Scaling laws
Empirical relationships between model size, data volume, and computational resources in AI, typically showing performance gains with increased scale (e.g., loss decreasing as ). Early work (e.g., Kaplan et al., 2020) established foundational scaling behavior.
Key principles:
- Performance scales predictably with model size and data
- Data efficiency and compute efficiency are critical factors
Recent debates:
- The 2025-2026 surge in large model releases (e.g., Granite models) has challenged traditional scaling law assumptions
- Panel discussions (e.g., IBM Mixture of Experts) debated whether scaling laws remain valid amid architectural innovations like Mixture of Experts (model)
- Amazon’s blocking of chatgpt’s shopping agent highlighted practical constraints on agent deployment tied to scaling limitations
Related concepts:
- Mixture of Experts (model)
- Agent (AI)
- large-language-models
2026 04 14 IBM Mixture of Experts
Source Notes
- 2026-04-23: Engine Survival: The Critical Role of Oil Pressure and Warning Lights · ▶ source
- 2026-04-14: IBM Mixture of Experts