As AI matures from a novelty into a strategic asset, 79% of organisations now prioritise sovereignty to mitigate vendor lock-in and secure critical data. Despite this, the market remains paralysed by a paradox: while open models have achieved performance parity with proprietary systems at a fraction of the cost, they remain massively under-utilised due to perceived friction and trust gaps.

This session dissects this market inefficiency, with insights from LF Research revealing how closed-source dominance is often driven by inertia rather than superior capability. We will explore how to operationalise true independence by rejecting "open-washing" in favour of rigorous frameworks that demand full model completeness: verifying everything from training data to weights. Join us to learn how to transition from renting black-box APIs to architecting a transparent, reproducible, and economically superior AI stack.