The September 2026 TIOBE Index shows Python still holding first place but falling below 18% for the first time in the index's recent history, at 17.76% — down from 18.53% the prior month. C also weakened, falling to 10.28%. The gradual decline in both top languages points to a broadening ecosystem rather than a single language dominating.
Python's steady decline
Python's rating has been drifting downward through much of 2026. At 17.76%, it still sits far ahead of second-place C, but the trajectory is clear: the language that rode the AI and data science wave to dominance is seeing its share dilute as more specialized tools gain traction. Python remains non-negotiable for AI work — but when models scale or simulations grow complex, speed becomes critical, and that's where challengers step in.
Julia nears the top 20
At position 21 with a 0.74% rating, Julia sits just one slot away from re-entering the top 20 — a threshold it first touched in summer 2023. TIOBE CEO Paul Jansen noted that Julia has taken some of MATLAB's territory in numerical and scientific computing, modeling, and data processing. MATLAB itself fell another two places to number 27, continuing a decline that drew attention last month.
Julia combines Python-like syntax with C-like execution speed, positioning it for engineers who demand both. Financial firms run risk models on it, and automotive engineers use it for vehicle dynamics simulations. The niche is real, even if broader adoption outside academia and specialized industries remains limited.
The top 10 holds steady
C++ rose slightly to 8.67%, widening its lead over Java at 7.54% to 1.13 percentage points. R held at 1.69%, and Rust stayed at number 10 with 1.34% — its third consecutive month in the top 10. Fortran sits directly behind at number 11 with 1.24%, separated by just a tenth of a percentage point.
What the AI coding wave means
AI coding tools reached 84% adoption among developers, and the best models now clear 80% on SWE-bench Verified. The labs are openly optimizing for systems work — concurrency bugs, race conditions, and architectural flaws. As one developer put it: "The best argument for Rust in 2026 is not memory safety or performance. It is that AI writes better Rust than it writes C++." The compiler feedback loop is so tight that models self-correct in real time.
The real story in 2026 is that the winning stack is polyglot by design — Python for AI prototyping, Rust for performance-critical extensions, Go for cloud-native infrastructure, and Julia for numerical work that needs speed without leaving a familiar syntax.