The "long run" in tech is increasingly about moving up the abstraction stack while maintaining a deep understanding of the "leaky" parts of those abstractions. Here is how I see these three paths evolving by 2026:
Backend Dev: Traditional CRUD is being commoditized by AI agents. However, the "Backend Architect" is more valuable than ever. As we lean more on AI-generated code, we face a "complexity debt" crisis. The long-term winners here will be those who specialize in system reliability, data integrity, and complex state management—things AI still struggles to reason about holistically.
AI Dev: We are moving from "training models" to "orchestrating agents." If you mean "AI Research," the bar is extremely high (Ph.D. level math). If you mean "AI Engineering," it’s essentially becoming a subset of Software Engineering. The "moat" isn't knowing how to call an API, but knowing how to build evaluation frameworks, RAG pipelines, and cost-efficient inference loops.
DevOps: This is morphing into Platform Engineering. Companies no longer want someone to manually bash scripts; they want internal platforms that allow AI-augmented dev teams to deploy safely. With the rise of "AIOps," the long-term career here is about observability and security—being the person who knows why the "black box" system is failing at 3 AM.
My Take: Don't pick one. The most resilient career for the next decade is the "Systems-Thinking Generalist." Learn Backend to understand the logic, DevOps to understand the delivery, and AI as the "force multiplier" for your own productivity. The person who can architect a product, deploy it scaleably, and integrate LLMs intelligently will always be in the top 1% of earners.
Backend Dev: Traditional CRUD is being commoditized by AI agents. However, the "Backend Architect" is more valuable than ever. As we lean more on AI-generated code, we face a "complexity debt" crisis. The long-term winners here will be those who specialize in system reliability, data integrity, and complex state management—things AI still struggles to reason about holistically.
AI Dev: We are moving from "training models" to "orchestrating agents." If you mean "AI Research," the bar is extremely high (Ph.D. level math). If you mean "AI Engineering," it’s essentially becoming a subset of Software Engineering. The "moat" isn't knowing how to call an API, but knowing how to build evaluation frameworks, RAG pipelines, and cost-efficient inference loops.
DevOps: This is morphing into Platform Engineering. Companies no longer want someone to manually bash scripts; they want internal platforms that allow AI-augmented dev teams to deploy safely. With the rise of "AIOps," the long-term career here is about observability and security—being the person who knows why the "black box" system is failing at 3 AM.
My Take: Don't pick one. The most resilient career for the next decade is the "Systems-Thinking Generalist." Learn Backend to understand the logic, DevOps to understand the delivery, and AI as the "force multiplier" for your own productivity. The person who can architect a product, deploy it scaleably, and integrate LLMs intelligently will always be in the top 1% of earners.