A former Nvidia engineer says people looking to stay competitive in the age of AI need to start building now.
“Doesn’t matter what you build, as long as you do it end to end: starting from an idea and deploying it so that a friend can use it,” Chip Huyen, an AI researcher who worked on Nvidia’s NeMo platform and taught machine learning at Stanford, wrote to Business Insider.
Huyen’s advice extends beyond her fellow AI researchers and engineers. She said when she holds workshops for companies, it’s incredible to see what people without even a coding background can do, thanks to AI coding agents.
“After that, they become so much more confident in themselves and also have a much better understanding of AI,” she said.
If you’re stuck figuring out what to do, Huyen says she has a simple exercise.
“For a week, note down everything that frustrates you,” she wrote in a guide she shared with Business Insider. “Why is this taking so long? Why do I have to repeat this every single time? etc. For each of these problems, try to think about how to solve it. Then pick one problem to solve.”
Learning about the foundations of AI and how it works is also important, Huyen said.
“Learning only by building is like learning a new language only by speaking,” she wrote.
That’s why it’s imperative that those looking to upskill supplement their own AI exploration by learning about the tools they’re using.
“You should also bring structure into your learning, like picking a curriculum, books, courses,” she wrote.
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If you’re looking for where to start, Huyen shared a list of AI-related resources with Business Insider that she compiled when writing her latest book, “AI Engineering.”
What computer science students should do
As for those studying computer science or engineers looking to expand their knowledge base, Huyen said the best thing they can do is master systems thinking.
“Coding is just a means to an end. CS is about system thinking, using coding to solve actual problem,” Huyen said during a recent episode of “Lenny’s podcast.”
Problem-solving will never go away, Huyen said, because as AI can automate more stuff, the problems will just get bigger.”
Huyen said that AI “can help automate a lot of disjointed skills,” but combining the separate knowledge to solve problems will remain difficult. She pointed to a webinar hosted by Stanford computer science department chair Mehran Sahami and Andrew Ng, founder of DeepLearning.AI and a former founding member of Google Brain, in which the pair discussed the future of CS. At the time, Sahami said that it is “critical for students to understand the code that’s generated” by AI tools.
In talking to companies, Huyen said that she is hearing examples of senior engineers spending more time reviewing code written by junior engineers. Across tech, the fear is that more junior engineering roles will be replaced as generative AI becomes more skilled at writing code itself. That’s why developing skills can help, Huyen said.
Companies “appreciate engineers who have a good understanding of the whole systems and be able to have good problem-solving skill, are thinking holistically instead of locally,” she said.
