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).

The Financial Times recently found that ChatGPT (along with Bard, Google's own experimental chatbot) can tell a joke at least passably well, write an advertising slogan, make stock picks, and imagine a conversation between Xi Jinping and Vladimir Putin. It is understandable that a new technology with such seemingly vast powers would raise concerns. But much of the distress is misplaced. AI's detractors tend to understate the pace of technological change that advanced economies have already been living through. In 1970, US employment was roughly evenly divided across occupations, with low-skill, medium-skill and high-skill jobs accounting for, respectively, 31 per cent, 38 per cent, and 30 per cent of total hours worked.

If you want to imagine the future of healthcare, you can do no better than to read cardiologist and bestselling author Eric Topol's trilogy on the subject: "The Creative Destruction of Medicine," "The Patient Will See You Now," and "Deep Medicine." "Deep Medicine" bears a paradoxical subtitle: "How Artificial Intelligence Can Make Healthcare Human Again." The book describes the growing interaction of human and machine brains. Topol envisions a symbiosis, with people and machines working together to assist patients in ways that neither can do alone. In the process, healthcare providers will shed some of the mind-numbing rote tasks they endure today, giving them more time to focus on patients.

The future comes too soon and in the wrong order." "It is our moral responsibility not to stop the future but to shape it." The pandemic is hurtling the world into a Technology 4.0-transformed "future of work" much earlier than anticipated in the ILO's Centennial Declaration of 2019. India's global significance in mastering the future of work through technology-adaptive and high-productivity human capital employing the largest global cohort of 820 million youth is huge. Along with declining fertility rates and women's empowerment, this could yield a large demographic dividend of high growth rates for decades, despite short-term shocks.

Creative destruction is the process whereby innovation replaces existing economic structures with new ones. Countries that embrace creative destruction, like England during the Industrial Revolution or Deng Xiaoping's post-Mao trade-friendly China, leave some behind but flourish as a whole. Problems arise when the elites quash creative destruction for their own benefit. When Francis I of Austria-Hungary banned the construction of railroads and factories in the early 1800s to preserve his feudal agrarian order, the empire's economy suffered leading to poverty and famine. Today, with a combination of increasing minimum wages and near-zero federal interest rates, our governments are incentivizing, not quashing, creative destruction.

Will AI take away our jobs? Will AI destroy the world and/or become our ruler? I argue it is great to think and ask this kind of question and they should not be taken as pessimistic thinking. The goal of these questions is not to know the right answer, the goal is to understand different visions of the future, or at least how our experts are thinking about this important question. But before asking or thinking about these important questions let us think about something more basic, the relation between economic growth and innovation.

A briefing from the European Parliament [1] provides accessible introductions to some of the key techniques that come under the Artificial Intelligence banner, grouped into three sections to give a sense the chronology of its development. The first describes early techniques, described as'symbolic AI' while the second focuses on the'data driven' approaches that currently dominate, and the third looks towards possible future developments. By explaining what is'deep' about deep learning and showing that AI is more maths than magic, the briefing aims to equip the reader with the understanding they need to engage in clear-headed reflection about AI's opportunities and challenges. In 2019, community groups, researchers, policymakers, and workers demanded a halt to risky and dangerous AI. AI Now's 2019 report [2] spotlights these growing movements, examining the coalitions involved and the research, arguments, and tactics used.

Steve Woods is co-founder and CTO of Nudge.ai, a relationship intelligence platform. When the machines took over farming, a new set of industrial jobs blossomed. When the robots took over the factories, we moved to IT jobs that had never previously existed. Now that AI is taking over another swath of jobs, a wave of as-of-yet-unheard-of jobs, will soon flourish. The thinking that leads to this conclusion has a long, decorated history going back to Joseph Schumpeter's description of creative destruction in the 1940s.

Steve Woods is co-founder and CTO of Nudge.ai, a relationship intelligence platform. When the machines took over farming, a new set of industrial jobs blossomed. When the robots took over the factories, we moved to IT jobs that had never previously existed. Now that AI is taking over another swath of jobs, a wave of as-of-yet-unheard-of jobs, will soon flourish. The thinking that leads to this conclusion has a long, decorated history going back to Joseph Schumpeter's description of creative destruction in the 1940s.

A recent article in The Guardian dons the foreboding title "Robots will destroy our jobs--and we're not ready for it." The article claims, "For every job created by robotic automation, several more will be eliminated entirely. According to an article in MIT Technology Review, business researchers Erik Brynjolfsson and Andrew McAfee believe that rapid technological change has been destroying jobs faster than it is creating them, contributing to the stagnation of median income and the growth of inequality in the United States. If technology is destroying jobs faster than it's creating them, it is the first time in human history that it's done so. Actually, the number of jobs is unlimited, for the simple reason that human wants are unlimited--or they don't frequently reveal their bounds.