DeepSeek's R1 model has accumulated over 92,000 GitHub stars, indicating sustained production adoption of reasoning-focused architecture patterns. GitHub stars correlate with implementation velocity in enterprise deployments. This metric suggests teams are integrating chain-of-thought reasoning models into existing workflows at scale, rather than treating them as experimental. The star count implies R1 is being forked, benchmarked, and deployed across multiple internal codebases—a baseline signal of operational adoption beyond research interest. For builders, this means reasoning models are no longer optional architectural components. Teams standardizing on non-reasoning models will face integration pressure as downstream dependencies shift toward reasoning-native inference. This accelerates the obsolescence of single-pass completion patterns for complex reasoning tasks. Operators should expect increased demand for inference infrastructure that supports longer token sequences and variable compute costs. The infrastructure implication is clear: reasoning model deployment requires different scaling assumptions than prior generations, making cost-per-inference less predictable and making batching strategies more critical for profitability.