Across Texas, computer science majors are staring down the future of work, and it looks a lot like artificial intelligence. As entry-level software hiring cools, students say they are more anxious about landing that first stable job and are increasingly putting off the hunt. Many are opting for graduate programs or specialized AI training instead, while computer science departments at UT Austin, UTSA, and Baylor race to bake AI into core coursework so degrees are less vulnerable to automation.
Job market data show a pullback
National numbers are not exactly calming anyone’s nerves. The Federal Reserve Bank of New York reports elevated unemployment and underemployment among recent college graduates, with technology majors facing some of the weakest early-career outcomes. Listings for software-development roles that spiked in 2021 and 2022 have also eased, and Indeed’s software-development postings, tracked on FRED, remain well below their 2022 peak.
Universities are rewriting degrees for an AI era
Department chairs told reporters they have added AI-focused classes and reworked degree plans so students learn to collaborate with the same tools that are reshaping software work, The Texas Tribune reports. UT Austin recently won board approval for a new School of Computing that will fold computer science, information, and statistics into a single unit and expand AI and data instruction across campus, the university said. Administrators say the goal is to broaden students’ skill sets so graduates can supervise and apply AI tools rather than be replaced by them.
Faculty: risk and practical fixes
Faculty leaders are blunt about the challenges and the path forward. UTSA chair Fred Martin told The Texas Tribune that “definitely, it’s harder to get jobs,” while Baylor’s Jean Gao urged students to build distinguishing, applied skills as computer science becomes “glue” across industries. Both chairs are overseeing updates that integrate foundational AI topics into undergraduate curricula, university materials show.
Students and early-career engineers are adjusting
Students are responding with a mix of patience and pragmatism. Local reporting describes UTSA senior Vivian Tran saying several classmates chose master’s programs to sidestep a crowded entry-level market, and that she submitted hundreds of internship applications before finally landing a summer role. The same reporting follows a recent UT Austin graduate who said he was laid off after his employer reorganized around AI-driven efficiencies and has since moved into machine-learning work, a shift that highlights how uneven this transition can be for new engineers. Click2Houston includes the student and faculty interviews cited here.
What students can do next
Professors and administrators say the near-term advantage will belong to students who pair AI literacy with internships, cross-disciplinary work and concrete, demonstrable projects. Universities across Texas are betting that revamped curricula plus real-world experience will keep new graduates competitive even as employers lean harder on automation.
