The Geopolitics of Digital Technology Innovation: Assessing Strengths and Challenges of Germany's Innovation Ecosystem

The COVID-era public and private investment influx into Germany’s digital technology R&D is reversing amid inflation, fiscal consolidation, and geopolitical pressures coming from the Zeitenwende. Germany's future in an EU that is among the top-tier technology powers requires a profound and rapid transition of the country's R&D strengths into data-intensive, systems-centric areas of IoT and deep technology that are linked to the domestic manufacturing base. New policy approaches in three areas - money, markets, and minds - are needed. New technologies such as robotics, artificial intelligence (AI), advanced material science, biotech, and quantum computing tend to have broad general-purpose applications. But uncoordinated funding vehicles, universities' civil clauses, and restrictive visa and onboarding guidelines for skilled foreign workers slow innovation in these sectors and hamper German techno-geopolitical competitiveness

New York Hearts Tech: Tech in the City Under Bloomberg

From Austin’s Silicon Hills, to Louisiana’s Silicon Bayou, Southern California’s Silicon Beaches to the Midwest’s Silicon Prairie, cities across the country have been actively courting tech. New York City is no exception. Since the financial crisis, the Bloomberg administration has pursued tech as a way to diversify the City’s economy. Over the past four years, the government has introduced a suite of tech-focused economic development initiatives, including competitions, incubators, and a well publicized (and expensive) campaign to overhaul the state of the City’s engineering education. Simultaneously, the growth of the City’s tech industry has been remarkable by any measure – companies, jobs, investment, or community network. Much of the government’s attention has been directed at the purported shortage of local tech talent. Less evaluated has been the government’s impact in directing tech’s growth. This thesis asks thus to what extent the Bloomberg administration’s tech agenda has impacted the tech community and contributed to its resurgence in the City. I derive my answer from both a detailed survey of the tech community, as well as from targeted interviews with winners of government-sponsored competitions, academics and industry leaders. My research finds the tech community considerably more impacted by and reliant for its growth on the City’s existing competitive advantages (its strength and diversity across industries, its skilled labor force, its global economy and its livability) than on the direct and visible support of the government

Startup Biases

This Article provides an original descriptive account of bias in the startup context and explains why litigation is eschewed and what happens when it is used as a mechanism to combat bias in the venture capital ecosystem. Further, this Article identifies two particular phenomena in the startup context that exacerbate gender and racial bias. First, homophily—the idea that like attracts like—abounds and has been part of the DNA of venture capital since its inception. The thick networks that developed as venture capital made its way from the East Coast to the West Coast were limited to an elite group that were predominantly white and male. Second, because startups are not subject to a robust set of rules and regulations, they operate under a private ordering structure which allows startups to focus on growing the company. Therefore, whether gender or racial bias is addressed (if at all) depends on the company’s priorities. This lack of rules and regulations is both a blessing and a curse in the startup world. While it allows startups maximum flexibility, it also prevents startups from being subject to a set of laws like it would be in a public company setting. Indeed, evolving practices in the public company realm have limited applicability in the private company setting. This Article offers both legal and non-legal tools to address startup biases, including legal reform applicable to unicorns and investors of a certain size, the diversification of deal leads and referrals, the recruitment of more investors who are women and racial and ethnic minorities, among others. The proposed solutions are intended to disrupt the homophily-influenced infrastructure of startups so that long-lasting changes can occur. Otherwise, we will be where we always are—with women and racial and ethnic minorities receiving a paltry share of venture capital funding

The future of venture capital decision making : the impact of quantitative sourcing and machine learning on the VC Investment process

Investing in early-stage startups is a difficult endeavor. Venture Capitalists use heuristics and base their decisions on past experiences, which can lead to biases. Recently, Venture Capitalists are increasingly using artificial intelligence and quantitative sourcing to support their investment process, while others still rely on traditional investment mechanisms. This research investigates the usage and impact of artificial intelligence and machine learning throughout the venture investment cycle to make investment decisions. This dissertation is an exploratory study that employs a qualitative research approach in the form of semi-structured interviews with ten European Venture Capitalists. The results show that Venture Capitalists utilize machine learning and web scraper tools, particularly during the deal origination, firm-specific screening, and general screening stages of the investment process, to solve the identification and selection challenges. As a result, investment processes become more efficient and less biased, allowing for more time to be spent advising and mentoring portfolio startups. It adds to the existing literature on how artificial intelligence and data can augment existing investment mechanisms during the venture capital decision-making process.Investir em startups na sua fase inicial exige um elevado empenho. Os investidores de capital de risco baseiam as suas decisões em pesquisa e experiências passadas, o que pode levar a enviesamentos. Embora muitos investidores de capital de risco ainda utilizem mecanismos de investimento tradicionais, tem havido um aumento na utilização de inteligência artificial e sourcing quantitativo para apoiar o processo de investimento. Esta investigação estuda a utilização e impacto da inteligência artificial e de machine learning ao longo do ciclo de investimento de risco para tomar decisões de investimento. Esta dissertação é um estudo empírico que utiliza uma abordagem de investigação qualitativa sob a forma de entrevistas semi-estruturadas com dez empresas de investimento de capital de risco europeias. Os resultados mostram que os investidores de capital de risco utilizam machine learning e ferramentas de recolha de dados na web, em particular durante o início da oportunidade de negócio, a seleção específica da empresa, e fases gerais de análise do processo de investimento, para resolver os desafios de identificação e seleção. Consequentemente, os processos de investimento tornam-se mais eficientes e menos tendenciosos, permitindo que se utilize mais tempo a aconselhar e a orientar as empresas do portfolio. Este estudo complementa a literatura existente relativamente a como a inteligência artificial e os dados podem elevar os mecanismos de investimento existentes durante o processo de tomada de decisão de capital de risco

Patent Demands & Startup Companies The View from the Venture Capital Community

With the high level of interest in patent monetization and its effects on US companies, data on the topic is increasingly important. This study examines one aspect of the topic, focusing on the effects of the rising patent monetization market on startup companies. The study provides one of the rare glimpses of monetization activity outside of lawsuits. It provides both quantitative and qualitative information on the startup community’s experience with and perspectives on patent demands. Among other issues, the study tests a narrative that has circulated suggesting that patent monetization creates for venture capital investment. According to the theory, venture capitalists will be attracted to the possibility of monetizing a startup company’s patents if the company fails, and this attraction spurs investment. The study tests that narrative through the eyes of the venture-backed community itself. Results include the following: When making funding decisions, the vast majority of venture capitalists do not consider the potential for selling to assertion entities if the company fails. Thus, patent monetization does not appear to provide investment incentives. In addition, both the companies and the venture capitalists overwhelming believe that patent demands are having a negative impact on the startup community, and all or most of the demands they experience are coming from those whose core activity involves licensing or litigating patents. The effects of these demands are described in terms including the specific costs expended by the companies and the distraction to management, engineers, and other employees. Most important, participants detail the human toll that patent demands have had on entrepreneurs

Startup Governance

Although previously considered rare, over three hundred startups have reached valuations over a billion dollars. Thousands of smaller startups aim to follow in their paths. Despite the enormous social and economic impact of venture-backed startups, their internal governance receives scant scholarly attention. Longstanding theories of corporate ownership and governance do not capture the special features of startups. They can grow large with ownership shared by diverse participants, and they face issues that do not fit the dominant principal-agent paradigm of public corporations or the classic narrative of controlling shareholders in closely held corporations. This Article offers an original, comprehensive framework for understanding the unique combination of governance issues in startup companies over their life cycles. It shows that venture-backed startups involve heterogeneous shareholders in overlapping governance roles that give rise to vertical and horizontal tensions between founders, investors, executives, and employees. These tensions tend to multiply as the company matures and increases the number of participants with varied interests and claims. This framework of startup governance offers new insight into issues of current debate, including monitoring failures by startup boards and late-stage governance complexity, and suggests that more attention should be paid to how corporate law principles apply in the startup context

Using big data in startup selection : exploring machine learning as a tool to predict successful startups in the age of social media

This research aims to further explore the possibilities in the usage of Machine Learning within the Venture Capital industry. Building on previous research the goal of this paper is to determine whether social media analyses can improve the accuracy of Machine Learning models to predict startup outcomes and valuations for startup companies. The research is built on the following models: Multilayer Perceptron, XGBoost, RandomForest, Naive Bayes, and Voting Regressor. The data used in this research comes from Crunchbase, USPTO, and Twitter. The models in this research achieved an adjusted R2 of 0.5281 for value prediction, which shows that exit value is explainable to a large extent by using publicly available qualitative and quantitative data. Outcome prediction had precision for IPO between 0.1447 to 0.4193 and F1-scores between 0.2360 to 0.4449 for models built from Series A to Series C funding rounds. The results of this research show that Venture Capital firms investing from Series A to Series C would be able to outperform the market in terms of returns by implementing Machine Learning in their investment decision-making process. To further improve these results extracting further social media data is a beneficial future resource. Compared to previous models this research built models for 3 specific early funding rounds and can outperform the markets with data available for VCs at these points in time.Esta investigação visa explorar mais profundamente as possibilidades de utilização da aprendizagem mecânica na indústria do Venture Capital. Com base em pesquisas anteriores, o objectivo deste trabalho é determinar se as análises dos meios de comunicação social podem melhorar a precisão dos modelos de Machine Learning para prever os resultados e as avaliações das empresas em fase de arranque. A investigação baseia-se nos seguintes modelos: Multilayer Perceptron, XGBoost, RandomForest, Naive Bayes, e Voting Regressor. Os dados utilizados nesta pesquisa provêm de Crunchbase, USPTO, e Twitter. Os modelos nesta pesquisa alcançaram um R2 ajustado de 0,5281 para previsão de valor, o que mostra que o valor de saída é explicável em grande medida através da utilização de dados qualitativos e quantitativos disponíveis publicamente. A previsão de resultados teve precisão para IPO entre 0,1447 a 0,4193 e pontuações F1 entre 0,2360 a 0,4449 para modelos construídos das séries A a séries C de financiamento. Os resultados desta investigação mostram que as empresas de Venture Capital que investem da Série A à Série C seriam capazes de superar o mercado em termos de retorno, implementando a Machine Learning no seu processo de tomada de decisões de investimento. Para melhorar ainda mais estes resultados, extrair mais dados dos meios de comunicação social é um recurso futuro benéfico. Em comparação com modelos anteriores, esta investigação construiu modelos para 3 rondas de financiamento antecipado específicas e pode superar os mercados com dados disponíveis para VC nestes pontos no tempo

Private Company Lies

Rule 10b-5’s antifraud catch-all is one of the most consequential pieces of American administrative law and most highly developed areas of judicially-created federal law. Although the rule broadly prohibits securities fraud in both public and private company stock, the vast majority of jurisprudence, and the voluminous academic literature that accompanies it, has developed through a public company lens. This Article illuminates how the explosive growth of private markets has left huge portions of U.S. capital markets with relatively light securities fraud scrutiny and enforcement. Some of the largest private companies by valuation grow in an environment of extreme information asymmetry and with the pressure, opportunity, and rationalizing culture that can foster misconduct and deception. Many investors in the private markets are sophisticated and can bear high levels of risk and significant losses from securities fraud. It is increasingly evident, however, that private company lies can harm a broader range of shareholders and stakeholders as well as the efficiency of allocating billions of dollars for innovation and new business. In response to this underappreciated problem, this Article explores a range of mechanisms to improve accountability in the private markets and ultimately argues for greater public oversight and enforcement

Matching Startup Founders to Investors: a Tool and a Study

The process of matching startup founders with venture capital investors is a necessary first step for many modern technology companies, yet there have been few attempts to study the characteristics of the two parties and their interactions. Surprisingly little has been shown quantitatively about the process, and many of the common assumptions are based on anecdotal evidence. In this thesis, we aim to learn more about the matching component of the startup fundraising process. We begin with a tool (VCWiz), created from the current set of best-practices to help inexperienced founders navigate the founder-investor matching process. The goal of this tool is to increase efficiency and equitability, while collecting data to inform further studies. We use this data, combined with public data on venture investments in the USA, to draw conclusions about the characteristics of venture financing rounds. Finally, we explore the communication data contributed to the tool by founders who are actively fundraising, and use it to learn which social attributes are most beneficial for individuals to possess when soliciting investments.Comment: MIT Master's of Engineering in Computer Science thesis. June 2018. 152 page