AI’s Synthetic Summer: The 2025 Mid-Year Data & Trend Outlook

Source: Nate’s Newsletter Date: 2025-06-13 URL: https://natesnewsletter.substack.com/p/ais-synthetic-summer-the-2025-mid

Summary

Natural training data is tightening — organizations are restricting access, reducing the pool of genuine human-generated text — while synthetic data production is accelerating. Nate’s mid-2025 data trend outlook challenges the assumption that synthetic data is inferior: evidence suggests synthetic training methods improve model quality, and leading models already incorporate them. The critical open questions are alignment risk (as synthetic data dominates, do models maintain human value alignment?) and what the next bottleneck becomes after natural data scarcity is resolved.

Implications

AI economics thread. Synthetic data as a training input changes the economics of model development: labs that can generate high-quality synthetic data at scale decouple from the natural data supply constraint that limited smaller players. This doesn’t eliminate the data moat, it shifts it — from “who has the most data” to “who can generate the best synthetic data,” which is itself a capability that favors well-resourced labs.

Capital thread. The natural data tightening is partly strategic — organizations restricting access to prevent competitive use — and partly exhaustion. Either way, it reshapes the frontier model training cost curve and creates new licensing market dynamics. Labs that locked in data access agreements before restrictions tightened hold a structural advantage that compounds as restrictions increase.

Watch: Whether synthetic data’s apparent quality improvements hold at scale and across modalities, or whether value alignment degradation emerges as synthetic training percentage increases — this is the central empirical question for model quality through 2026-2027.