The Compound Infeasibility of AGI: Why Replicating and Exceeding Human Cognition Is Not a Scaling Problem

The dominant formal case against the feasibility of artificial general intelligence (AGI) is complexity-theoretic (van Rooij et al., 2024) and has been answered by the observation that the proof relies on adversarial-distribution assumptions real human behavior does not satisfy (Guerzhoy, 2024). The literature now sits at an impasse: a formal impossibility argument that proves too much, alongside an empirical scaling consensus that 76% of surveyed AI researchers no longer share (AAAI, 2025). We argue this impasse is methodological. The question of AGI feasibility has been treated as single-axis (computational, architectural, or definitional), and single-axis arguments admit single-axis rebuttals. We propose instead a compoundinfeasibilitythesisorganizedaroundfourreinforcingaxes—threeempirical, one conceptual—none individually fatal, which jointly preclude any plausible path from current machine-learning paradigms, on the timeline the inevitability literature describes (single-digit years), to a system matching and exceeding human general intelligence. The operative claim is infeasibility-on-current-paradigms, not in-principle impossibility (Section 7.9). The central methodological move is a cross-axis dependency (Section 3.5): any plausible mitigation of one axis re-imports demands on at least one other, so single-axis rebuttals do not aggregate into a rebuttal of the conjunction. The four axes are (i) energetic-computational, (ii) developmental and cumulative-cultural, (iii) causal-embodied generalization, and (iv) the absent domain-general specification of exceeding human general intelligence. We ground each axis in measured 2024–2026 evidence (BabyLM, the ARC-AGI-2 closure and ARC- AGI-3 launch, GSM-Symbolic, Apple’s “Illusion of Thinking,” Epoch AI data-exhaustion projections, theAAAI2025panel). We engage Hendryck et al. (2025) directly: the framework substantially advances the matching specification but does not address exceeding, and its own “jagged-profile” diagnosis is the Goodhart pattern our argument predicts. We conclude with three falsifiable refutation conditions: a sample-efficiency threshold, a transfer-without- retraining threshold, and a documented specification-and-realization breakthrough on the exceeding problem. Until any is met, the burden of proof sits with the inevitability claim, not with the skeptic.