper share in tlie 1990s against tlie 1980s. Tlie middle and riglit panel plot tliese clianges for tlie subseguent decades. Scientific publication sample is limited to tliose witliin tlie top 10% of forward paper citations in tlieir field-year. Patent citations to papers are limited to tliose tliat occur up to five years after paper publication. Geology and environmental science are omitted. Scientific publication data is sourced from SciSciNet (Lin et al., 2023).
Figure 7: Commercialization Regions
Notes: This graph plots the regions in the ( VC , θ)-space when internal R&D, division of innovative labor, and no innovation occur. Parameter values are α = 12 , ψ = 12 and λ = 14
Figure 8: Socially Optimal Investment
Notes: The region above red line is where a Division of Innovative Labor is socially efficient. The region below the red line is where internal R&D is efficient. Parameter values are α = 12 , ψ = 12 and λ = 14 as in Figure 7.

Figure 9: Commercialization of Scientific Fields by Types of Users

co
OT

00

00 @

.1	.2 .3 .4 .5 .6
Share of VC Citations

Biology
Materials science

ChemistryComputer science

Engineering
Medicine
Physics Mathematics

.1	.2 .3 .4 .5 .6
Share of Large Firm Citations

Notes: Scatterplots at tlie scientific field level grapli tlie sliare of papers cited by patents against tlie tlie types of users citing science. "Sliare of VC citations" in tlie x-axis of tlie left panel divides tlie number of papers cited by at least one VC-backed startup patent by papers cited by any patent (pairwise correlation witli forward patent citations is 0.74). "Sliare of Large Firm Citations" in tlie middle plot clianges tlie numerator to papers cited by patents of U.S. headquartered listed firms from tlie DISCERN project (r=0.10). "Sliare of University Citations" in tlie riglit plot clianges tlie numerator to papers cited by patents of universities and national laboratories (r=0.59). VC-backed firms, public entities and national laboratories are identified tlirougli data from Ewens and Marx (2024). Sample is limited to papers tliat are in tlie top 10% of forward paper citations for tlie field and year. We count patent citations of papers tliat occur up to five years after tlie focal paper's publication. Scientific publication data is sourced from tlie SciSciNet database (Lin et al., 2023).
CO CT)
	Patent Citation Rate
Top 5 Patentees	10.00%	Patent Citation Rate
Top 5 Patentees	25.57%	Patent Citation Rate
Top 5 Patentees	12.62%	Patent Citation Rate
Top 5 Patentees	10.04%	
	Genentecli	519	Univ of California	3390	Genentecli	10350	Moderna	3081	
	Merck	343	INCYTE Pharma	2696	Pioneer Hi Bred	1778	Univ of California	2421	
	Cetus Co	317	ISIS Pharma	1959	Univ of California	1476	Pioneer Hi Bred	2406	
	Univ of California	298	Genentecli	1841	Genomatica	1364	Broad Institute	2040	
	Scripps Clinic	269	Merck	1571	ISIS Pharma	970	Harvard Univ	1775	
Chemistry	1980-89		1990-99		2000-09		2010-2019		
	Patent Citation Rate
Top 5 Patentees	8.25%	Patent Citation Rate
Top 5 Patentees	14.93%	Patent Citation Rate
Top 5 Patentees	9.96%	Patent Citation Rate
Top 5 Patentees	7.31%	
	Du Pont	768	Univ of California	1480	Genentecli	1331	Univ of California	1506	
	Merck	722	ISIS Pharma	1318	Univ of California	923	MIT	1045	
	Eli Lilly	405	Merck	1267	Univ of Illinois	841	ISIS Pharma	964	
	Monsanto	395	Eli Lilly	978	Bristol Myers Sguibb	634	CalTech	869	
	Dow	330	Univ of Texas	861	MIT	585	Incyte Pharma	743	
Computer science	1980-89		1990-99		2000-09		2010-2019		
	Patent Citation Rate
Top 5 Patentees	7.99%	Patent Citation Rate
Top 5 Patentees	15.14%	Patent Citation Rate
Top 5 Patentees	10.46%	Patent Citation Rate
Top 5 Patentees	8.74%	
	IBM	471	IBM	2538	Microsoft	5317	IBM	10655	
	Bell Labs	440	Microsoft	1215	IBM	3805	Microsoft	5264	
	GE	264	Sun Microsys	1062	Intel	791	AT&T IP	5141	
	RCA	150	Lucent	1008	Sun Microsys	689	Google	4068	
	Bell Communications	141	Intel	785	Google	672	Microsoft Tech Licensing	4028	
Engineering	1980-89		1990-99		2000-09		2010-2019		
	Patent Citation Rate
Top 5 Patentees	5.92%	Patent Citation Rate
Top 5 Patentees	8.28%	Patent Citation Rate
Top 5 Patentees	5.61%	Patent Citation Rate
Top 5 Patentees	5.01%	
	Bell Labs	149	Micron	488	IBM	742	AT&T IP	2250	
	GE	130	IBM	411	Micron	441	IBM	1682	
	IBM	104	Motorola	292	Intel	342	Qualcomm	755	
	Texas Instruments	101	Lucent	286	Numerical Technol	328	Google	555	
	Motorola	96	Texas Instruments	169	Innovative Sillicon	312	GE	509	
Table 1: Top Users of Science by Field (1/2)
Biology	1980-89	1990-99	2000-09	2010-2019
Notes: "Patent Citation Rate" refers to the share of papers that receive one or more citations from U.S. patents within five years of publication. "Top 5 Patentees" downward sorts the number of paper-patent citation pairs by a patent assignee (which are listed next to the focal company name). Grey-shaded cells refer to firms that have either received Venture Capital Financing based on Ewens and Marx (2024) or are younger than 10 years at the beginning of the decade.

Table 2: Top Users of Science by Field (2/2)
Materials science	1980-89		1990-99		2000-09		2010-2019		
	Patent Citation Rate
Top 5 Patentees	11.95%	Patent Citation Rate
Top 5 Patentees	14.08%	Patent Citation Rate
Top 5 Patentees	11.09%	Patent Citation Rate
Top 5 Patentees	7.39%	
	Bell Labs	1034	Micron	1277	Micron	3225	IBM	3215	
	IBM	641	IBM	1068	Nantero	1574	Lockheed Martin	1915	
	Texas Instruments	565	Lucent	1014	IBM	1570	MIT	1592	
									
	GE	459	Motorola	849	Univ of California	954	Monolithic 3D	1353	
	DuPont	363	Univ of California	620	Univ of Illinois	916	CalTecli	1252	
Mathematics	1980-89		1990-99		2000-09		2010-2019		
	Patent Citation Rate	1.84%	Patent Citation Rate	3.91%	Patent Citation Rate	3.24%	Patent Citation Rate	2.45%	
	Top 5 Patentees		Top 5 Patentees		Top 5 Patentees		Top 5 Patentees		
	Bell Labs	63	Microsoft	193	Microsoft	531	IBM	514	
	IBM	56	Lucent	164	IBM	233	Microsoft	439	
	GE	35	Motorola	155	LSI Co	147	Adobe	354	
	RCA	31	IBM	150	Cadence Design Sys	141	Qualcomm	320	
	Schlumberger	26	Xerox	132	Mitsubishi Electric	139	Google	299	
Medicine	1980-89		1990-99		2000-09		2010-2019		
	Patent Citation Rate
Top 5 Patentees	2.68%	Patent Citation Rate
Top 5 Patentees	6.71%	Patent Citation Rate
Top 5 Patentees	4.42%	Patent Citation Rate
Top 5 Patentees	3.42%	
	Τ Jefferson Univ	151	Merck	746	Dexcom	1043	Moderna	2378	
	Univ of Texas	124	Univ of California	632	Genentecli	980	Array Biopharma	1523	
	Univ of California	121	Eli Lilly	562	Univ of California	510	Immunomedics	1355	
	Warner Lambert	121	Univ of Texas	525	Bristol Myers Squibb	483	Cyberonics	1241	
	ER Squibb	118	Genentch	444	Adv Cooling Therapy	398	Pharmacyclis	1194	
Physics	1980-89		1990-99		2000-09		2010-2019		
	Patent Citation Rate
Top 5 Patentees	2.80%	Patent Citation Rate
Top 5 Patentees	3.41%	Patent Citation Rate
Top 5 Patentees	3.26%	Patent Citation Rate
Top 5 Patentees	2.76%	
	Bell Labs	186	Micron	288	General Hospital	294	Lockheed Martin	1385	
	IBM	81	Lucent	210	MIT	281	AT&T IP	888	
	Stanford Univ	69	MIT	162	Univ of California	212	IBM	650	
	Rockwell Intl	56	IBM	138	Lucent	204	CalTecli	514	
	GE	54	Univ of California	128	CalTecli	175	MIT	478	
Notes: "Patent Citation Rate" refers to the share of papers that receive one or more citations from U.S. patents within 5 years of publication. "Top 5 Patentees" downward sorts the number of paper-patent citation pairs by a patent assignee. Grey-shaded cells refer to firms that have either received Venture Capital Financing based on Ewens and Marx (2024) or are younger than 10 years at the beginning of the decade.
References
Ahmed, N., Wahed, M., & Thompson, N. C. (2023). The Growing Influence of Industry in AI Research. Science, 379 (6635), 884–886.
Alston, J., & Pardey, P. (2022). Are Ideas Really Getting Harder to Find? (Tech. rep.). University of Minnesota, Department of Applied Economics.
Anderson, G., & Moris, F. (2023). Federally Funded R&D Declines as a Share of GDP and Total R&D (NSF 23-339). National Science Foundation. Alexandria, VA.
Ando, Y., Bessen, J. E., & Wang, X. (2025). The Rising Returns to R&D: Ideas Are not Getting Harder to Find. Boston Univ. School of Law Research Paper, (25-16).
Arora, A., Belenzon, S., Cioaca, L., Sheer, L., Shin, H., & Shvadron, D. (2024). DISCERN 2.0: Duke Innovation & SCientific Enterprises Research Network. Zenodo.
Arora, A., Belenzon, S., Cioaca, L. C., Sheer, L., & Zhang, H. (2023, November). The Effect of Public Science on Corporate R&D. 31899.
Arora, A., Belenzon, S., & Patacconi, A. (2018). The Decline of Science in Corporate R&D. Strategic Management Journal, 39 (1), 3–32.
Arora, A., Belenzon, S., & Sheer, L. (2021). Knowledge Spillovers and Corporate Investment in Scientific Research. American Economic Review, 111 (3), 871–98.
Arora, A., Belenzon, S., & Suh, J. (2022). Science and the Market for Technology. Management Science, 68 (10), 7176–7201.
Arora, A., Cohen, W., Lee, H., & Sebastian, D. (2023). Invention value, inventive capability and the large firm advantage. Research Policy, 52 (1), 104650.
Arora, A., Fosfuri, A., & Rønde, T. (2024). The Missing Middle: Value Capture in the Market for Startups. Research Policy, 53 (3), 104958.
Arora, A., & Gambardella, A. (1994). The Changing Technology of Technological Change: General and Abstract Knowledge and the Division of Innovative Labour. Research Policy, 23 (5), 523–532.
Block, F., & Keller, M. R. (2009). Where Do Innovations Come from? Transformations in the US Economy, 1970–2006. Socio-Economic Review, 7 (3), 459–483.
Bloom, N., Jones, C. I., Van Reenen, J., & Webb, M. (2020). Are Ideas Getting Harder to Find? American Economic Review, 110 (4), 1104–1144.
Bush, V. (1945). Science: The Endless Frontier. Transactions of the Kansas Academy of Science (1903-), 48 (3), 231–264.
Ceccagnoli, M., Higgins, M. J., & Palermo, V. (2014). Behind the Scenes: Sources of Complementarity in R&D. Journal of Economics & Management Strategy, 23 (1), 125–148.
Chen, C., Nguyen, D. T., Lee, S. J., Baker, N. A., Karakoti, A. S., Lauw, L., Owen, C., Mueller, K. T., Bilodeau, B. A., Murugesan, V., et al. (2024). Accelerating computational materials discovery with machine learning and cloud high-performance computing: from large-scale screening to experimental validation. Journal of the American Chemical Society, 146 (29), 20009–20018.
Cohen, W. M., & Klepper, S. (1996). A reprise of size and R & D. The Economic Journal, 106 (437), 925–951.
Cohen, W. M., & Levinthal, D. A. (1989). Innovation and Learning: The Two Faces of R & D. The economic journal, 99 (397), 569–596.
Dalla Fontana, S., & Nanda, R. (2023). Innovating to Net Zero: Can Venture Capital and StartUps Play a Meaningful Role? Entrepreneurship and Innovation Policy and the Economy, 2, 79–105.
Deng, N., Gu, X., Fan, Y., Havlin, S., & Zeng, A. (2025). The critical role of persistent disruption in advancing science. Nature Computational Science, 1–10.
Deng, N., & Zeng, A. (2023). Enhancing the robustness of the disruption metric against noise. Scientometrics, 128 (4), 2419–2428.
Elder, A. L. (1970). The History of Penicillin Production. American Institute of Chemical Engineers.
Ewens, M., Nanda, R., & Rhodes-Kropf, M. (2018). Cost of Experimentation and the Evolution of Venture Capital. Journal of Financial Economics, 128 (3), 422–442.
Ewens, M., & Marx, M. (2024). Firm Age and Invention: An Open-Access Dataset.
Fort, T. C., Goldschlag, N., Liang, J., Schott, P. K., & Zolas, N. (2025). Growth is Getting Harder to Find, Not Ideas (tech. rep.). US Census Bureau, Center for Economic Studies.
Goldfarb, B. (2005). Diffusion of General-Purpose Technologies: Understanding Patterns in the Electrification of US Manufacturing 1880–1930. Industrial and Corporate Change, 14 (5), 745–773.
Gordon, R. J. (2012). Is US Economic Growth over? Faltering Innovation Confronts the Six Headwinds. National Bureau of Economic Research.
Guedj, I., & Scharfstein, D. (2004, November). Organizational Scope and Investment: Evidence from the Drug Development Strategies and Performance of Biopharmaceutical Firms. 10933.
Hartmann, P., & Henkel, J. (2019). The Rise of Corporate Science in AI [Working Paper].
Holst, V., Algaba, A., Tori, F., Wenmackers, S., & Ginis, V. (2024, February 7). Dataset Artefacts are the Hidden Drivers of the Declining Disruptiveness in Science. arXiv: 2402.14583 [cs]. Retrieved August 27, 2024, from http://arxiv.org/abs/2402.14583
Jones, B. F. (2009). The Burden of Knowledge and the “Death of the Renaissance Man”: Is Innovation Getting Harder? The Review of Economic Studies, 76 (1), 283–317.
Kim, C. (2024). From Research to Development: How Globalization Shapes Corporate Innovation. University of Maryland, PhD Dissertation.
Lerner, J., & Nanda, R. (2020). Venture capital’s role in financing innovation: What we know and how much we still need to learn. Journal of Economic Perspectives, 34 (3), 237–261.
Lin, Z., Yin, Y., Liu, L., & Wang, D. (2023). SciSciNet: A Large-Scale Open Data Lake for the Science of Science Research. Scientific Data, 10 (1), 315.
Macher, J. T., Rutzer, C., & Weder, R. (2024). Is there a secular decline in disruptive patents? Correcting for measurement bias. Research Policy, 53 (5), 104992.
Masclans, R. (2025). Science, Startups, and the Problem of Value Capture:Thin Acquisition Markets, Weak Outside Options [Doctoral dissertation, Fuqua School of Business, Duke University].
Medawar, P. B. (1982). Pluto’s Republic. Oxford University Press.
Moreira, S., Klueter, T. M., & Asija, A. (2023). Market for technology 2.0? reassessing the role of complementary assets on licensing decisions. Research Policy, 52 (7), 104787.
Mowery, D. C. (2009). Plus ca Change: Industrial R&D in the “Third Industrial Revolution”. Industrial and corporate change, 18 (1), 1–50.
National Center for Science and Engineering Statistics (NCSES). (2024). National Patterns of R&D Resources: 2021–22 Data Update (NSF 24-318). National Science Foundation. Alexandria, VA.
National Research Council. (1980). Industrial Research Laboratories of the United States, Including Consulting Research Laboratories.
Park, M., Leahey, E., & Funk, R. J. (2023). Papers and Patents Are Becoming Less Disruptive over Time. Nature, 613 (7942), 138–144.
Smith, A. (1778). An Inquiry Into the Nature and Causes of the Wealth of Nations: By Adam Smith, LL. D. and FRS Formerly Professor of Moral Philosoophy in the University of Glasgow; in Two Volumes (Vol. 1). W. Strahan, and T. Cadell, in the Strand.
Stigler, G. J. (1951). The Division of Labor is Limited by the Extent of the Market. Journal of political economy, 59 (3), 185–193.
Stokes, D. E. (1997). Pasteur’s Quadrant : Basic Science and Technological Innovation. Brookings Institution Press.
Teodoridis, F. (2018). Understanding team knowledge production: The interrelated roles of technology and expertise. Management Science, 64 (8), 3625–3648.
U.S. Department of Agriculture, Agricultural Research Service. (2023). The Rescue of Penicillin: How USDA Lab Strains Made Wartime Production Possible.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polo- sukhin, I. (2023, August 1). Attention Is All You Need. arXiv: 1706.03762 [cs].
Whitehead, A. N. (1925). Science and the modern world. The Macmillan Company.
Woolf, S. H. (2008). The Meaning of Translational Research and Why It Matters. JAMA, 299 (2), 211–213.
Appendix Proof for Model
In the main text, we state the following:
Lemma 2: Efficiency of equilibrium. The market equilibrium exhibits three types of inefficiency. (i) Some regions are characterized by insufficient investment in R&D. Other regions feature either (ii) division of innovative labor when integrated R&D would be more efficient, or (iii) integrated R&D when division of innovative labor would be more efficient. The region in (ii) exhibiting inefficient DOIL vanishes as the appropriability parameter α tends to one.
That there can be three regions where the market equilibrium is socially inefficient is shown by example in Figure 8. In this appendix, we prove the second part of Lemma 2.
Let
f(θ)= α(1λ-ψ)θ, g(θ) = α(1 - ψθ), h(θ) = 1-1 λ(1-θ).
All three lines are illustrated in Figure 8.
A necessary condition for the DOIL region to be always socially efficient is that the point θ where f(θ) and g (θ) intersect in on or above the line defined by equation h(θ).
(Note that this is also a sufficient condition. The reason is that the absolute value of slope of the h function, 1-1λ ≥ 1, is always greater that the absolute value of the slope of the g function, αψ ≤ 1. See Figure 8 for some intuition.)
Simple algebra shows that f (θ) = g(θ) implies θ= 1-ψλ+ψλ∈[λ,1] and f(θ)= (1α-(1ψ-+ψψ)λ).
Moreover
1	1 - ψ - λ + ψλ
h(θ) =	.
1 - λ 1 - ψ + ψλ
f(θ) ≥ h(θ) implies α(1 - ψ)(1 - λ) ≥ 1 - ψ - λ + ψλ.
This only holds for α = 1.
to papers that do not cite the focal paper but at least one of its references. We use data made available by Holst et al. (2024) that corrects for the bias introduced by papers with zero backward citations. The data is available for papers published until 2010. We use CD5, which calculates disruption measures based on forward citations received up to five years after publication.