大家怎么看未来AI大规模取代码农
kwirky
楼主 (北美华人网)
Rebuild this Angular 6 project in the latest version of React
大概也就是未来一到两年,码农职位因为AI会减少25%。未来十年会码农职业会消失。
https://levelup.gitconnected.com/chatgpt-will-replace-programmers-within-10-years-91e5b3bd3676
Phase 2: Advanced IDE Tooling and Consolidation (1 — 2 yr) Phase 2 will organically emerge from Phase 1, as IDE tooling gets more sophisticated. The entire codebase will provide context to the AI, which will then be able to make project-wide suggestions like:
Add 100% unit-test coverage to the project
Refactor all model classes into a separate library using Gradle and Java 17
Change the security model from OAuth to SAMLThis will drastically improve legacy codebase maintenance and migration. As millions of engineers apply such changes, usage patterns will emerge and be used to train the next generation of tools. As the traditional IDE becomes a mere vessel for AI, such suggestions will be auto-applied. For example, why wouldn’t every codebase get 100% unit-test coverage? In fact, it should happen automatically in the background as we code. This period will be known as the great consolidation. Long-held competitions between frameworks will be decided by which is the most AI-friendly. For example, imagine one could safely upgrade a legacy JS codebase to React with a single button click, but doing the same migration to Vue would take a week. Even if there are viable reasons to choose Vue, ultimately frameworks that adapt quickest to AI integration will prevail. Such an arms race will occur in programming languages as well. For example, imagine you inherited a slow-performing code base written in Python. Your IDE suggests it be translated to Rust. You click “yes” and redeploy, and the app is suddenly 10X faster. The same goes for protocols — why bother with HTTP when everything can easily be gRPC? Let’s get off of FTP and .md5 while we’re at it. Finally, Phase 2 will also bring the advent of the AI-CD pipeline. It’s not difficult to imagine a CD pipeline that can learn deployment patterns. For example, an AI that tweaks Kubernetes configuration based on traffic daily. Or even goes as far as to rebuild the application in different tech stacks to optimize performance and server cost. Job Loss Prediction: 25%