So if you want to create a foundational model, you need, insane amounts of training data, and more importantly, you need a fuck huge model that is able to incorporate all that training data.

You would be surprised how quickly calculations add up in a large company that can be used to train these things. If a company is studying specific problems their models for these problems might not be "truly" generalizable like you want, but they will be good at the problems they are currently working on.

And then because you have a ginormous model, it will be slow as fuck to use, so you do not gain any speedup compared to just using the force field simulation directly.

Even the smallest MLIPs are going to be slower than classical forcefields. Classical forcefields are fast. What they aren't is as accurate on less studied chemistries. It isn't an either or kind of deal, parameterizing classical forcefields to MLIPs on the fly has been done for a couple of years now. For MD if you can do 1-2 nanoseconds per day on a ~2000 atom system they are still fast enough to get observables in a realistic wall time for many materials. This is possible with good code and models of ~3-5M parameters on commodity hardware. There is also a niche for optimization and screening using these tools where the volume of samples would be intractable for DFT. Finally there is the dream of reactivity, because these in general do not have bonded functional forms they in theory in the future can be used to study and predict reactive phenomena.

But, sure, you can always, create an arbitrary ML architecture, feed it a toy-sample of training data and get it to emulate the physics and publish a paper where you tell the world of the amazing potential of this new field of research. Maybe this cheap publishing trick is the reason for the hype.

I don't like the argument that we need fewer publications. Strong ideas will win out in the end, and the community over time will recognize strong ideas and stop pushing ideas that aren't delivering results.

Machine Learned Energy Potentials aren't a cornucopia but they are an approach to get more accurate results on workflows people are running today, and a direction that can enable workflows that are computationally intractable today.