[2606.18141] Behind Python: The Languages That Power AI

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Computer Science > Programming Languages

arXiv:2606.18141 (cs)

[Submitted on 16 Jun 2026]

Title:Behind Python: The Languages That Power AI

Authors:Juan P. Licona-Luque, Beatriz A. Bosques-Palomo, Nezih Nieto-Gutiérrez, Gustavo de los Ríos-Alatorre, Luis A. Muñoz-Ubando (Tecnológico de Monterrey, Monterrey, Mexico)<br>View a PDF of the paper titled Behind Python: The Languages That Power AI, by Juan P. Licona-Luque and 6 other authors

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Abstract:Python dominates AI development, yet the numerical work behind frameworks like PyTorch and NumPy is executed in C, C++, or Rust. When a developer must implement an algorithm without such libraries -- because none exists, the target is resource-constrained, or a new system is being built -- which language should they choose? This paper answers that question empirically. Five algorithms covering data mining (k-means), machine learning (k-NN), neural networks (MLP with backpropagation), computational intelligence (genetic algorithm), and fuzzy systems (Mamdani inference) are implemented from scratch in Python, C, C++, Rust, Go, and Julia. All implementations share a common pseudo-random generator, consume identical inputs, and produce bit-identical outputs, so every measured difference reflects the language rather than the computation. Three performance tiers emerge: C and C++ are effectively tied; Rust trails them by 9% (geometric mean); Julia runs 3.3x slower than C and Go 5.0x; Python sits at 315x. Memory tells a different story -- Julia's JIT runtime carries a fixed ~224 MiB footprint regardless of workload, while C, C++, and Rust stay below 6 MiB. Crucially, rankings are not stable: Go's slowdown swings from 2.6x on k-NN to 8.0x on k-means, showing that workload characteristics can shift a language's position by a full tier. The results provide concrete, per-workload guidance for choosing an implementation language in AI systems.

Subjects:

Programming Languages (cs.PL)

ACM classes:<br>D.3.4; I.2.6

Cite as:<br>arXiv:2606.18141 [cs.PL]

(or<br>arXiv:2606.18141v1 [cs.PL] for this version)

https://doi.org/10.48550/arXiv.2606.18141

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Juan Licona-Luque [view email]<br>[v1]<br>Tue, 16 Jun 2026 16:41:24 UTC (256 KB)

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