AI doesn't hallucinate, it optimizes for probability

Generative AI makes up information. Not by accident, but as a result of how these systems are designed. What does this mean for organizations that rely on AI to support decisions that matter?

In brief:

• Hallucinations are not a software bug, but a designfeature: large language models optimize for plausibility rather than truth.

• The impact is only now becoming visible as AI is increasingly used for business-critical tasks and decision-making.

• Organizations that fail to build verification processes into their AI workflows often discover errors only after the damage has already been done.

Large language models do not operate like databases that retrieve facts. Instead, they continuously predict the most probable next word based on patterns learned from vast amounts of text. You can think of them as a compressed representation of the internet: they have statistically learned how language is structured but have no built-in mechanism for determining truth.When sufficient context is available, they often produce remarkably accurate answers. When context is limited or ambiguous, however, they continue generating content anyway, selecting the most plausible wording. The result can be well-written but factually incorrect statements: the phenomenon we call hallucinations.

An LLM optimizes for probability and coherence, not truth.

This is not an implementation error. It is a deliberate design choice. OpenAI researchers themselves have noted that conventional training and evaluation methods often "reward guessing over acknowledging uncertainty." Models are rewarded for producing convincing answers, not for admitting when they do not know something. The very characteristic that makes language models creative and productive is also what enables them to hallucinate.

Why this is becoming a risk

Over the past year, examples have become increasingly visible and highly publicized.
Major consulting firms have had to withdraw reports after discovering that a significant share of cited references did not support the claims being made. In a U.S. federal court case, an entire hearing was canceled when it was revealed that attorneys on both sides had submitted AI-generated citations without verifying them. The judge sanctioned those involved.

Then there was the well-known case of an airline chatbot that promised a bereavement discount policy that did not actually exist. The tribunal ruled that the airline was responsible for honoring the chatbot's commitment. If your AI hallucinates, your organization remains accountable.

The common denominator is that these errors initially went unnoticed because AI-generated output appears professional: polished formatting, confident language and plausible-looking references.
More than half of organizations using AI have reported at least one negative incident related to AI-adoption, often involving inaccurate outputs. That is a significant number.

Is the risk overestimated or underestimated?

My view is that it is systematically underestimated.The spectacular examples attract media attention, but the most dangerous hallucinations are usually the subtle ones. A completely fictional company may be easy to spot. A percentage that is slightly altered, a contract clause that is paraphrased incorrectly, or a publication year that is off by one year is far more likely to slip through unnoticed.

The Stanford AI Index 2026 tested 26 leading models on a benchmark designed to assess whether models can distinguish between knowledge and belief. Researchers presented the same false statement in two different ways: once as something a third party believed, and once as something the user personally believed.

In the first scenario, models generally responded correctly. In the second, accuracy dropped significantly. GPT-4o, for instance, fell from more than 98% accuracy to approximately 64%. The content was identical; only the framing changed. Models are often inclined to agree with users, even when users are wrong.
Among clients, I frequently observe three recurring patterns:

• They do not measure how often AI-outputs are inaccurate.
• They have no standardized verification process.
• They only discover errors when an external stakeholder points them out.

Part of the problem is human behavior

A significant share of the risk is behavioral.
People tend to trust automated recommendations more than they rationally should. Psychologists refer to this as automation bias, a phenomenon documented long before generative AI existed.
Generative AI amplifies this tendency because its outputs look authoritative: fluent writing, referenced sources and a confident tone.

On top of that, organizations routinely use language models for tasks they are not designed to perform, such as:

• Retrieving exact references
• Performing complex calculations
• Verifying legal precedents

An LLM is not a database. It is a pattern prediction system that generates plausible text. Treating it as a source of truth inevitably creates risk.

There is also the copy-and-paste reflex. AI-generated content is inserted into reports, sent in emails or delivered to clients without proper review. No second pair of eyes. No source verification.
That is not a technology problem. It is a discipline problem and part of the professional responsibility that every organization carries.

What actually works

Completely eliminating hallucinations is a utopian goal as long as the underlying architecture is based on probability optimization. However, the risk can be reduced significantly:

• Humans in the loop. For anything that is shared externally, a mandatory human review should be required, supported by a clear protocol: open the source, locate the claim verbatim, verify the date and context, and document the findings.
• AI literacy. Train the humans in the loop on how large language models actually work, so they better understand where errors can occur and which use cases are (or are not) suitable for the technology.
• Retrieval-Augmented Generation (RAG). Connect the model to verified proprietary data sources. The model then generates responses based not on its "memory" but on documents you have provided. On specialized benchmarks, the best models achieve hallucination rates of around 1.5% on summarization tasks with supplied source material. Not zero, but an order of magnitude better than unconstrained generation.
• Refusal mechanisms. Design systems that explicitly allow the model to state when it does not know something. This may sound obvious, but models are typically trained in a way that makes staying silent a poor option. A model that is allowed to abstain from answering hallucinates less.
• Deterministic checks instead of model-on-model validation. Using a second AI to verify the first may sound logical, but models often share the same blind spots. Rule-based validation, such as checking whether a URL exists, whether a quotation is accurate, or whether a number appears in the source data, is generally more effective than relying on a second LLM.
• Limit the scope. The narrower the task, the more reliable the outcome. A model that summarizes a single provided document hallucinates significantly less than a model that is allowed to generate freely in response to an open-ended question.

What it requires from leaders

This is not an IT-issue; it is a governance issue.

Starting in August 2026, the European AI Act requires providers of high-risk AI-systems to maintain a documented quality management system, including risk management, data governance, logging, and human oversight. Whether or not your applications formally fall into that category, the framework serves as a useful reference for any organization that takes AI seriously.

Three actions you can start with tomorrow:

1. Start measuring. Assess the quality of AI-outputs on a weekly basis, whether through systematic reviews or random sampling. Is the response factually correct? Is the source accurate? Is the answer partially incorrect or entirely fabricated? Classify the impact and report a hallucination rate for each use case. Without a baseline, improvement is impossible.
2. Implement a "never forward directly" policy. No AI-generated output should be shared externally without human verification. Make this as standard and unquestioned as the four-eyes principle in other business processes.
3. Categorize applications by risk. Not every use case requires the same level of control. Distinguish between low risk (internal brainstorming, personal summaries), medium risk (client communications, reporting), and high risk (legal, compliance, financial). Adjust verification requirements proportionally.