“In the Age of AI Coding, Why Are We Still Using Python?” — How Infrastructure Costs and Execution Speed Are Redefining Next-Gen Language Selection

In the world of software engineering, Python has reigned supreme as the king of “development efficiency” for decades. However, with the rise of generative AI, a quiet but decisive tectonic shift is occurring in its absolute position. The question being asked is: “If AI is the one writing the code, why should we continue using Python, a language whose primary focus is human readability?”

Taking the viral Medium essay “If AI writes your code, why use Python?” as a starting point, we will decode the essence of the “language selection paradigm shift” that engineers will face from 2026 onwards. We are currently at a historical turning point where the development paradigm is reverting from “reducing human cognitive load” back to “optimizing computational resources.”

1. The Curse of Development Efficiency: Why We Were “Made to Choose” Python

Until now, the reason Python was the de facto standard was extremely simple: it was “optimized for the human brain.”

Its intuitive syntax, vast libraries, and expansive community are all parts of an ecosystem designed to lower the “cognitive load” when humans understand, write, and debug code. Even with structural weaknesses like slow execution speeds and heavy memory consumption, Python offered an economic rationale—“Time to Market”—that more than compensated for those flaws.

However, this rationale is built on the premise that “humans are the primary actors writing the code.” This premise is collapsing in the current AI-native era.

2. The “Ultimate Coder” AI Destroys the Existing Language Hierarchy

For AI agents like ChatGPT, GitHub Copilot, or Cursor, “syntactic complexity” is no longer a barrier. AI can instantly output compilable code even for Rust’s strict ownership checks or C++’s complex memory management—tasks that would take a human hours to master.

In other words, Python’s greatest weapon—its “human-friendliness”—is rapidly losing its relative value when mediated by AI.

3. Flag-Bearers of the Post-Python Era: Rust, Mojo, and the Redefinition of System Languages

In terms of languages optimized for the AI era, two major trends deserve close attention:

• Rust (The Synthesis of Safety and Speed): The steep learning curve was previously the main barrier to adopting Rust. However, if AI can generate code that clears the complex borrow checker, humans can enjoy only the benefits: “safety and a lightning-fast execution environment.”
• Mojo (The Rising Star of AI-Native Languages): Designed specifically for AI development, Mojo maintains the familiarity of Python while enabling C-level performance and direct access to GPUs. With the potential to inherit existing Python assets while extracting 100% of hardware performance, this language has the potential to become a next-generation standard.

4. The “Inertia” of the Ecosystem: Realistic Reasons Why Python Persists

That said, not every project will abandon Python tomorrow. The biggest hurdle is the “gravitational pull of the ecosystem” accumulated over many years. Libraries like NumPy, Pandas, and PyTorch, which form the foundation of scientific computing and machine learning, are now akin to a massive piece of social infrastructure.

What’s noteworthy is the change in the “internal structure” of these libraries. Currently, the core logic of many major libraries is being rewritten in Rust or C++, making Python a mere “thin interface.” Even if users think they are using Python, high-speed non-Python binaries are running in the background. This “hidden migration” represents the reality of the transition period.

5. FAQ: Core Doubts Regarding Language Strategy in the AI Era

Q: Do beginner engineers no longer need to learn Python? A: It remains useful. There is no better “teaching material” than Python for learning logical structures and algorithms. However, being in a state where you can “only write Python” will likely become a career bottleneck within a few years.

Q: Is the difference in infrastructure costs significant enough to matter for small to mid-sized development? A: For small APIs, the difference might be negligible. However, in architectures like LLM-integrated agent systems that involve massive token processing and repeated inference, even a few percentage points of difference in execution efficiency can manifest as cost differences of millions of yen annually.

Q: If we leave the code to AI, doesn’t it matter what language we use? A: Since you are controlling the environment where the final binary is executed, selecting a language is synonymous with selecting an “execution model.” Considering maintenance, debugging, and traceability, the industry will favor “languages that are abstracted enough for humans to verify intent, yet close enough to the hardware to be efficient.”

Conclusion: The Value of Technology Shifts from “Ease of Writing” to “Sincerity to the Machine”

“If AI writes the code, it no longer needs to be Python.” This seemingly radical thesis suggests that the essence of software development is returning to its roots: shifting from “human optimization” to “resource optimization.”

The winners of the future will be engineers who can accurately instruct AI to “build an ultra-fast backend in Rust and optimize the frontend with WebAssembly,” while taking responsibility for the overall system architecture and cost-performance maximization.

Redefining the evolution of programming languages through the lens of AI—only those prepared to do so will be able to navigate the next tech frontier.

This article is also available in Japanese.