Since 2013, we’ve been metaphorically peering over the shoulders of programmers to create our annual interactive rankings of the most popular programming languages. But fundamental shifts in how people are coding may not just make it harder to measure popularity, but could even make the concept itself irrelevant. And then things might get really weird. To see why, let’s start with this year’s rankings and a quick refresher of how we put this thing together.
In the “Spectrum” default ranking, which is weighted with the interests of IEEE members in mind, we see that once again Python has the top spot, with the biggest change in the top five being JavaScript’s drop from third place last year to sixth place this year. As JavaScript is often used to create web pages, and vibe coding is often used to create websites, this drop in the apparent popularity may be due to the effects of AI that we’ll dig into in a moment. But first to finish up with this year’s scores, in the “Jobs” ranking, which looks exclusively at what skills employers are looking for, we see that Python has also taken 1st place, up from second place last year, though SQL expertise remains an incredibly valuable skill to have on your resume.
Because we can’t literally look over the shoulders of everyone who codes, including kids hacking on Minecraft servers or academic researchers developing new architectures, we rely on proxies to measure popularity. We detail our methodology here, but the upshot is that we merge metrics from multiple sources to create our rankings. The metrics we choose publicly signal interest across a wide range of languages—Google search traffic, questions asked on Stack Exchange, mentions in research papers, activity on the GitHub open source code repository, and so on.
But programmers are turning away from many of these public expressions of interest. Rather than page through a book or search a website like Stack Exchange for answers to their questions, they’ll chat with an LLM like Claude or ChatGPT in a private conversation. And with an AI assistant like Cursor helping to write code, the need to pose questions in the first place is significantly decreased. For example, across the total set of languages evaluated in the TPL, the number of questions we saw posted per week on Stack Exchange in 2025 was just 22 percent of what it was in 2024.
With less signal in publicly available metrics, it becomes harder to track popularity across a broad range of languages. This existential problem for our rankings can be tackled by searching for new metrics, or trying to survey programmers—in all their variety—directly. However, an even more fundamental problem is looming in the wings.
Whether it’s a seasoned coder using an AI to handle the grunt work, or a neophyte vibe coding a complete web app, AI assistance means that programmers can concern themselves less and less with the particulars of any language. First details of syntax, then flow control and functions, and so on up the levels of how a program is put together—more and more is being left to the AI.
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