Elon Musk: Advocates for Broad Learning, Covering Humanities, Arts, Science, and Engineering, to Build a Strong General Knowledge Foundation
Tesla CEO Elon Musk discussed education in the era of artificial intelligence during an exclusive interview aimed at a Chinese audience, advocating for broad learning that encompasses humanities, arts, science, and engineering, emphasizing the need to build a strong foundation of general knowledge first.
He provided the operational logic: to give commands to robots, one must first be able to organize questions; the broader the knowledge base, the better one is at asking questions, which is essentially writing prompts for artificial intelligence. His viewpoint equates school education, prompt engineering, and directing physical robots as the same capability, rather than treating prompts as a separate course.
This aligns with his educational stance in recent English interviews: while school socialization remains, knowledge acquisition can be increasingly entrusted to infinitely patient individualized models; Tesla's hiring should not rigidly require a university diploma but must recognize evidence of outstanding abilities. He has set up small-scale schools for his children that do not advance by traditional grades and advocates for using models as personalized teachers.
The industrial context is that Tesla views the Optimus humanoid robot as a general-purpose end effector, aiming to use natural language to make machines work, rather than writing industrial programs for each task separately. xAI's Grok is positioned by him as a conversational and reasoning interface. Directing robots and writing prompts for models, in his expression, is the same set of capabilities of "clearly articulating goals."
He repeatedly emphasizes that artificial intelligence and robots will rewrite labor: work may become optional, and economic output will trend towards digital intelligence multiplied by the number of physical robots. If education only trains for memory and procedures that can be replaced by models, it will misalign with this output curve; if it trains for questioning, cross-domain organization, and aesthetic judgment, it will be closer to the human-machine division of labor he describes.
The market mechanism is the rewriting of talent specifications. Buyers are manufacturing and software companies deploying humanoid robots and intelligent agents, while sellers are still educational systems sliced by industrial-era subjects. The event is driven by entrepreneurs framing prompts as general capabilities. Beneficiaries are those who can teach both science engineering and expressive organization, while those under pressure are recruitment forms that treat diplomas as the sole filter. The funding does not change hands on the day of the interview; the turnover occurs in who first allocates "questioning individuals" to obedient robots.
Source: Public Information
ABAB AI Insight
Musk's history in education is not about writing textbooks but about modifying entry points according to engineering shortages. SpaceX and Tesla have long replaced degree screening with performance and exceptional abilities, while experimental schools like Ad Astra eliminate grade locks, transforming classrooms into project-based learning. Framing prompts as general knowledge pushes the product interfaces of xAI and Optimus back into the classroom: only those who understand models can direct humanoid machines in factories.
The capital path follows the interface. Tesla shifts production line space towards robots, xAI treats conversational models as operating systems, while chips and energy are manufactured separately. Money is invested in the closed loop of "understanding natural language to work," demanding education to output operators capable of organizing questions, rather than producing repetitive jobs that will be consumed by the closed loop. Humanities and arts here are not mere decoration; they ensure that commands have goals, constraints, and aesthetics, avoiding the issuance of only short-lived commands.
This is analogous to Steve Jobs juxtaposing humanities with technology, Jensen Huang treating prompts as new programming, and the industrial revolution turning literacy into factory discipline. The current industry is transitioning from keyboard commands to verbal directives: software agents arrive first, humanoid robots follow, and education lags behind the interface.
Structural changes involve technology replacing labor while shifting pricing power of human capital. What is being replaced are standard answers, while questioning structures are being marked up. The mechanism is: models make knowledge retrieval a free layer, with scarcity shifting to how tasks are defined; those who can compress cross-domain knowledge into executable questions will be able to schedule increasingly cheaper robot time.
ABAB News · Cognitive Laws
- Prompts are not a new subject; they are an old capability of organizing questions clearly.
- The breadth of knowledge determines what level you can ask, while models only determine how quickly you can get answers.
- Before directing robots, one must first manage their own list of questions.