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The author argues that while LLMs make certain optimizations cheaper, software can still be slow due to factors like increased tolerance for latency in asynchronous workflows and other practical constraints.
The article argues that LLMs and AI tools are lowering the barrier for software performance optimizations, enabling custom adaptations like JIT compilers and regex engines that were previously too costly to implement.
The article discusses how LLMs are reducing friction in programming language choice, leading developers to adopt performance-oriented languages like Rust and Zig for fast, small software and enabling work with complex technologies previously considered difficult.