Impact of Artificial Intelligence on Firms and Workers
Sunday, Jan. 3, 2021 10:00 AM - 12:00 PM (EST)
- Chair: Alex Xi He, University of Maryland
Engineering Value: The Returns to Technological Talent and Investments in Artificial Intelligence
AbstractEngineers, as implementers of technology, are highly complementary to the intangible knowledge assets that firms accumulate. This paper describes how technical talent is a source of rents for corporate employers, particularly for the case of Google’s surprising open-source launch of TensorFlow, a deep learning software package. First, I present a simple model of how employers intangible assets expose them to the returns to their employees’ skill acquisition efforts. Then, using over 180 million position records and over 52 million skill records from LinkedIn, I build a panel of firm-level skills to measure the market value of exposure to newly available deep learning talent. AI skills are strongly correlated with market value, though variation in AI skills from 2014-2017 does not explain contemporaneous revenue productivity within firms. AI-intensive companies rapidly gained market value following the launch of TensorFlow, while companies with opportunities to automate relatively larger quantities of labor with machine learning did not. Using a difference-in-differences approach, I show that the TensorFlow launch is associated with an approximate market value increase of 4-7% for AI-using firms. AI superstar firms in the top quintile also appear to benefit, but show pre-trends in market value growth.
The Adoption of Artificial Intelligence at the System Level
AbstractWe examine how the adoption of AI is impacted by factors beyond task productivity. We show that AI adoption can be constrained by the degree of interrelatedness amongst decisions in an organisation with low modularity being a constraint on adoption. However, this critically depends on the type of prediction offered by the AI. With certain types of prediction, lower modularity increases the case for AI adoption.
Does Big Data Improve Financial Forecasting? The Horizon Effect
AbstractWe study how data abundance affects the informativeness of financial analysts' forecasts at various horizons. Analysts forecast short-term and long-term earnings and choose how much information to process about each horizon to minimize forecasting error, net of information processing costs. When the cost of obtaining short-term information drops (i.e., more data becomes available), analysts change their information processing strategy in a way that renders their short-term forecasts more informative but that possibly reduces the informativeness of their long-term forecasts. We provide empirical support for this prediction using a large sample of forecasts at various horizons and novel measures of analysts' exposure to abundant data. Data abundance can thus impair the quality of long-term financial forecasts.ty of long-term forecasts.
Lisa B. Kahn,
University of Rochester
University of Wisconsin
- O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights
- J2 - Demand and Supply of Labor