Should I Get a Masters for AI?
The Honest Answer.

A Masters makes sense in specific situations: 1) You want to do AI research at top labs (DeepMind, OpenAI research roles), 2) You're targeting roles that explicitly require advanced degrees (some quant firms, research scientists), 3) Your employer will pay for it while you work, 4) You're making a career change from a completely unrelated field with no technical background, 5) You want the academic network and structured learning environment. For most people wanting AI engineering roles, it's overkill.

Skip the Masters if: 1) You already have a technical background (CS, math, physics, engineering), 2) You want to be an AI/ML engineer rather than a researcher, 3) You learn well independently or with mentorship, 4) You can't afford 2 years out of the workforce, 5) You want to work at startups or companies that value skills over credentials. In 2026, most AI engineering roles care about what you can build, not your degree.

A PhD is for people who want to push the boundaries of AI knowledge and publish research. It's 4-6 years, often funded, but the opportunity cost is massive. Only pursue a PhD if you're genuinely passionate about research and want roles at places like Google DeepMind, OpenAI's research team, or academia. For industry AI engineering roles, a PhD is rarely necessary and can sometimes be seen as overqualified.

In 2026, the fastest paths into AI are: 1) Structured self-study with a strong portfolio (3-6 months if you're technical), 2) 1:1 coaching with an industry professional ($2K-$5K, 8-12 weeks, personalized to your gaps), 3) Online specializations from Coursera/fast.ai (low cost, self-paced), 4) Building real AI projects and contributing to open source. These paths get you job-ready faster and let you start earning sooner. The key is demonstrating skills, not collecting credentials.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

If your employer covers tuition AND you can do it part-time while working, it changes the math significantly. You eliminate the biggest costs (tuition and lost income). Consider it if: the program is well-regarded, you can apply learnings immediately at work, and you're not sacrificing too much personal time. But even then, ask yourself if a Masters is the most efficient use of your learning time or if targeted courses and projects would serve you better.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.