Like many in the tech space, I was initially intrigued by the promise of ChatGPT. The idea of a conversational AI that could answer anything, assist with projects, and even help debug code sounded like a game-changer. But after several months of real-world use, that optimism has turned into disappointment—and not without reason.
The Cracks Begin to Show
At first, ChatGPT seemed like a brilliant tool. But the deeper I went, the more I noticed something was off. It wasn’t just minor inaccuracies—it was confidently wrong about things that were easy to verify.
Take this example: it claimed a widely used web service didn’t have an API, even though one had been released in January 2021—well before the AI’s supposed September 2021 knowledge cut-off. That wasn't just a simple oversight. It was a complete failure to represent publicly available information.
Then came the gaming-related missteps. I asked about the game Crypto Hack and was told that a certain mode was introduced after September 2021. In reality, that mode had been released in July 2021. The AI’s timeline was off, again. And this wasn’t a one-off error. Any topic that wasn’t mainstream tech or pop culture seemed to get hand-waved or misdated.
Feedback Goes Nowhere
I didn’t keep quiet. I submitted critical feedback, hit the dislike button on bad responses, posted concerns in support forums, and even left negative reviews. I was hoping someone at OpenAI would take it seriously.
They didn’t. Or if they did, there was no evidence of it.
The same errors kept showing up. There were no updates acknowledging the mistakes, no transparent changelogs, and certainly no fixes. It was as if feedback from regular users like me didn’t matter at all.
The DIY AI Rabbit Hole
After months of frustration, I decided to do what any technophile might think about doing: build my own AI.
Sounds ambitious? It is—and I learned that the hard way.
I began exploring datasets like Common Crawl, thinking I could spin up a lean language model for more specialized use. But then I saw the cost: $400 per month just for full dataset archival storage. That’s before you even touch training costs, GPUs, or cloud infrastructure.
Training even a basic large language model costs millions of dollars. That’s not hobbyist money—that’s Silicon Valley startup capital. The idea that you can just "build your own" is, sadly, not grounded in reality unless you have very deep pockets.
Thinking Bigger: Should There Be Accountability?
When corporations charge for a product that’s flawed, refuse to fix reported issues, and lock average users out of the creation process—something feels wrong.
At one point, I seriously considered whether legal action was even on the table. Not because I wanted to make headlines, but because these companies profit from users, while showing minimal responsibility in return.
If OpenAI and others want to dominate the AI space and monetize it aggressively, they should be held to a higher standard of accuracy, transparency, and affordability.
Conclusion: The Illusion of Intelligence
ChatGPT—and models like it—are impressive on the surface. But once you use them consistently, you’ll see they’re not nearly as intelligent or reliable as advertised. Worse, when feedback is ignored and the cost of alternatives is out of reach, regular users are left without options.
This isn’t just a gripe. It’s a call for accountability in AI. We deserve better—from both the technology and the companies behind it.