Bing Chat changed its name to Copilot and launched Copilot Studio. What is the difference between it and GPT?

The fundamental difference between Microsoft Copilot and GPT is that Copilot is a branded, productized application of generative AI, while GPT is the underlying large language model architecture developed by OpenAI that powers it and many other systems. Microsoft Copilot, formerly Bing Chat, is a specific AI assistant integrated across Microsoft's ecosystem, including Windows, Edge, and Microsoft 365. It is built upon a sophisticated orchestration layer that can call upon various AI models, including OpenAI's GPT-4 and GPT-4 Turbo, but also Microsoft's own models like Phi for specific tasks. Its primary identity is as a user-facing product with features like web grounding with citations, image generation via DALL-E 3, and deep integration with Microsoft Graph for accessing a user's documents, emails, and calendar. In contrast, GPT (Generative Pre-trained Transformer) refers to the core AI model technology itself—a neural network architecture trained on vast datasets to predict and generate text. Users do not interact with a raw GPT model; they interact with applications like ChatGPT or Copilot that utilize it as an engine.

The introduction of Copilot Studio further delineates this ecosystem by providing a low-code tool for customizing and extending the Copilot product for organizational use. Copilot Studio allows businesses to create tailored Copilots that can answer domain-specific questions, automate workflows, and connect to internal data sources and APIs. This moves beyond the general-purpose capabilities of a base GPT model into the realm of enterprise-grade, secure, and actionable AI agents. While one could use OpenAI's GPT models via an API to build similar custom solutions, Copilot Studio is pre-integrated with the security, compliance, and user identity frameworks of the Microsoft 365 environment. It essentially productizes the process of building a GPT-based assistant for a specific business context, managing the underlying complexity of model orchestration, data grounding, and user interface.

Mechanistically, Copilot's response generation is a result of a complex pipeline where a user's prompt is processed, potentially enhanced with real-time web search or internal company data, and then routed to the most suitable AI model. This orchestration decides whether to use a powerful but costly model like GPT-4 for complex reasoning or a smaller, faster model for simpler tasks. A raw GPT model, accessed via an API, does not have this automated orchestration or built-in augmentation with search or proprietary data unless the developer explicitly builds such systems. Therefore, Copilot offers a curated, multi-modal experience, while GPT provides the foundational generative capability. The implications are significant for adoption: Copilot offers a streamlined, supported product for consumers and enterprises within the Microsoft suite, whereas leveraging GPT directly offers greater flexibility for developers building entirely new applications but requires substantial expertise in AI engineering, safety, and integration.

In summary, comparing Copilot to GPT is akin to comparing a finished automobile to an internal combustion engine. GPT is the advanced engine—the core power source. Microsoft Copilot is the complete vehicle, incorporating that engine (along with others) into a chassis with a user interface, safety systems, navigation (web search), and custom interiors (Copilot Studio). The launch of Copilot Studio emphasizes Microsoft's strategy to dominate the enterprise AI assistant space by enabling customization on top of its managed platform, abstracting the raw complexity of the GPT architecture into a deployable business tool.