the ChatGPT store is about to launch… let’s get rich

OpenAI’s upcoming GPT Store launch is set to turn custom “GPT agents” into a direct monetization channel for developers—so quickly that the marketplace may be flooded with low-quality “crapware.” With ChatGPT already surpassing 100 million daily active users about a year after launch, the pitch is straightforward: if even 1% of users pay $1 per month for a custom agent, a developer could reach decamillionaire-level revenue within a year. That math, paired with a very low barrier to entry, makes the store feel less like a slow-building platform and more like a near-term gold rush.

The immediate expectation is a race to publish. Creating a custom GPT agent is described as largely a configuration task: go to “my GPTs,” create a new agent, add custom instructions and conversation starters, optionally upload files with proprietary data, and then release it on the store. The suggested go-to strategy is to “slap the hood on” and market the agent as solving users’ problems—fast enough that quality may lag. The transcript also flags uncertainty around the economics: the revenue split between OpenAI and developers is unknown, with speculation ranging from Apple-like 70/30 to something less favorable if OpenAI is doing most of the heavy lifting.

Competition may not be limited to OpenAI. Leaked screenshots for Google’s “B Advanced” suggest Bard could introduce its own developer pay tier and marketplace tied to Gemini Ultra, though that remains speculation. Still, the core bet is that a leaderboard will reward usefulness, giving genuinely helpful agents a path to significant earnings.

What to build is treated as the real bottleneck. The transcript lists example agent concepts—smart-home automation, a stock broker agent connected to APIs like Alpaca, and an AI personal trainer that uses daily messaging to push behavior change—then dismisses them as “dumb” once spoken aloud. The more cynical, practical advice is to identify existing “trendy AI SaaS” products, copy their ideas, and package them as GPT Store offerings.

The concrete build walkthrough centers on adding real capabilities to a GPT agent via “actions” (formerly plugins). The described approach is to create a custom API that ChatGPT can call over HTTP, documented using an OpenAPI specification (YAML or JSON). The transcript emphasizes that ChatGPT can execute only limited code by default (notably Python), but an action-backed agent can trigger server-side logic—such as running JavaScript in a sandbox on the developer’s server. A Nitro-based TypeScript setup is suggested for routing and deployment, and the GPT action is configured through an AI-plugin JSON file placed in a well-known directory on the developer’s site or URL. Once deployed, the agent can accept user chat input, send it to the API, and return results—positioned as an “artificial slave” that can be monetized through the store next week.