Use an AI assistant like Claude Code to create a persistent corporate memory. Instruct it to save valuable artifacts like customer quotes, analyses, and complex SQL queries into a dedicated Git repository. This makes critical, unstructured information easily searchable and reusable for future AI-driven tasks.

Snyk founder's new venture, TESOL, posits that AI will make code disposable. Instead of code being the source of truth, a durable, versioned 'spec' document defining requirements will become the core asset. AI agents will generate the implementation, fundamentally changing software development.

Tools like Git were designed for human-paced development. AI agents, which can make thousands of changes in parallel, require a new infrastructure layer—real-time repositories, coordination mechanisms, and shared memory—that traditional systems cannot support.

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'.

Moving PRDs and other product artifacts from Confluence or Notion directly into the codebase's repository gives AI coding assistants persistent, local context. This adjacency means the AI doesn't need external tool access (like an MCP) to understand the 'why' behind the code, leading to better suggestions and iterations.

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.

The current model of separate design files and codebases is inefficient. Future tools will enable designers to directly manipulate production code through a visual canvas, eliminating the handoff process and creating a single, shared source of truth for the entire team.

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.

AI developer environments with Model Context Protocols (MCPs) create a unified workspace for data analysis. An analyst can investigate code in GitHub, write and execute SQL against Snowflake, read a BI dashboard, and draft a Notion summary—all without leaving their editor, eliminating context switching.