LLM workspaces & other paradigms I believe in

Introduction

The AI landscape started with simple chatbots—the frustrating ones on websites that barely handled customer service, or platform-locked assistants like Siri and Alexa designed primarily for purchases. During my final year at Champlain College, everything shifted with the release of GPT-3.5 and ChatGPT. The GPT architecture, originally developed by Google and implemented by OpenAI, transformed these basic systems into tools capable of complex tasks like coding and tool manipulation.

At the time, I didn't grasp the implications for design work. Then these AI systems suddenly evolved beyond simple chat interfaces—they started calling APIs, executing functions, and working alongside humans in meaningful ways. The trajectory since then has been fascinating, and I see four distinct branches emerging in AI's evolution: integrated workspaces, real-time interactions, background AI agents, and enhanced computational processing.

The AI + Workspace Paradigm: Where We Are Now

We've moved beyond chat interfaces into purpose-built AI workspaces. Take Google's NotebookLM: it processes research from PDFs and YouTube links, generating everything from study materials to AI-voiced podcasts. ChatGPT's Canvas enables real-time code and document collaboration. Tools like Cursor.ai streamline the coding process with inline AI assistance, while v0 generates full applications from natural language descriptions. Each platform demonstrates how AI can transform raw content into structured, actionable work products.

I believe these emerging paradigms will reshape how we interact with AI systems:

Real-Time APIs

The technical shift from request-response patterns to continuous token streaming enables truly fluid interactions. Current implementations already show decreased latency, but the real advancement comes from AI systems that can process and respond to information as it develops—summarizing meetings in progress, flagging key decisions, and assigning action items without breaking the conversation flow.

AI Agents

Background processes handle tasks that exceed real-time constraints or require ongoing attention. These agents coordinate complex operations like monitoring rental availability or managing appointment scheduling. The implications for existing service platforms are significant—when AI can directly interface with scheduling systems and booking platforms, the role of intermediary services will fundamentally change.

The Thinking Paradigm

Test-time compute enables AI to tackle more complex cognitive tasks. Planning a group trip illustrates this well: instead of the usual back-and-forth collecting preferences and constraints, an AI system with sufficient computational resources can process multiple variables simultaneously—budgets, activities, transportation, accommodations—and generate comprehensive plans that account for all parameters. Models like gpt-o1-preview and DeepSeek's latest release demonstrate this capability, particularly when trained on high-quality, domain-specific data.

The Convergence of Design and Development

The traditional boundaries between product design and development are dissolving. Modern design systems and tools have democratized consistency and technical implementation—what used to require specialized knowledge can now be achieved through platforms like Figma or Sketch. The differentiator has become holistic problem-solving: understanding both user experiences and technical integration at a deep level. AI tools are accelerating this convergence, enabling designers to engage with technical implementation and developers to influence user experience decisions directly.

Looking Ahead

This field's rapid evolution defies precise prediction—even leading experts limit their forecasts to 12-24 months. Success in this environment demands continuous adaptation and learning. Whether that means expanding technical capabilities, questioning established knowledge, or finding novel applications for existing tools, the key is maintaining flexibility while creating genuine value.

These observations come from watching these technologies evolve firsthand. I'm curious about your perspective, especially if you see different patterns emerging in these spaces.