The Model's the Easy Part: Why Infrastructure Control is the New AI Moat

Autumn 2022. IT budgets froze for 2023. ChatGPT launched November 30, 2022. CEOs were impressed by early-stage GenAI.

By 2024, approximately 90% of those GenAI pet projects were culled because they weren't promising. Only about 10% started going through GRC for deployment. In 2025, applications entered production with varying guardrailing and cost tracking. Agentic coding became real in late 2025. By 2026, internal and external usage of GenAI in production is expected to explode.

The most important shift in AI over the last two years has been adoption velocity, not model capability. Model capabilities have improved dramatically over the last two years, especially for open source and open weight models. AI in production has gone from a handful of well-resourced tech companies to thousands of teams across every sector.

But control is the new moat in AI infrastructure. Otari is a control plane for LLMs, open-source at its core. Open source is an absolute requirement for adoption in the most important industries, agencies, and organizations. Organizations that instrument for control now will have an operational advantage that compounds.

The future of AI is about who controls the layer above the models. ServiceNow's Amit Zavery said: 'Every customer, when they're thinking of AI adoption and agentic, they're worried about control.'

Most teams are not using one model; they are using dozens of different models. Each model has its own API, pricing, latency profile, and rate limits. AI inference scales non-linearly, causing costs to increase disproportionately with usage. Most teams don't find out what they're on the hook for until they get the invoice.

There is no native tooling across providers to surface AI costs before it's too late. As AI moves into regulated industries, compliance requires knowing which model said what, when, to whom, and why. Current infrastructure has no answer for compliance requirements around AI model usage. This multi-provider architecture is not sustainable.

Control means routing requests intelligently by cost, capability, latency, and compliance. Control means real-time observability into what your AI is doing and why. Control means setting policies at the org level and having them enforced consistently. Control means swapping providers without rewriting your application layer.

Michael Dell of Dell Inc said: 'What cloud delivered was elastic scale: what it didn't promise, and cannot perhaps deliver, is cost-predictable agentic AI at scale on sensitive enterprise data.'

Palantir's Alex Karp said: 'What the technical customers want is control over their compute, their models, their data stack and their alpha. They want to know they own the means of production.'

Organizations should own their inference and AI stack.