Enterprise AI doesn’t need another app: it needs its language
08.07.2026
Author - Enrique Dans
The history of computing contains a recurring pattern: the infrastructure arrives first, and the language that makes it productive arrives later.
The most revealing example is the web. The internet worked before the World Wide Web. TCP/IP moved packets. DNS resolved names. Email connected institutions. FTP moved files. Servers existed. Universities, laboratories, and technically sophisticated organizations could use the network.
But for ordinary organizations, the internet was not yet a business environment. It was infrastructure.
Then came a thin, almost deceptively simple layer: URLs, HTTP, HTML, browsers, and servers. CERN’s history of the web explains that Tim Berners-Lee invented the World Wide Web at CERN in 1989 and that its basic idea was to merge computers, data networks, and hypertext into a global information system. The W3C’s architecture of the web later described the web as an information space in which resources are identified by URIs. RFC 9110, in turn, defines HTTP as a stateless application-level protocol for distributed hypertext information systems.
None of that invented networking. It made networking legible. The web did not create the internet. It collapsed the cost of using it.
That is where enterprise AI is today. Frontier models are available as managed services. GPU compute is elastic. Vector databases are mature. Storage is abundant. Retrieval, orchestration, evaluation, security, and reinforcement learning infrastructure all exist in some form.
And yet, most enterprise AI still feels artisanal because the new substrate is being programmed by hand. Before high-level languages, everything could be built in assembly.
Languages change the cost structure, take properties that previously required custom engineering and make them native. They turn repeated effort into grammar.
We need a language in which agents are persistent by default, also execution. In which every meaningful state change becomes part of the system’s biography and permissions, constraints, and auditability are structural properties. And business outcomes are reward signals that systems can learn from. A language in which the learning loop is native.
That language will appear soon.
