<img src="https://spectrum.ieee.org/media-library/laptop-with-code-and-programming-language-labels-on-a-bright-yellow-background.jpg?id=61631329&width=980"/> <div class="rm-embed embed-media"><div class="flourish-embed flourish-chart" data-src="visualisation/24825595?820658"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img alt="chart visualization" src="https://public.flourish.studio/visualisation/24825595/thumbnail" width="100%"/></noscript></div></div> 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<a href="https://www.python.org/" target="_blank"> Python</a> has the top spot, with the biggest change in the top five being<a href="https://javascript.info/" target="_blank"> JavaScript</a>’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<a href="https://www.w3schools.com/sql/" target="_blank"> SQL</a> 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<a href="https://modrinth.com/mods" target="_blank"> Minecraft</a> servers or academic researchers developing new architectures, we rely on proxies to measure popularity. We detail <a href="https://spectrum.ieee.org/top-programming-languages-methodology-2025" target="_blank">our methodology here</a>, 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<a href="https://stackexchange.com/" target="_blank"> Stack Exchange</a>, 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<a href="https://claude.ai/" target="_blank"> Claude</a> or <a href="https://chatgpt.com/" target="_blank">ChatGPT</a> in a private conversation. And with an AI assistant like <a href="https://cursor.com/" target="_blank">Cursor</a> 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<a href="https://spectrum.ieee.org/chatgpt-code" target="_self"> using an AI to handle the grunt work</a>, 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.Although code-writing LLM’s are still very much a <a href="https://spectrum.ieee.org/ai-for-coding" target="_self">work in progress</a>, as they take over an increasing share of the work, programmers inevitably shift from being the kind of people willing to fight religious wars over whether source code should be indented by<a href="https://youtu.be/cowtgmZuai0?si=R4uNNt2P3Ynxb0wG" target="_blank"> typing tabs or spaces</a> to people who care less and less about what language is used.After all, the whole reason different computer languages exist is because given a particular challenge, it’s easier to express a solution in one language versus another. You wouldn’t<a href="https://github.com/mnr/rpigpior" target="_blank"> control a washing machine</a> using the<a href="https://www.r-project.org/" target="_blank"> R programming language</a>, or conversely do a statistical analysis on large datasets using <a href="https://en.wikipedia.org/wiki/C_(programming_language)" target="_blank">C</a>.But it is technically possible to do both. A human might tear their hair out doing it, but LLMs have about as much hair as they do sentience. As long as there’s enough training data, they’ll generate code for a given prompt in any language you want. In practical terms, this means using one—any one—of today’s most popular general purpose programming languages. In the same way most developers today don’t pay much attention to the instruction sets and other hardware idiosyncrasies of the CPUs that their code runs on, which language a program is vibe coded in ultimately becomes a minor detail.Sure, there will always be some people who care, just as today there are nerds like me willing to debate the merits of writing for the<a href="https://spectrum.ieee.org/chip-hall-of-fame-zilog-z80-microprocessor" target="_self"> Z80</a> versus the<a href="https://spectrum.ieee.org/chip-hall-of-fame-mos-technology-6502-microprocessor" target="_self"> 6502</a> 8-bit CPUs. But overall, the popularity of different computer languages could become as obscure a topic as the relative popularity of railway track gauges.One obvious long-term consequence to this is that it will become harder for new languages to emerge. Previously, new languages could emerge from individuals or small teams evangelizing their approach to potential contributors and users. Presentations, papers, demos, sample code and tutorials seeded new developer ecosystems. A single well-written book, like Leo Brodie’s<a href="https://www.forth.com/starting-forth/" rel="noopener noreferrer" target="_blank"> Starting Forth</a> or Brian Kernighan and Dennis Ritchies’<a href="https://ia903407.us.archive.org/35/items/the-ansi-c-programming-language-by-brian-w.-kernighan-dennis-m.-ritchie.org/The%20ANSI%20C%20Programming%20Language%20by%20Brian%20W.%20Kernighan%2C%20Dennis%20M.%20Ritchie.pdf" rel="noopener noreferrer" target="_blank"> The C Programming Language</a>, could make an enormous difference to a language’s popularity.But while a few samples and a tutorial can be enough material to jump-start adoption among programmers familiar with the ins and outs of hands-on coding, it’s 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 data they can crunch, they better they are. Consequently programmers have noted that<a href="https://www.reddit.com/r/ChatGPTCoding/comments/1gf9aap/training_ai_on_lesser_known_languages/" rel="noopener noreferrer" target="_blank"> AIs give noticeably poorer results</a> when trying to code in less-used languages.There are research efforts to<a href="https://news.mit.edu/2025/making-ai-generated-code-more-accurate-0418" rel="noopener noreferrer" target="_blank"> make LLMs more universal coders</a>, but that doesn’t really help new languages get off the ground. Fundamentally new languages grow because they are scratching some itch a programmer has. That itch can be as small as being<a href="https://www.codewithc.com/why-python-doesnt-use-semicolon-pythons-syntax-design/?amp=1" rel="noopener noreferrer" target="_blank"> annoyed at semicolons</a> having to be placed after every statement, or as large as a philosophical argument about the<a href="https://writings.stephenwolfram.com/2019/05/what-weve-built-is-a-computational-language-and-thats-very-important/" rel="noopener noreferrer" target="_blank"> purpose of computation</a>.But if an AI is soothing our irritations with today’s languages, will any new ones ever reach the kind of critical mass needed to make an impact? Will the popularity of today’s languages remain frozen in time?<h2>What’s the future of programming languages?</h2>Before speculating further about the future, let’s touch base again where we are today. Modern high-level computer languages are really designed to do two things: create an abstraction layer that makes it easier to process data in a suitable fashion, and stop programmers from shooting themselves in the foot.The first objective has been around since the days of<a href="https://fortran-lang.org/" rel="noopener noreferrer" target="_blank"> Fortran</a> and<a href="https://developer.ibm.com/languages/cobol/" rel="noopener noreferrer" target="_blank"> Cobol</a>, aimed at processing scientific and business data respectively. The second objective emerged later, spurred in no small part by Edgar Dijkstra’s 1968 paper “<a href="https://homepages.cwi.nl/~storm/teaching/reader/Dijkstra68.pdf" rel="noopener noreferrer" target="_blank">Go To Statement Considered Harmful</a>.” In this he argued for eliminating the ability for a programmer to make jumps to arbitrary points in their code. This restriction was to prevent so-called spaghetti code that makes it hard for a programmer to understand how a computer actually executes a given program. Instead, Dijkstra demanded that programmers bend to structural rules imposed by the language. Dijkstra’s argument ultimately won the day, and most modern languages do indeed minimize or eliminate Go Tos altogether in favor of structures like functions and other programmatic blocks.These structures don’t exist at the level of the CPU. If you look at the instruction sets for Arm, x86, or RISC-V processors, the flow of a program is controlled by just three types of machine code instructions. These are conditional jumps, unconditional jumps, and jumps with a trace stored (so you can call a subroutine and return to where you started). In other words, it’s Go Tos all the way down. Similarly, strict <a href="https://en.wikipedia.org/wiki/Data_type" target="_blank">data types</a> designed to label and protect data from incorrect use dissolve into anonymous bits flowing in and out of memory.So how much abstraction and anti-foot-shooting structure will a sufficiently-advanced coding AI really need? A hint comes from recent research in AI-assisted hardware design, such as<a href="https://ieeexplore.ieee.org/abstract/document/11103838" rel="noopener noreferrer" target="_blank"> Dall-EM</a>, a generative AI developed at Princeton University used to create RF and electromagnetic filters. Designing these filters has always been something of a black art, involving the wrangling of complex electromagnetic fields as they swirl around little strips of metal. But Dall-EM can take in the desired inputs and outputs and spit out something that looks like a QR code. The results are something no human would ever design—but it works.Similarly, could we get our AIs to go straight from prompt to an<a href="https://en.wikipedia.org/wiki/Intermediate_representation" rel="noopener noreferrer" target="_blank"> intermediate language</a> that could be fed into the interpreter or compiler of our choice? Do we need high-level languages at all in that future? True, this would turn programs into inscrutable black boxes, but they could still be divided into modular testable units for sanity and quality checks. And instead of trying to read or maintain source code, programmers would just tweak their prompts and generate software afresh.What’s the role of the programmer in a future without source code? Architecture design and algorithm selection would remain vital skills—for example, should a pathfinding program use a classic approach like the<a href="https://en.wikipedia.org/wiki/A*_search_algorithm" rel="noopener noreferrer" target="_blank"> A* algorithm</a>, or instead should it try to<a href="https://www.quantamagazine.org/new-method-is-the-fastest-way-to-find-the-best-routes-20250806/" rel="noopener noreferrer" target="_blank"> implement a new </a>method? How should a piece of software be interfaced with a larger system? How should new hardware be exploited? In this scenario, computer science degrees, with their emphasis on fundamentals over the details of programming languages, rise in value over coding boot camps.Will there be a Top Programming Language in 2026? Right now, programming is going through the biggest transformation since compilers broke onto the scene in the early 1950s. Even if the predictions that much of <a href="https://spectrum.ieee.org/ai-index-2025" target="_self">AI is a bubble</a> about to burst come true, the thing about tech bubbles is that there’s always some residual technology that survives. It’s likely that using LLMs to write and assist with code is something that’s going to stick. So we’re going to be spending the next 12 months figuring out what popularity means in this new age, and what metrics might be useful to measure. What do you think popularity should mean? What metrics do you think we should consider? Let us know in the comments below.

The article discusses the top programming languages expected to dominate in 2025, highlighting trends in technology and software development. Understanding these languages is crucial for developers and businesses as they adapt to the evolving tech landscape, ensuring they remain competitive and relevant in the industry.

The Top Programming Languages 2025

<img src="https://spectrum.ieee.org/media-library/image.jpg?id=61634716&width=980"/> Our <a href="https://spectrum.ieee.org/top-programming-languages-2024" target="_self">Top Programming Languages interactive</a> tries to tackle the problem of estimating a language’s popularity by looking for proxy signals. We do this by constructing measures of popularity from a variety of data sources that we believe are good proxies for active interest for each programming language. In total, we identify 64 <a href="https://spectrum.ieee.org/tag/programming-languages" target="_self">programming languages</a>. We then weight each data source <a href="https://spectrum.ieee.org/the-top-programming-languages-2023" target="_self">to create an overall index of popularity</a>, excluding some of the lowest scorers. Below, we describe the sources of data we use to get the measures, and the weighting scheme we use to produce the overall indices.By popularity, we mean we are trying to rank languages that are in active use, including activity from maintaining legacy systems. We look at three different aspects of popularity: languages in active use among typical IEEE members and working software engineers (the “Spectrum” ranking), languages that are in demand by employers (the “Jobs” ranking), and languages that are in the zeitgeist (the “Trending” ranking).We gauged the popularity of languages using the following sources for a total of seven metrics (see below). We gathered the information for all metrics in July—August 2025. In the past we relied heavily on APIs to gather data from sources, but now the data is gathered manually to the difficulty of keeping up with API changes and terminations, and because many of the programming language’s names (C++, Scheme) collided with common terms found in research papers and job ads or were difficult for a search engine to parse. When a large number of search results made it impractical to resolve ambiguities by examining all of the results individually, we used a sample of each data source, and determined the relevant sample size based on estimating the true mean with 95 percent confidence. Not all data sources contain information for each programming language and we interpret this information as the programming language having “no hits” (that is, not being popular).The results from each metric are normalized to produce a relative popularity score between 0 and 1. Then the individual metrics are multiplied by a weight factor, combined, and the result renormalized to produce an aggregate popularity score.In aggregating metrics, we hope to compensate for statistical quirks that might distort a language’s popularity score in any particular source of data. Varying the weight factors allows us to create the different results for the Spectrum, Jobs, and Trending rankings. We fully acknowledge that, while these weights are subjective, they are based on our understanding of the sources and our prior coverage of software topics. Varying the weight factors allows us to emphasize different types of popularity and produce the different rankings. We then combined each weighted data source for each program and then renormalized the resulting frequency to produce an aggregate popularity score.The Top Programming Languages was originally created by data journalist <a href="https://communication.northwestern.edu/faculty/nicholas-diakopoulos.html" target="_blank">Nick Diakopoulos</a>. Our statistical methodology advisor is <a href="https://www.linkedin.com/in/hilary-wething-52414b10/" target="_blank">Hilary Wething</a>, although all the actual data gathering and calculation is performed by us. Rankings are computed using <a href="https://www.r-project.org/" target="_blank">R</a>. <h2>Google</h2><a href="https://www.google.com/" target="_blank">Google</a> is the leading search engine in the world, making it an ideal fit for estimating language popularity. We measured the number of hits for each language by searching on the template, “X programming language” (with quotation marks) and manually recorded the number of results that were returned by the search. We took the measurement in July 2025. We like this measure because it indicates the volume of online information resources about each programming language.<h2>Stack Overflow</h2><a href="https://stackoverflow.com/" target="_blank">Stack Overflow</a> is a popular site where programmers can ask questions about coding. We recorded the number of questions tagged to each program within the last week prior to our search (August 2025). For the Mathematica/Wolfram language, we relied on the sister <a href="https://mathematica.stackexchange.com/" target="_blank">“Stack” for the Mathematica platform</a> and tallied the number of programming-related questions asked in the past week. These data were gathered manually. This measure indicates what programming languages are currently trending.<h2>IEEE Xplore Digital Library</h2>IEEE maintains a <a href="https://ieeexplore.ieee.org/Xplore/home.jsp" target="_blank">digital library</a> with millions of conference and journal articles covering a wide array of scientific and engineering disciplines. We searched for journal, magazine, and early access articles that mention each of the languages in the template “X programming” for the 2025 year-to-date. For search results that returned thousands of articles, we identified the correct sample size for a 95 percent confidence interval (usually a little over 300) and pulled that number of articles. For each language we sampled, we identified the share of articles that utilize the programming language and then multiplied the total number of articles by this share to tally the likely total number of articles that reference a given programming language. We conducted this search in August 2025. This metric captures the prevalence of the different programming languages as used and referenced in engineering <a href="https://spectrum.ieee.org/tag/scholarship" target="_self">scholarship.</a><h2><a href="https://spectrum.ieee.org/tag/scholarship" target="_self"></a>IEEE Jobs Site</h2>We measured the demand for different programming languages in job postings on the <a href="https://jobs.ieee.org/" rel="noopener noreferrer" target="_blank">IEEE Job Site</a>. For search results that returned thousands of listings, we identified the correct sample size for a 95 percent confidence interval (usually around 300 results) and pulled that number of job listings to manually examine. For each language we sampled, we identified the share of listings that utilize the programming language and then multiplied the total number of job listings by this share to tally the likely total number of job listings that reference a given programming language. Additionally, because some of the languages we track could be ambiguous in plain text—such as Go, J, Ada, and R—we searched for job postings with those words in the job description and then manually examined the results, again sampling entries if the number of results was large. The search was conducted in August 2025. We like the IEEE Job Site for its large number of non-U.S. listings, making it an ideal to measure global popularity.<h2>Career Builder</h2>We measured the demand for different programming languages on the <a href="https://www.careerbuilder.com/" rel="noopener noreferrer" target="_blank">CareerBuilder</a> job site. We searched for “Developer” jobs offered within the <a href="https://spectrum.ieee.org/tag/united-states" target="_self">United States</a>, as this is the most popular job title for programmers. We sampled 400 job ads and manually examined them to identify which languages employers mentioned in the postings. The search was conducted in August 2024. We like the career builder site to identify the popularity of programmer jobs among U.S.-based companies<h2>GitHub</h2><a href="https://github.com/" target="_blank">GitHub</a> is a public repository for many volunteer-driven open-source software projects. We used <a href="https://innovationgraph.github.com/global-metrics/programming-languages" target="_blank">Github’s listing</a> of it’s top 50 programming languages, filtering out entries for things like <a href="https://docs.docker.com/reference/dockerfile/" target="_blank">Docker configuration scripts</a>. The data cover the first quarter of 2025. This measured provides a strong indication what languages coders choose to work in when they have a personal choice.<h2>Trinity College Dublin Library</h2>The<a href="https://www.tcd.ie/library/" target="_blank"> library of Trinity College Dublin</a> is one of six legal deposit libraries in Ireland and the <a href="https://spectrum.ieee.org/tag/united-kingdom" target="_self">United Kingdom</a>. A copy must be deposited with the library of any printed material published or distributed in Ireland, and on request any U.K. publisher or distributor must also deposit a book. We searched for all books published in the year to date that matched the names of programming languages and checked the results for false positives. The search was conducted in July 2025. We like this library collection because it represents a large and categorized sample of works, primarily in the English language.<h2>Discord</h2>Discord is popular chat-room platform where many programmers exchange information. We counted the number of tags that correspond to each language. In the case of languages that could also be names of nonprogramming topics (many nonprogramming-related topics also have dedicated Discord <a href="https://spectrum.ieee.org/tag/servers" target="_self">servers</a>; for example, “Julia” could refer to the <a href="https://julialang.org/" target="_blank">programming language</a> or the <a href="https://muppet.fandom.com/wiki/Julia" target="_blank">Sesame Street puppet</a>), results were manually examined. <a href="https://disboard.org/en" rel="noopener noreferrer" target="_blank">Disboard</a> was searched in August 2025. Disboard lists many public discord servers and many young coders use the site, contributing a different demographic of coders.

The Top Programming Languages Methodology for 2025 aims to assess the popularity of programming languages by analyzing various data sources that serve as indicators of active interest. This approach is significant as it helps developers and organizations understand which languages are gaining traction, ultimately guiding their learning and project decisions.

Top Programming Languages Methodology 2025

تستكشف المقالات أبرز لغات البرمجة المتوقعة أن تتصدر في عام 2025، مع التأكيد على أهمية فهم هذه اللغات للمطورين والشركات. تتضمن منهجية تقييم شعبيتها تحليل مصادر بيانات متنوعة، مما يساعد المعنيين على اتخاذ قرارات مستنيرة في بيئة تكنولوجية تتطور بسرعة.

Los artículos exploran los principales lenguajes de programación que se anticipa dominarán en 2025, enfatizando la importancia de comprender estos lenguajes para desarrolladores y empresas. La metodología para evaluar su popularidad implica analizar diversas fuentes de datos, ayudando a las partes interesadas a tomar decisiones informadas en un panorama tecnológico en rápida evolución.

Les articles explorent les principaux langages de programmation qui devraient dominer en 2025, soulignant l'importance de comprendre ces langages pour les développeurs et les entreprises. La méthodologie d'évaluation de leur popularité implique l'analyse de diverses sources de données, aidant les parties prenantes à prendre des décisions éclairées dans un paysage technologique en évolution rapide.

Emerging Trends in Programming Languages for 2025

Emerging Trends in Programming Languages for 2025

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