Introducing a definition of AGI from the perspective of expertise compression

We introduce Experience-Compressed Intelligence (ECI), a novel framework for measuring artificial general intelligence that shifts focus from human-like performance to the efficiency of experience compression and reuse. Traditional AGI definitions emphasize behavioral similarity to humans or economic productivity, obscuring fundamental questions about how systems acquire, represent, and transfer knowledge. We propose that intelligence should be quantified by measuring: (1) how much human experience can be compressed into learned representations, (2) the rate of extracting tacit knowledge from limited examples, (3) the efficiency of cross-domain knowledge transfer, and (4) epistemic confidence through activation manifold analysis. We formalize ECI as a composite metric integrating compression ratio, tacit knowledge extraction rate, cross-domain retention, and experience efficiency index, weighted by epistemic confidence derived from Statistical Path Density (SPD). Our experimental validation on MNIST demonstrates that ECI provides meaningful discrimination between in-distribution, near-out-of-distribution, and far-out-of-distribution samples (AUROC = 1.0 for noise detection, 0.73 for FashionMNIST), with overwhelming statistical significance (p < 10⁻²²⁰). We argue that ECI offers a measurable, comparable, and scalable alternative to existing AGI definitions, with clear implications for evaluating progress toward general intelligence.