RESEARCH

Question

Can AI make learning more engaging and effective without turning education into another addictive feed?

Why I am researching this

I previously co-founded an AI-powered learning platform used by more than 300,000 people across three countries. I have also researched AI/ML-driven learning experiences.

That experience left me with a question I still care about: if technology can make learning more compelling, what should it optimise for?

Retention is not the same as understanding. Engagement is not the same as growth. A good learning product should help people become more capable, not merely more likely to return.

What this project will examine

• Motivation, attention, and learning outcomes
• AI tutors and personalised learning
• The difference between engagement and dependence
• Feedback systems that create real progress
• The risks of treating education as a growth product

Question

Where does AI automation create real leverage, and where should people remain responsible for the decision?

Why I am researching this

Working on AI automation has made one thing clear to me: many organisations do not need more AI for its own sake. They need better systems, cleaner data, clearer workflows, and a stronger understanding of where human judgment still matters.

Automation is powerful when it removes repetitive work. It becomes dangerous when it removes context, accountability, or responsibility.

What this project will examine

• AI automation inside teams and organisations
• The difference between assistance and delegation
• Data infrastructure as a prerequisite for useful AI
• Which decisions should remain human-led
• Why operational leverage is not the same as organisational intelligence

Question

What makes a digital public system legitimate enough for people to trust it?

Why I am researching this

Building I-VOTE taught me that public technology is never only a technical problem.

A voting platform can be fast, accessible, and well-designed, but none of that matters if people do not believe in the result. Trust is created through transparency, fairness, accountability, accessibility, and the feeling that the system represents people rather than processes them.

What this project will examine

• Digital voting and civic participation
• Trust, legitimacy, and transparency
• The difference between usability and public confidence
• Technology in low-trust environments
• The role of AI in public institutions

Question

Why do people with similar ability end up with radically different access to opportunity?

Why I am researching this

I do not think the opportunity gap is only about talent, effort, or information.

It is often a context gap.

People need examples, trusted relationships, useful introductions, practical knowledge, and environments that make action feel possible. The right conversation can change a person’s direction. The right system can make a path visible before someone gives up on finding it.

What this project will examine

• How opportunities actually form
• Networks, trust, and context
• The role of geography in ambition
• How young people enter high-agency environments
• Whether technology can make opportunity more reachable

Question

What does it mean to build ambitious technology companies outside the world’s obvious startup centres?

Why I am researching this

I have seen how geography shapes ambition.

Talent is widely distributed. Access to capital, examples, networks, and high-context communities is not. The question is not whether great companies can be built outside traditional centres. They can.

The more interesting question is what founders outside those centres need in order to compete globally without losing the advantages of where they started.

What this project will examine

• Startup ecosystems beyond traditional hubs
• The role of global networks
• Education, migration, and access
• Building companies across borders
• What places such as Tirana and Munich make possible