What's the Best Way to Learn AI?
It Depends on You.

There's no universal 'best'—it depends on your background, learning style, and goals. Self-study works if you're disciplined and have strong fundamentals. Courses provide structure but move at a fixed pace. Bootcamps suit career changers who need full immersion. The cohort offers the fastest path for developers who want personalized guidance. The best approach matches your constraints: time, budget, and how much accountability you need.

Self-study is free and flexible but has a 5-10% completion rate for a reason—it requires exceptional discipline. Structured courses provide deadlines and community but often teach at a pace that's too slow or too fast for you. The middle ground: use courses for foundational concepts, then self-study for projects. Or get coaching to create a custom curriculum that moves at your exact pace.

Bootcamps ($10K-$17K) make sense for career changers starting from zero who need full immersion and job placement support. For developers or technical professionals, you're often paying for material you could learn faster independently. The cohort pace doesn't adapt to what you already know. Consider alternatives like targeted coaching or structured self-study with mentorship.

The cohort gives you a custom path based on your existing skills and specific goals, with no wasted time on what you already know. You get immediate answers when stuck instead of spending hours debugging alone. Most importantly, you have accountability. Someone is tracking your progress and adjusting the plan when life gets in the way. It's the fastest path for people who value time over money.

Depends entirely on your starting point and depth. Basic ML concepts: 1-2 months. Building production applications: 3-6 months. Becoming job-ready as an AI engineer: 6-12 months. These timelines assume consistent, focused effort. Most people dramatically underestimate the time because they don't account for getting stuck, losing motivation, or relearning concepts they didn't fully grasp.

At minimum: basic Python programming and high school math. Helpful but not required: linear algebra, statistics, and software engineering experience. The more programming experience you have, the faster you'll progress—AI engineering is mostly engineering, not math. If you're missing prerequisites, address those first. Trying to learn AI without Python fundamentals is like learning to run before you can walk.

It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.

Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.

You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.

You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.

It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.

Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.