Jensen Huang just said something that’s making people uncomfortable, and honestly, it should.
The NVIDIA CEO recently challenged our entire understanding of what makes someone “smart” in an interview that’s now making waves across tech communities. His answer wasn’t what anyone expected, and the backlash in the comments reveals exactly why we needed to hear it.
Here’s what happened: When asked to define intelligence, Huang didn’t talk about IQ scores or computational prowess. Instead, he said something that initially sounds counterintuitive—AI solving software engineering first doesn’t mean programming was easy. It means we’ve been measuring intelligence wrong the entire time.
The Uncomfortable Truth About Technical Brilliance
We’ve spent decades worshipping at the altar of technical competence. Software developers became the modern-day priesthood, speaking in languages mere mortals couldn’t comprehend. The ability to think like a machine, to translate human problems into computer logic, became the ultimate marker of intelligence.
Huang challenges this notion head-on. According to him, being technically astute is just table stakes. Real intelligence—the kind that actually moves humanity forward—requires something entirely different: the ability to see around corners.
What does that mean in practical terms? It means understanding not just how to solve a problem, but which problems are worth solving in the first place. It means recognizing the second-order and third-order effects of your solutions. It means having the foresight to anticipate how humans will actually use what you build, not just how they should use it.
The most revealing part of his definition? Empathy. That single word cuts through decades of Silicon Valley mythology about the brilliant but socially awkward genius. Huang suggests that someone who lacks empathy, regardless of their technical prowess, fundamentally lacks intelligence.
Why AI Conquered Code Before It Conquered Laundry
One commenter on the Reddit thread made an astute observation that deserves deeper exploration. AI didn’t solve programming first because programming is easy—it solved programming first because of data availability and verifiability.
Think about what makes a good training dataset for AI: massive scale, clear success criteria, and immediate feedback loops. Programming has all three in abundance. GitHub alone hosts hundreds of millions of repositories. Stack Overflow contains decades of questions and validated answers. Every piece of code either runs or doesn’t, compiles or throws errors—there’s an objective measure of success.
Compare that to, say, designing a beautiful room or coaching a youth soccer team. The data is sparse, scattered, and highly subjective. Success isn’t binary—it exists on a spectrum influenced by cultural context, personal taste, and countless other factors that resist quantification.
This reveals something crucial about intelligence that Huang hints at but doesn’t fully articulate: the domains AI struggles with aren’t necessarily the ones requiring “less” intelligence. They’re the ones requiring fundamentally different kinds of intelligence—the messy, contextual, deeply human kinds that resist reduction to patterns and probabilities.
The Coming Reckoning at the Human-AI Interface
Here’s where Huang’s argument gets genuinely interesting, and where most people misunderstand what he’s actually saying.
He’s not suggesting we should all become less technical. He’s suggesting that pure technical ability, divorced from wisdom and foresight, is about to become commoditized. When AI can generate functional code from natural language descriptions, the programmer who simply translates requirements into syntax becomes obsolete.
But the person who can anticipate unintended consequences? The one who asks “should we build this?” before diving into “how do we build this?” The individual who recognizes that a technically perfect solution might create more problems than it solves? That person becomes infinitely more valuable.
This shift is already happening, and most people aren’t prepared for it. One Reddit commenter captured the tension perfectly: “We need not only technical geniuses there. We need philosophers, politicians, religious people there helping to process the change.”
The future doesn’t belong to people who can code—AI can increasingly do that. It belongs to people who can think holistically about what should be coded, why it matters, and how it fits into the broader human ecosystem.
The False Choice Between Intelligence and Empathy
Several commenters pushed back against Huang’s definition, arguing that intelligence shouldn’t be redefined to include empathy. They’re missing the point entirely.
Huang isn’t saying empathetic people are smart and technical people are dumb. He’s saying that someone who possesses technical brilliance without empathy is operating with incomplete information—and incomplete information leads to suboptimal solutions, regardless of how elegant the code might be.
Consider the most consequential failures in tech over the past decade. Were they failures of technical execution? Rarely. Facebook’s role in spreading misinformation, algorithmic bias in hiring tools, privacy breaches that exposed millions of users—these weren’t problems that could be solved with better code. They were failures of foresight, wisdom, and empathy.
The engineers who built these systems weren’t unintelligent. They were simply solving the wrong problems, or solving the right problems while ignoring crucial context about how humans actually behave. That’s not intelligence—it’s sophisticated ignorance.
Looking Around the Corner: What This Means for Your Career
If you’re reading this and feeling uncomfortable, good. That discomfort means you’re starting to see around the corner yourself.
The question isn’t whether AI will continue automating technical tasks—it will. The question is what human capabilities become more valuable as that automation accelerates.
Based on Huang’s framework, here’s what to develop:
Systems thinking becomes paramount. Understanding how complex systems interact, how changes propagate through networks of dependencies, how second-order effects emerge—these are the skills that AI struggles with because they require holding massive amounts of context while navigating ambiguity.
Ethical reasoning matters more than ever. As AI capabilities expand, the ethical implications of what we build become increasingly complex. The ability to reason through competing values, anticipate unintended consequences, and make principled decisions under uncertainty becomes a core competency.
Human understanding deepens in importance. Knowing how to motivate teams, navigate organizational politics, understand customer psychology, anticipate market shifts—these deeply human skills don’t just complement technical ability. They multiply its impact exponentially.
Notice what’s missing from that list? Raw technical execution. Not because it’s unimportant, but because it’s increasingly automated. The technical genius who can’t translate their brilliance into actual human value is facing obsolescence.
The Smartest People in the Room
One commenter shared an observation about the smartest person they know: “He lives at least five minutes in the future, probably more.” This captures Huang’s point perfectly.
Intelligence isn’t about knowing more facts or executing algorithms faster. It’s about pattern recognition across domains, anticipation of future states, and the wisdom to act on incomplete information. It’s about seeing the game several moves ahead while everyone else is still figuring out the current board position.
Interestingly, this definition of intelligence aligns more closely with traditional Eastern philosophy than Western analytical thinking. It values wisdom over knowledge, synthesis over analysis, contextual understanding over abstract truth.
As AI takes over tasks that require pure analytical horsepower, the pendulum swings back toward these older, deeper forms of intelligence. The future belongs to generalists with deep expertise, to ethicists who understand technology, to technologists who understand humanity.
Your Next Move: Where Opportunity Meets Intelligence
Understanding what intelligence means in the AI age is one thing. Positioning yourself to capitalize on that understanding is another entirely.
Whether you’re a software developer recognizing that pure coding skills won’t future-proof your career, a manager trying to build teams for the AI era, or a professional looking to make your next strategic move, the opportunities are already emerging on HireSleek.com.
The platform showcases roles that value exactly what Huang describes: positions requiring technical competence paired with strategic thinking, empathy, and the ability to navigate the human-AI interface. From AI ethics researchers to technical product managers who bridge engineering and human needs, from strategic advisors helping organizations integrate AI responsibly to roles that didn’t exist five years ago—this is where the future of work is being built.
For companies, HireSleek offers access to professionals who embody this new definition of intelligence: people who don’t just execute tasks but anticipate implications, who don’t just build features but solve human problems. The traditional hiring playbook focused on credentials and technical tests is becoming obsolete. The future requires identifying candidates who think in systems, act with empathy, and see around corners.
Don’t wait until the AI transformation forces your hand. Explore the opportunities shaping the future at HireSleek.com—because the smartest move is often the one you make before everyone else realizes they need to.
The Uncomfortable Conclusion
Huang’s definition of intelligence is threatening precisely because it’s true. It suggests that many people who’ve built their identity around being “smart” in traditional ways are about to face a reckoning.
But it also suggests something hopeful: the skills that make us most human—empathy, wisdom, foresight, ethical reasoning—are exactly the skills that become most valuable as AI handles more of the technical heavy lifting.
The question isn’t whether you’re intelligent by yesterday’s standards. It’s whether you’re developing the intelligence that matters for tomorrow. Can you see around corners? Do you understand not just how to build things, but what’s worth building? Can you anticipate how your solutions will ripple through complex human systems?
Those are the questions that separate the truly intelligent from the merely technically competent. And in a world where technical competence is increasingly automated, that distinction matters more than ever.
The future doesn’t belong to the best programmers. It belongs to the best thinkers—and thinking, it turns out, is far more complex than we imagined.