// AI scaling

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Enterprise AI scaling is hitting operational bottlenecks, not model limits

Companies running pilot programs have discovered that deploying autonomous agents at scale requires solving unglamorous infrastructure problems—orchestration, monitoring, failure handling, integration with legacy systems—that no LLM vendor has packaged into a turnkey solution. This explains the sudden market interest in "agent gateways" and middleware: enterprises are willing to pay for governance and operational visibility layers precisely because the hard part of AI scaling isn't making smarter models, it's making them reliable and auditable in production. The constraint has shifted from capability to operability, which means the next wave of AI winners will likely be infrastructure vendors solving orchestration problems, not more foundation model companies.