Ask Claude, ChatGPT or a rival AI chatbot to list Europe’s top venture capital investors, and the AI chatbot hallucination risk becomes immediately apparent: the tools confidently produce lists that bear little relationship to reality. Which raises an obvious question: if this is how these models perform on a relatively well-documented topic, what happens when people rely on them for higher-stakes research?
Confident answers, shaky foundations
The broader problem with AI-generated rankings is well-documented. NIH/PMC research found that ChatGPT and Bing exhibited a critical degree of hallucination when tested. That finding sits alongside work from MIT Sloan EdTech, which reported on a Stanford HAI study showing that general-purpose AI chatbots hallucinated on between 58% and 82% of legal research queries when tested on 2023-era models. Legal research, like VC rankings, involves specific named entities, dated events and verifiable facts, exactly the territory where confident-sounding errors are most dangerous.
The numbers from OpenAI’s own testing are no more reassuring. According to Trading Central, one of OpenAI’s recent tests found that its newest o3 and o4-mini models hallucinated 30–50% of the time. These are the company’s most capable and most recent releases. If the flagship models produce false information at that rate under test conditions, the implications for anyone using these tools to screen investors, funds or market participants are hard to ignore.
Research published by UC San Diego Today adds another dimension: large language models hallucinated 60% of the time when answering user questions where the correct answers fell outside their original training data. That is a structural limitation, not a bug to be patched. If a VC firm is newer, smaller, or simply less covered in English-language text, an AI chatbot may invent plausible-sounding details about it rather than admit ignorance.
What the AI chatbot hallucination risk means for investor research
The specific problem with AI-generated European VC rankings is that the models appear to conflate sources, invent credentials and surface names that fit a pattern of “what a top investor sounds like” rather than reflecting actual track records. The hallucination risk is compounded in a domain like European venture capital, where the underlying data is less standardised and less publicly available than, say, US public-market data.
There is also a subtler issue. When a chatbot produces a list, it typically does so with no uncertainty attached. A user who does not already know the European VC landscape has no obvious signal that the output is unreliable. The model does not say “I am not sure about this one”; it presents a fabricated fund name with the same tone it would use to state a verified fact. That asymmetry between the model’s apparent confidence and its actual reliability is arguably more dangerous than the raw hallucination rate alone.
The pattern is consistent across domains. Whether the query is about legal precedent, investment rankings or medical information, AI chatbots have repeatedly been shown to generate plausible-sounding but incorrect answers at rates that would be unacceptable in a professional research context. The European VC ranking exercise is a useful illustration of that problem, not an outlier.
For anyone using these tools to support due diligence or market mapping, the practical implication is straightforward: AI-generated outputs on named entities need independent verification against primary sources before any weight is placed on them. The chatbot hallucination risk does not disappear with newer models, as OpenAI’s own o3 and o4-mini testing suggests. It shifts, and sometimes narrows, but it does not go away.
