The Data on AI
New York, NY
Our estimates on data scientist numbers for S&P trace back to several sources that we check on a monthly basis, including the company's website, career boards and other methods.
The number of data scientists a company has is important because it reflects its capability to innovate, scale AI initiatives, and leverage data-driven decision-making to maintain a competitive edge in AI development and application.
AI Mentions in Quarterly Earnings Calls (2017-Q3 to 2022-Q3): 2.37
AI Mentions in Quarterly Earnings Calls (2022-Q4 to current): 22.56
Post ChatGPT Release, AI Mentions Moved 4.93 Standard Deviations
S&P holds 0 patents focused on AI subjects. We have combed through the records of the United States Patent Office for filings related to AI for more than 500 companies. We consider a qualifying patent to be one that primarily concerns artificial intelligence or machine learning. Patents that simply mention these things do not figure in our count.
Examining CapEx shows how much S&P invests in physical assets, highlighting its commitment to growth and the infrastructure needed to support AI initiatives.
This graph provides a comprehensive view of S&P's strategic allocation of resources by comparing free cash flow, R&D costs, and CapEx over time, highlighting how investments in AI and innovation are balanced against financial health and infrastructure commitments. It offers insights into whether a company is sustainably prioritizing AI development amidst its broader financial strategies.