Over the past decade, neural networks — particularly large language models — have transformed how we interact with information. They can write, summarise, translate, and assist with increasingly complex tasks. They are fast becoming embedded in everyday life.
And yes — they make mistakes.
But these mistakes aren’t evidence that the technology is broken. They’re a direct, unavoidable consequence of the way neural systems work. The same processes that let them improvise, connect disparate ideas, and produce unexpected brilliance also guarantee that they will sometimes be wrong.
Why mistakes happen
Neural networks don’t reason or understand in the way humans do. They don’t follow logic like other computer systems. Instead, they generate outputs by identifying statistical patterns in their training data and predicting the most probable next token in a sequence.
That process brings both power and fragility. Power, because it allows the system to operate fluidly across any domain of language, without needing rigid, pre-programmed rules. Fragility, because there’s no internal grounding to check whether a sentence is true, consistent, or complete.
The result? Mistakes that appear in familiar forms:
- Hallucinated facts – confidently stated information that has no grounding in reality.
- Flawed logic – conclusions that don’t follow from their premises.
- Missing context – answers that ignore relevant constraints or background.
These are not occasional glitches. They are baked into the architecture itself.
The temptation to “fix” them
When faced with mistakes, the natural instinct is to eliminate them at the source — to adjust the model until it never hallucinates, never slips logically, never forgets context.
But with neural systems, this is not like fixing a bug in a traditional program. A language model’s “mistakes” are the same mechanism that enables its creativity. Remove the possibility of error entirely and you risk removing the possibility of surprising, high-value insight.
It’s like asking a jazz musician to only play from sheet music: the dissonant notes disappear, but so does the magic.
Where control really belongs
This is why the most reliable path forward isn’t to try to turn neural networks into flawless truth engines. It’s to keep them as they are — fast, fluid, generative — and place the necessary controls around them, not inside them.
External verification layers can catch factual errors, validate reasoning, and enforce the rules only when it matters. That way, the model is free to explore the full space of possibilities, while the system as a whole guarantees correctness where it’s required.
This approach gives us the best of both worlds: the creativity of neural generation, with the precision of symbolic reasoning and deterministic checks.
Mistakes as part of the design
Seen in this light, AI’s mistakes aren’t a sign of immaturity. They’re a reminder that we’re working with a fundamentally different kind of intelligence — one that thrives on possibility, not certainty. The goal isn’t to erase that difference, but to embrace it, and build the right scaffolding around it.
If we succeed, we won’t just have more reliable AI. We’ll have AI that retains its ability to surprise and delight us — while meeting the demands of trust and safety where it truly matters.