I was supposed to write about agentic AI this week. I had my notes ready, my research outlined, my arguments structured. But then I was cooking dinner, half-listening to an interview with NVIDIA CEO Jensen Huang on the “A Bit Personal” podcast with Jodi Shelton, when he said something that made me stop chopping vegetables mid-slice.
The host asked him a simple question:
“Who is the smartest person you’ve ever met?”
In the tech world, you expect a name. For example, a legendary engineer or a Nobel laureate. Instead, Huang paused. And then he said something that did not just challenge my parenting approach. It exposed a fundamental tension in how we have constructed our entire educational and economic system around a definition of intelligence that is rapidly becoming obsolete.
Huang paused after the question. You could hear him thinking. Then he said:
“I can’t answer that question. And I know what people are thinking. The definition of smart is somebody who is intelligent, solves problems, technical… but I find that that’s a commodity. And we are about to prove that artificial intelligence is able to handle that part easiest.”
This is no hyperbole.
The CEO of one of the world’s most valuable technology companies just called traditional intelligence — the kind we test for, the kind we push our kids to develop, the kind that gets you into good colleges — a commodity.
Then he continued:
“Let me give you another example. Everyone thought that software programming is the ultimate smart profession. Look what is the first thing AI is solving? Software programming. And so it turns out that the definition of smart is very different than most people think.”
Think about that.
For decades, we have treated coding as the ultimate "future-proof" skill. Yet, Huang pointed out that programming is among the first domains AI has mastered, not the last. If the pinnacle of 20th-century intelligence can be replicated by a large language model, then we are facing a paradigm shift in human capital.
But here’s where Huang’s answer transformed from alarming to electrifying. Because he didn’t stop at telling us what intelligence is not anymore. He told us what it is:
“I think long term the definition of smart and my personal definition of smart is someone that sits on that intersection of being technically astute but human empathy and having the ability to infer the unspoken around the corners, the unknowables… To be able to preempt problems before they show up just because you feel the vibe.”
He defined a new frontier of intelligence. He moved away from “processing power” toward something more nuanced: Human Heuristics.
While "vibe" might sound informal, in a technical context, it refers to high-dimensional pattern recognition. It is a sophisticated form of intelligence that combines multiple inputs: technical knowledge, yes, but also emotional intelligence, systems thinking, historical context, and the ability to notice subtle patterns in human behavior and organizational dynamics. It is the intelligence of a seasoned executive who can walk into a meeting and immediately sense the unspoken tensions, or the engineer who anticipates a design flaw not through calculation but through deep intuition built on years of experience. AI can process data, but it cannot yet synthesize experience, emotion, and intuition the way humans can.
You can even think of it as the intelligence of the ambiguous.
And then came the kicker, the line that made me immediately text my friends with high-school kids:
“This I think is going to be the future definition of smart. And that person might score horribly on the SAT.”
Let that sink in. The person running a company at the absolute cutting edge of artificial intelligence just said the smartest people of the future might bomb the SAT. Not because they lack intelligence, but because the SAT measures only one narrow band of it. Huang is not dismissing analytical thinking or problem-solving skills; he is saying that someone could have extraordinary intelligence in reading people, anticipating problems, and navigating complex human systems, yet score poorly on a test designed to measure speed and accuracy in standardized problem-solving. The SAT is not necessarily becoming irrelevant, it is revealing itself as incomplete. It measures computational intelligence, but misses entirely the forms of intelligence that might matter most when machines handle the computational heavy lifting.
This hit me like a freight train because of something I had done just that morning. My three-year-old daughter wanted to show me a stone she had found in the park yesterday. I was trying to get her dressed for preschool. “Not now, baby,” I said. “We don't have time. We need to go.”
But what was so important? Getting to preschool on time? Making sure she sat still during circle time? Am I preparing her for a world that's about to exist, or am I training her for one that's disappearing? Maybe stopping to look at that stone.. her noticing something small.. wanting to share it.. being curious about the world.. maybe that was actually the important thing.
We have built an entire educational infrastructure around computational intelligence: memorization, standardized testing, and linear problem-solving. We are essentially training our children to compete with machines at the very tasks where machines have an insurmountable advantage. By prioritizing the “schedule” over the “discovery,” we may be systematically de-emphasizing the exact traits that lead to the "vibe" intelligence Huang prizes.
If technical proficiency is now the baseline rather than the pinnacle, how do we recalibrate? I have identified five pillars for parenting in the age of AI:
Empathy as a Core Competency: The other day at the playground, my daughter stopped playing to sit with another little girl who was crying. She did not know her. She just sensed distress and responded. She brought her a stick (the universal toddler currency) and sat there with her.
In traditional educational frameworks, this would be praised as “nice” but not particularly intelligent. But now that I am thinking about it, what my daughter demonstrated was precisely what Huang describes: the ability to read emotional states, the willingness to respond to unspoken need, the capacity for unprompted empathetic action.
We must stop viewing empathy as a “soft skill.” It is a sophisticated sensory tool for navigating complex human systems. It is the ability to read the “unspoken” that AI cannot yet grasp.
The Architecture of Ambiguity: Our educational system privileges “The Right Answer.” A is correct. B is wrong. Show your work. Follow the steps. Get the right answer. This reflects what philosopher Jean-François Lyotard called "performativity", i.e., the reduction of knowledge to that which can be measured and optimized. But the future belongs to those comfortable with “The Unknown.” We must encourage our children to sit with confusion and reward the quality of their questions over the speed of their solutions.
Breadth Over Premature Specialization: The future does not belong to people who are excellent at one thing. The future belongs to the integrator. Huang emphasizes the intersection of the technical and the human. We should resist the urge to “track” our children into narrow silos (STEM vs. Arts) and instead encourage polymaths who can bridge different worlds.
The Value of Unstructured Wisdom: Life experience cannot be simulated. A teenager working a retail job or a child navigating a playground conflict is learning “vibe” intelligence that a packed schedule of enrichment classes cannot provide.
AI as Cognitive Augmentation: We shouldn’t ask if AI will replace our children; we should ask how our children will use AI to amplify their humanity. The goal is not to be a better calculator than the computer; it us to be the visionary who knows what to ask the calculator to do.
Let me be absolutely clear: I am not suggesting we abandon math and language skills. These remain crucial foundations for logical reasoning and articulate expression. Rather, we need to go beyond treating them as the endpoint of education and recognize them as the baseline from which deeper human capacities emerge.
Since listening to that podcast, I have been rethinking my approach:
Different conversations: Not just “What did you learn?” but “Who did you help? What did you notice about how people were feeling? What surprised you about someone’s reaction?”
Protecting unstructured time: Time to be bored. Time to figure things out. Time to observe the world and the people in it without adult-directed activities.
Redefining achievement: Celebrating when she notices someone’s feelings. When she shows unprompted kindness. When she demonstrates curiosity about why people behave as they do.
Introducing complexity: Age-appropriate exposure to moral ambiguity, social complexity, and situations where there is not a clear right answer.
Reconceptualizing AI: Not as something to fear or compete against, but as a tool she will learn to leverage so she can focus her cognitive resources on distinctly human problems.
Most importantly: trusting different forms of intelligence. The kind that can’t be measured by standardized tests but may matter more than anything else.
There is an inherent anxiety in realizing our old maps no longer fit the terrain. The metrics we trusted, the pathways we understood, the definitions we relied upon.. all suddenly uncertain. But there is also a profound liberation in this uncertainty.
If standardized tests measure only computational intelligence, we are free to stop optimizing childhood for a single score. We can value the whole child: the one who stops to examine insects, who notices when someone needs help, who asks questions that have no clear answers.
We are moving from a Knowledge Economy to a Wisdom Economy. From valuing what can be quickly recalled to what must be slowly cultivated through experience, observation, and human connection.
Tomorrow morning, when my daughter stops to show me a bug or a stone and we are running late, I will pause. Because that moment — her noticing something small, wanting to share it, being present enough to see it in the first place — that is the intelligence no algorithm can learn. It is the foundation of the observation, the curiosity, the human attentiveness that Huang describes. And it only develops when we give it space.
What are you thinking about this shift in intelligence? How are you approaching it with your kids? I would love to hear your thoughts in the comments.
One last thing: If you found this valuable, please like or share this post. Your support helps ensure this information reaches other parents when they need it most. Thank you for reading!
Until next time,
Anastasia
P.S. If you want to hear the full interview with Jensen Huang, it’s on the “A Bit Personal” podcast with Jodi Shelton. The question about intelligence starts at about 1 hour and 14 minutes. Fair warning: it might change how you think about everything.
About the author: She is a Senior Computer Scientist based in Silicon Valley, where she uses her expertise in mathematics and artificial intelligence to help ensure the safety and reliability of critical systems (think airplanes and beyond!) She is also the parent of a curious 3-year-old daughter. Each night, she reflects on how AI is reshaping the world her daughter is growing up in. This newsletter is her space to explore those reflections on technology, the future, and what it truly means to raise children in an age of rapid and often unpredictable change.
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