🎙️ Timo Spring, Co-Founder of Alma🎧 About this episodeThe latest episode of The In-Between Tech & Trust Podcast explores how AI systems quietly extract human and organizational knowledge, and what it takes to design infrastructure that preserves sovereignty instead. The conversation is for leaders, founders, and technologists working with AI in real organizational settings, especially where trust, regulation, and long-term control matter.Rather than focusing on models or product features, the discussion stays at the infrastructure level - where decisions about coordination, ownership, and data flow shape what AI systems ultimately do to people and institutions.🧭 Episode overviewEva Lihotzky speaks with Timo Spring, founder of MIMMS, about why AI became powerful faster than it became governable. Drawing on years of work across healthcare, energy, and public sector systems, Timo argues that the real constraint in AI adoption is not intelligence, but coordination.They examine how AI agents turn human experience into executable algorithms, why this creates a one-way export of institutional knowledge, and how alternative system designs could allow collaboration without raw data sharing. The conversation also touches on Europe’s strategic position in the AI stack, the limits of current procurement models, and why trust in infrastructure cannot be claimed but must be structurally proven.🧩 Key themes discussedHow AI agents extract implicit human knowledge and why organizations pay for thisWhy coordination and orchestration matter more than smarter modelsThe idea of individual expertise as executable algorithmsContext transfer as an alternative to large-scale data transferEnergy use, efficiency, and infrastructure design choices in AI systemsSovereignty, ownership, and trust at the operating system layerTensions between value-based AI systems and existing institutional structuresOpen sourcing as a long-term trust mechanism for foundational infrastructure

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