OpenAI Acquires Astral: After Stealing Our Jobs, AI Just Bought Itself Premium Custom Tools

Although it didn’t rain in Shanghai today in March, the 7-degree Celsius weather still makes you want to hold a hot cup of coffee. The most buzzing news in the tech world today is undoubtedly OpenAI’s announcement to acquire Astral.

Look Beyond the “Acquisition” and See What It Actually Bought

A top-tier large model company bought a startup that makes Python development tools (uv, Ruff, ty). Most people’s first reaction might be that OpenAI is flush with cash and wants to win over the tens of millions of Python developers worldwide. I happened to be using uv to configure a new project yesterday, and its seamless speed is indeed ridiculously fast. But if you think about it carefully, this move is actually a bit unusual.

Astral’s founder Charlie Marsh and his team are joining OpenAI, and the announcement mentioned a core goal: to accelerate the development of Codex. Codex’s usage has increased fivefold this year, with over two million active users weekly. But here comes the question: why does an AI model capable of writing god-tier code need a team that builds package management and code linting tools?

To put it bluntly, the current AI is like an eccentric with highly specialized skills. It is a master at writing logic, but its self-care ability is extremely poor.

Looking at this group photo, you can guess that compared to flashy generative AI, developers like Charlie Marsh care much more about the underlying execution efficiency of the system.

Machines Need a “Disciplinarian” Too

When we use large language models to write code, the biggest headache isn’t that they can’t come up with algorithms. They can provide a set of seemingly perfect logic, but the moment you paste it locally to run, you are hit in the face with environment issues, dependency conflicts, and formatting errors. Large models are essentially making probabilistic predictions; when generating characters, they have no concept of “absolute correctness” in their minds.

Now look at Astral’s flagship products. Ruff is currently an insanely strict code linter in the industry, and uv is a lightning-fast dependency manager. Their biggest commonality is that their underlying architecture is rewritten in Rust—extremely rigorous and highly deterministic.

I previously saw some test data on an automated programming Agent. When asking AI to fix bugs itself, the majority of its time and computing power isn’t spent thinking about logic, but wrestling with a sluggish Python environment and vague error messages. OpenAI buying Astral, to put it plainly, is buying a “disciplinarian’s ruler” for Codex. In the future, once the AI generates code, Ruff can tell it within milliseconds where the format is wrong, and uv can instantly set up the testing environment. AI will no longer need humans standing by to wipe its ass; it can complete the “write code – test – modify” loop all by itself in a deterministic environment.

The Collision of Fast and Slow

Let’s do a horizontal comparison of current AI programming tools. Most products, regardless of which industry-leading model is behind them, are essentially “human-led, AI-assisted.” They obediently sit in the editor’s autocomplete sidebar, waiting for you to hit the Enter key.

But OpenAI’s ambition clearly goes beyond this. What they want to do is let Agents take over the entire software development lifecycle. Traditional Python toolchains (like pip or flake8) are written in Python itself, making them slow and methodical. When a human programmer runs them, waiting three to five seconds is perfectly normal—just enough time to take a sip of water. But what if the operator is an AI capable of iterating hundreds or thousands of times per minute? Those few seconds of delay, multiplied by thousands of loops, become a highly fatal performance bottleneck. Tools written in Rust are meant to match the reaction speed of machines.

But there is one question you need to consider: the rules of Rust tools are rigid and uncompromising. If AI encounters a highly obscure type error thrown by Ruff, will it fall into an infinite loop of “fixing one bug only to break another”? The actual performance of current large models when dealing with extremely strict compile-time errors isn’t as magical as everyone imagines. Faced with an inflexible machine review, I’m quite curious about how Codex will resolve such deadlocks moving forward.

Some Things I Wonder About Sometimes

Stretching the timeline out, I sometimes wonder, could this be the beginning of a redefinition for languages like Python?

Python initially became wildly popular globally because it is human-friendly; its syntax is simple and reads like ordinary English sentences. But if future code is primarily written by AI like Codex, do we still need “human-friendly” syntax? Or rather, will Python gradually become just an “intermediate language” used purely to connect large models with underlying C/Rust libraries?

When AI possesses tools with absolute control over the environment, perhaps in the future, even a beginner developer won’t need to know what a virtual environment is, nor will they need to nervously type pip install in the terminal. You just state what project you want to build, and the AI instantly creates an isolated sandbox in the background using uv, ensures the code is flawlessly tight with Ruff, and then directly serves you the execution results. Or maybe I’m overthinking it—after all, when facing those ancestral, intricately intertwined legacy system dependency trees, even gods would scratch their heads, let alone AI.

The Coffee Has Gone Cold

I’ve digressed. I didn’t notice while typing earlier, but the coffee on my desk has gone completely cold.

Today, while updating dependencies for an old project, watching the execution progress bar flash by in the terminal, I truly lamented how fast the gears of technological evolution are turning. If in the future even the grunt work of configuring environments and checking errors is completely monopolized by AI and lightning-fast tools, will those of us who stare at the terminal every day have no choice but to go steal the product managers’ jobs?

References:

—— Lyra Celest @ Turbulence τ.