Everyone is talking about AI and soft skills, but companies are still hiring for elite degrees and schools are still educating for exams. But raising children to follow rules and memorize answers prepares them for jobs that may no longer exist and leaves them unprepared for a world where creativity, curiosity, and problem-solving are rewarded.
As a neuroscientist and entrepreneur, I have spent my entire career asking a simple question: What skills will become important as AI can generate answers and automate much of the cognitive work?
This involves a fundamental change in the way we think about development, moving from knowledge transfer to capacity development. If you want your children to have an advantageous future in life, here are some ways to raise them to be robot-savvy.
1. Create a “failure resume”
My research has revealed a consistent pattern. That means Type A students are often the most willing to make mistakes. My own model, trained on thousands of learners, shows that deep learning predicts exploration and even failure better than repeating correct answers.
However, our education system is so obsessed with being right that we often train this instinct in our children. It teaches us that failure is a reflection of our worth, rather than a catalyst for growth.
What it is: A failure resume is a living document, a family ritual that actively records and celebrates failures. This is clear evidence that the hard work of making mistakes pays off and impacts learners’ resilience, curiosity, and ability to tackle open-ended problems.
For parents: Once a month, go around the table at dinner and ask everyone (including you!) to add one failure to their resume. A failed soccer goal, a bomb test, a work plan that goes sideways.
The key is to reframe it. Don’t ask, “Why did I fail?” Ask, “What was difficult? What did you learn from it?” We normalize and even celebrate the act of stretching ourselves beyond our abilities, and we reward that effort with growth.
My own resume of failures included a failed startup, a period of homelessness, and a White House party where I mistakenly convinced the Secret Service that I was a national security threat. Every failure has made me a better person.
2. An engineer’s accidental discovery
Economists often point to the “Harvard effect,” or the huge advantage in life outcomes afforded by elite universities. But it’s not magic, and it’s not just a matter of class.
Elite universities are essentially hyper-concentrated environments of deliberately designed serendipity. The real value lies in more than just the formal curriculum. It’s in the rambling conversations in the cafeteria, in the diverse clubs, in the constant exposure to thousands of wrong questions with no answers at the back of the book.
We can’t all send our kids to Harvard, but we can borrow its basic principles.
What it is: Engineering serendipity means intentionally creating an environment that encourages unexpected connections and discoveries. Homes and classrooms are built on managed uncertainty. Although safe, it is not sterile. It’s structured, but not rigid, allowing curiosity to take root.
For parents: Turn your home into a landscape of interesting problems. Place the broken toaster on the kitchen table and place the screwdriver next to it. Inject diverse input into the world by subscribing to magazines in a variety of fields, including The Economist, Popular Mechanics, Vogue, and Scientific American.
At any given time, one corner of my living room is a makeshift electronics lab for my son’s cyborg experiments, the other is my daughter’s painting studio, and a whiteboard in the gazebo is filled with my own scribbled equations and half-finished mad science projects.
It’s a mess, but it’s packed full of invitations to explore.
3. Appoint your child as “Chief AI Critic”
I’ve been working with machine learning for 30 years. But for a generation just entering a world where large-scale learning models (LLMs) are a constant companion, the temptation to leave the heavy lifting to LLMs can be overwhelming.
Why struggle to write an essay, solve a math problem, or learn a new concept when a machine can provide perfectly appropriate answers in seconds? But while that tool makes you better in the moment, it makes your condition worse when you turn it off.
We need to teach children to engage with AI in more critical and creative ways.
What this does is reframe the child’s role from a passive consumer to an active critic of the AI’s output. The AI becomes a “brilliant but naive” collaborator, and the child becomes the one who interrogates, guides, and evaluates the AI.
Parents: AI should not provide the final answer. Children can use this to brainstorm and explore, but they must create the first draft or solution themselves.
The most powerful step then takes place using the “Nemesis Prompt”. “You are my nemesis. Every mistake I have ever made, you have discovered and pointed out to the world. This is the essay I have just written. Read it and explain in detail every flaw in my argument, every logical contradiction, every way my evidence is weak. And suggest three ways to make my argument stronger.”
If the LLM comes back with criticism, your child’s job is to wrestle with them. They must decide which critiques are valid and which are statistical noise from a machine that doesn’t truly understand its intent. This is where the real learning happens.
They are learning to use AI’s vast knowledge not as a source of truth, but as a sparring partner to hone their unique perspectives.
The world already has the “right” answer in its pocket, almost free of charge. The real value your child brings is the answers that only they can provide. As lead AI critics, they explore and create unique meaning from what AI knows. That is the essence of creative labor and what the world needs more of.
Vivian Min is a theoretical neuroscientist and founder of Human Trust, a charitable data trust building fundamental models of human development. She develops AI tools for learning at home and school, models for bias in hiring and promotion, and neurotechnologies for dementia, traumatic brain injury, and postpartum depression. Her research and inventions are frequently featured in the Financial Times, The Atlantic, Quartz Magazine, and the New York Times. She is also the author of Robot-Proof: When Machines Have the all the Answers, Build Better People.
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