Research

Future Shock introduces three concrete, falsifiable predictions for artificial general intelligence using a five-signal ensemble model. The signals — PDM, prediction markets, expert positions, LLM reasoning, and editorial practitioner judgment — produce point estimates of March 2027 (Domain-Specific AGI), July 2027 (Recursive Self-Improvement), and October 2027 (Multi-Domain AGI), with confidence intervals and full signal-level data.

We expand the Precondition Density Model dataset from 1,699 to 3,179 events and test the H3 temporal compression hypothesis. Result: rejected (p = 1.0). The apparent shrinking of gaps between parallel discoveries is fully explained by increasing event density. The core holdout remains robust at Cohen's d = 9.80 across all six dataset versions. We also characterize the model's predictive boundaries and contribute a verification pipeline for AI-generated research data.

We introduce the Precondition Density Model (PDM), a framework that quantifies the relationship between accumulated foundational knowledge and the emergence of specific scientific and technological breakthroughs. Using a dataset of 1,699 historical events and text embeddings, we show that the model ranks the correct discovery in the top 3 of all candidates for 68% of holdout events, with a mean rank of 3.9 versus a random baseline of 12.9 (Cohen's d = 9.80, p < 10⁻¹⁶).