November 6, 2023
Are AI-exposed stocks a golden ticket, or is over-optimism in the mix? Interview with Rochester Cahan, U.S. Portfolio Strategist at Empirical
In the overhyped landscape of artificial intelligence, promises may often overshadow reality. Companies, eager to ride the AI wave, declare their intent loudly, but how many are genuinely committed? To separate genuine AI integration from mere buzzwords, Empirical Research delved into a unique metric: AI job ads.
We interviewed Rochester Cahan, U.S. Portfolio Strategist at Empirical on the key findings of their approach, the significance of AI hiring, and the implications for investors, as revealed by their recent paper “AI Jobs: Oxymoron? AI hiring as a measure of firm-level AI penetration”.
Empirical Research Partners
We see AI jobs are a more substantive measure of true AI adoption, as opposed to the fickle news cycle.
Like most exciting new technologies there’s an element of hype surrounding AI. Almost every firm is quick to say they’re going to harness the potential of AI. Some will of course, but many more are simply saying what they think investors want to hear. To separate tangible progress from mere hot air, we think focusing on firms that are putting their money where their mouth is — by hiring expensive AI talent — is a good approach. Deploying AI talent across an organization at least gives it a fighting chance of implementing AI in its day-to-day operations.
First, we manually examined a subset of job ads, identifying those that were AI-related. Then we looked for common features in those ads (e.g., words used, technologies mentioned) that could help us automatically identify AI-related job ads in the rest of the sample. Once we had an algorithm for automatically identifying AI job ads, we looked at the percent of a firm’s job ads over a trailing one-year period that were AI-related.
We’ve used RavenPack for new sentiment data for many years now, so we had confidence in the technology and algorithms they deploy to scrape web data and analyze it, particularly regarding entity recognition. Job ads seemed like a natural place where RavenPack’s technology could help us quickly process a large body of online content.
Our work suggests that AI is more likely to enhance rather than destroy margins, for the businesses that provide AI algorithms. Like the adoption of the Cloud, AI requires immense processing power, data, capital, and talent to operationalize. The very biggest tech firms have the raw ingredients needed to offer AI services at scale; rather than disrupting such firms it seems more likely that AI deepens their moat. For the users of AI, the outcome is less apparent: some firms might be able to reduce their costs using AI but others might see their businesses disrupted.
Like most exciting new technologies there’s an element of hype surrounding AI. To separate tangible progress from mere hot air, we think focusing on firms that are putting their money where their mouth is — by hiring expensive AI talent — is a good approach.
AI news stories are closely tied to the hype cycle surrounding AI, and surged after the initial release of ChatGPT. However, the volume of news quickly cooled as the news cycle moved on to other things. In contrast, AI hiring has continued to increase. Again, we see AI jobs are a more substantive measure of true AI adoption, as opposed to the fickle news cycle. We put more weight on AI jobs rather than AI news.
Valuation matters. There’s a price for everything and that’s true with AI too. Creating a basket of the firms that are doing the most AI hiring allows us to track how valuations are evolving, both compared to the stocks’ own history and other stocks in the market. We can also look back over history to compare current valuations to other big tech booms in the past, e.g., the rise of the mainframe in the 1960s, the start of the PC era in the 1980s, and of course the Dot Com era of the 1990s. At this point, the valuations of firms doing lots of AI hiring look reasonable relative to the free cash flows they’re producing.
For the full paper,
Empirical Research Partners.
is the U.S. Portfolio Strategist at Empirical Research Partners LLC, having joined the firm in 2013 to focus on a broad spectrum of research topics ranging from bottom-up stock-selection to top-down macroeconomic analysis. Prior to joining the firm, Mr. Cahan was the head of U.S. Quantitative Strategy for Deutsche Bank in New York. During his tenure there the team was top-ranked for quantitative research in the Institutional Investor All-America Research Team survey for three consecutive years from 2011 through 2013. Before Deutsche Bank he also held quantitatively-focused positions at Macquarie Bank and Citigroup in both New York and Sydney, Australia from 2003 through 2010. In addition to his highly regarded practitioner research, Mr. Cahan has published a number of academic articles in top journals including The Journal of Empirical Finance, The Journal of Banking and Finance, and The Journal of Portfolio Management. Mr. Cahan received a double degree in Mathematical Physics and Finance from Massey University in New Zealand in 2003, and is also a CFA charterholder.
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