Legibility Framework

Mis à jour le 2025-01-05Confiance : high

legibility-frameworksarah-guoai-strategyagent-labsmodel-labsuntrainable-tasksstrategic-positioningcompetitive-moatsdomain-specializationintent-scarcity

Strategic framework developed by sarah-guo for categorizing AI companies and understanding competitive positioning based on the "legibility" or trainability of different business functions. Core concept distinguishing between agent-labs and model-labs based on what can be systematically trained versus what requires human judgment and domain expertise.

Core Concept

The framework centers on identifying which business functions and capabilities are "trainable" (can be systematically learned and benchmarked) versus "untrainable" (require human judgment, domain expertise, and contextual understanding). This distinction drives different competitive strategies and moats.

Framework Categories

Trainable Territory

Foundation model capabilities
Standardized benchmarks and evaluations
Generic AI functionalities
Reproducible technical performance
Scalable computation

Untrainable Territory

Domain-specific integration work
Customer relationship management
Private reality arrangement and translation
Intent identification and prioritization
Contextual judgment calls
Maintenance and ongoing adaptation

Strategic Implications

Agent Labs Positioning

Companies operating in "untrainable" territory focus on:

Integration work: "Arranging a company's private reality so a model can act on it"
Tool provision: "Handing the model the tools to act"
Workforce transformation: "Working with the customer to change the reality of its workforce"
Domain specialization: Deep expertise in specific verticals
Ongoing relationships: "Integration and maintenance run as long as the relationship does"

Model Labs Positioning

Companies in "trainable" territory focus on:

Foundation model development
Benchmark optimization
Scalable capabilities
Generic AI advancement
Research and development

Intent Scarcity Concept

Key insight from the framework: Intent scarcity may be a more limiting factor than compute power. As Guo notes: "Even harder is offense, choosing what to build in the first place... The model is no help there. It will do whatever you point it at and can't tell you what's worth pointing it at, and you can't benchmark that, so you can't train it."

This suggests that knowing what problems to solve becomes the ultimate scarce resource, not the ability to solve known problems.

Competitive Dynamics

Defensibility Patterns

Agent Labs: Build moats through customer relationships, domain expertise, and integration complexity
Model Labs: Build moats through computational scale, research capabilities, and technical advancement

Market Evolution

The framework explains why "incumbents don't take everything: they keep the ground they have, and the next thing comes from someone who finds a use before the rest of us."

Practical Applications

Company Assessment

Framework helps evaluate AI companies by asking:

Which territory do they primarily operate in?
What type of work creates their competitive advantage?
How defensible is their position over time?
Where do they create unique value?

Investment Strategy

Particularly relevant for understanding:

Different risk/reward profiles between Agent and Model Labs
Timeline to defensible market positions
Capital requirements and scaling patterns
Market opportunity assessment