The company I work for makes a killing on applying a 1980s-era ML technique to a really tough modern business problem -- the resulting product is probably the best in the world at what it's doing.

Old techniques have several things going for them, with one of the more important ones for us being explainability. A random person off the street could hypothetically, with an hour or two of training, diagnose problems just by looking at the structure of the model. That's very helpful for adapting to market needs.

Generality is another big plus. Since the model encodes intuitive ideas there's a lot of room for using it in innovative ways.

Older techniques also tend to produce better results with less data, because big data wasn't as much of a thing back then.

Unless you have a crazy amount of resources, I think it's far better to be bleeding edge in as few things as possible. Solving a new business problem? Perhaps don't spend too much time on also solving all the childhood diseases of a new technology.