AI has already had a major effect on code generation, especially for routine CRUD applications, although its reliability and usefulness still vary depending on the task and workflow. That raises a bigger question: what will happen to software engineering jobs as AI tools improve?
The discussion seems very polarized. Some people say AI assistants now complete nearly all of their work, while others still spend significant time correcting hallucinations, reviewing generated code, and guiding the tools. Smaller teams may be able to produce more with fewer people, and some roles could become less specialized or more focused on end-to-end delivery.
I'm currently trying to find a backend development position and haven't received an interview yet. It makes me wonder whether my Python and FastAPI experience has become less valuable, or whether the difficulty is mostly due to the broader hiring slowdown. Should I focus on system design, DevOps, and production engineering, or would it be necessary to learn highly specialized areas such as CUDA or operating-system development? I'm looking for practical perspectives rather than predictions based only on hype or fear.
4 Answers
The important distinction is between typing code and doing engineering. AI is increasingly good at scaffolding endpoints, tests, and routine features, but people are still needed for architecture, requirements, security, debugging production failures, data modeling, and judging whether a generated solution will remain maintainable. Developers who understand those areas are better positioned than people whose main skill is assembling snippets.
Not getting interviews is probably not evidence that FastAPI or Python has become worthless. Hiring has been weak in many areas, and junior and backend positions can be very competitive. I’d keep Python and FastAPI, then add skills that show you can own a service: SQL and data modeling, testing, authentication, observability, deployment, containers, cloud basics, and system design. CUDA or kernel development only makes sense if you specifically want infrastructure, graphics, or machine-learning systems roles.
A strong portfolio project can demonstrate this better than listing more technologies. Build and deploy a service, explain the architecture and trade-offs, include tests and monitoring, and show how you handled failure cases. AI can help you build it, but you should be able to defend every important decision.
Some teams are reporting that one experienced developer can now deliver what used to require several people, and the tools can be surprisingly effective when they are allowed to run tests and iterate. That can reduce hiring, especially for junior and narrowly specialized roles. Still, productivity gains do not automatically translate into every company eliminating most of its engineers; reliability, product decisions, and accountability remain constraints, particularly in regulated software.
Claims that AI has completely eliminated hallucinations are too strong. Agentic workflows and automated tests reduce the problem, but they don’t guarantee that the requirements, tests, or assumptions are correct.
AI is likely to compress teams and reduce some specialization, particularly for routine application work. However, many companies still have enough process, legacy systems, communication overhead, and compliance requirements that they can’t simply replace everyone with an AI assistant. The biggest near-term effect may be that existing engineers are expected to deliver more, while entry-level roles become harder to find.
There are cases where companies use AI as a justification for layoffs, even when the affected developers could have used or built those tools themselves. That doesn’t necessarily mean AI is independently replacing every developer, but it can give management a reason to restructure and eliminate repetitive work.
Routine CRUD work was already becoming commoditized through frameworks, templates, and outsourcing. AI has accelerated that trend, but it hasn’t made system understanding irrelevant. It makes knowing why the code is correct more valuable than memorizing syntax.