Are there any particular disadvantages of OpenAI versus other LLMs when using Generative AI for effective Code Modernization?

We decided to ask OpenAI itself. Here's exactly what we got. (It's just an okay answer, we think. You?)

While OpenAI's Generative AI models like Codex have shown great potential for code generation and modernization, there are a few specific disadvantages or limitations that users may face when compared to other LLMs (Large Language Models):

1. Lack of precision and control: OpenAI models are based on large-scale pre-training, which means that they grasp the patterns and trends from a wide variety of sources, including potentially low-quality or incorrect code examples. Consequently, this can lead to generated code being syntactically incorrect or semantically flawed.

2. Bias in training data: The training data used to build OpenAI models can introduce bias as it mirrors the datasets used for training. If the training data contains biases, such as a particular coding style or language preference, the generated code might unintentionally reflect those biases.

3. Limited language support: While OpenAI models like Codex are proficient in multiple programming languages, their support and knowledge might vary across different languages. They may not perform equally well in all programming paradigms, frameworks, or niche languages that are less popular.

4. Difficulty in customizing behavior: OpenAI models usually have limited customization options. Users might face challenges when trying to fine-tune or adapt the model's behavior to tailor it to their specific use case or company guidelines. This can hinder the code modernization process if the model doesn't align perfectly with the desired outcomes.

5. Ethical concerns: OpenAI models are trained using vast amounts of data from the internet, including code snippets from various sources. This data often includes potentially copyrighted or proprietary code, raising concerns around intellectual property rights and legal compliance when utilizing the generated code for commercial purposes.

6. Limited performance understanding: OpenAI models lack a deep understanding of code execution and performance considerations. While they can generate syntactically correct code, the efficiency, scalability, and performance of the generated code might not be optimal. Manually optimizing generated code might still be necessary.

It's important to consider these disadvantages and take necessary precautions while using OpenAI's Generative AI models for code modernization to ensure the reliability, correctness, and security of the generated code.