AI scaling laws
Relationships describing how model performance scales with increased parameters, data, and compute. Typically follow power-law patterns (e.g., loss ∝ N-α for parameter count N). Key implications:
- Predictable performance gains from scaling
- Efficiency trade-offs between data/compute/parameters
- Basis for model development strategies
Recent Discussions
- Mixture of Experts podcast panel (hosted by Tim Hwang) featuring:
- gabe-goodhart (Chief Architect, AI Open Innovation)
- Abraham Daniels (Sr. Technical Product Manager, Granite)
- Aaron Baughman (IBM Fellow, Master Inventor)
- Key debate points:
- Validity of traditional scaling laws amid “Fun-cember” model releases (end-of-year surge in major model launches)
- Analysis of amazon blocking chatgpt as indicator of competitive dynamics
- Questions about diminishing returns in scaling efficiency
2026 04 14 Ibm panel
Source Notes
- 2026-04-23: Engine Survival: The Critical Role of Oil Pressure and Warning Lights · ▶ source
- 2026-04-14: “But OpenClaw is expensive…”