From what I've gathered and read from OP's comments, it sounds like their analog model makes sense and is precise enough for them to work with. People talk abo

From what I've gathered and read from OP's comments, it sounds like their analog model makes sense and is precise enough for them to work with. People talk about how much more powerful a "32-core Xeon machine" is and they're not wrong, but just because new technology is available you also have to look at is it worth it to transition over/update it. I have an example from my daily work. We were hired to update a company's model from 6 years ago. Their model is an Excel VBA model that we're rebuilding in Python with an increase resolution of detail and accuracy. However, for our "more accurate" model to be accepted, we have to also prove the following: - We have to show that our model's outputs are the same as the VBA's model for the same input/starting condition (makes sense at a higher level, but at a higher resolution this means you have to spend more time looking at why a specific value is in a specific way and can be different from the lower resolution model) - Train the new users on the new model (knowledge transfer) - Ability to update and modify the new model wherever they need to change it or understand it - Understand the limitations and the constraints of the new model This basically boils down to how much time is needed in training the new model, how much money and time is needed to invest in building a new model, and is it really worth it to build a new model when the old model has worked good enough for now (and you understand the limitations of said model and is within your needs)? There's only so much time in the day and you obviously have a ton of other tasks that also needs your attention, and getting these newer models verified and accepted by the board is great but it's also additional meetings and time that you need to setup with them and get everything OK-ed... doubly hard when everyone else is also super busy. I'd say modern computational platforms are better when it comes to raw power, but the logic and just resources required to build, verify, accept, and train (human resources-wise) the new model might just not be fully worth it. Remember, there are also other industries other than tech that uses computer hardware to get jobs done.