A summary of a Chinese essay, with a few short translated excerpts. All views below are the author's; quotes are my translations.
On 14 September 2026, a DeepSeek engineer writing as intlsy published a WeChat essay titled 我不得不把才华埋葬在昨天 ("I have to bury my talent in yesterday"). He wrote the main attention kernel of DeepSeek v4.1 (the head-dim-512 MQA attention). The essay is a personal reflection on AI taking over his specialty, and it ends with an argument for open-weight frontier AI.
The essay went viral in Chinese Internet: WeChat shows it at "100k+" reads (the platform's display cap), with about 41,000 likes, 84,000 shares, 31,000 recommendations and 1,601 comments as of 7 October 2026. It is probably one of the most widely shared Chinese-language article of what AI automation looks like from inside a frontier lab, and thus a sample of what a large Chinese technical audience is reading and reacting to.
I think it might interest Lesswrong readers.
His argument
1. AI is catching up in his own specialty
He describes AI going very fast, from only an assistant that looked up documentation and found bugs to a system that can read CUDA, PTX and SASS, profile the stall time of each instruction, and optimize kernels on its own, in just a year. He expects that AI-written kernels can match or even beat his own within six months to a year, and expects AI to soon design and evaluate kernel schedules as well. In his opinion, the gap is structural. AI can keep scaling thinking effort, tool calls and parallelism (translator's note: Just like the concept of RSI), while he cannot.
2. Why he keeps speeding up his own replacement
Better kernels make training and inference faster, which speeds up capability gains, which brings his replacement sooner, he says. Anyway, he continues to do it. The first reason is basic. Writing kernels feels like a game to him. The second, which he calls more important, is race logic. If he slacked off, or even sabotaged training, other labs' models would keep advancing and replace him all the same. In his words: "I would rather not be disrupted. But if I must be, I would rather be the one who does it."
3. Not out of work, but forced to change what the work is
He separates losing his job from losing his craft. He expects to stay employed by becoming what he calls a "mech pilot" for agents. Before, his interests, his strengths and industry demand were aligned; AI has split them, shifting demand from people who write fast kernels to people who use AI to produce fast kernels faster. He illustrates this with a master knitter who adopts a knitting machine: still better than competitors who use the same machine, but without the quiet afternoons that made the work worth doing. His summary of the change: "more gears in my hands, but fewer beats in my heart."
4. Worries about everyone else
Three questions:
Students will likely outsource hands-on assignments to AI, leaving them weak at organizing code, designing systems and abstraction.
It is unclear whether those engineering skills will become obsolete, like writing x86 assembly, or stay essential, like understanding a computer system end to end. If they stay essential, AI lets weak engineers produce bad code several times faster, making systems more fragile.
In a future society, power may matter more than skill or intelligence.
5. Two futures, and his case for open AI
He frames the future as heading toward one of two extremes: "communism," where productivity gains are broadly shared, or "Cyberpunk 2077," where a few companies control the strongest AI and you need the strongest AI to climb the social ladder, a closed loop (translator's note: typical opinion from a Chinese). His conclusion is that frontier intelligence should be supplied to everyone openly and cheaply. He says he does not trust Amercian companies to do this and compares it, by his own admission as an exaggeration, to Hitler getting the atomic bomb before the Allies (translator's note: take it easy). He gives this as his reason for staying at DeepSeek.
He says he does not trust Amercian companies to do this and compares it, by his own admission as an exaggeration, to Hitler getting the atomic bomb before the Allies (translator's note: take it easy).
To clarify, he said, "he especially don't wish Anthropic to have best AI or AGI first", he did NOT make the Hilter reference for ALL US labs.
This make sense as the extreme hawkishness of Dario's speech had made some Chinese believe Dario is against China as a whole including Chinese people not just the government.
I think it's very important to represent these type of stuff accurately, I would recommend a direct translation for future reference instead of summerisation.
A summary of a Chinese essay, with a few short translated excerpts. All views below are the author's; quotes are my translations.
On 14 September 2026, a DeepSeek engineer writing as intlsy published a WeChat essay titled 我不得不把才华埋葬在昨天 ("I have to bury my talent in yesterday"). He wrote the main attention kernel of DeepSeek v4.1 (the head-dim-512 MQA attention). The essay is a personal reflection on AI taking over his specialty, and it ends with an argument for open-weight frontier AI.
The essay went viral in Chinese Internet: WeChat shows it at "100k+" reads (the platform's display cap), with about 41,000 likes, 84,000 shares, 31,000 recommendations and 1,601 comments as of 7 October 2026. It is probably one of the most widely shared Chinese-language article of what AI automation looks like from inside a frontier lab, and thus a sample of what a large Chinese technical audience is reading and reacting to.
I think it might interest Lesswrong readers.
His argument
1. AI is catching up in his own specialty
He describes AI going very fast, from only an assistant that looked up documentation and found bugs to a system that can read CUDA, PTX and SASS, profile the stall time of each instruction, and optimize kernels on its own, in just a year. He expects that AI-written kernels can match or even beat his own within six months to a year, and expects AI to soon design and evaluate kernel schedules as well. In his opinion, the gap is structural. AI can keep scaling thinking effort, tool calls and parallelism (translator's note: Just like the concept of RSI), while he cannot.
2. Why he keeps speeding up his own replacement
Better kernels make training and inference faster, which speeds up capability gains, which brings his replacement sooner, he says. Anyway, he continues to do it. The first reason is basic. Writing kernels feels like a game to him. The second, which he calls more important, is race logic. If he slacked off, or even sabotaged training, other labs' models would keep advancing and replace him all the same. In his words: "I would rather not be disrupted. But if I must be, I would rather be the one who does it."
3. Not out of work, but forced to change what the work is
He separates losing his job from losing his craft. He expects to stay employed by becoming what he calls a "mech pilot" for agents. Before, his interests, his strengths and industry demand were aligned; AI has split them, shifting demand from people who write fast kernels to people who use AI to produce fast kernels faster. He illustrates this with a master knitter who adopts a knitting machine: still better than competitors who use the same machine, but without the quiet afternoons that made the work worth doing. His summary of the change: "more gears in my hands, but fewer beats in my heart."
4. Worries about everyone else
Three questions:
5. Two futures, and his case for open AI
He frames the future as heading toward one of two extremes: "communism," where productivity gains are broadly shared, or "Cyberpunk 2077," where a few companies control the strongest AI and you need the strongest AI to climb the social ladder, a closed loop (translator's note: typical opinion from a Chinese). His conclusion is that frontier intelligence should be supplied to everyone openly and cheaply. He says he does not trust Amercian companies to do this and compares it, by his own admission as an exaggeration, to Hitler getting the atomic bomb before the Allies (translator's note: take it easy). He gives this as his reason for staying at DeepSeek.