The Next AI Platform Isn’t a Model — It’s Your Context

Quick Overview

The next AI platform is context, not models, shifting focus from large language models (LLMs) to providing rich, specific context—like data, files, and environment—to make AI tasks plausibly solvable, a concept being actively developed by major players like Slack, Google (Gemini Enterprise), and Perplexity (Email Assistant).

Key Points: The next major AI platform shift is away from model size toward context engineering, which focuses on providing the right background, files, and environment for LLMs to solve tasks. Tobi Lutke coined the term "context engineering" over "prompt engineering" to describe the art of providing the necessary context for LLMs to be plausibly solvable (0:05). Slack is positioning itself as the "agentic OS" by integrating external AI agents like Claude, ChatGPT, and Perplexity via its new Real-Time Search API, enabling agents to access the full context of Slack chats (2:00, 4:06). Google introduced Gemini Enterprise to address the need for context, allowing it to pull context from Gmail, Google Drive, Calendar, and other Workspace apps, positioning it as a major competitor to Microsoft's enterprise AI offerings (10:41). Perplexity launched its Email Assistant, which integrates directly with a user's email account to draft replies, organize meetings, and analyze shared documents, emphasizing data privacy and SOC 2/GDPR compliance (9:31, 10:00). An industry expert (Mi Kang) suggests the winner in the platform war will be the product with the richest personalized context, prioritizing long session time and broadest information collection (8:55). The core engineering problem is optimizing the utility of tokens against LLM constraints to consistently achieve desired outcomes, moving beyond simple prompt crafting (1:48).

Context: This video discusses the emerging paradigm shift in Artificial Intelligence development, moving the competitive focus from the raw power or size of Large Language Models (LLMs) to the quality and accessibility of the context provided to these models. The discussion centers on the rise of "context engineering" as a core skill for building effective AI agents, referencing recent announcements from major tech companies like Slack, Google, and Perplexity, all vying to own the enterprise data layer.