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Deep Tech Dynamics: Mechanics of Research & Development [Part-1]
Upfront Highlights
Deep tech startups are currently experiencing a boom of funding opportunities and talent flowing into the space. Although software startups have dominated the startup scene for decades, companies working in the world of atoms rather than bits are moving faster than at any time in recent history. The acceleration in the field bears examination of the historical structure of deep tech innovation and why recent policy changes give cause for optimism for the future.
Over the last 30 years, venture investment returns have come overwhelmingly from information technology companies. However, deep tech fields such as energy, biotech, and materials science address more fundamental societal and economic challenges than software by tackling physical problems that are often more critical than those addressed by software. To quote Vannevar Bush, “Science and technology open up new frontiers of economic activity.” [1] Put more strongly, deep technology opens new frontiers in a way that new business models or service delivery cannot because of the opportunity it opens for subsequent technological developments in its wake. The success of information technology companies based on business model, platform, or service innovation in this light can be viewed as following in the wake of deep tech innovations. Deep tech work on the circuits, semiconductors, and infrastructure built the foundations that subsequent businesses operated on. Tracking the history of deep tech, the dynamics of the capital flows in these industries, and the broader trends in society and government that affect technology investment can help elucidate the future of deep tech.
A clarifying concept here is Schumpeter's notion of “creative destruction,” which posits that the emergence of new goods, services, markets, and technologies is fundamentally disruptive to older methods. The upside is that the new methods are better, faster, and cheaper and will eventually be supplanted themselves. New developments do destroy the profits and position of the old, but offer improvements via their adoption and serve as an engine for growth. Innovation is also more capital-intensive than conducting business as usual, and not many players in the economy have the capital or the stomach for risk to allocate resources to projects that might not pay off. [2]
Most conventional economic theory regards efficient allocation of resources as the goal of rational agents. However, there is a natural tension between the efficient allocation of resources and the pursuit of innovative work, as that work will necessarily cause a great deal of waste along the way. This presents a challenge to capital allocators and managers making investment decisions. They can choose to allocate capital towards risky R&D projects that individually have a low probability of producing returns on investment in the future and produce a great deal of waste, or they can allocate resources more efficiently towards existing known quantity projects that are more certain to produce a, albeit more modest, return on investment. A rational manager deciding how to allocate their company’s resources will look at their options on the basis of resulting future cash flows and probabilities of success for each possible investment. Risky R&D will naturally be at the bottom of the list on this basis. Compounded with other market incentives and personal incentives that we will look at more in Part 2, managers will tend not to invest in R&D. This incentive structure to efficiently allocate resources, unfortunately, means that the natural tendency is towards less innovation in the aggregate. Less inefficient creative destruction, and more highly efficient stasis. [3]
Thankfully, several mechanisms within private markets and built out of public policy help maintain economic innovation and offer sufficient incentives to overcome the tendency toward over-optimizing for efficiency. Bill Janeway presents a model in his book Doing Capitalism in the Innovation Economy where innovation and commercialization work sits at a midpoint in this model between basic research upstream and financial markets offering a return downstream. [4]
Upstream, research labs and R&D groups within businesses engage in the trial and error of scientific research, which may or may not yield commercializable outcomes. At the midpoint, commercialization work takes the successes of R&D to turn scientific developments into actual products or services. Downstream, speculative financial markets are waiting to reward successful innovation work for the high risk involved in its pursuit. This model provides a helpful framework for examining the system dynamics that power the deep tech economy, and in turn can help us find ways to support the researchers, entrepreneurs, and the broader economy benefiting from technological dynamism. [5]
Upstream - Research & Development
The upstream component of this system can be defined as all of the research and development efforts in the different research institutions, both public and private. It is hard to overstate the importance that research and development played in the material betterment of society over the 20th century. Although many important inventions were made early in the 20th century by truly lone inventors picking low-hanging fruit, significant discoveries increasingly require huge amounts of capital and coordination to make progress. As the problems got harder to solve, the modern research lab emerged as the main venue for innovation rather than the garages and basements of inspired inventors.
Research labs have differing funding models depending upon the type of institution, but the majority of research dollars come from one of two sources– the federal government or private corporations. One important commonality between public and private research labs is the importance of risk tolerance. Most research does not yield commercial results. There is necessarily a long process of trial, and error, and a good deal of failure that goes into any commercial success. While there is waste involved in this process, it is a necessary waste, and in the long run, it is beneficial. The government both funds and conducts its own research through federal labs and agencies like the Department of Defense, Department of Energy, or NASA. It also pays for most scientific research in university research labs through funding agencies like the Department of Health and Human Services or other institutions like the National Science Foundation. Private labs are typically an embedded subdivision of a larger corporation and are funded by allocating profits from existing product lines towards research in the promise of future profit from the commercialized output of R&D.
Billions of dollars are spent annually on research and development. Today, the amount of investment in research and development (R&D) by businesses conducting their own in-house research is nearly 4 times greater than that of government spending on research. In the year 2020, the private sector allocated $517.4 billion towards R&D efforts, while public sector funding stood at $142.8 billion. Given the different funding sources, the rationale and incentives for private versus public funding for research vary a great deal. [6]
Richard Nelson’s analysis of government spending was written in 1959 shortly after the successful launch of Sputnik. It appeared at the time that the US was, if anything, underfunding basic research during its early lag in the Space Race and Nelson attempted to model what “the right amount” would be for the government to spend on basic science. He shows that there is social value from basic research that the government should be interested in promoting. Private firms produce social value when they engage in basic research just like publicly funded organizations, much of which comes from information spillover effects where the knowledge generated is useful, but does not necessarily align with the goals of the specific project that produced the results. An example might be the World Wide Web emerging from an ancillary project at CERN. However, the amount of research that private firms engage in will always be less than the social optimum because the firms will not be able to capture all of the benefits from this spillover themselves. It still remains the case, as Nelson identified, that the government is in a position to tolerate a much longer lag between research and commercialization than the private sector. [7]
Ken Arrow also shows that because innovation has social benefits that private firms cannot capture themselves, private firms will tend towards funding narrower-scoped work that they can benefit from more directly. Arrow emphasizes the fact that the government is able to support capital allocation that may be economically inefficient, but will have the social benefits of jobs, technological improvements, and a net positive impact on the economy through new frontiers opening up for businesses. The fact that the government is maximizing for social good rather than profitability can allow it to absorb the expense incurred by the risk of innovation funding as a means of achieving the social good it is pursuing. [8]
Both Richard Nelson and Ken Arrow demonstrate that the government can support capital allocation that may be economically inefficient, but will have the social benefits of jobs, technological improvements, and a net positive impact on the economy through new frontiers opening up for businesses. The fact that the government is maximizing for social good rather than profitability can allow it to absorb the expense incurred by the risk of innovation funding as a means of achieving the social good it is pursuing.
No agent whether an individual or a collection of individuals has been able to predict with certainty which technologies or research projects will be the ones that go on to be a major success. History is filled both with examples of massive programs that failed to meet expectations and small side projects that went on to be major successes. Although it does create waste, the process of research is non-deterministic. Trial and error despite the risk of failure is the only means forward, and the extent to which organizations can abide by this risk determines whether or not innovative work takes place at all.
Midstream – Commercialization
Once the science and implementation of a new technology have matured within a research setting, a promising technology can move to the next stage of the stream; commercialization. This is the engine room where startups and new product development groups work out how to take inventions from research to market.
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Proving an invention in a lab is no guarantee that it will be a commercial success as entrepreneurs and intrapreneurs go through the process of actually putting everything together into a product. Extensive research and interviews need to be conducted to ensure that an actual market exists for whatever kind of product might come out of the work. Moreover, verifying that there is an actual problem for end users that the invention could solve. Intrapreneurs and commercialization groups within large companies especially will need to align the new product line to the existing business around aspects like branding and technical competencies. For startup teams, this mostly takes the form of building a team that can execute. Developing a business model that can be profitable and evolve from the outset is also critical.
Private organizations can use a combination of their R&D departments and product management teams, business development units, design groups, and consultants to roll out new products. In the case of federally funded research, however, the pathway to market is slightly more complicated. As the labs and universities are not principally in the business of selling the resulting products that their researchers might be developing, the typical pathway to market is through a licensing agreement between the lab and an outside party via a technology transfer department.
Startups and entrepreneurs interested in developing deep tech companies are typically without the extensive means of these larger organizations. Technology transfer from universities, federal labs, or even private companies can be a great option, but there are still numerous challenges to developing a new technology product without the support structure of a larger organization. Compared with a software startup, deep tech companies are inherently more capital intensive and have substantial costs associated with early development to get to a minimal viable product that can be an order of magnitude higher than the costs of software development. However, there is a rapidly developing ecosystem of startup studios, incubators, accelerators, and funding programs like SBIRs help to add the support and capital necessary to make these companies successful.
Downstream
All of this work needs a strong incentive. That incentive is provided for research and development organizations by the promise of huge windfall gains, not just in the form of future cash flows, but from profits in financial markets when share prices increase. This goal is clear enough for corporate research and development or startups that are commercializing deep tech. Even in the case of public sector labs, the ultimate goal of the spending is still a combination of economic gain, social value, and national security. The expectation of a return on taxpayer investment in the form of jobs created and economic gain from tech development keeps the endeavor ultimately rooted in financial success.
From all of the failed research programs, dead-end projects, and failed startups, only a small percentage find their niche and flourish. Even amongst the research promising enough to solicit the funding required to advance the Technology Readiness Level to commercialization, very little of that work yields viable products. There are more ways for the progression from a research project to a real product to go wrong than to go right at every turn. Accordingly, the returns on investment for the projects that are successful need to be massive to justify the level of risk.
Becoming a modestly profitable business over the short to medium term does not justify such a high chance of failure. For startups commercializing research, there are essentially two means of generating a massive return– to get acquired by another company or to have a public offering. In either of these cases, it is the cash generated from the sales of shares, and thereby the promise of expected future cash flows, that makes it all worthwhile, not just the cash flow from operations that the business can produce.
So where the vast majority of these companies are not successful or only modestly so, the ones that can get acquired or IPO are the ones that justify the whole endeavor. This structure is reflected in venture capital returns. For a fund making investments into startups, the return on investment follows a power law distribution where a very small number of the investments make up the vast majority of the returns on investment. Out of ten investments a fund makes, a plurality will have a return from 0.75x to 1.5x the initial investment. Maybe just 1 in 10 will produce the types of 10x return on investment that make up for the losses on other investments. Throughout the ten-year life of a given fund, the portfolio needs to generate really outstanding returns such that, relative to the more modest returns of less risky assets like debt, equities, or real estate, the fund is investible on a risk-adjusted basis.
The incentive structure for corporate R&D is different in some interesting and important ways. As stated above, the main incentive for startups to commercialize risky research is a windfall from an acquisition or public offering. Corporations that are already publicly listed or matured only indirectly have this option in the form of spinning out a new company and retaining stock in the new venture. The real justification that managers have to make is that the research will turn into additions to an existing product line and allow the company to retain a competitive edge in the future. Compared to a startup basing all of its future revenues on commercializing R&D, going concerns allocate a portion of their present revenue in the expectation of enabling a portion of future revenue growth. However, as will be discussed in parts 2 & 3, the incentive structures at large organizations increasingly make it difficult for managers to justify spending on R&D.
Looking Forward
If Schumpeter’s vision of creative destruction is the engine that keeps the economy moving, then examining the structure and evolution of the systems that produce innovations is critical to harnessing it in the future. Private and public funders engage in this work for very different reasons. The private sector conducts the bulk of the research and development that goes on in the economy, but the public sector complements private funding in four important ways;
The role of research and development (R&D) is central to innovation in the economy, laying the groundwork for additional advancements in the wake of deep tech innovation. However, the journey from R&D to market-ready products is fraught with challenges and high risks, requiring a delicate balance of resources between innovation and commercial viability. Ultimately, the substantial financial rewards from successful ventures, often realized through acquisitions or IPOs, serve as the primary justification for the significant investments and risks undertaken in the deep tech sector.
In part 2 of this series, we will take a historical perspective on the relationship between the government, financial capital, and the broader economy that innovation is a driving force behind.
References
Author
John Boyer
John Boyer is an Associate at FedTech , where he works on deep tech startup studios and innovation strategy. He was a product manager prior to joining FedTech, and he currently focuses on the biotech, climate, aerospace, and defense industries. John received his BA in Philosophy from the University of Pittsburgh.