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- Columns 2 and 4 also include the baseline controls all measured as of 2010: firm-level characteristics (log sales, cash/assets, R&D/sales, log markup, and log number of resumes), log industry wage, and characteristics of the commuting zones where the firms are located (the share of workers in IT-related occupations, the share of college-educated workers, log average wage, the share of foreign-born workers, the share of routine workers, the share of workers in finance and manufacturing industries, and the share of female workers). Standard errors are clustered at the 5-digit NAICS industry level and reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
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- Columns 2, 3 , 5, 6, 8, and 9 also control for the baseline controls all measured as of 2010: firm-level characteristics (log sales, cash/assets, R&D/sales, log markup, and log number of resumes), log industry wage, and characteristics of the commuting zones where the firms are located (the share of workers in IT-related occupations, the share of college-educated workers, log average wage, the share of foreign-born workers, the share of routine workers, the share of workers in finance and manufacturing industries, and the share of female workers). Columns 3, 6, and 9 additionally control for state fixed effects. Standard errors are clustered at the 5-digit NAICS industry level and reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
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- Columns 2, 4, and 6 also include the baseline controls all measured as of 2010: firm-level characteristics (log sales, cash/assets, R&D/sales, log markup, and log number of resumes), log industry wage, and characteristics of the commuting zones where the firms are located (the share of workers in IT-related occupations, the share of college-educated workers, log average wage, the share of foreign-born workers, the share of routine workers, the share of workers in finance and manufacturing industries, and the share of female workers). Standard errors are clustered at the 5-digit NAICS industry level and reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
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- Columns 2, 4, and 6 also include the baseline controls all measured as of 2010: firm-level characteristics (log sales, cash/assets, R&D/sales, log markup, log number of job postings), log industry wage, as well as characteristics of the commuting zones where the firms are located (the share of workers in IT-related occupations, the share of college-educated workers, log average wage, the share of foreign-born workers, the share of routine workers, the share of workers in finance and manufacturing industries, and the share of female workers). Standard errors are clustered at the 5–digit NAICS industry level and reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
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- Finally, we use our Cognism resume data to measure the share of all fresh university graduates from each university who get AI-skilled jobs in each year between 2006 and 2018. These data allow us to validate our premise that ex-ante AI-strong universities are able to increase the supply of AIskilled graduates following the increase in commercial applications in AI in the first half of the 54 Electronic copy available at: https://ssrn.com/abstract=3651052 2010s, discussed below.
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- In Appendix Table 11, we further show that firms that are more exposed to AI-strong universities are not growing faster before 2010, which supports the exclusion restriction that the exposure to AI-strong universities only affects firm growth through firms’ AI investments after 2010.
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- Instrument Validation. We first validate several core assumptions underlying the intuition behind our instrument. Confirming our key argument, we show that the increase in AI-trained graduates during the 2010s was much more pronounced in AI-strong universities than in nonAI -strong universities. Figure 5 plots the share of fresh graduates that are AI-trained from AIstrong and non-AI-strong universities from 2006 to 2018. In 2006, there were few AI graduates across the board, with the share of AI graduates below 0.3% for both AI-strong and non-AI-strong universities. Even in 2012, the share of AI graduates remained below 0.5% in both groups of universities. From 2012 to 2018, however, the share of AI graduates tripled (to about 1.5%) in AI-strong universities, while the share of AI graduates remained under 0.5% in non-AI-strong universities.
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- Internal structure of Qualcomm’s AI workforce. Between 2000 and 2018, the majority of Qual70 Electronic copy available at: https://ssrn.com/abstract=3651052 comm’s AI employees have been engineers focused on the improvement of the core product being developed at each point in time, supported by an auxiliary staff of patent counsels and data scientists.
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- Manufacturing Wholesale & Retail Finance Other ∆ Log ∆ Log ∆ Log ∆ Log ∆ Log ∆ Log ∆ Log ∆ Log Sales Employment Sales Employment Sales Employment Sales Employment (1) (2) (3) (4) (5) (6) (7) (8) ∆ Share AI Workers 0.135** 0.125* 0.321*** 0.357*** 0.239** 0.264** 0.177*** 0.125* (0.057) (0.072) (0.061) (0.061) (0.107) (0.103) (0.061) (0.067) Industry FE Y Y Y Y Y Y Y Y Controls Y Y Y Y Y Y Y Y Adj R-Squared 0.321 0.281 0.817 0.857 0.473 0.478 0.473 0.363 Observations 516 516 109 109 149 149 278 278 82 Electronic copy available at: https://ssrn.com/abstract=3651052 Table A7. AI Investments and Firm Growth in Tech Sectors Using the Resume-based AI Measure This table reports the coefficients from long-differences regressions of firm growth from 2010 to 2018 on the contemporaneous changes in AI investments among U.S. public firms in tech sectors.
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- Regressions in columns 2 and 4 also include industry-level controls for log total employment, log total sales, and log average wage in 2010. Standard errors are robust against heteroskedasticity and reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
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- Specifically, in Table 10, we find no significant relationship between the change in the share of fresh graduates from AI-strong universities in the pre-period (from 2005 to 2010) and our instrument.
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- The group includes a healthcare arm (UnitedHealthcare) established in 1977 and a new technology arm founded in 2011 (Optum). While the UnitedHealthcare arm makes use of AI techniques to optimize operations ranging from cost projections to fraud detection in medical claims, the launch of Optum highlights the way in which firms such as UNH can leverage AI technologies to expand operations by creating new products and entering new market segments. UNH is one of very few companies with access to detailed patient, patient-physician, and drug-patient interaction data for large portions of the U.S. and many additional global locations, making it perfectly placed to harness AI in its operations.
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- Timeline of AI investments at JPM. As highlighted by Figure A2, investments in AI at JPM began at the turn of the century, with a steady increase through the first decade turning into an exponential growth in the second decade. The explosion in AI investments at JPM during the 2010s is marked by the acquisition of the multimedia recommendations patent in 2011; an underscoring of the risks associated with data security following a data leak in 2016; and finally the establishment of a dedicated AI research initiative (Machine Learning Center for Excellence) spearheaded by Dr.
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- Timeline of AI investments at Qualcomm. As can be seen from the timeline in Figure A4, the presence of AI employees at Qualcomm began earlier than in the other firms, and by 2007 the firm initiated dedicated AI research projects in its research arm. The ramp up continued through 2013, marked by collaborations with outside partners such as Brain Corp and internal projects on problems such as face detection. After 2013, Qualcomm saw notable consequences of the earlier investments, including the first release of SNPE and the formation of an organizationally separate AI research group, but the share of Qualcomm’s overall workforce that is skilled in AI remained approximately flat from 2013 to 2018.
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- Timeline of AI investments at UNH. The use of AI technologies at UNH traces further back than at most firms. As early as the 1990s, UNH piloted AdjudiPro, an AI-powered platform for processing claims from physicians. However, the presence of AI-skilled labor at UNH remained low throughout the 1990s and 2000s, noticeably picking up in 2011 with the launch of the Optum platform. Thereafter, UNH’s investment in AI human capital rose steadily throughout the 2010s.
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- We compare the coverage of our university graduates data with official statistics from universities and show that our resume data cover a sizable proportion of university graduates in the U.S. In particular, we aggregate the data to university-year level by calculating the total number of fresh graduates from each university in each year. We compare these numbers with the total numbers of all degrees (bachelors, masters, and PhDs) conferred by each university in each year, using the Integrated Postsecondary Education Data System (IPEDS) data, which contain the total enrollment and the number of degrees conferred each year for all post-secondary institutions in the U.S. As of 2012 (the latest year of the IPEDS data), our resume data cover, on average, 59% of all fresh graduates at each university. The number of fresh graduates in the resume data is also highly correlated with graduates in the cross-section of universities (correlation=0.73).
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