Scaling Hypothesis

Definitions

1. The idea that simply scaling up model size, data, and compute can continue to produce substantial capability gains. The scaling hypothesis is influential in modern AI, though it remains debated where its limits lie.

   In plain English: The belief that bigger models, more data, and more compute can keep producing better AI capabilities.

   Example: "Their roadmap implicitly followed the scaling hypothesis by prioritizing larger training runs over more handcrafted model changes."

Related Terms

• Chinchilla Scaling
• Compute Optimal
• Data Scaling
• General Intelligence
• Intelligence Explosion
• Seed AI
• Self-Improvement
• Utility Function AI