Last week, I discovered myself hunched over my laptop computer at 10 p.m. (hey, that’s late for me!), wrestling with a coding drawback. After hours of frustration, I stepped away and made a cup of tea. When I returned, I did what any self-respecting technologist in 2025 would do: I backtracked, reformulated my query, and requested ChatGPT for assist.
I’m consistently requested questions like (*5*) and “What skills do they actually need in this AI world?” I ponder about this too. I imply, if AI can now write code higher than most people, ought to we nonetheless be instructing youngsters to do it? How will we put together them for the longer term, particularly as issues are shifting so shortly?
Perhaps counterintuitively, this AI revolution may make a liberal arts schooling extra useful. A poetry main learns find out how to specific humanity. A historian learns classes from the previous. A philosophy scholar learns to query assumptions and moral frameworks. These timeless human expertise grow to be much more essential as AI handles the technical heavy lifting. With these foundational talents to grasp and specific the human situation, what’s doable with creativity turns into boundless.
The End of Coding Is the Beginning of Problem-Solving
As AI begins writing code, we’re getting into what my good friend Tim O’Reilly calls “the end of programming as we know it.” We’ve gone from punch playing cards to meeting language to C, Python, and JavaScript—and now we’re simply telling computer systems what to do in plain language. That shift opens the door for extra folks to form know-how. The future isn’t about understanding code; it’s about understanding what to construct and why.
Stanford researchers, together with Noah Goodman (who’s each a pc scientist and a psychologist finding out human cognition), not too long ago printed a captivating paper inspecting how totally different AI methods strategy problem-solving.
What makes Goodman’s perspective so useful is his twin experience in how minds, each human and synthetic, work. His paper reveals that the considering patterns that make sure AI methods extra profitable mirror these of efficient human problem-solvers: The most profitable methods confirm their work, backtrack when caught, break huge issues into manageable subgoals, and work backward from desired outcomes.
It’s a profound discovery: The expertise that make people efficient problem-solvers will stay useful no matter how AI evolves. It made me notice that these cognitive behaviors—not coding syntax—are what we must be nurturing in our kids.
Five Essential Skills Kids Need (More than Coding)
I’m not saying we shouldn’t train youngsters to code. It’s a helpful talent. But these are the 5 true foundations that may serve them no matter how know-how evolves.
1. Loving the journey, not simply the vacation spot
When homework appears unimaginable or a LEGO construction collapses for the fifth time, it’s simple for youths to get discouraged. But instructing them that setbacks are studying alternatives builds the bounce-back capacity they’ll want in a quickly altering world. The capability to soak up real setbacks and proceed ahead—discovering one thing new even once they don’t attain their preliminary purpose—may be the one most vital talent we will nurture in our youngsters.
Developing a love of studying helps them to see robust issues as attention-grabbing puzzles moderately than scary roadblocks. This doesn’t simply apply to educational topics. Genuine curiosity concerning the world prepares kids to adapt repeatedly. The most profitable folks I do know aren’t those that memorized essentially the most details or mastered one particular talent; they’re those who stayed curious and stored going by fixed change.
We usually speak about intrinsic motivation as a prerequisite for studying, but it surely’s additionally a muscle you construct by the training course of. As kids sort out challenges and expertise the satisfaction of overcoming them, they’re not simply fixing issues; they’re creating the motivation to sort out the following one.
2. Being a question-asker, not simply an answer-getter
When you’re a scholar, you’re judged by how properly you reply questions.…But in life, you’re judged by how good your questions are.—Robert Langer, MIT Professor and Cofounder of Moderna
Anyone can ask AI for solutions. Those who ask considerate questions will get essentially the most from it. Good questions stem from understanding what you don’t know, being clear about what you’re actually searching for, and framing them in a means that results in significant solutions.
One of essentially the most highly effective metaskills we might help kids develop is self-awareness about their very own studying fashion. Some are project-based learners who have to construct one thing in order to grasp it. Others study by dialog, writing, visualization, or instructing others. When a toddler discovers how their mind works finest, they will strategy any new topic by the lens that works for them, turning what may need been a wrestle right into a pure course of.
When a toddler asks, “Why is the sky blue?,” they’re doing one thing highly effective: noticing patterns, questioning what others take without any consideration, and looking for deeper understanding. Children who study to ask good questions will direct the world moderately than be directed by it. They’ll know find out how to break huge issues into solvable items—an strategy that works in any area.
3. Trying, failing, and attempting otherwise
When fixing issues, scientists don’t transfer ahead in a straight line. They make guesses, check them, and sometimes uncover they had been fallacious. Then they use that info to make higher guesses. This try-learn-adjust loop is one thing all profitable problem-solvers use, whether or not they’re fixing code or determining life.
When one thing doesn’t work as anticipated—together with an AI-generated reply—youngsters want to determine what went fallacious after which strive totally different approaches. This means getting snug with saying issues like “Let me try a different way” or “That didn’t work because…”
Whether they’re troubleshooting a tool or navigating on a regular basis challenges, this mindset helps them strategy issues with confidence moderately than giving up.
4. Seeing the entire image
The greatest challenges we at present face, from local weather change to healthcare, require understanding how totally different items join and affect one another. This “big-picture thinking” applies equally to on a regular basis conditions, reminiscent of understanding why a classroom will get noisy or why a household price range doesn’t stability.
This mindset is about recognizing patterns and understanding how altering one factor impacts every little thing else. It helps us anticipate unintended penalties and create options that really work.
When we train youngsters to see connections moderately than remoted details, we put together them to sort out issues that AI alone can’t remedy. They grow to be administrators moderately than followers, capable of mix human wants with technological prospects.
5. Walking in others’ sneakers
In my current op-ed for the Chicago Tribune, I argued that effectivity and empathy aren’t opposing forces. They want one another. This precept is very vital as we elevate the following technology.
Technology with out human understanding results in options that may look good on paper however overlook the true folks they’re meant to assist. I’ve seen this firsthand in authorities methods that course of folks effectively however fail to acknowledge their dignity and distinctive conditions.
Children who develop deep empathy will create applied sciences that actually serve humanity moderately than simply serving statistics. They’ll ask not solely “Can we build this?” however “Should we build this, and who will it help or harm?” They’ll keep in mind that behind each knowledge level is a human story, and that essentially the most significant improvements are people who strengthen our connections to 1 one other.
The Real Future: Amplifying Human Creativity
These 5 expertise converge in what I see as essentially the most thrilling side of our AI-augmented future: democratized creation. As extra folks acquire the power to form know-how, even with out conventional coding expertise, we’ll see an explosion of native, purpose-driven options.
As I not too long ago wrote, I helped put collectively ai/teenagers, the primary international AI convention for and by teenagers. I wished to study from the primary AI-native technology, which intuitively understands know-how’s potential in methods many adults don’t.
Imagine a world the place younger folks not solely use know-how however actively form it to unravel issues in their communities, designing accessibility instruments for associates with disabilities, creating platforms that join native assets with those that want them, or constructing academic experiences tailor-made to totally different studying types.
This future isn’t about AI changing human creativity; it’s about amplifying it, making it doable for extra folks to convey their distinctive views and options to life.
Let’s Build This Future Together!
The fantastic thing about this strategy—specializing in resilience, questioning, adaptation, methods considering, and empathy—is that it really works no matter how know-how evolves. The most technologically superior future nonetheless wants individuals who can embrace challenges, ask significant questions, study repeatedly, see connections, and perceive one another.
In some ways, we’re returning to the perfect of a classical schooling for the AI age. These expertise type a contemporary trivium—not grammar, logic, and rhetoric however maybe curiosity, creativity, and compassion—foundational talents that unlock all different studying and doing.
Let’s work on this as a neighborhood! I’m crowdsourcing concepts, actions, and approaches that assist develop these important expertise. What different expertise do you assume we must always deal with? I’m desperate to study with all of you.