Why AI adoption rates differ: workflow chunk size and integration friction, explained
2C_ornot2C · x · 2026-11-12
Replying to Ethan Mollick, jjpalano explains why AI diffusion rates vary so much across industries: it hinges on application shape, model capabilities, and industry dynamics.
Core argument:
- Adoption accelerates when AI can handle a bigger "chunk" of a workflow and plug into one or more points straightforwardly. "Write this code" or "diagnose from well-defined inputs" is far easier than "add value at an ambiguous point in M&A deal creation."
- This changes as capabilities improve and industries reconstruct workflows — already visible everywhere.
- SWEs are prototypical early adopters: most tech-savvy, cash-flush industry, huge pressure to innovate, which explains why coding boomed first.
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