Python posted a reported gain of about 7 percentage points in the TIOBE index in 2025, reaching a 25.35% rating. That was described as the highest rating for any language since Java in 2001. Its reported 15-point lead over the next language was also a record for the index.

For a language released in 1991, those figures suggest considerable staying power. They also support a practical argument for technology teams: Python's accumulated libraries, documentation, and developer experience remain valuable as more projects involve AI.

The figures discussed here reflect the position as of November 9, 2025. They make a case for considering Python in new investments, rather than a reason to replace working systems simply because another language ranks higher.

An established ecosystem fits the current demand

Python's appeal has never depended mainly on execution speed. Its stronger advantage is the amount of work already done around it. Teams can often find an established library, a documented pattern, or an example for the problem at hand rather than build every component from scratch.

That matters especially in machine learning. According to JetBrains' State of Python 2025 report, 41% of Python developers use the language for machine learning. TensorFlow, PyTorch, and scikit-learn have made Python central to their developer-facing ecosystems, helping it become the standard choice for much AI development.

The survey figure describes developers, not the share of all Python code or development hours devoted to AI. Even with that distinction, it shows how closely Python's user base is connected to machine learning.

Python's maturity also reduces some of the uncertainty around adopting it. Its strengths include:

  • Established libraries across a wide range of problem domains.
  • Production practices developed over decades.
  • Extensive official and community documentation.
  • Experience at organizations ranging from startups to Google.

Those resources can shorten the path from an idea to a working prototype. They can also make it easier to change an implementation after user feedback, which is often more useful than optimizing runtime performance before the requirements are settled.

AI coding assistants could extend that advantage

AI creates two kinds of demand for Python. Developers use Python to build AI systems, and they use AI coding assistants to write Python. The second relationship could make the existing ecosystem more useful, although the size of the productivity gain needs more evidence than popularity figures alone provide.

GitHub reported a 22.5% year-over-year increase in Python contributions, and Python overtook JavaScript as the platform's most-used language. That activity adds to a large public collection of examples, libraries, and implementation patterns.

Tools such as GitHub Copilot, Cursor, and Claude Code can help developers work with familiar Python patterns. A large public codebase offers substantial material for model training and examples, though it doesn't establish what any particular assistant was trained on or how reliably it will handle a task.

For a developer with 15 years of Python experience, the potential benefit is less time spent writing boilerplate, plus help spotting possible edge cases or recalling an unfamiliar library's conventions. The developer still has to assess whether the suggestion fits the system.

For a mid-level developer, an assistant can explain a pattern and suggest an implementation while the work is underway. A bootcamp graduate may also be able to make useful contributions sooner with that support. These are plausible benefits, not evidence that a less experienced developer has acquired years of judgment simply by gaining access to a tool.

This distinction matters for hiring. AI assistance may make a wider range of candidates productive, but the strongest case is for using it alongside learning and review. The claim that assistants let teams operate with substantially fewer senior developers is less secure.

The numbers behind the 2025 investment case

Several other figures describe Python's momentum, although they cover different measures and periods:

  • 1.19 million LinkedIn job listings reportedly required Python skills.
  • 9.3% growth in 2024 was cited for Python, compared with 2.3% for Java, 1.4% for JavaScript, and 1.2% for Go.
  • 41% of Python developers used the language for machine learning, according to JetBrains' State of Python 2025 report.
  • A 26.14% peak TIOBE rating in August 2025 was associated with AI adoption and tooling improvements.

The August peak and the 25.35% rating represent separate reported readings. Neither should be treated as a direct measure of the percentage of production software written in Python.

The hiring figures suggest strong demand, while Python's position as a widely taught language supports a substantial talent pipeline. They don't, by themselves, show that hiring will be easy or that employers who act sooner will get better terms. The useful implication for AI projects is more specific: Python skills are likely to be relevant, and hiring plans should account for them.

AI-assisted onboarding could help less experienced hires contribute earlier. That possibility belongs in workforce planning, but it shouldn't be treated as a guaranteed reduction in training time or staffing cost.

Performance and typing still need explicit decisions

Python's execution speed remains a valid concern. A rough planning argument is that it is fast enough for 90% of use cases, with the remaining 10% requiring another approach. Those proportions are a rule of thumb, not a measured result that applies to every organization.

One increasingly popular approach is Python + Rust. Python provides the application logic and access to its libraries, while Rust handles performance-critical components. This allows a team to keep much of Python's development convenience without requiring every demanding operation to run in Python.

The relevant question is where performance limits the application. A prototype, a data workflow, and a real-time processing system can have very different requirements. Python's ecosystem is a strong advantage when development time dominates the cost. It is less persuasive when the workload cannot meet its runtime requirements.

Typing also deserves a more precise discussion than a claim that the problem has been solved. Python 3.14 continues the language's development around type hints. Tools such as mypy provide static analysis, which checks code without running it, while Pydantic supports runtime data validation. These tools address related but different problems; their availability doesn't make every Python program type-safe.

AI coding assistants may help identify type-related mistakes earlier, but claims that they catch such bugs more consistently need evidence. Type hints, analysis tools, and validation remain deliberate engineering choices rather than automatic benefits of using an assistant.

Where Python makes sense for new investment

For AI and machine-learning initiatives, Python is a strong default candidate because of its established ecosystem and available tooling. The case is particularly good when the work depends on libraries that already make Python the natural interface.

For existing systems, popularity is a weaker reason to change languages. A rewrite introduces work that a ranking cannot justify. A new service or a planned re-architecture offers a more reasonable point to compare Python's advantages with the requirements and skills already in place.

Python's growth may also reinforce itself. More developers can produce more libraries and public examples. Those resources can make the language easier to adopt and provide more material for AI-assisted development, which may attract further use. That is a plausible network effect, though a 15-point index lead doesn't establish how long it will last.

The practical opportunity is to combine a mature ecosystem with useful assistance. That can improve the time it takes to reach a working result without assuming that AI removes the need for technical judgment.

What could weaken the case

Python's advantages don't remove the risks. Three developments could reduce its appeal over a three-to-five-year planning horizon:

  • More demanding runtime requirements. A stronger shift toward edge computing or real-time processing could make Python's performance limitations more costly.
  • A compelling AI-native language. A language designed specifically for AI workloads could attract enough use to fragment the ecosystem, although that remains a speculative possibility.
  • Governance problems. Poor stewardship could damage confidence or slow progress, even after a long period of stable development.

These seem relatively unlikely to displace Python within that planning horizon. For individual projects, the immediate constraints are more concrete: runtime requirements, access to the right libraries, and the team's ability to maintain the result. Python's 2025 momentum strengthens its case, but those constraints should determine the choice.