The main issue with EPL is that compiling or interpreting the language (it supports both paradigms) is extremely resource intensive and incurs significant compu

The main issue with EPL is that compiling or interpreting the language (it supports both paradigms) is extremely resource intensive and incurs significant compute overhead, often unavailable without resorting to fixed infrastructure. Inference requires access to the massive n-dimensional memetic matrix that comprises the execution environment since most of the processing is done in the data structure itself. Is silicon this implies a large amount of memory and processing capacity, and in carbon the training process takes decades, even though the cost is (amazingly) lower. The big downside of carbon based solutions is that each instance has to be individually trained, so scaling is a serious problem. Not only that, but the success of the training stage is not predictable, and retraining is usually ineffective. Interestingly, EPL is considered by some to be an exemplary example of the “data as code” paradigm, since the majority of the processing is pre compiled into the n-dimensional memetic matrix. Even though the mematrix is so massive that resource consumption is high just to move tiny fragments in and out of working memory, it enables the use of simple algorithms to produce surprising inference performance. The main requirement for extracting inference from the dataset is that the extraction algorithm perform statistical prediction of sequential tokens, which is easily accomplished in both silicon and carbon based neural networks. It is also worth noting that each data output is also code, which is then recursively be added to the mematrix even if the output is an error. This requires careful code hygiene and error checking and correction or inference results can quickly go off the rails.