AI sees extensive use across many industries, but its reliability still leaves something to be desired.
In the year 2026, AI is everywhere. Schools, online journals, laboratories, and an ever-increasing number of private companies employ AI programs for a vast variety of tasks, typically in the name of speed and efficiency.
As anyone who has used an AI chatbot before can tell you, however, that speed can come at the cost of truthfulness.
AI’s Hallucination Problem
If you were to tell a chatbot that you would be “willing to pay a million bucks to have pizza right now,” the LLM powering that chatbot might interpret that statement literally; rather than reading your statement as a humorously exaggerated desire for pizza, it may well think you would actually be willing to pay 1 million male deer to get a piece of pizza.
This kind of misinterpretation, or “hallucination” in the context of AI, is relatively harmless when asking a chatbot silly questions. What’s not so harmless is when AI provides incorrect or even entirely made-up information for a pharmaceutical company testing drug interactions, or a supply chain manager trying to predict the best shipping routes during a period of political turmoil.
AI hallucination might be more manageable if it were always clear when a program was inventing or misconstruing information, but because most LLMs are built to sound confident and agreeable, it can be difficult to tell fact from falsehood without some thorough fact-checking. Of course, fact-checking the program that’s meant to check the facts for its users is counterintuitive and, frankly, self-defeating.
There are two more important causes for AI hallucination: one, most LLMs aren’t programmed to let users know if they don’t know something, and two, much of the training data LLMs learn from is itself riddled with inaccuracies and opinions.
These combined factors effectively make it possible for LLMs to not only be wrong but confidently so, and when entire businesses build their models on the assumption that AI almost always provides clear, relevant, and accurate responses, those businesses and the people that work with them can sometimes end up basing their actions on false information.
This isn’t to say that businesses aren’t aware of the fact that AI can and does hallucinate; however, some companies have started going out of their way to develop AI models that directly address the root causes of AI hallucination.
Improving AI Reliability in the Real World: A Case Study
One such company working to improve AI’s reliability is Vertus, an AI company based in the Isle of Man. Its founders, Julius Franck, Alex Foster, and Michal Prywata, built a cognitive reasoning system designed to recognize when certain patterns are and aren’t applicable, thereby helping it avoid making the same kinds of assumptions many other LLMs might perpetuate under similar circumstances.
To test their AI, Vertus had its system trade on financial markets throughout 2025. In this time, the company reported positive results.
Vertus attributes its success to its system’s ability to quickly adapt to new patterns in the market. To do this, the AI is designed to ask whether a given pattern still applies to a certain situation. When it doesn’t, the system recognizes the shift, stops, and rebuilds its reasoning around what’s actually happening.
As a kind of failsafe, the AI is also designed to tell its user when it cannot come up with an answer to a question, reducing its likelihood of inventing a confident but factually unsound response.
With Vertus’s tests having come back positive, the company has started extending its AI solutions into healthcare, scientific research, and supply chain management.
Although Vertus is by no means the only organization working to improve AI reliability, its accomplishments serve as a useful indicator of an approach that, so far, has proved valuable. Building AI systems to check new information against what they already know and tell users when they don’t know something are important first steps toward mitigating AI hallucination, though whether those systems will become commonplace any time soon remains to be seen.
Still Work to be Done
Even in the few years since chatbots were popularized with the introduction of ChatGPT in 2022, AI’s practical and theoretical uses have expanded substantially. While that rapid expansion has helped many businesses improve their bottom line by cutting costs, such substantial growth over a short period of time has its consequences.
AI hallucination remains a serious issue, and as AI becomes increasingly ingrained into medicine, finance, education, and many other integral industries, the need to address its tendencies toward providing quick, confident answers at the cost of veracity will only become more vital.
AI’s ability to collect, organize, and analyze massive swathes of information in only a few moments could prove instrumental for organizations in the coming years, but any further progress must be tempered by efforts to improve AI’s reliability before people consider expanding upon its currently flawed foundations.
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