September 6, 2026, (Inside AI) — A father of three who has studied artificial intelligence for 15 years watched his eight-year-old daughter laugh while reading a Dr Seuss-style story generated by ChatGPT in seconds. The story, based on a childhood idea he never wrote, became a moment of creative play. But it also raised a harder question: how do children embrace AI without losing core skills?
The father is Daniel Susskind, an economist and author who has advised governments and businesses on AI’s impact on work and society. His personal reckoning came during a family car trip to Suffolk. The family listened to a podcast called History’s Not Boring, narrated by two children. His wife, a podcast and documentary maker, later discovered the children did not exist. The entire podcast was AI-generated.
“It was a strange moment, learning that people we had grown fond of were not actually people at all,” Susskind writes. “It was also remarkable that AI could create such high-quality educational content. But for my wife, a podcast and documentary maker, it was an unsettling moment as well: here was something that would have taken her and a talented team several days to make, and now it was being done without people.”
The discovery crystallized a dilemma facing parents and educators worldwide. Teachers worry traditional methods no longer work. Parents wonder what children actually know when AI answers every question. Employers doubt whether exams and coursework still signal real ability. Susskind argues that banning AI is the wrong response.
Future-Proofing Failed Before Kids Left School
In 2013, then-UK Prime Minister David Cameron announced England would become the first country where all primary and secondary school children would learn to code. Education secretary Michael Gove said it would “equip every child with the computing skills they need to succeed in the 21st century.” Many advanced countries followed.
By January 2026, Anthropic reported that 90% of the code for Claude Code, its AI-powered coding assistant, was written by AI. The skill meant to protect children from disruption became largely redundant before they left school. Susskind calls this a policy failure rooted in a flawed belief: that we can identify “future-proof” skills AI will not master for decades.
“The truth is that we know only two things about what lies ahead,” he writes. “One is that it will be full of technologies that are far more capable than today. And the other is that we know little else.”
Instead of guessing which skills will survive, Susskind proposes a return to basics. Literacy and numeracy have fallen globally since 2009, according to the OECD’s Pisa assessments. These skills matter more, not less, because AI systems hallucinate and make simple mistakes. The computer scientist Geoffrey Hinton called them “idiot savants.” Users need sharp basics to tell when AI is being a savant or an idiot.
Teach Both, Test Both
Susskind points to a forgotten British maths professor, Wilfred Halliday Cockcroft. In 1982, the Cockcroft Report responded to fears about electronic calculators. Cockcroft proposed splitting maths education into two parts: teach students to use a calculator, and teach them to cope without one. Crucially, test both.
That principle became the global gold standard. Susskind argues the same approach should apply to AI now. Every subject, from history to English literature, should be divided in two. Students learn to use AI in one part and flourish without it in the other. Both parts get examined.
“A teacher cannot monitor whether or not a student uses AI in the quiet solitude of their bedroom,” he writes. “But nothing can replace the feeling of sitting in an exam, looking at the paper, and feeling that cold sweat when you realise you haven’t prepared for both parts.”
Susskind also warns against conflating social media with AI. Screen time debates miss the point. What matters is what children do on those screens. He cites personal tuition as an example. AI can provide tailored instruction at far lower cost than human tutors. A student quoted in The New Yorker in April 2025 said: “I don’t think anyone has ever paid such pure attention to me and my thinking and my questions. It’s made me rethink my interactions with people.”
For career advice, Susskind tells young people to choose a profession because the problem interests them, not the job. Jobs will change. Problems will remain. He points to Sebastian Thrun, a computer scientist who co-authored a 2017 Stanford University study on AI skin cancer detection. Thrun knew little about medicine but built a system rivaling leading dermatologists.
If starting over, Susskind would run toward AI and science. In 2024, creators of DeepMind’s AlphaFold2 won the Nobel Prize in chemistry for solving the protein folding problem. Frontier mathematics discoveries are accelerating. The main constraint on AI’s use, he argues, is the limits of human imagination.
