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# Data scaling, not model architecture, drives AI progress
- URL: https://adjacent.media/signals/data-scaling-not-model-architecture-drives-ai-progress/
- Published: 2026-09-08T23:09:25.000Z
- Updated: 2026-09-08T23:09:25.000Z
- Description: Dwarkesh Patel argues that recent AI capability gains stem from larger training datasets and compute resources, not algorithmic breakthroughs. This shifts where competitive advantage concentrates: companies with data moats and infrastructure budgets outpace those relying on novel algorithms.
- Author: Jonathan Greene
- Tags: #signal, theme-ai, AI & ML, model training, llm capabilities

Source: [Dwarkesh Patel](https://open.substack.com/pub/dwarkesh/p/pretraining-progress-is-mostly-data)

Dwarkesh Patel argues that recent AI capability gains stem from larger training datasets and compute resources, not algorithmic breakthroughs. This shifts where competitive advantage concentrates: companies with data moats and infrastructure budgets outpace those relying on novel algorithms. The implication is straightforward—sustained leadership depends on access to scale, and bottlenecks in synthetic data, labeling, and energy become the real constraints on progress.