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My old student would have bombed every test Huang is calling outdated.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.

From $14 Billion to $125 Million: What Killed Chegg

I had a Chegg account for years. For my electrical engineering classes, I would check my  own homework solutions using Chegg before handing them to students, just to double check my work. It was a good tool and worth the money at the time.

Then one night I tried Google Gemini on the same kind of problem. It gave me the answer, walked through the steps, and did a better job explaining what was going on. I sat there for a minute and thought, why am I paying for Chegg? I cancelled the next morning. These days Gemini is what I lean on for problem solving in my classes.

Turns out a lot of people made the same call. Chegg was worth around $14 billion in 2021. Today it is worth about $125 million. The company laid off almost half its remaining staff last October. Revenue last quarter dropped 42% from the year before. The CEO points the finger at ChatGPT and at Google's new AI answers that show up right at the top of the search page. Students get their answer before they ever click through to a paywall.

Khan Academy is a different story but feels some of the same heat. It is a nonprofit, so there is no stock price to watch. A few years back they rolled out Khanmigo, an AI tutor, and got it in front of more than 1.4 million students. On paper, big win. In April, Sal Khan admitted most students barely touched it. The reason is not hard to figure out. Free AI is already open in another tab. Kids are not going to switch over to a separate tutor when help is right there.

Both companies were built to sit between a student and an answer. AI now sits closer to the student than either of them. When that happens, the middleman business gets thin in a hurry.

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.

Car Buyer vs User

Image Google Gemini Generated

I stopped at a dealership recently to get some parts for my wife’s car and, as always, took a peek in the showroom. I ended up looking at stickers and counting the screens in a mid-range SUV. Four. One in the dash, one in the middle, one for the passenger, one in the back. A salesperson grabbed me and started walking me through them. I stopped paying attention about 30 seconds in. Paddle shifters, massage seats, gester controls, ten drive modes, ambient lighting, self parking, some kind of fragrance thing. I nodded. I would use none of it.

Apparently I am not alone. The JD Power 2024 Tech Experience Index rates the so-called advanced driver features near the bottom of what owners actually value. Gesture controls hit 43 problems per 100 vehicles, which is a polite way of saying they do not work. People pay for this stuff, give up on it, and then never bring it up again until they trade the car in.

I keep thinking about the iPhone in this context. Two billion of them have shipped. None came with a manual. Kids figure them out before they can read. Apple kept taking things away. The home button. The headphone jack. Each cut made the phone easier. Carmakers do the opposite. They keep piling features on and call it progress.

I drive a 2020 Tesla Model 3. The software is great. The maps work, the updates show up overnight, the menus make sense. The car also has a turn signal stalk, a gear stalk, and two scroll wheels on the wheel. Tesla removed all of those in the 2024 Highland refresh. You signal now by pressing a button on the spoke. You shift gears by swiping a slider on the screen. Owners are paying around $400 for aftermarket kits to put the stalks back in. Tesla rolled out a version in China with the stalk returned. The best software in the industry could not save the worst ergonomic decision in the industry.

A few years ago BMW went the other way and tried to charge for hardware. Eighteen dollars a month to turn on the heated seats already wired into the car. About 90 percent of BMWs leave the factory with the hardware in place. Customers were essentially being charged twice. BMW killed it in September 2023. New Jersey introduced a bill to make hardware paywalls illegal on cars, which tells you how badly that went over.

None of this is accidental. Cars get designed for the showroom because that is where the sale happens. Three knobs and two screens looks cheap next to haptic glass and 47 menu layers. Marketing wants the acronym count on the window sticker. So you walk in as a buyer, feel reassured by the complexity, sign, and drive home. About ten minutes later you become the user. The user wants the heat on without looking at a screen, the mirrors adjusted without a settings tree, and the left turn signaled with one finger (not that one! )

Things are starting to turn. Mazda kept the knobs and gets credit for it every time someone reviews the car. Volkswagen pulled the buttons, watched sales slip, and admitted in 2025 the buttons are coming back. Their design chief said the reversal is permanent. Euro NCAP is moving toward requiring physical controls on five core functions to keep a five-star rating, which means the regulators are about to do what the marketing departments would not.

The buyer is in the showroom for an afternoon. The best interface is one you do not have to think about, and you should not need a manual to signal a left turn.

DC, AC and GROUND Coupling Settings on the Oscilloscope

A student stopped me in lab last week. She was staring at a trace that kept drifting off screen. She had the coupling set to DC and a large offset was pushing her signal out of range. The question she asked was a good one: what is the difference between AC, DC, and ground coupling, and when do you use each?

The short answer is that coupling controls what the oscilloscope passes to its vertical amplifier before it draws anything on screen. With newer scopes from different manufacturers it is sometimes difficult to find where to set coupling so you can either hunt around (usually the way I do it!) or consult the user manuals.The choice changes what you see, and getting it wrong means you either miss something or start chasing a problem that is not there. Here's a pic with short definitions for each following.

DC Coupling

DC coupling passes everything through: the full signal including any DC offset. If you have a 10 mV sine wave sitting on top of a 12V supply, you see both. This is the correct default for most measurements. Power supply work, logic signals, anything where the offset carries information requires DC coupling. You want the complete picture. That student had the scope set to DC Coupling (often the default.)

AC Coupling

AC coupling inserts a series capacitor that blocks DC and attenuates frequencies below roughly 10 to 20 Hz, depending on the scope. That same sine wave now appears centered on zero, and you can use full vertical scale to examine it. This is useful for noise on a supply output, audio signals, or serial data eye diagrams where the DC level is irrelevant. The tradeoff is real: the capacitor charges and discharges, so the trace drifts for a few seconds after you switch modes. Square waves also suffer at low frequencies because the flat tops droop as the capacitor charges through the input impedance.

Ground Coupling

Ground coupling disconnects the input entirely and connects the vertical amplifier to ground. The signal disappears. You get a flat line at 0V. This is a calibration step, not a measurement mode. Use it to find your ground reference position before you connect a live signal, especially when you need to compare multiple channels on the same scale.

The student switched to AC coupling, centered the trace, and saw exactly what she wanted to see: about 80 mV of sine wave.

A Different Path to Quantum Computing Hits 99% Accuracy

Two research teams, one in Germany and one in Switzerland, published results in Nature on the same day showing quantum logic operations with better than 99% accuracy. The technique they used has been worked on for almost thirty years.

To get a quantum computer to do anything, you have to make individual atoms interact in a controlled way. Most neutral-atom companies do this by hitting atoms with lasers to push them into a high-energy state. The atoms interact, then the state decays quickly. The operation has to finish before the decay.

These two teams used a different method. They cooled lithium atoms to near absolute zero, trapped them in a grid made of laser light, and let neighboring atoms overlap slightly. No high-energy state needed. The atoms touch in a controlled, predictable way, and that touch becomes the logic operation. The ETH Zurich group and the Max Planck group used different control schemes and landed at similar fidelities.

The teams also reported Bell-state lifetimes over 10 seconds. A Bell state is a pair of qubits locked together so that measuring one determines the other, no matter how far apart they sit. The lifetime is how long that link survives before noise breaks it. Many quantum platforms measure this in microseconds or milliseconds. Ten seconds is a long window, and it matters because every gate operation has to finish before the link decays.

Lithium matters here for a specific reason. Its electrons follow a rule that no two of them can occupy the same state at the same time. That rule, built into the physics, prevents a whole category of errors automatically. The hardware handles some of the error checking that software would otherwise have to do.

The likely first use for machines like this is quantum chemistry. Simulating a drug molecule or a battery material means simulating how electrons behave. Lithium atoms in this setup follow the same rules as those electrons, so the hardware and the problem use the same physics. The phys.org writeup has details.

Winter Park, 1978

 Every man dies twice, Hemingway wrote. Once when he stops breathing. And again, the last time someone speaks his name.

Spring break 1978. We left Amherst Wednesday after the two o’clock class. Nobody said anything about Thursday or Friday. Meatball had already packed the Vega. It had a crack in the dash and the heater worked on one side. We had forty dollars each and a week. My father had given me an extra twenty that I kept folded in my wallet. Just in case. Dave said forty was plenty. Nobody argued.

Meatball had a box of eight-tracks behind the seat. He played Aerosmith through Connecticut and Foreigner through New Jersey and nobody said much about the music or anything else.

I drove from somewhere in Virginia through the Carolinas. The road was flat and straight and the pines came right to the edge of the shoulder. Dave slept in the back with his jacket over his face. Meatball watched the road.

“You think forty bucks is enough,” he said.

“For what.”

“The week.”

“It’ll have to be,” I said.

He nodded and looked out the window. We didn’t talk about it again.

The sun came up outside Valdosta. Dave woke up and Meatball put in a Stones tape and we rode that way for a while with the windows cracked and the palms going by. None of us had ever seen a palm tree.

Dave’s cousin was at Rollins College in Winter Park on a golf scholarship. He had a single room and we put our bags against the wall and slept on the floor. In the morning I walked the campus. The buildings were Spanish tile and stucco and the trees were old and there was a lake with water ski boats tied to a dock. The boats were nice. I stood there a while looking at them and then I walked back.

We ate McDonald’s when we had to and walked into the Rollins cafeteria when we could. Nobody stopped us. The food was good and there was a lot of it. Before we left I went to the campus store and bought a Rollins t-shirt. It cost four dollars. I figured we were even.

We drove out to Winter Haven for the game. The Red Sox trained there and the park was small and the grass was very green. We sat on the grass in left field behind the wall. There were maybe a dozen other people out there. You could see the whole field from low down like that and the players were close.

When the Yankees took the field Reggie Jackson trotted out to right. He was maybe thirty feet away.

Dave waited until it was quiet. “Hey Reggie.”

Reggie watched the infield.

“Hey Reggie. You’re a bum.”

Nothing.

“Reggie. Bum. You’re a bum, Reggie.”

Meatball looked at me. I watched the field.

Dave got up on one knee. “Hey Reggie. You hear me? Bum.”

Reggie turned. He didn’t say anything. He looked at Dave the way you look through something that isn’t there. Dave stopped.

Reggie hit two home runs that day. I don’t think Dave crossed his mind again.

Dave sat back down.

We didn’t say anything about it. Meatball got three beers and we sat in the sun and watched the rest of the game. It was warm and the sky was very blue and it was a good day.

Dave and I lost touch after college. We ran into each other occasionally over the years but not often. I didn’t know him the way I once did.

David J. Hoey aka Hooker. He played baseball, basketball, soccer and rugby. After UMass he worked U.S. Customs in Boston and then fire jumped in Colorado. Later he sold plumbing supplies. He loved the Patriots and the Red Sox. The most important thing in his life was his son Alex. They went to ComiCon together in New York and Boston. He was fifty-nine when he died. He was the strongest and most athletic person I ever knew.

My mother was his elementary school teacher.