[HighFreqAsuka]

Quite a lot of techniques in deep learning have stood the test of time at this point. Also new techniques are developed either depending on or trying to solved deficiencies in old techniques. For example Transformers were developed to solve vanishing gradients in LSTMs over long sequences and improve GPU utilization since LSTMs were inherently sequential in the time dimension.

[ldjkfkdsjnv]

Sure, but if you were an expert in LSTM, thats nice, you know the lineage of algorithms. But it probably isnt valuable, companies dont care, and you cant directly use that knowledge. You would never just randomly study LSTMs now.

[opportune]

There are plenty of transferrable skills you get from being an expert something that gets made obsolete by a similar-but-different iterative improvement. Maybe you're really good at implementing ideas from papers, you have a great intuitive understanding of how to structure a model to utilize some tech within a particular domain, you understand very well how to implement/use models that require state, you know how to clean and structure data to leverage a particular feature, etc.

Also, being an "expert in LSTM" is like being an "expert in HTTP/1.1" or "knowing a lot about Java 8". It's not knowledge or a skill that stands on its own. An expert in HTTP/1.1 is probably also very knowledge about web serving or networking or backend development. HTTP/2 being invented doesn't obsolete the knowledge at all. And that knowledge of HTTP/1.1 would certainly come in handy if you were trying to research or design something like a new protocol, just as knowledge of LSTMs could provide a lot of value for those looking for the next breakthrough in stateful models.

[HighFreqAsuka]

Transformers have disadvantages too, and so LSTMs are still used in industry. But also it's not that hard to learn a couple new things every year.