You do have a point... some companies do run their own models that are specifically tailored for their clients. But if they're anything like the RPM, they may be causing more problems than solutions
Like I said though, the vast majority of forecasting data that private forecasters use are available for free or very cheaply. Do the tailored models eliminate meteorologists (meaning forecasters) completely, as is suggested by the thread topic? No. As I said before, as computers get more accurate, meteorologists will still find other ways to improve upon the existing forecast, if not in the more conventional ways that we're used to. It won't take as many human forecasters to cover all the work in the future (as programmers become more prominent), but there will still be human forecasters.
Heck, there could even be other ways to make an automated forecast that we haven't even thought about yet, and there could be new methods for human forecasters to beat computer forecasts that have yet to be discovered. Our field is still relatively young, and I think there's still huge gains to be made both by humans and by forecasters in the years to come. I guess my point since I started posting in this thread is to not think of human forecasters as static while the world of computing remains dynamic. We learn as the computers learn. We evolve as the computers evolve. Will certain areas of forecasting be replaced by computers eventually? Yes. Will forecasting as a profession go away as a result? No. Forecasters will just find other things to forecast and be better at.
Also, just look at it from a practical standpoint. In the 80s and 90s, the models gained a bunch of skill very quickly as the knowledge of the weather from a mathematical standpoint grew with the better technology. Since about the early 2000s, notable increase in skill has been hard to come by (at least looking at the more traditional models like the GFS, NAM, ECMWF, UKMET, CMC, etc.) as the mathematical discoveries and revelations that helped create significantly better models waned. More and more we are relying on leaps in technology than we are in leaps in meteorology in order to advance the model skill. How much could be debated, but it's clear that the trend for increased skill is much slower than it was just a decade or two ago. Finer resolution models offer higher skill in select areas, but suffer in other areas. There's always a trade-off.
Computers are limited by spatial and temporal resolution, availability and accuracy of observations (in the horizontal and vertical), what equations and assumptions are used, rounding errors, compounding errors as you extend out into time, computing power, etc., etc. Each of these are MAJOR hurdles to overcome to the point where computers consistently out-forecast humans. With all of these ways for a computer to fail, you can see why I'm very skeptical about computers completely taking over the forecasting field.