ChatGPT Stopped Being Interesting When Everyone Started Using It Correctly

The Novelty Problem Nobody Talks About

ChatGPT hit 100 million users faster than any consumer app in history. Two years later, most of those users treat it like a slightly better search engine. They've learned the "right" way to use it—structured prompts, clear instructions, iterative refinement—and in doing so, they've turned the most flexible tool we've ever built into something predictable.

The interesting stuff happened when nobody knew what they were doing. People tried to jailbreak it, manipulate it into saying things it shouldn't, or push it into creative territory OpenAI never intended. Now we have prompt engineering courses, best practices documentation, and an entire industry built around "proper" usage. The chaos that made ChatGPT fascinating got replaced by optimization.

Why the API Matters More Than the Interface

The real story of ChatGPT isn't the chat interface—it's that OpenAI made the underlying model accessible enough that thousands of companies could build on top of it without needing their own research teams. The API turned ChatGPT from a product into infrastructure.

Perplexity, Jasper, Copy.ai, and dozens of other companies exist because OpenAI decided to commoditize the model layer. They're betting they can build better user experiences for specific use cases than OpenAI can build for general ones. Some of them are right. Most of them are discovering that wrapping GPT-4 with a nicer interface isn't a defensible business model.

The companies that survive won't be the ones with the best prompts or the cleanest UI. They'll be the ones who figured out how to capture proprietary data that makes their version of ChatGPT meaningfully better than the generic one.

The Context Window Changed Everything (Again)

When ChatGPT launched, the 4K token context window felt generous. You could paste a few pages of text and get useful responses. Then GPT-4 shipped with 32K tokens. Then Claude pushed to 100K. Now we're talking about million-token context windows like they're inevitable.

This progression isn't just about cramming more text into a single prompt. It fundamentally changes what the tool is for. At 4K tokens, ChatGPT was a conversation partner. At 100K tokens, it became a document analysis tool. At a million tokens, it's something closer to a persistent working memory that can hold an entire codebase or months of chat history.

The problem is that most people are still using it like it has a 4K context window. They're breaking tasks into small chunks, summarizing aggressively, and treating each conversation as disposable. The mental model hasn't caught up to the capability.

Why Custom GPTs Failed to Matter

OpenAI launched custom GPTs with the pitch that anyone could build their own specialized version without coding. Thousands of people did. Almost none of them gained meaningful traction outside the creator's immediate network.

The issue wasn't technical—the tools worked fine. The issue was distribution. Building a custom GPT is easy. Getting people to use your custom GPT instead of just typing their question into vanilla ChatGPT is nearly impossible. There's no app store dynamics, no viral loops, no reason for users to remember that your specialized version exists.

The only custom GPTs that succeeded were the ones that didn't need distribution because they were internal tools. Companies built them for their own employees, loaded them with proprietary data, and restricted access. Those actually provided value because they solved the cold start problem—the GPT already knew your company's context.

The Multimodal Shift Nobody Noticed

ChatGPT started as text-only. Then it added image understanding. Then image generation. Then voice. Then video analysis. Each capability launched with hype, got integrated into the interface, and became invisible within weeks.

This is the actual innovation curve for AI tools right now—not the models getting smarter, but the modalities getting merged so seamlessly that users stop thinking about them as separate features. You can upload a photo, ask a question about it, get a text response, generate an image based on that response, and have the whole thing read aloud without switching tools.

The companies building single-modality AI tools are competing with a product that treats modality switching as trivial. That's a brutal position to be in, and most of them haven't adjusted their strategy to account for it.

What Actually Threatens ChatGPT

It's not Claude or Gemini or any other chat interface. Those are competing for the same users with roughly equivalent capabilities. The real threat comes from products that make the chat interface obsolete.

Cursor and other AI-native IDEs are eating ChatGPT's developer use case by embedding the model directly into the workflow. You don't copy code into ChatGPT, get a response, and copy it back—you just write a comment describing what you want and the code appears. The chat interface was always friction.

The same pattern is playing out in other domains. AI-native writing tools, research assistants, and data analysis platforms are all building experiences where you don't chat with an AI—you just work, and the AI handles the parts you'd normally delegate. ChatGPT's flexibility becomes a liability when a specialized tool can eliminate the prompting step entirely.

The Commoditization Treadmill

OpenAI keeps making ChatGPT better. The models get faster, cheaper, and more capable. Context windows expand. New modalities get added. And somehow, none of it feels like it matters as much as the initial launch did.

That's what commoditization looks like in real time. The technology keeps improving, but the competitive advantage keeps shrinking. Every capability ChatGPT adds gets matched by competitors within months. Every price cut gets undercut. Every new feature becomes table stakes.

The question isn't whether ChatGPT will stay dominant—it probably will for a while. The question is whether being dominant in chat interfaces will matter when the next generation of tools makes chatting with AI feel as outdated as typing commands into a terminal.