Over the past year, the way of programming has been completely rewritten. You no longer need to type code line by line; just tell the AI tool your needs, and it can generate functionality. This seems like a golden age of personal productivity, but a neglected premise is that AI does not create from scratch but reorganizes the existing wisdom of humanity.
Its core capability is built on learning and calling upon the vast open-source code available on platforms like GitHub. Recently, a study from an academic institution revealed a hidden crisis behind the prosperity of this 'ambient programming': it may be systematically undermining the open-source ecosystem that supports the entire software world.
To understand this concern, one must first see the position of open source software. It is like the sewers of the digital age, omnipresent yet rarely noticed. The operating system of your phone, the backend database of social software, and the players of video websites all run on open source code. The Log4j vulnerability incident in 2021 is a clear example; an inconspicuous logging framework vulnerability exposed the global internet infrastructure in an instant.
The essence of open source is a type of "public good." Maintainers often cannot charge directly; their rewards depend on the reputation, job opportunities, or donations that come from user interaction. For decades, this model has operated through a closed loop of "direct feedback": users download, read documentation, submit issues, and give likes. This attention translates into motivation for maintainers to keep updating.
However, AI programming is severing this connection. Now, you only need to describe the functionality to AI, which automatically selects and combines code in the background. The code runs, but you do not know which libraries it specifically called upon, and you will not pay attention to the projects and maintainers behind it. Research has termed this change as "intermediation"—the attention and feedback that originally flowed to maintainers is being entirely intercepted by the AI layer.
Simulation models reveal two forces. In the short term, AI enhances efficiency and lowers the barrier to developing new tools. But in the long term, when "demand shifts" outweigh "efficiency gains," the system will slide into decline. Developer returns decrease, and only the highest quality projects are worth sharing, while medium-quality projects disappear, leading to a decline in both the quantity and average quality of market software packages. Ultimately, the long-term welfare of the entire ecosystem declines.
This is not a theoretical deduction. After the popularity of generative AI, the public Q&A traffic on Stack Overflow has significantly declined. Projects like Tailwind CSS have seen an increase in downloads, but documentation access and commercial revenue are declining. Projects are widely used but fail to translate into effective returns for maintainers.
The more fundamental issue lies in the distribution of benefits. AI platforms extract immense value from the open source ecosystem without needing to pay the corresponding costs to maintain it. Users pay AI platforms, while the open source projects and maintainers being called upon often gain nothing.
Reconstructing the distribution mechanism is a possible idea, similar to the revenue-sharing model of music streaming: AI platforms track the open source projects called upon and proportionally return part of the revenue to maintainers. Additionally, foundation grants, corporate sponsorship, and government support for digital infrastructure are also important means to compensate for lost revenue.
This requires a shift in industry perspective: from viewing open source as a "free resource" to recognizing it as a "public infrastructure that requires long-term investment and maintenance." Open source software has deeply embedded itself in the digital world and will not disappear. However, the era supported by idealism and scattered attention may be approaching its limits.
This efficiency revolution brought about by AI programming concerns not only the development experience but also serves as a pressure test on how "public technology can be sustained." When the foundation becomes thinner, all digital innovations built upon it, including the vast open source tech stack relied upon by $BTC and $ETH, will face scrutiny regarding their long-term robustness.
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