Ruby, once a top language, has plummeted to 30th place. The programming language that powered the early 2010s web startup boom now sits near the bottom of the Tiobe index, a widely watched popularity ranking. Its March 2025 rating of 0.55% marks a new low. This shift reflects deeper changes in the tech landscape, from the rise of artificial intelligence to the evolution of web development itself.
Why Did Ruby Fall So Sharply?
The Tiobe index for March 2025 places Ruby at 30th overall, down from 25th last month. Tiobe CEO Paul Jansen offered a blunt explanation: “The main reason for Ruby’s drop is Python’s popularity. There is no need for Ruby anymore.” That single sentence captures a decade-long trend. Python absorbed the web development niche that once belonged to Ruby, then expanded far beyond it into data science, machine learning, and automation.
Ruby was the Tiobe language of the year in 2006, having shown the highest growth rate in popularity that year. Its peak came later, in May 2016, when it reached 8th place. The descent from 8th to 30th over nine years tells a story of a language that failed to expand beyond its original stronghold. Ruby on Rails made building database-backed web applications fast and elegant, but that single use case became a ceiling rather than a foundation for growth.
Python, by contrast, kept adding domains. It moved from scripting and web development into scientific computing, data analysis, artificial intelligence, and education. Each new domain brought new developers, new libraries, and new search-engine visibility. The ruby popularity decline is not about Ruby getting worse. It is about Python getting broader.
Which Languages Gained or Lost Ground This Month?
The March 2025 Tiobe index shows movement beyond Ruby’s drop. SQL and R swapped places in the top 10. SQL now ranks 8th with a rating of 2.0%, while R sits at 9th with 1.88%. This swap reflects steady demand for database querying and statistical computing, both of which remain essential in data-driven organizations.
Swift re-entered the top 20 with a rating of 1.04%. Apple’s language has maintained a loyal following among iOS and macOS developers, and its return to the top 20 suggests sustained interest in native Apple development. Kotlin fell to 22nd with a rating of 0.82%, a modest decline that may reflect Android developers shifting focus or the maturation of the Kotlin ecosystem.
Dart ranked 25th this month with a rating of 0.69%. Google’s language, once positioned as a rival to JavaScript, continues its slow climb. The Flutter framework has given Dart a second life in cross-platform mobile development, and its steady presence near the top 25 indicates real-world usage that search-engine metrics may undercount.
How Does the Tiobe Index Measure Popularity?
The Tiobe Programming Community Index does not count lines of production code or number of active developers. Instead, it uses a formula that assesses the number of skilled engineers worldwide, courses, third-party vendors, and web page mentions from popular sites such as Google, Amazon, Bing, Wikipedia, and more than 20 others. The index reflects visibility and mindshare rather than raw usage.
This methodology means that languages with strong documentation, active tutorial ecosystems, and frequent blog posts tend to rank higher. Python benefits enormously from this approach because its community produces an immense volume of educational content, library documentation, and conference talks. Ruby, with a smaller but still passionate community, generates less search-engine footprint by comparison.
The ruby popularity decline in Tiobe may overstate the drop in real-world production usage. Ruby on Rails still powers notable websites, and many businesses continue to run Ruby code in production. But the index captures a shift in attention and investment that is real, even if the exact numbers are debatable.
Will Large Language Models Change the Index?
Some observers have asked whether the Tiobe index should switch from search engines to large language models for its ratings. Tiobe CEO Paul Jansen addressed this question directly. “The answer is no,” Jansen said. “The Tiobe index measures how many internet pages exist for a particular programming language. LLMs ultimately rely on the same sources — they are trained on and analyze these very same web pages. Therefore, in essence, there is no real difference.”
This means that the rise of tools like ChatGPT and GitHub Copilot will not fundamentally change how language popularity is measured. LLMs draw from the same corpus of web pages, documentation, and forums that Tiobe already counts. If Ruby content is scarce on the web, LLMs will reflect that scarcity, and the index will continue to show the same trend.
For Ruby advocates, this is a sobering reality. Improving Ruby’s Tiobe ranking would require generating more web-visible content, not just writing better code. More tutorials, more library documentation, more community resources — all of these would increase Ruby’s footprint in the sources that Tiobe and LLMs both depend on.
The Ruby on Rails Framework: Blessing or Curse?
Ruby has been around since 1995 and still gets regular releases. The language itself is mature, stable, and well-designed. But its identity became inseparable from Ruby on Rails, the web framework that launched in 2004 and defined a generation of web development. Rails introduced concepts like convention over configuration, database migrations, and integrated testing that influenced every framework that followed.
The blessing was clear: Rails made Ruby famous. Startups could build and ship web applications faster than ever before. The framework attracted a wave of developers who learned Ruby specifically to use Rails. The curse was equally clear: Ruby became a one-trick language in the public imagination. When the web framework landscape diversified with Django, Laravel, Node.js, and later Next.js, Ruby’s single-use identity left it vulnerable.
Rails itself remains a excellent framework, still maintained and still capable. But the association between Ruby and Rails meant that when developers moved toward other backend approaches — serverless, microservices, API-first architectures — they often moved away from Ruby entirely. The language never developed a second major use case that could sustain growth when web framework popularity shifted.
Python’s Rise in Data Science and AI
Python is cited as a reason for Ruby’s drop in the Tiobe index, and the data supports that claim. Over the past decade, Python became the default language for data science, machine learning, and artificial intelligence. Libraries like NumPy, pandas, scikit-learn, TensorFlow, and PyTorch created an ecosystem that Ruby simply cannot match.
Ruby never developed a comparable scientific computing ecosystem. There is no Ruby equivalent of Jupyter notebooks, no Ruby library with the depth of scikit-learn, and no Ruby framework for deep learning that competes with PyTorch. The Ruby community focused on web development almost exclusively, and that focus left the language on the sidelines during the AI boom.
Consider a startup CTO who built their MVP in Ruby on Rails five years ago. Today, that same CTO needs to integrate machine learning models, process large datasets, and hire engineers who can work across both backend and data pipelines. Python offers a unified ecosystem where one language handles web APIs, data processing, and model training. Ruby offers excellent web development but requires integration with other languages for everything else. The choice becomes pragmatic, not ideological.
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What the Tiobe Index Actually Measures
Ruby’s March rating is 0.55%, down from its position at 25th last month. Ruby was ranked 25th last month, meaning it dropped five places in a single month. Such a rapid decline raises questions about what the index truly captures. Does Ruby have 0.55% of the world’s developer mindshare, or does it have 0.55% of the world’s search-engine-visible content about programming languages?
The distinction matters. Many Ruby on Rails applications run quietly in production, maintained by small teams who do not write blog posts or create tutorials. Those applications generate no Tiobe points. Meanwhile, every new Python tutorial, every PyCon talk video, and every Stack Overflow answer about pandas adds to Python’s score. The index rewards visibility, not utility.
For a senior Ruby developer who has invested a decade in the ecosystem, the ruby popularity decline in Tiobe does not mean their skills are worthless. It does mean that their language is becoming less visible to hiring managers, less likely to appear in course curricula, and less likely to be chosen for new projects. The index reflects a real shift in momentum, even if it overstates the decline in existing production usage.
The Consolidation of the Ecosystem
Ruby’s highest Tiobe position was 8th in May 2016. That peak came during a period when many general-purpose languages coexisted with healthy communities. Today, the landscape looks different. Python, JavaScript, Java, C#, and Go dominate the conversation, while smaller languages struggle for visibility. The ecosystem appears to be consolidating around a few winners.
This consolidation affects developer choices at every level. Computer science students choosing between Ruby and Python for their first serious language face an obvious answer: Python offers more job opportunities, more learning resources, and more long-term career stability. Even developers who prefer Ruby’s syntax and philosophy must acknowledge the practical advantages of Python’s ecosystem.
The consolidation is not total. Languages like Rust, Go, and TypeScript continue to grow in specific niches. But the general-purpose language space is contracting, and Ruby sits on the wrong side of that contraction. The language that once represented the cutting edge of developer productivity now represents a legacy choice, maintained by loyalists but rarely chosen for new initiatives.
Ruby’s Dedicated Community
Ruby was the Tiobe language of the year in 2006, a testament to its early impact on web development. The community that formed around Ruby and Rails was famously welcoming, creative, and productive. Matz’s philosophy of “developer happiness” attracted thousands of programmers who found joy in writing Ruby code. That community still exists, and it still produces excellent work.
Can a language survive on loyalty and niche enthusiasm without mainstream growth? The answer is yes, but with limits. Ruby will not disappear. It will continue to receive updates, maintain its ecosystem, and serve the businesses that rely on it. But without new developers entering the community, the language will gradually shrink. Fewer contributors means fewer libraries, slower innovation, and a smaller safety net when security issues arise.
The Ruby community faces a choice. It can accept a smaller, quieter role as a niche language for specific use cases, or it can invest in expanding Ruby’s reach beyond web development. The latter path would require building or adopting libraries for data science, cloud infrastructure, and AI — areas where Ruby currently has little presence. Either path is valid, but the current trajectory suggests the community is choosing the former.
Frequently Asked Questions
What specifically about Python’s ecosystem made it absorb the web development niche that once belonged to Ruby?
Python’s ecosystem absorbed web development through a combination of framework maturity and domain expansion. Django offered a full-featured alternative to Ruby on Rails with similar conventions, while FastAPI and Flask provided lighter options for API development. More importantly, Python allowed developers to use one language for both web development and data science, reducing the cognitive overhead of switching between languages for different parts of a project.
If I am starting a new web project today, are there any scenarios where Ruby on Rails still makes more sense than Python’s Django or FastAPI?
Ruby on Rails still makes sense for projects where rapid prototyping and convention-over-configuration are the top priorities. Rails ships with integrated testing, database migrations, and a mature ecosystem of gems that can accelerate development. If your team already has deep Rails expertise and your project does not require data science or AI integration, Rails remains a productive choice. However, for projects that may later need machine learning or data processing, Python offers a more natural path forward.
Will the emergence of large language models that generate code shift popularity rankings away from search engine page counts toward actual usage patterns?
No, because LLMs are trained on the same web pages that the Tiobe index uses for its ratings. As Tiobe CEO Paul Jansen explained, LLMs ultimately rely on the same sources and analyze the same web pages. If Ruby has less content on the web than Python, both the Tiobe index and LLM-based code generation will reflect that imbalance. The measurement methodology would need to change fundamentally — for example, by counting actual compiler downloads or production deployments — to capture real usage patterns rather than web visibility.
