For decades, software interaction has been defined by users adapting to the specific languages and structures of various interfaces, but the rise of Large Language Models (LLMs) is challenging this paradigm. According to Dhyey Mavani, in a January 3, 2026, article, the fundamental question is shifting from "Which API do I call?" to "What outcome am I trying to achieve?"

This shift signifies a move from code-centric interaction to language-based interaction, where users can express their intent in natural language, and the system interprets and executes the necessary functions. Mavani introduces the concept of Model Context Protocol (MCP) as a crucial abstraction in this new era. MCP allows models to understand human intent, discover relevant capabilities, and execute workflows, effectively translating natural language requests into software functions.

The traditional approach to software interaction involved users learning specific commands, memorizing HTTP methods, and integrating Software Development Kits (SDKs). In the 1980s, users typed commands like 'grep', 'ssh', and 'ls' into a shell. By the mid-2000s, they were invoking REST endpoints like 'GET users'. The 2010s saw the rise of SDKs, such as 'client.orders.list()', which abstracted away the underlying HTTP complexities. However, all these methods required users to understand and adhere to the structured form in which software capabilities were exposed.

LLMs are changing this by enabling a more intuitive and accessible interface. Instead of requiring users to know the specific function or method signature, LLMs can interpret natural language and determine the appropriate actions. This has significant implications for society, potentially democratizing access to software and reducing the technical barrier to entry.

The development of MCP is a key step in realizing this vision. By providing a standardized way for models to understand context and access capabilities, MCP can facilitate the creation of more intelligent and user-friendly systems. The article emphasizes that MCP is not merely a buzzword but a tangible approach to bridging the gap between human intent and software execution.

The implications of this shift are far-reaching. As LLMs continue to evolve, we can expect to see more applications that leverage natural language as the primary interface. This could lead to more intuitive and efficient workflows, as well as new opportunities for innovation. The focus will be on defining the desired outcome, rather than wrestling with the technical details of how to achieve it.