The lines between IDEs and terminals are blurring as both adopt features from the other. The future developer workbench will be a hybrid prioritizing a natural language prompting interface, relegating direct code editing to a secondary, fallback role.
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The workflow with an AI coding assistant is described as feeling like the human is the robot, not the programmer. The primary role shifts from writing code to shuttling information between different contexts and the AI model, which performs the heavy lifting of code generation and problem-solving.
Figma CEO Dylan Field predicts we will look back at current text prompting for AI as a primitive, command-line interface, similar to MS-DOS. The next major opportunity is to create intuitive, use-case-specific interfaces—like a compass for AI's latent space—that allow for more precise control beyond text.
AI's impact on coding is unfolding in stages. Phase 1 was autocomplete (Copilot). We're now in Phase 2, defined by interactive agents where developers orchestrate tasks with prompts. Phase 3 will be true automation, where agents independently handle complete, albeit simpler, development workflows without direct human guidance.
The emerging paradigm is a central coding agent with multiple specialized input tools. A canvas tool (like Paper) will be for visual prompting, an IDE (like Cursor) will be for code refinement, and a text prompt will be for direct commands, all interoperating with the same agent to build software.
Despite the rise of terminal-based AI, IDEs remain essential because source code is meant for human consumption. Visual interfaces are the best way for developers to review, understand, and build context around what AI agents produce, preventing the 'death of the IDE'.
The next frontier for AI in development is a shift from interactive, user-prompted agents to autonomous "ambient agents" triggered by system events like server crashes. This transforms the developer's workbench from an editor into an orchestration and management cockpit for a team of agents.
The primary interface for managing AI agents won't be simple chat, but sophisticated IDE-like environments for all knowledge workers. This paradigm of "macro delegation, micro-steering" will create new software categories like the "accountant IDE" or "lawyer IDE" for orchestrating complex AI work.
Future coding interfaces will move beyond read-only chat logs. They will treat the AI conversation as an editable 'multi-buffer'—a new type of document that aggregates code snippets from across a project. This will allow developers to directly manipulate code within the conversational flow itself.
Instead of becoming obsolete, IDEs like IntelliJ will be repurposed as highly efficient, background services for AI agents. Their fast indexing and incremental rebuild capabilities will be leveraged by AIs, while the human engineer works through a separate agent-native interface.
For over a decade, software development fragmented into siloed roles (PM, Design, Eng) with their own tools. AI code editors are collapsing these boundaries by creating a unified workspace where a single "maker" or a streamlined team can build, iterate, and ship, much like in the early days of computing.
