Getting into AI engineering, especially at the
junior level... whether you're excited about the thrill of the new technology or learning new
skills... if your goal is a quick and easy job hunt, AI is really hard... but despite all these
challenges, if you do make it the payoff is going to be amazing... Hey everyone, it's Jean,
your trusted engineering mentor, and today, I really want to address a topic that's been on my
mind for a while now. I get tons of messages from people who are interested in becoming software
engineers and especially, more specifically, AI Engineers. there seems to be this magnetic pool
that's drawing people into AI engineering right now, but sometimes, the reality doesn't quite
match up with the dream. it's because there are a bunch of misconceptions about being a software
engineer and especially an AI engineer, and I want to make sure you know what you're getting into.
I want to empower you to make your own decisions based on what is actually true so that you can
find success in soft software engineering or AI engineering careers of your own. but before
we dive into the big question of whether or not you should be an AI engineer, I have a favor
to ask. please give me a favor and hit that like button! it's not just about stroking
my egos, but it really helps the YouTube algorithm understand that you are interested
in content like this. plus, it helps spread the word so other soon-to-be software Engineers
can also benefit from our discussion today. so, if you're ready for some real talk about AI
engineering and software engineering, give that like button a tap, and let's get into it!
First off, I do want to address some of the other common advice that I have seen online. now, there
are some people out there who might be telling you not to jump into software engineering because
of reasons like it's too much math or saying that it's not all about money. honestly, when it
comes to math and physics, most regular Software Engineers don't really use much of it. You
won't be solving crazy equations every day, and it's more about coding and logic. but if you're
thinking about getting into machine learning, you do need to get ready for some math. and if math
isn't really your thing, you probably don't want to go into machine learning. software engineering,
on the other hand, is completely fine even if you hate math. so there is some good news for you.
now, about the money, some people say don't go into software engineering for money, but often the
people who are saying those things have never been worried about money. money definitely played a big
role in my decision to become a software engineer. Achieving Financial stability was a big priority
for me as a teenager. so if you are attracted to the financial side of things, I think that's
perfectly fine. and I think we should just be more honest about money. there is often judgment
about wanting to pursue money, but making money and having enough of it is a big consideration
and a priority for a lot of us. if you are eyeing those high-paying software engineering gigs, just
don't assume that it's going to be easy money. Money doesn't grow on trees, and the reason why
AI and software engineering roles pay so well is because they demand a unique set of skills and
expertise. it's not a shortcut to easy money, but it's a reflection of the value and effort
required for those roles. and this brings me to reason number one: AI is really hard. You guys
probably all know Andrew Ng, he's a big name in AI, and he said in a recent Stanford lecture
that the best way to learn AI engineering and become a really good AI engineer is by reading
tons of research papers. and I have to ask you, have you ever tried reading any of those research
papers? if you're in a bachelor's program, you probably have never even come across any of these
research papers. so if you have not given it a shot, I do recommend you try reading them. if you
try reading them and it sounds like it's written in a different language, that's probably okay. it
means you're just a normal human being. but the challenge is that you will be competing for the
same job with people who read these papers for fun, as a hobby. so if you're really serious about
becoming a good AI engineer, start diving into those research papers now. keep reading them until
it makes sense. and this is not to scare you off but it's a reality check again. the competition
here is no joke, which is my reason number two: the competition. working in Tech is already pretty
fiercely competitive and add all the AI hype it becomes a super challenge. every new and shiny
thing usually comes with a lot of excitement. and AI is no exception. Are you ready to dive
into competing in the most challenging field out there? history teaches us, right? during the Wild
Wild West or the Gold Rush sure like some people struck gold, but many folks pay the price with
their lives. the promise of hitting big in AI is real. let's be honest but most of you won't make
it. and again this is not just about AI or machine learning, but it's a pattern in history. Whenever
there is a lot of money and power involved, people want a piece of it. this means that
you're not going to have that work-life balance, which is my reason number three: burnout. high
paying jobs come at a cost, and this is not just about software engineering, but it applies to most
jobs. most well-paying jobs demand hard work. you just can't have it all. so pick your priority. do
you want big pay or a balanced work life? Have you been following all the drama on the news with Sam
Alman and Open AI? he got fired, and he fought his way back. the board that tried to oust him got
ousted themselves. it's almost like real-life squid games. do you think work-life balance is
a priority for Sam Altman today? probably not! and you saw how the news was changing so
quickly during the whole ousting drama? that is my reason number four: rapid changes.
in the AI universe, everything is a constant Evolution. it's like a never-ending dance of
updates and Innovations. the technology itself, the inner workings of the companies, and the
leadership; it's all Dynamic and constantly moving. it's like playing competitive Sports
where the rules and the opponents change every day. playing in the field of AI is like playing
a dynamic game. you just have to be on your toes all the time, ready to adapt to anything
that changes instantly. Winning doesn't just require skills here, but it also requires your
ability to quickly adapt to a new environment. does the idea of constantly changing and
shifting strategies with the company or the tech excite you? Are you the type of person who
feels alive when things are constantly changing around? if the answers to those questions
were yes, yes, yes, this could be the perfect fit for you. every day can be an adventure.
okay, I said I'm going to give you five reasons and we have talked about the first four so far.
AI is really hard, it's really competitive, it carries the risk of burnout, and it's constantly
changing. now this all all adds up to the fifth and perhaps the most important reason. But before
we get into this, if you've been watching so far and you found value in this video, I would
really appreciate a tap on that like button, and with that, let's get into the last reason.
for all of these challenges to be an issue for you, you would first need to land a job, right?
And this is reason number five: lack of Junior roles. getting into AI engineering, especially at
the junior level, which means one to three years of experience, really isn't easy. If you went
to a school like Waterloo, where they do four internships or co-ops before graduating, you would
have two years of work experience already under your belt. Now, if you didn't attend a school
like that with a structured internship program, you're on your own to find your own internships.
you have to really hustle to find your own internships and get that experience. even with
the internship experience, getting a job in Tech or AI is really hard. There just aren't that many
roles available because even the most experienced people who read these research papers that I
mentioned earlier for fun. they want to do it, too. if your goal is a quick and easy job hunt,
AI engineering may not be the best option for you. other fields of software engineering are
challenging, too, but relatively speaking, AI engineering jobs are a little bit harder to
come by for junior engineers. and I'm not saying that it's not possible. there are going to be
people commenting, "Well, I found a job." Well, good for you! but for the majority of people,
it's going to be really tough because there just aren't that many roles. it's all about
supply and demand. the supply is pretty low, and the demand is really high right now. but
despite all these challenges, if you do make it, the payoff is going to be amazing. if you land
that gig, it is not going to be just a job. you're going to be part of a conversation that
shapes the future of the entire world. imagine being in the Inner circle of the most Innovative
technology before it even hits the public. you get to play around with it and have a voice in the
direction of the products. if you're watching this video and thinking that all of this sounds
really overwhelming, that's okay. Just be honest. This is not for everyone. There may be other
fields in software engineering like full stack, front end, and back end mobile all shaping tag in
the future and probably with less competition. so don't jump into AI just because it's trendy.
choose a path that is aligned with your values and passion. whether you're excited about the
thrill of new technology or learning new skills, there is a place for you in software engineering.
If you feel like AI is really the thing for you and you really want to know how to learn AI
engineering, watch my other video where I lay out the three-step strategy for becoming
an AI engineer. I'll see you there [Music]