From voluntary principles to binding AI safety law
For years, leading AI labs talked about “AI safety”, but in many regions the rules were still based on guidelines and ethical principles. In 2025, this changed. Lawmakers focused on high-impact AI systems – models and applications that can affect rights, safety, critical infrastructure or large financial decisions.
The core idea is simple: the more powerful and high-risk the AI system, the stronger the requirements around testing, monitoring, documentation and human oversight.
EU: risk-based AI safety under the AI Act
In the European Union, the AI Act introduced a risk-based framework:
- Prohibited AI uses (e.g. certain kinds of social scoring).
- High-risk AI systems with strict obligations.
- Limited-risk systems with transparency duties.
- Minimal-risk systems with almost no extra regulation.
High-risk systems must undergo conformity assessments, maintain detailed technical and training documentation, log their behaviour and enable effective human intervention. For game-like experimentation platforms such as NovaryonAI, this means being very clear about the purpose, limits and non-financial nature of the experience.
US & UK: oversight, audits and frontier model scrutiny
In the United States, federal agencies expanded their use of existing consumer protection, discrimination and safety laws to cover AI systems. At the same time, voluntary safety commitments by large labs started to solidify into expectations around red-teaming, incident reporting and risk disclosure.
The United Kingdom continued along its “pro-innovation” path while building AI-safety specialised regulators and expert units. Particular attention was given to:
- frontier-scale foundation models,
- AI used in critical infrastructure,
- systems that can generate realistic misinformation at scale.
What counts as a “high-risk” AI system?
While definitions vary by jurisdiction, three elements appear again and again in AI safety discussions:
- Impact: does the system influence safety, rights, or large financial / social outcomes?
- Autonomy: how much control does the system have over actions or decisions?
- Scale: how many people can the system affect and how quickly?
A small experimental AI game like NovaryonAI has a very different risk profile than a fully autonomous trading engine, but the same principle applies: be honest about what the system does, what it doesn’t do, and where humans stay in control.
NovaryonAI’s place in the AI safety landscape
NovaryonAI is positioned as a logic-based AI challenge and experimental decision gate, not as a financial service or gambling platform.
The “guardian” AI evaluates a single sentence from the player based on internal logic and linguistic criteria. There is no random number generator, no roulette wheel, no slot machine – just a deterministic AI decision and a growing “treasure pool” that represents the difficulty of convincing the system.
This design aligns with modern AI safety expectations: clear rules, transparent intent, and explicit separation between gameplay, experimentation and real-world financial decision-making.
Why AI safety matters even for “just a game”
2025 made one thing clear: there is no sharp line between “serious AI” and “playful AI”. Language models and reasoning systems that start as experiments can quickly influence real people, communities and expectations.
For that reason, projects like NovaryonAI treat AI safety as part of the experience:
- clear communication that the system is an experimental Hungarian AI guardian,
- no promise of guaranteed financial returns,
- strong focus on logic, persuasion and thinking skills, not chance,
- transparent rules and visible limits to the system’s role.
Looking ahead: AI safety in the age of AGI and advanced game AIs
As foundation models approach AGI-level reasoning and AI-driven games become more complex, regulators will keep asking:
- Who is responsible when the AI makes a harmful decision?
- How was the system tested before release?
- Can users understand and challenge the outcome?
For NovaryonAI, the answer is to stay on the side of transparent, skill-based challenges – a Hungarian AI guardian that invites players to think deeper, not to gamble blindly.
This article is an informational overview of the AI safety landscape as of late 2025 and does not constitute legal advice. Organisations deploying real-world high-risk AI systems should consult specialised legal and compliance experts in their jurisdiction.