Andrej Karpathy

Interactive exploration of 342 BLS occupations covering 143 million US jobs, with layers for employment outlook, pay, education and digital AI exposure. Its customizable LLM scoring pipeline illustrates uneven exposure across digital and physical work. Exposure scores are rough estimates of occupational change, not predictions of job disappearance; they omit demand responses, regulatory barriers and preferences for human workers. The project explicitly describes itself as a development tool rather than a rigorous economic publication.

Author-posted, AI-cleaned summary and transcript, which Karpathy says he read. Describes a late-2025 jump in coding-agent usefulness, professional orchestration and evaluation, and verifiability as an explanation for uneven progress. Current enthusiasm updates the older decade-of-agents interview; the edited text is not an exact quotation transcript.

His review connects verifiable rewards to reasoning gains, criticizes benchmark overfitting, describes jagged intelligence and the growing application layer around models. Provides concrete mechanisms and builder vocabulary rather than a universal intelligence forecast.

Karpathy’s primary interview frames agents as a decade of engineering work. Discusses cognitive deficits, continual learning, the gap between self-driving demos and deployment, and education. The forecast is dated and intuitive, not a calibrated deadline.

His README demonstrates agents editing a training file, running five-minute experiments and retaining improvements against a fixed validation metric. The introduction’s future agent civilization is playful fiction, not a report of current events. Human-authored instructions and a bounded setup remain essential.