Enterprise AI doesn’t need another app: it needs its language

For the past two years, companies have been asking the same question in slightly different forms: which AI application should we build next? A customer service agent? A sales copilot? A procurement assistant? A coding agent? A research assistant? A workflow automation layer? A chatbot connected to internal data? A model wrapped in a user interface and connected to tools? All of that makes sense. It is how every new computing era begins. First, people try to build applications directly on top of the new substrate. They use whatever tools already exist, wrap the new capability in familiar interfaces, and assemble the missing pieces by hand. But there is a moment in every major computing cycle when that approach reaches its limit. The problem is no longer whether something can be built. It can. Given enough talented engineers, almost anything can be assembled. The real question becomes whether it can be built repeatedly, safely, cheaply, and at scale. That is when the language appears. And enterprise AI may now be reaching exactly that point. The substrate is already here The history of computing contains a recurring pattern: the infrastructure arrives first, and the language that makes it productive arrives later.