WTC Live: AI, tech or talent race?

World Trade Center Amsterdam fosters connection, growth and international collaboration. As a catalyst for the new economy we welcome new ideas and voices that shape tomorrow. During the first edition of the salon series ‘WTC Live’ we invited idealists, policymakers, businesses, and impact organisations to discuss one of the most pressing questions for Europe’s AI economy: who will build it, and do we have the talent to compete?

The Dutch AI paradox

Merve Karaman is a Digital Economy Strategist at the Dutch Ministry of Economic Affairs and one of the co-authors of the Wennink Report, the most-discussed analysis of the Netherlands’ long-term economic resilience. She opened the evening with a macro-level challenge. The Netherlands excels at scientific research and academic publications, she states. Yet of the roughly ten AI unicorns that exist globally, none are Dutch. Meaning that while the Netherlands generates substantial AI knowledge, its commercial value is largely captured in the United States and China.

“We are producing the backbone, but others are commercialising our strengths.”

The structural gap is stark: R&D investment in the Netherlands significantly lags behind the US, where investment levels are approximately six times higher. ASML stands as a notable exception, but it also underlines just how isolated that example is. To maintain the country’s current economic model, Karaman cited a need for €180 billion in growth investment.

The report identifies four domains requiring urgent investment: talent, rules and licensing frameworks, access to affordable energy, and the strengthening of the digital infrastructure as the backbone of everything else. Critically, 70% of the investment pipeline of 51 projects must come from the private sector. This cannot be a government project alone.

Value outcome over output

Merle Bartsch is the founder of Rosaia, a platform she describes as a “Google Maps for your career”. It deploys AI to help people understand where they are in their career, where they want to go, and what it takes to get there. As a labour market specialist, she brought a sharp provocation to the room. She asked how many people planned to retire in the next 18 months. Then she referenced a widely-cited forecast that most white-collar tasks will be automated within that same timeframe. Something crucial is being overlooked here, she adds. We’re confusing output for outcome. Output can be automated. Outcome is shaped by processes and those carry value that goes far beyond any single deliverable. Skipping that valuable process, she argues, can cost you a great deal.

Organisation is the new AI strategy

Ruben Timmerman is the co-founder of School for Moral Ambition, an organisation based right here at WTC Amsterdam that works at the intersection of purpose, technology and systemic change. His opening position was characteristically direct: the AI talent race is not about which tools you use. It is about how you organise.

His recommendation was to pick one primary platform, he cited Claude, and require that any other tools in use connect to the same organisational backend. This creates coherence without restricting experimentation. He also encouraged leaders to create psychologically safe spaces where teams can share learnings and mistakes openly.

“You will make mistakes. The race is simply about who makes them first and learns fastest”.

Attitude over credentials

Rob Steur is Regional Commercial Director for Northern Europe at Robert Walters, the international recruitment agency with an office at WTC Amsterdam. He reframed the talent question from a recruitment perspective. In the AI era, he argued, the critical question is not who you hire, it is how you hire. Career paths have become fundamentally non-linear, and the skills required are shifting faster than any hiring process built for a previous era can track. His framework for AI readiness splits across two levels. For the whole team: adaptability, learning agility, and critical thinking. For those taking on an “AI champion” role: curiosity, an improvement mindset, and risk awareness. The formula he landed on: recruit for attitude, train for skills.

With structured interview techniques (situational and behavioural methods) that reveal how candidates actually think under pressure. Some companies, he noted, are going further: presenting candidates with a real business problem, asking them to use AI to solve it, and then asking them to walk through their process. The output matters less than the reasoning.

What are we not seeing?

The evening closed with an open panel. The speakers were asked: what will change more than most people currently expect, if we meet again in three years?

Timmerman predicted that job displacement, though widely discussed, remains something few people have actually felt yet. That will change and the scale of it will surprise most people. Karaman pointed to underestimated dependency on US and Chinese infrastructure, and the urgency of carving out strategic niche positions in the European digital stack. Steur observed that what happened to financial roles outsourced to lower-wage countries will happen again, this time to AI, but that new roles, such as AI process controllers, will emerge in their place.

Perhaps the most striking point came from an audience member who noted that children are growing up with an openness to AI that adults simply do not have. They are not being left behind by the technology, they are racing ahead of it in ways that are difficult to anticipate from within a current-knowledge framework. We are using our understanding of today," he said, “to imagine a world we cannot yet see.”

“We’re all busy worrying about the effect of AI on ourselves and we’re underestimating what it will do for the next generation.”

Four key learnings

1. Talent without scale doesn’t win
   Europe has the knowledge base, but lacks the talent ecosystems and investment needed to turn that into globally competitive AI companies. The race isn’t about producing talent alone, but about enabling it to create economic value.
2. The real talent edge is in process, not output
   As AI automates tasks, the value of talent shifts from producing outputs to shaping processes and outcomes. Those who understand how to work with AI stand out.
3. The talent race is won by how you organise
   AI advantage comes from organisational design, like shared systems, rapid learning cycles, and cultures where experimentation (and failure) is safe. The fastest learners will lead.
4. Hire for mindset, train for AI
   In a rapidly shifting landscape, adaptability, curiosity, and critical thinking matter more than credentials. The most competitive organisations recruit for attitude and build AI capability internally.

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