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.…
Enterprise AI is reaching a critical juncture similar to other major computing eras. While the technical substrate for AI is now remarkably complete, applications still feel heavily manual and ad-hoc. This suggests the need for a language layer analogous to previous computing revolutions. The infrastructure for enterprise AI - frontier models, GPU compute, vector databases, storage, security, and more - is already in place.
However, building AI applications manually creates a pile of glue code for issues like persistent state, durable execution, observability, security, and more. Every team must recreate these missing primitives. This highlights that standard programming languages are absent for enterprise AI. Languages matter because they change the economics of software development - turning expensive, fragile, and non-repeatable manual effort into cheap, repeatable, and durable native functionality.
At certain cost levels, this is indistinguishable from creating new capabilities. The key question is whether enterprise AI is now approaching a point where it needs a language layer to make the infrastructure productive at scale. The substrate is there, but an enabling language could be the multiplier to bring enterprise AI into the mainstream, just as the web multiplied the utility of the internet.
Written by urgent.news from Fast Company's reporting — not their text. Machine-written; read the original for the full account.
