What drives the choices people make — and how can technology learn to understand them?
In a new episode of EVOLVE Voices, Aampe Co-Founder and CEO Paul Meinshausen joined Edilsa Bueno to explore what sits at the heart of both human and machine intelligence: the ability to learn from signals we don’t yet understand.
Why We Don’t Know What We Don’t Know
In the episode, Paul dives into how humans and machines both grapple with limited information:
“People don’t know what they don’t know. The best systems — human or AI — are the ones that keep learning anyway.”
From the subconscious cues people rely on — what they wear, when they engage, what they notice — to the invisible biases that shape decisions, Paul explains how agentic systems can help companies uncover and respond to those underlying patterns in real time.
Beyond Data — Toward Understanding
Much of today’s technology focuses on collecting more data. But as Paul notes, more data doesn’t automatically mean more understanding. The real challenge is making systems adaptive — enabling them to respond dynamically to new information instead of just describing what happened in the past.
That’s the foundation of Aampe’s agentic infrastructure: software that doesn’t just predict user behavior, but experiments, learns, and evolves alongside users.
“We shouldn’t expect people to bend to static systems. We should build systems that flex to human complexity.”
A Different Kind of Conversation
The episode also touches on the human side of technology building — the honesty that happens behind conference stages, how founders can collaborate with investors as real teammates, and how curiosity, not certainty, is often the most valuable mindset in innovation.
For anyone thinking about the intersection of AI, decision science, and human behavior, this conversation offers an authentic, wide-ranging perspective on how to make technology more human — not by simplifying people, but by designing systems that can learn from them.
