AI or not AI ?
We've all heard about the massive compensation packages offered to top AI engineers to join Meta.
And let's be honest, we've all thought the same thing: Why not me?
If you're a data scientist like me, but not deeply involved with AI, LLMs, and the latest technologies, you might be asking the most hyped question: “Should I pivot from data science to AI?”
But here's the thing: just because there's hype doesn't mean you should jump to it.
We've seen different cycles of hype before: the Data Scientist Hype, the Data Engineer Hype, and now, it's the AI Data Scientist Hype.
Let's dive into some thoughts that will help you to guide through.
AI Jobs Are Growing
Two years ago, in the UK and USA combined, there were at most 2k positions focused on AI. Now, there are at least 5k, depending on how you search for them.
The demand is real.
Salaries
From LinkedIn positions where companies disclose salaries, AI engineers earn roughly 30% more than the average data science position.
It's important to understand that you won't immediately earn the astronomical salaries you hear about in the news. Initially, your compensation might be on the same scale or at most 30% higher, but with experience, your earnings can grow significantly.
Is the Hype real?
Here are three reasons why AI engineers are truly in demand:
AI is expanding.
Name a company or person who doesn't use AI products—it's tough. AI is enhancing efficiency across various processes, despite its ups and downs.
It’s really affordable.
API calls are quite cheap now, and they're becoming even more affordable with technological advancements.
Investment
Everyone is investing in AI. You might call it an AI bubble, and that could be true, but it's the current reality, and we can't predict what will happen in five years.
What actually you are gonna do?
There are two main roles in AI field.
AI Engineers : They transform AI into products that people actually use.
AI researchers : They build new models, sometimes from scratch, and develop new architectures that save time and improve efficiency, both from academic and practical perspectives.
Your Advantage as a Data Scientist
The good part is that data scientist role overlaps a lot with AI engineer/researcher
You understand:
How to work with data → AI backbone
Statistics and testing → AI evaluation
How to approach business problems → AI solutions
So, should you pivot to AI?
It depends.
One thing is clear: you should definitely start working more closely with AI. From there, it's your choice whether to fully pivot into the AI field or simply use AI to boost your efficiency.
The best time to start learning AI was yesterday, but there's still time to catch this train.
Master, I love your insights on this.