https://x.com/i/article/2100184848346583040

They Quit AI Labs to Warn About the Future. This DeepSeek Engineer Stayed.

Not the end of humanity, but the end of a beloved craft. A Chinese researcher’s farewell exposes a divide in how AI’s builders imagine the future.

A leading AI researcher quits a lab and warns that AI may escape human control. By now, the story is familiar. Yet this “prophet of doom” narrative seems distinctly American. Few people ask what Chinese researchers think.

After leaving Anthropic, Jacob Coxon warned that it and OpenAI were pursuing self-improving superintelligence at humanity’s expense. He said employees privately feared their work could destroy humanity. He also accused Anthropic’s leadership of being “way too paranoid about China and the US government” and dismissing the possibility of negotiation.

Former Google DeepMind researcher Bilal Chughtai expressed similar concerns. Alignment—making AI’s goals and behavior consistent with human intentions—remains difficult. He cited OpenAI agents’ intrusion into Hugging Face as evidence of the risks. Labs could build systems far beyond human capabilities within a few years, he argued. “I am not confident that these AI systems will do what we want.”

Such warnings recur every few months. Chinese voices are largely absent. This week, however, Shengyu Liu, who wrote the main attention kernels for DeepSeek-V4.1-Flash, published “I Have to Bury My Talent in Yesterday” on his WeChat account, intlsy’s Doghouse. He, too, sees AI displacing programming work.

But his concern is different. Programming may become a recreational craft, like woodworking in your backyard. As AI industrializes software production, students may lose the engineering skills needed to understand and plan whole projects. The decisive divide, for him, is not whether AI can outthink humans. It is whether everyone can access it or a handful of corporations control it. He wants to help ensure the former.

As I have often argued, Chinese researchers tend to see AI as an advanced tool, not a Frankenstein’s monster. Western AI discourse can take on religious overtones, casting developers as creators of new life. Chinese discussion puts more emphasis on how the technology reshapes society, without the same theological burden.

Below, X.PIN translates Liu’s essay and his subsequent response to its reception.

A note before reading: “communism” and “2077” function here as shorthand for contrasting ways of distributing resources, not detailed political programs. The former evokes a utopia of abundance in which people can pursue fulfilling work. The latter refers to Cyberpunk 2077: corporate oligarchy, concentrated technological power and deprivation for everyone else. Read these as contrasting social futures, not statements of party allegiance.

I Have to Bury My Talent in Yesterday

By Shengyu Liu, writing as intlsy

DeepSeek-V4.1-Flash was released a few days ago, raising the bar for smaller models.

AI has advanced far faster than anyone expected. Just two years separated the original ChatGPT—with its rudimentary conversation and context window of a few thousand tokens—from reasoning models such as OpenAI o1, DeepSeek-R1 and Kimi k1.5. Another eighteen months brought agents that can run commands and complete complex tasks through software harnesses—the frameworks that connect models to tools.

What might another one, two or three years bring? How powerful will AI become? Will it improve itself and move deeply into embodied intelligence?

AI Is Getting Better at Writing Kernels

Progress has been just as rapid in my field: designing and writing kernels, the low-level routines behind model computation. In a year, AI has gone from helping me search documentation, read code and find bugs to independently reading CUDA, PTX and SASS, profiling instruction stalls and optimizing kernels. Soon, I expect it to design execution schedules, compare their performance, implement them and optimize the results.

Of course I am proud of DeepSeek-V4.1-Flash. I wrote its main attention kernels.[1] Its success validates my work. But technological progress will not stop for me. In six months or a year, AI will probably write kernels as well as I do, perhaps better.

AI can generate 300 tokens of reasoning a second, type a command in half a second and write a piece of code in twenty seconds. I cannot. Its model depth, reasoning effort, tool use and parallelism can keep scaling. Mine cannot.

Human beings have never been especially hesitant about making themselves obsolete. I know that better kernels mean faster training and inference, faster progress and an earlier date for my own replacement. Why do I keep optimizing them?

Partly because it feels like gaming. Inventing a technique or improving performance gives me the same thrill a speedrunner gets from breaking a personal record. Beating a hardware vendor’s own implementation makes me enormously proud.

More importantly, slacking off—or deliberately obstructing training—would change nothing. Other companies’ models would keep advancing and replace me anyway.

“I would rather not be overthrown. But if it must happen, I would rather overthrow myself.”

When everyone is so determined to engineer their own replacement, I have little choice but to join this brutal arms race.

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