Your hackathon kit

Define the actor, the decision, the dependency, and the consequence. This keeps AI from becoming a decoration instead of a useful system.

Look for constraints in data, compute, skills, regulation, procurement, standards, feedstocks, and market access.

A good intervention rarely helps one startup alone. Ask what the university, region, investors, and infrastructure providers must do together.

Compare futures instead of pretending one forecast is enough: cost, continuity, accountability, readiness, dependency, and adoption.

Decide what needs to be simulated: compute access, data flows, institutional capability, supply chains, or regional ecosystems.

Ask AI to expose assumptions, compare governance options, list missing data, and produce a framework that a human expert can challenge.

Turn the decision logic into a dashboard, intake form, scoring matrix, dependency map, or workshop tool.

Map which capabilities must be built or governed locally: data, compute, talent, regulation, procurement, standards, finance, and market access.

Identify where the system depends on external platforms, proprietary models, imported inputs, single suppliers, or missing talent.

Do not only score the technology. Score whether institutions can procure, govern, maintain, and learn from it.

Ask where value is created and captured: data, IP, jobs, infrastructure, learning loops, public value, and venture ownership.

Show the stakeholder, the choice, the evidence, the trade-off, and the next action. Keep the demo focused.

Lead with the decision, then the evidence, assumptions, trade-offs, and recommended next experiment.

A framework that looks clear gets used. Use hierarchy, contrast, spacing, and plain labels so the idea travels.

Show how the output could support a pilot, workshop, venture pathway, policy memo, or ecosystem partnership.