Chip Huyen, a prominent AI researcher and former Nvidia engineer who worked on the company’s NeMo platform and taught machine learning at Stanford, is offering critical guidance for professionals seeking to remain competitive in the rapidly evolving AI landscape. Her central message is clear: start building AI projects now, regardless of your technical background.
Huyen emphasizes that the specific project matters less than the complete experience of taking an idea from conception through deployment. “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,” she told Business Insider. This hands-on approach applies not just to AI researchers and engineers, but to professionals across all disciplines.
Remarkably, AI coding agents have democratized development to the point where individuals without coding backgrounds can create functional applications. Huyen reports witnessing this transformation firsthand during corporate workshops, where participants gain both technical confidence and a deeper understanding of AI capabilities through practical building exercises.
For those uncertain where to begin, Huyen recommends a simple but effective exercise: spend a week documenting daily frustrations—tasks that take too long or require repetitive effort—then select one problem to solve using AI tools. This approach grounds learning in real-world applications and personal pain points.
However, Huyen cautions against learning solely through building. She compares this to “learning a new language only by speaking,” stressing the importance of understanding AI fundamentals. Structured learning through curricula, books, and courses should complement hands-on experimentation. She has compiled a comprehensive list of AI resources in her latest book, “AI Engineering.”
For computer science students and engineers specifically, Huyen advocates mastering systems thinking over pure coding skills. “Coding is just a means to an end. CS is about system thinking, using coding to solve actual problem,” she explained on Lenny’s podcast. As AI automates more discrete tasks, the ability to combine separate knowledge domains to solve increasingly complex problems will become invaluable.
Huyen notes that senior engineers are already spending more time reviewing code written by junior colleagues, reflecting concerns about AI potentially replacing entry-level engineering roles. Companies increasingly value engineers with holistic understanding and strong problem-solving abilities rather than narrow technical skills.
Key Quotes
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, former Nvidia engineer and AI researcher, emphasizes the importance of complete project experience over specific technical choices. This advice applies to professionals at all skill levels seeking to understand AI capabilities.
After that, they become so much more confident in themselves and also have a much better understanding of AI
Huyen describes the transformation she witnesses in workshop participants without coding backgrounds who successfully build projects using AI coding agents, highlighting how hands-on experience builds both competence and confidence.
Coding is just a means to an end. CS is about system thinking, using coding to solve actual problem
Speaking on Lenny’s podcast, Huyen articulates why computer science students should prioritize problem-solving and systems thinking over pure coding skills, especially as AI automates more routine programming tasks.
Learning only by building is like learning a new language only by speaking
Huyen warns against exclusively hands-on learning, emphasizing that understanding AI fundamentals through structured curricula, books, and courses is essential for developing deep expertise alongside practical building experience.
Our Take
Huyen’s advice represents a pragmatic response to widespread anxiety about AI’s impact on technical careers. What’s particularly insightful is her dual emphasis on both building and foundational learning—a balanced approach that avoids the extremes of either pure theory or mindless tool usage. Her observation that AI coding agents enable non-programmers to build applications isn’t just democratization rhetoric; it’s a fundamental shift in who can create technology solutions. The most telling insight may be her point about problems getting bigger as AI handles more tasks. This suggests that rather than eliminating technical work, AI is raising the complexity floor—tomorrow’s entry-level problems will be today’s advanced challenges. For professionals, this means the imperative isn’t just learning AI tools, but developing the systems thinking and problem-solving frameworks that allow you to tackle increasingly complex challenges. The future belongs not to those who can code, but to those who can architect solutions to problems we haven’t yet imagined.
Why This Matters
This guidance from a respected AI researcher comes at a critical inflection point for the technology industry. As generative AI tools become increasingly capable of writing code and automating technical tasks, the traditional career path for software engineers faces disruption. Huyen’s advice reflects a broader industry shift where technical skills alone no longer guarantee job security.
The democratization of AI development through coding agents represents a fundamental change in who can build technology products. This accessibility could reshape workforce dynamics, enabling career transitions and empowering non-technical professionals to solve problems independently.
For businesses, Huyen’s observations about senior engineers spending more time on code review signals an emerging organizational challenge. Companies must balance AI productivity gains against the risk of developing a workforce that lacks deep technical understanding. Her emphasis on systems thinking and problem-solving provides a roadmap for both individuals and organizations navigating AI integration.
The implications extend beyond tech: as AI handles routine tasks, human value increasingly lies in holistic thinking, creative problem-solving, and the ability to integrate AI tools effectively—skills that require intentional development and cannot be easily automated.
Recommended Reading
For those interested in learning more about artificial intelligence, machine learning, and effective AI communication, here are some excellent resources: