Hungary's AI Window Is Closing: What Government Leaders Must Do Now
Hungary has a short window to establish itself in AI. Without immediate steps, the country risks losing ground on building applications, structuring public data, and influencing the operation of major data centers.
Local research centers are already doing intensive work. The priority now is a regulatory and testing environment that reduces friction, attracts filings, and accelerates deployment. With the right framework, foreign companies could patent their inventions in Hungary.
Funding is on the table: a HUF 73 billion tender for early-stage AI companies by year-end and another HUF 50 billion later. This is the moment to connect policy, procurement, education, and industry incentives into a single plan that delivers outcomes, not reports.
Immediate policy priorities
- Create AI sandboxes and fast-track filings: Stand up legal and technical sandboxes for testing models, data use, and safety. Add a fast-track path for AI-related patents and certifications to make Hungary the easiest place in the region to file and deploy.
- Launch the HUF 73b tender with clarity: Define focus areas (applied AI, public sector data projects, safety and evaluation), transparent criteria, and milestone-based disbursement. Prepare the HUF 50b follow-on to scale winners quickly.
- Structure public data: Set standards, metadata, and secure access policies for government datasets. Prioritize high-value domains such as health, transport, energy, and justice, with clear data stewardship and audit trails.
- Expand AI instruction on a fixed timeline: University programs are growing now; vocational school materials start end of October on a trial basis. Begin secondary school instruction in February and primary schools next September, as recommended.
- Government as a flexible guide: Issue practical guidance for risk management, procurement, model evaluation, and privacy. Support businesses with templates, KPIs, and technical checklists.
- Engage data center operators: Align incentives, energy and location policy, and compliance oversight so Hungary can influence operations while meeting security and sustainability requirements.
Funding architecture that drives results
- Set a public scoring rubric: problem importance, feasibility, data access plan, safety evaluation, and commercialization path.
- Favor consortia that pair startups with universities and agencies to speed adoption into public services.
- Use staged tranches tied to live pilots and measurable adoption in healthcare, education, transport, and finance.
- Publish quarterly dashboards: recipients, milestones, and impact metrics to keep trust high and delays low.
Education rollout and workforce readiness
- University: scale AI modules across engineering, health, economics, and public policy; require practical projects with real datasets.
- Vocational (trial from end of October): focus on practical tools, safety, and productivity in admin, manufacturing, and services.
- Secondary (from February) and primary (from next September): develop age-appropriate curricula, teacher training, and safety-first guidelines.
- Public sector upskilling: mandate short, role-based AI training for agencies, procurement teams, and regulators.
Public data and infrastructure
- Build a national data catalog with role-based access and logs. Start with 10 priority datasets and expand quarterly.
- Adopt interoperability standards and secure APIs so startups and agencies can build on the same foundation.
- Offer compute credits and shared evaluation services for startups participating in the tender or sandboxes.
KPIs for the next 12 months
- 30+ sandbox participants; 10+ public sector pilots live.
- 50+ AI-related patent filings in Hungary.
- 100,000+ students engaged across university, secondary, and vocational programs.
- 100+ high-value datasets structured and accessible under clear governance.
- 1,000+ SMEs supported through vouchers, credits, or testing access.
Risks to manage early
- Compliance, safety, and bias: require risk assessments, documented data lineage, and model evaluations for high-impact use cases.
- Procurement pitfalls: avoid vendor lock-in with open standards, clear exit clauses, and shared IP where public funds de-risk development.
- Energy and resilience for data centers: align capacity planning, renewable integration, and security protocols.
There is a broader context: the EU's position in technology investment is currently weak, and catching up will be hard without decisive action. Align national rules with the emerging EU framework and move faster on testing and adoption where possible. For reference, see the EU's regulatory framework for AI here.
Next steps for ministries this quarter
- Form a cross-ministry AI task force with a 90-day mandate to deliver the sandbox, tender design, and data catalog plan.
- Publish the tender by year-end with a plain-language guide for applicants and evaluators.
- Issue a decree enabling regulatory sandboxes and fast-track procedures for AI-related patents and certifications.
- Kick off the vocational school trial, finalize secondary and primary curricula, and schedule teacher training.
- Open structured consultations with banks, telecoms, energy firms, healthcare providers, and data center operators.
As MNB governor Mihály Varga noted, Hungary cannot afford to miss the early adoption stage. Education must prepare people to use AI, the state should guide with flexibility, and businesses need to move on clear opportunities now.
If your teams need practical, role-based upskilling to support this agenda, explore focused programs here.
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