Recent research and reporting show a sudden and painful reversal for many young computer science graduates who entered university during the tech boom only to find an A.I.‑reshaped labour market that no longer guarantees a fast track to high‑paying engineering jobs. A New York Times investigation, supported by new labour data from the Federal Reserve Bank of New York and enrollment figures from the Computing Research Association, documents that unemployment among recent computing graduates has risen, that undergraduate production has surged even as entry‑level hiring contracts, and that generative A.I. coding tools together with widespread tech layoffs are disrupting the traditional path from degree to software job (New York Times; New York Fed; CRA Taulbee Survey). The change matters for Thai students, universities and policymakers as Thailand pushes an ambitious national A.I. plan while preparing the next generation of digital workers.
The short version: after more than a decade of messaging that “learn to code, get a CS degree, and the money will follow,” a confluence of forces — generative A.I. coding assistants that can produce and debug large amounts of code, mass layoffs at major technology firms, and automated applicant‑screening systems — has made it harder for new graduates to land the entry‑level software jobs that traditionally absorbed fresh talent. The New York Times interviewed dozens of recent graduates who described applying to thousands of roles, completing repeated coding tests and interviews, and in many cases being ghosted or rejected; national statistics show unemployment among 22–27‑year‑old computer science and computer engineering majors at some of the highest levels across majors (New York Times; New York Fed).
Why this matters to Thai readers: Thailand has been accelerating public and private investment in AI and digital skills, with a national AI Strategy and Action Plan (2022–2027) and government efforts to build AI literacy across schools and colleges (AI Thailand national strategy; dig.watch summary). Thai students and families who saw computer science as a secure path to well‑paid tech work now face the same global labour shifts documented in the U.S. reporting. Because Thailand’s economy relies heavily on tourism, manufacturing and increasingly on digital services, a rising cohort of skilled but underemployed computing graduates could affect household finances, labour mobility and the country’s ability to supply the new kinds of AI‑literate workers firms say they need.
Key facts and recent developments are clear from multiple sources. National survey data tracked by the Federal Reserve Bank of New York show elevated unemployment among recent computing graduates: the Fed reported unemployment rates for recent graduates in computer science and computer engineering among the highest across majors for young graduates aged 22–27 (New York Fed college labour interactive). The Computing Research Association’s Taulbee Survey documents that undergraduate enrollment and degree production in computing have more than doubled in the last decade at many North American universities — a boom in supply that now collides with cooling of demand (CRA Taulbee Survey 2024 report).
The labour shock has two technical drivers. First, adoption of generative A.I. tools that write, refactor and test code reduces the marginal time and labour some companies need from junior engineers. Economists point out that entry‑level roles are often the most automatable in knowledge work because they involve routine coding tasks that A.I. tools can replicate in part (New York Times; Seattle Times analysis). Second, large technology employers have carried out rounds of layoffs in 2024–25 — including at major chipmakers, cloud and platform firms — shrinking the number of openings for new graduates even as venture capital and startup hiring remains uneven (TechSpot tracking of layoffs).
Voices on the ground capture the human toll. New graduates in the U.S. told the New York Times of applying to hundreds or thousands of jobs, of being asked to pass online coding tests only to be rejected or ghosted, and of taking temporary or unrelated work to cover living costs. One recent graduate described the “most demoralizing” job search, another said automated screening rejected their applications “in three minutes.” Some employers and policy advocates argue that A.I. will also create new, higher‑value jobs and that training programs can help graduates pivot; major technology firms have responded by pledging large retraining commitments. Microsoft, for example, announced a multi‑year program called Microsoft Elevate that pledges more than US$4 billion in cash, cloud services and training to expand A.I. education and reskilling globally (Microsoft Elevate blog). But those programs will take time to roll out and do not immediately replace lost hiring pipelines.
Experts quoted in the reporting offer a mix of concern and pathways forward. A former U.S. federal computing education official warned that computer science students who would have competed for multiple offers in previous years now struggle to get any offers. An economist noted that automation tends to hit entry‑level roles first, while academic researchers cautioned that many universities only recently began integrating A.I. tool training into curricula, lagging employer expectations (New York Times; CRA Taulbee 2024).
For Thailand, the implications are immediate and strategic. Thai universities have expanded computing programs and the government has promoted AI readiness as part of national development. That expansion helped produce a larger pool of numerate, tech‑literate young people — a national asset. But the U.S. case signals that supply alone will not guarantee stable employment. Thailand’s National AI Strategy emphasizes building AI capability across education and the public sector; successful implementation could help Thai graduates move into roles where human domain knowledge and interdisciplinary skills are combined with A.I. fluency — for example, AI applied to agritech, tourism management, healthcare informatics, and public sector services (AI Thailand national plan summary; dig.watch briefing). At the same time, Thailand must prepare for transitional challenges: potential underemployment, wage pressure for entry‑level roles, and mismatches between university teaching and employer expectations.
Historical and cultural context sharpens the stakes. Many Thai families place high value on securing a profession perceived as stable and prestigious; degrees in engineering, medicine and accountancy have long been pathways to middle‑class status. In the last decade, computing and software became a similar “safe bet,” attracting more students into metropolitan universities in Bangkok, Chiang Mai and Khon Kaen. A reversal in market signals — where coding skills remain valuable but the pathway to an engineering job narrows — can create social stress if graduates face unemployment or must accept work outside their field. Thai students traditionally rely on family networks, local jobs and sometimes government or alumni connections to absorb early graduate transitions; those informal buffers can help but are uneven across regions and income groups.
What might happen next? Several plausible developments are visible from current evidence and policy moves. First, demand for graduates who can combine AI tool literacy with domain expertise (health, law, agriculture, tourism) will rise; employers need people who understand the context in which A.I. operates and can translate technical outputs into business value. Second, credentialing and micro‑credentials — short, skills‑focused certificates in A.I. tooling, prompt engineering, data stewardship and human‑centred design — are likely to proliferate. Microsoft’s Elevate pledge and similar corporate programs indicate the private sector will play a major role in shaping such credentials (Microsoft Elevate). Third, governments and universities may expand internship programmes, apprenticeships and work‑study partnerships to preserve on‑ramp opportunities for early‑career talent; the New York Times reporting showed students often lose the chance to gain on‑the‑job experience when companies cut internships and entry positions (New York Times).
Practical recommendations for Thai readers — students, families, educators and policymakers — follow from the evidence.
For students and recent graduates: diversify your skillset beyond textbook coding. Learn A.I. toolchains (how to use code‑generation assistants responsibly), but pair them with domain knowledge (e.g., public health, logistics, hospitality management). Build a portfolio of projects that show problem‑solving in an applied context and demonstrate human strengths — communication, product sense, ethical judgment. Seek internships, freelance work or volunteer projects with NGOs and local firms; real‑world results matter more than test scores.
For university educators: accelerate integration of A.I. tool training into coursework, but teach it critically — emphasise prompt engineering, evaluation of model outputs, testing and verification, reproducibility and ethics. Expand partnerships with local industry to guarantee supervised internships and project‑based learning that mirrors the teamwork and systems thinking employers need. Consider modular micro‑credentials tied to employer demand that allow students to acquire job‑relevant competencies quickly.
For policymakers and employers in Thailand: protect and expand entry‑level pathways. Encourage tax credits or matching funds for companies that create paid internships and apprenticeships for new graduates. Invest in public reskilling programmes targeted at both computing majors and adjacent fields, and ensure equitable access across provinces so students outside Bangkok are not left behind. Use Thailand’s AI Strategy implementation to prioritise sectors where local knowledge confers a comparative advantage — e.g., agritech, tourism, smart cities and public health — and create targeted fellowships and public sector placements.
For families and communities: support graduates in building diverse career plans. Encourage language and communication skills that broaden employability (English remains a global lingua franca for tech but local language expertise and cross‑cultural skills have value). Recognize entrepreneurship as a viable option — small digital firms and localised AI applications can create household‑level resilience.
The global shift shows both risks and windows of opportunity. A workforce that learns to use A.I. as a force multiplier — combining tools with judgement, creativity, sector knowledge and ethical sensibility — will be in demand. Thailand’s policy architecture already recognises this: the national AI action plan, and capacity‑building initiatives such as the AI Governance Clinic and public‑private partnerships, are steps in the right direction (AI Thailand national strategy; UNESCO on Thailand AI Governance Clinic). But implementing those plans to preserve entry pathways and to reskill at scale will determine how many new graduates make a successful transition into the AI‑enabled economy.
Sources: reporting and analysis in this article is based on the New York Times investigation “Computer Science Grads Struggle to Find Jobs in the A.I. Age” (New York Times), labour market data and interactive feature from the Federal Reserve Bank of New York (New York Fed college labour market), the Computing Research Association Taulbee Survey 2024 report (CRA Taulbee Survey 2024), Microsoft’s Microsoft Elevate blog post summarising its US$4 billion AI skilling commitment (Microsoft Elevate), and recent industry coverage of widespread tech layoffs and the debate over A.I. as a cause (TechSpot layoffs tracker; Seattle Times analysis). For Thailand’s policy context and national AI plan, see the AI Thailand portal summarising the national AI strategy and action plan (AI Thailand national strategy) and dig.watch’s briefing on Thailand’s Action Plan (dig.watch summary).