Alright – so today we’ve got the honor of introducing you to Marta Morawska. We think you’ll enjoy our conversation, we’ve shared it below.

Marta , looking forward to hearing all of your stories today. We’d love to have you retell us the story behind how you came up with the idea for your business, I think our audience would really enjoy hearing the backstory.

I’ve always been fascinated by people’s inner architecture — the part you only notice when you really care. The way someone thinks, feels, or operates under pressure reveals more than any credential, yet most systems flatten that into stiff categories that miss the dynamics underneath. That gap—between how complex people truly are and how simplistically they’re understood—was impossible to ignore.
The idea to build MINDACC emerged from watching AI systems scale into billion-parameter architectures, mapping language, proteins, and markets with extraordinary precision, while the frameworks we use to understand human capability remained stagnant. It was contrast so large it felt structural: intelligence itself was being redesigned, but our models of human intelligence remained vastly two-dimensional.
That was the moment I realized: the tools we use to understand and collaborate with people haven’t been updated. They are still built on a 20th-century model of the mind — linear, symptomatic, part-by-part. But the mind doesn’t work like that. It is a living, adaptive, pattern-forming system running in a world increasingly mediated by other intelligent systems.
This wasn’t just a scientific discrepancy—it was a human cost. People were misread, misallocated, or misunderstood not because they lacked potential, but because the tools interpreting them lacked depth. I kept seeing talented individuals struggle in environments that didn’t match their cognitive patterns, and organizations misdiagnose problems that were fundamentally about perception, interpretation, and meaning—not motivation.
That’s when the idea crystallized: if AI is moving toward adaptive architectures, human development needs one too. So I built MINDACC around that gap — modeling how cognition moves in real situations, not just describing it. Today, this gives leaders something that didn’t exist before: a living model of how their people operate, perform and scale — generated in minutes, not months. It’s an intelligence system that evolves with the people it serves. Early results are clear: users make better people decisions, teams align faster, and complex dynamics become navigable. The foundation is built and validated. Now it’s about scale.

Marta , love having you share your insights with us. Before we ask you more questions, maybe you can take a moment to introduce yourself to our readers who might have missed our earlier conversations?

I’m a Polish-Norwegian founder working at the intersection of human behavior and AI. My background spans interdisciplinary research, co-founding a medical practice above the Arctic Circle that served local and Indigenous Sámi communities, government consulting in Oslo, and clinical work in New York. These environments taught me that understanding how people think, decide, and perform under pressure isn’t theoretical — it’s operational.
I founded MINDACC to address a gap I kept seeing across those settings: bringing the level of precision we expect in medicine and finance to how humans actually perform, decide, and adapt — especially as AI systems continue to accelerate. The cost of misreading people and team dynamics is enormous, yet the tools we rely on to understand human capability have barely evolved

Learning and unlearning are both critical parts of growth – can you share a story of a time when you had to unlearn a lesson?

I spent time in New York working with engineers, quants, and operators, making high-stakes decisions every day. They weren’t interested in understanding themselves. They wanted an edge. That gap — between self-insight and actual performance — is where everything I thought I knew about cognition started to break.
What I had to unlearn was the idea that the mind is static. Most systems still treat cognition as something you can describe, label, or fix — as if it were a stable object. Traits, diagnoses, habits. Clean categories applied to something that, in reality, behaves more like a living system — fluid, adaptive, continuously reshaped by its environment. That framing started to feel insufficient. Not wrong, but incomplete. It assumes cognition is contained within the individual. In practice, it’s distributed — across tools, relationships, incentives, and systems. Thinkers like Bateson, Maturana, and Gibson pointed to this decades ago: the mind is not an isolated unit. It is ecological.
The shift mirrored what happened in biology when it moved from observation to design. Synthetic biology didn’t just study life — it began to engineer it. Moving from describing systems to building with them. That became the bridge. It suggested that cognition, too, could be approached as something that can be structured, interfaced, and evolved.
So we started experimenting. Instead of treating thoughts and emotions as narratives, we tried to look at them as noisy, high-dimensional data — translating subjective experience into representations we could test, iterate, and refine. We started with the hardest case: if we could translate poetry into mathematical structure without losing what makes it human, we could translate anything. The point wasn’t the output. It was building a bridge between languages that rarely meet.
From there, the work deepened. Clinical psychology, neuroscience, systems engineering, and quantitative modeling began to converge into shared frameworks. What that made possible wasn’t better descriptions of people — it was the ability to map how cognition actually moves across time. Under pressure. In context. Alongside the systems people work within.
At the same time, another question started to emerge: what happens when human cognition doesn’t operate alone, but in constant interaction with intelligent systems? We began exploring early interfaces between human cognition and AI — how perception, judgment, and identity begin to shift when intelligence is no longer purely human. Not as a speculative idea, but as something observable in how people think and perform when working alongside increasingly capable systems.
The question was no longer ‘How do we understand people?’ It became ‘How do we design systems where human and machine intelligence can operate together — without distortion or loss of capability?’
This work eventually evolved into a cognitive systems lab I now lead — an environment designed to test these ideas in practice, not just theory. A place where cognition is treated neither as fixed nor as chaotic, but as distributed and adaptive — something that can be shaped, aligned, and extended as the surrounding systems evolve. In parallel, it shaped MINDACC as the commercial platform — where these breakthroughs translate into competitive advantage for organizations making critical people decisions at scale. The timing is perfect. There’s a fundamental mismatch between how fast organizations need to move and how their people tools actually work. MINDACC solves that.

Have any books or other resources had a big impact on you?

While many books, essays, videos, and works of art or science have moved me deeply — Stanislas Dehaene’s How We Learn among them — I’ve come to believe that the most powerful influence on my thinking comes from direct contact with other people’s living thought processes.
Outstanding texts can illuminate ideas with clarity and beauty — they compress decades of insight into portable form. But nothing quite matches the stimulation of sitting across from someone whose mind works differently from yours, wrestling with a hard problem together.
There’s an older tradition that resonates here. The dialectic — from ancient Greece through Hegel — where knowledge doesn’t arrive fully formed but emerges through the collision of perspectives. Not debate. A generative process where thinking is shaped, challenged, and refined in real time.
What has shaped me most are those rarer moments where that kind of exchange actually happens — when thinking is still fluid, before it becomes polished or finalized. The hesitations, the sudden reframes, the shift in direction mid-conversation. A single hour like that can restructure something you’ve held for years. That level of signal is difficult to access in finished work. It tends to appear only in fluid exchange — when there’s enough alignment and mutual curiosity for ideas to actually evolve, not just be presented.
In many ways, that perspective continues to shape how I build. It moved me from static knowledge toward living, adaptive systems — from understanding how people think in isolation to creating conditions where those patterns become visible, structured, and actionable.

Contact Info:

• Website: https://mindacc.com
• Linkedin: https://www.linkedin.com/in/marta-morawska-010517296
• Twitter: @martiimay