Hello! I'm your friend, an economist and financial journalist. This article that has gone viral on platforms like Zhihu and WeChat seems on the surface to be a programmer's farewell to the era of writing code by hand. However, beneath the emotional surface, it actually provides a microcosm of the restructuring of labor value in the AI era.
Many readers have been misled by the discussions at the end of the article about DeepSeek vs Anthropic, as well as the references to "communism" and "Cyberpunk 2077." But these are precisely the author's most frustrating aspects—the public is focused on political stances, while what the author really wants to convey is the powerlessness and need for adaptation of individuals in the face of technological advancements.
Let me break down the article into five key aspects to help you see beyond the surface:
1. The core issue: The anxiety of changing from a "craftsman" to a "supervisor"
[Simple explanation:] In the past, you were the master who built the bricks; now you're the foreman directing the robots to do the work. This loss of control is distressing.
The author's main concern is not about losing his job, but rather about losing the joy of his work. Back then, writing the underlying code instructions was like a craftsman polishing a piece of jade—each variable name, each line of logic was carefully crafted by the author. Although the process was tedious, it brought a sense of empowerment and achievement. It was similar to the dopamine rush a gamer feels when breaking a record. But with the advent of AI agents, things have changed. AI can write code faster and more accurately. The author now has to shift his focus from how to write code to how to make the AI write it better.
- Past joy: I made this sword with my own hands, and I was proud of it.
- Current pain: I have to hand over the task of creating the sword to the machine; I just have to check if it was done correctly.
It's like a seasoned chef who used to enjoy chopping vegetables and stirring the pan, but now has to figure out how to set the parameters for an automatic stir-fry machine. The result might be better, but the joy of interacting with the ingredients is gone. This is what the author means by "burying one's talent in yesterday"—your core competencies (manual skills) are being diminished by tools (AI), forcing you to transition from an executor to a manager, a process that is painful and passive.
2. Economic logic: The devaluation of skills in a world where efficiency is king
[Simple explanation:] When machines can do work faster and better than you, your ability to do things slowly and meticulously becomes less valuable; the market only cares about the results.
The author mentions a harsh reality: To improve efficiency and code performance, AI is necessary. In economics, this is called the transfer of comparative advantages.
- In the past: Humans had a comparative advantage in building complex logical systems because machines don't understand context or business logic.
- Now: AI has a comparative advantage in code generation and basic optimization.
The author says that current AI can do the same work faster than humans, and in the future, it will do it even better. This means that the economic value of handwriting code is rapidly declining. If a company can use AI to complete a task that used to take you three hours in one hour with fewer bugs, they will definitely choose AI. The author has to accept this because the pressure to survive outweighs nostalgia. This is not just a personal choice but a trend in the entire tech industry: Basic coding tasks are being automated, and human value is shifting to higher-level roles such as architecture design, requirement definition, and AI optimization.
3. Social metaphors: Open intelligence vs. closed monopolies (2077 vs. utopia)
[Simple explanation:] Is AI a public resource, or a private club accessible only to the wealthy? This is the author's real concern about the future.
Although the author doesn't emphasize this point, it's what made the article so popular. It raises a significant economic and social issue: the distribution of AI technology.
- Anthropic (representing a closed/elite approach): The author implies that Anthropic's approach is unappealing, possibly due to its high API prices, strict usage restrictions, or its focus on serving a few large corporations or specific groups. This resembles the world in *Cyberpunk 2077*, where technology is highly advanced but monopolized by large companies, leaving ordinary people marginalized and exacerbating wealth disparities.
- DeepSeek (representing an open/egalitarian approach): The author prefers DeepSeek because it is open and affordable. This reflects his ideal of technical sharing (not political systems)—the most powerful intelligence should be available to everyone, just like air and water.
The author is worried that AI could create a new digital divide, with only large companies and individual developers being left behind. He hopes for a future where everyone can use AI to enhance their productivity, rather than being excluded by it. This concern about technical fairness resonates with many readers.
4. Cognitive biases: Why does the public focus on superficial issues?
[Simple explanation:] People tend to align with certain groups and avoid thinking about complex personal struggles. Emotions spread faster than logic.
The author is frustrated that people didn't understand his emotional connection to writing code by hand; instead, they focused on the discussion of communism and *Cyberpunk 2077* at the end of the article.
- Zhihu/WeChat: Discussions turned into arguments about the political stances of Anthropic and DeepSeek, even involving personal attacks.
- Rednote: The topic was simplified to "DeepSeek vs Anthropic."
- International forums: Political debates erupted around the term "communism."
This reflects a common pattern in media dissemination:
1. Emotional labeling: Complex personal concerns are hard to spread, but topics like "Sino-US tech competition," "ideology," and "corporate monopolies" easily spark debates.
2. Simplification of information: Readers don't have the patience to understand technical details; they grasp keywords like DeepSeek, Anthropic, and communism and interpret them based on their preconceptions.
3. Dilution of the author's message: The author intended to discuss the struggle of individuals in the changing times, but the public interpreted it as a corporate PR battle or a political debate. This shows that in today's media landscape, pure technological humanism struggles to gain mainstream attention and often needs to be packaged with political or commercial themes to gain traction.
5. Looking to the future: Finding hope in a challenging landscape
[Simple explanation: Although old skills are becoming obsolete, we can't just give up. We need to learn new skills and stick to our values.
In the article's conclusion, the author offers a mature and positive strategy for those facing the impact of AI:
1. Proactive evolution, not passive elimination: "I want to be the one who revives myself." This means you should learn how to work with AI, not wait for it to replace you. It's about upgrading your skills, not erasing them.
2. Sticking to core values: The author still believes in the idea of open sharing. No matter how technology evolves, he hopes to promote its benefits for all while achieving commercial success.
3. Redefining happiness: The author acknowledges that it may be difficult to find the same pure joy as before, but he chooses to accept this and seek new sources of fulfillment in the new technological landscape.
In summary, this article is not just a programmer's complaint; it's a guide for survival in the AI era. It tells us that:
- Skills will become obsolete: Your traditional skills may quickly lose their value.
- Tools will become more powerful: AI is not an enemy; it's a new tool for production.
- Values are crucial: We need to consider for whom technology is being used.
- Adaptation is the only way forward: Instead of dwelling on the past, we should embrace the future, even if it's challenging.
For ordinary people, there's no need to worry too much about losing jobs, but we should be concerned about stagnation. The core competitiveness of the future will not be what you can do, but what you can make AI do for you.