Glean discovered an unexpected growth loop: as usage of its intelligence layer *inside* other platforms like Claude grows, it creates a "spillover effect" that brings users back into the native Glean UI to build more powerful, team-wide agents and assets.

The true power of the AI application layer lies in orchestrating multiple, specialized foundation models. Users want a single interface (like Cursor for coding) that intelligently routes tasks to the best model (e.g., Gemini for front-end, Codex for back-end), creating value through aggregation and workflow integration.

Glean spent years solving unsexy enterprise search problems before the AI boom. This deep, unglamorous work, often dismissed in the current narrative that credits AI for its success, became its key competitive advantage when the category became popular.

Superhuman Go is not just another AI assistant; it's a platform designed to be the "mass transit" for third-party AI agents. By providing the underlying infrastructure, they enable partners like Radical Candor to embed their unique knowledge directly into users' workflows across any application, a powerful distribution strategy.

Enterprise AI vendors are moving beyond simple search or chat applications. The real value and defensibility lie in the underlying 'context engine' that connects and understands siloed company data, user activity, and permissions. This engine provides the accuracy and relevance that generic LLMs fundamentally lack.

Using generic AI assistants means starting from scratch with each query. An AI second brain connects these tools to your personal, ever-growing knowledge vault. This creates a compounding effect, making your AI progressively smarter and more context-aware than any generic tool.

The "all-in-one" SaaS pitch is making a comeback because AI agents thrive on comprehensive context. Fragmented point solutions starve AI models of the necessary data to perform at a high level. Therefore, building a single platform that holds all the context is now a critical competitive advantage, not just a convenience.

To combat reliance on a single AI provider, users can build a personal context layer—a collection of documents, data connections, and skill playbooks. This system acts as personal "alpha," allowing any capable AI model to quickly understand a user's context and perform tasks effectively, ensuring portability and reducing vendor lock-in.

The planned Superapp combining coding, browsing, and chat is more than a UI consolidation. The deeper, more critical goal is to merge multiple backend systems into a single, unified 'AI harness' that manages context, actions, and interaction loops. This creates a powerful, efficient AI layer for various applications.