Source URL: https://simonwillison.net/2025/Sep/29/armin-ronacher-90/
Source: Simon Willison’s Weblog
Title: Armin Ronacher: 90%
Feedly Summary: Armin Ronacher: 90%
The idea of AI writing “90% of the code" to-date has mostly been expressed by people who sell AI tooling.
Over the last few months, I’ve increasingly seen the same idea come coming much more credible sources.
Armin is the creator of a bewildering array of valuable open source projects – Flask, Jinja, Click, Werkzeug, and many more. When he says something like this it’s worth paying attention:
For the infrastructure component I started at my new company, I’m probably north of 90% AI-written code.
For anyone who sees this as a threat to their livelihood as programmers, I encourage you to think more about this section:
It is easy to create systems that appear to behave correctly but have unclear runtime behavior when relying on agents. For instance, the AI doesn’t fully comprehend threading or goroutines. If you don’t keep the bad decisions at bay early it, you won’t be able to operate it in a stable manner later.
Here’s an example: I asked it to build a rate limiter. It “worked” but lacked jitter and used poor storage decisions. Easy to fix if you know rate limiters, dangerous if you don’t.
In order to use these tools at this level you need to know the difference between goroutines and threads. You need to understand why a rate limiter might want to"jitter" and what that actually means. You need to understand what "rate limiting" is and why you might need it!
These tools do not replace programmers. They allow us to apply our expertise at a higher level and amplify the value we can provide to other people.
Via lobste.rs
Tags: armin-ronacher, careers, ai, generative-ai, llms, ai-assisted-programming
AI Summary and Description: Yes
Summary: The text discusses the increasing reliance on AI in programming, particularly emphasizing its potential and the challenges it presents. Armin Ronacher notes that while AI can generate a significant portion of code, it requires a strong understanding from programmers to ensure the reliability and correctness of AI-generated solutions.
Detailed Description: The author, Armin Ronacher, highlights the current landscape of AI-assisted programming, noting a trend where serious programmers are utilizing AI to generate up to 90% of their code. This brings both benefits and challenges:
– **AI Integration in Programming**: The rise of AI-generated code signaling a shift in how software development operates, with many credible sources starting to recognize the extent of AI’s involvement.
– **Need for Human Expertise**: Despite AI’s capacity to produce functioning code, it lacks comprehensive understanding in critical areas such as threading, concurrency, and data management. This can lead to potential issues if programmers aren’t careful.
– For example, when tasked with creating a rate limiter, the AI’s output may function but can lack critical elements such as jitter or optimal storage solutions.
– **Skill Amplification**: Ronacher emphasizes that while AI tools can assist in writing code, they do not replace the programmer’s job. Instead, these tools allow skilled developers to leverage their expertise and enhance the quality of their output.
– **Understanding Key Concepts**: To effectively engage with AI tools, programmers must be well-versed in advanced programming concepts (e.g., goroutines vs. threads, rate limiting) to make informed decisions and correct potential flaws in AI-generated code.
Overall, the text serves as both a cautionary note and an encouragement for programmers to adapt to the growing role of AI in software development, underscoring the importance of human knowledge and intervention.