In just four years, the dominant narrative about computer science careers took a sharp turn: from “crisis-proof future” to structural uncertainty. Universities that once struggled to keep up with teaching are now seeing graduates celebrating a single offering instead of the multiple they used to have. What has changed — beyond AI — and what can students, companies, and academic centers do to reframe professional preparation?
What Changed (beyond the slogan “AI changed everything”)
1) Over-demand → oversupply (local and temporary).
The surge in enrollments from 2016-2022 generated very large cohorts entering the job market during a deceleration cycle: stabilization of staff, reordering of priorities, and more filtering in selection processes.
2) Reconfiguration of tasks due to generative AI.
Tools for coding assistance and agents reduce development times for standard tasks (scaffolding, basic refactoring, testing), which compress the value of junior profiles focused only on implementation. Productivity increases, while the need for headcount in certain teams decreases or shifts.
3) Organizational “thinning”.
Many companies have streamlined middle layers and outsourced non-differentiating work. Remaining teams are smaller, more versatile, and with product responsibility.
4) Shift in salary premiums.
The premium is no longer just for “writing code,” but for solving problems with products that affect metrics, integrate AI with data, and operate systems. The gap between standard contribution and differential impact has widened.
Signals on Campus (and in Job Postings)
- Fewer multiple offers for internships and entry-level positions; hiring processes are longer and more technically demanding.
- Greater emphasis on questions about architecture, data, and product beyond just algorithms.
- Search for “T-shaped” profiles: broad foundation (systems, networks, data, security) + specializations connected to business value.
Is “Computer Science” dead? No: the skill set basket has changed
What retains value (and is increasing):
- Fundamentals: data structures, concurrency, networks, operating systems, computer architecture.
- Applied data science: data prep, model evaluation, prompting, and tooling for MLOps.
- Product engineering: API design, metrics, basic UX, A/B testing.
- Platform and operations: cloud, containers, CI/CD, observability, cost/finops.
- Security: secure by design, threat modeling, basic compliance.
Things that commoditize faster:
- Repetitive implementation without product context.
- “Knowing a library” without understanding patterns or trade-offs.
Recommendations for roles
For students (or bootcampers):
- Learn to work with AI, not against it.
Use copilots and agents daily: clear specs, verification, and evaluation of results are now part of professional competence. - Impactful portfolio, not just demos.
Projects that show end-to-end delivery: problem → data → solution → metric (e.g., latency, retention, cost). Document decisions and post-mortems. - Broaden your base and “adjacent skills”.
Combine CS with a domain (health, finance, logistics, legal). Industry context makes you stand out compared to “code for code”. - Communicate as a product engineer.
Tell stories with data, prioritize, estimate, negotiate scope. The “why” is as important as the “how”. - Iterate your job search like a sprint.
Measure your funnel: applications → interviews → callbacks. Adjust CV/portfolio and technical practice every 2–3 weeks; seek specific feedback.
For universities:
- Strong core curriculum (systems, networks, architecture, data, security), with labs that integrate AI and platform.
- Bridge courses with law, health, business, humanities: real case solving, ethics, and AI responsibility.
- Multi-partner capstone: projects with companies/NGOs where the deliverable lives beyond the course (not toy apps).
- Portfolio-oriented career center and external validations (selective certifications, open source contributions).
For employers:
- More realistic evaluations: fewer riddles; more challenges with AI integrated, debugging, observability, and design decisions under constraints.
- Junior + AI apprenticeship: onboarding with mentors and goals measuring learning, quality, and impact, not just ticket closing.
- Internal re-skilling: create pathways from QA/Support/IT to platform, data, or security with learning sprints and practice.
Concrete strategies for the AI era
- Design with “AI-first”: identify where copilots/agents save time and where require human review. Document the checkpoints.
- Value metrics: adopt combined KPIs (e.g., lead time, defect rate, GPU cost per feature).
- Pragmatic ethics and compliance: responsible AI use checklists, privacy, and security integrated into PRs and pipelines.
- Continuous learning: rotate every 6–9 months to an adjacent area (data↔backend↔platform).
Misleading narratives (and how to correct them)
- “AI takes CS jobs” → AI redefines tasks; shifts value toward integration, evaluation, and operation.
- “Just learn library X” → Patterns and fundamentals matter; the library changes, but the criteria remain.
- “It’s all prompting” → Without clean data, robust systems, and metrics, prompts won’t save the project.
Signs of recovery (what to observe in 2025–2026)
- Increase in job openings mentioning agents, AI observability, MLOps, and cost per inference.
- Practices returning to pre-2020 levels (offer/applicant ratio) in cloud, data, and security.
- Growth in platform and reliability roles linked to AI deployments (AI SRE, AI Infra).
The essentials
Computer science still holds value; it’s no longer automatic. The advantage is no longer in “knowing how to program” in the abstract, but in converting real problems into measurable solutions supported by AI — with technical judgment, domain understanding, and responsibility. In this space, those who adopt and orchestrate AI will outperform those who ignore.
For students, universities, and companies, the task is not to go back to 2019: it’s to design 2026 with a more balanced, interdisciplinary, and operational skill set. And far from being a threat, this is an opportunity for computer science to return to what it has always promised: solving important problems with technology and judgment.
via: techspot