Vivienne Ming has built AI for refugee reunifications, autism communication tools, and an early-warning system for manic episodes. She has also built one for her son. When he was diagnosed with Type 1 diabetes in 2011, the standard of care was a finger-prick test strip and a paper log. So she wrote a machine learning model that predicts his blood sugar one to three hours out, hooked it into his medical gear, and ran it.
That résumé matters because Ming, a theoretical neuroscientist and former Chief Scientist at one of the first AI hiring companies, is one of the few AI insiders who are bearish on how the technology is being deployed. Her book, Robot-Proof: When Machines Have All the Answers, Build Better People, argues that generative tools, as currently designed, make most people measurably worse at thinking. Her EEG studies show gamma-band activity dropping by roughly 40% in users who hand questions to the model and take whatever comes back. She calls them the Automators and the Validators. Together, they are 95% of users. The other 5%, what Ming calls the Cyborgs, outperform everyone.
We sat down with Ming to talk about the 95/5 divide, the Jiffy Lube economy, and what to do about both.
You argue that 95% of people will see AI hurt their brains and economic lives, while 5% will become “the most powerful creative force on the planet.” What separates these two groups—is it education, access, or mindset? And more importantly, can someone move from the 95% to the 5%, or is this divide becoming permanent?
The divide isn’t education, access, or even raw intelligence. It’s a set of cognitive habits that determine whether AI makes you sharper or hollows you out. In Robot-Proof, I call this meta-learning: your ability to learn how to learn.
In my hybrid intelligence studies, I identified three archetypes. Automators handed the question to the model and accepted whatever came back. Validators used AI to confirm what they already believed. Both groups perform worse with AI than they did without it. Worse, EEG data show their brains are doing measurably less work, with gamma-band activity often dropping by 40% under AI conditions. Those are the 95%.
My hope for humanity comes from that final 5%, the Cyborgs, who argue with the model, ask it to steelman positions they disagree with, and treat it as an interlocutor rather than an oracle. They outperform everyone, including the best AI-only runs.
What predicts Cyborg behavior? Intellectual humility, curiosity, fluid intelligence, and perspective-taking—measurable traits, present before anyone touched a keyboard.
The good news is that these meta-learning skills are not fixed endowments. They can be developed. The bad news is that almost nothing in our current educational, professional, or technological environment is designed to cultivate them. We’ve built AI systems optimized for autonomy and efficacy. They give us the answer and “save” us from thinking, which is exactly the wrong design for human flourishing. Every “just ask AI” interaction trains the wrong muscles.
We can definitely move from the 95 to the 5, but it is effortful and difficult. You won’t get there by using AI more. You’ll get there by using it differently, and by deliberately resisting the products that promise to do “the boring stuff” so you can do “the fun stuff.”
The divide becomes permanent only if we let the current trajectory continue. That’s a design choice, not a destiny.
You’ve shared the gripping story of building an illegal AI to manage your son’s Type 1 diabetes when the hospital couldn’t provide answers. What did that experience teach you about the gap between what AI can do and what our institutions allow it to do? And how should parents think about taking matters into their own hands when their children’s health is at stake?
When my son was diagnosed with Type 1 in 2011, the standard of care was decades behind what was technically possible. My wife and I are both scientists, and we collected enough data about our son’s blood sugar, heart rate, and carbohydrates to regularly crash Google Docs. But we were sent home with finger-prick test strips and printed sheets to write a couple of numbers by hand. On the other hand, the open-source community had already developed loop algorithms that could read continuous glucose data and automatically dose insulin. The technology existed. The institutions hadn’t caught up, and in many cases were actively prohibited from doing so.
So we built it ourselves. Using some of that open-source code, I hacked my son’s medical equipment and did what I knew how to do: built a machine learning model to predict his blood glucose levels one to three hours into the future. I wish every parent could experience a “superpower” that could change their child’s life.
What that taught me is that the gap between what AI can do and what institutions allow it to do is now a life-and-death gap, and it widens every year. Regulatory frameworks were built for a world where medical innovation moved on pharmaceutical timelines—decade-long trials, slow diffusion. AI moves on weekly timelines. The mismatch isn’t a bureaucratic inconvenience. It’s a moral problem. Every month a system isn’t approved is a month of worse outcomes for the people it would have helped.
That said, I’m careful with the parental advice here. We had specific advantages. I study machine learning and brains, and I had the community of #WeAreNotWaiting parents who had already done the engineering work. Telling every parent to “take matters into their own hands” without that scaffolding is irresponsible.
What I’d say instead is, be a sharp consumer of your child’s care. Ask what’s possible, not just what’s offered. Find the patient communities where parents share what’s actually working. And push your providers, your insurers, and your representatives to close the gap between technical capability and institutional reality. Your kid doesn’t have time to wait for the system to catch up.
You describe AI as “deprofessionalizing elite careers”—from doctors to lawyers. For Worth’s audience of business leaders and investors, what does this mean for how they should think about their own careers, their companies, and where they’re allocating capital? Which professions are most vulnerable, and which human capabilities become more valuable?
Here’s the unflattering truth about a lot of professional work: it consists of pattern-matching against precedent, with the credentialing barrier doing most of the economic work. Once a system can match patterns at human-or-better quality, the barrier collapses, and what’s left is what couldn’t be patterned in the first place.
That’s why I call it the Jiffy Lube economy. We didn’t lose mechanics when oil changes got systematized. We lost the premium on routine mechanical work. The diagnostic mechanics who reason about novel failures still command real fees, but the vast majority of that labor is now done by high school grads who hook your car up to a machine that tells them what to do. The same shape is coming, at varying paces, for medicine, law, consulting, financial analysis, and large parts of software engineering. The jobs aren’t vanishing. They’re deprofessionalizing.
For your readers, three implications matter.
On their careers: the question isn’t whether AI can do parts of your job. It’s which parts of your job actually generated the premium you’ve been paid, and whether those parts survive. If your value-add was the synthesis of complex documents, that’s commoditizing fast, as we are already seeing in software engineering. If your value-add was judgment under genuine uncertainty, relationship capital, or the ability to define which problem is worth solving, that’s appreciating.
On their companies: organizations spending the next five years using AI to make existing roles incrementally more productive will be outcompeted by organizations that restructure around hybrid intelligence from the ground up. The efficiency story is a trap. The restructuring story is the real one. There is already not enough elite labor to meet demand, and reskilling from one pattern-matching job to another won’t change that. Invest in lifting the meta-learning capacity of your human capital.
On capital: I’d be skeptical of any business whose moat is regulatory protection of pattern-matching work. Everyone has access to the same AI models. They get the same answers in their pockets. I’d be very interested in businesses building the infrastructure for hybrid intelligence—tools, training, measurement, governance—because that market barely exists yet and every serious organization will need it.
The capabilities that become more valuable are the ones AI is structurally bad at: navigating genuine ambiguity, taking moral responsibility, building trust across long time horizons, and asking the questions no one has asked yet. My research shows the value of these human qualities increasing dramatically as machines become more intelligent.
What Makes Kids Robot-Proof
You’ve analyzed decades of large-scale human development data. What does the research actually reveal about raising adaptable, resilient thinkers? Are there specific traits or experiences in childhood that predict who thrives in an AI-augmented world?
After thirty years of studying natural and artificial intelligence, I can tell you what doesn’t predict thriving: test scores, GPA, IQ above a fairly modest threshold, prestige of school, and almost everything that anxious affluent parents currently obsess over.
What predicts an amazing life—health, wealth, and well-being—is a more interesting set of traits. Curiosity that survives schooling, because most kids start curious, and we systematically extinguish it. Resilience working on ill-posed problems with no answer key. Intellectual humility is the comfort of being wrong in public. Perspective-taking, which turns out to predict not just social outcomes but cognitive ones, including the Cyborg behaviors we see in adults working with AI. And what I’d loosely call narrative agency: the sense that you are the protagonist of your own life rather than someone executing a script handed to you.
In Robot-Proof, I explore these and many other factors—purpose, working memory span, subjective utility—that predict positive life outcomes. They predicted career outcomes for the 122 million people I analyzed as Chief Scientist at one of the first companies to use AI in hiring. They predicted flourishing during the pandemic. And they arise again in my latest research on hybrid intelligence. Prodigies with “perfect” university applications don’t go on to change the world. Kids rich in meta-learning do.
The childhood experiences that build these are unglamorous. A recent study found that rewarding good questions rather than “right” answers causally increased curiosity in kids. Others have shown that open-ended play that adults don’t direct has positive impacts on creativity and agency. Real responsibility—chores that matter, decisions with stakes, the chance to fail meaningfully and recover—lifts resilience and courage. Cross-age and cross-context relationships, not just same-age peers in the same school, grow perspective-taking. Reading widely and deeply, including fiction, is the most efficient perspective-taking technology our species has ever invented.
The traits that matter are the ones most easily killed by optimization. Every parent-managed activity, every algorithmically curated piece of content, is time the child isn’t building the muscle of figuring out what they think and what they want.
Robot-proofing isn’t about teaching kids to use AI well. I don’t even know what “AI literacy” is supposed to mean. It’s about protecting the developmental conditions for a child to become an explorer.
Beyond commercial applications, you’ve built AI systems to reunite refugee children with family, help autistic kids read facial expressions, and predict manic episodes. What have these projects taught you about building technology that makes us more human rather than less? And how do you think about the ethics of deploying AI in such sensitive contexts?
The reunification work, the autism communication tools, the early-warning systems for manic episodes—these projects have a common structure. They start with understanding the human dimension of the problem. Too often, “AI for Good” is treated as an engineering problem with an ethical checklist. Doing any kind of good in the world means owning the whole problem and not stopping until the world is changed.
When I built the system to reunite refugee children with their families, the AI did the part humans cannot do at scale: matching faces across thousands of records in poor lighting. That was technology I’d originally learned from a CIA-sponsored academic project, but when I was introduced to the Refugees United program, it became more than just an engineering project. The Google Glass tool that helps autistic kids read facial expressions doesn’t replace social interaction. It scaffolds it, then fades as the kids no longer need the help. The signals of a manic episode that my collaborators and I discovered don’t replace human intervention. They specifically trigger warnings to loved ones and clinicians whom the user has named ahead of time.
We can use AI and machine learning models to deepen human connection or to substitute for it. Doing good with AI almost always starts by observing humans struggling with the problem in advance, rather than by analyzing data or optimizing models. It succeeds only when people own the whole problem, not when they assume AI will solve it for them.
I’m wary of frameworks that treat ethics as a checklist applied at the end. The hard ethical work happens in the original framing: who is this for, what does success look like for them, who bears the cost if we’re wrong, and who has the standing to decide we should stop. I’ve refused work and shut down projects mid-build when the answers to those questions stopped being answers I could defend. Practice saying no. It’s the only ethics that holds up under pressure.
For parents reading Worth—many of whom have the resources to give their children every educational advantage—what practical advice do you have? What should they be doing differently to prepare their kids for a world where machines have all the answers?
The cruelest irony of resourced parenting is that the more advantages you provide, the more you can accidentally optimize away the very traits your child needs. I see this constantly with founders, executives, and investors who run their children’s development with the same intensity they run their portfolios, and produce kids who are excellent at executing other people’s instructions and lost when nobody is grading them.
Here are a few practical recommendations, in roughly increasing order of difficulty.
Protect unstructured time. Your child choosing to do something creative is very different than being forced to do it. A child with no white space on the calendar never has to find out what they’re interested in.
Let them be bad at things in front of you. Pick up an instrument, a language, a sport you’re struggling with and practice in their presence. Don’t train them to give “right” answers. Train them to explore. In a study my wife and I conducted analyzing discussion forum interactions among university students, the vast majority were afraid to be wrong in public, even when they weren’t penalized. The best students explored and were regularly wrong, but this forced them to confront their own misconceptions and allowed learning and growth to occur. Give them real responsibility, not the theater of responsibility: cooking the family dinner once a week, managing their own appointments by middle school, handling a budget, negotiating with a real adult on their own behalf.
Read fiction together past the age when it feels necessary. Argue about the characters’ choices. This is something my family does each night over dinner.
The biggest influence you will have on your kids after genes is what you role-model. Be the people you want your kids to be. This includes how you use AI. Use it with them, out loud, the way you’d want them to use it. Argue with your favorite agent. Ask it for the strongest case against your view. Catch it being wrong, and struggle with explaining why it’s wrong. Most kids are going to see adults using AI to skip thinking. Show them the power of being different.
Hybrid Intelligence
You’ve spent 30 years building at the intersection of explain I and human potential. When will companies like your Possibility Sciences? What’s your vision for hybrid intelligence? Is the future really about augmentation rather than replacement—and if so, what needs to change about how we’re building and deploying AI today?
The vision is straightforward to state and harder to build: AI systems whose success is measured by what the human-AI pair can do together over time, not by what the AI can do alone on a benchmark.
Almost every dollar in AI right now flows the other direction. We benchmark models on tasks they perform autonomously. We celebrate when they beat humans. We design products around the fantasy that the right amount of AI is a “magic genie that solves all problems.” That trajectory isn’t just bad for human flourishing. It’s bad business. The actual frontier of value creation is hybrid, and we’re not even measuring it.
At Possibility Sciences, we’re building a platform for innovation itself. Our machine learning models map the trajectories of disruptive innovation in science, economics, policy, and culture. We feed that with agents specifically designed to measure differing facets of innovations: cost, safe next steps, unexpected combinations, and more. All of that shows us a likely future emerging from the traditions of the past. The hybrid component is a distinct, massive innovation engine that enables humans to ideate and explore alternative trajectories. Like the show For All Mankind, our system allows people to explore the what-ifs of innovation. What if there had been more funding for mRNA research in the 1990s? What if solid-state batteries had hit scale before lithium-ion? What if the U.S. had treated semiconductor manufacturing as critical infrastructure twenty years earlier? The point isn’t nostalgia. It’s the same machinery, run forward, that lets a foundation, an investor, or a national lab ask: which non-obvious trajectory should I back right now to shape a breakthrough I care about in five to ten years?
At my nonprofit, The Human Trust, we are beginning to develop a Hybrid Intelligence Index (HIX) to measure the impact of AI agents on human outcomes. Which models lower mortality risks in their users? Which lifts creativity the most? Which produces the most economic gains accumulated over a lifetime? None of this can be measured by testing agents in isolation with questions that have definably right answers. HIX puts the human-AI pairing as the unit of analysis.
The most striking result so far: a model we trained to never give answers—”Socrates,” which scores zero on every standard benchmark—produces the highest average hybrid intelligence we’ve measured. When the AI refused to hand over answers, more than twice as many users switched into Cyborg mode and started exploring. That result should be embarrassing to the field. But it’s also the most hopeful finding I’ve seen in a decade.
