Key Takeaways
- Enrollment in computer‑science programs in Texas and nationwide has fallen roughly 20 % as AI‑driven efficiencies reshape hiring for software engineers.
- Recent graduates face elevated unemployment (≈7 % for CS, 7.8 % for computer‑engineering majors) and a competitive job market, yet median early‑career wages remain the highest among all disciplines.
- University faculty acknowledge student anxiety but emphasize that internships, networking, and solid technical fundamentals still lead to strong job outcomes.
- Departments at UT Austin, UTSA, and Baylor are revising curricula to include foundational AI coursework for all undergraduates.
- Students are responding by pursuing graduate degrees, building extensive project portfolios, or targeting niches where AI augments rather than replaces human work.
Rising Anxiety in Texas Computer Science Programs
Across Texas campuses, a palpable sense of unease has settled over computer‑science classrooms. “At the very beginning, it was a joke,” recalled Derek Do, a third‑year CS major at the University of Texas at Austin. “The industry took it seriously, but a lot of the students didn’t.” The joke, however, has given way to genuine concern as students watch AI tools automate coding tasks that once defined the profession. The fear is not abstract; many worry they will be the first cohort displaced by a future where machines write, test, and deploy software faster and cheaper than humans can.
Declining Enrollment and a Cooling Job Market
Admissions to computer‑science programs have slipped about 20 % both in Texas and nationally, mirroring a slowdown in software‑engineer hiring. Data compiled by the Federal Reserve Bank of St. Louis from Indeed show that U.S. software‑development job postings have plunged since the 2022 hiring boom. Consequently, recent graduates face a tougher landscape: the Federal Reserve Bank of New York reports unemployment rates of roughly 7 % for CS degree holders and 7.8 % for computer‑engineering graduates. Despite these headwinds, the same study notes that median early‑career wages for CS and engineering grads still exceed those of any other major, and the underemployment rate—grads working outside their field—remains comparatively low.
Student Experiences on the Front Lines
Parth Patki, a spring 2025 UT Austin graduate, embodies the turbulence many students now encounter. After an internship at an international cybersecurity firm, he secured a full‑time software‑engineer offer at the same company. “The use of artificial intelligence was essential to his work,” Patki said, noting that AI could accomplish in seconds what took him a week of trial‑and‑error. The efficiency, however, came at a cost. In December, Patki was laid off alongside about half of the software engineers in his office, a decision leadership attributed to AI‑driven restructuring. He then applied to an average of 25 jobs per day for two months before receiving an offer from one of only two firms that called him back for an interview.
Faculty Perspectives: Reasons for Optimism
Professor Fred Martin, chair of the computer‑science department at the University of Texas at San Antonio, concedes that “definitely, it’s harder to get jobs,” yet he highlights a silver lining. “Our kids, the ones who have internships, who know how to talk to people and have the chops, they totally have jobs. They get great jobs.” Martin points to the post‑COVID hiring surge that left many tech firms overstaffed; now, amid persistent inflation and broader economic uncertainty, companies are cautious about adding headcount. He hopes enrollment is merely stabilizing after a period of inflated numbers.
Professor Jean Gao of Baylor University adds that AI may actually expand demand for software developers by lowering the barrier for non‑tech firms to adopt intelligent tools. “Computer science is just like glue, in every field you need it, like health care, insurance, finance, cybersecurity, everywhere needs computer science,” Gao asserted. She urges students to differentiate themselves—not merely chase a high salary but cultivate genuine passion and unique skills that AI cannot readily replicate.
Curricular Shifts in Response to AI
All three department chairs confirmed they have revised undergraduate curricula to address AI’s growing role. Gao and Stone said they each introduced mandatory foundation‑level AI classes for every CS student, ensuring graduates understand both the capabilities and limits of machine‑learning tools. These courses aim to move students beyond treating AI as a novelty and toward integrating it responsibly into software‑development workflows.
How Students Are Adapting
Vivian Tran, a senior at UTSA and president of the campus Association for Computing Machinery chapter, described a peer group split between two strategies. Many friends opted to remain in school and pursue master’s degrees, “just because they wanted to avoid the job market for now,” Tran said. Others have doubled down on résumé‑building: solving elite coding problems, crafting personal projects, attending networking events, and positioning themselves as “LinkedIn warriors.” Tran herself submitted 250 internship applications before landing a summer software‑engineer role at Uber, which she hopes will convert to a full‑time offer. She acknowledged the market is “not impossible, but it is certainly more difficult.”
A Freshman’s Hopeful Outlook
Danielle Nyame, a first‑year CS major at UT Austin, expressed optimism despite the prevailing anxiety. “AI will not be able to take over every single aspect of this field, but it will be able to help aid with the work that is done in this field,” she said. Nyame hopes to apply AI at the intersection of business or social justice, using the technology to build tools that serve societal needs rather than merely chasing profit.
From Layoff to Machine‑Learning Engineer
Parth Patki’s story did not end with his December layoff. After months of relentless applications, he secured a position as a machine‑learning engineer at PayPal, where he now employs AI daily to accelerate software creation. “It’s both as bad and not as bad as people think,” Patki reflected, noting the work remains exciting but also reminding him daily of his replaceability. “I know that I’m replaceable,” he said. “Every day, I’m reminded that I’m replaceable.” His response has been to prioritize savings as a hedge against future volatility, a pragmatic stance shared by many of his peers navigating an AI‑augmented job market.
In sum, Texas computer‑science programs are navigating a period of rapid transformation. While AI threatens to automate traditional coding tasks, educators and students alike are discovering that adaptability—through internships, continuous learning, niche specialization, and a solid grasp of both software fundamentals and AI principles—remains the most reliable pathway to enduring career success.
AI is changing how Texas universities teach computer science as job market slows