A Realistic AI Timeline (vintagedata.org)

The piece argues we’re exiting the era of ever-larger pretraining and entering a new cycle where reasoning, reinforcement learning (RL) and mid/post‑training “agentification” drive progress. Instead of chasing generalist scale, practical deployment will favor smaller, specialized models trained with opinionated curricula and RL-style objectives that reward features of a “good answer” rather than next-token likelihood. That shift—combined with stricter accuracy thresholds in industry (often 0–2%)—is expected to trigger a 2026 commercial boom as domain-tuned, agentic systems replace brittle generalists. Key technical needs are operationalized reward design, rubric engineering, classifiers/LLM-as-judge, and structured-generation techniques that make even tiny models performant on math and regulated tasks. Practically, the author predicts widespread use of emulators: faithful simulated environments where models are trained on action traces and system constraints, enabling verticalized pipelines (banking, telecom, driving) and live fine-tuning. This raises new infrastructure priorities—token‑level accuracy metrics, model interpretability (traceability, graph views of LM internals), monitoring, and safety tooling—because agent failures can cascade (e.g., mass trading anomalies). Paradoxically, a nascent “general agent” or AGI may emerge as a small, recursive model trained across many emulated domains rather than via brute-force scale. For ML practitioners this implies a pivot from scaling experiments to reward engineering, domain emulation, robust evaluation, and observability to meet real-world accuracy and safety requirements.