The best programming languages ​​2025

Since 2013, we look metaphorically over the shoulders of programmers to create our annual interactive rankings of the most popular programming languages. But fundamental changes in the way people coded can not only make popularity more difficult, but could even make the concept itself out of words. And then things could become Really weird. To see why, let’s start with this year’s ranking and a rapid refreshment of the way we assemble this thing.

In the “Spectrum“The default classification, which is weighted with the interests of the members of the IEEE in mind, we see that once again Python in first place, with the biggest change in the first five being used to create web pages from last year in sixth place. With this year’s scores, in the classification of “jobs”, which exclusively examines the skills that employers are looking for, we see that Python has also taken 1st Place, until second place last year, although SQL’s expertise remains an incredibly precious skill to have on your CV.

Because we cannot literally look over the shoulders of all those who code, including children who hack Minecraft servers or university researchers developing new architectures, we are counting on proxies to measure popularity. We detail our methodology here, but the result is that we were the measures of several sources to create our classification. The metrics that we choose publicly indicate the interest in a wide range of languages: Google’s research traffic, the questions posed on the exchange of battery, the mentions in research articles, activity on the Open Source Github, etc.

But programmers turn away from many of these public expressions of interest. Rather than browsing a book or looking for a website like Stack Exchange for answers to their questions, they will chat with an LLM like Claude or Chatgpt in a private conversation. And with an AI assistant like the cursor helping to write code, the need to ask questions in the first place is considerably reduced. For example, through the total set of languages ​​evaluated in the TPL, the number of questions we saw displayed per week on Stack Exchange in 2025 was only 22% of what it was in 2024.

With less signal in measures accessible to the public, it becomes more difficult to follow popularity in a wide range of languages. This existential problem for our classification can be addressed by seeking new measures, or trying to investigate programmers – in all their variety – directly. However, an even more fundamental problem is looming in the wings.

Whether it is a experienced coder using an AI to manage growls, or a neophyte atmosphere coding a complete web application, the help of AI means that programmers can be concerned less and less the details of any language. The first details of the syntax, then the control and the flow functions, and so on the levels of the way a program is put in place – more and more is left at AI.

Although the LLM of code writing are always a work in progress, while they take care of an increasing part of the work, programmers inevitably pass the kind of people wishing to fight language is used.

After all, the reason why the various computer languages ​​exist is because given a particular challenge, it is easier to express a solution in one language compared to another. You do not control a washing machine using the programming language R, or inversely do a statistical analysis on large data sets using C.

But East Technically possible to do both. A human could tear their hair by doing it, but LLM has about as much hair as sensitivity. As long as there is enough training data, they will generate code for a given prompt in any language you want. In practical terms, this means using one – of everything – most popular general programming languages ​​today. In the same way that most developers do not pay much attention to the sets of instructions and other material idiosyncrasia of the CPUs on which their code works, which language in which a program is coded is ultimately a minor detail.

Of course, there will always be people who care, just like today, there are nerds like me ready to debate the advantages of writing for Z80 processors against 6502 8 bits. But overall, the popularity of different computer languages ​​could become such an obscure subject as the relative popularity of railway gauges.

An obvious long -term consequence on this subject is that it will become more difficult for new languages ​​to emerge. Previously, new languages ​​could emerge from individuals or small teams evangering their approach to contributors and potential users. Presentations, articles, demos, example of code and tutorials adrenned to new developer ecosystems. A single well -written book, like Leo Brodie Departure or Brian Kernighan and Dennis Ritchies’ Programming language Ccould make a huge difference in the popularity of a language.

But while a few samples and a tutorial may be sufficient to relaunch adoption among the familiar programmers with the ins and outs of the practical coding, this is not enough for today’s AIS. Humans build mental models that can extrapolate from relatively small amounts of data. LLMs rely on statistical probabilities, so the more they can eat data, they are better. Consequently, the programmers noted that AIS give significantly less poor results when they try to code in less used languages.

There are research efforts to make the LLM more universal, but that does not really help the new languages ​​to start. The fundamentally new languages ​​develop because they are giving itching that a programmer a. This itching can be as small as being annoyed to semi-finals to be placed after each declaration, or as important as a philosophical argument on the goal of the calculation.

But if an AI soothes our irritations with today’s languages, will the new ones ever reach the type of critical mass necessary to have an impact? Will the popularity of today’s languages ​​be frozen in time?

What is the future of programming languages?

Before speculating further on the future, let’s touch the base where we are today. Modern high -level computer languages ​​are really designed to do two things: create an abstraction layer that facilitates data processing appropriately and prevent programmers from getting in the foot.

The first objective has existed since the time of Fortran and Cobol, aiming respectively to process scientific and commercial data respectively. The second objective appeared later, largely stimulated by the 1968 Edgar Dijkstra document “go to the declaration considered harmful”. In this, he pleaded for eliminating the ability of a programmer to jump to arbitrary points of their code. This restriction consisted in preventing the spaghetti code which makes it difficult for a programmer to understand how a computer actually executes a given program. Instead, Dijkstra demanded that the programmers look at the structural rules imposed by the language. Dijkstra’s argument has finally won the day, and most modern languages ​​minimize or eliminate structures to go completely as functions and other programmatic blocks.

These structures do not exist at the CPU level. If you look at the instructions for ARM, X86 or RISC-V processors, the flow of a program is controlled by only three types of machine code instructions. These are conditional jumps, unconditional jumps and jumps with a stored trace (so that you can call a sub-program and come back to the place where you started). In other words, he is passing all along. Likewise, strict data types designed to label and protect data from incorrect use dissolve in anonymous bits entering and leaving the memory.

So, how much abstraction and anti-shooting structure A sufficiently advanced coding AI will really need it? An index comes from recent research in AI assisted hardware design, such as Dall-EM, a generation in AI developed at Princeton University used to create RF and electromagnetic filters. The design of these filters has always been something of a black art, involving the dispute of complex electromagnetic fields while swirling around small metal strips. But Dall-EM can take the desired entries and exits and spit something that looks like a QR code. The results are something that no human can ever conceive, but it works.

Likewise, could we bring our AI to go directly from the invitation to an intermediate language which could be introduced in the interpreter or the compiler of our choice? Do we need high-level languages ​​in this future? Admittedly, this would transform programs into impenetrable black boxes, but they could always be divided into modular testable units for mental health and quality checks. And instead of trying to read or maintain the source code, the programmers were simply going to change their prompts and again generate the software.

What is the role of the programmer in the future without source code? The design of architecture and the selection of algorithms would remain vital skills – for example, a Path orientation program Use a classic approach such as algorithm A *, or should it try to implement a new method? How should software be interfaced with a larger system? How to use the new equipment? In this scenario, computer diplomas, emphasizing the fundamental principles on the details of programming languages, increase in value compared to the start -up camps.

Will there be a higher programming language in 2026? Currently, the programming has passed through the greatest transformation since the compilers broke into the stage in the early 1950s. Even if the predictions that a large part of the AI ​​is a bubble on the point of realizing, the thing about technological bubbles is that there is always a residual technology that survives. It is likely that using LLMS to write and help with the code is something that will remain. We will therefore spend the next 12 months to determine what popularity means in this new age and what measures could be useful to measure. What to do You Do you think popularity should mean? What measures do you think we should consider? Let us know in the comments below.