Will you need a degree to get a job in AI? Five years ago the answer was yes, and the minimum level of education required to apply for most AI jobs was a bachelor’s degree. To work in research roles? You needed a PhD or a master’s from a respected ML program.
In 2026 the trend of dropping degree requirements from job postings is definitely a thing. That said, AI skills in many cases command a higher pay premium than ever before. Which means getting entry-level work of any kind is increasingly difficult regardless of having or not having a formal degree.
There’s no one-size-fits-all answer to this, as the best path will depend on the job you’re looking to get into, where you’re starting from, and what you’re willing to sacrifice in the short-term. Here’s what the hiring data actually looks like.
What employers actually ask for now
Although degree requirements have fallen somewhat for jobs that traditionally require one, the drop off is not as pronounced as the media has led us to believe with their ‘skills over degrees’ headlines.
According to a September 2026 analysis from Lightcast, the degree requirements for jobs that traditionally required a degree have decreased by about 14% since 2019. For roles in IT and computer science, such as for an AI engineer, the degree requirements have actually decreased since 2024.
Notably, many of the postings that ask for AI skills also ask for a four-year degree (as opposed to postings that do not ask for AI skills). There is a strong background of professional skills required for most roles that incorporate AI. For example, a hospital may use AI in healthcare, but would still require someone with a degree in healthcare (as an example). A bank using AI in finance would similarly require a degree in finance.
Not all tech is created equal, however. In fact, the degree requirements for IT and computer science jobs have actually increased since 2019, the second-highest increase among career areas tracked by Lightcast.
An Oxford study of around 11m job ads in the UK found that university requirements in AI jobs fell by 15% between 2018 and 2023.
This means that a degree is still the biggest filter employers use when recruiting, for now at least.
Where a degree still pays off
Degree requirements remain largely unchanged for three types of work.
Research and model-building: If you want to train frontier models or write research papers for publication, then graduate school is currently the norm. The Oxford study found AI skills carry a 23% wage premium, higher than every education level except the PhD at 33%.
Within established professions (healthcare, finance, engineering) that begin to incorporate AI into existing roles, the degree required for those professions will generally remain in place while the individual also learns to apply AI.
Career switchers, if you came from marketing, teaching or law for example, a degree structures your studies and helps with recruitment. Many employers will only look at applications from people with the right degree and then look for experience on top of that.
The format of the program is almost as important as the degree itself for working adults. Many of the best master’s programs in artificial intelligence are now offered online and can be completed part-time while still holding a full-time role (in or outside of the tech space).
Where you can skip it
The case for skipping a degree is strongest in very applied roles, such as building on top of others’ models, automating tasks, etc. What one has built and can continue to build is far more relevant than where they got their education.
The researchers from Oxford have identified the same pattern, that is high rewards for skills in high demand AI jobs and low rewards for degrees in such jobs.
This is particularly true for those already working in tech. Why would a software engineer or data analyst need to go back to school for another degree? A single focused AI certificate or even a few projects are typically sufficient to prove one’s point.
For AI in particular, there are many, many options: online courses; on the job training; degree programs that combine study with work experience; micro-credentials of all kinds (including online ones); and AI bootcamps.
So, what replaces the diploma as proof of skills? Well, the deployed app, your public repository on GitHub or a write-up of how you fine-tuned and then evaluated a model. It’ll take the hiring manager 5 minutes to look at the work that you’ve done online. Put that work front and center on your CV too, since technology trends are changing what employers look for in CVs.
What each path really costs
First, the price of the degree is obvious: According to a Programs.com analysis of 100 U.S. programs, the median price for an AI master’s degree is $33,450 in total tuition, ranging from $10,000 to $129,600 for the 100 programs analyzed. (Note that for online programs, the price of AI master’s degrees is lower, with a median of $24,495 in total tuition.) Students who study full-time also forfeit a year or two of income.
Of course, for in-state students at public universities the median cost of an AI master’s is lower, at $28,688. But 20 of the 100 degree programs we studied can be had for under $20,000 for a credible AI education that doesn’t have to cost an arm and a leg.
Note that while online courses may seem cheap, experience is key when applying for junior positions. And, surprisingly, entry-level positions in AI are even disappearing for everyone. In a Stanford study, workers between the ages of 22 and 25 in the most AI-exposed occupations saw a 13% relative decline in employment.
The buffer of experience holds for older workers. In the same set of occupations where employment declined for younger, less-experienced workers, employment actually rose or held steady for their more experienced counterparts. It’s the kind of task automation versus assistance that drives these outcomes.
PwC’s 2026 AI Jobs Barometer revealed that the most AI exposed junior jobs have senior skills such as leadership required 7 times more than the least exposed junior jobs. While early-career postings in AI exposed sectors have remained flat, the number of “seniorised” entry-level roles has grown by 35% since 2019.
The upshot: diplomas and online courses alone won’t get you hired. Either one requires proof of the value an employee can bring to a company.
How to decide
Match the path to the job you want, not to the headlines.
- Researcher/build models at an AI lab? Get a graduate degree. Specifically, a PhD in order to try to get a Research Scientist job.
- You come from a non-technical background. A degree would be worth your while to get you up to speed with the required knowledge, and online programs are cheap to earn while you are working.
- You already code or work with data. Go straight for the skills you need in AI, build a project or two and look at a certificate if required for specific skills in the postings you want.
- You’re not sure if a degree is needed for your desired path right now. Take one course and complete one real project for the lowest cost possible to confirm a degree is required for the postings you want to apply for.
Stop confusing “do you need a degree” with “should you get a degree”. The real question is what proof does a posting represent for the job you want to do, and how to get that as cheap and fast as possible.
FAQs: Do You Need a Degree to Get a Job in AI?
1. Do you need a degree to get a job in AI?
No, a degree is not always required to get a job in AI. Employers may prioritize practical skills, project experience, and knowledge of AI tools for applied roles. However, research positions and specialized roles may require a bachelor’s, master’s, or PhD.
2. Can I get an AI job without a degree?
Yes, you can pursue AI jobs without a degree by learning relevant skills, building real-world projects, earning certifications, and showcasing your work on GitHub or a portfolio.
3. Which AI jobs do not require a degree?
Some applied AI roles may be accessible without a degree, including AI automation specialist, AI application developer, chatbot developer, and AI workflow engineer. Requirements vary by employer and experience level.
4. Is a master’s degree necessary for an AI engineer?
No, a master’s degree is not always necessary to become an AI engineer. Strong programming skills, machine learning knowledge, and practical experience can be sufficient for some positions, although certain employers prefer or require an advanced degree.
5. Do AI research jobs require a PhD?
Many AI research scientist positions prefer or require a PhD, particularly those focused on developing new machine learning methods or conducting advanced research. Some research engineering and applied research roles accept candidates with a master’s degree or equivalent experience.
6. Can online AI courses help me get a job?
Yes, online AI courses can help you develop skills in machine learning, Python, generative AI, and model deployment. However, completing projects and demonstrating practical abilities are often more valuable than relying on course certificates alone.
7. How can I prove my AI skills without a degree?
You can demonstrate your skills through deployed AI applications, GitHub repositories, portfolio projects, model evaluation reports, and documented examples of solving real-world problems with AI.
8. Is an AI degree worth it in 2026?
An AI degree can be worthwhile if your target roles require formal qualifications, advanced mathematics, or research expertise. If you want to build AI applications or automate business workflows, focused courses and practical projects may offer a more affordable starting point.
9. Can non-technical people get jobs in AI?
Yes, non-technical professionals can transition into AI-related roles by learning relevant tools and developing practical skills. Their existing industry knowledge in areas such as marketing, finance, healthcare, or education can also be valuable when applying AI to specific business problems.
10. What skills should I learn to get an AI job without a degree?
Start with Python, basic mathematics, machine learning fundamentals, generative AI tools, APIs, and prompt engineering. Depending on your target role, learn model evaluation, data handling, AI application development, and deployment, then build projects that demonstrate your abilities.
