What Jensen Huang Thinks Smart Looks Like Now

Years back I had a student who was terrible at exams. Multiple choice, timed problems, placement exams, all of it. He barely squeaked by in lecture. In lab he was someone else. He would stand next to a partner who was building a circuit and just watch. Then he would point at a cap and ask what happens if it fails open. He was usually right before the thing ever got powered up.

He got a field tech job after he graduated. Two years in, customers were calling and asking for him by name. He still cannot tell you the formula for a low-pass filter off the top of his head. He can tell you, on the phone, that the problem you think you have is not the one you actually have.

I thought about him this week reading about Jensen Huang on Jodi Shelton’s A Bit Personal Podcast. She asked him who the smartest person he has ever met is. He would not answer. He said the question itself is the wrong one now.

Huang’s point: the kind of smart we have always rewarded, the technical problem solver, is becoming a commodity. He used software programming as the example. For decades that was the smart-person job. Now it is the first thing AI is doing well.

So what does he think smart looks like going forward? Three things stacked. Technical understanding, human empathy, and the ability to pick up on what nobody is saying. That last one he called seeing around corners. It comes from data, analysis, first principles, life experience, wisdom, and reading the people in the room. People who have that mix tend to spot problems before they happen.

This is part of why I started doing oral exams in my Circuits class. A written test will tell me a student got the right number. It will not tell me whether they know why they used mesh instead of nodal, what a time constant actually means, or where the sign error came from. Sit them down and make them talk through it and you find out fast. 

Most of what engineering school grades on is the part AI is taking over. Timed problem sets. Multiple choice. Take-home work that gets judged on how polished it looks. The stuff Huang is talking about, reading a situation, asking the question nobody else thought to ask, knowing something is off before the numbers say so, is harder to grade. It is also what is going to matter.

My old student would have bombed every test Huang is calling outdated. He read a lab bench the way some people read a chessboard. Twenty years ago that made him a good tech. Today it makes him the kind of engineer you cannot swap out for a chatbot.

The Oral Exam Experiment Worked

Last fall I posted that I was dropping homework from the grade book and adding an oral portion to every exam in the spring. Students were running Engineering homework problems through Gemini and handing in solutions they could not explain when I asked. I was grading a chatbot, so I stopped.

Spring semester is over. It worked. The oral portion runs about ten minutes per student. They pick one problem from their written work and walk me through it. Why mesh and not nodal. What the time constant tells you about the circuit. Where the negative sign came from. I learn more about what a student actually understands in that ten minutes than I used to learn from a semester of graded homework.

A new Lumina Foundation-Gallup study says 57% of US college students use AI in their coursework at least weekly, and one in five use it every day. At the same time, 53% say their school discourages or prohibits it. Daily use is highest among men and among business, tech, and engineering students. The students avoiding AI mostly cite ethical concerns and school policy, so the ones following the rules are falling behind on a tool they will use the rest of their careers.

My position on AI in education is simple. If we are preparing students for the jobs they are about to take, AI has to be in every class. Every engineering job they walk into will expect them to use these tools well. A program that prohibits AI is training students for a job market that no longer exists. The work is not to keep AI out of the classroom. The work is to teach students how to use it, where it fails, and when to check it against first principles.

That still leaves the assessment problem. If students use AI on everything, how do you know what they understand? You change how you measure them. Oral exams catch what written work cannot. In-class paper problems catch it. Hands-on labs, where a student wires a circuit on a breadboard, takes scope measurements, and explains what they are seeing, catch it cold. Take-home essays graded on polish do not catch anything anymore.

The AI can solve the circuit. It cannot explain why this student chose the loop they chose, and it cannot wire the breadboard when the lab is due at five. That is what we should be assessing, and that is the work employers are hiring for.