Intent Scarcity

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

intent-scarcitysarah-guoai-strategylegibility-frameworkcompetitive-advantageproblem-identificationstrategic-directionuntrainable-tasksstartup-strategy

Concept introduced by sarah-guo suggesting that identifying what problems to solve may be a scarcer resource than the computational power to solve known problems. Core insight: "Maybe intent is an even scarcer input than compute."

Core Concept

The fundamental challenge in AI applications is not technical capability but strategic direction. As Guo explains: "Even harder is offense, choosing what to build in the first place. That's what I spend the year looking for, and I find it maybe three times."

Model Limitations

AI models fundamentally cannot address intent scarcity because they:

"Will do whatever you point them at"
"Can't tell you what's worth pointing them at"
Cannot be benchmarked on problem identification
Cannot be trained to discover valuable use cases

This creates an inherent limitation where technical capability advances but strategic insight remains scarce.

Strategic Implications

Competitive Advantage

Intent scarcity explains why technical superiority alone doesn't guarantee market dominance. Companies that identify valuable applications before others gain first-mover advantages that persist despite technical convergence.

Innovation Patterns

The concept explains market dynamics: "the 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."

Investment Focus

For investors like Guo, intent scarcity shifts evaluation criteria from "can this be built?" to "should this be built?" and "who will figure out the valuable applications first?"

Relationship to Legibility Framework

Intent scarcity operates in the "untrainable" territory of the legibility-framework. While models can be trained to execute on identified intents, the identification itself requires:

Domain expertise
Customer understanding
Market insight
Creative problem-solving
Contextual judgment

Practical Applications

Startup Strategy

Focus on problem discovery, not just solution development
Spend significant time on customer development and use case validation
Prioritize domain expertise and market understanding
Build hypothesis-testing capabilities for rapid intent validation

Enterprise AI

Invest in teams that understand business context, not just technical implementation
Create processes for identifying high-value AI applications
Develop capabilities for translating business problems into technical solutions
Build relationships with domain experts who understand real pain points

Market Dynamics

Winner-Take-Most vs Winner-Take-All

Intent scarcity suggests markets may be less winner-take-all than pure technical capability would imply. Different companies may identify different valuable intents, creating room for multiple winners.

Timing Advantages

First to identify and execute on valuable intent gains advantages that persist even as technical capabilities commoditize. This explains why some "inferior" technical solutions maintain market position.

Measurement Challenges

Intent scarcity is inherently difficult to measure because:

No objective benchmarks for problem identification quality
Success only becomes apparent in retrospect
Market feedback loops can be slow
Value depends heavily on context and timing