Computer science graduates are confronting an unexpectedly challenging job market, with many struggling to secure positions despite sending hundreds of applications. The stark contrast from pandemic-era hiring frenzies has led to a phenomenon dubbed the “panic master’s” – students pursuing graduate degrees to delay their job search in hopes of better market conditions.
Samhita Parvatini, who graduated from Penn State University in May, exemplifies this trend. After submitting 250-300 applications with minimal success, she enrolled in a master’s program as a “last-minute decision.” She entered college in 2021 during peak tech hiring when computer science degrees were “highly sought out,” but now finds the Big Tech landscape has become “the opposite” of what it was five years ago.
According to ADP Research Institute data, software developer employment largely declined between late 2019 and early 2024, with job postings dropping back to pre-pandemic levels per Indeed’s tracking. Meanwhile, the tech sector has experienced brutal layoffs: over 165,000 employees cut in 2022, more than 264,000 in 2023, and nearly 150,000 so far in 2024 across over 520 companies.
Fresh graduates now compete against experienced laid-off engineers for fewer positions. Emos Ker, a recent NYU graduate, noted that while AI and LLM sub-industries are booming as Big Tech invests heavily, these specialized fields require higher-level training that many universities don’t yet provide. Companies prefer hiring “midlevel, senior engineers” over graduates who need more guidance, particularly for AI roles that require training “from the ground up.”
The graduate school trend is measurable: MIT’s EECS Master of Engineering program saw enrollment jump from 241 students (2023-2024) to 303 this year, a significant spike. The Council of Graduate Schools reported computer science as the “only field to increase in first-time enrollment (5.4%) between Fall 2021 and Fall 2022.”
Despite the challenges, not everyone is pessimistic. The US Bureau of Labor Statistics projects 18% employment growth for software developers by 2033. Samuel Onabolu landed a full-time role after sending over 1,000 applications, attributing success to “perseverance and a little bit of luck.” PayScale data shows master’s degree holders earn an average of $112,000 compared to $72,000 for bachelor’s degree holders in CS.
Key Quotes
With all the tech firings, they’re looking for people who are like midlevel, senior engineers. And unfortunately, for people like us who want to come out and work in AI, it’s not really easy to get into because you kind of need to train us from the ground up.
Emos Ker, a recent NYU graduate, highlights the critical challenge facing CS graduates interested in AI careers: while AI and LLM fields are booming with investment, entry-level positions are scarce because companies prefer experienced engineers who don’t require extensive training in these specialized domains.
Although sub-industries within computer science, like AI and LLM, are booming as Big Tech invests heavily, these fields often require a higher level of training.
This observation from Emos Ker underscores the disconnect between AI industry growth and employment opportunities for new graduates, revealing that the AI boom hasn’t translated into entry-level job creation despite massive corporate investment.
I knew that I wasn’t going to go anywhere after graduation. So I thought, might as well apply, and we’ll take a couple of classes, you know, do something better with my time during this period.
Samhita Parvatini’s decision to pursue a master’s degree reflects the pragmatic calculation many CS graduates are making: rather than remain unemployed, they’re investing in additional education, hoping both to improve their credentials and wait out the market downturn.
I feel like every CS major is going through the exact process I went through. I feel like it just takes that one acceptance, that one offer, to kind of break into that career.
Samuel Onabolu, who sent over 1,000 applications before landing a role, captures both the widespread struggle and the persistence required in today’s tech job market, offering a realistic but hopeful perspective for fellow graduates.
Our Take
This article exposes a critical inflection point for AI workforce development. The irony is striking: as AI transforms industries and companies pour billions into AI infrastructure, the next generation of potential AI engineers faces unprecedented barriers to entry. This suggests the AI talent shortage narrative may be overstated – or more accurately, mischaracterized. The shortage isn’t of people interested in AI careers, but of candidates with specific, advanced skills that traditional CS programs don’t provide. Universities are scrambling to add AI-focused programs, but the lag creates a lost generation of graduates caught between old curriculum and new demands. The “panic master’s” trend may actually benefit the industry long-term by producing more specialized talent, but it also raises equity concerns: only those who can afford additional education can access AI careers. Companies need to reconsider their hiring strategies, potentially investing more in training entry-level talent rather than competing exclusively for scarce senior AI engineers.
Why This Matters
This story reveals critical tensions in the AI and tech labor market that will shape the industry’s future. While AI investment surges and companies race to develop advanced systems, a paradox emerges: entry-level talent struggles to break in while specialized AI roles go unfilled due to skills gaps. This signals a fundamental mismatch between traditional CS education and evolving AI industry needs.
The “panic master’s” phenomenon indicates universities are becoming de facto holding patterns for talent during market corrections, potentially creating a more educated but frustrated workforce. For the AI industry specifically, the barrier to entry for specialized roles like LLM development means companies may face talent shortages in cutting-edge areas even as general CS graduates flood the market.
This trend could accelerate the bifurcation of tech careers: routine software development roles face commoditization and competition, while AI-specialized positions command premium compensation but require advanced training. The implications extend to workforce development policy, university curriculum design, and corporate hiring strategies as the industry navigates this transformation.
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