Is Python Losing Its Edge? How Rust Is Eating Its Lunch

Foundation Crumbling: The Python Foundation Loses Money

In 2025 the Python Software Foundation reported a loss of $1.46 million, forcing a pause on its sponsorship programs. Unlike other language foundations that rely on strong corporate backing, Python now survives largely on donations and community goodwill.

Rust Is Eating Python’s Lunch

At the 2025 Python Language Summit, it was announced that one‑third of new native PyPI modules are written in Rust. Developers are rewriting performance‑critical components—such as Pandas (now Polars) and Pydantic—in Rust because pure Python cannot keep up.

Pandas? Replaced by Polars (Rust‑based).

Pydantic? Core rewritten in Rust to avoid Python’s performance ceiling.

The Growth Blind Spot: Many Users, Shallow Experience

Python’s user base grew 7 % last year, but half of the users have less than two years of experience, and only 15 % are on the latest stable release (3.13). This reflects a “turnover” problem rather than genuine ecosystem maturity.

Developers often cling to outdated code from 2018, calling it “stable engineering,” while the reality is stagnation.

The AI Myth We Believed

It’s common to hear that “Python carries AI,” yet many core AI libraries (TensorFlow, PyTorch) are actually written in C++/CUDA, and NumPy still relies on legacy Fortran code.

When AI workloads move to mobile, embedded, or edge devices—where performance is critical—Python’s heavy runtime becomes a liability.

The “Easy” Trap

Python’s simplicity ("pip install and go") has driven massive adoption, but it also creates a false sense of security. Developers often ignore memory management, rely on increasing AWS budgets to mask performance issues, and resort to frequent service restarts for debugging.

Meanwhile, Rust developers produce fast, safe code because the compiler forces them to understand low‑level details.

Big Companies Have Already Moved On

In production systems that must handle millions of users, large tech firms favor Java, Go, or C# over Python. Python is relegated to scripting, data analysis, and glue code.

When performance problems arise, blaming the Python garbage collector is not a credible excuse for engineering leaders.

Toolchain Chaos

Python’s dependency management is fragmented: pip, poetry, and conda each have their own ecosystems, and virtual environments are unreliable. Setting up a project often feels like defusing a bomb, consuming half an hour of “environment rescue” time.

Typical commands illustrate the contrast: cargo new for Rust, go build for Go, and a vague “reserve your morning” for Python.

The community’s default answer—"just use Docker"—doesn’t solve the underlying performance and maintainability issues; it merely hides them.

How Languages Turn Into Zombies

Python won’t disappear overnight, but it risks becoming a legacy language that lives without growth, similar to Perl, PHP, or COBOL. Signs include foundation losses, core libraries shifting to other languages, stagnant releases, major companies abandoning Python for production, and a user base that leaves at the first performance wall.

The Uncomfortable Truth

Spending hours improving Python today may not align with the most in‑demand languages over the next five years. Betting everything on Python is akin to buying a house in a town that just announced massive layoffs.