500 New AI Degrees Just Launched: Most People Learning AI Will Never Need OneTake it seriously when I tell you that most marketers, creators, and business owners reading this should probably skip the AI degree entirely.First, the boom itself, because the numbers are genuinely wild. In 2018, Carnegie Mellon launched the first bachelor's degree in artificial intelligence in the US. According to Programs.com, there are now 193 AI degree programs. AI master's programs grew from 116 in 2022 to 310 today. I've tracked degree programs for years, and I've never seen a credential category form this fast. Cybersecurity took about 15 years to go through the same curve. AI did it in four. Universities are responding to a real signal. Lightcast, which analyzes over a billion job postings, found that roles mentioning AI skills pay roughly 28% more, about $18,000 more per year. Two or more AI skills push the premium to 43%. When money like that shows up in the job market, enrollment follows, and 500+ new degree programs get built. But there's a second set of numbers inside that same research, and it tells a completely different story about who actually captures that premium. The AI job market isn't hiring AI graduatesHere's the stat that reframes everything: 51% of job postings that require AI skills are now outside IT and computer science entirely. Marketing. Sales. Operations. HR. Finance. More than half of the demand for AI skills comes from roles where no one has ever asked to see a technical diploma. Lightcast measured roughly 800% growth in generative AI requirements appearing in non-tech job postings since 2022. Read those postings closely, and you'll notice something. They don't ask for a Master of Science in Artificial Intelligence. They ask whether you can use the tools: automate a reporting workflow, produce content at 10x your old speed, build a customer-facing chatbot that doesn't embarrass the brand, and cut a week of video production down to an afternoon. The salary premium attaches to demonstrated skills. The degree is one way to signal those skills, and for most applied roles, it's the slowest and most expensive way available. Two markets wearing one labelThe mistake people make (including universities, honestly) is treating "learning AI" as one market. It's two. The first market is builders: the people designing, training, and deploying the models themselves. For them, a rigorous AI degree makes real sense. The work runs on graduate-level math; employers gate those roles hard. If you're switching careers into machine learning engineering, a program like Georgia Tech's or UT Austin's online master's (both under $11,000 total, remarkably) is a legitimate path. If that's you, go with my blessing. I have a whole website for you. The second market is users: everyone applying AI inside an existing craft. Marketers, video creators, agency owners, e-commerce operators. This market is much larger than the first one, and it accounts for half of AI demand outside tech departments. For the second market, a two-year degree has a structural problem that no university can fix. The tools reinvent themselves every few months. A curriculum approved by a faculty committee in 2025 describes a toolchain that won't exist by graduation. What holds its value in the builder track (math, statistics, systems fundamentals) is exactly the part that the applied track doesn't need day to day. The market already voted on thisOne more number, and it's my favorite in this whole dataset. Roughly 57 million Americans say they want to learn AI skills. About 8.7 million are actively learning them. And as of last year, only around 7,000 were doing it through a credit-bearing university program. Seven thousand out of 57 million. Higher ed, even with 500 shiny new programs, is capturing about 0.01% of the demand. Everyone else is learning from YouTube, from courses, from documentation, and increasingly from the AI tools themselves, which have gotten strangely good at teaching you how to use them. That's usually framed as a failure of universities. I'd frame it differently: the applied market figured out the efficient path on its own. When the skill is "use the tools well," the tools are the classroom, and the tuition is your time. What actually works for the applied trackSince I've talked you out of a $60,000 credential, here's what I'd do instead, based on watching which self-taught people actually convert AI skills into income. Pick one workflow in your business and go embarrassingly deep on it. One. If you make marketing videos, that might be scripting with an LLM, cutting drafts with AI editing tools, then finishing in a template so the output stays on-brand. Depth in one revenue-touching workflow beats surface familiarity with forty tools, which is what most "AI course" completists end up with. Then document it in public. The applied AI market runs on proof of work, and proof of work is the one thing a diploma can't fake. A LinkedIn post showing you cut video production from 5 days to 1, with the before-and-after, does more for your rates than any certificate. I've built two companies on exactly this principle: show the receipts, and the credibility follows. The 28% premium is real. It's mostly being collected by people who never set foot in a lecture hall. They learned the tools by shipping with them, showed their work, and let the results argue for them. The universities will keep launching programs because the demand signal is too loud to ignore, and I'll keep listing them all. But the biggest AI education story of this decade isn't happening on any campus. It's 8.7 million people teaching themselves, one workflow at a time, and quietly getting paid for it. | Use our online video maker to create a marketing video Choose a video template and edit it with our online editor Video Kategorileri En Son Makaleler |