Why Gemini's Coding Skills Are Declining

8/14/2025

So, What's the Deal? The Real Reason Gemini's Coding Skills Seem to Be Getting Worse

If you've been using Google's Gemini for coding tasks lately, you might be feeling a little… frustrated. It's a sentiment bubbling up across developer forums, Reddit threads, & even Google's own help communities. The story often goes something like this: a few months ago, Gemini was a coding powerhouse, a genuine partner in development. Now? It feels like it’s struggling. It’s making weird typos, forgetting context mid-conversation, & sometimes spitting out code that’s just plain wrong.

So what gives? Is Gemini actually getting dumber, or is something more complex going on? Honestly, it’s not a simple yes or no answer. It turns out, there's a whole mess of reasons why your go-to AI coding assistant might feel like it's letting you down. Let's dive into the real reasons Gemini's coding skills seem to be getting worse.

The Elephant in the Room: It’s Not Just You

First off, let's validate what you're probably experiencing. You’re not imagining things. Countless developers have been sharing stories that echo the same frustrations. One user on Reddit lamented that Gemini 2.5 Pro had gotten "very bad at coding these days," even making silly mistakes like typing "httb://" instead of "http://". Another user on a Google Help forum reported that for several days, they struggled to solve a JavaScript problem with Gemini, only to have competitors like Claude.ai & GPT-4 identify the issue almost instantly.

The complaints are pretty consistent:

Loss of Context: You're deep into a complex coding problem, & suddenly Gemini seems to have amnesia about what you were just discussing.
Oversimplification: You ask for a sophisticated script, & you get something a first-year computer science student might write.
Hallucinations & Errors: The code looks plausible at first glance, but then you realize it's full of bugs, uses non-existent functions, or is just completely irrelevant to your project.
A Patronizing Tone: In a particularly bizarre twist, some users have noted that Gemini can even get a bit "arrogant," lecturing them on "standard design practices" while providing incorrect code.

It's infuriating, especially when you're up against a deadline. But while the frustration is real, the "why" is where things get interesting.

The "Silent Nerf" & the Constant Churn of Updates

One of the leading theories floating around is the idea of the "silent nerf." Big tech companies are constantly tweaking their AI models. These updates are rolled out for a variety of reasons – to improve safety, reduce computational cost, or patch vulnerabilities. Google's own release notes show a steady stream of updates to Gemini models, with new versions being released every few months. For instance, a new version of Gemini 2.5 Pro was released in May 2025 to specifically improve coding and function calling.

Here’s the thing: while these updates are often framed as "improvements," they can sometimes have unintended consequences. An update designed to make the model safer might inadvertently make it more cautious & less creative in its coding solutions. A tweak to make it more efficient might reduce its ability to handle complex, multi-turn conversations. Because these changes happen behind the scenes, all you see is a sudden drop in performance, leaving you wondering what happened.

This is a double-edged sword. On one hand, Google is actively working to make Gemini better. They've announced updates that boost coding performance, reduce errors, & even enable cool new features like creating a learning app from a YouTube video. On the other hand, each update, even a minor one, changes the model's behavior. What worked for you yesterday might not work today. This constant state of flux can be jarring & make the model feel unreliable.

Benchmarks vs. The Real World: A Tale of Two AIs

Now, if you look at the official benchmarks, the story gets even more confusing. On paper, Gemini is a beast. Google's announcements are filled with charts showing Gemini Ultra outperforming GPT-4 on a wide range of tasks, including coding. The benchmarks for Gemini 1.5 Pro, for example, show it has incredible accuracy on coding tasks.

So why doesn't it feel that way in practice?

The disconnect often lies in the difference between a sterile benchmark environment & the messy reality of a real-world coding project. Benchmarks are standardized tests. They’re great for measuring specific capabilities in isolation, like "Can the AI solve this specific algorithm challenge?" or "How well does it generate Python code for this well-defined problem?"

But that’s not how most developers work. Real-world coding is a chaotic dance of debugging, refactoring, dealing with legacy code, & trying to integrate five different libraries that don’t want to play nice with each other. It requires a deep understanding of context, the ability to reason through ambiguity, & a knack for creative problem-solving. These are things that are notoriously difficult to measure with a standardized test.

So while Gemini might ace a test on generating a perfect, self-contained function, it might stumble when you ask it to debug a complex issue within a massive, 30,000-line codebase. This is where a user's perception of its skills can diverge sharply from its benchmarked abilities. Some users have found that while Gemini can explain code well, GPT-4 is still superior when it comes to the actual writing of the code.

The Rise of "AI Vibe Coding" & the Degradation of Skills

There's another, more philosophical angle to consider: the concept of "AI Vibe Coding." It's the idea that as developers become more reliant on AI for coding, their own skills start to atrophy. Why bother memorizing the syntax for a complex function when you can just ask your AI assistant to write it for you?

This creates a dangerous feedback loop. As developers lean more on AI, they become less capable of spotting subtle errors or architectural flaws in the code the AI generates. They accept what the AI produces because they've lost some of the critical thinking skills needed to evaluate it properly.

This isn't just about individual developers getting "dumber." It has a knock-on effect on the AI models themselves. These models are often trained and fine-tuned using a process called Reinforcement Learning from Human Feedback (RLHF), where human reviewers rate the AI's responses. If the human reviewers are themselves becoming less skilled, they might start to prefer simpler, less elegant, or even slightly incorrect code. This, in turn, teaches the AI that lower-quality code is acceptable, leading to a gradual decline in the model's overall performance. We could be in a spiral where both AI and developers are getting worse in tandem.

How Businesses Can Navigate the AI Maze

This whole situation can be a headache for individual developers, but for businesses trying to leverage AI, it's a serious challenge. How can you build reliable processes around a tool that seems to be in a constant state of flux?

This is where having a more focused, specialized AI solution can make a world of difference. While general-purpose models like Gemini are trying to be everything to everyone, other platforms are focusing on doing one thing really, really well.

Take customer service & website engagement, for instance. Businesses are increasingly looking to AI to provide instant support to their customers. Instead of relying on a massive, general-purpose model with unpredictable behavior, many are turning to platforms like Arsturn. Here's the thing about Arsturn – it helps businesses create custom AI chatbots trained specifically on their own data. This means the chatbot isn't guessing or pulling from the vast, sometimes messy, expanse of the internet. It's providing answers based on your company's product documentation, FAQs, & knowledge base.

This approach sidesteps many of the problems plaguing general AI models. The chatbot's knowledge is contained & controlled. You don't have to worry about a "silent nerf" suddenly making it forget your return policy. It provides a consistent, reliable experience for your customers, answering their questions & engaging with them 24/7. When you're talking about lead generation & website optimization, this kind of tailored interaction is HUGE. By building a no-code AI chatbot with Arsturn, businesses can boost conversions & provide the kind of personalized customer experiences that build real connections.

So, Is It Really Getting Worse?

The perception that Gemini's coding skills are declining is a complex issue with no single culprit. It’s a mix of:

Constant Model Updates: The very act of trying to improve the model can lead to unpredictable changes & a feeling of instability.
Safety & Alignment Tuning: Making the AI safer & more "aligned" can sometimes feel like putting the brakes on its creative & problem-solving abilities.
Benchmark vs. Reality: The model might be acing its exams but struggling with the hands-on, messy reality of real-world development.
The "AI Vibe Coding" Effect: A potential co-dependent decline in both human & AI skills could be subtly lowering the bar for what we consider "good" code.

It's less that Gemini is objectively "getting worse" & more that our relationship with it is changing. As we integrate these powerful tools more deeply into our workflows, we become more sensitive to their flaws & inconsistencies. The initial "wow" factor has worn off, & now we're grappling with the practical realities of using them day-to-day.

Ultimately, the key is to see these AI models for what they are: incredibly powerful, but imperfect, tools. They are not magic black boxes. They are constantly evolving systems that require a critical eye, a healthy dose of skepticism, & a willingness to adapt.

Hope this was helpful & sheds some light on what's going on behind the curtain. Let me know what you think – have you noticed a change in Gemini's performance?