We need to treat AI hallucinations as a feature, not a bug

When generative AI makes things up, it's often called a "hallucination." Many see this as a critical flaw — a bug that needs to be fixed. While that view is understandable, it's also limiting because it fundamentally misinterprets how these systems work. Instead, we need to adopt a different perspective: AI hallucination isn't a system failure; it's the natural result of a new kind of computing that works on probability, not on strict logic.

This shift from the certainty of old computers to the possibility of new AI is a major turning point in technology. Learning how to use this "hallucination engine" safely and effectively is one of the biggest challenges we face today. We're no longer just building calculators; we're learning to manage tools that can invent. Our future depends on us being able to tell the difference between useful creations and mistakes.

From old certainty to new possibilities

For decades, the promise of computing was its deterministic consistency, a guarantee that the same input would always yield the same output every single time. Old computers, from the earliest mechanical calculators to the device you're using now, were built like a fortress of logic. Even at their most sophisticated their fundamental premise was simple: give them an input, and they will follow the instructions to produce the same correct output every single time. Any mistake was a bug in the code or a problem with the hardware. This reliable, predictable system gave us spreadsheets, databases and the entire digital world we know.

The rise of large-scale generative models, like large language models (LLMs), marks a complete break from that tradition. These systems are not logical engines; they are probabilistic ones. They don't retrieve perfect answers from a database. Instead, they create new information by predicting the next most likely word in a sentence. A "hallucination" is what happens when this chain of likely-sounding words doesn't match reality.

Contrary to what many people think, this isn't a malfunction; it's the system working exactly as it was designed to. Interestingly, this behavior is surprisingly human. Think of a student in an exam who doesn't know the answer to a question. They don't leave the page blank; they write around the topic, using what they do know to construct a plausible answer, hoping the effort is acknowledged. We often pardon this in humans but see it as a failure in machines, creating a double standard. An AI model, when asked an obscure question it has no direct data for — like "Name three 17th-century poets who wrote about cheesemaking"—acts like that student. Its goal is to create a believable answer, mixing its knowledge of poets' names and writing styles with the concept of cheese to build something that feels right, even if it's completely made up.

This ability to connect ideas and create something new is the model's main function, and it stems directly from its training data. While training on verified, high-quality sources is crucial to reduce errors, it can never completely eliminate the risk that they’ll happen. The model's core job is still to generate, not just retrieve. The same process that lets an AI write a poem is what lets it invent a "fact." Creativity and hallucination are two sides of the same coin.

The trust matrix: Managing cognitive load

This new kind of computing has two very different sides. On one hand, the hallucination engine is an amazing tool for creativity. On the other, the fact that it can just make things up is a major risk. The primary purpose of using a tool is to reduce our cognitive load, but if we have to constantly verify its output, are we actually making more work for ourselves?

The answer depends on the task. Instead of a blanket rule to "always verify," a smarter approach is to use a risk-based framework. We can think of this as a 2x2 matrix, balancing the impact of a wrong answer against the probability of hallucination.

1. Low impact/low probability (Green zone: safe to trust). This is for low-stakes tasks where the AI is well-grounded, like summarizing a simple, factual document using retrieval-augmented generation (RAG). Here, you can largely trust the output and reduce your cognitive load.

    

2. Low impact/high probability (Yellow Zone: use for inspiration). This includes creative tasks like brainstorming blog post ideas or writing a poem. The output doesn't need to be factually correct, so you can use it freely as a creative partner without verification.

    

3. High impact/low probability (Yellow Zone: verify). This is for critical tasks where the AI is grounded but the stakes are high, such as summarizing a company's official earnings report. The tool reduces the initial work, but the output must be verified against the source document before making any decisions.

    

4. High impact/high probability (Red zone: avoid or use with extreme caution). This is the danger zone. Asking an ungrounded AI for medical or legal advice falls here. The risk of a harmful, made-up answer is too high, and using the tool in this way dramatically increases cognitive load and risk.

A new deal: Better governance and critical thinking

This shift in computing requires a new social contract with technology. The old deal was that we could trust a computer's output. The new deal must be that we apply risk-based judgment to its output. This has huge consequences for our laws, our ethics and our schools.

Our current laws are not ready for a world with automated, believable falsehoods. Who is responsible if an AI gives dangerously wrong medical or financial advice? This will likely mean new rules for testing AI, being transparent about how it works and clearly labeling AI-generated content.

Most importantly, this new era requires a major change in education. A strong foundation of factual knowledge is more crucial than ever for spotting errors. The most critical skill is no longer knowing the answer, but knowing how to question the answer. Using frameworks like the trust matrix, students need to be taught how to assess the risk of a given task and apply the appropriate level of skepticism. They must learn to use AI not as a perfect oracle, but as a powerful and sometimes flawed partner that requires their judgment.

Conclusion

The arrival of the hallucination engine signals the end of an era of computational certainty. To see hallucination as just a bug is to miss the point entirely. It is the signature of a new kind of machine that computes with possibilities. Our job isn't to destroy this engine, but to build its steering wheel, brakes and guardrails.