Computer science is not losing value; it is undergoing the kind of professional reclassification that medicine experienced when it matured from craft to formal discipline. For years, a four-year CS degree functioned as both intellectual foundation and implicit license to practice. Graduates entered industry, and the early years of employment—implementing features, debugging systems, repeating known patterns—served as an informal residency through which judgment slowly formed.

Artificial intelligence systems are compressing that residency.

Much of the structured, pattern-driven work that once trained junior engineers—scaffolding services, wiring integrations, refactoring, generating tests—now falls within the competence of AI agents. As execution becomes automated, the human role shifts upward toward the domains where patterns fail: system design under uncertainty, security reasoning, performance tradeoff modeling, distributed correctness, and responsibility for failure in complex environments. These problems are less about writing code and more about modeling reality, risk, and consequence.

Medicine offers the clearest analogy. Technology did not eliminate physicians; it raised the baseline of competence and moved the physician’s work toward synthesis, oversight, and high-stakes judgment. Likewise, AI lowers the cost of producing functional software while increasing the premium on producing systems that are resilient, secure, and conceptually sound.

In this landscape, the bachelor’s degree in Computer Science does not become obsolete; it becomes preparatory. It establishes the language and theory of the field but no longer maps cleanly to its highest levels of responsibility. Scarcity shifts away from syntax and frameworks—now abundant through tools—and toward judgment under ambiguity, architectural foresight, and cross-system reasoning. Those capacities emerge through deeper study and sustained engagement with complexity, the territory traditionally associated with advanced education.

The field is not shrinking; it is professionalizing. As machines absorb the mechanical layers of construction, human value concentrates around abstraction, interpretation, and accountability. The definition of a “computer scientist” moves upward not by academic decree, but because the problems that remain demand a deeper kind of thinking.