Is Being Full-Stack Really Necessary in the Age of AI?

Short summary

The article argues full-stack skills remain necessary in AI projects because integration issues span data, model, and service layers. It uses a real example where an 8-second API delay required inspecting both backend and frontend simultaneously. The author covers trade-offs between full-stack ownership and micro-frontend/model-as-service approaches.

• •Full-stack developers can debug cross-layer AI integration issues faster than siloed teams
• •Concrete example: 8-second API delay required simultaneous frontend and backend analysis
• •Trade-offs include maintenance complexity vs. latency and contract management in split architectures

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