Industry is changing.   In the 20^th century, manufacturing was king, and statisticians studied quality control and process monitoring.   Today, the big business is information technology.   Google, Amazon, Apple and others have created new and complex economic ecologies.  These include recommender systems, which suggest which books a person would like to read, what music a person will enjoy, and whom that person should date.  Computational advertising is another component—everyone sees online advertisements, and the information technology companies have gotten very smart about deciding which ads to show to which customers.  Autonomous vehicles are emerging swiftly into the market, and will have profound positive and negative effects upon our economy.   The manufacturing industries that remain are becoming roboticized, and depend upon sophisticated supply chains to optimize production and profit.   And Large Language Models are changing the way that software engineers, lawyers, educators and many other professionals work.  

All of this evolution raises new questions for statisticians.  How can recommender systems match people to books/music/partners more accurately?  How can one improve the invisible auctions that underlie online ad buy?  What is the safety profile of an autonomous vehicle, and how does that change with weather conditions and other factors?  What is the statistical reliability of a manufacturing pipeline when robots can break down or supply chains falter?  And how can one study the “psychology” of a Large Language Model?   These are areas in which statisticians can make important contributions, but the traditional statistical education needs some retooling to prepare undergraduates and graduate majors for the information technology workplace.  This paper does a deeper dive on the emerging role for statisticians in the knowledge economy.