In the current AI landscape, many builders and investors feel despair because frontier models seem poised to absorb almost everything. But according to Sarah Guo, this view misses a critical category: untrainable work.

Measurable, public, and legible tasks (coding benchmarks, generic customer support scripts, standardized processes) are being commoditized rapidly from both below (open models) and above (labs folding scaffolding into weights). What remains valuable and defensible is work that is private, context-rich, high-stakes, and dependent on trust, permissions, accountability, and deep integration with proprietary systems.