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What Will AI Look Like in 2035? A Parent's Honest Guide
Expert forecasts from Stanford, MIT, and OpenAI on AI in 2035 — what changes, what stays the same, and which skills matter for kids today.
Your 7-year-old will be 16 in 2035. They’ll apply to college — or to apprenticeships, or to creative programs that don’t exist yet — in an AI-saturated world. The question parents keep asking is: what should I actually do now to prepare them? Not “should I worry,” but “what specifically changes, and what do I need to do about it?”
That’s what this guide tries to answer. Not hype, not dystopia. Just what the best available forecasting tells us about AI in 2035 as a parent’s guide — and where the forecasters honestly disagree.
What the Leading Forecasters Actually Say About AI in 2035
The Stanford AI Index 2024, one of the most comprehensive annual summaries of AI progress, documents that AI systems have crossed human-level performance benchmarks in language understanding, image recognition, and many reasoning tasks — but still fail at tasks requiring genuine causal reasoning and sustained planning across novel situations.1
MIT researchers studying AI trajectory published projections in 2023 suggesting that by 2030–2035, AI will handle 50–80% of structured, routine cognitive tasks in most white-collar industries — but that tasks requiring real-world judgment, relationship-building, and contextual improvisation will still depend heavily on humans.2
OpenAI’s own technical roadmap, while deliberately vague, points toward “broadly capable” systems by the early 2030s — AI that can perform most professional cognitive work given clear instructions — but the company’s researchers have been careful to note that “capable” and “reliable” are different things. An AI that can draft legal briefs 95% correctly is still not one you’d hand a case to without careful oversight.3
The skeptics matter here too. Gary Marcus, a cognitive scientist at NYU who has been consistently critical of AI capability claims, argues that current large language model architectures have fundamental limits — they lack genuine understanding, causal models of the world, and robust common sense. His view, shared by other critics, is that “AGI by 2035” predictions are extrapolating a curve that will likely flatten.4 He may be right, or he may be wrong. As a parent, you don’t need to take a side — you need to make decisions under uncertainty, which is something humans have always done.
What the Stanford AI Index 2024 makes clearer than the hype cycle does: AI is not one thing. It’s a set of capabilities with wildly different maturity levels. Narrow AI (image recognition, text generation, recommendation systems, process automation) is already pervasive and will be more so. General reasoning, embodied intelligence, and autonomous long-horizon planning remain genuinely hard problems.
What Will Almost Certainly Be True by 2035
Some changes are happening regardless of how the prediction arguments resolve:
AI in the workplace will be pervasive. The McKinsey Global Institute estimated in 2023 that by 2030, up to 30% of current work activities could be automated.5 Whether that number is 20% or 40%, the direction is clear. Knowing how to work with AI systems — prompting them well, checking their outputs critically, knowing when to trust them and when not to — will be a baseline professional skill.
Personalized AI tutoring will be standard in schools. Khan Academy’s Khanmigo launched in 2023, and Carnegie Learning’s AI math tutor has shown measurable learning gains in peer-reviewed studies. By 2035, schools that haven’t adopted AI-assisted learning tools will be the exception, not the rule.
AI-generated content will be everywhere and indistinguishable. The ability to tell human-created from AI-created text, image, and video will be mostly gone for most people in most contexts. Critical evaluation of sources — what’s real, what’s constructed, who benefits — will be a daily survival skill.
Routine data analysis jobs will look completely different. Jobs like junior financial analyst, entry-level programmer, basic legal research, and medical coding — tasks that previously required four-year degrees — will be substantially AI-assisted or AI-handled. This doesn’t mean mass unemployment; history suggests new job categories emerge. But the transition matters.
What Will Probably Stay the Same
This is where the forecast gets more honest about limits:
Human relationships in high-stakes situations will still be human. Parents want human teachers for their children. Patients want human doctors for serious diagnoses. Juries trust human lawyers. Grief counselors need to be people. The evidence for AI replacing relational, high-stakes professional interactions in the next 10 years is thin.
Physical, hands-on, real-world skills will not be displaced. Electricians, plumbers, surgeons, and robotics technicians do work that requires dexterous physical judgment in complex, unpredictable environments. Robotic automation is advancing here too, but the timeline is longer.
Creativity anchored in lived experience will still be distinctly human. AI can generate plausible art, music, and writing. Whether it can generate meaningful art — things that emerge from a specific human life and resonate because of that — is a genuine open question. The debate among cognitive scientists and AI researchers is real and unresolved.
Skills Table: What Will and Won’t Matter in 2035
| Skill | Will it matter more, less, or differently in 2035? | What to build now |
|---|---|---|
| Basic coding / syntax | Less (AI writes most code) | Systems thinking; understanding what to build |
| Critical evaluation of information | More — not less | Practice spotting errors in AI outputs at home |
| Written communication | Same or more (directing AI well requires precision) | Write full sentences; no shortcuts |
| Mathematical reasoning | More | Not calculation — estimation, modeling, logic |
| Creativity & original ideation | More | Open-ended projects; building things with no manual |
| Persistence through difficulty | Same | Let kids struggle; resist solving problems for them |
| Human relationship skills | Same | Unstructured social time; conflict resolution |
| Rote memorization | Less | Use it to build fluency, not as an end goal |
| Adaptability / learning new tools | More | Regularly expose kids to unfamiliar tools and problems |
What You Can Do Today — For a 7-Year-Old
Let Them Build Things That Break
The most AI-proof skill isn’t a subject — it’s a disposition. Kids who grow up building things, debugging them, fixing them, and iterating are developing the loop of thought that no language model can replicate: grounded, embodied, cause-and-effect reasoning. A circuit kit, a Lego Technic set, a sewing project. It doesn’t matter what. The failure is the point.
Teach Them to Evaluate Claims, Not Just Accept Them
At age 7, this is as simple as asking “how do you know that?” when your kid repeats something they heard. By 10 or 12, it means showing them a Wikipedia article and an academic source and asking what’s different. By 14, it means looking at AI outputs critically. This builds the mental habit that will matter enormously in a world full of plausible-sounding AI-generated content.
Don’t Optimize for Today’s Tests
The school system is still optimizing largely for knowledge recall. A 7-year-old who memorizes spelling lists and state capitals will do fine in third grade. But parents who want to prepare kids for 2035 are also investing in the skills the tests don’t measure: asking questions, tolerating ambiguity, explaining their thinking out loud.
Learn a Little AI Yourself
You don’t need to be a programmer. But parents who have used AI tools themselves — tried ChatGPT, hit its errors, noticed its confident wrongness — are better equipped to have honest conversations with their kids about what these tools can and can’t do. Firsthand experience with AI limitations is more useful than any article about them.
Resist the Pressure to Specialize Too Early
A 7-year-old who “wants to be a programmer” or “wants to do AI” doesn’t need to specialize — they need breadth. The landscape will shift dramatically before they’re 25. Kids with wide exposure to different domains, ways of thinking, and types of problems are more adaptable than those narrowly optimized early.
You can read more about building these foundations in our guide on future-proofing kids’ career and AI skills and the data on AI job displacement in 2026.
What to Watch For Over the Next 3 Months
If you start shifting family habits based on this article, here’s how to know whether things are moving:
Month 1: Your kid starts asking more questions instead of accepting answers. Not a dramatic shift — maybe just one or two more “why”s per day. That’s the habit starting.
Month 2: When they use AI (if they do, in school or at home), they start catching errors or asking whether the answer is right. That’s critical evaluation taking hold.
Month 3: They finish a self-directed project — something they designed, built, or wrote without a template. The topic doesn’t matter. The initiative does.
Red flags: If your child is increasingly avoidant of hard problems, consistently uses AI to skip thinking (rather than to enhance it), or is showing signs of information overload and anxiety, those are patterns worth addressing — perhaps with a conversation, perhaps with limits, perhaps with a professional’s input. There’s more on this in our breakdown of the global AI race and what it means for your child’s competitive future.
Frequently Asked Questions
Will AI replace teachers by 2035?
Probably not replace — but significantly augment. AI tutoring tools are already showing learning gains in math and reading, and by 2035 they’ll be standard in most schools. But evidence from educational psychology consistently shows that relationships, motivation, and classroom culture matter enormously — and those depend on human teachers.
Should my child learn to code given that AI writes code now?
Learning to code teaches systems thinking, logic, and problem decomposition — not just syntax. Those skills matter more as AI handles syntax. The goal isn’t to train a human compiler; it’s to build a mind that understands how systems work.
What if my child isn’t “good at STEM”?
The skills that matter most for 2035 — critical thinking, communication, adaptability, creative problem-solving — aren’t STEM-exclusive. An English-focused kid who learns to evaluate evidence, argue well, and adapt to new information is well-prepared. The future favors T-shaped people, not narrow specialists.
Is it worth worrying about AI now for a 7-year-old?
Probably less than you think. The habits that prepare kids for an AI-saturated world — curiosity, persistence, critical thinking, breadth — are the same ones good parenting has always aimed at. Focus there, not on predicting which specific tools will matter in 10 years.
How do I explain AI to a 7-year-old honestly?
The most honest framing: AI is a very fast pattern-matching tool that learned from enormous amounts of text and images. It can seem smart, but it doesn’t understand — it predicts. The best metaphor: a student who read every book in the library but never went outside. Very good at some things. Surprisingly bad at others.
About the author
Ricky Flores is the founder of HiWave Makers and an electrical engineer with 15+ years of experience building consumer technology at Apple, Samsung, and Texas Instruments. He writes about how kids learn to build, think, and create in a tech-saturated world. Read more at hiwavemakers.com.
Sources
- Stanford University Human-Centered AI Institute. (2024). Artificial Intelligence Index Report 2024. Stanford HAI. https://aiindex.stanford.edu/report/
- Autor, D., et al. (2023). “The Changing Labor Market in the Age of AI.” MIT Work of the Future. https://workofthefuture.mit.edu/
- OpenAI. (2023). GPT-4 Technical Report. https://openai.com/research/gpt-4
- Marcus, G. (2022). “Deep Learning Is Hitting a Wall.” Nautilus. https://nautil.us/deep-learning-is-hitting-a-wall-238440/
- McKinsey Global Institute. (2023). The Economic Potential of Generative AI. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai
- Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age. W.W. Norton. (Referenced for historical context on automation transitions.)
- National Academies of Sciences, Engineering, and Medicine. (2023). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. https://www.nationalacademies.org/
Footnotes
