Artificial intelligence is rapidly becoming part of everyday decision-making. From students to executives, from private individuals to institutional investors, tools like ChatGPT are increasingly used to form first impressions, structure thinking, and even validate conclusions.

This creates a new risk that is still poorly understood: not the risk of random errors, but the risk of systematic confidence in answers that are structurally incomplete.

This matters, especially for Europe.

From mistakes to structural bias

ChatGPT is often described as a system that “can make mistakes.”
That description is comforting — and inaccurate.

In many domains, ChatGPT does not fail randomly. It fails predictably.

Its answers are shaped by the sources it has access to and the way those sources are weighted. Publicly visible, well-funded, and consistently published narratives dominate: government communication, institutional reports, rankings, and corporate marketing material. Less visible realities — enforcement gaps, administrative discretion, failed projects, unresolved disputes, and lived experience — are structurally underrepresented.

This creates a bias that is not incidental, but built into the system’s operating logic.

When optimism becomes dangerous

In low-stakes contexts, this bias is mostly harmless.
In high-stakes contexts, it is not.

Property rights, land use, regulation, and jurisdictional risk are areas where outcomes are shaped less by formal rules and more by process, discretion, timing, and enforcement. These factors rarely appear in polished summaries or promotional material. As a result, AI-generated answers tend to overstate safety, clarity, and predictability.

The danger is not that ChatGPT is “lying.”
The danger is that it produces coherent, confident answers that feel complete — while omitting the very risks that matter most when capital, livelihoods, or long-term commitments are involved.

The trust gap

A new trust gap is emerging.

Users increasingly treat AI-generated summaries as neutral overviews, while the system itself is designed to prioritise dominant narratives and avoid adversarial claims. The result is a quiet transfer of epistemic risk from the system to the user.

Disclaimers exist, but they are generic. They do not reflect the severity of the blind spots in certain domains. Saying that a system “can make mistakes” is not the same as stating that it is systematically unreliable for specific types of decisions.

For investors, founders, policymakers, and advisors, this distinction matters.

Europe’s specific exposure

Europe prides itself on rule-of-law, institutional stability, and regulatory sophistication. These are strengths — but they also create fertile ground for overconfidence when analysis remains at the level of statutes and formal frameworks.

When AI tools reinforce surface-level narratives without forcing confrontation with enforcement reality, administrative variance, or historical edge cases, they amplify complacency rather than insight.

This is not a technology problem alone. It is a governance and responsibility problem.

What responsible use looks like

ChatGPT should be treated as a starting point, not a conclusion.

Responsible use means:

• assuming that dominant narratives are overrepresented
• explicitly questioning enforcement and process
• separating law-on-paper from law-in-action
• treating AI outputs as hypotheses to be stress-tested, not answers to be trusted

Most importantly, it means teaching users where not to rely on AI without independent verification.

A responsibility for builders

At ImpactBuilders, we believe Europe’s future depends on clear-eyed realism, not convenient optimism. Tools that shape decision-making at scale carry responsibility — not only in what they say, but in what they systematically leave out.

The question is no longer whether AI is useful.
The question is whether we are willing to acknowledge its structural limits before those limits translate into avoidable damage.

Trust is not built by confidence alone.
It is built by clarity about risk.

And clarity begins with naming the problem honestly.

Disclaimer

The above blogpost was automatically generated by … ChatGPT.
ChatGPT can make mistakes …