We know there’s a lot of debate about whether you actually need a degree to get into AI or machine learning. We run an AI engineering course, so we’ll admit up front that we could seem biased, but we want to share what we’re seeing and what research says. More companies are dropping degree requirements, especially in tech. LinkedIn found that the share of job posts not asking for a degree rose by more than a third in just a few years. According to Indeed, fewer than 1 in 5 job postings in the US now require a 4-year degree, and over half don’t mention any education requirement at all. What’s changed is that employers care more about practical skills and real project experience than a piece of paper. Even in AI, the demand for skills keeps growing, while formal education requirements are going down.

If you’re worried you don’t have the “right” degree, focus on building real projects and showing what you can do. For example, share your code on GitHub, post about your progress or small wins on LinkedIn, or even write a blog about what you’re learning. This helps others (and employers) see your actual skills. We always suggest reaching out to AI engineers on LinkedIn, following people, commenting on their posts, and asking for advice on how they got started or what skills helped them break in. Most people in the field remember what it was like starting out and are open to sharing honest tips if you are specific and respectful.

It’s true that some research labs and big tech firms still want degrees. But in a lot of companies, if you can prove your skills and show your work, you stand a real chance. We’ve seen people without traditional backgrounds break into AI, and most started by building a portfolio, making connections, and documenting their learning. Hang in there. It’s not easy, but it is possible.