The Illusion of Competence
The reader describes a historical curiosity: how experts perceive this transition? A year ago, they became fascinated by AI-assisted coding. Without formal computer science training, they used large language models to build a complex TypeScript system. It included APIs, PostgreSQL databases, and data pipelines. Initially, the experience felt magical. Artificial intelligence drastically reduced the gap between ideas and implementation.
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DeepSeek Partners with Huawei to Build Programming Tools for Ascend ChipsHowever, challenges emerged when converting the project into a production product. Debugging became cyclic. Fixing one error triggered others. Some components functioned in ways that were not fully understood. After months of refactoring, a disturbing realization occurred. The system exceeded their technical understanding. When everything works, this discrepancy is invisible. At the first error, it becomes obvious. Sometimes, they cannot determine next steps without consulting AI again. This raised the question: did they spend a year building a real product or just an illusion of sophistication?
Economic Skepticism and Personal Stance
Although not opposed to technology, the reader wants to work professionally with these systems. They remain unsure about the correct relationship with them. Seemann expresses his own position. He has not made a final decision but leans toward slight antipathy toward artificial intelligence. He knows this might be inevitable. Experiments often impress him but also frustrate him. Maximum frustration occurs when the technology performs best. When results are poor, he finds comfort in thirty years of study. When performance is excellent, he wonders where to sign up for a Butlerian Jihad.
This attitude is influenced by his socioeconomic status. Being older and successful, he can survive unemployment. He is less certain if a knowledge-based society would withstand such shocks. Large language models might take programmer roles first. Verifying code is easier than managing insurance claims. If mass unemployment hits the knowledge sector, societal stability could be threatened.
Seemann notes that he rarely discusses his economist background, but it is relevant here. Economically, he cannot imagine 30-40 percent unemployment having no significant impact. He knows historical arguments claim new jobs offset lost ones. This happened with the sewing machine, steam engine, and computers. This claim is only partially true. New jobs appeared, but often not for the same people. Coal miners did not become programmers overnight.
Foundations Before Automation
A similar argument was used when China joined the World Trade Organization. Many new positions were created, but not necessarily in the West. Based on lived experience and history, the author is skeptical that all will be well. He sincerely hopes to be wrong. He enjoys programming and would not mind continuing for ten more years. More importantly, he has young children and hopes there will be a place for them in the future.
The reader explicitly asks for his opinion. Is he glad he learned programming fundamentals before large language models existed? If starting today, would he still seriously study languages, data structures, databases, networks, operating systems, debugging, and architecture? Does he believe artificial intelligence allows people to build faster than they can understand? The answer to the first question is affirmative. Yes, he is certainly glad he mastered the basics before the era of artificial intelligence began.
