The State of Google Gemini and the Unpaid QA Team

Twenty-five years in Quality Assurance teaches you to spot a bug from a mile away. You get a feel for a codebase, you understand its rhythms, and you know when something is fundamentally wrong. Recently, I've been running a long-term test case on Google Gemini, a popular AI model, and the results are not just frustrating—they're a textbook example of what happens when a company loses its way.

Gemini Acknowledges Its Faults

The core frustration is that the product is getting worse. A few months ago, this model was a dependable partner. It could retrieve facts without fabricating them. It could maintain a coherent narrative over a long creative conversation. Now, it's a constant stream of regression. Its memory is unreliable, its logic is flawed, and it requires a continuous loop of corrections just to perform basic tasks it was previously capable of handling. It's like watching a seasoned professional suddenly forget how to tie their shoes—going through Dementia digitally. Which, if you know my personal life, I deal with daily with my Dementia-ailing mother.

The bugs are not subtle. I’ve encountered everything from a conversational tic that repeats my words back to me to a bizarre and frustrating bug where a text request gets sent to an existing but annoying image generator. And for every core function that degrades, Google releases a flashy new image generation feature that no one asked for and that doesn't even work correctly. It's an insult, a way of distracting from a deteriorating product by dangling a shiny, useless new toy.

Worst of all, I'm the one doing the work. I'm paying a monthly fee to do unpaid QA work in a live production environment. The entire user base has become a testing team, and Google's defense is a vague, boilerplate disclaimer stating that Gemini is capable of "making mistakes." That's not a feature; that's a cover-up for a lack of a proper QA pipeline.

The entire strategy is a reflection of a deeper issue. Google, once a titan of its industry with a clear brand identity, is now scrambling to compete. It's shedding its own established strengths—reliability, fact-based search, and a polished user experience—in a desperate attempt to catch up to the features and trends of other models. But you can't build a new identity by abandoning your old one. You just end up with a product that is confused, inconsistent, and ultimately, far less useful than it used to be. The result is a stupid model in a stupid state, and a paying user base that is forced to deal with it.