Recent discussions of AI scaling have emphasized compute (FLOPs) and parameter counts as the primary drivers of capability. While scaling laws such as Kaplan et al. (2020) and Chinchilla (Hoffmann et al., 2022) demonstrate empirical regularities, they risk obscuring the deeper mechanisms by which intelligence emerges.

This paper argues that intelligence is a product of feedback loops, not FLOPs. Environments are not just benchmarks, but operators on policy: they shape identity as much as they measure ability. I introduce the concept of feedback bandwidth (B), defined along dimensions of latency, veracity, granularity, and counterfactual richness, and sketch a relationship ΔPerf ∝ f(B)·T to capture how capability growth scales with loop efficiency and experience budget.

Examples from coding environments, curriculum learning, multi-agent interaction, and tool use illustrate how feedback geometry governs generalization and robustness. The commentary concludes with falsifiable predictions, grounded in recent literature, that improved feedback veracity, latency, granularity, and consolidation pipelines reduce sample complexity and enhance transfer.

By reframing scaling through the lens of loops, this paper positions environment design as the true bottleneck for AGI development and highlights feedback geometry as a substrate-neutral lever for capability, alignment, and safety.