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.

The labs are aggressively pursuing the obvious horizontal layer of agentic AI — general code generation, writing, image creation, and simple tool-calling agents. This is the well-trodden Yellow Brick Road, where raw model capability dominates and labs hold decisive advantages in scale, distribution, and architecture.

The real moats and durable value creation lie in The Rest of Oz: complex, vertical, multi-step workflows involving messy real-world data, legacy systems, regulatory constraints, multi-player coordination, and industry-specific tribal knowledge. These require deep scaffolding, data flywheels, governance, and deterministic outcomes that horizontal models cannot easily replicate.

Enterprise SaaS companies are reporting that AI is not disrupting their core businesses — it is dramatically expanding them.

Q1 2026 earnings calls revealed five consistent themes:

AI can fake effort. It cannot fake time.

Software features and intelligence can now be cloned in days. The enduring advantages are those accumulated slowly through real-world operation and physical reality.