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Python remained the most popular language in TIOBE’s August 2025 index, after reaching a record rating of 26.98% in July. TIOBE CEO Paul Jansen attributed part of that momentum to AI coding assistants, which may work especially well with languages that have large codebases, extensive documentation, and mature libraries.
That is a plausible explanation—not proof that AI caused Python’s rise. TIOBE measures popularity through indirect signals, not the amount of production code written or the quality of one language over another.
What TIOBE reported
Python held first place in the August 2025 TIOBE Programming Community Index with a rating of 26.14%. That followed a record 26.98% rating in July 2025.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteA TIOBE rating is a monthly share of the index’s measured popularity signals. It is not Python’s share of all software development, a count of Python programs, or a measure of how much code developers wrote in the language.
#1 Best Overall
The figures come from an August 4, 2025 InfoWorld report. They should not be described as the latest TIOBE results in 2026 without separately verifying the relevant 2026 index.
August 2025 TIOBE top 10
| Rank | Language | Rating |
|---|---|---|
| 1 | Python | 26.14% |
| 2 | C++ | 9.18% |
| 3 | C | 9.03% |
| 4 | Java | 8.59% |
| 5 | C# | 5.52% |
| 6 | JavaScript | 3.15% |
| 7 | Visual Basic | 2.33% |
| 8 | Go | 2.11% |
| 9 | Perl | 2.08% |
| 10 | Delphi/Pascal | 1.82% |
Why AI assistants could favor Python
AI coding assistants generate, complete, explain, refactor, and debug code using large language models. Jansen’s argument is that popular languages give those systems more material to learn from: public repositories, documentation, tutorials, examples, and widely used libraries.
That creates a possible feedback loop:
- Popular languages produce more public code and documentation.
- More examples give AI systems greater language-specific context.
- Better contextual assistance can reduce the friction of learning and using the language.
- Lower friction may encourage more developers to choose the same language.
Python is particularly exposed to this effect because it combines relatively readable syntax with a large ecosystem. It is widely used in machine learning, data science, automation, education, scripting, web development, and increasingly AI-related workflows.
The relationship may run in both directions. AI developers frequently use Python, creating more demand for Python assistance, while AI tools may make Python easier for beginners and professionals to adopt.
What the evidence does—and does not—show
Established fact: Python ranked first in the cited TIOBE data and reached a record rating in July 2025.
Attributed explanation: TIOBE CEO Paul Jansen said AI coding assistants helped Python continue growing because popular languages provide more training material.
Not established: The report does not measure how many developers use AI assistants, compare assistant accuracy across languages, or calculate how much of Python’s rating was caused by AI.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchIt also does not show that AI-generated Python is more reliable than code in another language, that AI is Python’s main source of popularity, or that Python will permanently remain number one.
What TIOBE measures
According to TIOBE’s methodology, the index is an indicator of programming-language popularity. Its inputs include estimates of skilled engineers, courses, third-party vendors, and search activity across Google, Amazon, Wikipedia, Bing, and more than 20 other websites. The index began in June 2001, according to the InfoWorld report.
TIOBE explicitly says the index is not a ranking of the best language and does not measure the amount of code written in each language. Search interest, educational demand, vendor support, and documentation volume can all influence an ecosystem-based measure.
That matters here: a large existing community can raise a language’s visibility, which can make it appear even more attractive to developers and AI systems. Monthly changes can also reflect measurement and search trends rather than a sudden change in production software.
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Python also led a different popularity index
The August 2025 PYPL ranking, which is based on Google searches for programming-language tutorials, also placed Python first at 30.5%. Its other leading entries were Java at 15.54%, C/C++ at 8.3%, JavaScript at 7.32%, C# at 5.32%, R at 5.19%, Objective-C at 3.57%, PHP at 3.49%, Rust at 2.63%, and TypeScript at 2.48%.
Python’s first-place position in both indexes supports the narrower conclusion that it had broad popularity in August 2025. It does not independently prove the AI-assistant explanation, because neither ranking establishes that relationship.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Perl’s surprise rise is a warning against simple explanations
Perl ranked ninth in August 2025 with a 2.08% rating, up from 25th place a year earlier. The report said Jansen had no clear explanation for the jump.
The result is useful context. Even TIOBE cannot explain every major movement in its index, so a correlation between Python’s popularity and the growth of AI coding tools should not automatically be treated as a proven cause. The reported rise in older languages such as Ada, Visual Basic, SQL, Fortran, and Delphi likewise indicates an index trend—not necessarily a broad migration back to legacy development.
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Python’s AI compatibility is a useful consideration, but it should not be the sole reason to select a language.
- Choose Python when its ecosystem and readability fit the project, especially for AI, data science, automation, education, and many web-back-end tasks.
- Consider Go, Rust, C++, Java, C#, or JavaScript/TypeScript when performance, concurrency, platform integration, browser delivery, runtime constraints, or an existing enterprise stack matters more.
- Check the real ecosystem for actively maintained libraries, deployment support, observability, security requirements, and available team expertise.
- Review AI-generated code regardless of language. Readable Python can still contain incorrect APIs, obsolete package usage, insecure authentication, vulnerable dependencies, concurrency bugs, numerical errors, or poor resource cleanup.
- Test and govern assistants with dependency pinning, automated tests, security review, secret-handling rules, and organizational policies covering proprietary code and data retention.
AI assistance can reduce the cost of writing code. It does not remove the need to understand the codebase, verify behavior on edge cases, or maintain the resulting system.
Bottom line
AI coding assistants may be reinforcing Python’s existing lead because Python already has the documentation, examples, libraries, users, and AI-related demand that make assistance easier and more valuable. TIOBE’s August 2025 data is consistent with that feedback-loop theory, but it is not a causal study. Developers should treat AI support as one selection factor alongside use case, performance, security, deployment, hiring, and long-term maintainability.
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