Let’s avoid the equation ai_fragility * software_fragility = very_bad_things

Anyone who has built classical, deterministic software systems can tell you how fragile they are. Such systems perfectly follow precise, explicit instructions, and are incapable of operating outside those instructions. That means that, if you have written instructions which do not specify the behaviour you want, which are inconsistent with one another, or do not cater for unexpected values, your system will fail. If you are lucky, that failure will be loud: a crash with an ugly error message. If you are unlucky, it will be quiet: steady, ongoing corruption of data values which you won’t discover until later.

Anyone who has built newer, AI driven, probabilistic software systems can also tell you how fragile they are. Such systems treat inputs (prompts, context, training data and so on) as factors which affect the probability of getting different outputs. That means that, if your inputs are insufficient to tilt the balance of probabilities in favour of your desired outputs, your system will fail. If you are lucky, that failure will be obvious: results which are factually incorrect or logically incoherent. If you are unlucky, it will be obscure: results with subtle biases which you won’t discover until later.