AI coding agents have fundamentally altered software development, shifting the focus from manual human prompting to architecting autonomous loops. While major model providers increasingly incentivize high token consumption through usage-based pricing and complex agentic workflows, the actual productivity gains remain debated, as many developers struggle to justify the massive costs. The industry is witnessing a transition where languages optimized for human creativity, such as Ruby and Zig, are being replaced by machine-friendly alternatives, often leading to "slop" or codebases that lack human oversight. Despite the hype surrounding infinite agentic loops, local open-weight models are emerging as a viable, cost-effective alternative for developers seeking control. Ultimately, the true value in software development continues to reside in well-maintained primitives and human-driven architectural decisions rather than the sheer volume of AI-generated code.
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