Bigger models are not the way

▤ Summary

This article argues that the AI industry is reaching the limits of the “bigger model is better” approach. It points to benchmark comparisons showing that smaller or more efficient models can come close to much larger proprietary systems. The piece also highlights the problem of hallucinations and poor uncertainty calibration in very large models. It concludes that teams should optimize for capability, truthfulness, and compute efficiency rather than parameter count alone.

▦ Linked Companies

Anthropic

$10M to $25M