PUBLISHED May 14th, 2025 06:05 am | UPDATED May 30th, 2025 07:08 am
The air buzzed with energy at Porto‘s historic Alfândega Congress Center as SIM 2025 (Start-up, Investment, and Matching) unfolded over two intense days of innovation and insight. Bringing together entrepreneurs, investors, and industry leaders from across Europe and beyond, the conference was a launchpad for big ideas. But amid the startup pitches and buzzword-laced panels, one talk stood apart for its intellectual heft and emotional resonance: Dr. Vivienne Ming’s keynote on Human-Centric AI, human potential, and the ethical challenge at the heart of technological progress.
Dr. Ming, a theoretical neuroscientist, entrepreneur, and founder of the think tank Socos Labs, is no stranger to provocation. Her work spans building AI that predicts manic episodes, optimises education, helps children with autism read facial expressions, and even enhances hearing in people with cochlear implants. But at SIM, she wasn’t here to dazzle with case studies. She was here to interrogate us.
“AI shouldn’t make your life easier,” she said. “It should make you better.”
And just like that, the tone was set.
From Coin Flips to Purpose
Dr. Ming opened with a confession: she didn’t pursue AI because she foresaw its cultural dominance. As a student, she flipped a coin between two research paths. Neuroscience won. That twist of fate eventually led her into computational neuroscience, and from there to decades of building AI systems that do more than automate: they augment human lives.
But augmentation, she warned, is not the same as convenience. The AI that writes your emails may save you time, but it won’t spark a better idea. The tutor that gives students instant answers may raise scores in the short term, but it stunts real learning. Her call was clear: we must stop designing technology that flatters our laziness and start creating tools that sharpen our thinking.
The AI Data Switch: Promise vs. Bait-and-Switch
Dr. Ming introduced what she called the “AI Data Switch”: the dual-edged promise that AI will enhance human capability while quietly replacing human roles. She illustrated it with healthcare. Imagine a general practitioner empowered by AI, able to access everything about a patient before the appointment begins, allowing for tailored, empathetic care. Now imagine a more likely outcome: replacing that doctor with a physician’s assistant and a chatbot, because it’s cheaper and “just as effective.”
“The sweet spot for AI,” she explained, “is de-professionalisation. Taking expensive humans out of the loop.”
Efficiency, she argued, has become a Trojan horse for disempowerment. But it doesn’t have to be this way.
What Real Learning Requires
Drawing on decades of educational research, Dr. Ming dismantled the utopian notion that AI tutors are the answer to modern education. The data shows: when students are given the answers by AI, they learn less than those who get no AI assistance at all. The only effective AI tutors are the ones that refuse to give answers and instead ask better questions, leading students through confusion, frustration, and eventually insight.
This pedagogical insight is a microcosm of her broader thesis: to build truly transformative AI, we must resist the instinct to make life easier. Challenge must be designed in.
Productivity Myths: Who Really Benefits From AI?
Citing studies from Harvard and Boston Consulting Group, Dr. Ming debunked the myth that generative AI universally boosts productivity. The evidence is clear: lower-skilled and less-experienced workers see dramatic gains when using AI to complete tasks. But more experienced professionals, such as writers, coders and consultants. These individuals often perform worse when relying on AI, especially when judged on creativity and insight.
Her point: AI lifts the floor, but does little to raise the ceiling. To truly empower talent, AI must serve as a partner in exploration, not a crutch for output.
“AI shouldn’t make your life easier: it should make you better.”
Better People, Not Just Better Machines
Dr. Ming’s body of work is a testament to the power of this philosophy. She has built AI systems that don’t just solve problems, but help people become better at solving them themselves. Whether hacking medical devices to help her diabetic son or developing AI that predicts postpartum depression from epigenetic data, her focus is not on efficiency but on transformation.
One of the most striking moments came when she described feeding an entire book manuscript into a large language model, not to write it, but to tear it apart. She asked the AI to become her toughest critic: “Find every place I lied, misled, or oversimplified.” This, she argued, is what AI is for. Not automation, but confrontation.
The 20/80 Problem: Innovation vs. Intimidation
Dr. Ming referenced a recent study that revealed a troubling dynamic. In a company deploying advanced AI tools to identify new protein structures, only 20% of scientists used the system effectively. These few boosted innovation and drove profitability by 40%. The remaining 80% were overwhelmed. They couldn’t interpret the AI’s suggestions and became less effective.
The implication? Better technology alone is not enough. We need better people: curious, courageous, and capable of engaging with complexity.
Towards a Human-Centric AI Future
Dr. Ming concluded with a radical idea: what if every AI system was designed like the best teachers, the ones that never give answers, the ones always asking better questions? What if instead of efficiency, our benchmark for success was growth?
At a conference dominated by fundraising metrics and scaling strategies, her message was a necessary disruption. Her vision for AI isn’t about getting more done. It’s about becoming more human.
As Porto’s sun dipped below the Douro and SIM 2025 wound to a close, her words lingered: “AI (Artificial Intelligence) should make your life harder, harder in the ways that make you better.”
In a world racing toward effortless everything, Dr. Ming wants AI that asks better questions. And that’s what we must demand from the smartest people in the field: not just faster algorithms or higher efficiency, but systems designed to provoke, to challenge, and to elevate human thinking. We should be building technologies that make us pause, reconsider, and grow. Not ones that pamper our habits or replace our effort.
True innovation isn’t about doing more with less, it’s about becoming more through struggle, curiosity, and rigor.
For more information on Dr. Vivienne Ming’s work, visit Socos Labs