The multi-model era is here — and most products aren't ready for it
No single AI model is best at everything, and the gap between specialists is widening, not narrowing. We think that changes how AI products should be built — and most haven't caught up yet.
Two years ago, picking an AI model was mostly a question of which big lab you trusted most. Today, it's a genuinely hard engineering problem: different models lead on different tasks, prices shift constantly, and the "best" model for a coding question is rarely the best for a creative-writing one.
Most AI products quietly made a single-model bet early on, and now face an awkward choice: rip out core infrastructure to support multiple providers, or keep telling users "sorry, that's not what this model is good at."
We think the products that win the next phase will be the ones built model-agnostic from the start — able to route a request to whichever model handles it best, swap providers as the landscape shifts, and let users benefit from competition instead of being locked into one lab's roadmap.
That's the architecture underneath ChatterMate and CleverImage today, and it's why we can add new providers as they emerge without asking users to change how they work. The model landscape will keep shifting under everyone's feet — the only durable strategy is to build for that motion, not against it.