You may have seen headlines suggesting “Computer Science is DEAD.” That’s not accurate.

What’s actually happening is a shift — not away from tech, but toward more specialized and interdisciplinary programs, especially those explicitly naming Artificial Intelligence (AI) in the degree title.

The question for families isn’t: “Is computer science over?”

The better question is: “How is tech education evolving AND how do we evaluate these new programs wisely?” Let’s break it down.

What the Data Actually Shows

1️⃣ Computer Science Has Grown Dramatically (and Is Stabilizing in Some Places)

• According to the National Science Foundation (NSF) and Computing Research Association (CRA), bachelor’s degrees in computer science more than doubled from 2012 to 2022.
  
• Many universities reached capacity limits in CS due to explosive growth.
  
• Some institutions have recently seen leveling enrollment — but this often reflects caps, not declining demand.

(Source: NSF NCSES data tables; CRA Taulbee Survey)

2️⃣ AI-Specific Degrees Are Expanding

In the past 3–5 years, more universities have launched:

• B.S. in Artificial Intelligence
• AI + X interdisciplinary programs
• AI concentrations within CS
• AI ethics and governance programs

Examples include:

• UC San Diego – B.S. in Artificial Intelligence (within the Halıcıoğlu Data Science Institute)
• University of Southern California (USC) – B.S. in Artificial Intelligence
• University at Buffalo (SUNY) – Department of AI and Society
• University of South Florida – College of Artificial Intelligence, Cybersecurity & Computing (launched 2023)

These programs reflect institutional recognition that AI is not just a tool -  it’s becoming a distinct discipline.

3️⃣ Workforce Outlook Remains Strong

According to the U.S. Bureau of Labor Statistics (BLS):

• Software developer roles are projected to grow ~25% from 2022–2032 (much faster than average).
• Data scientist roles are projected to grow ~35%.
• AI-related roles fall within these broader computing and analytics categories.

(Source: BLS Occupational Outlook Handbook)

While the tech industry has experienced hiring volatility and layoffs in recent years, long-term demand for advanced computing, automation, cybersecurity, and data skills remains strong.

The Four Major Pathways Families Will See

1️⃣ Computer Science (CS)

Focus: Programming, algorithms, systems, computing theory.

Strength: Broad and flexible foundation. Most AI systems are still built on core CS principles.

Good fit for students who:

• Like problem-solving at a deep technical level
• Enjoy math and abstract thinking
• Want maximum flexibility

2️⃣ Artificial Intelligence / Machine Learning

Focus: Designing, training, and evaluating AI systems (natural language processing, computer vision, neural networks).

Strength: Specialized and forward-looking.

Good fit for students who:

• Are specifically excited about building AI tools
• Want early immersion in machine learning
• Are comfortable with higher-level math (linear algebra, probability, calculus)

Important: Many AI majors are math-heavy and assume strong quantitative preparation.

3️⃣ AI + X (Interdisciplinary AI)

Examples:

• AI & Society
• AI & Public Policy
• AI & Business
• AI & Health

Focus: How AI impacts industries and decision-making.

Strength: Blends technical literacy with application and ethics.

Good fit for students who:

• Like tech but don’t want pure coding
• Care about ethical implications
• Enjoy connecting systems to real-world impact

4️⃣ Data Science / Data Analytics

Focus: Statistics, data modeling, visualization, predictive analysis.

Strength: High workforce demand across industries.

Good fit for students who:

• Enjoy math, patterns, and evidence
• Prefer applied analytics over building infrastructure

CS vs. AI: How Should Students Decide?

This is not about choosing the “safest” label. It’s about choosing the right skill foundation.

Consider Computer Science if:

• The student wants broad flexibility.
• They are unsure of a specific niche.
• They may pursue graduate school later.

Many AI careers still begin with a CS degree.

Consider AI-Specific Majors if:

• The student is strongly committed to AI development.
• The program is well-structured and math-intensive. Note: In many cases, an AI major without a strong math and CS core is a red flag, not an advantage.
• The school has demonstrated faculty depth in AI research.

Reality Check: What Families Should Know

1. AI tools change rapidly.
2. The strongest protection against job volatility is deep foundational skill.
3. Degree name matters less than curriculum depth.
4. Graduate education is common in advanced AI roles.
5. The field is competitive and workload-heavy.

The Mental Health & Academic Fit Lens

Tech majors often involve:

• Heavy weekly coding loads - It’s common for first‑year CS/AI students to spend 15–20 hours per week outside of class on coding assignments alone.
• Multi-hour problem sets
• Group projects
• Competitive grading environments
• Imposter syndrome in high-performing cohorts

Students with weaker executive functioning, time management struggles, or math anxiety should factor that into the decision.

Choosing AI because it sounds “future-proof” without assessing fit can lead to burnout.

The Bottom Line

The resilient path is not avoiding AI.

It’s building durable computing and analytical skills, whether under the name Computer Science, Artificial Intelligence, Data Science, or an interdisciplinary hybrid.

Tools will evolve. Foundations endure.

Get the Tech Major Evaluation Toolkit inside FutureU Academy, plus other resources that make college planning simpler.