The new software paradigm, driven by generative AI, moves away from complex interfaces. Instead, applications are designed to understand a user's natural language intent, removing the friction of learning how to operate the software and shifting the burden of learning from the user to the system.

Most users only scratch the surface of complex enterprise software. AI agents will bridge this gap by interpreting natural language requests and executing complex tasks on the user's behalf. This transforms the user experience from learning features to simply stating goals, unlocking decades of untapped capabilities.

Instead of being stuck with rigid software, a future powered by decentralized AI could allow users to modify their tools directly. For example, a doctor frustrated with an electronic medical record system could use natural language to instantly change the software to fit their workflow, reclaiming control over their digital environment.

The next evolution of software isn't a better interface; it's no interface. The founder envisions an "AI native" future where users interact with the system via voice commands, like a headset-wearing office manager directing agents to perform tasks ("check Nathan in," "send the bill") without ever clicking a button.

In this software paradigm, user actions (like button clicks) trigger prompts to a core AI agent rather than executing pre-written code. The application's behavior is emergent and flexible, defined by the agent's capabilities, not rigid, hard-coded rules.

Vanta is moving beyond chat-based AI to develop agents that can generate entire, task-specific user interfaces on the fly. This "on-demand software" can guide a user through a workflow with a custom-built UI that disappears once the task is complete.

Instead of integrating with existing SaaS tools, AI agents can be instructed on a high-level goal (e.g., 'track my relationships'). The agent can then determine the need for a CRM, write the code for it, and deploy it itself.

Future applications will move beyond static interfaces to systems that learn from user corrections. Instead of complex settings, users will train their software by simply replying in natural language, like, "That's a manufacturing domain, I don't care about those," creating an instant, personalized feedback loop.

When a user's personal agent (in an environment like Codex) interacts with an app, it can automatically share vast context about the user's goals and history. This eliminates tedious onboarding and enables a deeply customized experience from the first interaction, changing how software is designed.