Is It Too Late to Learn AI?
Have you missed the train on learning AI?
By Frederik Bussler, Head of Growth Marketing at Obviously AI
I regularly share resources to learn AI and data science, whether it’s courses from Google or Harvard or full-length YouTube tutorials.
At the same time, I hear the concern: “Is it too late to learn AI and data science?”
The concern is that, as millions of students are learning machine learning, the field is becoming saturated. After all, there are a limited amount of AI jobs, especially during a global recession.
Andrew Ng’s famous Machine Learning course on Coursera has close to 4 million students.
As of writing, if you search for “Machine Learning” on LinkedIn Jobs, you’ll find just over 100,000 jobs.
Clearly, there are far more students than open jobs — a ratio of almost 40:1, just looking at the number of students in a single Coursera course.
Why It’s Still Worth It
That said, learning AI is still worth it, for a number of reasons.
First off, let’s talk about intrapreneurship. AI has become far easier and faster to build and deploy than ever before — especially given no-code AI tools like Obviously.AI — which means that employees are in a position to add more value by adding AI to their skillset.
These intrapreneurs finding AI use-cases in their organizations don’t add to the number of open jobs on LinkedIn, but there are countless examples.
There’s a huge incentive for any employee to become an AI intrapreneur: The potential to automate the repetitive, boring parts of their work, and focus on the creative, human-centered tasks. Not to mention, AI skills can boost your salary and career.
For example, marketers could use AI to predict customer behavior, build personas and identify top demographics. Retail employees could optimize assortments, predict inventory burn, forecast staffing needs, and more. Insurance employees could use AI to predict insurance claims, litigation risk, subrogation opportunities and more.
The possibilities for AI intrapreneurs are endless.
There’s another massive field of opportunity not included in the ~100K machine learning jobs out there: Entrepreneurship.
Entrepreneurship is the riskier flipside of intrapreneurship. It means going your own way, finding new ways to add value in the market, often without backing, support, or stability of any kind.
At the same time, this high risk comes with the potential for high reward.
Let’s say you join a Silicon Valley startup as the 30th employee (still early on), and you’re one of the top engineers in your field. According to Holloway, you can expect 0.25%–0.5% equity.
If you strike out on your own, as a solo founder, you have 100% equity to start. By bringing own co-founders, employees, and investors, that number will decrease, but there’s a lot more potential to be had.
Even if you’re not interested in intrapreneurship, entrepreneurship, or landing a new role, there’s something to be said for constant learning.
AI is now found in every industry, from the recommendations you get on Amazon, Spotify, Netflix, or Tinder, to the search results you see on Google or YouTube, even to COVID-19 tracking, vaccine development, and vaccine roll-out.
To stay up-to-date on the latest technologies, and really to understand today’s world, learning AI is a must.
Learning AI is worth it, and always will be. Even if the job market gets saturated (which it isn’t yet, as there are still open jobs for those who are qualified), there’s always a potential for creative intrapreneurs and entrepreneurs. To stay relevant, AI skills are quickly becoming a must-have.
Bio: Frederik Bussler is Head of Growth Marketing at Obviously AI. He is on a mission to democratize AI.
Original. Reposted with permission.
- We Don’t Need Data Scientists, We Need Data Engineers
- Using NLP to improve your Resume
- Top 5 Artificial Intelligence (AI) Trends for 2021
Top Stories Past 30 Days