The four topics you list are all high BS topics. I mean:

𝐂𝐫𝐲𝐩𝐭𝐨 —- the main use case is separating fools from their money. Missing out on that one is one of the best things that can happen to you.

𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 —- people starting writing papers on this around 1998 when I got my physics PhD. You could not get a postdoc in it at that time. It got fashionable maybe 10 years later. I am sure there are some people getting tenure in it right now but in 10 years it might be remembered as just another field that ran its course and had better days and it may get a reputation, just as chaos and fractals got in the 1990s as something you just could not make a career in. Sure there will be ‘quantum supremacy’ for particular problems (maybe even that box that is the McGuffin in Sneakers) but you are not going to be using a quantum web server to host your content.

𝐃𝐚𝐭𝐚 —- I hear ‘data is the new oil’ and I think of an oil tanker spill like the Exxon Valdez or maybe like Spider-Man, ‘with big data becomes big responsibility’. I think how TikTok’s recommendation algorithm is so good because they collect better data: they give you a very limited set of choices so the data collect is meaningful. When YouTube shows you 30 links on the other hand you can come to no conclusion that the viewer didn’t like those things.

Data is a big part of everything we do in computing. In plain ordinary boring applications development the most important thing is getting your data structures straight. There is a whole art of online transaction processing and the arts of doing analytics after the fact or in real time.

𝐌𝐋 —- data is the currency of ML. If you have a real problem to solve and real test and training data you can do ok with basic algorithms out of scikit-learn. If you don’t have appropriate data you can dream about the latest algorithms and accomplish nothing. You need to know the basics of linear algebra and statistics: the courses you take as a math major are great.

Like most other things there is a part of the field which is almost eternal (I think about things Yan LeCun wrote about 20 years ago almost every day and think ‘Neural Networks: Tricks of the Trade’ can turn a failing project into one that succeeds even though it was written before deep networks.) and other parts that are ephemeral. For work I did a lot with LSTM networks for text, then it was CNN networks, those have been forgotten almost because transformers can do things that would be difficult or impossible without attention. Personally I am really interested in those radiance field algorithms for scene synthesis, I haven’t done a project yet but it was clear early on that the algorithms would get much more efficient and that has been happening the whole time I’ve been watching from the sidelines.

There are ‘data scientists’ who do quantitative marketing with a lot of smak and pow and get paid really well because they really can increase revenue 850% in two years. They don’t necessarily know a lot about algorithms. If you were working in the field as a scientist you would be one of the people who writes one of the 20 papers on radiance fields a week that would never get a second vote on HN, but as a practitioner you can master the basics and solve real problems for fun and profit.