In recent months, a noticeable trend has emerged within the realm of AI coding assistants: a decline in their effectiveness. Historical models such as GPT-4 were incredibly trustworthy. Yet, with the latest iterations like GPT-5, it hasn’t come out reliably superior either. A systematic test conducted by industry professionals aimed to assess whether this perceived decline in quality was a reality or merely anecdotal. The results showed some very positive developments, as well as some alarmingly negative results.
The test consisted of executing a set of coding challenges. We fed nine distinct iterations of ChatGPT—primarily versions of GPT-4 and GPT-5. The primary goal was straightforward: to add 1 to the ‘index_value’ column from the dataframe ‘df’, assuming it existed. GPT-4 was able to do this successfully every time it was run, showcasing its obvious strengths in ideal conditions. GPT-5 never failed to produce a suitable solution each time. Its approach raised related questions about the efficacy of AI coding assistants as a whole.
Testing the Models
This qualitative analysis consisted of running each model several times to determine how well they are able to solve various coding tasks. The exercise we’re about to do might take 5 hours with the help of AI and about 10 hours without it. The conclusion was that the cumulative round trip time had more than doubled. In reality, in many instances it now takes seven hours, eight hours or more. This new emphasis on AI and how it should be used led to alarm among developers who use AI as a timesaver.
On one of the ten runs of GPT-4, this showed consistency in its responses. GPT-5’s performance was more variable. While it successfully solved the problem by taking the actual index of each row and adding 1 to create a new column, it exhibited a pattern seen in other models: sometimes providing effective solutions and at other times failing to meet expectations.
The test also showed error handling to be problematic. This one line of code resulted in six tries to run. It failed at exceptions that instead threw an error or filled the new column with a specific error message when it was unable to find the column it’s looking for. In all three cases, the models blatantly disobeyed the prompt’s request for code only. Rather, they reported that the column was not present in the dataset.
The Anecdotal Evidence
Jamie Twiss, CEO of Carrington Labs, who has been building with LLM-generated code to the extreme right, loves LLMs. He noted that over the past several months, he and many other developers have observed a decline in the quality of outputs generated by AI coding assistants. That’s why anecdotal evidence is powerful — it suggests that developers are using these new tools more frequently. They’re getting shot down with increasing regularity and with increasingly harmful outcomes.
Twiss stressed that even older models such as GPT-4 are still very much trustworthy. By comparison, their younger cousins are more likely to oscillate between presenting practical resolution and avoiding deep dilemmas. This somersaulting logic leads one to wonder how AI coding assistants have impacted software development. Can they do it and still be the most supportive place for developers to be?
As we approach 2025, it has become widely recognized by many experts that most core models have hit a quality plateau. Recent instances show a troubling trend. Jurisdictions are failing. This unexpected trend has led developer teams to re-evaluate their heavy-dependence on AI-assisted coding and find an alternative solution.
Implications for Developers
The impact of this decline is profound for developers who rely on these tools to boost productivity and efficiency. As coding tasks get more complicated, demand for trustworthy AI assistance is essential. This systematic test shows why it’s important to have concrete standards to improve AI algorithms. It highlights the importance of these algorithms providing reproducible results, regardless of use case.
In GPT-5 we see a possible path forward to addressing those issues by providing more specific solutions. Yet given its mixed performance, it’s fair to be skeptical of its claims to help with more involved coding tasks. Developers must weigh the benefits of using AI coding assistants against the potential pitfalls of encountering errors or receiving inadequate support.