I have a theory - tree based models require minimal feature engineering. They are capable of handling categorical data in principled ways, they can handle the most skewed/multimodal/heteroskedastic continuous numeric data just as easily as a 0-1 scaled normal distribution, and they are easy to regularize compared to a DL model (which could have untold millions of possible parameter combinations, let alone getting the thing to train to a global optimum).

I think if you spent months getting your data and model structure to a good place, you could certainly get a DL model to out-perform a gradient boosted tree. But why do that, when the GBT will be done today?

"Helion hopes that Polaris, will generate more energy than it takes in by 2024. When that target is reached, the company plans a manufacturing facility that can produce 20 fusion generators a day ..."

That is some of the most breathtaking hubris I've ever read. Basically, "As soon as we achieve the greatest advance in energy since chlorophyll ... 30 months should do it ... we plan to quickly distribute it to the world." I dearly wish them well.

Imagine living through the political and economic disruptions if clean, appliance sized fusion generators go on sale at Home Depot. A lottery ticket seems like a safer bet but it's a nice dream.