Human Intuition
Human is building machine-learning models that learn institutional judgment from recorded traces of decisions, actions, and outcomes, rather than requiring explicit rules. The company targets the disconnect between AI-driven growth and the broader economy's slow, labor-dependent productivity gains by converting tacit knowledge into compounding software capital. Its approach treats a business as a bounded world with defined systems, permissions, tools, histories, objectives, and feedback.
- Artificial Intelligence
- Enterprise Software
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Most of the economy still becomes more productive the old-fashioned way—by adding people, accumulating experience, and asking those people to make better decisions—while the AI sector captures a disproportionate share of growth. Trillions of dollars of existing economic activity remain constrained because the knowledge required to run institutions still lives in human beings, a limitation economics has long treated as fundamental.
Solution
Human works to convert the accumulated judgment of institutions into software that can compound, treating Hayek's knowledge problem as a research problem rather than an impossibility theorem. The company observes the bounded world in which an institution acts—its systems, permissions, tools, histories, objectives, and feedback—and infers the judgment encoded in its behavior from recorded traces of decisions, actions, and outcomes. By recovering what an organization is actually optimizing from the consequences of its decisions, Human aims to make tacit knowledge learnable without requiring it to be articulated. The company's approach targets the next productivity regime: turning human judgment into machine capital that can increase output without increasing labor in proportion.
Target Audience
Advanced economies and enterprises whose productive knowledge currently resides in people and cannot easily become capital, particularly institutions with rich recorded operational histories seeking to multiply output without proportional labor growth.
Features
- Builds machine-learning models that learn judgment from traces of decisions, actions, and outcomes rather than requiring explicit rules
- Analyzes bounded institutional worlds with defined systems, permissions, tools, histories, objectives, and feedback
- Focuses on converting the accumulated judgment of businesses into reproducible, compounding software capital
- Targets the disconnect between AI's concentrated economic growth and the broader economy's slow, labor-dependent productivity gains