An s-curve has a slow start, a rapid rise, then a plateau. With Python after years of success, you cannot have more success, and it had a good run. After 30 plus years, four key factors will cause a rift and slowly eat away at the peak of Python adoption.
Where Python could be useful in 2030 is using only the Python standard library without packaging, and also in a hosted Jupyter environment for adhoc exploration. This may exist for another 30 years. Data Engineering, LLMOps, MLOps and Backend engineering is much less compelling.
They are follows:
Bolt on Packaging
Too many solutions exist to do what is trivial in Rust, setup a project and install a package and deploy it.
Bolt on Threads
Decades ago, it might have been interesting to have true threads, but with modern compiled languages like Rust that have safety features built in like default immutable variables and a compiler that protects you from many common concurrency problems, true threads are “too little, too late”.
Bolt on Language Safety
Almost any rational Python developer lints and tests and formats their code, but there are essentially infinite combinations of tools to do this since it isn’t included with the language like Cargo, which ships with a compiler, linter and formatter. Additionally, the language itself has much less benefit of typing as a “bolt on” since there is no compiler. Many people feel the strength of Python is dynamic typing, and if Python remains dominant in “standard library only” installs, i.e. useful as a competitor to Bash, then this may remain the strength in 2030.
Putting multiple bandaids on a language to make up for design limitations of 2025 that seem obvious like using a compiler, immutable variables and static typing, is “too little too late”, and even worse, potentially useless.
Bolt on Performance
Ya, but, you can jump these hoops, is the typical apology for Python performance. Python is a scripting language and to suggest people compile C (an even older, unsafe language full of security risks), is not a reasonable way to suggest writing performant Python. A better suggestion to improve performance by 10-1000 is to not write Python and to write Rust.
Why does Python need to be used in every single domain? Instead a better domain is standard library only scripting, and hosted Jupyter experimentations that do not ship to production.
Conclusion
There is a lot of controversy around GenAI, but it can be helpful for experts to work on slightly harder problems. This puts even more emphasis on why using Rust in the era of coding assistants matches nicely with the eventually slow decline of Python as a solution to every problem.
To use a cooking metaphor, Python is being used as chicken, when it is rosemary. The meal is more likely Rust, with an occasional Python solution.