If you get into it now, you'll probably be on the losing end of a pork cycle [1].
All of the hype is creating overinvestment on the side of "producers" of AI. All that overinvestment will mature at roundabout the same time. When it all hits the market at the same time, they'll have to fiercely compete with each other at the same time as having to deal with "reality" kicking in, i.e. learning the difference between hype and real demand to create real value for real paying customers. There will be massive oversupply.
You'd have to find some way to be short that thing, i.e. to somehow take the other side of that trade.
You want to be on the receiving end of that investment with no exposure to the crash that will follow (if any). For example, if you had an AI background now, you could start an AI school. Your customers would be people taking the hype at face value. You'd take their money now, but when it later turns out that the skill isn't worth in the job market what they thought it would be, you're not exposed to that. ...that's what acting school does for wannabe Hollywood superstars. Running an acting school for wannabe stars is definitely a better business than trying to actually be a star.
I agree. I did my degree at a top 10 university in the world. Bachelor mixed, Master focused on AI.
All I can say is, the job market for data science, machine learning engineering and similar is heavily overcrowded. This means that due to competition (lots of supply, not so much demand) salaries will be (a lot) lower than e.g. software engineering. I didn't even bother going into the field, I heard enough horror stories from friends. Several of which got into high prestigious AI companies, for which they had to pass 6+ very challenging interviews and compete against hundreds or thousands of other candidates to get in. Yet, they get paid peanuts compared to what I now rake in as an SWE. When I do 6+ challenging interviews with a company for an SWE job, the least I (can realistically) expect is a TC of > $160k.
Sure, there's these mythical $500k+ salaries for data science / machine learning as well, but they are a lot rarer than for SWE, simply because the market is much smaller for them. So you're playing the game of trying to become a famous football player, where only the top of the top get dream contracts, the rest not in a long shot.
Money is not everything, true, but at some point you have to ask yourself what's worth more; chasing an elusive dream of meaning or focusing a little bit on your well-being as well.
I don't necessarily regret focusing on AI, but from a pragmatic point of view, I rather should have taken a couple more systems and cloud computing classes in hindsight.
Ugh, it's like we are living on a different planet. SW engs seem to be now commoditized, all the money and hiring is in the ML space. It's fairly straightforward to get a 2x rate as a ML eng compared to one would get as a fullstack SW eng.
There are no bootcamps for ML. You can get a graduate degree (MS) in 1 year if you get into UTexas' online MSDSO which has a bunch of ML classes (ML/DL/NLP). UTexas itself is a top 10 school in CS so that might put you on the map for recruiters though PhD is strongly preferred. Another option is to take Stanford AI graduate classes which are deeper than UTexas' but also more demanding.
I agree that this is possible, and definitely worth paying attention to.
But some hype cycles are "real", e.g., looking at the internet in early 2000, you might have thought it was about to crash or about to be huge. Either way, you'd be right. It was about to crash in the short term, but in the long term, it still made sense to "get into the internet" in 2000 because it was still a secular trend that ended up making a huge impact on the world.
This is overly simplistic. Some hype cycles are real, and drive real long term value, like mobile and cloud. These technologies rendered many businesses obsolete and created many new opportunities.
I wouldn’t be surprised if there was a new AI version of every SaaS out there, followed by a consolidation cycle in a few years.
...oh, I remember many many hype cycles. I'm old enough to have been around when people ripped out relational databases from underneath applications and replaced them with XML files because if you didn't you were clearly living under a rock.
All of the hype is creating overinvestment on the side of "producers" of AI. All that overinvestment will mature at roundabout the same time. When it all hits the market at the same time, they'll have to fiercely compete with each other at the same time as having to deal with "reality" kicking in, i.e. learning the difference between hype and real demand to create real value for real paying customers. There will be massive oversupply.
You'd have to find some way to be short that thing, i.e. to somehow take the other side of that trade.
You want to be on the receiving end of that investment with no exposure to the crash that will follow (if any). For example, if you had an AI background now, you could start an AI school. Your customers would be people taking the hype at face value. You'd take their money now, but when it later turns out that the skill isn't worth in the job market what they thought it would be, you're not exposed to that. ...that's what acting school does for wannabe Hollywood superstars. Running an acting school for wannabe stars is definitely a better business than trying to actually be a star.
[1] https://en.wikipedia.org/wiki/Pork_cycle