When ai is able to automatically write codes, generate interfaces, and complete basic tests, many question whether the computer profession has reached a turning point. The answer is clear and positive: it is worth it, and it is worth it, but the logic of the test has changed completely. Ai is not the end of the computer profession, but the catalyst for industrial upgrading, eliminating low-end duplicated work and magnifying the value of core skilled personnel。
I. Ai is not a substitute, but a restructuring of the post structure
It was never the computer profession itself, but rather the primary, repetitive, standardized programming position. Basic crud development, simple page construction and mechanical testing are being quickly replaced by low code and large models, and it is already true that primary jobs are shrinking and thresholds rising。
But that does not mean that demand disappears, but rather that it is a structural shift. The talent gap continues to widen in the areas of ai research and development, large model fine-tuning, algorithm engineering, network security, data intelligence, industrial software, autopilot, ai security and ethics. The data show that the computer and information technology occupations are growing at a much faster rate than the industry average over the next 10 years, and that ai-related jobs are growing at a much faster pace than the demand for high-end talent。
Ai is essentially a productivity tool, like a calculator that does not eliminate mathematicians, nor does ai replace a computer that understands systems, structures, businesses and solves complex problems。
Ii. Core values of the computer profession that ai cannot shake
A lot of people misread the computer = write the code, which is the biggest error. At the core of the computer profession is the ability to construct a technology system from 0 to 1。
- the base of ai is computers: large models, in-depth learning, distributed training, all based on computer systems structure, operating systems, compilation principles, linear algebras and algorithms. Without understanding the bottom of the computer, it can only be called at the ai application level and cannot access core development。
- unreplaceable human values: definition of needs, architecture, safety wind control, cross-industry integration, technical ethics, complex failure mapping, which require judgement, creativity and industry understanding, which ai cannot do on its own。
- universality and cross-border capacity: computers are the “common language” of the digital age, which is integrated with financial, medical, manufacturing, legal and educational depths, with a higher premium for complex talent and greater risk resistance。
Iii. Diverseness: ordinary players enter carefully and the strong eat
The computer industry has been polarized by ai:
- low end tracks: only basic grammar, copying codes, lack of engineering capacity, increased difficulty in employment and weak wage growth。
- high-end race lanes: personnel capable of managing ai tools, mastery of algorithms and systems, engineering landings and business thinking, and sustained high pay and opportunity。
To put it simply: the age of writing code by stacking time, the age of technological depth and problem-solving ability. Students with common scores, weak logic and lack of patience, blindness and probabilities are prone to falling into the inside; computers are still one of the most expensive professions for those who are well-defined, skill-loving and willing to farm。
Iv. Evaluation and learning results: common, approach ai
1. Select direction: prioritize the subdirectories of artificial intelligence, data science, network security, software engineering, intelligent science, etc., to avoid purely basic development。
Upgrading of capabilities: transforming ai as a tool, learning to hint engineering, model fine-tuning, ai-aided development from a “code-writer” to a “man who directs ai”。
3. Complex development: deep-farming of the computer base is accompanied by the accumulation of knowledge in the financial, medical and automobile sectors, creating irreplaceable barriers。
4. Focusing on practice: more projects, competitions, internships, and companies ' ability to solve real problems, rather than rhetorical。
Concluding remarks
In the ai era, computer science is not obsolete, it's just evolutionary. It is no longer a shortcut for everyone, but a stronger track. Computers remain the best ticket to high-paying, front-line and infinite possibilities for those who are willing to cultivate bottom logic, embrace technological change and have systemic thinking。
Would you like me to prepare a simplified list of the computer fields and the university gradients for direct reference
