The article explores the current challenges and prospects associated with AI coding assistants, particularly concerning the balance between open-source and closed-source models. While the excitement surrounding AI is palpable, there is an underlying concern about the limitations of closed-source solutions, especially as machine learning models depend heavily on high-quality data. Gergely Orosz emphasizes that open-source technologies provide better training data for large language models (LLMs), promoting a necessary shift towards transparency in software development. Though this won't entirely eliminate biases in AI, it presents a step toward fostering innovation.
It's not clear how we resolve this looming problem. We're still in the "wow, this is cool!" phase of AI coding assistants, and rightly so.
As much as closed-source options may have worked in the past, it's hard to see how they can survive in the future.
Open source code is high-quality training, and starving the LLMs of training data by locking up one's code, documentation, etc., is a terrible strategy.
It doesn't solve the problem of LLMs being biased toward older, established code and thereby inhibiting innovation, but it at least pushes us in the right direction for software.
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