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Large, established companies get a bad rap for failing to be innovative. Conventional wisdom suggests that these firms are less likely to create path-breaking new technologies. Some argue this is because established firms are less likely to pursue radically new ideas—they get complacent or they don’t want to cannibalize their own success. Others claim that established firms may try to innovate, but are too set in their old ways to succeed. In our research, we argue that it’s neither reluctance to pursue new technologies nor inability to think in new ways that limits established firms from developing new technologies. Rather, it’s when and where they choose to pursue such innovations that may sometimes be the problem. On the one hand, firms are most likely to pursue path-breaking innovation when their technological performance has fallen below their expectations. A firm that’s outcompeting its rivals may see no need to try something radically new; one that’s falling behind may be eager to look for a big breakthrough, especially if its performance is just slightly below expectations, so a big new discovery could push it over the hump. On the other hand, attempts to develop path-breaking new technologies are most likely to succeed when undertaken from positions of existing strength. Organizations tend to perform better when they have strong capabilities—better scientists and engineers, effective product development processes, and strong brand names—and these advantages are not only likely to persist over time, they are also likely to be helpful even when pursuing more radical inventions. The better you understand the possibilities and limits of the old technology, the better chance you have of combining it with more distant knowledge to develop something really new. Putting these two arguments together suggests a fundamental mismatch between the pursuit of path-breaking new innovations and their likelihood of success. Firms may be most motivated to go after radical new technologies when they are lagging (slightly) behind expectations, but they are most likely to successfully develop such technologies when they are technology leaders. Thus, firms that eagerly pursue path-breaking new technologies when their performance falls below expectations may be over-invested in looking for the next big thing, while firms that choose to play it safe because their performance far exceeds expectations may be under-invested in trying new things. Our study explores this dynamic through a massive, long-term dataset of organizational patenting behavior from 1980-1997. Starting with every US patent filed, we look for patents that make truly novel connections—bringing together technology areas that have rarely, if ever, been linked before. For example, if research in power generation hadn’t typically drawn on knowledge about gas separation (largely true before the 1990s), then a new patent using gas separation to innovate in power plants would represent a novel connection. We then look at a) which firms are most likely to introduce such patents, and b) which of these “radical” patents are most likely to have substantial impact, where impact represents a breakthrough patent that is among the 5% most highly cited patents in the same technology class and year (a common, non-commercial measure of patent success). Our results show strong support for our theory about the mismatch between motivation and ability: firms are most likely to introduce “radical” patents when their prior technological performance has been just below their aspirations, but such patents are most successful when introduced by firms performing substantially above their aspirations. Further, we find that this mismatch is especially pronounced in large, multi-technology firms—the more technological areas you compete in, the more eager you are to try radically new things in areas where you’re falling behind, and the less eager to mess with those technologies that are doing well.

Studying a sample of seven European countries, including the five largest economies of the continent, the firm size appears as a determinant of firm’s capacity to innovate, in terms of product, process and organizational innovation. This correlation between firm size and propensity to innovate holds even controlling for the different distributions of firm size across countries and across productive sectors. Some other factors appear as key determinants of innovation, like the expenditure in R&D, the “human capital” of the workforce (formal instruction and training programs), the professionalism of the management (if external to the family that owns or controls the firm). Such factors are disproportionally distributed across firm size (when the dimension increases, the quantity of such factors increases), and it is essentially this different amount of key resources for innovation to explain the lower innovative performance of small firms, not some “intrinsic” or “black box” motivations linked to the limited size. Only in process innovation small firms seem to have a disadvantage respect to medium firms not explained by such factors. As concerns Italy, we must premise that this study does not consider the micro firms, from 1 to 9 employees, which are particularly abundant in Italy (Onida, 2004); therefore the relative position in innovative performance emerging from this study is likely a bit shifted to a more favourable position respect to a framework which considers all the kinds of firms. Besides, innovation is measured through a survey, not with measures like patents or publications, which generally indicate a more radical innovation and where Italy is notoriously weak (Istituto Guglielmo Tagliacarne-Focus PMI, 204). Therefore, when positive signals emerge about Italian innovative performances, they should be taken cum grano salis. The descriptive statistics indicate for Italy, if compared with the other countries, a large presence of medium firms and a shortage of large firms. The innovative performance, measured as the percentage of firms who introduced in a three-year period at least one product, process and organisational innovation, is in line with the average of the sample in product and process innovation and below such average in organisational innovation. The international framework appears quite articulated: the multivariate analysis allows to do a “ranking” of the innovative performance of the countries, even excluding the effect of different distributions of productive sectors and of firm size; such ranking results different in different types of innovation; in the tree types of innovation, Italy occupies intermediate places. Comparing Italy with the whole sample, the statistically significant results are that medium Italian firms have an higher propensity to process innovation than the medium firms of the whole sample, while all firm sizes have a disadvantage in organisation innovation. Even in Italy, small firms have a disadvantage respect to medium and large firms in all three types of innovation. The amount of this disadvantage does not seem significantly different in Italy respect to the other countries. Trying to verify if the disadvantage of small firms in the innovative capacity is due to a different allocation of crucial resources, we discovered that there is a shortage, if compared with the other countries, of human capital (graduated workers and workers who participate to training programs) and of non-familiar management. This is probably the reason why in many cases the human capital and the management do not appear in Italy as key determinant of innovation. A more educated and trained workforce, a more professional management seem therefore the way for Italy to close the innovative gaps or to reinforce its strengths. This results goes together with another policy conclusion of our study: we unequivocally find an advantage given by a larger firm size in every kind of innovation; being the Italian industrial characterised by an average small dimension of firms, this suggests to implement policies aiming at increasing the firm size. Actually, looking closely, these two conclusions go together: the size of a firm increases if it is successful; but, a firm’s workforce becomes more trained, its management more professional, it certainly becomes more innovative, therefore more successful; in this way the firm grows. Becoming larger, it has more resources to undertake innovative projects, to hire more qualified personnel and management; the public intervention should operate to trigger such virtuous circle

The immense size of big tech firms has made it relatively easy for them to bid for defense and intelligence agency contracts, and some have been doing so for decades. The same cannot be said for small startup companies that are often starved for cash and need revenue streams to stay alive. According to some analysts, pilot projects launched by defense tech startups may succeed in creating prototypes, but frequently fail to cross the so-called “valley of death” lying between early prototype production and multi-year Pentagon contracts. Historically, the overhead costs associated with U.S. government procurement processes have made it difficult for smaller firms to compete.47 This began to change in 2015, when then Defense Secretary Ash Carter established DIUx. It was headquartered in Silicon Valley and designed as a venture capital fund: the goal was to quickly identify and invest in startups developing cutting-edge technologies that might have military applications.48 With DIUx, the Pentagon built its own startup accelerator to fund firms specializing in AI, robotics, data analytics, cybersecurity, and biotechnology. DIUx was intentionally located in the heart of Silicon Valley, near Amazon’s Lab126, Microsoft’s Silicon Valley campus, and Apple’s corporate offices. Carter, who had spent several years at Stanford University prior to his appointment as Defense Secretary, had reportedly been impressed with the Bay Area’s innovative entrepreneurial spirit.49 In 2018, DIUx was renamed Defense Innovation Unit (DIU), indicating that it was no longer experimental. Between June 2016 and September 2022, DIU awarded contracts worth a total of $1.2 billion to more than 320 companies.50 Carter modeled DIU after In-Q-Tel, a firm established by the CIA in the late 1990s to capitalize on innovations being developed in the private sector, particularly in Silicon Valley.51 By channeling CIA funds to nascent companies building surveillance, intelligence gathering, data analysis, and cyberwar technologies, the agency hoped to outdo global rivals by funding firms with creative engineers, hackers, scientists, and programmers. In-QTel has made more than 500 investments across an extraordinary range of startups.52 In-QTel’s portfolio includes firms with futuristic projects such as Cyphy, which manufactures tethered drones that can fly reconnaissance missions for extended periods using a continuous power source; Atlas Wearables, which produces fitness trackers that closely monitor body movements and vital signs; Fuel3d, which sells a handheld device that produces detailed three-dimensional scans of structures or objects; Sonitus, which has developed a wireless communications system, part of which fits inside the user’s mouth; and Saildrone, which produces autonomous maritime surveillance drones enabled by AI.53 In-Q-Tel has also invested in data-mining firms like Geofeedia, TransVoyant, and PATHAR.54 Once again, it’s worth reflecting on how funding for these new companies could have come from taxpayer-funded civilian agencies, rather than from the CIA or Pentagon. For example, financing for Cyphy’s tethered drones could have come from FEMA (Federal Emergency Management Agency), the USDA (the Department of Agriculture), or the Department of the Interior, since these agencies could presumably use new drone technologies to help survey disaster zones, farms, or federal lands. Since the tools being developed by Atlas Wearables clearly have medical applications, research and development for the company’s devices could have been supported by the NIH (National Institutes of Health). And Saildrone’s nautical vessels potentially have a ride range of oceanographic applications, and under different circumstances, could have been funded exclusively by NOAA (National Oceanographic and Atmospheric Administration) or the EPA (Environmental Protection Agency). Instead, all of these startups were partly supported by military agencies that presumably had a vital role in influencing the development of the new technologies. As noted by Hugh Gusterson: “When research that could be funded by neutral civilian agencies is instead funded by the military, knowledge is subtly militarized and bent in the way a tree is bent by a prevailing wind.”55 The public comes to accept that military institutions should rightfully dominate the direction of research and development work, and loses sight of long term consequences—namely, the ways in which this process binds the American economy ever more tightly to endless wars abroad and greater surveillance at home. DIU and In-Q-Tel are parts of an investment infrastructure that has expanded quickly over time. As noted above, the Defense Department created MD5—now called the National Security Innovation Network—shortly after DIUx was founded, and last year, it established the Office of Strategic Capital as a means of connecting defense tech startups to sources of venture capital and private equity. In addition, the DoD has rapidly developed a range of “accelerators,” “incubators,” and “hubs” to cultivate “innovation ecosystems” that bring small entrepreneurs and startup firms into contact with potential customers from U.S. defense and intelligence agencies. 56 Startups typically follow one of three paths. By far, the most common is failure. According to conservative estimates, 75 percent of startups do not succeed in bringing a commercially viable product to market, or its early investors fail to make a profit.57 Other estimates place the failure rate at approximately 90 percent. The few startups that do manage to survive either “go public”—that is, they emerge as publicly traded corporations—or they are purchased and merged with (or acquired by) a larger company. As they develop and incur expenses, successful startups generally organize several fundraising stages or “funding rounds” (Seed, Series A, Series B, Series C, Series D, etc.) to keep afloat. To illustrate how U.S. military and intelligence agencies have supported tech startups, consider the case of Keyhole, a small San Francisco-based company that developed software for creating three-dimensional models of the earth’s surface. By patching together satellite images and aerial photos, the program could essentially produce a high-resolution map of the entire planet. In-Q-Tel provided seed funding in 2003, and within two weeks, military and intelligence agencies were reportedly using Keyhole’s software to support the U.S. war in Iraq.58 The following year, Google acquired Keyhole for an undisclosed sum.59 It was renamed Google Earth and today, it is worth approximately $4 billion.60 In this case, In-Q-Tel’s investment paid off in monetary terms, but the bigger jackpot was the Keyhole program itself, which the CIA and Pentagon immediately deployed in support of military operations. Like many DIU and In-Q-Tel investments, Keyhole-Google Earth is a dual-use technology that can be adapted for both military and civilian applications. The vast majority of In-Q-Tel funded startups acquired by large tech companies have created dualuse products, such as AppThwack and Elemental Technologies (acquired by Amazon); Acompli and Perceptive Pixel (acquired by Microsoft); Tacit Software (acquired by Oracle); Cleversafe (acquired by IBM); MindMeld (acquired by Cisco); QD Vision (acquired by Samsung); and Pixim (acquired by Sony).61 But In-Q-Tel does not just provide promising startups with cash—it typically invests in companies at an early stage of development, when companies tend to be amenable to shaping their technologies to meet the CIA’s needs. Presumably DIU is taking the same approach. Tech startups are often open to suggestions and recommendations from military and intelligence agencies, since federal agencies are potentially a large customer base.62 Consequently, In-Q-Tel and DIU are able to play an influential role in steering the development of new technology for their own purposes. As mentioned above, these processes have the effect of “bending” innovation toward the needs of the CIA and Pentagon, respectively. One can only imagine the scientific opportunities that have been lost or delayed because they are not aligned with military priorities: for example, increased research to better understand and develop mitigation plans for accelerating climate change and its effects; better tools for forecasting epidemic diseases; and improved methods for sustainable agriculture and resource management. For small companies, In-Q-Tel and DIU funding can lead to two long-term benefits. First, In-Q-Tel and DIU offer an important advantage to defense tech startups: they typically have a “halo effect” that results in an ability to leverage much greater amounts of private investment—particularly VC funding—than would otherwise be the case.63 According to InQ-Tel, on average every dollar it invests in a company is leveraged into $28 of private VC funding.64 Another important benefit is that early funding from In-Q-Tel and DIU, which is typically granted for prototype development using an expedited “OT [other transaction] agreement,” often leads to high-value multiyear production contracts.65

DoD Is Awarding More (and Larger) Contracts to Big Tech Companies A growing portion of the Defense Department’s spending is going to large, well known tech firms, including some of the most valuable corporations in the world. These include Microsoft, Amazon, Alphabet (Google’s parent company), Oracle, Hewlett Packard, IBM, Intel, Motorola, Motorola, and Dell, among others.23 Contracts for hardware, software, and support services may have ceilings of hundreds of millions—or even billions—of dollars. According to the nonprofit research organization Tech Inquiry, three of the world’s biggest tech corporations were awarded approximately $28 billion from 2018 to 2022, including Microsoft ($13.5 billion), Amazon ($10.2 billion), and Alphabet, Google’s parent company ($4.3 billion). 24 It is important to note that this is a conservative estimate, since large defense and intelligence contracts are often classified. The top five contracts to major tech firms during this period have contract ceilings totaling at least $53 billion (see Table 1). In addition to the Pentagon, other non-DoD agencies (the CIA in particular) have also dramatically expanded their technology contracts while forging closer bonds with Silicon Valley firms. It is exceedingly difficult to untangle the web of Pentagon procurement contracts awarded to tech companies for several reasons. Like many defense contractors, tech firms frequently acquire or merge with other companies. Sometimes segments of a corporation are reorganized as independent entities (or “spinoffs”). For example, over the past decade the firm commonly known as “Hewlett Packard” has undergone a number of transformations that resulted in the creation of many entities, including a personal computer company (HP Inc.), a cloud services company (Hewlett Packard Enterprise or HPE), an IT provider that was spun off from HPE following a merger (DTX Technology), and a public sector IT provider that was spun off from DTX technology (Perspecta). Perspecta was later acquired by another entity (Peraton), which is owned by a private equity firm (Veritas Capital).25 Table 1. Five Largest Publicly Disclosed Tech Contracts Awarded by U.S. Military and Intelligence Agencies, 2019-2022.26 [Table Omitted for Brevity] Another complication that makes it challenging to analyze DoD procurement contracts is the fact that major tech firms are often awarded large subcontracts from relatively obscure intermediary or “passthrough” companies that are granted primary contracts from the Pentagon.27 Such arrangements make it difficult—but not impossible—to determine the extent to which the tech industry is involved in military work. Tech Inquiry is a non-profit research organization that uses FOIA (the U.S. Freedom of Information Act), among other methods, to collect, collate, and analyze public government records, particularly procurement contracts with companies that provide weapon systems, surveillance technologies, and cloud computing services to U.S. government agencies.28 In a 2020 report, Tech Inquiry concluded that Pentagon officials’ “alarmist claims” about Silicon Valley’s purported reluctance to conduct defense work were unwarranted, since major tech firms were awarded hundreds, and sometimes thousands, of Pentagon contracts and subcontracts.29 Some of these deals were worth hundreds of millions, or even billions, of dollars. After an endless series of summits, forums, and private meetings bringing together DoD officials, academics, Silicon Valley executives, and influential investors—as well as years of persistent messaging from Pentagon leaders and hawkish public policy think tanks about an “AI arms race” with China and the looming threats to U.S. national security posed by Russia and Iran—the Defense Department and the CIA now routinely award multiyear contracts to major tech firms.30

Until late November, when the epic saga of OpenAI’s board breakdown unfolded, the casual observer could be forgiven for assuming that the industry around generative AI was a vibrant competitive ecosystem.

But this is not the case—nor has it ever been. And understanding why is fundamental to understanding what AI is, and what threats it poses. Put simply, in the context of the current paradigm of building larger- and larger-scale AI systems, there is no AI without Big Tech. With vanishingly few exceptions, every startup, new entrant, and even AI research lab is dependent on these firms. All rely on the computing infrastructure of Microsoft, Amazon, and Google to train their systems, and on those same firms’ vast consumer market reach to deploy and sell their AI products.

For some, the idea of the military using Generative AI(GenAI) might conjure up a mental image of a commander asking ChatGPT to generate ten alternative battle plans, or asking it to identify the top five high-value enemy targets for the next five sorties. Whilst simplistic, these scenarios are not so far away from the truth.

In its favour are Generative AI’s speed, ease of use and ability to draw on vast volumes of data to generate responses to queries. On the other hand, the very public lessons that ChatGPT has taught us about GenAI’s limitations include ‘hallucinations’, its preference for historical versus real-time data, its unreliable mathematics functions, and risks to data security.

An issued patent is presumed valid. Courts require a firm that challenges a patent to prove its invalidity by “clear and convincing evidence.” This standard appears unjustified. A plethora of presumptions and procedures tip the scales in favor of the ultimate issuance of a patent, once an application is filed. In addition, as many have noted, the PTO is underfunded, and PTO patent examiners all too often do not have sufficient time to evaluate patent applications fully. These circumstances suggest that an overly strong presumption of a patent’s validity is inappropriate. Rather, courts should require only a “preponderance of the evidence” to rebut the presumption of validity.

The PTO works under a number of disadvantages that can impede its ability to reduce the issuance of questionable patents. Perhaps most important, the courts have interpreted the patent statute to require the PTO to grant a patent application unless the PTO can establish that the claimed invention does not meet one or more of the patentability criteria. Once an application is filed, the claimed invention is effectively presumed to warrant a patent unless the PTO can prove otherwise.

Another option to facilitate preclusion between the courts and the PTO would be to have both tribunals apply the same burden of proof on the question of invalidity. Congress has two basic options on that score. First, it could require the PTAB to apply the clear and convincing evidence standard that currently applies in court. That standard, however, is a form of judicial deference to agency decision making, 216 and it makes little sense to say that a three-judge panel of the PTAB should defer to a prior decision by a single examiner. The other option would be for both the courts and the PTO to apply a preponderance of the evidence standard. If, as many believe, the PTO issues too many patents that do not actually satisfy the requirements for patentability, then the clear and convincing evidence standard improperly insulates many patents from invalidity challenges. 217 Indeed, the decision to issue a patent, made by a single examiner with limited time to allocate to the task, does not seem like the type of reasoned decision making to which court deference is typically appropriate. 218

PREVAIL would amend § 316(e) of title 35 to provide that the original claims of a patent are subject to a presumption of validity that can only be overcome by clear and convincing evidence. In addition, PREVAIL would enact a new § 315(e)(2) that would mandate that the USPTO reject any PTAB petition that “presents” prior art that is the same or substantially the same as prior art that was previously “presented” to an examiner.

The principal rationale for requiring clear and convincing evidence of invalidity in civil litigation—deference to the expert agency that issued the patent—does not apply when a patent is being reconsidered by the same expert agency. The USPTO remains just as expert, if not more so, on the second evaluation of a patent. There is no reason for the agency to defer to an earlier decision that is the product of less deliberation and that considered less evidence.

The last four decades of the development of post-issuance proceedings at the USPTO have also coincided with a shift in how the courts resolve patent cases. As recently as 1978, judges decided over 90% of these cases, but as changes in Seventh Amendment jurisprudence have been absorbed by the plaintiff’s bar, three quarters of patent cases now go before a jury.4

Although district judges’ patent validity decisions have proven less accurate than those made by the PTAB, jury verdicts present even more difficulties. As compared to judges, juries are more likely to favor particular types of parties, more likely to favor the party that initiates the suit rather than the defendant (regardless of whether it is the patent owner or a declaratory judgment plaintiff), and more likely to decide all issues in favor of one party or another rather than distinguishing among the different questions before them.5

One thing that is apparent from the data is that the PTAB is doing a good job.

First, AIA trials overwhelmingly are completed within their one-year deadline, allowing them to serve as a timely substitute for district court litigation. This is no small matter. The system of post-issuance review that PTAB proceedings replaced, inter partes reexamination, was plagued with delays. Because that system was required to follow examination rules, either side could delay the proceeding at will and examiners lacked the tools to manage cases.

Several studies revealed that not all HCWs were ready to accept the vaccine for COVID-19, there have been increasing reports on hesitancy in receiving the vaccine. For example, a study conducted in Egypt (21%) (11), Ghana (39.3%) (12), and the USA (36%) (13) found that HCWs were intended to accept the COVID-19 vaccine. Most of HCWs reported that their concerns were about the safety and adverse side effects of the vaccines (12–15).

Healthcare workers bridge the gap between healthcare systems and patients. HCWs vaccine acceptance rates correlated with their willingness to recommend the COVID-19 vaccination to their patients. Individuals of the general population who agreed on the benefit of vaccination as recommended by HCWs are reported to have a higher willingness to pay for COVID-19 vaccines (16). People commonly depend on HCWs' information and actions to guide their decisions regarding the acceptance or refusal of the COVID-19 vaccine (17–19). Delays in COVID-19 vaccination among HCWs further prevent herd immunity and will cause an increase in COVID-19-related illnesses and deaths (3, 13).

These comparisons emphasise that, regardless of how the medical establishment feels about anti-vaccinationists, it is important to understand that they have deeply held beliefs, often of a spiritual or philosophical nature,11 and these beliefs have remained remarkably constant over the better part of two centuries. The movement encompasses a wide range of individuals, from a few who express conspiracy theories, to educated, well informed consumers of health care, who often have a complex rationale for their beliefs, related to a “mixture of world views held about the environment, healing, holism … and a critical reading of the scientific and alternative literature.”12

Vaccine hesitancy—the reluctance or refusal to vaccinate despite the availability of vaccines- is rising worldwide. Even though vaccines are considered one of the most important achievements of public health, preventing an estimated 2.5 million deaths each year worldwide and reducing disease-specific treatment costs [1], the World Health Organization (WHO) identified vaccine hesitancy as one of the top ten global health threats of 2019 [2].

Vaccine hesitancy is not a new trend. Since 1795, when Edward Jenner published the book titled “An Inquiry into the Cowpox”, vaccination has become a mainstream medical practice all over the world [3]. Nevertheless, from their beginnings, inoculations have had their detractors. The earliest ones were men of the church. They reasoned that infectious diseases were a God-given fact of life and death. Some medical doctors, those that were earning a lot of money from useless but lucrative cures, also enrolled in the anti-vaccination movement early [3]. Vaccination was associated with diverse hazards including tuberculosis, cancer, madness, blood poisoning, and syphilis [4]. From the middle of the 19th century, Great Britain and the countries under its influence made vaccination compulsory. Parents that refused to inoculate their children were sent to prison. The results were disastrous, with social riots that ended with the abolishment of the acts that made vaccination compulsory in 1909. The lessons provided by these first experiences were clear: the risks of vaccination must not be silenced, and compulsory vaccination was not the answer to the lack of public confidence in vaccines.

The world’s top 20 pharmaceutical companies collectively spent $145 billion on their research and development (R&D) investments in 2022-23*, up from $139 billion in 2021-22*, according to new research from Deloitte.

The Measuring the return from pharmaceutical innovation: Unleash AI’s potential report from Deloitte, the 14th in the series, analyses the current state of R&D return on investments (ROI) for a cohort of 20 leading biopharma companies. According to the study, the rise in R&D spend [sic] is attributed to several factors, including more complex trial requirements, regulatory changes and the impact of inflation.

In an op-ed debate in the Wall Street Journal, Els Torreele, director of our Access to Essential Medicines Initiative, argued that proposals to extend patents on pharmaceuticals would not increase innovation for critical health needs. Instead, they would solidify a broken innovation model that primarily serves the financial interest of the pharmaceutical industry at the expense of patients worldwide.

True medical innovation usually originates in academic research labs and research institutes, often supported with public funding, where researchers build upon the current state of medical knowledge to come up with new ideas and strategies to tackle diseases. It is the commercialization of these findings that heavily relies on patents. Patent monopolies allow pharmaceutical companies to block competition and sell their products at premium prices. Market opportunity, not innovation or medical advance, is the key motivator in the pharmaceutical industry.

Prior to the AIA, a challenger to a patent had to prove with clear and convincing evidence that the patent is invalid. The challenger had to be related to the patent in some way and generally that meant they needed to be sued for patent infringement. It was the challenger’s burden to prove the patent invalid. The challenger bore a significant cost – often as much or more than the inventor bore.

As a result of their higher level of indebtedness and strategic dependence on M&As, pharmaceutical companies have become more exposed – and thus vulnerable – to capital market developments as they pursue their financialised model. The current environment of extremely low interest rates and rising stock market valuations has fuelled a boom in M&As. However, the effects of a reversal of these market conditons may have a much more significant impact on pharmaceutical companies than in the past.

The financialised business model has emerged out of a specific historical environment as part of the broader process of financialisation that has taken shape in contemporary societies. Since the financial crisis, however paradoxically, financialisation has intensified rather than weakened. The conditions of extremely low interest rates have helped companies across sectors to increase their borrowing and embarking on new cycles of M&As and share buybacks. This tightening connection between finance and Big Pharma has increased systemic risk by promoting a fragile business model that generates high profits and rewards shareholders and management generously at the expense of the future drug development.

The pharmaceutical industry faces significant technological uncertainty and high costs from a lengthy and complicated drug development process. Strong intellectual property rules have long become been a key policy support for firms that must confront the uncertainty of blockbuster drug development. On the other hand, when a drug is approved and begins to generate revenues from its sale, this uncertainty is transformed into relatively stable and secure returns on investment. Hence, the market instability faced by drug makers is restricted mainly to patent disputes originating with generic drug producers, unexpected side effects of a new drug, or the introduction of a competitor drug within the same class of therapy. Without knowing the number of drugs they would launch, the largest pharma companies introduced the management of uncertainty. They put forward the promotion of ethical drugs to sustain growth and generate cash by assuring the maximum penetration on market with an intense marketing strategy and the longest period of patent protection possible.

This specific institutional environment favors particular profit strategies. First, socialized demand enables mass markets and strong growth and, therefore, exploitation of economies of scale. Second, patents and the expectation of a strong market incentivize innovation and the search for monopoly rents. Quality is less important as a means of generating profits (drugs are not a luxury or “distinctive” good), and cost-cutting strategy is not really relevant owing to the strong growth of demand, the relative weak role of prices and the “innovative” character of the industry. The profit strategies available to the pharmaceutical industry are volume (economies of scale), innovation and volume, volume and variety (scale and scope). Typically, generic models are based on a volume sales strategy. Old chemical conglomerates adopted a volume and variety strategy. The blockbuster model is primarily based on a profit strategy, which combines innovation with both volume and flexibility (see Appendix for a list of different productive models in the pharmaceuticals industry). The blockbuster pairing is made possible by the monopolistic rents created by patent systems. For the strategy to work, however, drug companies must develop a product policy based on intense marketing that positions new ethical drugs at the center of the drug market to increase profits assuring the finance of R&D. Blockbuster drugs represent the essential source of profits to be made that are complimented by increasing sales figures. Because of high R&D expenditures, large companies focus on blockbusters to generate sufficient profits to recover their sunk costs. For example, Procrit/Eprex of Johnson & Johnson was an orphan drug that has brought the company more than $42 billion in sales revenue since its release in 1991. Given the opportunities, larger firms began to increase their advertising expenditures considerably, directing their armies of sales representatives at physicians and their patients. According to Gagnon and Lexchin (2008), large pharmaceutical companies spend twice on marketing what they spend, on average, on R&D.

The combined value of all intangible assets on the balance sheet of 27 of the largest pharmaceutical companies has increased from 13 per cent of total assets in 2000 to 51 per cent in 2018, standing at a staggering US$857 billion dollars. This expansion of intangibles is an important aspect of the financialisation of Big Pharma. It shows how the sector has transformed itself from generating revenue through developing, producing and selling drugs to receiving income from owning and monopolising intellectual property. The large companies have spent years buying up competitors and biotechnology companies to boost pipelines, diminish competition and secure IPRs. As a result, Big Pharma increasingly operates more like a financial actor than an industrial sector.