Včera jsem byl na predátorovi. Vykuchali ho a předělali na Crioodsovi. Byl tam dokonce lenochod pásek. Strašné.

What policy challenges have you seen firsthand that could benefit from AI but lack technical capacity?

Source: OECD (2025), Governing with Artificial Intelligence: The State of Play and Way Forward in Core Government Functions, OECD Publishing, Paris, doi.org/10.1787/795d....

The opportunity: People who can bridge policy complexity and technical implementation. This is exactly why I'm transitioning from public affairs to machine learning, to help solve this critical divide.

#PolicyTech #CareerTransition

The bottleneck? Skills. About 70% of UK government bodies cite this as their biggest barrier. We're building AI gigafactories but need AI-capable governments in parallel.

OECD analyzed 200 government AI projects across 11 core functions. The finding? Most remain stuck in pilot phase. Some 70% of countries use AI internally but only 33% for policy design. UK Parliament found 'no systematic mechanism' for scaling successful pilots.

Už jen 14 500.

Timeline: Graduate 2028.

Focus: Interpretable AI for government decisions. Systems that respect institutional constraints and serve public interest.

The field needs practitioners before it needs a Wikipedia entry.

This October I started a BSc in Computer Science at University of London.

Target: ML engineer who understands how institutions actually work.

Building NLP systems for policy analysis. Documenting at github.com/sebekj

After 20 years in public affairs (British Embassy during Brexit, Mondelēz regulatory strategy), I've watched the gap grow:

Policy experts who can't build systems.
Data scientists who don't understand government constraints.