As AI models become locally runnable on consumer hardware, the application layer has been commoditized — any engineer with a capable model can replicate most SaaS features in a weekend. The real competitive moat has shifted down to infrastructure ownership. This is illustrated at the macro level by hyperscalers securing nuclear power for AI compute, and at the developer level by choosing data platforms like MongoDB Atlas that compound capabilities (full-text search, vector search, embedding generation, MCP server integration) without forcing architectural rewrites. A study showing developers are actually 19% slower with AI tools despite feeling 20% faster underscores that mass-produced application code is noise, while enterprises pay premiums for infrastructure that enables AI at scale. The thesis: hardware got cheap, code got cheap, so durable value now lives in the infrastructure layers that don't get replaced.

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