AI 产品是一间办公室，互联网产品是报纸

Core Insights - The article emphasizes the shift in product design focus from information presentation in the internet era to productivity organization in the AI era [4][51] - It highlights the need for a new design framework that accommodates AI's embedded productivity within products, moving away from traditional information containers [4][51] Group 1: Internet Product Design - Internet products are designed around information, addressing how it is produced, organized, distributed, and consumed [3][5] - The evolution of information containers can be categorized into three stages: physical (newspapers), digital (web pages), and algorithmic (recommendation systems) [8] - The design paradigm for internet products has consistently revolved around creating effective information containers [8] Group 2: AI Product Design - AI products are fundamentally different as they embed productivity directly, requiring a new approach to design that focuses on how to organize and utilize this productivity [9][10] - The evolution of work containers for AI can also be divided into three stages: physical (offices), digital (tools like Notion), and AI-native (products like Kuse) [10] - The design of AI products must consider how to effectively harness AI's productivity within a structured work environment [10] Group 3: Work State Management - Human work is a continuous process of moving from historical states to target states, necessitating stable expression, acquisition, and manipulation of work states [11][15] - Files serve as the minimal expression of state, allowing visibility and operability of work states [16][17] - Folders manage the context of work, defining the scope and continuity of tasks [19][20] Group 4: AI Work Context - AI operates by predicting and generating tokens based on given context, making the structure of context crucial for effective output [25][26] - Context is limited to a one-time window, requiring reconstruction for each computation, which adds complexity to AI product design [27][28] - The cost of context is significant, as each token contributes to computational expenses, necessitating efficient context management [29] Group 5: File Systems and AI Collaboration - File systems provide an external state space that allows for efficient context management, enabling AI to work without needing to load all information at once [30][32] - The structure of file systems has been validated in coding products, where continuous development relies on a well-maintained file system [34][36] - File systems enhance AI productivity by ensuring outputs meet expectations and allowing for continuous work progression [38][40] Group 6: Human and AI Collaboration - Collaboration shifts from instruction-based interactions to state-based teamwork, with files becoming the shared objects of work [42][43] - Outputs from AI become reusable work states rather than one-time results, creating a continuous trajectory of work [46][49] - The system's potential is realized as work progresses without constant human intervention, allowing for a collaborative environment between humans and AI [50]