Too early to tell, but IMO unlikely. Let's see what his take will be once an AI can propose a topic, write the script, animate, and narrate a good educational math video.
He won't care because he has established position and is set. It will be just like everybody else - if the individual is at the top of the food chain then he/she is all over it how you are using it wrong and how you should use it (don't forget to follow and subscribe).
Yep, that's what I see in the CS field -- people at the top are advocating AI and don't understand why the hell the others are hesitant. I meant the top engineers, not the management. I look up to them because of their skills, but that doesn't mean I need to agree with everything they said.
But you already have Claude-directed videos that utilize OP's manim project (which he uses himself to do 3b1b) that anyone else can create. Grant open-sourced manim and now anyone can point a model at it to create their own 3b1b-like videos. They're popping up all over the place as Opus 5.5 demo videos just in the last few weeks. So... AGI?
I guess I wasn’t clear, but using manim to make videos is not the hard part. The hard part is making them engaging and enlightening, and whatever else you want to attribute to 3b1b videos which make them a joy to watch and learn from.
I tried watching 3b1b over the years, really wanted to enjoy it. But I never found the way he presents things intuitive and never got a palpable intuition that helped me with math classes or work.
I saw some clearly AI slop made videos and got much better understanding of topics, since they don't do foreshadowing for an awe-inspiring connection that we'd admire together after 15 min videos, the clankers just get to the point. Not claiming those videos are too useful either, but I did however see accelerated learning gains from asking LLMs to explain (and do Bret Victor-style small interactive demos) math and algos, something I have never experienced from any of the educational STEM Youtube channels.
Even if you find them intuitive, they produce no learning gains for anyone. Take a lin alg test, watch the 'Essence of lin alg' videos, take comparable test again. I guarantee you won't see any change even if you 'understood matrices better'. They are pure mathporn.
No learning gains? I teach linear algebra. I can assure you that students who watch and study the "Essence of linear algebra" videos (which I recommend on the syllabus) do better on my tests that those who don't, because they learn stuff from them.
It's much more likely that the students who take the time to watch supplementary materials are the ones who'd do better regardless of the quality of the given material. If you have access to fresh learners, I'd say it's a good opportunity to test how good those series are.
I'm not sure anything I say can convince you, as your mind seems to be made up already, but my experience has been that those videos help my students understand the concepts of linear algebra, comparing the mastery level now vs. before the series was published on YouTube. Especially when it comes to getting a feeling for what a linear transformation is, and how the properties of matrices echo the properties of the transformation they represent.
As for "fresh learners", I can provide my own son, who watched the series before taking his first linear algebra class, and says that it helped him a lot.
I'm not defining education as passing tests. A test is just a measuring instrument.
In fact, your claim is strong and quite easy to test. If watching the series gives people a better fundamental understanding which makes subsequent learning more efficient, take matched novices, randomise them to watch the series or not, then teach both groups some later lin alg materials and measure learning gains on novel problems.
That's the kind of test I mean, not 'can you grind through the course exam'.
If your claimed deeper understanding neither improves what someone can do afterwards nor how efficiently they learn related material, I'm not sure what observable content the claim has.
This is not my experience. Having an intuitive grasp of e.g. the graphical interpretation of the determinant instantly lets you figure out most of the key determinant properties without having to memorize them. That's the primary thing you get from intuition imo, having a sense of what must be true without having to memorize or prove it.
I'm actually quite optimistic, was pointing out that, at least to to me, AI slop already produces better results than big educational content creators. For something concrete, even if not classic math, I credit direct LLM interaction highly for accelerating my understanding of CPU design and compiler tech.
fwiw the fact that brains work fundamentally differently was my big penny dropping realization after digging into what LLMs are doing. Everything (I assert) a brain does (the function between inputs and outputs) is due to training data. There is no "instinct" and there is no "ROM-driven way to think" in any brain. It's training data all the way down. As a consequence different brains do indeed think in fundamentally different ways.
This is so fundamentally divorced from what we know about cognition in humans or in animals. The idea that we are a pure tabula rasa and only shaped by our "training data" is absurd and false. Just look at the amount of data we need to be able to do things relative to ML models. Look at animals which can do complex tasks moments after birth. We're deeply, deeply shaped by instinct.
Well, everyone being tabula rasa has a point tho. Technically there's no unique physical cause that make development unfold the same for all organisms, or even all humans (see Waddington's epigenetic landscape). It just happens that we develop along very similar trajectories and can draw equivalences between how I think vs how you think. So, from an ontological perspective, if you squint a bit, we all are tabula rasa. But from a phenotypic perspective, of course it's bonkers, you can't teach a frog linear algebra.
~45% of people work for companies of less than 500 people, ~12% for the govrnment (federal, state, local), ~6% are self employed. That leaves less than a half for all the companies >500, and only a tiny minority of them have an outsized influence on politics.
EDIT: Also, if there were no big corporations, people would still work and pay taxes. No sane statesman would purposefully build their countries economy on non-state-controlled megacorps.
I'd say, in this analagy, step two is filling in half the numbers by gut feeling, and then three is seeing how wrong the guesses were and actually solving it.
Perhaps I'm uncommon in that I make diagrams and even sometimes pseudo-code (mostly in the form of mapping out function names, because they serve as a proxy for program flow paths) before I write actual code, but I don't find myself really going off gut feeling. I have a pretty good idea of what the program is going to look like and do before I start, and with e.g. Claude I give those to it, so it mostly looks like what I envisioned, unless it suggests changes.
For me, unstructured things (like non-formal diagrams, notes, pseudocode) are close to useless. I much prefer sketching the API surface in code right away and then iterating on it, building abstractions which cut the shapeless thought blob into neat, predictably interacting parts. The code is my notepad and the compiler is with me, every step of the way, catching contradictions and handwavyness.
AFAIU antirez is mostly writing in C, a verbose language where "create a hashtable of x->y" turns into a wall of boilerplate. In high level languages the length diffrence between a precise specification and the actual code is much smaller.
He also mentions using it for Python which is minimal boilerplate.
And he didn't limit his take to just C code. He said: state of the art LLMs are able to complete large subtasks or medium size projects alone, almost unassisted, given a good set of hints about what the end result should be.
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