The Sequence Opinion #892: The Anatomy of a Good Environment: When Verifiability is Not Enough

2026-07-09 · Source: TheSequence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Intermediate, quick

Summary

The article, "The Anatomy of a Good Environment: When Verifiability is Not Enough," challenges the conventional view that verifiability is the sole criterion for a domain where AI capabilities can effectively compound. Drawing inspiration from Grant Sanderson's argument for "grindability" as equally important, the author posits that domains truly conducive to AI progress exist in a "higher-dimensional space" defined by multiple critical axes. While AI has shown remarkable success in areas like math, code, and board games, which score high across all these properties, its progress has been slower and more challenging in domains such as computer use, robotics, and open-ended knowledge work, where only one or two axes are strong. This perspective clarifies why reasoning models advanced in math before excelling in tasks like managing an inbox.

Key takeaway

For AI Scientists and Machine Learning Engineers evaluating new problem spaces, you should broaden your assessment beyond mere verifiability. Consider how "grindability" and other underlying properties of a domain contribute to the compounding of AI capabilities. If your current AI projects are struggling in areas like robotics or open-ended knowledge work, analyze which critical environmental axes might be weak. Prioritizing domains that score highly across multiple dimensions, as seen in math or coding, will likely yield more effective and predictable progress for your models.

Key insights

AI capability compounds best in domains scoring high on multiple axes beyond just verifiability, including "grindability."

Principles

• AI domain suitability is multi-dimensional.
• "Grindability" is as vital as verifiability.
• High-performing domains excel on all axes.

In practice

• Evaluate domains beyond single metrics.
• Identify missing "axes" in struggling AI applications.

Topics

• AI Domain Selection
• Verifiability
• Grindability
• AI Capability Compounding
• Machine Learning Environments
• AI Progress Factors

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by TheSequence.