Normally you'd have to pay a tutor $50/hour. Multiply by 5 days a week, 52 weeks a year, you're at $13,000/year. Bringing that down to $499/year (26x cheaper) makes mathematical talent development accessible to many, many more people. Sure, that's not everyone, and there are still people who are priced out. But to me at least, providing a 26x cheaper option feels like a good starting point towards a goal of making mathematical talent development accessible to more and more people.
Of course, the following question still remains: "Why do you even need the learning experience to feel like working with a personal trainer? What is the benefit over a textbook, Khan Academy, MIT OpenCourseWare, etc.?"
The answer to that question: learning efficiency.
Let me tell you about my own experience. I self-studied a bunch of math subjects on MIT OpenCourseWare (OCW) when I was in high school. OCW is a good resource and I came a long way with it, but for the amount of effort that I put into learning on OCW, I could have gone a lot further if my time were used more efficiently. Just to name a handful of inefficiencies in OCW:
- not super scaffolded → you periodically run into situations where you bang your head on a wall thinking "how the heck did they get from here to there?" and it takes a long time to figure out what kind of logical leap is happening (if you figure it out at all)
- doesn't track your knowledge / make sure you've mastered the prerequisites for anything new you're supposed to learn → you often feel a large gap between your level of knowledge and the new material, which leads to more banging your head on a wall trying to figure out what prerequisite knowledge you're missing and how to learn it
- no spaced review → you quickly get rusty on a lot of what you learn, which not only means you come out of the course having forgotten a lot of content, but even during the course, you're constantly forgetting prerequisites
- doesn't adapt to your level of performance → you waste a lot of your time doing the wrong amount of work. Sometimes you grasp a topic quickly and end up doing way more practice problems than you need; other times you struggle with a topic and don't do enough practice problems to reach mastery
- leaves the definition of "mastery" open to interpretation by the learner → as a learner, it's hard to know when you've mastered something well enough to continue moving forward. You often think you've learned something well enough, when you actually haven't -- but you won't know unless there's an expert who is evaluating your knowledge. On the flipside, you can also take things too far being a perfectionist, spinning your wheels on the same topic for a week over some minor point that doesn't make perfect intuitive sense to you, when it would be more productive to just keep moving forward and solidify your understanding by building on top of it.
I could keep going with this list (happy to do so if you're interested, just let me know), but by now you probably get the point: all of these things introduce unproductive friction into the learning process, leading you to make less educational progress per unit time/effort that you put towards learning.
That's one reason why I've been so motivated to help build Math Academy. We take away as much of this learning friction as possible and maximize your learning efficiency. We address all the above issues and more.
That's our main value proposition: sure, it's possible to learn math elsewhere, but it's way more efficient with us.
Efficiency is important not only because you make faster progress, but also because you're less likely to quit.
In practice, people get off the train and stop learning math once it begins to feel too inefficient. In anything you do, once the progress-to-work ratio gets too low, you're going to lose interest and focus on other endeavors where your progress-to-work ratio is higher.
Efficiency keeps that progress-to-work ratio as high as possible, keeping you on the math learning train as long as possible.