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The Crisis of Venture Capital: Fixing America’s Broken Start-Up System

Despite all the attention and investment that Silicon Valley’s re­cent start-ups have received, they have done little but lose mon­ey: Uber, Lyft, WeWork, Pinterest, and Snapchat have consistently failed to turn profits, with Uber’s cumulative losses exceeding $25 billion. Perhaps even more notorious are bankrupt and discredited start-ups such as Theranos, Luckin Coffee, and Wirecard, which were plagued with management failures, technical problems, or even out­right fraud that auditors failed to notice.1

What’s going on? There is no immediately obvious reason why this generation of start-ups should be so financially disastrous. After all, Amazon incurred losses for many years, but eventually grew to become one of the most profitable companies in the world, even as Enron and WorldCom were mired in accounting scandals. So why can’t today’s start-ups also succeed? Are they exceptions, or part of a larger, more systemic problem?

Today’s big losses are not what Silicon Valley founders and futur­ists predicted. Artificial intelligence, driverless vehicles, ride sharing, blockchain, virtual reality, augmented reality, and the Internet of Things were supposed to change the world, enabling a dramatic in­crease in productivity growth. The resulting wealth was expected to be so huge that we would be able to support the unemployed with a universal basic income (UBI). Indeed, Andrew Yang, a successful entrepreneur, ran for president in 2020 on a platform whose most notable feature was a UBI pledge.

Furthermore, all these developments were supposed to be part of a larger technological revolution: accord­ing to many leading commentators and entrepreneurs, we are pur­portedly living in the most inno­vative time ever. And venture capital­ists seem to agree: their funding set a five-year record between 2015 and 2019, with investments in a wide variety of industries, and 2020 set a new single-year record.2

My analysis of start-ups, however, shows that the big losses suf­fered by Uber, Lyft, WeWork, Pinterest, and Snapchat—greater than 50 percent of revenues annually—are just the tip of the iceberg. More than 90 percent of America’s “unicorns”—start-ups valued at $1 bil­lion or more while privately held (before IPOs)—lost money in 2019 or 2020, even though more than half of them were founded over ten years ago. And a similar trend of losses holds for European, Indian, and Chinese start-ups. Of similar importance, recent analyses of venture capital (VC) firms show that returns on investments in VCs have barely exceeded those of public stock markets over the past twenty-five years, and their current losses suggest that returns will fall even further. Indeed, the low profitability of start-ups is a reflection of broader trends in the economy: the slowing productivity growth documented by Robert Gordon; stagnating innovation ob­served by Tyler Cowen; falling research productivity discussed by Anne Marie Knott, Nicholas Bloom, and others; and the declining impact of Nobel Prize research recently noted by Patrick Collinson and Michael Nielsen.3

In this article, I first discuss the abundant evidence for low returns on VC investments in the contemporary market. Second, I summarize the performance of start-ups founded twenty to fifty years ago, in an era when most start-ups quickly became profitable, and the most successful ones rapidly achieved top-100 market capitalization. Third, I contrast these earlier, more successful start-ups with Silicon Valley’s current set of “unicorns,” the most successful of today’s start-ups. Fourth, I discuss why today’s start-ups are doing worse than those of previous generations and explore the reasons why technological inno­vation has slowed in recent years. Fifth, I offer some brief proposals about what can be done to fix our broken start-up system. Systemic problems will require systemic solutions, and thus major changes are needed not just on the part of venture capitalists but also in our universities and business schools.

America’s Failing Venture Capital System

A 2020 report by Morgan Stanley4 documents several key trends in venture capital, particularly the falling rate of returns over the last forty years (these findings are summarized in figure 1). Investments in VC funds by individuals and institutions have risen over the last twenty years, as have VC investments in start-ups, the latter reaching a record high in recent years. Yet returns on VC investment fell dra­matically in the mid to late 1990s and have stayed low ever since, now barely higher than those of major stock market indices, an aston­ishing change when one considers the far higher risks associated with funding start-ups.

Within this twenty-year period, several major changes oc­curred in our start-up system that have contributed to these low returns.5 First, investor exits for most start-ups, at least until 2020, are now largely accomplished by acquisition, rather than by taking companies public through an initial public offering (IPO) on the stock market. The problem here is that the returns on investment from an acquisition are typically much smaller than those from an IPO, and thus the trend towards acquisitions is probably one reason for the falling returns for VC over the last twenty-five years. Second, among those start-ups that do go public, the percent that are unprofitable at the time of their IPO has increased dramatically over the last few decades, exceeding 80 percent in recent years, according to analysis by Jay Ritter of the University of Florida.6 This increase has continued despite the falling overall percentage of IPOs versus acquisitions, a change that should have caused the per­centage of unprofitable start-ups at IPO to fall.

These two trends together form a vicious circle. Lower profitability at the time of IPO naturally leads to smaller share price increases and thus to lower returns for start-up IPOs. These lower IPO returns in turn discourage start-ups from going public and thus drive the trend toward acquisition. But without profitability, incumbents will be unwilling to pay high prices for the start-ups that they acquire, and so returns on acquisitions will also fall. Lower acquisition valuations in turn affect IPO valuations. Poor IPO performance and low acqui­sition prices thus reinforce each other, and jointly lead to poor returns for the original investors in VC funds.

This decline in returns for venture capital is a serious problem for the U.S. economy, not only because it suggests that innovation is less profitable than in the past, but also because it significantly affects the investments of major institutions such as pension funds and uni­versity endowments. As shown in the previously cited Mor­gan Stan­ley report,7 these institutions have steadily in­creased their investments in VC funds since 1970. These institutions also invest in start-up IPOs, and the data discussed in the next section indicate that pre-pandemic returns for investors in unicorn IPOs were nega­tive.

Venture capitalists will disagree with this analysis. They will emphasize that some VC funds are actually making money be­cause returns are heavily skewed, and thus, overall, the system is working. This objection is partly correct: as shown in the Morgan Stanley report,8 a small percentage of investments does provide high returns, and these high returns for top-performing VC funds persist over subsequent quarters. Although this data does not demonstrate that select VCs consistently earn solid profits over decades, it does suggest that these VCs are achieving good returns. Therefore, such funds might still be considered good investments, despite broader trends.

From a public policy standpoint, however, market averages are more important than the individual returns of a few successful VCs, because most investors cannot reliably distinguish between high- and low-return funds. After all, the notion that high risk requires higher returns is an old one, taught in every introductory finance course, yet the VC system, on average, is not providing this risk premium. Thus, the VC system bears a closer resemblance to another form of risk-taking: gambling. Gambling also consistently offers low average returns but occasionally produces large payouts that are heavily skewed to a few big winners. And gamblers, like VC funds, tend to place their bets in the expectation that they will win one of these rare jackpots. But policymakers and the public should realize that average returns ought to be higher for risky investments than less risky ones. If they are not, it is time to rethink the value of VC.

A glance at the rest of the developed world suggests that these problems of VC-funded start-ups are not exclusively American. The rest of the world got a late start with venture capital, but by the end of the 2010s other nations were also setting new records in VC invest­ments, particularly China and India. By June 2020, the number of unicorn start-ups in China (227) had nearly equaled the number in America (233). And global unicorns have now reached an enormous $1.9 trillion in total value.9 But, as I shall discuss below, the lack of VC profitability is a global problem, with Chinese funds earning only slightly better returns than American ones.10

The Start-Up Successes of the Late Twentieth Century

There was a time when venture capital generated big returns for investors, employees, and customers alike, both because more start-ups were profitable at an earlier stage and because some start-ups achieved high market capitalization relatively quickly. Profits are an important indicator of economic and technological growth, because they signal that a company is providing more value to its customers than the costs it is incurring.

A number of start-ups founded in the late twentieth century have had an enormous impact on the global economy, quickly reaching both profitability and top-100 market capitalization. Among these are the so-called faamng (Facebook, Amazon, Apple, Microsoft, Net­flix, and Google), which represented more than 25 percent of the S&P’s total market capitalization and more than 80 percent of the 2020 increase in the S&P’s total value at one point—in other words, the most valuable and fastest-growing compa­nies in America in recent years.

Rapidly growing start-ups of this type merit attention both because they are arguably the main factor driving returns to venture capital—rather than the average profitability of all start-ups—and because they are the principal cause of the “creative destruction” celebrated by Joseph Schumpeter11 and other economists. As can be seen in figure 2, these giants reached profitability and top-100 market capitalization relatively quickly: Thirteen were profita­ble by year five, and another nine by year ten. Ten achieved top-100 market capitalization by year ten, and sixteen by year fifteen. Only one took longer than ten years to become profitable. Amazon and Qualcomm became profitable in year ten, a relatively long time within this group but far outpacing contemporary unicorns such as Uber, WeWork, Snapchat, and Pinterest, which still suffer losses greater than 50 percent of reve­nues at year ten or later.

Many of the successful start-ups of the late twentieth century created value for workers as well as investors. In addition to high-paying jobs for semiconductor engineers, software engineers, labor­atory scientists, and other white-collar workers, many of them also created well-paying blue-collar jobs—particularly the semiconductor manufacturers and other hardware companies that once employed thousands of workers in their production facilities. In this respect, also, they outshine the unicorns of more recent years, none of which employs production workers, and few—if any—pay adequate wages to their blue-collar employees. Uber, Lyft, and other gig-work start-ups are major contributors to income inequality and thus have drawn significant political backlash—a reaction resulting in regulations that could bring about their ultimate collapse.

A Cornibus Unicornium

In the contemporary start-up economy, “unicorns” are purportedly “disrupting” almost every industry from transportation to real estate, with new business software, mobile apps, consumer hardware, inter­net services, biotech, and AI products and services.12 But the actual performance of these unicorns both before and after the VC exit stage contrasts sharply with the financial successes of the previous generation of start-ups, and suggests that they are dramatically overvalued.

Figure 3 shows the profitability distribution of seventy-three uni­corns and ex-unicorns that were founded after 2003 and have released net income and revenue figures for 2019 and/or 2020.13 In 2019, only six of the seventy-three unicorns included in figure 3 were profitable, while for 2020, seven of seventy were. The six profitable start-ups in 2019 included three financial technology (fintech) firms (GreenSky, Oportun, and Square) and one company each in e-commerce (Etsy), video communications (Zoom), and solar energy services (Sunrun). In 2020, three of these firms became unprofitable (Oportun, Square, and Sunrun), and four others became profitable: three e-commerce com­panies (Peloton, Purple Innovation, and Way­fair) and one cloud storage service (Dropbox). Thus, a remarkably small fraction of start-up unicorns has achieved profitability, despite the fact that forty-five of the seventy-three analyzed here were found­ed over ten years ago. By contrast, twenty-two of the twenty-four start-ups listed above in figure 2—including Amazon and Qualcomm, which lagged in achiev­ing profitability—were earning profits already by year ten.

Furthermore, there seems to be little reason to believe that these unprofitable unicorn start-ups will ever be able to grow out of their losses, as can be seen in the ratio of losses to revenues in 2019 versus the founding year. Aside from a tiny number of statistical outliers—two recent start-ups with huge losses and a third founded in 2010, which has a ratio of 8.6 (not shown here)—there seems to be little relationship between the time since a start-up’s founding and its ratio of losses to revenues. In other words, age is not correlated with profits for this cohort.

Many of the start-up unicorns included in figure 3 had losses that were a significant fraction of revenues. In 2019, twenty-one of sev­enty-three had losses greater than 50 percent of revenues, and another thirteen (including Uber, Lyft, Pinterest, and Snapchat) had losses greater than 30 percent (not counting liquidations). In 2020, nineteen of seventy had losses greater than 50 percent of revenues, and another eleven had losses greater than 30 percent. Furthermore, given the large number of unicorns that provide cloud computing, video con­ferencing, and other services that were increasingly necessary during the Covid-19 lockdowns, it is surprising that the ratios of losses to revenue did not improve more than they did, and that there was not a higher fraction of profitable unicorns in 2020. These facts suggest that many unicorns have little chance of ever achieving profitability, and even if some do, they already have large cumulative losses (more than $25 billion for Uber) that will have to be covered by future profits.

When compared with profitability data from decades past, recent start-ups look even worse than already noted. About 10 percent of the unicorn start-ups included in figure 3 were profitable, much lower than the 80 percent of start-ups founded in the 1980s that were profit­able, according to Jay Ritter’s analysis, and also below the over­all per­centage for start-ups today (20 percent). Thus, not only has profit­ability dramatically dropped over the last forty years among those start-ups that went public, but today’s most valuable start-ups—those valued at $1 billion or more before IPO—are in fact less profitable than start-ups that did not reach such lofty pre-IPO valua­tions.

The heavy losses experienced by many unicorns have also impact­ed their performance in the stock market. A comparison of unicorns’ stock performance with overall market changes through March 9, 2020 (chosen to avoid the increased market volatility resulting from the Covid-19 pandemic) shows that many unicorns have seen changes in their share prices smaller than those of the overall nasdaq (the most relevant market index for start-ups). Of the forty-five ex-uni­corns that were taken public before 2019, only fourteen saw price increases larger than those of the nasdaq, and only a few of these were profitable in 2019 (Etsy, Square, Zoom) and 2020 (Etsy, Zoom).14

Likewise, their relatively small market capitalizations also show the poor performance of unicorn start-ups. None of the publicly traded ex-unicorns had the $98 billion market capitalization required to be among the top 100 companies in 2019 or the $109 billion required for top-100 status in the first half of 2020.15 A remarkably small number were even a fraction of the way toward top-100 status by early 2019: Uber had a market capitalization of $60 billion at that time; only two others, Square and Zoom, had greater than $20 billion; and ten had between $10 and $20 billion. There is clearly still a long road ahead for even the most valuable ex-unicorns.

Those unicorns that are still privately held do not look any more promising. Of the start-ups shown in figure 3, twenty-eight released their first net income figures after March 2020; none of these had profits in 2019—suggesting that few or none of the remaining private­ly held unicorns are profitable.

The general trend among foreign start-ups matches that seen in America: a few European unicorns are profitable (e.g., TransferWise), but none are in India, South Korea, or Singapore. The biggest exception to this trend is China, where about 60 percent of unicorns are unprofitable—a high figure, though not as high as the 90 percent seen in the United States. But significant differences exist in the Chinese and U.S. start-up systems that likely account for China’s superior performance. Notably, many Chinese unicorns or ex-uni­corns are either spin-offs of, or were founded by, large incumbents, a factor that likely increases the chanc­es of profitability and that is not typical for American unicorns. And most importantly, China’s rapid rate of economic growth in the twenty-first century means that its start-ups were competing with much weaker incumbents at their founding than were America’s start-ups. For instance, a Chinese start-up founded in 2009 entered a domestic economy that was about 40 percent its current size and thus had much weaker competitors than an American unicorn founded at the same time. Therefore, contrary to initial appearances, China’s unicorns are not performing significantly better than those of other nations (relative to their respective domestic economies). When analysis corrects for these differences, the percent­age of unprofitable start-ups in China is quite similar.

Finally, overall profitability in China, the United States, and else­where may actually get worse due to the easy availability of capital, thus enabling more start-ups to be formed and go public. One signifi­cant driver of this trend is the increasing presence of special-purpose acquisition companies (SPACs), informally known as “blank-check companies,” which make it easier for start-ups to go public while still suffering losses or even making little or no revenue. Blank-check companies are publicly traded shell companies that merge with pri­vate companies, enabling the private firms to sidestep the reporting requirements of an IPO. Mergers with blank-check companies have contributed to a record number of start-ups going public at aggressive valuations in 2020, despite earning little or no revenue. For instance, driven by easy money and the rapid rise of Tesla’s stock, a group of electric vehicle and battery suppliers—Canoo, Fisker Automotive, Hyliion, Lordstown Motors, Nikola, and QuantumScape—were val­ued, combined, at more than $100 billion at their listing.16 Likewise, dozens of biotech firms have also achieved billions of dollars in mar­ket capitalizations at their listings. In total, 2020 set a new record for the number of companies going public with little to no revenue, easily eclipsing the height of the dot-com boom of telecom companies in 2000.

The Causes of Poor Start-Up Performance

There are many reasons for both the lower profitability of start-ups and the lower returns for VC funds since the mid to late 1990s. The most straightforward of these is simply diminishing returns: as the amount of VC investment in the start-up market has increased, a larg­er proportion of this funding has necessarily gone to weaker opportunities, and thus the average profitability of these investments has de­clined.

The problem with the contemporary start-up system, however, extends beyond the average return on investment; it also includes the decrease in the number of start-ups founded after 2004 that have achieved top market capitalization. If we set aside the unique social and economic conditions of 2020, not a single start-up founded after 2004 is among the top-100 most valuable companies of 2019. Even if we include companies founded since 2000 in our analysis and count figures through 2020, there are only two exceptions to the trend: Facebook, founded in 2004, and Tesla, founded in 2003. But Tesla is a company that had less than 2 percent of the U.S. auto market in 2019 and did not achieve its first annual profit until that year. Further, it would not have achieved this profit if it had not enjoyed substantial tax and regulatory credits or if it had been obliged to split revenues with dealers or pay typical advertising costs as incumbents do.17 Thus, if we can ignore the hype around Tesla (a difficult thing to do in our hype-heavy media environment), it is clear that the start-ups founded during the last fifteen years have been far less successful than those of the preceding fifteen to thirty years.

A more plausible explanation for the relative lack of start-up suc­cesses in recent years is that new start-ups tend to be acquired by large incumbents such as the faamng companies before they have a chance to achieve top 100 market capitalization. For instance, YouTube was founded in 2004 and Instagram in 2010; some claim they would be valued at more than $150 billion each (pre-lockdown estimates) if they were independent companies, but instead they were acquired by Google and Facebook, respectively.18 In this sense, they are typical of the recent trend: many start-ups founded since 2000 were subsequently acquired by faamng, including new social media companies such as GitHub, LinkedIn, and WhatsApp. Likewise, a number of money-losing start-ups have been acquired in recent years, most notably DeepMind and Nest, which were bought by Google.

There are several potential objections that might be raised against this argument. First, all the successful start-ups of the late twentieth century listed in figure 2 also made acquisitions—in some cases more than fifty—that helped them increase their market capitalization. Cis­co pioneered this strategy in the 1990s and was able to become the most valuable company in the world for a short time during the dot-com bubble.19 Similarly, Microsoft obtained PowerPoint through the acquisition of Forethought in 1987; PowerPoint went on to become an important part of its Windows suite of products, the success of which is a significant reason why Microsoft itself has not been dis­rupted by more recently founded start-ups.

An even stronger objection to the acquisition argument is that it assumes new start-ups must challenge industries dominated by strong incumbents such as faamng—for instance, social media. But most of the successful start-ups listed in figure 2 also faced potential threats from strong incumbents; they avoided these threats, however, by initially commercializing new technologies that did not directly chal­lenge the incumbents’ interests, a tendency that was supported by the rapid evolution of Silicon Valley’s technologies. Silicon Valley was given its name because of the large number of semiconductor compa­nies established there between the 1950s and 1980s. But it soon diver­sified into disk drives, networking equipment, personal computers, workstations, and a wide variety of software products before the shift toward internet companies in the 1990s. Each of these technologies was commercialized by new start-ups partly because they involved genuinely new concepts, customers, and applications. By contrast, more recent start-ups that have been acquired by Silicon Valley in­cumbents—such as LinkedIn, Instagram, WhatsApp, YouTube, and even GitHub—mostly involve some form of a single technology: social media.

Overall, the most significant problem for today’s start-ups is that there have been few if any new technologies to exploit. The internet, which was a breakthrough technology thirty years ago, has matured. As a result, many of today’s start-up unicorns are comparatively low-tech, even with the advent of the smartphone—per­haps the biggest technological breakthrough of the twenty-first century—fourteen years ago. Ridesharing and food delivery use the same vehicles, driv­ers, and roads as previous taxi and delivery services; the only major change is the replacement of dispatchers with smartphones. Online sales of juicers, furniture, mattresses, and exercise bikes may have been revolutionary twenty years ago, but they are sold in the same way that Amazon currently sells almost everything. New business software operates from the cloud rather than onsite computers, but pre-2000 start-ups such as Amazon, Google, and Oracle were already pursuing cloud computing before most of the unicorns were founded. Fintech start-ups use algorithms to find low-risk borrowers or insur­ance subscribers, but economic contraction has shown how sensitive these algorithms are to changes in the overall economy; hence their technology cannot be considered revolutionary.

Other than artificial intelligence and data analytics, none of the technologies being devel­oped by unicorns is a breakthrough comparable to those seen by previous generations of start-ups. But here, too, the large losses taken by ex-unicorns developing AI technology, the exponentially rising costs of achieving increases in accuracy, and the small market for AI in 2020 ($17 billion) suggest that AI and data analytics are still a long way from being truly revolutionary.20

This lack of revolutionary technology has made it hard for uni­corns to create value at a scale necessary to be profitable. Intel and Microsoft created value for personal computer manufacturers and final users by capitalizing on the rapid improvements in microprocessors, memory, and hard disks that occurred over the last sixty years. But rapid improvements of this sort are actually very rare.21 Many of the other late twentieth-century start-ups shown in figure 2 directly benefited from the increase in memory described by Moore’s Law, notably computer manufacturers such as Compaq, Dell, and Sun, and integrated circuit suppliers such as nvidia and Qualcomm. And most of the others, specializing in internet content and services, benefited indirectly from Moore’s Law and from rapid improvements in fiber-optic speeds and bandwidth.22

The huge amount of value that was created by the exponential growth of computing technology in the late twentieth century ena­bled start-ups of that era to set high prices and thus secure high profits; today’s start-ups, however, cannot rely on such favorable technological circumstances, partly because cost per transistor has not declined since 2014.23 For instance, as noted above, rideshare services are only marginally more convenient than traditional taxi services, because they use the same vehicles, drivers, and roads as taxi services, so Uber and Lyft cannot transport more people per unit of time than do taxi companies.24 Without such productivity advantages and the additional value they create, it is all but impossible for contemporary start-ups to generate profits on the scale of Google or Amazon. This problem will only get worse as cities force Uber and other “gig economy” start-ups to pay higher wages to their workers. The late-twentieth-century start-ups listed in figure 2 were able to pay higher wages because they had much higher productivity than established firms in the industries they disrupted; lacking this advantage and the increased revenue that it brings, today’s start-ups will be unable to meet this standard.

In addition, some contemporary start-ups have also entered indus­tries that have traditionally been subject to significant state regulation and therefore face challenges much greater than those seen by the start-ups of previous decades. Taxi services, for instance, are regulated by city governments out of concern for congestion and other issues; these concerns and contests over city regulations continue to plague rideshare companies and also present an obstacle to new scooter- and bicycle-rental services. Fintech start-ups are trying to challenge tradi­tional banks, a class of firms that has been heavily regulated since the Great Depression. Education start-ups are fighting to enter an even more highly regulated industry and risk being caught up in political clashes over public and private schools.

In short, today’s start-ups have targeted low-tech, highly regulated industries with a business strategy that is ultimately self-defeating: raising capital to subsidize rapid growth and securing a competitive position in the market by undercharging consumers. This strategy has locked start-ups into early designs and customer pools and prevented the experimentation that is vital to all start-ups, including today’s unicorns. Uber, Lyft, DoorDash, and GrubHub are just a few of the well-known start-ups that have pursued this strategy, one that is used by almost every start-up today, partly in response to the demands of VC investors. It is also highly likely that without the steady influx of capital that subsidizes below-market prices, demand for these start-ups’ services would plummet, and thus their chances of profitability would fall even further. In retrospect, it would have been better if start-ups had taken more time to find good, high-tech business opportunities, had worked with regulators to define appropriate behavior, and had experimented with various technologies, designs, and markets, making a profit along the way.

Restarting the Start-Up System

Our current start-up system is clearly unable to produce or capitalize on revolutionary technologies, to generate significant profits or long-term growth, or to provide well-paying jobs for employees. The last of these deficiencies is the most immediately visible and politically compelling. The easy and, arguably, most popular thing to do would be to blame Uber and its fellow unicorns, require them to pay work­ers higher wages—and then watch them go bankrupt. But workers choose to work for Uber and other gig start-ups not because low wages are themselves appealing but because these are the best em­ployment opportunities that they can find. Thus, the employment practices of companies like Uber are not the ultimate problem, and simply legislating that they pay higher wages will not fix our broken start-up system. Rather, we need venture capitalists and start-ups to create new products and new businesses that have higher productivity than do existing firms; the increased revenue that follows will then enable these start-ups to pay higher wages. The large productivity advantages needed can only be achieved by developing breakthrough technologies, like the integrated circuits, lasers, magnetic storage, and fiber optics of previous eras. And different players—VCs, start-ups, incumbents, uni­versities—will need to play different roles in each in­dustry. Unfortunately, none of these players is currently doing the jobs required for our start-up economy to function properly.

Venture capitalists have shown themselves to be far less capable of commercializing breakthrough technologies than they once were. In­stead, as recently outlined in the New Yorker, they often seem to be superficial trend-chasers, all going after the same ideas and often the same entrepreneurs. One managing partner at SoftBank summarized the problem faced by VC firms in a marketplace full of copycat start-ups: “Once Uber is founded, within a year you suddenly have three hundred copycats. The only way to protect your company is to get big fast by investing hundreds of millions.” The article goes on to express skepticism of VCs in general:

For decades, venture capitalists have succeeded in defining themselves as judicious meritocrats who direct money to those who will use it best. But examples like WeWork make it harder to believe that V.C.s help balance greedy impulses with enlight­ened innovation. Rather, V.C.s seem to embody the cynical shape of modern capitalism, which too often rewards crafty middlemen and bombastic charlatans rather than hardworking employees and creative businesspeople.25

The poor performance of VCs and start-ups and the corresponding sense that they are mostly trend-chasing copycats are both indirect results of superficial training, and so part of the blame for these prob­lems must fall on business schools and universities. In recent decades, business schools have dramatically increased the number of entrepreneurship programs—from about sixteen in 1970 to more than two thousand in 201426—and have often marketed these programs with vacuous hype about “entrepreneurship” and “technology.”27 A recent Stanford research paper argues that such hype about entrepreneurship has encouraged students to become entrepreneurs for the wrong rea­sons and without proper preparation, with universities often presenting entrepreneurship as a fun and cool lifestyle that will enable them to meet new people and do interesting things, while ignoring the reality of hard and demanding work necessary for success.28

Even worse, academics have at times compromised their role as researchers, partnering with corporations to publish scholarship that functions more as public relations material than critical analysis. For instance, leading scholars have recently published distorted articles about Uber in top economics journals, thus helping the company mislead the world about both its technological and economic value and its treatment of employees.29 Not only have these economists not acknowledged their mistakes, few business schools have publicly addressed the declining performance of start-ups. For instance, Har­vard Business School’s online technology entrepreneurship course, which began in April 2020, does not mention losses, production slowdowns, or other problems experienced by recent start‑ups, instead regurgitating platitudes like “innovation is a burgeoning part of the economy.”30

A big mistake business schools make is their unwavering focus on business model over technology, thus deflecting any probing ques­tions students and managers might have about what role technological breakthroughs play and why so few are being commercialized. For business schools, the heart of a business model is its ability to capture value, not the more important ability to create value. This prioritization of value capture is tied to an almost exclusive focus on revenue: whether revenues come from product sales, advertising, subscriptions, or referrals, and how to obtain these revenues from multiple customers on platforms. Value creation, however, is dependent on technological improvement, and the largest creation of value comes from breakthrough technologies such as the automobile, microprocessor, personal computer, and internet commerce.

Platform business models for value capture are among the most discussed in our business schools and they are used by many of today’s money-losing start-ups, many of which developed platforms that match users and suppliers in different markets. This is what Uber’s platform does for riders and drivers and what other start-ups do in food delivery, peer-to-peer loans, used cars, business software, and other areas of the sharing and gig economies. All these start-ups have been quick to hype their platforms in their IPO filings and other documents. Business schools have similarly promoted these platform-based businesses in their strategy and entrepreneurship courses be­cause of the past success of start-ups such as Microsoft, Intel, and Google. But there have been significant technological changes over the past thirty years: PCs once created vast amounts of value through increases in processing power, which could then be shared among many members of the platform. But the end of Moore’s Law is in sight, and the successful platform-based businesses of the last genera­tion may no longer provide a viable model for contemporary start-ups.

The recent hype over Nikola, a problem-riddled vehicle start-up, is a perfect example of the dangers of focusing excessively on platform business models. According to JPMorgan, Nikola’s value lies in its big idea: to lease zero-emission hydrogen-powered vehicles to fleet oper­ators that can refuel at future Nikola hydrogen fueling stations for a flat per-mile fee. A JPMorgan analyst in September 2020 cited the lease model and Nikola’s partner-heavy approach as the company’s most compelling aspects.31

This analysis might have come right out of a business school textbook, but it ignores the relative challenges of producing, storing, and distributing hydrogen and electricity, as well as the metrics of energy density and efficiency—both important considerations in de­termining how much value Nikola might actually create. For instance, making hydrogen fuel from water uses electricity in a process called electrolysis, a rather low-efficiency transformation, whereas electric vehicles run directly on electricity; the extra steps mean that hydrogen vehicles will always be less energy-efficient than electric vehicles. Falling for a business model that completely ignores technical issues and their role in value creation is the road to poverty, but it is the road traveled by many business schools.

Innovation curricula need to be carefully rethought. Potential innovators and entrepreneurs must learn about value creation if they are to have the capability to introduce breakthrough technologies that can increase productivity. This requires careful economic analysis that will highlight where the greatest costs are found in an existing industry and how a new technology might affect those costs. Inves­tors might have avoided a massive bubble in Nikola and hydrogen vehicles, and start-ups might be able to generate better designs for ride-sharing, food delivery, business software, and other platforms, if the economics of these technologies and their industries were effec­tively taught.32 Furthermore, business school professors and students alike must understand how rapid improvements in integrated circuits, magnetic storage, and fiber optics enabled so many of the breakthrough technologies of the late twentieth century—in short, that technologies, not business models, enabled many of the successful start-ups of the previous generation to succeed. Further relaxing the stranglehold of business education on our venture capital and start‑up systems—for instance, by relocating innovation programs within the university to engineering schools, or by hiring fewer business school graduates into VCs—is another way to address this problem.

Restructuring Research

University engineering and science programs are also failing us, because they are not creating the breakthrough technologies that America and its start-ups need. Although some breakthrough tech­nologies are assembled from existing components and thus are more the responsibility of private companies—for instance, the iPhone33—universities must take responsibility for science-based technologies that depend on basic research, technologies that were once more common than they are now.

New technologies such as semiconductors, lasers, LEDs, glass fiber, and fiber optics played a major role in the story of start-ups and venture capital in the late twentieth century. Through their rapid improvements, these technologies provided start-ups with profits, VCs with great returns, and workers—including engineers and pro­duction workers—with well-paying jobs, jobs that are not appearing like they once did. In order to replicate that success now, some of today’s start-ups should be commercializing nanotechnology, super­conductors, quantum computers, and bioelectronics (Theranos tried but failed), as well as new forms of solar cells, transistors, and com­puters (e.g., neuromorphic computers), not just the low-end technologies of gig work, social media, and cloud computing. If these new technologies were being developed, we would also expect a boost in well-paying jobs for blue-collar workers.

This decline in technological breakthroughs cannot be attributed to a lack of funding: governments have been funding university re­search for more than half a century, yet research productivity has declined overall, including research into semiconductors, agriculture, and pharmaceuticals.34 Other than the internet being commercialized in the 1990s—the technological foundations of which were created in the 1960s and 1970s—few new science-based technologies have emerged in the last thirty years.35 And the small number of successes were mostly achieved by foreign competitors: lithium-ion batteries, OLEDs, and solar cells, for instance, were commercialized by Japa­nese, Korean, and Chinese companies.

Even Nobel Prize–winning research seems to lead to fewer techno­logical breakthroughs than in the past, according to a survey of top scientists. Asked to compare pairs of research projects that had won the Nobel Prize, scientists judged that the most important research in physics was done in the early part of the 1900s. Perhaps more importantly, few awards have been given to scientists for research done since 1990, not only in physics but also in chemistry and medicine.36 Yet we would expect to see a rising importance ascribed to later research in these fields, given the increase in university funding and the associated increase in PhD students and publications in recent dec­ades.

Furthermore, looking at some of these prizes in more detail reveals that much of the research work was done at corporate and not uni­versity labs. For instance, among Nobel Prizes for physics and chem­istry awarded since 2000 in lithium-ion batteries, LEDs, charge-coupled devices, lasers, integrated circuits, and optical fiber, nine of the seventeen recipients did their work at corporate labs. The only high-impact award that solely involved university research was gra­phene.37

Many scientists point to the nature of the contemporary university research system, which began to emerge over half a century ago, as the prob­lem. They argue that the major breakthroughs of the early and mid-twentieth century, such as the discovery of the DNA double helix, are no longer possible in today’s bureaucratic, grant-writing, administration-burdened university. The idea of scientists following their hunches to find better explanations and thus better products and services has yielded to the reality of huge labs pursuing grants to keep staff employed. Young scientists have become mere cogs in a grant-seeking machine, forced to suppress their curiosity and do what they are told by senior colleagues who are overwhelmed by administrative work. Two-author papers, like the one describing the structure of DNA, have been replaced by hundred-author papers. Scientific merit is measured by citation counts and not by ideas or by the products and services that come from those ideas. Thus, labs must push papers through their research factories to secure funding, and issues of scientific curiosity, downstream products and services, and beneficial contributions to society are lost.38

Nobel laureates have similar criticisms of the contemporary cul­ture of academic research. Various laureates in biochemistry, biology, computer science, and physics have claimed that they would now be denied funding for their prizewinning research because of grant-issuing bodies’ preference for less risky projects; one physicist even claims he could not get a job today. In today’s climate every project must succeed, and thus our scientists study only marginal, incremental topics where the path forward is clear and a positive result is virtually guaranteed.39

A first step toward fixing our sclerotic university research system is to change the way we do basic and applied research in order to place more emphasis on projects that may be riskier but also have the potential for greater breakthroughs. We can change the way proposals are reviewed and evaluated. We can provide incentives to universities that will encourage them to found more companies or to do more work with companies. But I think we should consider more radical changes, including a return to the culture of research that flourished in an era when more science-based technologies were being commercialized.

One option is to recreate the system that existed prior to the 1970s, when most basic research was done by companies rather than uni­versities. This was the system that gave us transistors, lasers, LEDs, magnetic storage, nuclear power, radar, jet engines, and polymers during the 1940s and 1950s. Apart from these past successes, there are a number of structural reasons why conducting basic research at corporate labs is likely to produce more useful results than in uni­versities. First, corporate scientists are focused more on solving prob­lems, whereas scientists in universities must also take on the administrative work of writing papers—often in collaboration with dozens of coauthors—managing PhD students and postdocs, reading dissertations and draft papers, writing letters of recommendation, and filing grant proposals to keep themselves, their students, and their staff em­ployed. Unlike their predecessors at Bell Labs, IBM, GE, Motorola, DuPont, and Monsanto seventy years ago, top university scientists are more administrators than scientists now—one of the greatest mis­uses of talent the world has ever seen. Corporate labs have smaller administrative workloads because funding and promotion depend on informal discussions among scientists and not extensive paperwork.

Second, the informal discussions and collaboration characteristic of corporate labs allows the scientists who work there to make better decisions about both the merits of different designs in the short term and problem-solving approaches for the long term. These informal discussions can also focus on issues of cost and performance: how to measure them and how to improve the technologies along these met­rics.40 Such discussions rarely occur in universities because their goal is the publication of research rather than the development of new products and services.

Third, conducting basic research at corporate laboratories can help avoid the problem of hyper-specialization in academia. Because publi­cations are the key output of university professors, there has been a growing number of journals over the last fifty years to accom­modate the growing number of university scientists, and these jour­nals have become increasingly specialized. For example, Nature now publishes more than 144 journals and the Institute of Electrical and Electronic Engineers more than 200. This growing specialization turns professors into narrowly focused researchers unable to under­stand not only the needs of the marketplace but also the metrics of cost and performance for a new technology, which should dictate long-term goals.41 Relocating more basic research to corporate labs can reduce this specialization by placing scientists in an organization whose goal is to commercialize new technologies.

We can return basic research to corporate labs by providing much stronger incentives for companies—or cooperative alliances of com­panies—to do basic research. A scheme of substantial tax credits and matching grants, for instance, would incentivize corporations to do more research and would bypass the bureaucracy-laden federal grant process. This would push the management of detailed technological choices onto scientists and engineers, and promote the kind of in­formal discussions that used to drive decisions about technological research in the heyday of the early twentieth century. The challenge will be to ensure these matching funds and tax credits are in fact used for basic research and not for product development. Requiring multi­ple companies to share research facilities might be one way to avoid this danger, but more research on this issue is needed.

Restoring America’s venture capital and start-up system to its suc­cessful past will require significant changes in how venture capitalists, start-ups, business professors, and university scientists and engineers do their work. We need less hype, more realistic economic analysis, and more breakthrough technologies. But achieving the latter will re­quire us to rethink completely how basic and applied research are conducted and commercialized.

This article originally appeared in American Affairs Volume V, Number 1 (Spring 2021): 3–26.

1 Jeffrey Funk, “Assessing Public Forecasts to Encourage Accountability: The Case of MIT’s Technology Review,” PLoS ONE 12, no. 8 (August 2017).

2 Alex Wilhelm, “In 2020, VCs Invested $428M into US-Based Startups Every Day,” TechCrunch, January 19, 2021

3 Robert J. Gordon, The Rise and Fall of American Growth: The U.S. Standard of Living Since the Civil War (Princeton: Princeton University Press, 2016); Tyler Cowen, The Great Stagnation: How America Ate All the Low-Hanging Fruit of Modern History, Got Sick, and Will (Eventually) Feel Better (New York: Dutton, 2011); Anne Marie Knott, How Innovation Really Works: Using the Trillion-Dollar R&D Fix to Drive Growth (New York: McGraw-Hill, 2017); Nicholas Bloom et al., “Are Ideas Getting Harder to Find?,” American Economic Review 110, no. 4 (April 2020): 1104–44; Patrick Collinson and Michael Nielsen, “Science Is Getting Less Bang for Its Buck,” Atlantic, November 16, 2018.

4 Michael J. Mauboussin and Dan Callahan, “Public to Private Equity in the United States: A Long-Term Look,” Morgan Stanley, August 4, 2020. See especially exhibits 6, 19, 35, 36, 38.

5 Mauboussin and Callahan.

6 Rani Molla, “Why Companies Like Lyft and Uber Are Going Public without Having Profits,” Vox, March 6, 2019.

7 Mauboussin and Callahan, “Public to Private Equity,” exhibit 8.

8 Mauboussin and Callahan, “Public to Private Equity,” exhibit 42.

9 Hurun Research Institute, Hurun Global Unicorn Index 2020 (Shanghai: Hurun Report, 2020).

10 Jeffrey Lee Funk, “Are There Any Industries in Which Ex-Unicorns Are Profitable?,” Medium, July 21, 2020; Jeffrey Lee Funk, “Most Chinese Ex-Unicorns Are Unprofitable, but Fewer Than in America,” Medium, October 12, 2020.

11 Joseph Schumpteter, Capitalism, Socialism and Democracy (New York: Harper, 1942).

12 Aileen Lee, “Welcome to the Unicorn Club: Learning from Billion-Dollar Startups,” TechCrunch, November 2, 2013.

13 Most of these start-ups have already gone public and thus release quarterly financials; some are still privately held and thus have only released figures for 2019. Therefore, a smaller number of companies is includ­ed for 2020 here. Further, some of the 2020 figures are for the first two quarters of 2020 only, while others are for the first three quarters.

14 Jeffrey Lee Funk, “Unicorn IPOs Continue to Disappoint Investors,” Medium, July 7, 2020.

15 Funk, “Unicorn IPOs”; “Global Top 100 Companies—June 2020 Update,” PricewaterhouseCoopers, July 2020.

16 Eliot Brown, “Electric-Vehicle Startups Are Wall Street’s Hot New Thing. No Revenue? No Problem,” Wall Street Journal, October 21, 2020.

17 Tesla buyers typically receive a $7,500 tax credit; Tesla itself received regulatory credits from automakers who did not sell enough electric vehicles, other automakers are required to maintain their existing dealer system (Tesla was formed after dealers became unnecessary), and Elon Musk’s popularity with the media and its small market share means it does not need to advertise to its small but vocal market. See Tina Bellon and Akanksha Rana, “Tesla Sets Revenue Record, Makes Profit Thanks to Pollution Credit Sales to Rivals,” Reuters, October 21, 2020.

18 Paul Sawers, “YouTube Revenue Shows Its Potential As a Standalone Company,” VentureBeat, February 4, 2020; Shobhit Seth, “Without Facebook, Instagram Valued at $100 Billion,” Investopedia, June 26, 2018.

19 David Mayer and Martin Kenney, “Economic Action Does Not Take Place in a Vacuum: Understanding Cisco’s Acquisition and Development Strategy,” Industry and Innovation 11, no. 4 (July 2010): 299–325.

20 Nathan Benaich, “State of AI Report 2020,” Medium, October 1, 2020; Brian Christian, The Alignment Problem: Machine Learning and Human Values (New York: Norton, 2020); Jeffrey Funk, “AI and Economic Productivity: Expect Evolution, Not Revolution,” IEEE Spectrum, March 2020; Gary Marcus and Ernest Davis, Rebooting AI: Building Artificial Intelligence We Can Trust (New York: Pantheon, 2019); Gary Smith, The AI Delusion (Oxford: Oxford University Press, 2018); Andrew Bartels and Mike Gualtieri, “Sizing the AI Software Market: Not as Big as Investors Expect but Still $37 Billion by 2025,” ZDNet, December 13, 2020.

21 Jeffrey L. Funk, “What Drives Exponential Improvements?,” California Management Review 55, no. 3 (May 2013): 134–52.

22 Jeffrey Funk, “Technology Change, Economic Feasibility, and Creative Destruction: The Case of New Electronic Products and Services,” Industrial & Corporate Change 27, no. 1 (February 2018): 65–82.

23 Jeffrey Lee Funk, “Deep Tech Unicorn Startups Are Unprofitable, Why?,” Medium, September 28, 2020.

24 Hubert Horan, “The Uber Bubble: Why Is a Company That Lost $20 Billion Claimed to Be Successful?,” ProMarket, November 20, 2019.

25 Charles Duhigg, “How Venture Capitalists Are Deforming Capitalism,” New Yorker, November 23, 2020.

26 “Infographic: The Growth of Entrepreneurship around the Globe,” Entrepreneur Middle East, January 26, 2017.

27 Jeffrey Funk, “What’s Behind Technological Hype?,” Issues in Science and Technology 36, no. 1 (Fall 2019): 36–42.

28 Rasmus Koss Hartmann, Anders D. Krabb, and André Spicer, “Towards an Untrepreneurial Economy?: The Entrepreneurship Industry and the Rise of the Veblenian Entrepreneur,” SSRN, November 12, 2019; Jeffrey Funk, “Entrepreneurship Is for Everyone according to This Full-Body Bus Stop Display in Singapore. Key Talent: You Must Be a Dreamer Who Sees Things Differently,” LinkedIn, October 2020.

29 Hubert Horan, “Uber’s ‘Academic Research’ Program: How to Use Famous Economists to Spread Corporate Narratives,” ProMarket, December 5, 2019.

30 Jeffrey Lee Funk, “Should We Fail Fast, Hard, and Often? Or Think Carefully about Investments?,” Medium, August 12, 2019.

31 Ben Foldy, Mike Colias, and Nora Naughton, “Long before Nikola Trucks, Trevor Milton Sold Investors on Startups That Faded,” Wall Street Journal, October 1, 2020.

32 Funk, “What’s Behind Technological Hype?”

33 Funk, “Technology Change.”

34 Roger Pielke Jr., “A ‘Sedative’ for Science Policy,” Issues in Science and Technology 37, no. 1 (Fall 2020): 41–47; Bloom et al., “Ideas”; Knott, How Innovation Really Works.

35 The material basics of the internet were created in the 1960s (glass fiber) and 1970s (lasers and packet switching).

36 Collinson and Nielsen, “Science Is Getting Less Bang for Its Buck.”

37 Jeffrey Funk, “Despite Extraordinary Growth in University Research over the Last 70 Years, Corporate Researchers Have Received Many High-Impact Nobel Prizes over Last 20 Years,” LinkedIn, September 2020.

38 Stephen P. Turner and Daryl E. Chubin, “The Changing Temptations of Science,” Issues in Science and Technology 36, no. 3 (Spring 2020): 40–46.

39 Stuart Buck, “Escaping Science’s Paradox,” Works in Progress, October 19, 2020.

40 Jeffrey Funk and Chris Magee, “Rapid Improvements without Commercial Production,” Research Policy 44, no. 3 (April 2015): 777–88.

41 Funk and Magee, “Rapid Improvements.”

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