Research

OpenAI released a 13-page industrial policy manifesto proposing a Public Wealth Fund, portable benefits, and auditing regimes for frontier AI. We respond with six specific counter-proposals: a federal 32-hour workweek, healthcare decoupled from employment, training data compensation through collective licensing, compute as public utility, concrete automation taxes, and a staged pathway to AI-enabled direct democracy. Where OpenAI was vague, we get specific. Where it stayed silent, we fill the gap.

We propose a six-layer taxonomy of how intelligence organizes itself around AI models, from bare-model reflexes through tool use, persistent memory, multi-agent coordination, emergent self-organization, and synthetic culture. Swapping the scaffold around the same model moves coding benchmark scores by 11 to 15 percentage points. The unit of analysis for AI intelligence is the coupled system, not the model alone.

Every operation inside a language model, explained step by step using Karpathy's microgpt — 200 lines of pure Python with zero dependencies. Interactive visualizations cover tokenization, embeddings, attention, backpropagation, and the training loop. The same algorithm powers GPT-4, Claude, and Gemini. The differences are scale, not structure.

We ran Karpathy's autoresearch framework on a $280 gaming GPU, letting an AI agent autonomously modify and retrain a small language model. In one hour: 12 experiments, 4 kept improvements, 12.3% better performance. The agent independently discovered that training speed beats model size on constrained hardware — the same principle behind DeepMind's Chinchilla scaling laws, found from scratch on a budget of $0.

We present a three-layer memory architecture grounded in Complementary Learning Systems theory, implemented in a production AI newsroom agent over 30 days. Evaluated across 80 human-designed test cases, unified retrieval improves accuracy from 40% to 77% on standard queries. Adding an LLM reasoning layer yields 81% overall, with +45-50pp gains on reasoning-intensive queries. Embedding-based vector search scores worst at 23% with 50% false positives, confirming that semantic similarity is not operational relevance.

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).