Wired: Python Is So Slow. Can Julia Solve the Two-Language Problem?

General Usage
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A reasonable take on the two language problem including Julia in Wired.com. It’s behind a paywall but viewable for free upon sign-up. A few quotes of interest:

Researchers prototype in slow, friendly Python but… rewrite in faster, less friendly languages like C++ or Rust. This limitation can’t be solved by spinning up a platoon of AI coding agents, because no matter how much you optimize a slow language, a faster one will outperform it.

As of 2026, Julia has come to attract a sober community of grown-ups… It leans academic… But you won’t find Julia…on the most popular languages… What went wrong?

First, Python’s [ecosystem and tooling are] far too robust to dislodge. Second, Julia has not been adopted by Big Tech… But third, and this is my answer: Nothing went wrong. Julia is a niche language, and for what it’s doing, it’s plenty successful… [It] will live on, small but beloved.

I’m not convinced it can solve the two-language problem—or that any language can… it exists in every software domain… valiant efforts to use Go or Rust for frontend development have utterly failed.

I don’t think there are any deep insights here. It’s a neutral opinion that avoids hype and hyperbole. But one thing stood out for me, rarely recognized in the interwebs: Julia is beloved.

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This is a tough question. I think Julia will live on but it’s missing some key things I would need for my day to day as a statistician. Dataframes.jl is fine. Plotting is fine. But packages around machine learning and any statistics based seems to have very low documentation and use. If we talk about SciML or anything neural networks, then this language is being improved week after week.

Also while base python is slow, it’s optimized. Yes this is a problem for the “two language” issue. But it still works at the end of the day.

I’m for Julia and I want to see it sky rocket! But we would need more developers and maintainers of packages.