"are there examples of people becoming very successful due to Anki?"

It's hard to answer that kind of question, because there are plenty of examples of people becoming successful without SRS, and people who use SRS don't only use SRS.

Personally, I use Anki for professional development (so not to pass exams, but for long-term mastery).  My biggest topics are math, algorithms (ex. machine learning research topics), and programming.

It's got a number of advantages.  Sometimes it helps by keeping technical details (like trig identities or programming syntax) close to the surface, so I can finish a project faster or plow through whiteboarding a problem with a colleague.  Other times it helps by cementing a deep conceptual intuition strongly, so that I can focus on doing more advanced things (instead of rehashing the same slippery basics).  Or by just increasing the breadth of what I know is available, so I don't have to agonize over the right Google terms. Sometimes it helps by making it easier to remember people's names, accelerating my ability to network (I recommend mnemonics here, since names are rote memorization, and rote memorization is difficult with Anki).

But above all, it ensures that I can keep learning, even if I only have 20 minutes a day to spare for deep/slippery topics like quantum computing; or 10 minutes a day to capture a fleeing professional experience that would otherwise be in one ear/out the other.  I'm just 32 years old, but that's old enough that I've forgotten much more than I've learned.  Anki changes that: it acts like compound interest for knowledge.  This spring (after a few years of regular SRS use) I'll reach 40,000 cards, with no sign of slowing down.

It's hard to reproduce that kind of growth with traditional learning methods.

Why "instead?"

I find that all of those activities enhance my SRS experience, and my SRS experience (sometimes dramatically) enhances all of those activities!

Particularly since I use SRS to capture concepts, intuitions, and rich relationships, not just isolated facts.  Rote memorization doesn't work very well with SRS anyway--as with everything, it works best when used to understand a topic in its full intuitive glory.

Not the OP, but got a few opinions there:

1. Algorithms can be Ankified with the same strategies that work for other subjects: chunk the basic intuition into a short overview card, use diagrams, and hit the "basic idea" of how it works before adding more details cards for particular tricks & minutia.  As always, most cards should take <15 seconds to review (including time spent reading the card).
2. Personally, I avoid burnout a few ways: 
   A) pair Anki with activities like taking a daily 1-mile walk or doing dishes (iOS voice commands help with the latter, so you can review hands-free),
   B) don't be shy about reviewing my professional development cards during work hours,
   C) deleting social media from my phone, so Anki is the go-to gap-filler app,
   D) always be learning a few new cards in each deck (since decks with 100% old material are less fun to review),
   E) refactoring: I set a reminder to work on marked cards every few days, so I can break down difficult cards into higher-quality material.  A little maintenance goes a long way.
   F) card design, card design, card design.  Well-designed cards are far less fatiguing to review than hastily made ones.
3. Not sure I understand.  Anki kind of abolishes the line between reviewing and learning: you do both with the same system.  If you mean motivating yourself to study old material---my trick is to review from large, combined superdecks, so there is no pause between "Oh, I finished my learning decks, time to move on to old decks."  If I give myself that break, I'm more likely to procrastinate, but if I treat it all as one big deck there's motivation to finish it all in one go.
4. I actually find it motivating.  Learning complex subjects is usually hard, because when you hit something hard there's an urge to procrastinate rather than figure out what's blocking you.  Anki removes these energy barriers for me: I can always learn a little more about quantum mechanics or whatever by adding a card or two.  It makes hard subjects easy, and makes it possible to say "I'll just study a little topology for 10 minutes" and actually make progress (which is usually impossible outside of SRS).
5. I very rarely try to tweak the algorithm.  90% of the usefulness of SRS is just being able to handle the spacing schedule for you.  Tweaking it doesn't lead to much gain in efficiency (with a few exceptions: adding learning stages to avoid ease hell, and changing the reset interval to something like 20% or 50% instead of 0% both help avoid a lot of unnecessary reviews).
6. Voice commands for everything, and audio cards for languages.  Hands-free, eyes-free reviews!

Anki's value depends a lot on how you use it.

If you use it for rote memorization, without grokking concepts deeply---then what you get is rote memorization.  Think of memorizing capitals without learning anything else about states and nations.

But if you work hard to identify conceptual landmarks and add good questions to tie it all together (more like a high quality PowerPoint presentation), then it can be amazing and facilitate retention of rich intuitions for years.  Think of memorizing major topographic landmarks on a map and a bit of history, so that you have something to relate capitals to in context.

Incidentally, rote-memorization cards are actually harder to review with Anki: the "glue" holding them in memory starts to fade after a month or so, so eventually the drift toward "ease hell" and otherwise become unpleasant to review.

Fantastic insights!  I especially like how you've articulated the value of using "easy" books to connect concept to the "real world."  I've certainly run into the problem of trying to Ankify a big dense book, and getting bogged down with it.

Incidentally I'm also working through the Book of Why and Causality by Pearl.  Great progression (starting with one, then returning to the other).

I've been using Anki for math and computing for several years now, and one word of warning is that your big, dense cards (i.e. screen-shotted definitions) are the sort of thing that, in my experience, is easy to remember for a month or two (because rote memorization is filling in a lot of the glue), but becomes much more difficult (and trends toward ease hell) once intervals reach the high months/years range.

So I wonder if after a couple more months you might feel inclined to refactor your cards to use many small cards (rather than few big cards) to express definitions and theorems.

Personally, I've converged on a highly visual approach to chunking concepts into their "basic idea," followed later by detail cards to drill specific aspects of a larger whole.  This is obviously a nice approach of diagram-friendly areas like topology:

But also works for equations, algorithms, etc. too.

I've got some more examples here:

 * Advance machine learning concepts

 * Statistics