Grounded Scaling: Why Agentic AI Needs Deterministic Environments

Liang Ding

Abstract: Long-chain agent execution fails exponentially in environments designed for human tolerance: with per-step determinism 𝛿 < 1, 𝑘-step chain success degrades as 𝛿𝑘. The AGI-to-ASI scaling debate (Genewein et al., 2026) has so far framed progress as a race between compute growth and a list of frictions (data wall, abstraction barrier, embodied bottleneck, multi-agent trust); we argue that environment determinism is a complementary binding axis cutting across all four, for the broad class of agentic AI tasks whose outcomes are verifiable economically, physically, or through multi-party settlement. Three formal results pin down the regime: a Determinism–Efficiency Bound on chain-task success, a Verifier–Goodharting Floor on flywheel ceilings under imperfect rewards, and a convergence condition for environment-side skill evolution. We operationalise the framework as a Supply Certainty Index (SCI) over five measurable properties, a five-level Determinism Maturity Model (DMM) as an adoption ladder, and a falsifiable open-question programme (OQ1–OQ5) with explicit null results that would force retraction. The position is platform-agnostic. We engage three competing positions: sim-to-real sufficiency, alignment sufficiency, and AI-as-normal-technology.