AI-Generated Code Has A Staggeringly Stupid Flaw

🌈 Abstract

The article discusses the impact of AI on the programming industry, arguing that despite claims of AI replacing programmers, AI-generated code is often inefficient and requires significant debugging and rework, making it less efficient than human programmers.

🙋 Q&A

[01] The problem with AI-generated code

1. What is the problem with AI-generated code according to the article?

• Even though AI can write code much faster than a human programmer, the code it generates is of such poor quality that it takes significantly more time to debug and make the code useful, making it less efficient than having a qualified human programmer do the job.
• A recent study found that on average, only 4% of the time did AI actually generate solutions that worked, and the vast majority of these were straightforward engineering issues.
• The AI model that performed the best (Claude 2) provided a good solution 4.8% of the time, while the popular ChatGPT-4 only provided a good solution 1.7% of the time.

2. Why is AI-generated code so inefficient according to the article?

• AI models are essentially overly developed predictive text programs that use statistics based on a large pool of data to generate the next character or word, but they do not actually understand the coding process or the rules of the coding language.
• As a result, AI models constantly get the coding wrong, as they are not trying to solve the problem but rather find an output that matches the statistics of their training data.
• This issue becomes even worse when the AI is asked to solve a problem it has never seen before, as its statistical models cannot be extrapolated to handle such cases.

[02] The limitations of AI

1. What are the limitations of AI according to the article?

• AI does not actually understand what it is doing and is not trying to cognitively solve the problem, but rather finding an output that matches the statistics of its training data.
• This issue is not just limited to AI-generated code but extends to other AI products like self-driving cars.
• The article suggests that this limitation of AI cannot be easily solved by simply providing more training data, as we are starting to hit a point of diminishing returns when it comes to AI training.

2. What is the solution proposed in the article?

• The article suggests that when we treat AI as what it actually is, a statistical model, we can have tremendous success, as seen in AI-generated structural designs like those used in the Czinger hypercar.
• However, the article warns against treating AI as a replacement for human workers, as AI is not intelligent and should not be treated as such.