Machine-Learning Models

It’s worth asking exactly what these tools do for programmers. How are they changing the basics of a decades-old job? Will they help programmers make more and better software? Or will they get bogged down in legal fights over IP and copyright? 

1. “There’s no way I could have learned Rust as quickly as I did without Copilot,” he [Gift] says. “I basically had a supersmart assistant next to me that could answer my questions while I tried to level up. It was pretty obvious to me that we should start using it in class.” 
2. “The way that I would think about it is that you have an experienced developer sitting next to you whispering recommendations,” says Marco Iansiti, a Keystone Strategy cofounder and a professor at Harvard Business School, where he studies digital transformation. “You used to have to look things up on your own, and now—whammo—here comes the suggestion automatically.” 
3. Gottschlich, who has been working on automatic code generation for years, is less impressed. “To be frank, code assistants are fairly uninteresting in the larger scheme of things,” he says, referring to the new wave of tools based on large language models, like Copilot. “They are principally bound by what the human programmer is capable of doing. They’ll never likely at this stage be able to do something miraculous beyond what the human programmer is doing.” 
4. The problem is that models are only as good as their training data. And Copilot’s models were trained on a vast library of code taken from GitHub’s online repository, which goes back 15 years. This code contains not only bugs but also security flaws that were not known about when the code was written. 
5. “There’s always going to be programmers,” says Synnaeve. “They will get a lot of help, but in the end what matters is understanding which problems need solving. To do that really well and translate that into a program—that’s the job of programmers.”