Just now! AI programming is "starving" the very foundation of the digital world. How much longer can the prosperity of $BTC and $ETH last?

Over the past year, the way we program has been completely rewritten. You no longer need to write code line by line; just tell AI tools what you need, and they can generate the functionality. This seems like a golden age of individual productivity, but an overlooked premise is that AI does not create out of thin air; it reorganizes human existing knowledge and achievements.

Its core capability is built on learning from and calling upon vast amounts of open-source code 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 destroying the open-source ecosystem that supports the entire software world.

To understand this concern, first, we need to see the position of open-source software. It’s like the sewers of the digital age—ubiquitous yet rarely noticed. Your phone’s operating system, the backend databases of social apps, the players on video sites—all run on open-source code. The Log4j vulnerability incident in 2021 is a clear example: a seemingly insignificant logging framework flaw exposed the entire global internet infrastructure instantly.

The essence of open source is a “public good.” Maintainers often cannot charge directly; their rewards depend on reputation, job opportunities, or donations driven by user interaction. For decades, this model has operated through a “direct feedback” loop: users download, read documentation, submit issues, and like. These attentions translate into motivation for maintainers to keep updating.

But AI programming is cutting this connection. Now, you only need to describe a function to AI, and it automatically selects and combines code in the background. The code runs, but you don’t know exactly which libraries it calls, nor do you pay attention to the underlying projects and maintainers. Researchers call this change “mediating”—the attention and feedback originally directed toward maintainers are now intercepted by the AI layer as a whole.

Simulation models reveal two forces at play. In the short term, AI improves efficiency and lowers the barrier to developing new tools. But in the long term, when “demand transfer” exceeds “efficiency gains,” the system will slide toward decline. Developer rewards decrease; only the highest-quality projects are worth sharing, medium-quality projects disappear, and both the quantity and average quality of software packages in the market decline. Ultimately, the long-term welfare of the entire ecosystem diminishes.

This is not just theoretical speculation. After the proliferation of generative AI, public Q&A traffic on Stack Overflow has significantly declined. Projects like Tailwind CSS see increased downloads, but documentation access and commercial revenue are falling. Projects are widely used but struggle to translate that into effective rewards for maintainers.

A more fundamental issue lies in the distribution of benefits. AI platforms extract enormous value from the open-source ecosystem but do not pay the corresponding costs to sustain it. Users pay AI platforms, but the open-source projects and maintainers they call upon often receive nothing.

Rebuilding a distribution mechanism is a possible solution—similar to revenue sharing models in music streaming: AI platforms track the open-source projects they call, and a portion of the revenue is returned to maintainers proportionally. Additionally, foundation grants, corporate sponsorships, and government support for digital infrastructure are important means to compensate for revenue loss.

This requires a shift in industry mindset: from viewing open source as “free resources” to recognizing it as “public infrastructure that requires long-term investment and maintenance.” Open-source software is deeply embedded in the digital world and will not disappear. But the era supported by idealism and scattered attention may be reaching its boundary.

This efficiency revolution brought by AI programming is not only about development experience but also a stress test for “how public technology can be sustainably maintained.” When the foundation weakens, all subsequent digital innovations—including the massive open-source tech stacks relied upon by $BTC and $ETH—will face questions about their long-term robustness.

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