Former Nvidia engineer reveals how to build real AI skills today

Former Nvidia engineer reveals how to build real AI skills today

AI is reshaping how we learn, work, and think, but former Nvidia engineer Chip Huyen says the best way to stay ahead is to build things yourself rather than just reading about them online.

According to Huyen, it doesn’t matter what you build, as long as you go from start to finish. The key is to take an idea, turn it into something real, and deploy it so others can use it. That hands-on approach, she says, is how people truly understand how AI systems work.

Start building from day one

Many learners waste time waiting for the perfect course or project idea. Huyen’s advice is simple: stop waiting and start building now. Even if you’re unsure, the act of creating helps you discover what you don’t know. The process itself teaches lessons that no tutorial ever could.

Building something end-to-end forces you to think about design, data, and deployment. It helps you see how each part of an AI system connects. That experience builds real confidence, especially when you see others using what you made. It’s the best way to learn how technology truly works.

Solve problems you care about

If you’re unsure what to build, start by listing things that frustrate you. Huyen suggests tracking daily annoyances, tasks that feel repetitive or slow. Once you have that list, think about how AI could fix one of them. That’s how real-world projects begin, grounded in problems that matter to you.

When you work on something personal, you naturally stay motivated. You’ll explore tools, test models, and learn by solving issues that affect your daily life. Huyen says this kind of curiosity-driven building gives people a deeper understanding of how AI can simplify real work and make life easier.

Building beats perfect planning

Some people spend months researching the “right way” to learn AI. Huyen says that the approach slows you down. She believes action teaches faster than endless planning. Mistakes are part of the process; they show what to fix and where to focus next. Each project makes the next one easier.

That’s why she encourages both beginners and professionals to experiment boldly. The more you build, the more patterns you start to recognize. You’ll begin to see what’s possible with AI tools and how small tweaks can lead to big results. Learning by doing is the fastest way forward.

Master systems thinking early

Huyen says coding alone won’t future-proof your career. What matters most is systems thinking, the ability to see how parts work together. In computer science, code is a tool to solve problems. The real skill is in understanding how data, models, and infrastructure connect to create solutions.

Systems thinking helps engineers move beyond surface-level fixes. It lets them design smarter, scalable products that solve real-world issues.

Companies value those who can look at the big picture and think holistically. That’s a mindset every aspiring AI professional should start practicing now.

Structure your AI learning

Building projects is crucial, but structure keeps you grounded. Huyen compares learning AI only by building to learning a language only by speaking it. You also need to study grammar, or in this case, theory. A solid foundation helps you understand the tools you’re using and why they work.

She suggests picking a clear learning path that includes books, courses, or online curricula. This mix of structure and practice ensures balanced growth. It turns random experimentation into targeted skill-building and keeps your progress steady instead of scattered.

AI helps non coders too

AI isn’t just for engineers anymore. Huyen says even people without coding backgrounds can now build useful tools thanks to AI coding assistants. She reports participants in her workshops creating impressive projects simply by combining ideas with the right AI tools.

That hands-on experience changes how people see themselves. Once they complete a project, they gain confidence and a deeper understanding of how AI works. Huyen believes that this new accessibility is opening doors for more creative thinkers to join the AI revolution.

Confidence grows through creation

Many people hesitate to start learning AI because they think it’s too technical. Huyen’s workshops prove otherwise. Once people build even a small project, their confidence skyrockets. The satisfaction of solving something practical makes them eager to explore more advanced tools.

That early sense of accomplishment pushes learners to keep going. Huyen says progress happens when curiosity meets action. The sooner you start creating, the faster you’ll develop the skills that make you stand out in this fast-changing field.

AI jobs are shifting fast

Across tech, companies are rethinking how engineering roles work. Generative AI tools can now write and review code, raising questions about the future of junior developers. Huyen says that’s why understanding full systems is more valuable than ever. It’s what separates problem-solvers from button-pushers.

AI might automate parts of programming, but it can’t replace people who see how everything fits together. The future belongs to those who can design, connect, and improve entire systems. That’s the kind of skill companies will always need, no matter how advanced AI becomes.

Problem solving never goes away

Even as AI automates repetitive work, new challenges appear. Huyen says problem-solving is timeless because technology constantly creates fresh puzzles. The more systems grow, the more coordination they need. Engineers who think across layers will always have an edge.

That’s why AI won’t make critical thinkers obsolete. It will only highlight the value of people who can define, analyze, and fix problems in smart ways. The tools may evolve, but the mindset remains the same; creative solutions never go out of style.

AI can’t replace big-picture minds

Huyen points out that while AI can automate small tasks, it still struggles with connecting the dots. Humans excel at linking separate ideas and adapting to change. That’s why people who can think holistically will stay in demand, even in a future full of automation.

AI tools can generate code or data, but they don’t understand the “why” behind a system. That’s where human judgment comes in. Those who combine technical skill with broad understanding will shape the next wave of innovation.

Computer science still matters

Some worry that AI tools will make computer science degrees less useful, but Huyen disagrees. She says CS remains crucial if students realize it’s not just about coding. It’s about understanding systems, solving real problems, and using code as a means to an end.

That’s why the future of CS education may focus more on system design and critical thinking. As AI handles more routine work, human creativity and insight will define the next generation of engineers. The core principles of computer science aren’t disappearing; they’re evolving.

Learn by linking AI tools

Many beginners start with isolated AI tools, but Huyen says the real growth happens when you connect them. Try combining models, automations, and simple APIs into a working system. It teaches how integration works and gives you practical experience with problem-solving at scale.

When you connect tools rather than just use them separately, you understand data flow, dependencies, and performance limits. That’s how professional AI engineers think. It’s the bridge between playing with tools and truly building solutions.

Industry leaders see AI differently

AI leaders can’t agree on what’s coming next. Nvidia’s Jensen Huang believes AI will create new kinds of jobs, while Anthropic’s Dario Amodei warns it could replace half of white-collar roles. Somewhere in between lies opportunity for those who keep adapting and learning.

Huyen’s message fits right in. Don’t wait to find out what AI will take or create. Start learning now so you’re ready for either future. Real builders will thrive no matter which direction the industry turns.

How companies view AI skills

Tech companies increasingly want employees who understand systems end-to-end. Huyen says businesses value those who can think beyond code and see the bigger picture. These engineers make better decisions, review AI outputs effectively, and spot potential issues early.

That holistic thinking helps teams work faster and smarter. As AI becomes part of every workflow, professionals who understand how to guide and improve these systems will be the ones shaping the industry’s future direction.

To learn more about why so many companies are struggling to make AI work, check out this MIT study, which finds AI failing at most companies that try to use it.

The future favors the builders

In the end, Huyen’s message is clear: the people who keep building will shape AI’s future. Whether you’re coding, designing, or just experimenting, every small project adds up. The goal isn’t to master AI overnight, but to keep creating until it feels natural.

AI is moving fast, and so should your learning. Build, test, break, and rebuild. Every time you do, you’re not just learning a tool; you’re developing the mindset that keeps you ahead in the age of intelligent machines.

Is DeepSeek AI really risky, or are companies overreacting? See why some firms are banning the tool and what it means for AI adoption.

What do you think about Nvidia’s engineer’s guide to learning AI? Share your thoughts in the comments.

Read More From This Brand:

• Sam Altman thinks Gen Z uses ChatGPT more wisely than adults
• Harvard professor warns of AI risks in classrooms
• Meta freezes AI hiring amid big changes and what it means for the future of AI

Don’t forget to follow us for more exclusive content right here on MSN.

If you like this story, you’ll LOVE our Free email newsletter. Join today and be the first to receive stories like these.

This slideshow was made with AI assistance and human editing.