As large-scale social data explode and machine-learning methods evolve, scholars of entrepreneurship and innovation face new research opportunities but also unique challenges. This chapter discusses the difficulties of leveraging large-scale data to identify technological and commercial novelty, document new venture origins, and forecast competition between new technologies and commercial forms. It suggests how scholars can take advantage of new text, network, image, audio, and video data in two distinct ways that advance innovation and entrepreneurship research. First, machine-learning models, combined with large-scale data, enable the construction of precision measurements that function as system-level observatories of innovation and entrepreneurship across human societies. Second, new artificial intelligence models fueled by big data generate'digital doubles' of technology and business, forming laboratories for virtual experimentation about innovation and entrepreneurship processes and policies. The chapter argues for the advancement of theory development and testing in entrepreneurship and innovation by coupling big data with big models. Key words: Entrepreneurship, venture funding, creative destruction, big data, digital doubles, embeddings, virtual experiment, artificial intelligence (AI), large language models (LLMs), deep neural networks (DNNs).

Many venture capitalists and technologists talk about artificial intelligence, big data and how everything from systems to objects are getting smarter. Devices talk to one another, and people are learning how to talk to devices. Google Home, Alexa and Siri are just a few examples of how spoken language can help automate our lives to get things done more quickly. Objects gather information, software is developed to make decisions, and our virtual assistants make sure it all runs smoothly. It's inevitable that this technology also will have a profound impact on the way businesses are started and accelerated.