The Business of AI Startups

New machine learning techniques have led to an acceleration of “artificial intelligence” (AI). Numerous papers have projected substantial job losses based on assessments of technical feasibility. But what is the actual impact? This paper reports on a survey of commercial AI startups, documenting rich detail about their businesses and their impacts on their customers. These firms report benefits of AI that are more often about enhancing human capabilities than replacing them. Their applications more often increase professional, managerial, and marketing jobs and decrease manual, clerical, and frontline service jobs. These startups sell to firms of different sizes, in different industries and nations, but the distribution of activity is distinct from that of larger firms. Firms serving EU customers appear to use higher levels of data protection

The Role of Data for AI Startup Growth

Artificial intelligence (“AI”)-enabled products are expected to drive economic growth. Training data are important for firms developing AI-enabled products; without training data, firms cannot develop or refine their algorithms. This is particularly the case for AI startups developing new algorithms and products. However, there is no consensus in the literature on which aspects of training data are most important. Using unique survey data of AI startups, we find that startups with access to proprietary training data are more likely to acquire venture capital funding

Ethics and AI Startups

Artificial Intelligence (AI) startups use training data as direct inputs in product development. These firms must balance numerous trade-offs between ethical issues and data access without substantive guidance from regulators or existing judicial precedence. We survey these startups to determine what actions they have taken to address these ethical issues and the consequences of those actions. We find that 58% of these startups have established a set of AI principles. Startups with data-sharing relationships with large high-technology firms (i.e., Amazon, Google, Microsoft), that were negatively impacted by privacy regulations, or with prior (non-seed) funding from institutional investors are more likely to establish ethical AI principles. Lastly, startups with data-sharing relationships with large high-technology firms and prior regulatory experience with GDPR are more likely to incur negative business outcomes, like dropping training data or turning down business, to adhere to their ethical AI policies

GDPR and the Importance of Data to AI Startups

What is the impact of the European Union’s General Data Protection Regime (“GDPR”) and data regulation on AI startups? How important is data to AI product development? We study these questions using unique survey data of commercial AI startups. AI startups rely on data for their product development. Given the scale and scope of their business models, these startups are particularly susceptible to policy changes impacting data collection, storage and use. We find that training data and frequent model refreshes are particularly important for AI startups that rely on neural nets and ensemble learning algorithms. We also find that firms with customers in Europe are significantly more likely to create a new position to handle GDPR-related issues or to reallocate firm resources due to GDPR

Three essays on inventors, inventions, and innovation

Utilizzando un campione di 415 inventori che hanno vinto il “R&D 100 award” tra il 2005 e il 2014 per la più importante innovazione radicale, questa tesi analizza empiricamente tre temi riguardanti le relazioni tra premi, invenzioni, ed innovazioni. La tesi si compone di tre saggi. Il primo saggio illustrerà i risultati di un'indagine statistica condotta sui vincitori del “R&D awards”: “The R&D 100 award inventor survey”. In questo saggio forniremo informazioni sulle caratteristiche degli inventori in termini di demografia e istruzione, il contesto in cui si è verificata la loro attività innovativa, e il valore delle loro innovazioni. Il confronto di alcuni dei nostri risultati con quelli di precedenti ed analoghe indagini ci permetterà di trarre interessanti conclusioni riguardanti le caratteristiche variabili degli inventori, l'evoluzione nel tempo della loro attività innovativa, le loro motivazioni, e i contesti industriali ed organizzativi in cui hanno operato. Nel secondo saggio prenderemo in considerazione un campione di importanti invenzioni premiate e stimeremo la probabilità che l'invenzione sia brevettata (o no) in funzione delle sue caratteristiche, delle caratteristiche dell'inventore, e delle caratteristiche dell'organizzazione. Sosterremo che un'analisi delle innovazioni che hanno vinto un premio importante (il “R&D 100 Awards”) ci permetterà di valutare le determinanti delle innovazioni che si verificano all'interno e all'esterno del sistema dei brevetti. Per eseguire l'analisi, utilizzeremo una serie di regressioni. In termini di propensione brevettuale, i nostri risultati mostreranno che la precedente esperienza brevettuale degli inventori, il contesto dell'organizzazione in cui lavorano e nella dimensione del team cui appartengono influiscono positivamente sulla probabilità di brevettare. Forniremo ulteriori prove sulle determinanti del valore e del valore di quelle invenzioni che sono state brevettate utilizzando gli indicatori tradizionali basati sulle citazioni del brevetto e gli indicatori alternativi tratti dal ‘The R&D 100 award inventor survey’. Un confronto tra il nostro campione di invenzioni premiate e brevettate ed un campione di controllo di innovazioni simili e brevettate (ma on premiate) suggerirà che le invenzioni brevettate vincitrici del premio sono di maggiore valore. Infine, il terzo saggio esplorerà la mobilità degli inventori pluripremiati (ovvero coloro che hanno vinto più volte il “R&D 100 award”). Utilizzeremo informazioni dettagliate riguardo l'eventuale spostamento dell'inventore dopo aver ricevuto il premio durante il periodo 2005- 2014 e costruiremo indicatori di esperienza diversi dal lavoro al momento del premio e indicatori occupazionali al momento del premio. Dapprima, utilizzeremo l'analisi non parametrica di Kaplan-Meier per evidenziare le differenze sistematiche tra diversi tipi di inventori in termini di mobilità. In seguito, utilizzando un modello complementary-log- logistic, studieremo le determinanti della probabilità che inventori pluripremiati siano più mobili ovvero si trasferiscano in un'altra organizzazione dopo aver vinto il premio. I risultati indicheranno che le prestazioni precedenti e attuali degli inventori con riferimento ai brevetti e alle pubblicazioni non hanno alcuna influenza sulla mobilità. I risultati forniranno invece prove che essere un imprenditore al momento del ricevimento dei premi è associato positivamente alla mobilità degli inventori.Drawing on a sample of 415 inventors who have won the ‘R&D 100 award’ for the most important breakthrough inventions between 2005 and 2014, this dissertation proposes empirical research on three topics within the field of inventors, invention and innovation. The work consists of three essays. The first essay will present the summary results of a comprehensive survey of R&D awards recipients: ‘The R&D 100 award inventor survey’. In this essay, we will provide information on the characteristics of the inventors in terms of demography and education, the context in which their innovative activity occurred, and the value of their innovations. Comparison of some of our findings with those of prior surveys will allow us to draw interesting conclusions concerning the changing characteristics of inventors and their inventive activity over time and across industrial and organizational contexts. In the second essay we will consider a sample of important awarded inventions from our survey and estimate the probability for the invention to be patented (or not) as a function of its characteristics, the characteristics of the inventor, and the characteristics of the organization. We will argue that by taking innovations that won an important prize (i.e. R&D 100 Awards) will allow us to evaluate the determinants of innovations occurring inside and outside the patent system. To perform the analysis, we will employ a logit regression. In terms of patent propensity our findings will show that inventors’ prior experience in patenting, the organization context they work in (i.e. firms) and the team size they belong to positively affect the probability to patent. We will further provide evidence on the determinants of the value and the quality of those inventions that are patented by employing traditional indicators based on forward patent citations as well as alternative indicators taken from ‘The R&D 100 award inventor survey’. Results will suggest that patented award-winning inventions are more valuable when matched to inventions in the same technological class that have been patented but not awarded. Finally, the third essay will explore the mobility of inventors that have won the ‘R&D 100 award’ for the most important breakthrough inventions multiple times. We will use detailed information concerning whether the inventor move or not after receiving the award during the 2005-2014 period, and construct indicators of experience other than job at the time of the award and indicators of job tenure at the time of the award. We will employ Kaplan-Meier non parametric analysis to highlight systematic differences across different type of inventors in terms of mobility. We will then investigate the probability of multi-award winners to move after being awarded the innovation prize by means of a complementary-log-logistic model. Results will indicate that inventors’ previous and current performance with reference to patents and publications have no influence on mobility. Results will instead provide evidence that being an entrepreneur at the time of the awards is positively associated with inventors’ mobility

Ethical AI Development: Evidence from AI Startups

Artificial Intelligence startups use training data as direct inputs in product development. These firms must balance numerous trade-offs between ethical issues and data access without substantive guidance from regulators or existing judicial precedence. We survey these startups to determine what actions they have taken to address these ethical issues and the consequences of those actions. We find that 58% of these startups have established a set of AI principles. Startups with data-sharing relationships with high-technology firms; that were impacted by privacy regulations; or with prior (non-seed) funding from institutional investors are more likely to establish ethical AI principles. Lastly, startups with data-sharing relationships with high-technology firms and prior regulatory experience with General Data Protection Regulation are more likely to take costly steps, like dropping training data or turning down business, to adhere to their ethical AI policies

The Role of Data for AI Startup Growth

Artificial intelligence (“AI”)-enabled products are expected to drive economic growth. Training data are important for firms developing AI-enabled products; without training data, firms cannot develop or refine their algorithms. This is particularly the case for AI startups developing new algorithms and products. However, there is no consensus in the literature on which aspects of training data are most important. Using unique survey data of AI startups, we find that startups with access to proprietary training data are more likely to acquire venture capital funding

The Business of AI Startups

New machine learning techniques have led to an acceleration of “artificial intelligence” (AI). Numerous papers have projected substantial job losses based on assessments of technical feasibility. But what is the actual impact? This paper reports on a survey of commercial AI startups, documenting rich detail about their businesses and their impacts on their customers. These firms report benefits of AI that are more often about enhancing human capabilities than replacing them. Their applications more often increase professional, managerial, and marketing jobs and decrease manual, clerical, and frontline service jobs. These startups sell to firms of different sizes, in different industries and nations, but the distribution of activity is distinct from that of larger firms. Firms serving EU customers appear to use higher levels of data protection