The author pushes back against the assumption that you always need the biggest, most powerful AI models. Yes, smaller models hallucinate—they'll confidently invent calendar events, describe tool calls as prose instead of actually executing them, and fabricate polished-sounding content when they don't know something. The author has seen this happen in production systems. But here's the thing: when you give clear signals about what information is missing, even the smallest models (referred to as "lite" tier) will admit they can't see the calendar instead of making stuff up. The bigger models, paradoxically, produce more convincing fabrications. This matters because it means you can build working systems without maxing out on model size and cost. The author is genuinely excited about the architecture they're building with something called Pepper. The setup uses a provider-neutral model layer that already routes through three different AI vendors through a single interface. This abstraction means adding a fourth backend isn't a full rewrite—it's just a translation adapter and a route key. You get flexibility and optionality without reinventing the wheel each time. The core argument is practical: match the model to the actual problem you're solving. Not every task needs superintelligence, and the infrastructure matters as much as raw capability. Build systems that can route requests intelligently, handle uncertainty explicitly, and swap backends without major surgery. That's more useful than chasing the biggest model available.