Edition #1: The First 100 People

How We Curated the Foundational Layer of the Merantix AI Campus

Five years ago, when we started the Merantix AI Campus, AI was not a mass-market topic. But the underlying challenge was already clear: the ecosystem was structurally siloed.

The teams building applied AI in Europe were small, fragmented, and often working in isolation. Researchers were disconnected from commercialization. Corporates lacked access to applied AI talent, AI-native solution and products. Startups couldn’t navigate regulation or validation. Policymakers lacked real-world feedback loops. Investors lacked proximity to technical teams.

We didn’t need a new building. We needed an operating system for collaboration.

Very early on, we made one decision that shaped everything that came after:

Selecting the first 100 people was the most critical decision we made. We were not looking for just residents, we were looking for system nodes, people capable of shaping how the ecosystem interacts. These first 100 people would define the culture, the velocity, and the ambition of the community.

How we selected the first 100

We mapped the most essential stakeholder groups required for a functional applied-AI environment: Founders, engineers, researchers, corporates, investors, policymakers, and infrastructure builders. The value emerged from the interaction between them.

So we screened for two things above all:

1. Contribution mindset: Before we talked about office space, we asked every early resident the same question: “What value are you excited to contribute to a shared AI community?” Some brought research expertise. Others had operational experience, networks, industry knowledge, regulatory insights, or access to real-world data. If someone couldn’t articulate their contribution, they weren’t the right fit.
2. Curiosity beyond their own vertical: We looked for founders who wanted to learn from researchers, researchers who welcomed industry friction, and corporates open to being challenged by startups. Without that cross-pollination instinct, community collapses back into silos.
3. A high bar for substance: We wanted people who were building something differentiated: new research directions, AI-native products, technically ambitious solutions, or infrastructure that moved the field forward.A community is only as strong as the substance inside it. This meant selecting teams who pushed boundaries, not those following trends, and individuals capable of elevating the level of discourse for everyone around them.

What we intentionally excluded

We said no — often — to teams who solely wanted office space.

We also avoided groups optimizing for visibility without the willingness to share learnings, challenges, or expertise.

A community built on extraction rather than contribution never scales.

Onboarding with intention

Every early resident went through a simple but revealing onboarding:

What value will you bring? And what specific value do you want to take out?

Those two answers told us almost everything about whether someone would thrive in a multidisciplinary environment.

Five years later

Those first 100 people made it possible to scale to 2,000+ members without diluting quality. In conjunction they created a feedback loop of trust, exchange, and collaboration. The community scales when the foundation is intentional, not when it is accidental.

Europe’s AI ecosystem doesn’t need more noise. It needs curated density, shared incentives, and places where the right people meet early. I needs people inside who care about the community as much as they care about their product.

If Europe wants stronger AI ecosystems, this is where it starts.