AI will NOT replace Software Engineers (ray.cat)

The headline claim—“AI will replace software engineers”—is misleading: LLMs excel at rapid prototyping and automating mundane tasks, but they don’t remove the long-term, systems-level work that makes software reliable, secure, and maintainable. A two-hour prototype can validate an idea, but production systems endure years of changing requirements, bugs, technical debt, and scale-related failures that only surface under load. Debugging, refactoring, secure deployments and architectural decisions require deep system understanding; an LLM can suggest fixes, but humans must implement and validate them safely. AI also depends on complex, physical infrastructure and multidisciplinary engineering: GPUs and cooling systems fail, network topologies and TDP constraints must be balanced, and training runs can cost millions and consume petabytes of data. Roles like data engineers, ML researchers, platform engineers, SREs, and product designers remain critical. Historically, tools from compilers to clouds expanded, not eliminated, engineering work—AI is another productivity multiplier that democratizes basic coding while increasing demand for experienced engineers who can bridge systems, business needs, and edge-case handling. Career winners will be those who pair AI tooling with deep systems, reliability, and design skills rather than those who only produce simple CRUD apps.