Revolut and Decagon reportedly move AI workloads to open-weight models

Interesting. Revolut reportedly only started seeing meaningful AI productivity gains after moving away from expensive OpenAI and Anthropic models to self-hosted GLM. Decagon reportedly runs ~90% of its workloads on open weight models. Why? Because when you're running AI agents in production for e.g. customer service, cost and latency matter enormously. You don't need a frontier model with PhD-level reasoning or even a model that knows the capital of Finland to handle every customer interaction. We're seeing similar dynamics across hypergrowth AI startups and increasingly the large enterprises we work with. But most importantly, the future isn't binary. When use cases are new, you want the smartest general-purpose model available. But once workflows mature and become predictable, smaller, faster, specialized models increasingly win. I wouldn't be surprised if 80–90% of routine enterprise AI workflows eventually run on open-weight and smaller models. This doesn't mean frontier labs lose. It means the market is splitting into two massive opportunities: frontier intelligence and specialized execution at scale.