In one sentence
Technological progress can preserve a person’s livelihood while destroying the particular craft, identity, and pleasure that made work meaningful. Knowing this, the author still advances AI because opting out would not stop replacement—and because he would rather help shape the force that replaces him than be passively overtaken by it.
Overview
Written after the release of DeepSeek v4.1, the essay begins with the astonishing speed of AI progress, from early conversational ChatGPT to reasoning models and tool-using agents. The author then applies that trajectory to kernel engineering, his own specialty. Within roughly a year, AI has moved from documentation and debugging assistance toward reading CUDA, PTX, and SASS, analyzing instruction stalls, and independently optimizing kernels.
The author takes pride in having written DeepSeek v4.1’s main attention kernel, but expects AI-generated kernels to match or surpass his work within six months to a year. He emphasizes a basic asymmetry: AI can scale speed, reasoning effort, tool use, and parallelism; an individual human cannot.
His continued effort is not simple denial. Kernel optimization gives him intense, game-like satisfaction, but deliberately slowing down would only leave competitors to advance and replace him anyway. Thus he participates in the race, hoping—through a pun on “revolution”—that if he must be displaced, he will help build the system that does it.
The personal conclusion is restrained but painful: he probably will not become unemployed, yet he may have to change professions and lose access to the work he genuinely loved. The title names that loss: his talent will remain real, but its economic and practical relevance may belong to yesterday.
The essay then widens from one engineer’s vocation to education, engineering quality, and political power. The author worries that students will rationally skip the practice through which judgment is formed, and that weak engineers equipped with AI may create bad systems faster. He ends by contrasting broadly shared abundance with a cyberpunk future in which a few companies control frontier intelligence. His commitment to open, inexpensive access—and his decision to remain at DeepSeek—follows from that political concern.
Core ideas
Capability progress can outrun occupational adaptation
The author treats AI progress as compounding across several dimensions at once: model depth, reasoning intensity, tool calls, environmental interaction, and parallelism. Human expertise improves more slowly and cannot scale in the same way.
Replacement may mean loss of vocation, not loss of income
The most important distinction is between employment and meaningful work. A person may remain economically useful after machines surpass their specialty, while losing the activity that supplied pleasure, pride, identity, and daily rhythm.
The innovator is trapped in an arms race
The author’s work accelerates the systems that threaten his own comparative advantage. Refusing to contribute would not prevent the transition because rival organizations would continue developing the same capabilities.
Craft can be intrinsically rewarding even when economically temporary
Kernel writing is compared to a speedrunning game: inventing techniques, improving performance, and surpassing official implementations provide immediate satisfaction independent of long-term career security.
Self-displacement offers agency, not safety
Helping create the technology that supersedes him does not eliminate the loss, but gives him some control over its direction, timing, and beneficiaries. The essay frames this as preferable to being passively displaced by someone else.
Access to intelligence is a question of power
The author does not treat model capability as politically neutral. If frontier intelligence remains concentrated inside a few companies, it may harden class divisions by making access to the strongest AI a prerequisite for mobility. Open-source, affordable intelligence is presented as a counterweight to that outcome.
The argument is personal rather than a universal forecast
The six-month-to-one-year estimate concerns the author’s specialized domain and his own judgment. It is a vivid insider warning, not proof that every occupation will follow the same timetable.
Practical takeaways
- Separate three questions that are often conflated: Will my job disappear? Will my current tasks be automated? Will I still be able to do the work I love? The essay is most concerned with the third.
- When evaluating automation risk, examine whether the system can perform the full feedback loop: understand the representation, use specialized tools, test results, measure performance, and iterate—not merely generate plausible code.
- Build career plans around capabilities that remain valuable when execution becomes cheap: problem selection, system-level judgment, evaluation, coordination, domain responsibility, and deciding what should be built.
- Do not confuse resistance with protection. If competitors can adopt the technology, refusing to use it may preserve neither the craft nor the job.
- Preserve intrinsic sources of satisfaction outside market value. A skill can stop being economically scarce without becoming personally meaningless.
- Use the essay’s emotional distinction in organizational planning: retraining someone for a new role may preserve employment while still failing to address the grief of losing a vocation.
Caveats and counterpoints
- The source is a first-person forecast from a highly specialized engineer, not a controlled study of AI capability or labor-market substitution.
- The essay’s account of what AI can already do is asserted from the author’s experience; it does not provide benchmarks, examples, or independent verification in the accessible translation.
- The comparison between AI’s scalable computation and human limits is directionally persuasive but leaves out complementary human strengths such as accountability, context, taste, and setting objectives.
- The conclusion assumes that rival competition makes nonparticipation ineffective. That may hold within a competitive AI industry, but regulation, coordination, safety pauses, or institutional choices could alter the outcome.
- The essay focuses on the loss of a beloved craft. Workers whose jobs are primarily instrumental may experience automation differently, especially if it improves safety, pay, autonomy, or working conditions.
- The author’s analogy between corporate control of AGI and Hitler obtaining atomic weapons is deliberately extreme. It communicates the intensity of his distrust but does not itself establish that the institutions or risks are historically comparable.
Questions worth revisiting
- If AI surpasses kernel specialists, which parts of kernel engineering remain valuable: architecture, benchmarking, debugging, hardware intuition, or directing agents?
- Can a craft survive as a hobby or art after it loses its economic necessity, or does professional context provide something that private practice cannot replace?
- Who should own and control the systems that automate scarce expertise?
- What would count as a successful transition if employment is preserved but the worker loses the activity they most wanted to do?
- Does open access to powerful AI mitigate displacement, or mainly broaden the number of people competing against one another?
- If the author’s prediction is correct, what forms of education still cultivate durable engineering judgment rather than temporary implementation skill?
Return to this when…
Return to this essay when thinking about AI and work beyond the slogan of “job loss.” Its distinctive contribution is the idea that automation may take away a vocation before it takes away a paycheck—and that the people building the replacement may do so knowingly, competitively, and with genuine love for the craft they are making obsolete.