When Apple launched the App Store in 2008, it changed the way we thought about software: instead of heavy installations, we had lightweight apps that followed us everywhere. Today we stand on the brink of another inflection point. ChatGPT apps aren’t just a novelty bolted onto a chatbot. They signal a new way of interacting with software, one that revolves around conversation rather than screens. These apps still run on servers and devices, but the experience now sits beyond the GUI layer, in the intelligence that understands and responds to what we say.
Beyond the App Store Moment
ChatGPT apps arrive at a moment when computing is ripe for reinvention. For decades, software depended on tapping and swiping; now, natural language is becoming the primary interface. In practice, ChatGPT apps still rely on a back‑end server and a front‑end component that runs in an iframe inside ChatGPT. But from the user’s perspective, the interaction flows through conversation rather than a discrete user interface. OpenAI has released the Apps SDK in preview and plans to open app submissions later this year. The promise is that, just as the App Store democratized mobile software, the ChatGPT ecosystem could democratize software that lives in dialogue.
From Prompts to Agents — The Inversion of the Software Stack
Traditional apps integrate AI as a feature: voice search or predictive text sits inside an app that is otherwise built on a standard operating system. ChatGPT apps invert that relationship. Developers define tools and UI components, but the intelligence layer, the GPT mode, becomes the primary runtime. Each app is anchored by an MCP server that exposes tools the model can call, enforces authentication, and packages structured data with an HTML template for the client to render. Instead of simply returning text, the app can trigger actions, display interactive widgets, and update its state via window.openai calls. In this sense, the AI isn’t just augmenting an app; it orchestrates the workflow itself.
Unified Conversational UX — Multimodality, Memory and Natural Language
Because ChatGPT understands language, images and even voice inputs, apps can span modalities. A single component can display a card with search results, accept follow‑up questions, call an external API via the MCP tool and return a refreshed dataset. Combined with ChatGPT’s built‑in memory, this creates experiences that feel continuous: the app remembers context across turns and adapts its output accordingly.
Each GPT App still has its own interface, built through the Apps SDK’s component system, but it lives inside the conversation instead of outside it. That makes it lightweight, contextual, and instantly actionable. Today, users still need to connect or explicitly trigger a GPT App, but the direction is unmistakable. Over time, apps will feel less like separate tools and more like natural extensions of the dialogue itself. The interface becomes conversational, living inside the dialogue rather than outside it.
Unprecedented Distribution for Developers
One of the most compelling aspects of ChatGPT apps is the distribution potential. ChatGPT counts hundreds of millions of weekly users globally, giving developers a large audience from day one. While getting into the ecosystem does require building an MCP server, hosting it on a secure HTTPS endpoint and registering a connector via ChatGPT’s developer mode, the friction is lower than traditional app stores. A single integration can instantly reach a global user base already inside ChatGPT.
ChatGPT as the Agentic Ecosystem for Apps
What makes the ecosystem truly novel is how apps cooperate. Each app defines one or more tools, functions the model can call, plus a corresponding UI component. ChatGPT orchestrates when to call which tool in MCP and merges the results into the conversation. Developers don’t need to worry about drawing windows or implementing navigation; they focus on the task their tool performs and the data it returns. The best apps are conversational, time‑bound and visually succinct; they extend ChatGPT rather than replicate existing web workflows. Think booking a flight, ordering food or summarizing the morning’s calendar — tasks that can be completed in a few turns and presented in a clear card. This agentic model turns ChatGPT into more than a host; it becomes the mediator that connects intent to action.
Monetization
Every platform needs an economic model, and ChatGPT apps are no exception. OpenAI already offers subscription plans for ChatGPT, and the company has signaled that monetization policies for apps will be announced when the submission process opens. Although details are still forthcoming, it is easy to see the potential: usage‑based billing, revenue sharing and premium features could create a marketplace akin to the App Store, but oriented around actions rather than downloads. For developers, the allure is clear: a single integration could reach millions of users and generate revenue without the overhead of mobile app development.
Why OpenAI Won’t Walk Away
OpenAI collaborating with Jony Ive on a new hardware device hint at where this ecosystem could go. Imagine a voice‑first device whose primary interface is conversation; instead of launching apps via icons, you call on GPT‑powered tools. In such a scenario, the Apps SDK would be the application layer, GPT the operating system, and the device the input/output surface. While no official device has been announced, the direction is logical: to deliver seamless, multimodal experiences across phones, laptops and dedicated hardware, OpenAI needs an app platform tightly integrated with its models. That is why it’s investing in MCP servers, design guidelines and developer policies now, because the apps created today will populate the devices of tomorrow.
The Intelligence Platform Era
We’re moving from a world where software lives on devices to one where software lives inside intelligence. ChatGPT apps still rely on web servers, SDKs and deployment pipelines, but the user experience is shifting into dialogue. For developers, the platform offers global reach and a chance to build on top of one of the most capable AI models. For users, it promises smarter, more integrated workflows that happen naturally in conversation. Just as the App Store defined the smartphone era, GPT‑powered apps are poised to define the intelligence era and OpenAI’s early moves suggest it intends to lead the way.
What’s Still Missing And What We’re Building Next
As exciting as this ecosystem is, building ChatGPT Apps today still isn’t easy.
Developers have to wire up MCP servers, handle authentication, define tools, and manually decide how each app is triggered within a conversation. There’s no simple framework for routing intent, managing context, or connecting multiple GPT Apps smoothly which makes rapid experimentation difficult even for experienced developers.
That’s the gap we’re working to close.
We released Chat.js, a Next.js-based framework that simplifies building and connecting GPT Apps the moment the SDK became available.
It abstracts away most of the boilerplate, handling communication with ChatGPT, structuring tool definitions, and rendering components so developers can focus purely on functionality and UX.
Next, we’re building a Python version, inspired by Django’s philosophy: opinionated, batteries-included, and accessible to backend developers who prefer Python over JavaScript.
And beyond that, we’re developing a visual builder, something like a Lovable for GPT Apps, so that non-technical creators can build conversational tools without writing code at all.
Because for this new platform to reach its potential, building a ChatGPT App should feel as natural as using one.
Simple, Intuitive, and Instant.