Claude's Secret Sauce Lives in Data

The strongest models are increasingly differentiated by invisible pipeline work: data quality, preference tuning, distillation, and eval loops. The transformer is still the chassis; the training recipe is what turns it into a better product.

• –Reasoning traces can be distilled into smaller models, which is why Opus-style outputs keep showing up in local fine-tunes
• –If Claude feels better than rivals, the edge may come more from curated post-training than from a radically different base architecture
• –DeepSeek made the recipe more legible to the market, but not trivial to reproduce at Anthropic-level quality
• –Competitors can copy behaviors with traces, yet the harder moat is the data flywheel and the feedback loop that produced those traces
• –For builders, base-model choice still matters, but post-training now looks like the bigger lever for practical capability