Transcript
00:00:00 The following is a conversation with Dan Kokotov, VP of engineering at rev.ai,
00:00:06 which is by many metrics, the best speech to text AI engine in the world.
00:00:12 Rev in general is a company that does captioning and transcription
00:00:16 of audio by humans and by AI.
00:00:20 I’ve been using their services for a couple of years now and I’m planning
00:00:23 to use Rev to add both captions and transcripts to some of the previous
00:00:28 and future episodes of this podcast to make it easier for people to read
00:00:32 through the conversation or reference various parts of the episode, since
00:00:36 that’s something that quite a few people requested.
00:00:39 I’ll probably do a separate video on that with links on the podcast website
00:00:45 so people can provide suggestions and improvements there.
00:00:48 Quick mention of our sponsors, Athletic Greens, All in One Nutrition Drink,
00:00:52 Blinkist app that summarizes books, Business Wars podcast, and Cash App.
00:00:59 So the choice is health, wisdom, or money.
00:01:02 Choose wisely my friends, and if you wish, click the sponsor links
00:01:06 below to get a discount and to support this podcast.
00:01:10 As a side note, let me say that I reached out to Dan and the Rev
00:01:13 team for a conversation because I’ve been using and genuinely loving
00:01:18 their service and really curious about how it works.
00:01:21 I previously talked to the head of Adobe research for the same reason.
00:01:25 For me, there’s a bunch of products, usually software, that comes along
00:01:30 and just makes my life way easier.
00:01:32 Examples are Adobe Premiere for video editing, iZotope RX for cleaning up audio,
00:01:37 AutoHotKey on Windows for automating keyboard and mouse tasks, Emacs as an
00:01:43 IDE for everything, including the universe itself.
00:01:47 I can keep on going, but you get the idea.
00:01:49 I just like talking to people who create things I’m a big fan of.
00:01:52 That said, after doing this conversation, the folks at Rev.ai offered to sponsor
00:01:58 this podcast in the coming months.
00:02:01 This conversation is not sponsored by the guest.
00:02:04 It probably goes without saying, but I should say it anyway, that you
00:02:08 can not buy your way onto this podcast.
00:02:11 I don’t know why you would want to.
00:02:13 I wanted to bring this up to make a specific point that no sponsor
00:02:17 will ever influence what I do on this podcast, or to the best of
00:02:21 my ability, influence what I think.
00:02:23 I wasn’t really thinking about this.
00:02:25 For example, when I interviewed Jack Dorsey, who is the CEO of Square that
00:02:30 happens to be sponsoring this podcast, but I should really make it explicit.
00:02:34 I will never take money for bringing a guest on.
00:02:37 Every guest on this podcast is someone I genuinely am curious to talk to or just
00:02:43 genuinely love something they’ve created.
00:02:45 As I sometimes get criticized for, I’m just a fan of people.
00:02:49 And that’s who I talk to.
00:02:51 As I also talk about way too much, money is really never a consideration.
00:02:56 In general, no amount of money can buy my integrity.
00:03:00 That’s true for this podcast, and that’s true for anything else I do.
00:03:05 If you enjoy this thing, subscribe on YouTube, review on the Apple podcast,
00:03:09 follow on Spotify, support on Patreon, a podcast on YouTube, and
00:03:14 support on Patreon, or connect with me on Twitter at Lex Friedman.
00:03:18 And now here’s my conversation with Dan Kokotov.
00:03:23 You mentioned science fiction on the phone.
00:03:25 So let’s go with the ridiculous first.
00:03:28 What’s the greatest sci fi novel of all time in your view?
00:03:32 And maybe what ideas do you find philosophically fascinating about it?
00:03:37 The greatest sci fi novel of all time is Dune.
00:03:40 And the second greatest is The Children of Dune.
00:03:43 And the third greatest is The God Emperor of Dune.
00:03:47 So I’m a huge fan of the whole series.
00:03:50 I mean, it’s just an incredible world that he created.
00:03:53 And I don’t know if you’ve read the book or not.
00:03:55 No, I have not.
00:03:56 It’s one of my biggest regrets, especially because a new movie is coming out.
00:04:01 Everyone’s super excited about it.
00:04:02 I used to, it’s ridiculous to say, and sorry to interrupt, is that I
00:04:07 used to play the video game.
00:04:09 It used to be Dune.
00:04:11 I guess you would call that real time strategy.
00:04:13 Right.
00:04:14 I think I remember that game.
00:04:15 Yeah, it was kind of awesome.
00:04:16 Nineties or something.
00:04:17 I think I played it actually when I was in Russia.
00:04:19 I definitely remember it.
00:04:21 I was not in Russia anymore.
00:04:22 I think at the time that I used to live in Russia, I think video games
00:04:26 were about like the suspicion of Pong.
00:04:29 I think Pong was pretty much like the greatest game I ever got to play in Russia,
00:04:33 which was still a privilege right in that age.
00:04:35 So you didn’t get color?
00:04:36 You didn’t get like, uh, so I left Russia.
00:04:39 I left Russia in 1991, right?
00:04:40 Okay.
00:04:41 So I was one of the few lucky kids because my mom was a programmer.
00:04:45 So I would go to her work, right?
00:04:47 I would take the, the Metro.
00:04:49 I’ve got our work and play like on, I guess the equivalent of like a
00:04:52 286 PC, you know, nice floppy disks.
00:04:56 Yes.
00:04:56 So, okay.
00:04:57 Put back to doing what you get back to doing.
00:04:59 And by the way, the new movie I’m pretty interested in, but the
00:05:04 skeptical, I’m a little skeptical.
00:05:06 I’m a little skeptical.
00:05:07 I saw the trailer.
00:05:08 Uh, I don’t know.
00:05:09 So there’s, there’s a David Lynch movie doing as you may know, I’m
00:05:12 a huge David Lynch fan, by the way.
00:05:14 So the movie is somewhat controversial, but it’s a little confusing, but it
00:05:20 captures kind of the mood of the book better than I would say like most any
00:05:24 adaptation and like doing so much about kind of mood and the world, right.
00:05:28 But back to the philosophical point.
00:05:29 So in the fourth book, God, emperor of doing, there’s a sort of setting where
00:05:36 Leto, one of the characters, he’s become this weird sort of God emperor.
00:05:41 He’s turned into a gigantic worm.
00:05:42 I mean, you kind of have to read the book to understand what that means.
00:05:44 So the worms are involved, the worms are involved.
00:05:47 You probably saw the worms in the trailer, right.
00:05:49 And in the video, you kind of like merges with the swarm, um, and becomes
00:05:53 this tyrant of the world and like oppresses the people for a long time.
00:05:56 Right.
00:05:56 But he has a purpose and the purpose is to kind of, uh, break through kind of
00:06:01 a stagnation period in civilization.
00:06:03 Right.
00:06:04 Um, but people have gotten too comfortable, right.
00:06:06 And so you kind of oppresses them so that they explode and like go on to
00:06:11 colonize new worlds and kind of renew the forward momentum of humanity.
00:06:15 Right.
00:06:16 And so to me, that’s kind of fascinating, right.
00:06:18 You need a little bit of pressure and suffering, right.
00:06:21 To kind of like make progress, not, not, not get too comfortable.
00:06:28 Maybe that’s a bit of a cruel philosophy to take away, but that seems to be
00:06:33 the case, unfortunately, obviously, I’m a huge fan of, uh, suffering.
00:06:39 So one of the reasons we’re talking today is that a bunch of people requested
00:06:46 that I do transcripts for this podcast and do captioning.
00:06:51 I used to make all kinds of YouTube videos and I would go on up work, I
00:06:56 think, and I would hire folks to do transcription and it was always a pain
00:07:01 in the ass, if I’m being honest, and then I don’t know how I discovered Rev.
00:07:07 But when I did, it was this feeling of like, Holy shit, somebody figured
00:07:13 out how to do it just really easily.
00:07:16 I I’m, I’m such a fan of just when people take a problem and they just make it easy.
00:07:25 Right.
00:07:26 You know, like just, uh, there’s so many, uh, there’s so many,
00:07:31 it’s like, there’s so many things in life that you might not even
00:07:35 be aware of that are painful.
00:07:37 Then Rev, you just like give the audio, give the video, you can
00:07:43 actually give a YouTube link.
00:07:45 And then it comes back like a day later or, uh, two days later, whatever
00:07:52 the hell it is with the captions, you know, all in a standardized format.
00:07:57 It was, I dunno, it was, it was, it was, it was truly a joy.
00:08:00 So I thought I had, you know, just for the hell of it, uh, talk to you
00:08:04 that one other product just made my soul feel good.
00:08:08 One other product that I’ve used like that is, uh, for people who might
00:08:12 be familiar is called isotope RX.
00:08:15 It’s for audio editing and like, and that’s another one where it was
00:08:21 like, you just drop it.
00:08:24 I dropped into the audio and it just cleans everything up really nicely.
00:08:28 All the stupid, like the mouth sounds and sometimes there’s a background
00:08:35 like sounds due to the malfunction of the equipment.
00:08:39 It can clean that stuff up.
00:08:40 It can, it has a general voice denoising.
00:08:43 It has like automation capabilities where you can do batch processing
00:08:47 and you can put a bunch of effects.
00:08:49 I mean, it just, I dunno, everything else sucked for like voice based
00:08:55 cleanup that I’ve ever used.
00:08:57 They’ve used audition, Adobe audition, and he’s all kinds of other things
00:09:01 with plugins and you have to kind of figure it all out.
00:09:04 You have to do it manually here.
00:09:05 It’s just, it just worked.
00:09:07 So that’s another one in this whole pipeline.
00:09:09 It just brought joy to my, to my heart.
00:09:12 Anyway, all that to say is, uh, uh, Rev put a smile to my face.
00:09:18 So can you maybe take a step back and say, what is Rev and how does it work?
00:09:24 And Rev or Rev.com?
00:09:26 Rev, Rev.com, the same thing, I guess, uh, that way we do have Rev.ai now as
00:09:31 well, which we can talk about later.
00:09:33 Like, do you have the actual domain or is it just, uh, the actual domain,
00:09:37 but we also use it kind of as a, as a sub brand.
00:09:41 Oh, so we’ve, so we use Rev.ai to denote our ASR services, right?
00:09:46 And Rev.com is kind of our more human and to the end user services.
00:09:49 So it’s like wordpress.com and wordpress.org, they actually have separate
00:09:53 brands that like, I don’t know if you’re familiar with what those are.
00:09:56 Yeah, they provide almost like a separate branch of a little bit.
00:10:00 I think with that, it’s like wordpress.org is kind of their open source, right?
00:10:04 And, uh, wordpress.com is sort of their hosted commercial offering.
00:10:07 Yes.
00:10:08 Um, and with us, the differential is a little bit different,
00:10:10 but maybe a similar idea.
00:10:11 Yep.
00:10:12 Okay.
00:10:12 So what is Rev?
00:10:13 Before I launch into, uh, what is Rev?
00:10:16 I was going to say, you know, like you, you were talking about like
00:10:18 Rev was music to your ears, your, your, your field was music to my ears.
00:10:21 To us, the founders of Rev, because, um, Rev was kind of founded to improve
00:10:27 on the model of Upwork that was kind of the original, um, or part of their
00:10:32 original impetus, like our CEO, Jason, was a early employee of Upwork.
00:10:38 So he was very familiar with their work, the company Upwork company.
00:10:41 Um, and so he was very familiar with that model and he wanted to make the whole
00:10:46 experience better because he knew like, when you go at that time, Upwork was
00:10:49 primarily programmers, so the main thing they offered us, if you want to hire,
00:10:54 you know, someone to help you code a little site, right.
00:10:56 Um, you could go on Upwork, um, you could like browse through a list of freelancers,
00:11:01 pick a programmer, you know, have a contract with them and have them do some
00:11:04 work, but it was kind of a difficult experience because, uh, for the, for you,
00:11:11 you would kind of have to browse through all these people, right.
00:11:13 And you have to decide, okay, like, well, is this guy good as, um, or somebody
00:11:18 else better and naturally, you know, you’re going to Upwork because you’re not
00:11:21 an expert, right?
00:11:22 If you’re an expert, you probably wouldn’t be like getting a programmer
00:11:24 from Upwork, uh, so, so how can you really tell?
00:11:27 So there’s a kind of like a lot of potential regret, right?
00:11:31 What if I choose a bad person, they’re like, going to be late on the work.
00:11:34 It’s going to be a painful experience.
00:11:36 And for the freelancer, it was also painful because, you know, half the time
00:11:39 they spent not on actually doing the work, but kind of figuring out how can I make
00:11:43 my profile most attractive to the buyer, right?
00:11:47 And they’re not an expert on that either.
00:11:49 So like Grav’s idea was let’s remove the barrier, right?
00:11:52 Like, let’s make it simple where we’ll pick a few, uh, verticals
00:11:56 that are fairly standardizable.
00:11:58 Now we actually started with translation, um, and then we added
00:12:01 audio transcription a bit later and we’ll just make it a website.
00:12:05 You go give us your files.
00:12:06 We’ll give you back, uh, the results, you know, as soon as possible.
00:12:11 You know, originally maybe it was 48 hours.
00:12:13 Then we made it shorter and shorter and shorter.
00:12:15 Um, yeah, there’s a rush processing too.
00:12:17 There’s a rush processing now, uh, and, uh, we’ll hide all the details from you.
00:12:22 Right.
00:12:23 Yeah.
00:12:24 And like, that’s kind of exactly what you’re experiencing, right?
00:12:27 You don’t, you don’t need to worry about the details of how the sausage is made.
00:12:29 That’s really cool.
00:12:30 The, so you picked like a vertical by vertically, you mean basically a
00:12:35 service, a service category.
00:12:36 Why translation is Rev thinking of potentially going into other verticals
00:12:41 in the future, or is this like the focus now is a translation transcription, like
00:12:46 language, the focus now is, is language or, uh, speech services, generally speech
00:12:52 to text language services, you can kind of group them however you want.
00:12:56 Um, so, but we, uh, originally the categorization was work from home.
00:13:01 So when, uh, work that was done by people on a computer, you know, we weren’t trying
00:13:06 to get into, you know, uh, task rabbit type of things and something that could
00:13:11 be relatively standard, not a lot of options.
00:13:14 So we could kind of present the simplified interface, right?
00:13:16 So programming wasn’t like a good fit because each programming
00:13:20 project is kind of unique, right?
00:13:21 We’re looking for something that, uh, Transcription is, you know, you have five
00:13:25 hours of idea, it’s five hours of audio, right?
00:13:27 Translation is somewhat similar.
00:13:29 In that, you know, you can have a five page document, you know, and then you
00:13:33 just can price it by that and then you pick the language you want and that
00:13:37 that’s mostly all that is to it.
00:13:39 So those were a few criteria.
00:13:40 We started with translation because we saw the need, um, and, uh, we picked up
00:13:48 kind of a specialty of translation, um, where we would translate things like
00:13:52 board certificates, uh, uh, immigration documents, things like that.
00:13:58 And so they were fairly, um, even more well defined and easy to
00:14:03 kind of tell if we did a good job.
00:14:05 So you can literally charge per type of document.
00:14:07 Was that, was, was that the, so what, what is it now?
00:14:10 Is it per word or something like that?
00:14:12 Like, how do you, like, how do you measure the effort
00:14:16 involved in a particular thing?
00:14:18 So now it looks like for audio translation, it’s like,
00:14:21 so now it looks a for audio transcription, right?
00:14:23 It’s a per audio minute.
00:14:24 Well, that, that yes, for, for, for our translation, we don’t really,
00:14:28 uh, actually focus it on anymore.
00:14:30 Uh, but you know, back when it was still a main business of Revit was per page,
00:14:35 right.
00:14:35 Or per word, depending on the kind of, uh, cause you can also do translation
00:14:38 now on the audio, right?
00:14:40 Mm hmm.
00:14:40 So like subtitles.
00:14:41 So it would be both, uh, transcription and translation.
00:14:45 That’s right.
00:14:45 I wanted to test the system to see how good it is to see like how, how, uh,
00:14:50 well, is Russian supported?
00:14:52 I think so.
00:14:53 Yeah.
00:14:54 And it’d be interesting to try it out.
00:14:55 I mean, one of the, now it’s only in like the one direction, right?
00:14:58 So you start with English and then you can have subtitles in Russian.
00:15:00 Not really, not really the other way.
00:15:02 Got it.
00:15:03 Because it’s, um, I’m deeply curious about this.
00:15:05 Um, when COVID opens up a little bit, when the economy, when the
00:15:08 world opens up a little bit, you want to build your brand in Russia?
00:15:12 No, I don’t.
00:15:13 First of all, I’m allergic to the word brand.
00:15:15 All right, I’m definitely not building, uh, any brands in Russia, but I’m going to
00:15:21 Paris to talk to the translators of Dostoevsky and Tolstoy, there’s this
00:15:26 famous couple that does translation.
00:15:29 And, you know, I’m more and more thinking of how is it possible to have a
00:15:35 conversation with a Russian speaker?
00:15:37 Cause I have just some number of famous Russian speakers that
00:15:42 I’m interested in talking to, and my Russian is not strong
00:15:47 enough to be witty and funny.
00:15:49 I’m already an idiot in English.
00:15:51 I’m an extra level of like awkward idiot in Russian, but I can understand it.
00:15:57 Right.
00:15:58 And I also like wonder how can I create a compelling English Russian
00:16:04 experience for an English speaker?
00:16:06 Like if I, there’s a guy named Grigori Perlman, who’s a mathematician who,
00:16:11 uh, obviously doesn’t speak any English.
00:16:14 So I would probably incorporate like a Russian translator into the picture.
00:16:21 And then it would be like a, not to use a weird term, but like a three, like a
00:16:25 three, three person thing where it’s like a dance of, like, I understand it one way.
00:16:32 They don’t understand the other way, but I’ll be asking questions in English.
00:16:38 I don’t know.
00:16:38 I don’t know the right way.
00:16:39 It’s complicated.
00:16:40 It’s complicated, but I feel like it’s worth the effort for certain kinds of
00:16:43 people, one of whom I’m confident of Vladimir Putin, I’m for sure talking to.
00:16:48 I really want to make it happen.
00:16:50 Cause I think I could do a good job with, but the, the right, you know,
00:16:54 understanding the fundamentals of translation is something I’m really
00:16:58 interested in.
00:16:58 So that’s why I’m starting with, um, the actual translators of like Russian
00:17:03 literature, because they understand the nuance and the beauty of the
00:17:06 language and how it goes back and forth.
00:17:08 But I also want to see, like in speech, how can we do it in real time?
00:17:13 So that’s, that’s like a little bit of a baby project that I hope to push forward.
00:17:18 But anyway, it’s a challenging thing.
00:17:19 So just to share, uh, my dad, um, actually does translation, um, not, not
00:17:25 professional, he’s a, uh, he writes poetry.
00:17:28 That was kind of always his, uh, not a hobby, but he’s, uh, he, you know, he
00:17:33 had a job, like a day job, but his passion was always writing poetry.
00:17:36 Uh, and then when I got to America, like he started also translating, um, first
00:17:43 he was translating English poetry to Russia.
00:17:45 Now he also like goes the other, uh, the other way, you kind of gain some small
00:17:50 fame in that world anyways, because, uh, recently this poet like Lewis
00:17:54 clock, I don’t know if you know of, uh, some American poet, um, she was
00:17:59 awarded the Nobel prize for literature.
00:18:01 Uh, and so my dad had translated, uh, one of her books of poetry in
00:18:05 to Russian, and he was like one of the few.
00:18:07 So he kind of like, they asked him and gave an interview to Radiosvoboda,
00:18:12 if you know what that is.
00:18:13 And he kind of talked about some of the intricacies of translating poetry.
00:18:17 So that’s like an extra level of difficulty, right?
00:18:18 Because translating poetry is even more challenging than
00:18:21 translating just, you know, interviews.
00:18:24 Do you remember any, any experiences and challenges to having to do the
00:18:28 translation that, that’s the God to you, like something he’s talked about?
00:18:32 I mean, a lot of it, I think is word choice, right?
00:18:35 It’s the way Russian is structured is first of all, quite different
00:18:38 than, um, the way English is structured, right?
00:18:40 Just there is inflections in Russian and genders, and they don’t exist in English.
00:18:43 That’s just one of the reasons actually why, um, machine translation is quite
00:18:47 difficult for English to Russian and Russian to English, because there’s
00:18:50 such different languages, but then English has like a huge number of words.
00:18:55 Um, many more than Russian, actually, I think.
00:18:57 So it’s often difficult to find the right word to convey the same emotional
00:19:01 meaning, yeah, Russian language.
00:19:03 They play with words much more.
00:19:06 So you, you’re mentioning that, uh, Rev was kind of born out of, um, trying to
00:19:12 take a vertical on the upwork and then standardize it.
00:19:15 So we’re just trying to make the, the freelancer marketplace idea better, right?
00:19:21 Um, better for both customers and better for the freelancers themselves.
00:19:27 Is there something else to the story of Rev finding the right word?
00:19:30 Rev, finding Rev, like what, what did it take to bring it to actually to life?
00:19:35 Was there any pain points?
00:19:37 Um, plenty of, plenty of pain points.
00:19:39 I mean, uh, as, as often the case it’s with scaling it up, right?
00:19:43 Um, and in this case, you know, the scaling is kind of scaling the,
00:19:48 the marketplace, so to speak, right?
00:19:49 Rev is essentially a two sided marketplace, right?
00:19:51 Because, you know, there’s the customers and then there’s the reverse.
00:19:55 Um, if there’s not enough Revers, Revers are world color freelancers.
00:19:59 So if there’s not enough Revers, then customers have a bad experience, right?
00:20:03 You know, it takes longer to get your work done.
00:20:06 Um, things like that, you know, if there’s too many done, the Revers have
00:20:09 a bad experience because they might log on to see like what work is available
00:20:12 and there’s not very much work, right?
00:20:15 Uh, so kind of keeping that balance, um, is, is, is a quite challenging problem.
00:20:20 And, you know, that’s, that’s like a problem we’ve been working on for many
00:20:22 years and we’re still like refining our methods, right?
00:20:25 If you can kind of talk to this gig economy idea, I did a bunch of different
00:20:30 psychology experiments on mechanical Turk, for example, I’ve asked to do
00:20:33 different kinds of very tricky computer vision annotation on mechanical Turk.
00:20:37 And it’s connecting, connecting people in a more systematized way.
00:20:43 I would say, you know, between task and, and, uh, what would you call that worker
00:20:50 is what mechanical Turk calls it.
00:20:52 What do you think about this world of gig economies, of there being a service
00:20:58 that connects customers to workers in a way that’s like massively distributed,
00:21:06 like potentially scaling to, it could be, it could be scaled to like
00:21:10 tens of thousands of people, right?
00:21:11 Is there something interesting about that world that you can speak to?
00:21:16 Yeah.
00:21:16 Well, we don’t think of it as kind of gig economy, like to some degree,
00:21:20 I don’t like the word gig that much, right?
00:21:22 Because to some degree it diminishes the words being done, right?
00:21:25 It sounds kind of like almost amateurish.
00:21:28 Well, maybe in like music industry, like gig is a standard term, but in work, it
00:21:33 kind of sounds like, oh, it’s, it’s, it’s frivolous, um, to us it’s, um, improving
00:21:41 the nature of working from home on your own time and on your own terms, right?
00:21:46 And kind of taking away geographical limitations and time limitations, right?
00:21:51 Uh, so, you know, many of our freelancers are maybe work from home moms, right?
00:21:56 And, you know, they don’t want the traditional nine to five job, but they
00:22:01 want to make some income and rough kind of like allows them to do that and decide
00:22:05 like exactly how much to work and when to work or by the same token, maybe someone
00:22:10 is, you know, someone wants to live the mountain top, you know, life, right?
00:22:17 You know, cabin in the woods, but they still want to make some money.
00:22:20 Um, and like generally that wouldn’t be compatible before, before this new world,
00:22:25 you kind of had to choose, uh, but like with Rev, like, if you like, you don’t
00:22:28 have to choose, can you speak to like, what’s the demographics like distribution?
00:22:36 Like where do rivers live?
00:22:38 Is there a way to, to, to, to, to, to, to, to, to, to, to, to, to, to, to, to, to,
00:22:40 to, to, to, but you really want to teach it how to, how to run, how to track
00:22:46 Once you’re out of the bush, 들어가, things like that, you know,
00:22:50 but like in the back of you know, like hard, but you
00:23:02 just as you approach, there’s a lot more control over you.
00:23:05 Like you, you may be Oh, like you know, one day you might go to the
00:23:08 For some years now, we’ve been doing these little meetings
00:23:10 where the management team will go to some place
00:23:12 and we’ll try to meet Revers.
00:23:13 And pretty much wherever we go, it’s
00:23:15 pretty easy to find a large number of Revers.
00:23:19 The most recent one we did is in Utah.
00:23:24 But anyway, really.
00:23:25 Are they from all walks of life?
00:23:26 Are these young folks, older folks?
00:23:28 Yeah, all walks of life, really.
00:23:30 Like I said, one category is the work from home.
00:23:33 Students who want to make some extra income.
00:23:37 There are some people who maybe have some social anxiety,
00:23:41 so they don’t want to be in the office.
00:23:43 And this is one way for them to make a living.
00:23:45 So it’s really pretty wide variety.
00:23:47 But on the flip side, for example,
00:23:49 one Rever we were talking to was a person
00:23:52 who had a fairly high powered career before
00:23:54 and was kind of like taking a break.
00:23:57 And she was almost doing this just to explore and learn
00:24:00 about the gig economy, quote unquote.
00:24:03 So it really is a pretty wide variety of folks.
00:24:06 Yeah, it’s kind of interesting through the captioning
00:24:09 process for me to learn about the Revers
00:24:13 because like some are clearly like weirdly knowledgeable
00:24:19 about technical concepts.
00:24:22 Like you can tell by how good they are
00:24:25 at like capitalizing stuff, like technical terms,
00:24:29 like a machine learning or deep learning.
00:24:30 Like I’ve used Rev to annotate, to caption
00:24:35 the deep learning lectures or machine learning lectures
00:24:38 I did at MIT.
00:24:39 And it’s funny, like a large number of them were like,
00:24:44 I don’t know if they looked it up
00:24:45 or were already knowledgeable,
00:24:47 but they do a really good job at like, I don’t know.
00:24:50 They invest time into these things.
00:24:52 They will like do research, they will Google things,
00:24:55 you know, to kind of make sure that they got it right.
00:24:57 But to some of them, it’s like,
00:24:59 it’s actually part of the enjoyment of the work.
00:25:01 Like they’ll tell us, you know,
00:25:03 I love doing this because I get paid
00:25:05 to watch a documentary on something, right?
00:25:07 And I learn something while I’m transcribing, right?
00:25:10 Pretty cool.
00:25:10 Yeah, so what’s that captioning transcription process
00:25:14 look like for the Revers?
00:25:16 Can you maybe speak to that to give people a sense,
00:25:18 like how much is automated, how much is manual?
00:25:23 What’s the actual interface look like?
00:25:25 All that kind of stuff.
00:25:26 Yeah, so, you know, we’ve invested a pretty good amount
00:25:28 of time to give like our Revers the best tools possible.
00:25:33 So typical day of forever,
00:25:34 they might log into their workspace,
00:25:37 they’ll see a list of audios that need to be transcribed.
00:25:41 And we try to give them tools to pick specifically
00:25:43 the ones they want to do, you know?
00:25:44 So maybe some people like to do longer audios
00:25:47 or shorter audios, people have their preferences.
00:25:52 Some people like to do audios in a particular subject
00:25:55 or from a particular country.
00:25:56 So we try to give people the tools to control,
00:25:59 things like that.
00:26:01 And then when they pick what they want to do,
00:26:04 we’ll launch a specialized editor that we’ve built
00:26:07 to make transcription as efficient as possible.
00:26:10 They’ll start with a speech drag draft.
00:26:12 So, you know, we have our machine learning model
00:26:15 for automated speech recognition, they’ll start with that.
00:26:18 And then our tools are optimized to help them correct that.
00:26:22 So it’s basically a process of correction.
00:26:26 Yeah, it depends on, you know, I would say the audio.
00:26:29 If the audio itself is pretty good,
00:26:31 like probably like our podcast right now
00:26:33 would be quite good.
00:26:34 So the ASR would do a fairly good job.
00:26:37 But if you imagine someone recorded a lecture,
00:26:41 you know, in the back of a auditorium, right?
00:26:45 Where like the speaker is really far away
00:26:47 and there’s maybe a lot of cross talk and things like that,
00:26:49 then maybe the ASR wouldn’t do a good job.
00:26:52 So the person might say like, you know what,
00:26:53 I’m just gonna do it from scratch.
00:26:55 Do it from scratch, yeah.
00:26:56 So it kind of really depends.
00:26:57 What would you say is the speed that you can possibly get?
00:27:00 Like, what’s the fastest?
00:27:02 Can you get, is it possible to get real time or no?
00:27:05 As you’re like listening, can you write as fast as?
00:27:09 Real time would be pretty difficult.
00:27:10 It’s actually a pretty, it’s not an easy job, you know.
00:27:13 We actually encourage everyone at the company
00:27:16 to try to be a transcriber for a day,
00:27:17 transcriptionist for a day.
00:27:20 And it’s way harder than you might think it is, right?
00:27:24 Because people talk fast and people have accents
00:27:28 and all this kind of stuff.
00:27:29 So real time is pretty difficult.
00:27:30 Is it possible?
00:27:32 Like there’s somebody, we’re probably gonna use Rev
00:27:34 to caption this, they’re listening to this right now.
00:27:39 What do you think is the fastest
00:27:42 you could possibly get on this right now?
00:27:44 I think on a good audio, maybe two to three X,
00:27:47 I would say, real time.
00:27:49 Meaning it takes two to three times longer
00:27:51 than the actual audio of the podcast.
00:27:55 This is so meta, I could just imagine the Revvers
00:27:58 working on this right now.
00:27:59 You’re like, you’re way wrong.
00:28:01 You’re way wrong, this takes way longer.
00:28:03 But yeah, it definitely works.
00:28:04 Or you doubted me, I could do real time.
00:28:06 Yeah.
00:28:08 Okay, so you mentioned ASR.
00:28:11 Can you speak to what is ASR, automatic speech recognition?
00:28:15 How much, like what is the gap
00:28:19 between perfect human performance
00:28:22 and perfect or pretty damn good ASR?
00:28:26 Yeah, so ASR, automatic speech recognition,
00:28:28 it’s a class of machine learning problem, right?
00:28:31 So take speech like we’re talking
00:28:34 and transform it into a sequence of words, essentially.
00:28:37 Audio of people talking.
00:28:38 Audio, audio to words.
00:28:41 And there’s a variety of different approaches
00:28:43 and techniques, which we could talk about later if you want.
00:28:46 So, we think we have pretty much the world’s best ASR
00:28:50 for this kind of speech, right?
00:28:52 So there’s different kinds of domains, right, for ASR.
00:28:55 Like one domain might be voice assistance, right?
00:28:58 So Siri, very different than what we’re doing, right?
00:29:02 Because Siri, there’s fairly limited vocabulary.
00:29:05 You might ask Siri to play a song
00:29:08 or order a pizza or whatever.
00:29:10 And it’s very good at doing that.
00:29:12 Very different from when we start talking
00:29:14 in a very unstructured way.
00:29:17 And Siri will also generally adapt to your voice
00:29:18 and stuff like this.
00:29:20 So for this kind of audio, we think we have the best.
00:29:22 And our accuracy, right now I think it’s maybe 14%
00:29:29 word error rate on our test suite
00:29:32 that we generally use to measure.
00:29:33 So word error rate is like one way to measure accuracy
00:29:37 for ASR, right?
00:29:37 So what’s 14% word error rate?
00:29:39 So 14% means across this test suite,
00:29:43 of a variety of different audios,
00:29:45 it would be, it would get in some way 14% of the words wrong.
00:29:53 14% of the words wrong.
00:29:55 So the way you kind of calculate it is,
00:29:59 you might add up insertions, deletions, and substitutions,
00:30:02 right?
00:30:03 So insertions is like extra words.
00:30:05 Deletions are words that we said,
00:30:07 but weren’t in the transcript, right?
00:30:10 Substitutions is, you said Apple, but I said,
00:30:14 but the ASR thought it was able, something like this.
00:30:17 Human accuracy, most people think realistically,
00:30:21 it’s like 3%, 2%, word error rate would be like
00:30:25 the max achievable.
00:30:28 So there’s still quite a gap, right?
00:30:29 Would you say that, so YouTube, when I upload videos,
00:30:33 often generates automatic captions.
00:30:35 Are you sort of from a company perspective,
00:30:38 from a company perspective, from a tech perspective,
00:30:41 are you trying to beat YouTube, Google?
00:30:44 It’s a hell of a, Google, I mean,
00:30:47 I don’t know how seriously they take this task,
00:30:49 but I imagine it’s quite serious.
00:30:51 And they, you know, Google is probably up there
00:30:56 in terms of their teams on, on ASR or just NLP,
00:31:02 natural language processing, different technologies.
00:31:04 So do you think you can beat Google?
00:31:06 On this kind of stuff, yeah, we think so.
00:31:08 Google just woke up on my phone.
00:31:11 This is hilarious, okay.
00:31:12 Now Google is listening, sending it back to headquarters.
00:31:17 Who are these rough people?
00:31:19 But that’s the goal?
00:31:20 Yeah, I mean, we measure ourselves against like Google,
00:31:23 Amazon, Microsoft, you know, some of the,
00:31:25 some smaller competitors.
00:31:28 And we use like our internal tests with it,
00:31:30 we try to compose it of a pretty representative
00:31:32 set of ideas, maybe it’s some podcasts, some videos,
00:31:36 some interviews, some lectures, things like that, right?
00:31:39 And we beat them in our own testing.
00:31:42 And actually Rev offers automated,
00:31:45 like you can actually just do the automated captioning.
00:31:49 So like, I guess it’s like way cheaper, whatever it is,
00:31:52 whatever the rates are.
00:31:54 Yeah, yeah.
00:31:55 So it’s a, by the way, it used to be a dollar per minute
00:31:57 for captioning and transcription,
00:32:00 I think it’s like $1.15 or something like that.
00:32:02 $1.25.
00:32:03 $1.25, yeah.
00:32:08 It’s pretty cool.
00:32:09 That was the other thing that was surprising to me,
00:32:10 it was actually like the cheapest thing you could,
00:32:15 one of the, I mean, I don’t remember it being cheaper.
00:32:18 You could on Upwork get cheaper,
00:32:20 but it was clear to me that this,
00:32:22 that’s gonna be really shitty.
00:32:23 Yeah.
00:32:24 So like, you’re also competing on price.
00:32:26 I think there were services that you can get,
00:32:29 like similar to Rev kind of feel to it,
00:32:34 but it wasn’t as automated.
00:32:35 Like the drag and drop, the entirety of the interface,
00:32:37 it’s like the thing we’re talking about.
00:32:39 I’m such a huge fan of like frictionless,
00:32:41 like Amazon’s single buy button, whatever.
00:32:47 Yeah, yeah.
00:32:48 That one click.
00:32:49 The one click, that’s genius right there.
00:32:52 Like that is so important for services.
00:32:54 Yeah.
00:32:55 And simplicity and I mean, Rev is almost there.
00:33:00 I mean, there’s like some, I’m trying to think.
00:33:04 So I think I’ve, I stopped using this pipeline,
00:33:10 but Rev offers it and I like it,
00:33:12 but it was causing me some issues on my side,
00:33:16 which is you can connect it to like Dropbox
00:33:20 and it generates the files in Dropbox.
00:33:22 So like it closes the loop to where
00:33:25 I don’t have to go to Rev at all and I can download it.
00:33:30 Sorry, I don’t have to go to Rev at all
00:33:32 and to download the files.
00:33:34 It could just like automatically copy them.
00:33:36 Right, you’re putting your Dropbox in a day later
00:33:39 or maybe a few hours later.
00:33:41 Yeah, it just shows up.
00:33:42 Just shows up, yeah.
00:33:43 Yeah, I was trying to do it programmatically too.
00:33:46 Is there an API interface you can,
00:33:48 I was trying to through like through Python
00:33:51 to download stuff automatically,
00:33:53 but then I realized this is the programmer in me.
00:33:56 Like, dude, you don’t need to automate everything
00:33:58 like in life, like flawlessly,
00:34:01 because I wasn’t doing enough captions
00:34:02 to justify to myself the time investment
00:34:05 into automating everything perfectly.
00:34:07 Yeah, I would say if you’re doing so many interviews
00:34:10 that your biggest roadblock is clicking on the Rev download,
00:34:14 but now you’re talking about Elon Musk levels of business.
00:34:18 But for sure, we have like a variety of ways
00:34:22 to make it easy.
00:34:22 You know, there’s the integration.
00:34:24 You mentioned, I think it’s through a company called Zapier,
00:34:26 which kind of can connect Dropbox to Rev and vice versa.
00:34:31 We have an API if you want to really like customize it,
00:34:33 you know, if you want to create the Lex Friedman,
00:34:37 you know, CMS or whatever.
00:34:40 For this whole thing.
00:34:41 Okay, cool.
00:34:42 So can you speak to the ASR a little bit more?
00:34:46 Like, what does it take?
00:34:51 Like approach wise, machine learning wise,
00:34:53 how hard is this problem?
00:34:55 How do you get to the 3% error rate?
00:34:57 Like, what’s your vision of all of this?
00:34:59 Yeah, well, the 3% error rate is definitely,
00:35:03 that’s the grand vision.
00:35:06 We’ll see what it takes to get there.
00:35:09 But we believe, you know, in ASR,
00:35:13 the biggest thing is the data, right?
00:35:15 Like, that’s true of like a lot of
00:35:16 machine learning problems today, right?
00:35:18 The more data you have and high quality of the data,
00:35:21 the better label the data.
00:35:24 Yeah, that’s how you get good results.
00:35:26 And we at Rev have kind of like the best data.
00:35:28 Like we have.
00:35:29 Like you’re literally,
00:35:30 your business model is annotating the data.
00:35:34 Our business model is being paid to annotate the data.
00:35:36 Being paid to annotate the data.
00:35:39 So it’s kind of like a pretty magical flywheel.
00:35:42 Yeah.
00:35:42 And so we’ve kind of like written this flywheel
00:35:44 to this point.
00:35:47 And we think we’re still kind of in the early stages
00:35:50 of figuring out all the parts of the flywheel to use,
00:35:53 you know, because we have the final transcripts
00:35:57 and we have the audios and we train on that.
00:36:01 But we in principle also have all the edits
00:36:05 that the Revvers make, right?
00:36:07 Oh, that’s interesting.
00:36:08 How can you use that as data?
00:36:10 Yeah, that’s something for us to figure out in the future.
00:36:13 But, you know, we feel like we’re only
00:36:15 in the early stages, right?
00:36:16 So the data is there.
00:36:17 That’d be interesting.
00:36:18 Like almost like a recurrent neural net
00:36:20 for fixing transcripts.
00:36:23 I always remember we did a segmentation annotation
00:36:28 for driving data.
00:36:30 So segmenting the scene, like visual data.
00:36:33 And you can get all,
00:36:35 so it was drawing, people were drawing polygons
00:36:37 around different objects and so on.
00:36:39 And it feels like it always felt like
00:36:41 there was a lot of information in the clicking,
00:36:45 the sequence of clicking that people do,
00:36:46 the kind of fixing of the polygons that they do.
00:36:50 Now there’s a few papers written about
00:36:52 how to draw polygons like with a recurrent neural nets
00:36:57 to try to learn from the human clicking.
00:37:00 But it was just like experimental,
00:37:04 you know, it was one of those like CVPR type papers
00:37:06 that people do like a really tiny data set.
00:37:08 It didn’t feel like people really tried to do it seriously.
00:37:13 Yeah, I wonder if there’s information in the fixing
00:37:16 that provides deeper set of signal
00:37:21 than just like the raw data.
00:37:24 The intuition is for sure there must be, right?
00:37:26 There must be.
00:37:26 And in all kinds of signals and how long you took
00:37:29 to make that edit and stuff like that.
00:37:32 It’s gonna be like up to us.
00:37:34 That’s why like the next couple of years
00:37:36 is like super exciting for us, right?
00:37:38 So that’s what like the focus is now.
00:37:40 You mentioned rev.ai, that’s where you want to.
00:37:43 Yeah, so rev.ai is kind of our way of bringing this ASR
00:37:49 to the rest of the world, right?
00:37:51 So when we started, we were human only.
00:37:55 Then we kind of created this Temi service.
00:37:59 I think you might’ve used it,
00:38:00 which was kind of ASR for the consumer, right?
00:38:02 So if you don’t wanna pay $1.25, but you wanna pay,
00:38:06 now it’s 25 cents a minute, I think.
00:38:08 And you get the transcript,
00:38:10 the machine generated transcript and you get an editor
00:38:13 and you can kind of fix it up yourself, right?
00:38:17 Then we started using ASR
00:38:18 for our own human transcriptionists.
00:38:21 And then the kind of rev.ai is the final step
00:38:23 of the journey, which is, you know,
00:38:25 we have this amazing engine.
00:38:27 What can people build with it, right?
00:38:28 What kind of new applications could be enabled
00:38:33 if you have SpeedTrack that’s that accurate?
00:38:36 Do you have ideas for this
00:38:37 or is it just providing it as a service
00:38:39 and seeing what people come up with?
00:38:40 It’s providing it as a service
00:38:41 and seeing what people come up with
00:38:43 and kind of learning from what people do with it.
00:38:45 And we have ideas of our own as well, of course,
00:38:47 but it’s a little bit like, you know,
00:38:49 when AWS provided the building blocks, right?
00:38:52 And they saw what people built with it
00:38:53 and they try to make it easier to build those things, right?
00:38:56 And we kind of hope to do the same thing.
00:38:59 Although AWS kind of does a shitty job of like,
00:39:02 I’m continually surprised, like Mechanical Turk,
00:39:05 for example, how shitty the interface is.
00:39:07 We’re talking about like Rev making me feel good.
00:39:11 Like when I first discovered Mechanical Turk,
00:39:15 the initial idea of it was like,
00:39:18 it made me feel like Rev does,
00:39:19 but then the interface is like, come on.
00:39:22 Yeah, it’s horrible.
00:39:24 Why is it so painful?
00:39:27 Does nobody at Amazon want to like seriously invest in it?
00:39:32 It felt like you can make so much money
00:39:35 if you took this effort seriously.
00:39:37 And it feels like they have a committee
00:39:39 of like two people just sitting back,
00:39:41 like a meeting, they meet once a month,
00:39:44 like what are we going to do with Mechanical Turk?
00:39:46 It’s like two websites making me feel like this,
00:39:49 that and craiglist.org, whatever the hell it is.
00:39:53 It feels like it’s designed in the 90s.
00:39:55 Well, Craigslist basically hasn’t been updated
00:39:59 pretty much since the guy originally built.
00:39:59 Do you seriously think there’s a team,
00:40:01 like how big is the team working on Mechanical Turk?
00:40:04 I don’t know.
00:40:05 There’s some team, right?
00:40:06 I feel like there isn’t.
00:40:08 I’m skeptical.
00:40:09 Yeah.
00:40:10 Well, if nothing else, they benefit from the other teams
00:40:14 like moving things forward in a small way.
00:40:18 But I know what you mean.
00:40:19 We use Mechanical Turk for a couple of things as well.
00:40:22 And yeah, it’s painful UI.
00:40:23 It’s painful, but yeah, it works.
00:40:25 I think most people, the thing is most people
00:40:27 don’t really use the UI, right?
00:40:29 Like we, for example, we use it through the API, right?
00:40:33 But even the API documentation and so on,
00:40:36 like it’s super outdated.
00:40:37 Like I don’t even know what to…
00:40:42 I mean, the same criticism, as long as we’re ranting,
00:40:47 my same criticism goes to the APIs
00:40:49 of most of these companies.
00:40:50 Like Google, for example, the API for the different services
00:40:55 is just the documentation is so shitty.
00:40:59 Like it’s not so shitty.
00:41:02 I should actually be…
00:41:05 I should exhibit some gratitude.
00:41:08 Okay, let’s practice some gratitude.
00:41:10 The documentation is pretty good.
00:41:14 Like most of the things that the API makes available
00:41:18 is pretty good.
00:41:19 It’s just that in the sense that it’s accurate,
00:41:23 sometimes outdated, but like the degree of explanations
00:41:27 with examples is only covering, I would say,
00:41:31 like 50% of what’s possible.
00:41:33 And it just feels a little bit,
00:41:35 like there’s a lot of natural questions
00:41:37 that people would wanna ask that doesn’t get covered.
00:41:41 And it feels like it’s almost there.
00:41:44 Like it’s such a magical thing.
00:41:46 Like the Maps API, YouTube API, there’s a bunch of stuff.
00:41:51 I gotta imagine it’s like, there’s probably some team
00:41:54 at Google responsible for writing this documentation
00:41:57 that’s probably not the engineers, right?
00:42:00 And probably this team is not where you wanna be.
00:42:04 Well, it’s a weird thing.
00:42:05 I sometimes think about this for somebody
00:42:09 who wants to also build a company.
00:42:12 I think about this a lot.
00:42:14 YouTube, the service is one of the most magical,
00:42:21 like I’m so grateful that YouTube exists.
00:42:24 And yet they seem to be quite clueless on so many things
00:42:30 like that everybody’s screaming them at.
00:42:33 Like it feels like whatever the mechanism
00:42:37 that you use to listen to your quote unquote customers,
00:42:40 which is like the creators, is not very good.
00:42:44 Like there’s literally people that are like screaming why,
00:42:47 like their new YouTube studio, for example.
00:42:51 There’s like features that were like begged for
00:42:55 for a really long time.
00:42:56 Like being able to upload multiple videos at the same time.
00:43:00 That wasn’t missing for a really, really long time.
00:43:03 Now, like there’s probably things that I don’t know,
00:43:08 which is maybe for that kind of huge infrastructure,
00:43:10 it’s actually very difficult to build
00:43:12 some of these features.
00:43:13 But the fact that that wasn’t communicated
00:43:15 and it felt like you’re not being heard.
00:43:19 Like I remember this experience for me
00:43:21 and it’s not a pleasant experience.
00:43:23 And it feels like the company doesn’t give a damn about you.
00:43:26 And that’s something to think about.
00:43:28 I’m not sure what that is.
00:43:29 That might have to do with just like small groups
00:43:32 working on these small features and these specific features.
00:43:35 And there’s no overarching like dictator type of human
00:43:40 that says like, why the hell are we neglecting
00:43:42 like Steve Jobs type of characters?
00:43:43 Like there’s people that we need to speak
00:43:48 to the people that like wanna love our product
00:43:50 and they don’t.
00:43:51 Let’s fix this shit.
00:43:52 Maybe at some point you just get so fixated
00:43:54 on the numbers, right?
00:43:55 And it’s like, well, the numbers are pretty great, right?
00:43:57 Like people are watching,
00:43:59 doesn’t seem to be a problem, right?
00:44:01 And you’re not like the person that like build this thing,
00:44:04 right?
00:44:04 So you really care about it.
00:44:05 You’re just there, you came in as a product manager, right?
00:44:09 You got hired sometime later,
00:44:10 your mandate is like increase this number,
00:44:13 like 10%, right?
00:44:15 And you just.
00:44:16 That’s brilliantly put.
00:44:17 Like if you, this is, okay, if there’s a lesson in this
00:44:21 is don’t reduce your company into a metric
00:44:24 of like how much, like you said,
00:44:28 how much people watching the videos and so on
00:44:30 and like convince yourself that everything is working
00:44:33 just because the numbers are going up.
00:44:36 There’s something, you have to have a vision.
00:44:39 You have to want people to love your stuff
00:44:43 because love is ultimately the beginning
00:44:46 of like a successful longterm company
00:44:49 is that they always should love your product.
00:44:51 You have to be like a creator
00:44:52 and have that like creator’s love for your own thing, right?
00:44:55 Like, and you’re pained by these comments, right?
00:44:59 And probably like Apple, I think did this generally
00:45:02 like really well.
00:45:03 They’re well known for kind of keeping teams small
00:45:06 even when they were big, right?
00:45:08 And, you know, he was an engineer,
00:45:10 like there’s a book, a creative selection.
00:45:12 I don’t know if you read it by a Apple engineer
00:45:15 named Ken Koscienda.
00:45:17 It’s kind of a great book actually
00:45:18 because unlike most of these business books where it’s,
00:45:21 you know, here’s how Steve Jobs ran the company.
00:45:24 It’s more like here’s how life was like for me, you know,
00:45:27 an engineer here, the projects I worked on
00:45:29 and here what it was like to pitch Steve Jobs, you know,
00:45:31 on like, you know, I think it was in charge of like
00:45:34 the keyboard and the auto correction, right?
00:45:36 And at Apple, like Steve Jobs reviewed everything.
00:45:39 And so he was like, this is what it was like
00:45:41 to show my demos to Steve Jobs and, you know,
00:45:43 to change them because like Steve Jobs didn’t like how,
00:45:46 you know, the shape of the little key was off
00:45:48 because the rounding of the corner was like not quite right
00:45:50 or something like this, right?
00:45:51 He was famously a stickler for this kind of stuff.
00:45:54 But because the teams were small,
00:45:55 he really owned this stuff, right?
00:45:56 So he really cared.
00:45:58 Yeah, Elon Musk does that similar kind of thing with Tesla,
00:46:01 which is really interesting.
00:46:03 There’s another lesson in leadership in that
00:46:05 is to be obsessed with the details.
00:46:07 And like, he talks to like the lowest level engineers.
00:46:11 Okay, so we’re talking about ASR
00:46:14 and so this is basically where I was saying
00:46:17 we’re gonna take this like ultra seriously.
00:46:20 And then what’s the mission?
00:46:22 To try to keep pushing towards the 3%.
00:46:26 Yeah, and kind of try to build this platform
00:46:30 where all of your, you know, all of your meetings,
00:46:33 you know, they’re as easily accessible as your notes, right?
00:46:38 Like, so, like, imagine all the meetings
00:46:41 a company might have, right?
00:46:43 You know, now that I’m like no longer a programmer, right?
00:46:46 Then I’m a quote unquote manager.
00:46:49 That’s less like my day as in meetings, right?
00:46:51 Yeah.
00:46:51 And, you know, pretty often I wanna like see
00:46:53 what was said, right?
00:46:55 Who said it, you know?
00:46:56 What’s the context?
00:46:57 But it’s generally not really something
00:46:59 that you can easily retrieve, right?
00:47:00 Like imagine if all of those meetings
00:47:03 were indexed, archived, you know, you could go back,
00:47:05 you could share a clip like really easily, right?
00:47:08 So that might change completely.
00:47:10 It’s like everything that’s said,
00:47:11 converted to text might change completely
00:47:14 the dynamics of what we do in this world,
00:47:16 especially now with remote work, right?
00:47:18 Exactly, exactly.
00:47:19 With Zoom and so on.
00:47:21 That’s fascinating to think about.
00:47:22 I mean, for me, I care about podcasts, right?
00:47:25 And one of the things that was,
00:47:30 you know, I’m torn.
00:47:31 I know a lot of the engineers at Spotify.
00:47:33 So I love them very much because they dream big
00:47:39 in terms of like, they wanna empower creators.
00:47:43 So one of my hopes was with Spotify
00:47:45 that they would use a technology like Rev
00:47:46 or something like that to start converting everything
00:47:51 into text and make it indexable.
00:47:55 Like one of the things that sucks with podcasts
00:47:59 is like, it’s hard to find stuff.
00:48:01 Like the model is basically subscription.
00:48:04 Like you find, it’s similar to what YouTube used to be like,
00:48:10 which is you basically find a creator that you enjoy
00:48:14 and you subscribe to them.
00:48:15 And like, you just kind of follow what they’re doing,
00:48:19 but the search and discovery wasn’t a big part of YouTube
00:48:24 like in the early days,
00:48:25 but that’s what currently with podcasts,
00:48:28 like is the search and discovery is like non existent.
00:48:33 You’re basically searching for like
00:48:35 the dumbest possible thing,
00:48:36 which is like keywords in the titles of episodes.
00:48:39 Yeah.
00:48:40 Even aside from a search and discovery, like all the time.
00:48:42 So I listened to like a number of podcasts
00:48:44 and there’s something said,
00:48:46 and I wanna like go back to that later
00:48:48 because I was trying to, I’m trying to remember,
00:48:49 what do you say?
00:48:50 Like maybe like recommended some cool product
00:48:52 that I wanna try out.
00:48:53 And like, it’s basically impossible.
00:48:54 Maybe like some people have pretty good show notes.
00:48:56 So maybe you’ll get lucky and you can find it, right?
00:48:59 But I mean, if everyone had transcripts
00:49:01 and it was all searchable, it would be so much better.
00:49:05 I mean, that’s one of the things that I wanted to,
00:49:08 I mean, one of the reasons we’re talking today
00:49:11 is I wanted to take this quite seriously.
00:49:13 The rough thing, I just been lazy.
00:49:16 So, because I’m very fortunate
00:49:19 that a lot of people support this podcast,
00:49:21 that there’s enough money now to do a transcription and so on.
00:49:24 And it seemed clear to me, especially like CEOs
00:49:29 and sort of like PhDs, like people write to me
00:49:36 who are like graduate students in computer science
00:49:38 or graduate students in whatever the heck field,
00:49:41 it’s clear that their mind,
00:49:43 like they enjoy podcasts
00:49:44 when they’re doing laundry or whatever,
00:49:46 but they wanna revisit the conversation
00:49:48 in a much more rigorous way.
00:49:50 And they really wanna transcript.
00:49:52 Like it’s clear that they want to analyze conversations.
00:49:56 Like so many people wrote to me
00:49:58 about a transcript for Yosha Bach conversation.
00:50:01 I had just a bunch of conversations.
00:50:03 And then on the Elon Musk side,
00:50:05 like reporters, they wanna write a blog post
00:50:09 about your conversation.
00:50:10 So they wanna be able to pull stuff.
00:50:13 And it’s like, they’re essentially doing
00:50:15 on your conversation transcription privately.
00:50:18 They’re doing it for themselves and then starting to pick,
00:50:21 but it’s so much easier when you can actually do it
00:50:23 as a reporter, just look at the transcript.
00:50:26 Yeah, and you can like embed a little thing,
00:50:28 like into your article, right?
00:50:29 Here’s what they said, you can go listen
00:50:31 to like this clip from the section.
00:50:33 I’m actually trying to figure out,
00:50:35 I’ll probably on the website create
00:50:39 like a place where the transcript goes,
00:50:41 like as a webpage so that people can reference it,
00:50:44 like reporters can reference it and so on.
00:50:46 I mean, most of the reporters probably want
00:50:50 to write clickbait articles that are complete falsifying,
00:50:54 which I’m fine with.
00:50:55 It’s the way of journalism, I don’t care.
00:50:57 Like I’ve had this conversation with a friend of mine,
00:51:01 a mixed martial artist, the Ryan Hall.
00:51:04 And we talked about, you know,
00:51:07 as I’ve been reading The Rise and Fall of the Third Reich
00:51:09 and a bunch of books on Hitler and we brought up Hitler
00:51:13 and he made some kind of comment where like,
00:51:17 we should be able to forgive Hitler
00:51:19 and, you know, like we were talking about forgiveness
00:51:23 and we’re bringing that up as like the worst case
00:51:25 possible things, like even, you know,
00:51:28 for people who are Holocaust survivors,
00:51:32 one of the ways to let go of the suffering
00:51:34 they’ve been through is to forgive.
00:51:38 And he brought up like Hitler as somebody
00:51:39 that would potentially be the hardest thing
00:51:42 to possibly forgive, but it might be a worthwhile pursuit
00:51:45 psychologically, so on, blah, blah, blah, it doesn’t matter.
00:51:48 It was very eloquent, very powerful words.
00:51:50 I think people should go back and listen to it.
00:51:53 It’s powerful.
00:51:54 And then all these journalists,
00:51:55 all these articles written about like MMA fight, UFC fight.
00:52:00 MMA fighter loves Hitler.
00:52:01 No, like, well, no, they didn’t.
00:52:03 They were somewhat accurate.
00:52:05 They didn’t say like loves Hitler.
00:52:07 They said, thinks that if Hitler came back to life,
00:52:13 we should forgive him.
00:52:14 Like they kind of, it’s kind of accurate ish,
00:52:18 but the headline made it sound a lot worse
00:52:23 than it was, but I’m fine with it.
00:52:27 That’s the way the world, I wanna almost make it easier
00:52:31 for those journalists and make it easier
00:52:33 for people who actually care about the conversation
00:52:35 to go and look and see.
00:52:37 Right, they can see it for themselves.
00:52:38 For themselves.
00:52:39 There’s the headline, but now you can go.
00:52:41 There’s something about podcasts,
00:52:42 like the audio that makes it difficult
00:52:44 to jump to a spot and to look
00:52:49 for that particular information.
00:52:53 I think some of it, I’m interested in creating,
00:52:58 like myself experimenting with stuff.
00:53:00 So like taking rev and creating a transcript
00:53:03 and then people can go to it.
00:53:05 I do dream that like, I’m not in the loop anymore,
00:53:09 that like, Spotify does it, right?
00:53:13 Like automatically for everybody,
00:53:16 because ultimately that one click purchase
00:53:19 needs to be there, like, you know.
00:53:21 Like you kind of want support from the entire ecosystem.
00:53:24 Exactly.
00:53:24 Like from the tool makers and the podcast creators,
00:53:27 even clients, right?
00:53:28 I mean, imagine if like most podcast apps,
00:53:33 you know, if it was a standard, right?
00:53:35 Here’s how you include a transcript into a podcast, right?
00:53:38 Like it’s just an RSS feed ultimately.
00:53:40 And actually just yesterday I saw this company
00:53:43 called Buzzsprout, I think they’re called.
00:53:46 So they’re trying to do this.
00:53:48 They proposed a spec, an extension to their RSS format
00:53:53 to reference transcripts in a standard way.
00:53:58 And they’re talking about like,
00:53:59 there’s one client dimension that will support it,
00:54:02 but imagine like more clients support it, right?
00:54:04 So any podcast, you could go and see the transcripts
00:54:08 right in your like normal podcast app.
00:54:10 Yeah.
00:54:11 I mean, somebody, so I have somebody who works with me,
00:54:15 works with helps with advertising, Matt, this awesome guy.
00:54:20 He mentioned Buzzsprout to me, but he says,
00:54:22 it’s really annoying because they want exclusive,
00:54:24 they want to host the podcast.
00:54:26 Right.
00:54:27 This is the problem with Spotify too.
00:54:29 This is where I’d like to say, like F Spotify,
00:54:33 there’s a magic to RSS with podcasts.
00:54:38 It can be made available to everyone.
00:54:40 And then there’s all, there’s this ecosystem
00:54:42 of different podcast players that emerge
00:54:45 and they compete freely.
00:54:47 And that’s a beautiful thing,
00:54:48 that that’s why I go on exclusive,
00:54:50 like Joe Rogan went exclusive.
00:54:53 I’m not sure if you’re familiar with,
00:54:54 he went to Spotify as a huge fan of Joe Rogan.
00:54:59 I’ve been kind of nervous about the whole thing,
00:55:01 but let’s see, I hope that Spotify steps up.
00:55:05 They’ve added video, which was very surprising
00:55:07 that they were able to put on.
00:55:07 Exclusive meaning you can’t subscribe
00:55:10 to the RSS feed anymore.
00:55:11 It’s only in Spotify.
00:55:12 For now you can until December 1st.
00:55:15 And December 1st, it’s all, everything disappears
00:55:18 and it’s Spotify only.
00:55:21 I, you know, and Spotify gave him a hundred million dollars
00:55:25 for that.
00:55:26 So it’s an interesting deal, but I, you know,
00:55:29 I did some soul searching and I’m glad he’s doing it.
00:55:34 But if Spotify came to me with a hundred million dollars,
00:55:38 I wouldn’t do it.
00:55:40 I wouldn’t do, well, I have a very different relationship
00:55:42 with money.
00:55:43 I hate money, but I just think I believe
00:55:46 in the pirate radio aspect of podcasting, the freedom.
00:55:50 And that there’s something about.
00:55:50 The open source spirit.
00:55:52 The open source spirit, it just doesn’t seem right.
00:55:54 It doesn’t feel right.
00:55:55 That said, you know, because so many people care
00:55:58 about Joe Rogan’s program,
00:56:00 they’re gonna hold Spotify’s feet to the fire.
00:56:02 Like one of the cool things with what Joe told me
00:56:06 is the reason he likes working with Spotify
00:56:10 is that they, they’re like ride or die together, right?
00:56:16 So they, they want him to succeed.
00:56:19 So that’s why they’re not actually telling him what to do
00:56:22 despite what people think.
00:56:23 They, they don’t tell them,
00:56:24 they don’t give them any notes on anything.
00:56:26 They want him to succeed.
00:56:28 And that’s the cool thing about exclusivity with a platform
00:56:31 is like, you’re kind of wanting each other to succeed.
00:56:36 And that process can actually be very fruitful.
00:56:39 Like YouTube, it goes back to my criticism.
00:56:44 YouTube generally, no matter how big the creator,
00:56:47 maybe for PewDiePie, something like that,
00:56:50 they want you to succeed.
00:56:51 But for the most part, from all the big creators
00:56:53 I’ve spoken with, Veritasium, all of those folks,
00:56:57 you know, they get some basic assistance,
00:56:59 but it’s not like, YouTube doesn’t care
00:57:02 if you succeed or not.
00:57:03 They have so many creators.
00:57:04 Yeah, like a hundred other.
00:57:06 They don’t care.
00:57:07 So, and especially with, with somebody like Joe Rogan,
00:57:12 who YouTube sees Joe Rogan,
00:57:15 not as a person who might revolutionize the nature of news
00:57:19 and idea space and nuanced conversations.
00:57:23 They see him as a potential person
00:57:26 who has racist guests on,
00:57:30 or like, you know, they see him as like a headache,
00:57:33 potentially.
00:57:34 So, you know, a lot of people talk about this.
00:57:37 It’s a hard place to be for YouTube, actually,
00:57:40 is figuring out with the search and discovery process
00:57:46 of how do you filter out conspiracy theories
00:57:49 and which conspiracy theories represent dangerous untruths
00:57:53 and which conspiracy theories are like vanilla untruths.
00:57:58 And then even when you start having meetings
00:58:00 and discussions about what is true or not,
00:58:03 it starts getting weird.
00:58:05 Yeah, it’s difficult these days, right?
00:58:07 I worry more about the other side, right?
00:58:09 Of too much, you know, too much censorship.
00:58:13 Well, maybe censorship is the right word.
00:58:14 I mean, censorship is usually government censorship,
00:58:17 but still, yeah, putting yourself in the position
00:58:21 of arbiter for these kinds of things.
00:58:22 It’s very difficult and people think it’s so easy, right?
00:58:25 Like, cause like, well, you know, like no Nazis, right?
00:58:27 What a simple principle.
00:58:30 But you know, yes, I mean, no one likes Nazis,
00:58:32 but there’s like many shades of gray,
00:58:35 like very soon after that.
00:58:37 Yeah, and then, you know, of course everybody, you know,
00:58:39 there’s some people that call our current president a Nazi
00:58:42 and then there’s like, so you start getting a Sam Harris.
00:58:45 I don’t know if you know that is wasted, in my opinion,
00:58:49 his conversation with Jack Dorsey.
00:58:51 Now I’ll also, I spoke with Jack before in this podcast
00:58:54 and we’ll talk again, but Sam brought up,
00:58:58 Sam Harris does not like Donald Trump.
00:59:01 I do listen to his podcast.
00:59:03 I’m familiar with his views on the matter.
00:59:06 And he asked Jack Dorsey, he’s like,
00:59:08 how can you not ban Donald Trump from Twitter?
00:59:12 And so, you know, there’s a set, you have that conversation.
00:59:15 You have a conversation where some number,
00:59:18 some significant number of people think
00:59:20 that the current president of the United States
00:59:22 should not be on your platform.
00:59:24 And it’s like, okay.
00:59:26 So if that’s even on the table as a conversation,
00:59:29 then everything’s on the table for conversation.
00:59:32 And yeah, it’s tough.
00:59:34 I’m not sure where I land on it.
00:59:37 I’m with you, I think that censorship is bad,
00:59:39 but I also think the show…
00:59:41 Ultimately, I just also think, you know,
00:59:43 if you’re the kind of person that’s gonna be convinced,
00:59:46 you know, by some YouTube video, you know,
00:59:49 that, I don’t know, our government’s been taken over
00:59:53 by aliens, it’s unlikely that like, you know,
00:59:56 you’ll be returned to sanity simply because, you know,
00:59:58 that video is not available on YouTube, right?
01:00:01 Yeah, I’m with you.
01:00:02 I tend to believe in the intelligence of people
01:00:04 and we should trust them.
01:00:06 But I also do think it’s the responsibility of platforms
01:00:10 to encourage more love in the world,
01:00:12 more kindness to each other.
01:00:14 And I don’t always think that they’re great
01:00:16 at doing that particular thing.
01:00:19 So that, there’s a nice balance there.
01:00:25 And I think philosophically, I think about that a lot.
01:00:28 Where’s the balance between free speech
01:00:32 and like encouraging people,
01:00:35 even though they have the freedom of speech
01:00:37 to not be an asshole.
01:00:39 Yeah, right.
01:00:41 That’s not a constitutional, like…
01:00:44 So you have the right for free speech,
01:00:48 but like, just don’t be an asshole.
01:00:50 Like you can’t really put that in the constitution
01:00:52 that the Supreme Court can’t be like,
01:00:54 eh, just don’t be a dick.
01:00:56 But I feel like platforms have a role to be like,
01:00:59 just be nicer.
01:01:00 Maybe do the carrot, like encourage people to be nicer
01:01:04 as opposed to the stake of censorship.
01:01:06 But I think it’s an interesting machine learning problem.
01:01:11 Just be nicer.
01:01:12 Machine, yeah, machine learning for niceness.
01:01:15 It is, I mean, that’s…
01:01:16 Responsible, yeah, I mean, it is.
01:01:17 It is a thing, for sure.
01:01:20 Jack Dorsey kind of talks about it
01:01:22 as a vision for Twitter is,
01:01:23 how do we increase the health of conversations?
01:01:26 I don’t know how seriously
01:01:28 they’re actually trying to do that though.
01:01:30 Which is one of the reasons that I’m in part considering
01:01:35 entering that space a little bit.
01:01:37 It’s difficult for them, right?
01:01:38 Because, you know, it’s kind of like well known
01:01:40 that people are kind of driven by rage
01:01:45 and you know, outrage maybe is a better word, right?
01:01:49 Outrage drives engagement.
01:01:51 And well, these companies are judged by engagement, right?
01:01:55 So it’s…
01:01:56 In the short term, but this goes to the metrics thing
01:01:58 that we were talking about earlier.
01:01:59 I do believe, I have a fundamental belief
01:02:02 that if you have a metric of long term happiness
01:02:06 of your users, like not short term engagement,
01:02:09 but long term happiness and growth
01:02:11 and both like intellectual, emotional health of your users,
01:02:15 you’re going to make a lot more money.
01:02:17 You’re going to have long…
01:02:18 Like you should be able to optimize for that.
01:02:21 You don’t need to necessarily optimize for engagement.
01:02:24 And that’ll be good for society too.
01:02:26 Yeah, no, I mean, I generally agree with you,
01:02:28 but it requires a patient person with, you know,
01:02:33 trust from Wall Street to be able
01:02:35 to carry out such a strategy.
01:02:36 This is what I believe the Steve Jobs character
01:02:39 and Elon Musk character is like,
01:02:41 you basically have to be so good at your job.
01:02:45 Right, you got to pass for anything.
01:02:46 That you can hold the board
01:02:48 and all the investors hostage by saying like,
01:02:52 either we do it my way or I leave.
01:02:56 And everyone is too afraid of you to leave
01:02:59 because they believe in your vision.
01:03:01 But that requires being really good at what you do.
01:03:04 It requires being Steve Jobs and Elon Musk.
01:03:06 There’s kind of a reason why like a third name doesn’t
01:03:09 come immediately to mind, right?
01:03:10 Like there’s maybe a handful of other people,
01:03:12 but it’s not that many.
01:03:13 It’s not many.
01:03:14 I mean, people say like, why are you…
01:03:15 Like people say that I’m like a fan of Elon Musk.
01:03:18 I’m not, I’m a fan of anybody
01:03:20 who’s like Steve Jobs and Elon Musk.
01:03:23 And there’s just not many of those folks.
01:03:26 It’s the guy that made us believe
01:03:27 that like we can get to Mars, you know, in 10 years, right?
01:03:31 I mean, that’s kind of awesome.
01:03:32 And it’s kind of making it happen, which is like…
01:03:35 And it’s kind of gone like that kind of like spirit, right?
01:03:40 Like from a lot of our society, right?
01:03:42 You know, like we can get to the moon in 10 years
01:03:44 and like we did it, right?
01:03:45 Yeah.
01:03:46 Especially in this time of so much kind of existential dread
01:03:52 that people are going through because of COVID,
01:03:55 like having rockets that just keep going out there
01:03:58 now with humans.
01:04:00 I don’t know that it, just like you said,
01:04:03 I mean, it gives you a reason to wake up in the morning
01:04:05 and dream, for us engineers too.
01:04:09 It is inspiring as hell, man.
01:04:14 Let me ask you this, the worst possible question,
01:04:17 which is, so you’re like at the core, you’re a programmer,
01:04:21 you’re an engineer, but now you made the unfortunate choice
01:04:29 or maybe that’s the way life goes
01:04:30 of basically moving away from the low level work
01:04:35 and becoming a manager, becoming an executive,
01:04:38 having meetings, what’s that transition been like?
01:04:43 It’s been interesting.
01:04:44 It’s been a journey.
01:04:44 Maybe a couple of things to say about that.
01:04:46 I mean, I got into this, right?
01:04:49 Because as a kid, I just remember this like incredible
01:04:54 amazement at being able to write a program, right?
01:04:57 And something comes to life that kind of didn’t exist before.
01:05:01 I don’t think you have that in like many other fields,
01:05:03 like you have that with some other kinds of engineering,
01:05:07 but you’re maybe a little bit more limited
01:05:09 with what you can do, right?
01:05:10 But with a computer,
01:05:11 you can literally imagine any kind of program, right?
01:05:14 So it’s a little bit godlike what you do
01:05:16 like when you create it.
01:05:19 And so, I mean, that’s why I got into it.
01:05:21 Do you remember like first program you wrote
01:05:23 or maybe the first program that like made you fall in love
01:05:25 with computer science?
01:05:28 I don’t know what was the first program.
01:05:29 It’s probably like trying to write one of those games
01:05:31 and basic, you know, like emulate the snake game
01:05:34 or whatever.
01:05:35 I don’t remember to be honest, but I enjoyed like,
01:05:37 that’s why I always loved about, you know,
01:05:40 being a programmer, it’s just the creation process.
01:05:41 And it’s a little bit different
01:05:43 when you’re not the one doing the creating.
01:05:47 And, you know, another aspect to it I would say is,
01:05:50 you know, when you’re a programmer,
01:05:52 when you’re a individual contributor,
01:05:54 it’s kind of very easy to know when you’re doing a good job,
01:05:57 when you’re not doing a good job,
01:05:58 when you’re being productive,
01:05:59 when you’re not being productive, right?
01:06:00 You can kind of see like you trying to make something
01:06:03 and it’s like slowly coming together, right?
01:06:05 And when you’re a manager, you know, it’s more diffuse,
01:06:08 right?
01:06:09 Like, well, you hope, you know, you’re motivating your team
01:06:12 and making them more productive and inspiring them, right?
01:06:15 But it’s not like you get some kind of like dopamine signal
01:06:18 because you like completed X lines of code, you know, today.
01:06:22 So kind of like you missed that dopamine rush
01:06:24 a little bit when you first become,
01:06:28 but then, you know, slowly you kind of see,
01:06:30 yes, your teams are doing amazing work, right?
01:06:32 And you can take pride in that.
01:06:34 You can get like, what is it?
01:06:38 Like a ripple effect of somebody else’s dopamine rush.
01:06:41 Yeah, yeah, you live off other people’s dopamine.
01:06:46 So is there pain points and challenges you had to overcome
01:06:50 from becoming, from going to a programmer to becoming
01:06:54 a programmer of humans?
01:06:55 Programmer of humans.
01:06:58 I don’t know, humans are difficult to understand,
01:07:00 you know, it’s like one of those things,
01:07:03 like trying to understand other people’s motivations
01:07:06 and what really drives them.
01:07:08 It’s difficult, maybe like never really know, right?
01:07:10 Do you find that people are different?
01:07:13 Yeah.
01:07:14 Like I, one of the things, like I had a group at MIT
01:07:18 that, you know, I found that like some people
01:07:24 I could like scream at and criticize like hard
01:07:30 and that made them do like much better work
01:07:32 and really push them to their limit.
01:07:35 And there’s some people that I had to nonstop compliment
01:07:39 because like they’re so already self critical,
01:07:42 like about everything they do that I have to be constantly
01:07:45 like, like I cannot criticize them at all
01:07:50 because they’re already criticizing themselves
01:07:52 and you have to kind of encourage
01:07:54 and like celebrate their little victories.
01:07:58 And it’s kind of fascinating that like how that,
01:08:00 the complete difference in people.
01:08:03 Definitely people respond to different motivations
01:08:06 and different loads of feedback
01:08:07 and you kind of have to figure it out.
01:08:10 It was like a pretty good book,
01:08:13 which for some reason now the name escapes me,
01:08:15 about management, first break all the rules.
01:08:18 First break all the rules?
01:08:19 First break all the rules.
01:08:20 It’s a book that we generally like ask a lot of
01:08:23 like first time managers to read it rough.
01:08:26 And like one of the kind of philosophies
01:08:28 is managed by exception, right?
01:08:31 Which is, you know, don’t like have some standard template
01:08:34 like, you know, here’s how I, you know,
01:08:36 tell this person to do this or the other thing.
01:08:39 Here’s how I get feedback, like manage by exception, right?
01:08:41 Every person is a little bit different.
01:08:42 You have to try to understand what drives them.
01:08:45 And tailor it to them.
01:08:47 Since you mentioned books,
01:08:48 I don’t know if you can answer this question,
01:08:50 but people love it when I ask it, which is,
01:08:53 are there books, technical fiction or philosophical
01:08:56 that you enjoyed or had an impact on your life
01:08:59 that you would recommend?
01:09:01 You already mentioned Dune, like all of the Dune.
01:09:04 All of the Dune.
01:09:05 The second one was probably the weakest, but anyway,
01:09:07 so yeah, all of the Dune is good.
01:09:09 I mean, yeah, can you just slow little tangent on that?
01:09:13 Is, how many Dune books are there?
01:09:16 Like, do you recommend people start with the first one
01:09:18 if that was?
01:09:19 Yeah, you gotta have to read them all.
01:09:21 I mean, it is a complete story, right?
01:09:23 So you start with the first one,
01:09:25 you gotta read all of them.
01:09:27 So it’s not like a tree, like a creation of like
01:09:31 the universe that you should go in sequence?
01:09:33 You should go in sequence, yeah.
01:09:35 It’s kind of a chronological storyline.
01:09:38 There’s six books in all.
01:09:40 Then there’s like many kind of books
01:09:43 that were written by Frank Herbert’s son,
01:09:47 but those are not as good.
01:09:48 So you don’t have to bother with those.
01:09:50 Shots fired.
01:09:51 Shots fired.
01:09:52 Okay.
01:09:53 But the main sequence is good.
01:09:56 So what are some other books?
01:09:58 Maybe there’s a few.
01:09:59 So I don’t know that like, I would say there’s a book
01:10:02 that kind of, I don’t know, turned my life around
01:10:05 or anything like that, but here’s a couple
01:10:07 that I really love.
01:10:08 So one is Brave New World by Aldous Huxley.
01:10:16 And it’s kind of incredible how prescient he was
01:10:20 about like what a brave new world might be like, right?
01:10:25 You know, you kind of see genetic sorting in this book,
01:10:28 right, where there’s like these alphas and epsilons
01:10:30 and how from like the earliest time of society,
01:10:34 like they’re sort of like, you can kind of see it
01:10:36 in a slightly similar way today where,
01:10:40 well, one of the problems with society is people
01:10:42 are kind of genetically sorting a little bit, right?
01:10:45 Like there’s much less, like most marriages, right,
01:10:48 are between people of similar kind of intellectual level
01:10:53 or socioeconomic status, more so these days than in the past.
01:10:57 And you kind of see some effects of it
01:10:59 in stratifying society and kind of he illustrated
01:11:03 what that could be like in the extreme.
01:11:05 There’s different versions of it on social media as well.
01:11:07 It’s not just like marriages and so on.
01:11:09 Like it’s genetic sorting in terms of what Dawkins called
01:11:13 memes as ideas being put into these bins
01:11:17 of these little echo chambers and so on.
01:11:20 Yeah, I know, so that’s the book
01:11:21 that’s I think a worthwhile read for everyone.
01:11:23 I mean, 1984 is good, of course, as well.
01:11:25 Like if you’re talking about, you know,
01:11:26 dystopian novels of the future.
01:11:28 Yeah, it’s a slightly different view of the future, right?
01:11:30 But I kind of like identify with Brave New World a bit more.
01:11:33 Yeah, speaking of not a book,
01:11:38 but my favorite kind of dystopian science fiction
01:11:41 is a movie called Brazil,
01:11:42 which I don’t know if you’ve heard of.
01:11:44 I’ve heard of and I know I need to watch it,
01:11:46 but yeah, because it’s in, is it in English or no?
01:11:50 It’s an English movie, yeah.
01:11:52 And it’s a sort of like dystopian movie
01:11:55 of authoritarian incompetence, right?
01:11:58 It’s like nothing really works very well, you know,
01:12:03 the system is creaky, you know,
01:12:05 but no one is kind of like willing to challenge it,
01:12:07 you know, just things kind of ample along
01:12:10 and kind of strikes me as like a very plausible future
01:12:13 of like, you know, what authoritarianism might look like.
01:12:16 It’s not like this, you know,
01:12:19 super efficient evil dictatorship of 1984.
01:12:21 It’s just kind of like this badly functioning, you know,
01:12:25 but it’s status quo, so it just goes on.
01:12:30 Yeah, that’s one funny thing that stands out to me
01:12:33 is in whether it’s authoritarian, dystopian stuff,
01:12:37 or just basic like, you know,
01:12:39 if you look at the movie Contagion,
01:12:42 it seems in the movies,
01:12:44 government is almost always exceptionally competent.
01:12:49 Like it’s like used as a storytelling tool
01:12:53 of like extreme competence.
01:12:55 Like, you know, you use it whether it’s good or evil,
01:12:58 but it’s competent.
01:12:59 It’s very interesting to think about
01:13:01 where much more realistically is it’s incompetence
01:13:06 and that incompetence isn’t itself has consequences
01:13:11 that are difficult to predict.
01:13:13 Like bureaucracy has a very boring way of being evil,
01:13:19 of just, you know, if you look at the show,
01:13:21 HBO show at Chernobyl,
01:13:23 it’s a really good story of how bureaucracy, you know,
01:13:30 leads to catastrophic events,
01:13:32 but not through any kind of evil
01:13:34 in any one particular place,
01:13:36 but more just like the…
01:13:37 It’s just the system kind of system.
01:13:40 Distorting information as it travels up the chain
01:13:43 that people unwilling to take responsibility for things
01:13:46 and just kind of like this laziness resulting in evil.
01:13:50 There’s a comedic version of this,
01:13:52 I don’t know if you’ve seen this movie,
01:13:53 it’s called The Death of Stalin.
01:13:54 Yeah, I liked that.
01:13:58 I wish it wasn’t so…
01:14:00 There’s a movie called Inglourious Bastards
01:14:02 about, you know, Hitler and so on.
01:14:07 For some reason, those movies pissed me off.
01:14:09 I know a lot of people love them,
01:14:11 but like, I just feel like there’s not enough good movies,
01:14:16 even about Hitler.
01:14:18 There’s good movies about the Holocaust,
01:14:21 but even Hitler, there’s a movie called Dawnfall
01:14:23 that people should watch.
01:14:24 I think it’s the last few days of Hitler.
01:14:26 That’s a good movie, turned into a meme, but it’s good.
01:14:30 But on Stalin, I feel like I may be wrong on this,
01:14:33 but at least in the English speaking world,
01:14:35 there’s not good movies about the evil of Stalin.
01:14:38 That’s true.
01:14:39 Let’s try to see that.
01:14:40 Actually, so I agree with you on Inglourious Bastard.
01:14:43 I didn’t love the movie
01:14:46 because I felt like kind of the stylizing of it, right?
01:14:50 The whole Tarantino kind of Tarantinoism, if you will,
01:14:55 kind of detracted from it
01:14:56 and made it seem like unserious a little bit.
01:15:00 But Death of Stalin, I felt differently.
01:15:02 Maybe it’s because it’s a comedy to begin with.
01:15:03 This is not like I’m expecting seriousness,
01:15:06 but it kind of depicted the absurdity
01:15:10 of the whole situation in a way, right?
01:15:13 I mean, it was funny, so maybe it does make light of it,
01:15:15 but something goes probably like this, right?
01:15:18 Like a bunch of kind of people,
01:15:20 they’re like, oh shit, right?
01:15:22 You’re right.
01:15:23 But like the thing is,
01:15:25 it was so close to like what probably was reality.
01:15:32 It was caricaturing reality
01:15:35 to where I think an observer might think that this is not,
01:15:39 like they might think it’s a comedy.
01:15:41 Well, in reality, that’s the absurdity
01:15:45 of how people act with dictators.
01:15:48 I mean, that’s, I guess it was too close to reality for me.
01:15:54 The kind of banality of like what were eventually
01:15:57 like fairly evil acts, right?
01:15:59 But like, yeah, they’re just a bunch of people
01:16:02 trying to survive.
01:16:04 Cause I think there’s a good,
01:16:05 I haven’t watched it yet, the good movie on,
01:16:07 the movie on Churchill with Gary Oldman,
01:16:12 I think it’s Gary Oldman.
01:16:14 I may be making that up.
01:16:15 But I think he won,
01:16:16 like he was nominated for an Oscar or something.
01:16:17 So I like, I love these movies about these humans
01:16:20 and Stalin, like Chernobyl made me realize the HBO show
01:16:26 that there’s not enough movies about Russia
01:16:28 that capture that spirit.
01:16:33 I’m sure it might be in Russian there is,
01:16:35 but the fact that some British dude that like did comedy,
01:16:39 I feel like he did like hangover or some shit like that.
01:16:42 I don’t know if you’re familiar
01:16:43 with the person who created Chernobyl,
01:16:44 but he was just like some guy
01:16:45 that doesn’t know anything about Russia.
01:16:47 And he just went in and just studied it,
01:16:49 like did a good job of creating it
01:16:51 and then got it so accurate, like poetically.
01:16:56 And the facts that you need to get accurate,
01:16:58 he got accurate, just the spirit of it
01:17:01 down to like the bowls that pets use,
01:17:03 just the whole feel of it.
01:17:05 It was incredible.
01:17:05 It was good, yeah, I saw the series.
01:17:07 Yeah, it’s incredible.
01:17:08 It’s made me wish that somebody did a good,
01:17:10 like 1930s, like starvation that Stalin led to,
01:17:17 like leading up to World War II
01:17:20 and in World War II itself, like Stalingrad and so on.
01:17:23 Like, I feel like that story needs to be told.
01:17:27 Millions of people died.
01:17:30 And to me, it’s so much more fascinating than Hitler
01:17:32 because Hitler is like a caricature of evil almost
01:17:37 that it’s so, especially with the Holocaust,
01:17:41 it’s so difficult to imagine that something like that
01:17:45 is possible ever again.
01:17:47 Stalin to me represents something that is possible.
01:17:52 Like the so interesting, like the bureaucracy of it
01:17:56 is so fascinating that it potentially might be happening
01:18:01 in the world now, like that we’re not aware of,
01:18:03 like with North Korea, another one that,
01:18:06 like there should be a good film on.
01:18:08 And like the possible things that could be happening
01:18:10 in China with overreach of government.
01:18:13 I don’t know, there’s a lot of possibilities there.
01:18:15 I suppose.
01:18:16 Yeah, I wonder how much, you know,
01:18:18 I guess the archives should be maybe more open nowadays,
01:18:20 right, I mean, for a long time, they just, we didn’t know,
01:18:22 right, or anyways, no one in the West knew for sure.
01:18:25 Well, there’s a, I don’t know if you know him,
01:18:27 there’s a guy named Stephen Kotkin.
01:18:29 He is a historian of Stalin that I spoke to on this podcast.
01:18:33 I’ll speak to him again.
01:18:34 The guy knows his shit on Stalin.
01:18:38 He like read everything and it’s so fascinating
01:18:44 to talk to somebody, like he knows Stalin better
01:18:50 than Stalin himself, it’s crazy.
01:18:53 Like you have, so he’s, I think he’s a Princeton,
01:18:55 he is basically, his whole life is Stalin.
01:18:58 Fighting Stalin.
01:18:59 Yeah, it’s great.
01:19:00 And in that context, he also talks about
01:19:03 and writes about Putin a little bit.
01:19:06 I’ve also read at this point,
01:19:07 I think every biography of Putin, English biography of Putin,
01:19:12 I need to read some Russians.
01:19:14 Obviously, I’m mentally preparing
01:19:15 for a possible conversation with Putin.
01:19:17 So what is your first question to Putin
01:19:19 when you have him on the podcast?
01:19:22 I, it’s interesting you bring that up.
01:19:26 First of all, I wouldn’t tell you, but.
01:19:27 You can’t give it away now.
01:19:30 But I actually haven’t even thought about that.
01:19:34 So my current approach, and I do this with interviews often,
01:19:38 obviously that’s a special one,
01:19:40 but I try not to think about questions until last minute.
01:19:45 I’m trying to sort of get into the mindset.
01:19:50 And so that’s why I’m soaking in a lot of stuff,
01:19:52 not thinking about questions, just learning about the man.
01:19:56 But in terms of like human to human,
01:19:59 it’s like, I would say it’s,
01:20:01 I don’t know if you’re a fan of mob movies,
01:20:03 but like the mafia, which I am, like Goodfellas and so on,
01:20:07 he’s much closer to like mob morality, which is like.
01:20:11 Mob morality, maybe, I could see that.
01:20:14 But I like your approach anyways of this,
01:20:16 the extreme empathy, right?
01:20:18 It’s a little bit like Hannibal, right?
01:20:21 Like if you ever watched the show Hannibal, right?
01:20:22 They had that guy, well, you know Hannibal of course, like.
01:20:27 Yeah, Silence of the Lambs.
01:20:30 But there were those TV shows as well,
01:20:31 and they focused on this guy, Will Durant,
01:20:34 who’s a character like extreme empath, right?
01:20:36 So in the way he like catches all these killers,
01:20:38 as he pretty much, he can empathize with them, right?
01:20:42 Like he can understand why they’re doing
01:20:44 the things they’re doing, right?
01:20:45 It’s a pretty excruciating thing, right?
01:20:48 Like, because you’re pretty much like spending
01:20:49 half your time in the head of evil people, right?
01:20:52 Like, but.
01:20:54 I mean, I definitely try to do that with others.
01:20:57 So you should do that in moderation,
01:20:59 but I think it’s a pretty safe place, safe place to be.
01:21:04 One of the cool things with this podcast,
01:21:06 and I know you didn’t sign up to hear me
01:21:08 listen to this bullshit, but.
01:21:10 That was interesting.
01:21:11 I, and what’s his name?
01:21:15 Chris Latner, who’s a Google,
01:21:17 oh, he’s not Google anymore, SciFi.
01:21:19 He’s legit, he’s one of the most legit engineers
01:21:21 I talk with, I talk with him again on this podcast.
01:21:23 And one of the, he gives me private advice a lot.
01:21:27 And he said for this podcast, I should like interview,
01:21:31 like I should widen the range of people
01:21:34 because that gives you much more freedom to do stuff.
01:21:38 Like, so his idea, which I think I agree with Chris
01:21:41 is that you go to the extremes.
01:21:44 You just like cover every extreme base
01:21:46 and then it gives you freedom to then go
01:21:48 to the more nuanced conversations.
01:21:50 And it’s kind of, I think there’s a safe place for that.
01:21:53 There’s certainly a hunger for that nuanced conversation,
01:21:56 I think, amongst people where like on social media,
01:22:00 you get canceled for anything slightly tense,
01:22:04 that there’s a hunger to go full.
01:22:06 Right, you go so far to the opposite side.
01:22:08 And that’s like demystifies it a little bit, right?
01:22:10 Yeah, that’s.
01:22:11 There is a person behind all of these things.
01:22:15 And that’s the cool thing about podcasting,
01:22:17 like three, four hour conversations
01:22:19 that it’s very different than a clickbait journalism,
01:22:24 it’s like the opposite, that there’s a hunger for that.
01:22:26 There’s a willingness for that.
01:22:28 Yeah, especially now, I mean,
01:22:29 how many people do you even see face to face anymore?
01:22:31 Right, like this, you know?
01:22:33 It’s like not that many people like in my day today,
01:22:36 aside from my own family that like I sit across.
01:22:39 It’s sad, but it’s also beautiful.
01:22:41 Like I’ve gotten the chance to like,
01:22:44 like our conversation now, there’s somebody,
01:22:47 I guarantee you there’s somebody in Russia
01:22:50 listening to this now, like jogging.
01:22:52 There’s somebody who is just smoked some weed,
01:22:55 sit back on a couch and just like enjoying.
01:22:58 I guarantee you that we’ll write in the comments right now
01:23:00 that yes, I’m in St. Petersburg, I’m in Moscow, whatever.
01:23:05 And we’re in their head and they have a friendship with us.
01:23:10 I’m the same way, I’m a huge fan of podcasting.
01:23:14 It’s a beautiful thing.
01:23:15 I mean, it’s a weird one way human connection.
01:23:18 Like before I went on Joe Rogan and still,
01:23:22 I’m just a huge fan of his.
01:23:24 So it was like surreal.
01:23:25 I’ve been friends with Joe Rogan for 10 years, but one way.
01:23:28 Yeah, from this way, from the St. Petersburg way.
01:23:31 Yeah, the St. Petersburg way and it’s a real friendship.
01:23:34 I mean, now it’s like two way, but it’s still surreal.
01:23:38 And that’s the magic of podcasting.
01:23:40 I’m not sure what to make of it.
01:23:42 That voice, it’s not even the video part.
01:23:45 It’s the audio that’s magical.
01:23:48 I don’t know what to do with it,
01:23:50 but it’s people listen to three, four hours.
01:23:53 Yeah, we evolved over millions of years, right?
01:23:57 To be very fine tuned to things like that, right?
01:24:00 Oh, expressions as well, of course, right?
01:24:02 But back in the day on the Savannah,
01:24:06 you had to be very attuned to whether
01:24:09 you had a good relationship with the rest of your tribe
01:24:11 or a very bad relationship, right?
01:24:13 Because if you had a very bad relationship,
01:24:15 you were probably gonna be left behind
01:24:17 and eaten by the lions.
01:24:18 Yeah, but it’s weird that the tribe is different now.
01:24:22 Like you could have a one way connection with Joe Rogan
01:24:26 as opposed to the tribe of your physical vicinity.
01:24:30 But that’s why it works with the podcasting,
01:24:33 but it’s the opposite of what happens on Twitter, right?
01:24:35 Because all those nuances are removed, right?
01:24:38 You’re not connecting with the person
01:24:40 because you don’t hear the voice.
01:24:42 You’re connecting with like an abstraction, right?
01:24:44 It’s like some stream of tweets, right?
01:24:48 And it’s very easy to assign to them
01:24:52 any kind of evil intent or dehumanize them,
01:24:56 which it’s much harder to do when it’s a real voice, right?
01:24:59 Because you realize it’s a real person behind the voice.
01:25:02 Let me try this out on you.
01:25:05 I sometimes ask about the meaning of life.
01:25:07 Do you, your father now, an engineer,
01:25:12 you’re building up a company.
01:25:14 Do you ever zoom out and think like,
01:25:16 what the hell is this whole thing for?
01:25:19 Like why are we descended to vapes even on this planet?
01:25:24 What’s the meaning of it all?
01:25:26 That’s a pretty big question.
01:25:29 I think I don’t allow myself to think about it too often,
01:25:32 or maybe like life doesn’t allow me
01:25:34 to think about it too often.
01:25:36 But in some ways, I guess the meaning of life
01:25:39 is kind of contributing to this kind of weird thing
01:25:44 we call humanity, right?
01:25:45 Like it’s in a way, you can think of humanity
01:25:47 as like a living and evolving organism, right?
01:25:50 That like we all contributing in a sway way,
01:25:52 but just by existing, by having our own unique set
01:25:55 of desires and drives, right?
01:25:58 And maybe that means like creating something great.
01:26:01 And it’s bringing up kids who are unique and different
01:26:06 and seeing like, they can join what they do.
01:26:09 But I mean, to me, that’s pretty much it.
01:26:11 I mean, if you’re not a religious person, right?
01:26:13 Which I guess I’m not, that’s the meaning of life.
01:26:16 It’s in the living and in the creation.
01:26:20 Yeah, there’s something magical
01:26:22 about that engine of creation.
01:26:24 Like you said, programming, I would say,
01:26:27 I mean, it’s even just actually what you said
01:26:29 with even just programs.
01:26:30 I don’t care if it’s like some JavaScript thing
01:26:32 on a button on the website.
01:26:36 It’s like magical that you brought that to life.
01:26:38 I don’t know what that is in there, but that seems,
01:26:41 that’s probably some version of like reproduction
01:26:47 and sex, whatever that’s in evolution.
01:26:49 But like creating that HTML button has echoes
01:26:55 of that feeling and it’s magical.
01:26:58 Right, well, I mean, if you’re a religious person,
01:27:00 maybe you could even say, all right,
01:27:01 like we were created in God’s image, right?
01:27:04 Well, I mean, I guess part of that is the drive
01:27:07 to create something ourselves, right?
01:27:09 I mean, that’s part of it.
01:27:11 Yeah, that HTML button is the creation in God’s image.
01:27:15 Maybe hopefully it’ll be something a little more.
01:27:18 So dynamic, maybe some JavaScript.
01:27:20 Yeah, maybe some JavaScript, some React and so on.
01:27:25 But no, I mean, I think that’s what differentiates us
01:27:29 from the apes, so to speak.
01:27:32 Yeah, we did a pretty good job.
01:27:34 Dan, it was an honor to talk to you.
01:27:36 Thank you so much for being part of creating
01:27:38 one of my favorite services and products.
01:27:42 This is actually a little bit of an experiment.
01:27:45 Allow me to sort of fanboy over some of the things I love.
01:27:49 So thanks for wasting your time with me today.
01:27:52 It was really fun.
01:27:53 Well, it was awesome.
01:27:53 Thanks for having me on and giving me a chance
01:27:55 to try this out.
01:27:57 Awesome.
01:27:59 Thanks for listening to this conversation
01:28:00 with Dan Kokotov and thank you to our sponsors,
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01:28:25 And now let me leave you with some words
01:28:27 from Ludwig Wittgenstein.
01:28:29 The limits of my language means the limits of my world.
01:28:33 Thank you for listening and hope to see you next time.