The Data on AI
Redmond, WA
The top 20 U.S. companies who employ the most data scientists control 45% of the talent in the field overall, a staggering share.
Microsoft Apple Alphabet Amazon Meta JP Morgan Walmart Unitedhealth Oracle Accenture Pfizer IBM Uber Intel Citi Paypal Capital One Ford Iqvia Cognizant
We combed through the United States Patent Office data for every company in the S&P 500—these are the 10 companies with the most AI related patents.
Microsoft Alphabet Amazon Oracle Adobe Accenture Qualcomm IBM Intel Capital One
Comparing mentions of AI-related terms in earnings transcripts of 'magnificent 7' tech companies from 2015 to 2024.
Microsoft Apple Nvidia Alphabet Amazon Meta Tesla
Microsoft and BlackRock already dominate their fields in AI. With a 30B fund, they look to cement their place.
Microsoft Blackrock
Our estimates on data scientist numbers for Microsoft 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): 13.53
AI Mentions in Quarterly Earnings Calls (2022-Q4 to current): 38.33
Post ChatGPT Release, AI Mentions Moved 3.43 Standard Deviations
Microsoft holds 683 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 Microsoft invests in physical assets, highlighting its commitment to growth and the infrastructure needed to support AI initiatives.
This graph provides a comprehensive view of Microsoft'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.