Who Is the Best Programming Language of 2025?

Who Is the Best Programming Language of 2025?

In the freshly released IEEE Spectrum 2025 programming language rankings, Python has once again claimed the title of most popular programming language. Not only is it the champion, but it leads by an overwhelming margin—the gap between first-place Python and second-place Java is remarkably wide.

Even more striking is that Python continues to maintain explosive growth, with the chasm between first and second place widening further still.

Who Is the Best Programming Language of 2025?

Python’s Tenth Consecutive Victory

After dominating the IEEE Spectrum rankings for nine consecutive years, Python has surged ahead once more, securing its tenth straight championship in the comprehensive programming language rankings.

Moreover, this marks the first time Python has topped all three categories—comprehensive ranking, growth rate, and employment orientation—making it the first triple crown winner in the 12-year history of the IEEE rankings.

Back when Python first emerged, it gained widespread recognition for its simplicity, gradually replacing Java as the teaching language in universities.

Subsequently, libraries like NumPy, SciPy, matplotlib, and pandas came onto the scene, rapidly transforming Python into the powerhouse for scientific research, finance, and data analysis.

Throughout this process, network effects played a tremendous role.

More and more programmers chose Python and developed various tools based on it, contributing massive amounts of code and solutions to the open-source community, gradually building Python’s ecosystem moat.

AI has further amplified this advantage.

Compared to other programming languages, Python possesses richer training data, which enables large language models like Claude to perform better when using Python. In turn, this attracts more AI users with no programming background to choose Python as their first language.

Unexpectedly, Python has reaped the benefits of artificial intelligence before programmers themselves.

Winners and Losers

While Python has become AI’s “golden child,” JavaScript has suffered a major setback.

This year, JavaScript experienced the largest fluctuation in the comprehensive rankings, plummeting from third place last year directly to sixth.

JavaScript’s strength lies in web development, but this peak is being submerged by the rising tide of Vibe Coding, with fewer and fewer people discussing JavaScript.

The throne of SQL, the “king of data analysis,” has also come under assault.

For years, SQL has been the skill most desired by employers and an essential item on programmers’ resumes.

But even this long-standing defensive line has been breached by Python.

However, SQL’s situation remains far more secure than JavaScript’s. Even if it’s no longer the brightest star, it remains an extremely valuable employment skill, with a gap to Python that isn’t particularly large.

This is because SQL occupies a more vertical position—it’s the standard language for all enterprise databases.

Even though Python excels at general-purpose programming, accessing databases still requires SQL.

Behind the Rankings: The Deceleration Moment for Programming Languages

As the dominance of top programming languages becomes increasingly pronounced, programmers’ working methods are also undergoing subtle changes.

The most visible shift is the decline of once-thriving “community culture.”

Programmer activity on Q&A websites has dropped sharply. When encountering problems, they no longer instinctively post in communities first, but instead habitually ask large language models directly or rely on intelligent agents like Cursor for auto-completion.

The data confirms this trend:

In March 2023, Stack Overflow had approximately 87,000 new questions, but by March 2024, this number had fallen to about 58,800—a 32.5% decline in just one year.

By December 2024, the situation had become even more dire, with year-over-year declines reaching 40%.

Many programmers admit that the community atmosphere can sometimes be uncomfortable, and they now prefer consulting AI when problems arise:

The people there are just too harsh. They’ll criticize you for not searching beforehand, make you feel stupid, and most of the time, they just glance and walk away without actually helping you. It’s gotten much better since LLMs appeared—no AI will bully you for being dumb and then walk away.

It’s not just learning methods—programmers’ work logic is also quietly changing.

Whether it’s veterans using AI to handle tedious tasks or newcomers attempting to write complete web applications, AI intervention is gradually freeing programmers from obsession with programming details.

First syntax details, then flow control and functions… these topics that once required intense focus are increasingly being handed over to AI.

Soon, the minutiae of source code will no longer matter, and no one will fight over whether to use tabs or spaces for indentation.

Programmers may not even need to worry about which language to choose, because there may only be one answer left.

Programming languages have flourished in the past because for specific tasks, certain languages always had advantages—you wouldn’t use R to control a washing machine, nor would you use C for large-scale statistical analysis.

But from a technical standpoint, this isn’t impossible; it’s just extraordinarily difficult for humans. We don’t have the energy for such thankless tasks.

AI, however, doesn’t have this problem. With sufficient training data, it can solve any problem using a specified language.

This means the Matthew effect will intensify further, and programming language diversity will significantly decline.

On one hand, mainstream general-purpose languages will experience exponential growth through positive user feedback, solidifying a “the strong get stronger” pattern.

On the other hand, non-mainstream languages will be further marginalized due to lack of training data, making AI support difficult and raising the barrier to entry for programmers.

Just as today’s developers rarely concern themselves with CPU instruction sets and low-level assembly language, whether to use Python or Java for programming may become an irrelevant detail in the future.

Is AI Ending Programming Languages?

Currently, programming is undergoing its biggest transformation since compilers emerged in the early 1950s.

The original mission of high-level languages like Python was actually quite straightforward:

Create abstraction layers so programmers no longer need to manually manipulate registers, memory, and instruction sets.
Provide error messages when code fails instead of crashing directly, preventing programmers from “shooting themselves in the foot.”

From a computer’s perspective, high-level languages have never been necessary; it’s simply that humans need a more convenient way to interact.

Unfortunately, the emergence of high-level languages hasn’t made programming much simpler. For most people, natural language is the most ideal interface.

Today, AI is still just helping us improve coding efficiency, but in the future, will it be possible to skip this unnecessary step and allow us to communicate directly with compilers through prompts?

This approach has one drawback: it would make programming as inexplicable as a large language model—a black box.

However, while we can no longer directly read source code, we can break programs into modular functional units and ensure program reliability by monitoring these individual units.

At that point, programmers won’t need to maintain tens of thousands of lines of source code; they’ll simply adjust prompts and optimize software with one click through AI.

So in a future without source code, what will the programmer’s role be?

There’s no definitive answer, but we can be certain that low-level architectural design and algorithm selection will remain core competencies.

Programming languages may exit the stage, but the underlying logic of code will not.