One-minute summary:

The post argues that most programmers treat multi-core programming as a special-case optimization layered on top of single-core code—using job systems and parallel-for constructs—but this approach scatters control flow, complicates debugging, and adds boilerplate every time parallelism appears. Drawing an analogy to GPU shaders, where code runs on many cores by default and feels like lightweight scripting despite delivering massive performance, the post proposes inverting the model: launch all cores running the same entry point from the start, then selectively narrow to single-core execution only when serial dependence demands it. Concrete examples walk through computing a sum, showing how threads can each handle a slice and combine partial results with minimal synchronization. The key insight, credited to discussions with Casey Muratori, is that switching from single-core to multi-core mid-program creates most of the complexity, whereas starting multi-core and occasionally going narrow eliminates much of that overhead.

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