Is there a paper comparing the cost of technical debt, as created by using a machine learning solution- versus the permanent rising cost of having a over-engine

Is there a paper comparing the cost of technical debt, as created by using a machine learning solution- versus the permanent rising cost of having a over-engineered, abstraction heavy enterprise solution - that automatically reduces the people who can work effective on it? Maybe it is just me, but this paper reads from the paragraph "System-level Spaghetti" on-wards like written by one of those java-consultants for 1000 $ a hour. Someone - who is desperately trying to sell this software-engineering toy-toolbox, which has been replaced with a database, lots of graphic-cards server-racks, some admittelty tangled clot statistics plus library glue-code. There is some merits to the point, that a NN should be trained on a well defined problem, and that there is a need for a higher system logic bundling the specialist-abilitys of the NNs, also having a eye on some key-output-parameters drift within boundaries. But that sounds a lot like - you need good "craftsmanship" to train a NN. Which is true- true for nearly all things.