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Trusted AI
Europe is building sovereign AI capacity. But organizations still need the decision layer that helps them evaluate tools, risks, governance, vendor dependency, and adoption choices in practice.
AI capability is accelerating, but trust, governance, adoption, and control are not keeping pace. Organizations are being asked to make AI decisions before they have the language, structures, or confidence to evaluate what those decisions actually mean.
The gap is not only compute, models, or regulation. It is the layer between "AI infrastructure exists" and "this organization can make a defensible, auditable, use-case-specific deployment decision."
Which AI tools can we use for which data?
What level of sovereignty is actually required?
Which vendor dependencies are acceptable?
What needs human oversight?
What should be documented before deployment?
Who is responsible for adoption, monitoring, and accountability over time?
Essays and field notes exploring trusted AI, sovereign AI, governance, public-interest infrastructure, and the practical adoption layer organizations need.
After TEDxBerlin, one idea stayed with me: Europe's public broadcasting model may offer a useful analogy for AI. Not because broadcasting and AI are the same, but because both rais…
Sovereign AI is no longer an abstract policy topic. It shows up in regulatory guidance, procurement rules, cloud contracts, and increasingly in ...
Artificial Intelligence, automation, and advanced tooling can spark incredible innovation. But what happens the day after launch? Who ensures that the system ...
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