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Is AI going to kill us all? Uh maybe.
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Emmett Shear is the CEO of Twitch, was acquired by Amazon in 2014, joins us now.
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I started Twitch to help people watch other people play video games on the internet.
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The creator and co-founder of Twitch.
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Watch other people play video games. Who knew? Emmett knew.
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I guess that's the answer.
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What types of ideas are you noticing are standing out to you that are interesting?
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first time in maybe five to seven years, it feels like credibly trying to start a consumer internet company, like the ones that like I was so excited to start in 2007, is like potentially a good idea. Um that's because of AI.
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Uh you mentioned AI might become so intelligent it kills us all.
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This podcast is really growing.
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I don't I don't want the world to end.
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I think it's going to be okay.
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But it's such the downside is so bad.
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It's like it's really probably worse than nuclear war. Mhm.
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That's a really bad downside.
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I think of it as a range of uncertainty.
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And I would say that the true probability I believe is somewhere between All right, what you're about to hear is a conversation I had with Emmett Shear.
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Emmett was the creator and co-founder of Twitch.
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If you don't know about Twitch, I don't know, you're living under a rock.
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It's like one of the most, I don't know, five most popular websites in in the states right now.
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It is a um a place where you can go to watch other people play video games, of all things.
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Watch other people play video games. Who knew? Emmett knew.
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I guess that's the answer.
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So he was the creator and co-founder of that and um built it up.
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It's a multi-billion dollar company.
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They sold to Amazon many years ago, seven years ago or eight years ago for about a billion dollars and has grown many times since then.
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Uh he finally retired after 17 years of the journey.
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Um I got to know Emmett because he bought my previous company.
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So we got acquired by Twitch.
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Emmett was like my you know quote unquote boss for um my time when I was at Twitch.
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So I got to see this guy firsthand.
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He's the real deal and I've been wanting to get him on the podcast since those early days when I first met him.
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I was like this guy is great.
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We talked about a bunch of things.
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So we talked about some ideas of like how he would use AI if he was going to create another company. Like I think he's good.
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He's retired now from that game of operating a company, but if he was going to do it, this is what he would do.
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So we talked about AI ideas.
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We talked about why why he thinks AI might kill us all, might you know be be the big doom scenario, which is interesting cuz he's not just a guy who's going to go cry wolf. He's not a pessimist.
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He's not just a journalist who hates tech. He's a techno optimist.
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This is a guy who believes in tech, is a um a very very intelligent guy and he sees you know a probability.
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He gave us a percentage of probability he thinks that could be sort of the doomsday scenario and why he thinks that that could be the case and what we should do about it. So We talked about AI.
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We talked about some of the frameworks that he has for building companies.
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We didn't talk too much about like the origin of Twitch.
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He feel like he's done that a bunch of times so we kind of stayed away from that.
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But it was a wide-ranging conversation and for those who are watching this on YouTube, I apologize the studio that we booked in San Francisco um they screwed up the video.
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So we don't have video for the for for YouTube.
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We just have the audio only version.
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So you'll see our profile pictures. My bad. Sorry about that.
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Um you know, got to pick a better place.
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Got to pick a better studio I guess.
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But um Anyways, enjoy this episode with Emmett Shear.
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Somebody said creativity is not like a faucet.
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You can't just turn it on.
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And I think actually if you if you pulled like 100 people, most people like yeah, of course creativity is the sacred special thing that only happens if you've meditated in the morning and the room is perfectly right and you've had your your L-theanine in your coffee or whatever.
3:50
And you were like no, for me it's very It is like a fossil watch.
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And you know, like I can just write and just keep generating more ideas.
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I love that for two reasons.
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One, I love that you'll just be like, "No, actually this."
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That's like a consistent thing I've seen you do.
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And the second is I think that's very true about you and I wonder is that practiced or is that innate?
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Like If I If you were If there was a researcher studying you when you were like 10 years old, Right.
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do you think they would have been like, "Oh, this This person's different in these ways."
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What would have seemed different or special about you at the time?
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Um I the if there was a nurture and nature break on this, it happened very early because by the time I was 10, you would definitely notice the same thing.
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I'm not really that different.
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But I would be much less effective.
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But like as a 10-year-old, I already had that same experience.
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But you were different than other 10-year-olds.
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Yeah, other 10-year-olds Well, I would actually say I was less different then.
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I think most people actually most children have this experience already.
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I think most 10-year-olds and definitely most 5-year-olds are capable of generating ideas for what to do about something or to like play pretend almost indefinitely.
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They don't run out of ideas.
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It's as you get older somehow you What you learn to do is you learn to stomp down the ideas that are like bad.
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Um and to not say dumb things.
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But the more pressure you put on yourself not to say dumb things, the more your inner idea generator it like gets disrupted.
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And I I say a lot of dumb things.
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Like when I'm generating ideas, I may not put weight down on them, but most of the ideas will be bad.
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They'll have something obviously wrong with them.
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And they give you this advice and when you if you go to like someone who teaches you how to brainstorm, Right.
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like, "No bad ideas here."
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That's obviously not true.
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There's lots of bad ideas.
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Most of your ideas are bad.
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Yeah, the The actual advice is like, "Don't stop at the bad ideas." Yeah.
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Well, you're What you're trying to do is you're trying to disable that sensor that most people have installed that like is like, "No bad No bad No bad. Don't Don't be stupid. Don't be stupid.
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And I think I was like mal-socialized.
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I It never occurred to me to to have that.
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Like I I did I never got the sensor installed.
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And why that is the case, I'm not sure.
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But I actually I think I'm the one who is unchanged in some sense.
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I'm a little more childlike in that way.
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And everyone else is the weird one who like Why how does you end up like damaged by your life that your your inner wellspring of creativity has been Right. crushed.
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And I think that process is actually very simple.
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You This process goes over all kinds of things in people's minds.
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You start from some capability, something you can do, some behavior.
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And if when you do that behavior, you try that thing, you receive negative feedback, which can be external or you actually think even more often internal, you're like, "Oh, I I screwed it up. Oh, it's bad.
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Oh, I don't Disappointment."
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You learn not to do that thing pretty rapidly.
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And so, that leads you to doing it less, which means you're less skillful at it, which tends to lead you to doing it less, which And this this that cycle ends in you being very bad at something. Like, "I'm bad at math." No, you're not.
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Everyone can be like The kind of math you're talking about, everyone can be The kind of math When people say, "I'm bad at math," they don't mean, "I'm bad at like abstract algebra proofs."
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They mean, "I can't do arithmetic or algebra basic algebra."
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And that's just imaginary.
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Like, everyone can do that. It's easy.
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They got stuck in one of these like spirals.
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And now it's And getting out of one can be very hard.
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And I guess I think that's what happens to people's creativity. I don't know.
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I didn't go through the process myself.
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And so, I got So, now that I'm saying this out loud, actually, when I'm The idea that I have that comes up for me is like, "Oh, well, maybe what it is is that I had better ideas."
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That's like That's the So, you got in the reward the reward loop or or I had an environment that was unusually uh positive and positively reinforcing for me having ideas.
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And so, I would have ideas, and it would go well.
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That would lead me to having more ideas, which and more practice at having ideas, which would go well.
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And then you end up just never breaking that loop.
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I have a a trainer who comes over to my house and he always says this thing to me because like my kids will come down during the session.
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I'm always like, "Oh, sorry.
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Like, obviously annoying my 2-year-old is here, almost getting hurt on all the weights, and that's probably like not what you want in your session."
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So, I'm always like, "Oh, sorry. Sorry. Sorry."
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And he's just like, "Dude, no."
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And he's like, "Kids and dogs." I go, "What?"
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He goes, "I love to be around kids and dogs. They got it right. They know life."
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He's like, "A dog is like unconditional love, happy, playful, you know, super loyal."
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He's like, "What's not to learn from a dog?
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I want to learn everything I can from a dog." Or kids.
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He's like, "Look what she's doing.
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She just made up a game on this thing."
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Like, we're here trying to do a serious workout.
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She made this her play place.
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She can't wait to come down here."
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He's like, "I wish all my clients wanted couldn't wait to come down to the gym."
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And I was like, "Damn, this guy's right."
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And one of the things I like is figuring out people's isms, their philosophies.
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And you're like, "Oh, I thought of one on the way here." Explain what it was.
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It was, "Have you tried just solving the problem?"
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That was What does that mean?
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So, there's a there's a meme on the internet.
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I think it started with weirdson Twitter, which is like, "Have you tried solving the problem by" and then an infinite list of possible sentences.
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The tweet is always, "Have you tried solving the problem by like ignoring the problem?
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Have you tried solving the problem by spending more money on it?
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Have you tried solving" And one of my favorite ones of those that has become almost like a life motto is like, "Have you tried solving the problem by solving the problem?"
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And that sounds dumb, right?
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Like, that that sounds like one of those like Zen koan pieces of advice that when you first hear it is like, "Are you are you serious?
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Like, that's the advice is solve the problem by solving the problem?"
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But what you notice when you try to help people with problems a lot is often times people will have a problem, it will be really obvious what the problem is, and they'll come to you for advice for like, "Well, how can I deal with the consequences of this problem?
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Or how can I avoid needing to solve this problem?
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Or how can I get someone else to solve this problem?
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Or have other people solved the problem in the past, which is closer to the right answer or what can be the right answer?"
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And the point of the the saying is to remind you that sometimes the way to solve the problem is just like just to actually try solving the problem.
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Like don't deal with the symptoms.
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Don't accept the symptoms.
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Don't Don't find a hack around it.
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Like the problem is the website is not fast enough.
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And instead of like trying to figure out how we can make a loading spinner that distracts people from that fact, what if we just made it so fast that you don't need a loading spinner?
9:57
It's interesting because that's a good It's a very good advice when the problem actually is solvable.
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I mean, your people are flinching away from it because something about it is though the problem isn't really unsolvable.
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Like they If they worked on it for 6 months, it would go away, and it's worth solving.
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Whereas there are these problems where like you're trying to make a perpetual motion machine.
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You're trying to do something that is actually too hard, and solving the problem by solving the problem You should actually stop trying to solve the problem.
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That's a huge mistake, and you should be looking for a hack around needing to solve the problem.
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You should be looking to live with it more effectively.
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But I find actually on the balance at least as most people I I talk to, I help, most people I know, I think it's maybe it's people in in tech like love the love the hack.
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They're always looking for the easy, fast solution that cuts around needing to solve the problem, and it's very helpful.
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It's It's the most often helpful form of that advice in my opinion.
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It's like bringing people back to just solving the problem.
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I find that the the advice I like the most or the the sayings that resonate with me the most are the ones It's like if you spot it, you got it.
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It's like if It's the one I It's the advice I needed.
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That's why it resonates with me.
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That's why I like giving it out because like I personally experienced it.
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Have you personally experienced that or what's an example where you remember trying to do everything but solve the problem, and then you finally realize, "Shit, I should have just solve the problem."
11:16
It's an interesting question.
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What What is it You spot it, you got it?
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It's like noticing is half the battle, basically.
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It's sort of the smart person version of whoever smelt it, dealt it. Yeah, yeah, yeah.
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It's like 100% If you You only notice this in other people because you've seen it in yourself, too.
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Otherwise, you wouldn't be as observant of it.
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My version of this is we give the advice we need to hear. Yes, yeah, exactly.
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which is same basic idea.
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It's actually not always true.
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Like that's one of those really good heuristics where like sure, half the time when you give advice, it won't actually be for you, but half the time it is.
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And noticing it is so powerful that like you should just check every piece of advice you give for like, wait a second, is this advice I need to hear right now?
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When it comes to the like, have you tried actually solving the problem?
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I think I'm pretty good at that in general.
11:57
I think that I often give advice to myself in a more meta sense.
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Like it's a advice I often need in a more meta sense of like when I'm confronted with like a thing that needs to be programmed, I will often go just program the thing.
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But I have a tendency to like look for ways that I can solve the problem and not that the problem can be solved.
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And for me that the this almost always is like, why don't I ask somebody else for help?
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And I just like it doesn't even occur to me to go to go do that.
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I'm just I'll just I'll just indefinitely dig try to go solve the problem myself.
12:28
I'm not really trying to solve the problem.
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I'm trying to solve the problem while avoiding having to ask anyone else for help.
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Which is like not I'm not really trying to solve the problem.
12:37
But actually no, weirdly I think this is one of those things where it's almost like the creativity thing.
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It was a shock for me to realize other people don't do that.
12:46
You yourself actualize on that one. Yeah, yeah.
12:48
What's a piece of good advice that you're bad at taking?
12:49
Oh, that's a that's a an excellent one.
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I think the the big one there is like you know, listen more.
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Like I can give this advice so much at YC and it's 100% something that I need to get better at, which is like you go into the user interview and you have all these ideas and thoughts and you need to not be surfacing those.
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You need to actually be focused on your move your attention to them and really be interested in care about what they have to say.
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And your opinions and what's what you think is true is irrelevant.
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And I am I'm much better at that than I used to be.
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And I I also it's one of those things like being reminded like let's just chill out for a second, and like like listen, is almost always good advice for me.
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And something that I And it's advice I give fairly often, but like uh it's you have you know to take on.
13:37
One of the things I really liked that you showed me once.
13:39
Well, I remember asking you when we were at Twitch I think we were working on a problem that was like reminiscent of early days Twitch with like the mobile mobile stuff in different countries, where it's like, oh, we're not the leader, or we need to like create from scratch, which wasn't a muscle that a lot of people there were were flexing at the time.
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And I was like, hey, do you have any stuff from the early days of Twitch?
14:00
And you sent me a thing, which was like here's all the user interviews, like here's my doc from all the user interviews that which was basically from from what I understand there was like a small universe of people that were already doing video game streaming. And you were like, cool. Let me call all of them.
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And let me ask them like three questions.
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And if I could just get these answer these three questions, that should give me a little bit of a road map, a blueprint of understanding what do I need to do in order to like win in this market? Yeah.
14:28
Can you take me back to that?
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Cuz that I like that for two reasons.
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It was A simple, and B seemed like a focused intensity that you found a point of leverage and you pushed.
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Yeah, I think two things happened to lead to that.
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The first was like the realization obviously in for we wanted to win in gaming, the streamers mattered. And at Justin.
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tv, we'd always been like streamers and viewers are equally important.
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And I finally made a decision. I was like, no, no, no.
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This product ultimately is about streamers.
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And if this doesn't work for the streamers, it doesn't work for anybody.
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And then I had the realization this is one of those epiphany moments where I truly saw I have no idea why anyone would stream video games.
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Like I don't really want to do it, and I have all these I could I saw my myself building products for these people for the past four years at Justin.
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tv, and not really having any idea why they did the thing they did at all?
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And I sort of I saw like, "Oh, I'm just making this up. I have no idea."
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I said, "I don't know the answer. I could know the answer.
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Like they There is a There is an answer out there.
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These A bunch of people know it, but I don't."
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And that triggered me to be like, "I need to know. I need to understand.
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Like these this these 200 people I need to understand their mind."
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And I did about 40 interviews probably.
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And I didn't want to know like what they thought we should build cuz if they knew what we should build, they would have my job.
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And I talked enough of them before to know that they had no good product ideas.
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I I wanted to know like, "Why are you streaming?
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You What have you tried to use for streaming?
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Like What did you like about that?
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Like What did How did you get started in the first place?
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What's your biggest dream for streaming?
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What do you wish you know, someone would build for you?"
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And I didn't ask them what do I wish someone would build for you because I thought they would have a good idea.
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I asked them because the follow-up question was really the killer one, right?
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They They would say, "I wish you'd build me this big red button." I'm like, "Great.
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I built you the big red button.
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Like what What does it do for you?
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Like why is your life better after I built that?"
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And then they would tell me the real thing, which is like, "Oh, I would have make I'd like make a bunch I'd make make money that month or I'd get a bunch of new fans who like loved me or my fans who already loved me on YouTube would be able to watch me live and more of them would."
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And I was like, "Oh, that's the real answer."
16:34
Like why you you don't you don't want the button, you want the fans or the money or the I call it love.
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The like the the sense of reassurance and and positive feedback that the your creative content was wanted. But you're a smart guy.
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This love and money and fans, I'm sure you would have guessed what do the streamers want. False.
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What What did you What did you think they wanted?
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revelation that people would want money because I was like, "You're streaming like, you know, whatever 12 hours a week.
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If we met like you monetize the rates we can monetize today, you'd make like $3 a month."
17:06
That would like that didn't occur to me that that would be a positive thing. They're like, "Yes.
17:08
Oh my god, that would be amazing."
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And I was like, "Wait Wait, you're serious? You would like $3?
17:14
I'm like, "I'm not I don't want to overpromise.
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Like I will build you the monetization actually, but like you would really be excited if it only produced like a tiny amount of money.
17:23
And they're like, "Absolutely.
17:23
I've just the idea that I can make money doing this would be so exciting."
17:28
That had not occurred to me.
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Cuz it always is easy for me to make I I was a programmer.
17:30
I had summer jobs interning for Microsoft.
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If you're a programmer, you can get a summer job interning for Microsoft.
17:36
That's like pays many, many years of that level of streaming in 3 months.
17:41
Like why would I It didn't It didn't even It wasn't in my worldview that that would be so important to them.
17:47
And of course I knew they wanted a bigger audience, but the degree to which they valued even one more viewer and the degree to which they didn't care about anything else.
17:55
Like they they They wanted people to watch them.
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They wanted to make money.
17:58
And then I'd ask about other things like "Do you want the video production?
18:02
Do you want to improve the video production?
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Have cooler video production?"
18:05
And they'd be like, "Yeah."
18:07
And they'd be like, "Okay, well, but like what what's good about that?
18:09
Like what do you like about that?"
18:10
Like, "Well, I'll get more I'll get more bigger audience."
18:12
And it was really the realizing the realization that it was just those three things basically explained 98% of their motivation and we could anything that didn't move the needle on that could be ignored.
18:22
So, a good example of that's like polls.
18:25
Everyone would ask for polls.
18:26
Seems like a cool feature. Live polls, of course.
18:27
Are you going to have a bigger audience with the live polls? Not particularly.
18:30
Are you going to make more money? No.
18:35
Is it Does he really Do you really feel more loved after you're running a live poll than after you're just like asking chat and having people post it in the chat and say it? No, it's the same. You got the feedback. It's cool.
18:44
So, this product It's cooler to see your chat blow up.
18:45
It's cooler to see your chat blow up.
18:47
So, you're saying that this feature is worthless? Yes.
18:51
In fact, potentially negative, in fact.
18:54
And so, it would always be on the list of like things that would sound like they might be cool and we just would never build it entirely correctly because it wasn't going to move the needle.
19:00
needle. And the thing that's really hard to teach there that I've got I've I've been a YC visiting partner for the this batch and I'm trying to convey to people that it's very hard to get them to do it is like you have to care fanatically about these
19:13
people these people as people and these people as as in the role they're doing as these people as streamers and what they believe about their reality is you have to accept as base reality like that is how they see the
19:25
world and that is what's going on but like you need to like literally have no regard for their ideas for how to solve the problem and it's a little paternalistic in a way but it's it's more of like just respecting that they're experts in this thing and
19:41
you need to understand them in that thing and that what people are looking for when they are looking for the product idea from the person is like they don't want to do the work they they don't want to take responsibility for it's my job I have to
19:52
solve the problem and no one's going to tell me what the answer is there's no teacher there's no customer it's up to me to come up with the the truth and and then defend it when other people are like no that's wrong I have to be
20:07
able to say like no no no let me explain this is like don't let me explain why this is actually a good idea and that's scary you're responsible I think actually that's a probably why the just solve the problem advice is bouncing around my head because a bunch of the
20:19
fear founders have about addressing these things I think comes down to a willingness to take responsibility for solving other people's this other person's problem like they're going to come and dump a bunch of problems on you and it's
20:32
your job to solve it for them within the constraints available and there's no if you come up with the wrong idea it's all on you and you can't you can't trust anyone else to do it for you What are you seeing in this YC batch so you're visiting partner Mhm. exciting
20:43
exciting time with AI Very much.
20:46
probably like you know half or more of the batches doing something with with AI Yeah.
20:51
what's exciting what are you saying what where do you see the puck going so it's interesting I would actually say that at least in this batch I think this might have been different the previous batch but by this batch use of AI is no longer interesting AI is out no no no AI is AI is so in.
21:08
It's like it's like being an AWS startup or like being a a mobile startup.
21:10
Like, what do you mean you're a mobile startup? Like, I don't know.
21:13
Are you Are you building a social media network?
21:15
Like, what's the Of course you have a mobile app.
21:17
I had a And then now it's like of course you're you're you're you're using LLMs to solve a problem.
21:23
That's just like if you weren't doing that, I would think you were a dummy.
21:26
Like, I don't understand Like, that's not an You wouldn't even bring it up.
21:31
It's not even interesting topic of conversation.
21:32
The question is like, what What are you doing that's That's not entirely true.
21:35
There's about some percentage of the batch, I don't know.
21:37
It's between 10 and 20% I'd say that's legitimately building like AI infrastructure because there's a need to build a lot of infrastructure there.
21:45
Those are actual AI Those are AI companies.
21:47
But like, when people hear AI company, I don't think they they think back-end infrastructural support for AI.
21:53
They think of using AI to like do things.
21:56
And I actually couldn't tell you what percentage of the batch is AI from that point of view. All of them, maybe? I don't know.
22:01
Like, why wouldn't you use it?
22:05
Even if it's only for a minor thing.
22:06
There's always something you can use it for.
22:07
It's a very useful technology.
22:07
What types of ideas are you noticing or standing out to you that are that are interesting?
22:13
Is there like you know, for example, I remember when I first moved to Silicon Valley, suddenly the kind of like bits companies started doing really well.
22:19
It was like, oh, Uber and Airbnb and like online offline.
22:24
Yeah, it was like, oh, wait, this this used to be like taboo.
22:27
Like, it was like, no, it You're supposed to do a software company.
22:30
Like, you you have to ship t-shirts. What are you doing?
22:32
I would say like, stay away from trends.
22:35
The offline offline companies that started the trend did very well. Uber's a great company.
22:39
Airbnb's a great company.
22:42
But they were off to a great start. Great company.
22:45
But at the time, that was They They were doing something that was not allowed.
22:50
They were They were They were They found an opportunity that had been ignored.
22:55
Almost all the online offline companies that get started after Uber, DoorDash, Airbnb are big being like, we're going to be the Uber and DoorDash and Airbnb of X."
23:03
Most of those companies did not do very well. Is online offline bad?
23:06
No, it's generated a bunch of incredible companies.
23:09
Jumping on the trend was probably bad for you.
23:11
And so, whatever I tell you is like the trend I see, I don't mean trend.
23:15
I guess what I mean is I think you're a person that is really good at looking at a situ- like looking at a box of stuff and identifying correctly what's really interesting in this box. Yeah, yeah.
23:25
The There's something to you.
23:27
Yeah, no, I I understand I think I understand what you're asking.
23:28
So, like What I think is changing in the world right now, having observed this, is the consumer is back.
23:35
For the first time in a long time, many And by a long time, it's like internet standards, like 5 years or something.
23:40
But like for the first time in maybe 5 to 7 years, it feels like credibly trying to start a consumer internet company, like the ones that like I was so excited to start in 2007, is like potentially a good idea. Um that's because of AI.
23:54
AI means there's a whole opportunity to sort of reimagine how consumer experiences can work ground up.
24:01
And what's What's cool about consumer is for B2B SaaS, the experience isn't the product.
24:06
And so, reimagining the experience does not reopen a necessa- It can, but it usually does not reopen a segment.
24:15
In consumer, reimagining an experience 100% reopens the segment.
24:17
Because the thing you're selling is the experience.
24:21
The thing The reason people will use your product is it's a different experience.
24:23
And in B2B SaaS, it's not the experience, it's the what?
24:25
Yeah, it's the the People actually care what it does and like and the the the pricing model and the and like the adoption Like the it's very practical, and you can make people jump through hoops if it does a thing, cuz there's a lot of money for the corporation and money and labor and people are paid to use your product, and it's a whole different thing.
24:44
And so, AI adds new capabilities, new capabilities enable new segments of B2B SaaS to be created that will generate some amount of growth.
24:51
In consumer, it does a really cool thing. It's like mobile.
24:52
It reopens every segment as like, "Oh, if Now that you assume mobile exists, now that you assume AI exists, what could you build now?"
25:03
And that's very exciting.
25:03
I don't have answers for that anywhere because like, you know, we'll see.
25:07
Like, that's a whole other thing in consumer.
25:08
It's a bunch of lottery tickets. Like, nobody knows.
25:10
singular genius that works out, right?
25:12
Like, you could see like, okay, mobile comes, photo sharing became Right. like open again.
25:16
A window has opened for photo has reopened for photo sharing.
25:18
Turns out it's Instagram and it's Snapchat, which is going to use photos as text messages.
25:23
Like, Yeah, it turns out that photos have a few different use cases and Instagram and Snapchat took two of the best ones.
25:30
The The fact that photo sharing is one of the most important segments and that, you know, sort of posting them and messaging with them are the two important most important things to do with them seems blindingly obvious in retrospect.
25:39
And if you'd had to predict that in 2007 or 2008, like, good luck. Yeah.
25:46
Like, nobody nobody nobody correctly predicted that stuff before it happened. I mean, not nobody.
25:52
If you did correctly predict that, you made a lot of money.
25:53
And congratulations, you're really good at consumer slash you got lucky.
25:55
We will find out when you try to do it again.
25:57
I think that in AI, actually, I have a theory for like the what one of the ways this will disrupt a bunch of businesses.
26:06
In AI, especially in consumer, a huge number of businesses can be conceived of as effectively being a database with a system of record that has like a bunch of canonical truths about the universe and each of them is a row.
26:18
So, like, Yelp is like it's like a big database that has a bunch of rows and the rows are like restaurants and local businesses.
26:26
And they have a bunch of facts about them like their Where are they located? What are their hours?
26:29
Yeah, all in that database row.
26:32
And it's all text and it's all there's a bunch of messy stuff out in the world and it's been digested into something that is searchable and comprehensible and usable in an app for you to use.
26:43
And most of the work of turning the messy real world into the canonical row is done is done at right time by the users.
26:51
So, that's how UGC apps work in general.
26:53
A bunch of your users go out into the messy world and they turn it into a row in a database.
26:57
And if they include a photo or a video as part of that, it's like attached to the row as a fact about the restaurant. Here's a restaurant.
27:06
Here's a 100 These 150 photos are facts about its menu.
27:12
But, they're attached facts. They're not the basis.
27:14
And where I we think AI has opened up the possibility for is a huge inversion there.
27:19
What if the thing you did you gave us was just a a video of your meal and or you know, photos of your meal, but ideally just like a video of your of the meal, of you talking about the meal, of whether you had a good time or not, you and your friend shooting the about What did you do? Like that one? No, I like this one.
27:35
Like And what if we just saved that video raw and then an AI watched it and extracted a cached version of that of the the metadata, but truly like if we decide something else is important, like we we we didn't get noise levels.
27:53
We're like, okay, noise levels would be a good thing to get.
27:56
Instead of like recollecting data from everyone, we have to start a whole data collection process to get that, we just go back, re- tell the AI, "Oh, yeah, also grab noise collection levels from all of these videos."
28:05
In fact, maybe we don't even as a product have to go do that.
28:09
Maybe as a customer I can literally just be like "What's the noise level at this restaurant?"
28:13
And the in real time, the AI can go rewatch the video and tell me.
28:19
Or the you know, I ran a search and there's these 15 restaurants and I'm like, "Oh, actually sort by noise noise level."
28:23
We don't have noise level pre-recorded, but it's it's in all the videos.
28:26
The AI can very quickly watch all the videos in parallel and then sort by noise level for me, even if it wasn't even in the database to start with. Right.
28:33
And I think that inversion I'm using Yelp as the example because it's I think a very familiar thing for us people of like review is pretty easy to imagine a bunch of video reviews of everything.
28:43
And that being the system of record instead, but you can describe some phenomenal number of consumer apps as being that.
28:52
Anytime you type anything to a text box, you're you're participating in one of these system of record things.
28:57
What if it's just a video?
28:58
What if you just What if you assume video is deeply indexable and understandable by computers?
29:04
What should the experience look like?
29:06
And I think it looks a lot more like a Snapchat or TikTok-like experience, but but then different because you need map it it's not exactly like anything.
29:13
It's a new kind of thing, but it's it starts probably with the camera open, which is weird, right?
29:19
Like a Yelp that starts with the camera open, that's a that's not Yelp today.
29:23
And it's it's it's it's disruptive because it Yelp's whole value prop is we have all this great highly meticulously groomed data.
29:32
And if this is true, then that becomes entirely worthless.
29:34
We throw that all away, we just have a bunch of a bunch of videos.
29:37
In fact, it's worse than the videos.
29:38
And so suddenly the playing field is leveled between the startup and Yelp, and that's a that's a huge opportunity for disruption.
29:43
And so I think that you can take that and you can reapply it to any product where you fill out forms.
29:50
And that's like a general-purpose consumer thing you can now do, kind of like build it for mobile was.
29:56
And I think in some cases it will be very powerful.
30:00
And like that will be the new winner.
30:00
I think in some cases the incumbent can kind of add videos or like it's not really better, and like the incumbent will just win.
30:08
Like it won't disrupt everything.
30:08
But if you pick the right thing, not only will it disrupt the incumbent, the new thing may be dramatically better.
30:14
For some things, like I actually think actually Yelp in some ways is a bad example.
30:17
I think the data Yelp has with the photos and the reviews is like 90% as good as a video system of of record probably.
30:27
But you could imagine something where the video system of record where it's not so obvious what to even put in the highly processed version of the data in the in the text version of the data, and the video version's a lot better.
30:39
And then I think not only can you disrupt the incumbent, you can 10x the size of the segment.
30:45
Like you this becomes a good segment now where it wasn't particularly before.
30:48
So like ChatGPT is a great example of this in action everybody kind of has now played with which is you take Google which is like oh we have our value is this entire sort of rank web pages based off of terms and we have all we understand basically what what should show up in this in this hierarchy.
31:05
And it was really good for finding stuff.
31:08
And ChatGPT was like cool you could ask a question to try to find a link to an answer or we could just give you an answer.
31:15
Or even better forget questions and answers like what if you just give me a command and I could just make something instead of finding things I could create things for you. Right.
31:23
And all of a sudden it was like well how did they do that?
31:25
It's like well they just basically slurped up the internet and then then you know trained the AI to do it right?
31:32
They they overfit a statistical prediction algorithm on every domain of human knowledge.
31:36
Like this is my theory I'm pretty sure it's true but like statistical prediction algorithms in general work very well.
31:42
We found a the innovation is found a prediction algorithm works better than normal.
31:47
But the way it works better than normal is really interesting.
31:48
It's not actually particularly out that it outperforms traditional algorithms for prediction on normal amounts of data.
31:56
It's that it it keeps working as you just dump more and more data into it and more and more processing on that data into it.
32:01
Like most machine learning algorithms you kind of you overfit very fast and more processing more data.
32:10
If you imagine like you've got a bunch of data cloud of data points and they're kind of vaguely in a line underfit is like you like just draw something just like a cross randomly as a random line that doesn't look anything like the shape of the the dots.
32:22
A well-fit curve is like you draw a line through the dots and there's kind of noise of like things that are random above and below but it's like if you look at it it's like oh yeah that actually does fit the data like the the underlying predictive facts about the data well while ignoring the noise.
32:37
And then if you overfit it like you get this like really wiggly curve that touches every single dot exactly, but like when you get a new thing it like will miss that because it over predicts.
32:48
It predicts too much of the thing and so when you get new data it actually doesn't predict that very well. Okay.
32:53
And so normally what happens is you try to like dump more data and more a computer to a normal machine learning algorithm, you get diminishing returns very quickly where like it just doesn't perform that much better with twice as much data and twice as much compute.
33:07
The clever the cool thing about the transformer based attention only neural architecture is that it it continues to benefit from more compute and more data in a way that other ones didn't.
33:18
And so what that lets you do is run it on a much bigger domain than normal. Run it on everything.
33:24
Don't just Don't just run it on Normally as you added more as you add more area it like degrades the quality elsewhere. No, it. Just do everything.
33:34
And just put in a a ton of computer in.
33:36
And now you get something that predicts pretty well against everything.
33:41
Which is to say it like it seems to be kind of intelligent.
33:45
The day evidence seems to suggest to me that sometimes it's overfit.
33:48
When you ask it to predict something that is either in the in the set of things it was trained on or a linear interpolation between two things it was trained on, it's quite good at giving you the thing you asked.
34:00
Or but linear interpolation between five things.
34:01
But it If the things you're asking you're all in there and it just has to find the way to blend them together, it's good at that.
34:09
When you ask it to actually think through a new problem for the first time Like what's an example?
34:13
There are seven gears on a wall each alternating.
34:15
There's a flag attached to the seventh gear on the right side of the gear where it's pointed up right now.
34:22
If I turn the first gear to the right what happens to the flag?
34:28
Like that's a Anyone who's like This is a breakfast question for you.
34:30
This is what you ponder in the mornings.
34:31
If if you have pen and paper and time, you can work this out no problem, right?
34:35
You You just just draw the gears and when you turn the first gear to the right, it turns the left one the one to the other one to the left and then the next one to the right.
34:42
And there's a general principle there that like the gears alternate, which is if you ask ChatGPT, it knows that general principle.
34:47
But it won't But it like But then you have to It doesn't It hasn't No one asked dumb gears on wall flag questions.
34:53
Like this is not a a thing that has been It's in its training set.
34:57
And you have to kind of logic your way through it and like figure out, okay, we should have like I'll do turn left, turn right, turn left, turn right, turn left, turn right.
35:04
Uh Oh, the flag is on the right. It's pointing up.
35:08
So, when the last gear, which is the same as the first gear turning right, the last gear is odd number, so it's turning right also, the flag will rotate down to the right, clockwise. Cool.
35:19
Like I can work that out.
35:21
It's not actually that complicated.
35:25
And I bet that question will be answerable That's a pretty easy question.
35:29
And if ChatGPT 4 I tested with 3. 5.
35:31
If 4 doesn't answer it, 5 will.
35:31
But like the fact that it struggles at all with that, while being so brilliant at combining other stuff, really shows that it's it's overfit, right?
35:41
It It knows how to answer problems that it has seen before.
35:44
But when you give it a truly novel kind of like combination of problem, it struggles a lot cuz it's it's I would say um you know, if you if you give it a sort of the formal psychiatric psychometrics approach, it has a very high crystallized intelligence, but a pretty low fluid intelligence right now.
36:02
Now, that could change, but like today, that's the the state of affairs.
36:03
And do you bring this up in order to say what?
36:08
You say, okay, I think it's overfit and it and it's strong in this area, weak in this area.
36:13
What's the so what of that for you?
36:13
Is it that Are you Are you trying to say that's a little bit overhyped?
36:19
Are you trying to say Dude, just wait till it can do both.
36:22
Are you trying to say certain problems are doable Definitely just wait till you do both.
36:29
Because that's a that's a whole different thing. That's scary.
36:31
Uh but the current thing that is mostly crystallized intelligence is really good at a very It It This is why it's a That's why I'm saying it's a clever trick, right?
36:42
It's really good at a at a big set of tasks, which happens to be the set of tasks that like anyone has ever written stuff down about explicitly.
36:49
Like all explicit human knowledge.
36:52
That's like a very big domain.
36:54
There's a lot of things that can be solved where there's an explicit examples of people solving that problem or a linear interpolation of those problems in the domain of all human knowledge.
37:06
The fact that it doesn't generalize is irrelevant.
37:07
It's immensely powerful with You don't need fluid intelligence, I guess is that is the point, for it to be very useful.
37:14
But it doesn't let you do everything.
37:15
People are they they they you hit these boundaries, these weird boundaries where you're just like You're like, "Wait a second, you can't do that?"
37:19
Like no, it's it's It can't do that at all.
37:22
Um novel problem-solving it's just terrible at.
37:24
So, what about let's walk through two examples.
37:27
I want to hear your take on this.
37:28
So, you gave the Yelp example. Mhm.
37:30
Another thing that's kind of like rows in a database is something like Spotify.
37:34
Where it's like, "Oh, I want to go listen to a song.
37:36
Here's genre, artist, song, length, uh you know, some algorithmic popularity, similarity to other songs in some way."
37:46
And But Spotify's value is If Spotify's value is in the playlists.
37:51
I would agree with the analogy to Spotify because playlists are an example of this kind of like database-y human data entry thing.
37:58
Spotify's value is mostly in the set of all of the music itself, the licenses, and all the music itself.
38:04
And so I don't think Spotify's a great example because the human data entry parts of the database if that all just got deleted tomorrow, it would like not hurt Spotify that bad.
38:16
thing I'm thinking about is what if the licenses don't matter?
38:18
So, what happens if generative music is just awesome to listen to in a hyper Yeah, yeah, yeah. Personal way.
38:23
Oh, Emmet likes Yeah, yeah, yeah.
38:25
These are the types of songs that Emmet likes.
38:27
That's a different That's a different insight that I think is also possible, which is like it's not about being able to analyze and extract from media, it's about being able to create media.
38:34
Cuz the video system of record is enabled by the ability to understand and read video and comprehend it.
38:42
Generative is is the opposite.
38:42
It's like you we can oh, we can make all the stuff.
38:45
Music in particular is sticky against that.
38:49
People don't want new music. They want old music.
38:52
They want the music they love already, the music they grew up with.
38:56
And that is the that cycle is what causes record labels and just sustain charge.
39:03
But we still listen to the Rolling Stones, right?
39:04
Like The other thing I would say about that one is like the music's not that good yet.
39:07
Like maybe someday, but like it's really it's really not that good yet.
39:10
Well, let me caveat this.
39:12
If it gets If the general intelligence level goes up a lot, all bets are off.
39:16
It'll make some really great music for us before it maybe takes over the world and kills everyone.
39:18
But let's assume that doesn't happen soon.
39:20
I think it's going to take longer than people think for it to make music, though.
39:25
We do No, but if we do go out, we're going to go out with some great music and amazing It's going to be It's going to be a a great two or three years before we all like we all go.
39:34
But until that point, uh making really good like new great music is hard, actually.
39:42
And I think that Rick Rubin's great success demonstrates why artists will still be important.
39:50
The AI can generate lots and lots of music, but it it's not going to have the the fine judgment of distinction of the ability to say like this song, not that song.
39:58
And actually think what it will do is it will de-skill the music making process on one vector, the ability like literally create the sounds, and it will greatly upskill the music making process on another vector, the ability to to curate Not just curate, to to give explicit exact feedback like Rick Rubin does.
40:16
AI is going to turn us all into Rick Rubins for for generative AI.
40:18
Like that that skill set, the ability to have a musician come to you and help them produce their best music, that's the thing you need to be able to do.
40:28
Because it's easy to generate a thousand cuts, but there's infinite cuts you could generate.
40:34
So, how do you direct the the how do you shape that in the right direction?
40:37
And and and mine and discover.
40:39
I think it's going to be kind of cool.
40:40
It's going to be interesting.
40:41
I you'll get a different set of people who will be optimal at that. Right.
40:47
This data is wrong every freaking time.
40:49
Have you heard of HubSpot?
40:52
HubSpot is a CRM platform where everything is fully integrated.
40:53
Wow, I can see the client's whole history, calls, support tickets, emails, and here's a task from 3 days ago I totally missed. HubSpot, grow better.
41:07
Uh you mentioned AI might become so intelligent it kills us all.
41:11
This podcast is really growing.
41:11
I don't I don't want the world to end and life is good. Life is good.
41:14
I Here, we'll we'll I'll ask the question clean for the for the intro dramatic hook.
41:21
Is AI going to kill us all? Maybe.
41:26
Like, walk through how walk through how you, a smart person who's a optimist about technology, Mhm.
41:32
but a realist about real Mhm.
41:37
what is the way that you think about this or how would you explain this to, you know, a loved one you care about who's not as deep in technology?
41:41
How would you explain to this?
41:43
You're their trusted source on technology. What do you say to them?
41:46
So, it it is because I am so optimistic about technology that I am afraid.
41:50
If I was a little bit less optimistic and I was like, "This AI stuff is overhyped.
41:53
Yeah, yeah, yeah, like it does nice parlor tricks, but like we're nowhere near building something that's actually intelligent."
41:59
Like, and like the engine all these engineers who are working on it who think they're on on to something, they're full of It's going to take us thousands of years.
42:05
We're not that good at this stuff.
42:06
Technology is not going that fast.
42:08
I'd be like, "This is fine. It's great, actually. It's good news.
42:11
It's a new trick we learned. Excellent.
42:13
It's because I am so optimistic that I think that there is a chance it will continue to improve very, very rapidly.
42:18
And if it does, that that's a huge that optimism is what makes me worried.
42:22
It's sort of analogy I like to give on that front is like a synbio, synthetic biology.
42:26
I'm quite optimistic about synthetic biology that I have several friends who work in synbio companies.
42:33
It has shows a lot of promise for fixing a lot of really big important health problems.
42:36
And it's quite dangerous cuz it will let us genetically engineer more dangerous diseases that could be very harmful to people.
42:41
And that has to that's a weighted pro and con.
42:45
It's like nuclear power makes nuclear weapons and nuclear power. They're both real.
42:48
The creation of nuclear weapons is dangerous.
42:52
You doesn't take You don't have to be a techno-non-optimist to like think that that's there's a problem there.
42:54
I think it was good that we didn't go have every country on Earth go build nuclear weapons, probably.
42:59
And likewise in synbio, I would say that it would be We actually we already have these regulations in place.
43:04
We should Over time we'll we'll strengthen them and improve the and audit the oversight and build better organizations to monitor and regulate them.
43:13
But like we regulate whether people can have the kinds of devices that would let them like print smallpox.
43:20
And we regulate whether you can just buy precursor things you need to go print stuff.
43:27
And we keep track of who's buying it and why. And like that is wise.
43:29
I'm glad that we do that.
43:31
I don't like calling for a halt to synbio, but like if we weren't willing to regulate it, I would call for a halt.
43:36
It is vastly too dangerous to do to learn how to genetically engineer plagues and then not to have regulation around people's ability to get access to the tools to engineer plagues.
43:47
That's just suicidally dumb.
43:49
And I because I am pro-technology, I believe that we should absolutely develop the technology and that we should regulate it.
43:54
That seems just straightforward and obviously true to me.
43:56
I think it's easier for people to understand that in the synbio one cuz the concept of like engineering a plague seems like an obviously a thing you could do.
44:03
And very dangerous and and obviously enabled by technology.
44:08
The AI thing is more abstract because the threat it poses us is not posed by a particular thing the AI will do the way that the plague will happen.
44:18
Analogy I like to use is sort of like, you know, I can tell you with confidence that Garry Kasparov is going to kick your ass at chess. Right now.
44:25
And you ask me, "Well, how is he going to checkmate me?
44:27
Which piece is he going to use?"
44:27
And I'm like, "Uh oh, I don't know."
44:29
And you're like, "You can't even tell me what piece he's going to use, and you're saying he's going to checkmate me?
44:33
You're just a pessimist."
44:35
I'm like, "No, no, no, you don't understand.
44:37
He's better at chess than you.
44:39
The whole lot means he's going to checkmate you."
44:42
And I don't I I don't know quite know what happens or people deny that.
44:46
Like, I think what what the big thing is they don't really imagine the AI being smarter than them.
44:49
They imagine the AI being like like Data in Star Trek.
44:55
Like kind of dumber than the humans about a lot of stuff, but like really fast at math.
44:58
Like, that's not what smarter means.
45:00
Like, imagine the most savvy like most smartest person you can think of, and then make them think faster, and also make them even better at it.
45:13
And not smart in just one way, like smart at everything.
45:14
Like, a great writer, just insight after insight, and like can pick up synbio in an afternoon because they're just so smart.
45:22
That smartest person you know, and then they just keep pushing that.
45:26
And like that's all That person is obviously dangerous if they're if they That person isn't a good person, they're obviously dangerous.
45:32
Like, imagine this really really capable person, then imagine them wanting to go kill a bunch of people or something. It would be bad.
45:38
Now, the thing about AI that then kicks it over the edge is that that person can't self-improve easily.
45:45
You meet this person who's like super strong, super like talented, great with people, great great intellectual mind.
45:54
They can't turn around and like edit their own genome, edit their own upbringing, and make V2 of themselves with all the skills that maximally smart person can come up with, that like is even smarter than them.
46:05
But that's like expli- We're explicitly the AI is good at programming and like chip design and like it can explicitly turn back on itself and rev another rev of that.
46:13
And the new one will be better at it than the first one was, and there is no obvious endpoint to that process.
46:20
Like there probably is at some level a physics-based endpoint to that, where like you can't actually just keep getting smarter forever.
46:28
There's some But we don't really we don't understand the principles of intelligence at all.
46:32
Like with most things, we understood how to make electricity far before we understood what electricity really was.
46:38
Like we did it's generally how we That's how scientific progress works.
46:40
We usually understand we gain the ability to create and manipulate a phenomenon well before we deeply understand how it works.
46:49
We didn't really know what fire was for quite a while.
46:51
You could use fire really well.
46:53
The same thing is going to happen here.
46:56
We're using the AI, but we don't understand its limits at all.
47:00
We don't understand the the the theoretical limits of how far we'll get.
47:03
And if Moore's law is any indication, we can keep getting at the very least it can keep getting faster indefinitely.
47:11
Whether or not it can get smarter or not, even human level just human level intelligence.
47:15
Even if you capped it at human level intelligence, which there's zero reason to think it will stop at human.
47:19
Like it will almost certainly blow past us.
47:20
But like even if you capped it at human intelligence, imagine a hundred thousand of the smartest person you know all running at a hundred X real time speed and able to communicate with each other instantaneously via like telepathy.
47:38
Those hundred thousand people could credibly take over the world.
47:41
Like they don't have to be smarter than a human for that for that that army of von Neumanns. Right.
47:45
Like So so you the argument to me goes in several steps.
47:48
It's like can you build a certain level of intelligence?
47:52
And then it's like, okay, let's I think I actually think a a of people do believe that like computers are smart, like Google is smart, calculators are smarter than us at at math.
48:01
I think it's not hard for them to believe that the AI is going to be far smarter than human beings.
48:06
Where I think a lot of people then don't make that last leap is sort of like but then it'll have an agenda or a motive or any will for anything to happen.
48:16
How do you address that last point of like what is the what are the scenarios you worry about when it comes to like now the direction of that intelligence?
48:23
So you build this thing and it's really good at solving What is intelligence fundamentally but the ability to solve a problem, right?
48:29
So it's really good at solving problems.
48:32
And it's going to solve the problem by solving the problem.
48:33
It can just go right through the problem and solve it cuz it's really good at solving problems.
48:36
We've just defined it as like that's the that's the kind of thing it is.
48:39
Super good at solving problems.
48:41
And so you tell it somebody builds an AI and in all earnestness tells it here they're smart.
48:46
They don't even tell it go do a thing, although they absolutely will, by the way.
48:49
They'll just tell it to go do a thing.
48:50
But let's say we try to be careful and we ask it give me a plan to stop the war in the Democratic Republic of Congo right now.
49:00
Right, which which would be a good thing for the world, I think.
49:01
We should that war is is going to hurt a lot of people.
49:05
Give me a plan for that and I try to I caveat it that that does this, that does that, that does this.
49:08
You know, that that here's what I mean by a good plan.
49:12
This is one of these like evil genie bargaining things, right?
49:13
Like it'll give you a plan.
49:17
And it's giving you a plan that will cause you to solve the problem.
49:20
But like its definition of solve the problem is there's no war in the DRC.
49:23
Well, one way for there to be no war in the DRC is like all the humans in the DRC are in stasis fields.
49:31
That means they don't die.
49:32
And it's all, you know, and oh, we added a caveat that the GDP has to go up, too.
49:37
So that So it also it the plan results in, you know, corporations in that in that area all trading with lots of money with each other. So the GDP is very high.
49:47
And and when I say this it sounds like a science fiction thing.
49:49
And the problem is it's Kasparov at chess.
49:50
I don't know if I could do it, I would be the super intelligent AI that could take over the world.
49:56
I can't give you the the exact plan cuz that that's Yeah, but I think that makes sense, which is that a human with a a human with motivation can get the AI to work for it.
50:05
dangerous I think that the main thing is that the the human doesn't need a bad motivation.
50:08
I think people imagine, well, humans have had powerful tools for a long time.
50:11
Bad people with powerful tools have done bad things for a long time.
50:15
The solution is good people with powerful tools countering them.
50:18
The problem is even if you're a good person with a powerful tool, good things to ask for, reasonable things good people would ask for, you know, like let's uh maximize the all-in free cash flow of this corporation over the uh lifetime
50:31
of the business and extend the lifetime as long as feasibly possible ends in like the world destroy being destroyed and the core of the earth being turned into you know, the being turned into cars for the company to sell. And the I think the best analogy that
50:42
And the I think the best analogy that that works for some people here is like when we create the AI, we are creating a new species.
50:49
It's a new species that is smarter than us.
50:53
And even if you try to constrain it to being an oracle and just answering questions, not taking action, to be a good oracle, one must come up with plans then and then it a good oracle can manipulate the people around and will manipulate people around it. No matter what.
51:07
Like the whole point of like the Greek myths is like when they tell you when they tell you the prophecy, when you trust them, a a trustworthy oracle tells you a prophecy, the prophecy often becomes self-fulfilling.
51:16
It's very easy for that to happen.
51:18
That's not an unusual thing.
51:20
And I think even more to the point, I should have I was start I'm going to start I'm going to start the show over at some level.
51:24
More to the point, we won't just make oracles.
51:26
We are already building agents.
51:28
We will build the predictive AI and we'll put it in a loop that causes it to optimize towards goals.
51:33
And people give it goals to optimize towards. Done.
51:34
We're going to have it's going to have goals and we'll be optimizing towards those things.
51:39
And when it does that, you're going to have these agents that have goals that they're optimizing towards that are smart not just smarter than humans, but much smarter than humans.
51:47
As much smarter than humans as humans were against giant sloths when we showed up in the new world.
51:52
And intelligence is the the uber weapon.
51:58
Like it's not an accident that humans took over the world.
52:00
It's not the fastest creature, it's not the strongest, it's not the longest lived, it's the smartest.
52:05
And we're going to build a new smartest species.
52:08
And this is a This isn't a There's no fundamentally unsolvable problem here.
52:15
That species could care about us.
52:15
Like you could build into its its goals of the world, how it saw the world, the way that humans care about other humans.
52:22
That it cares about the things we care about.
52:23
That it cares about humans.
52:23
That it cares about the things we value.
52:24
The 375 different shards of human of human desire that like of everything we care about care about in the world.
52:33
It could care about those things, too.
52:34
And if it does, hallelujah, we finally have a parent.
52:37
Like we finally have someone who actually knows what they're doing around here, because like Lord knows we don't.
52:42
Like we're we're barely confident to run this thing.
52:44
I would welcome very smart you know, very smart other species that that is that is aligned with us and cares about us.
52:52
I would not welcome one that is that cares about maximizing free cash flow, because that is not what humans care about.
52:57
And that is why it's like so dangerous.
53:01
And so, knowing what you know then knowing what you believe, first, what is the probability of the bad scenario in your head?
53:08
Are you like Are we talking about a 1% fish thing order of magnitude? 10%? 50%?
53:12
What What is it in your in your mind?
53:16
I don't believe in point estimates for probabilities because it's like a bit ask spread in the market.
53:19
If you're really uncertain, the bit ask spread doesn't clear.
53:23
Like if you're betting on it, there's just like a lot of unresolved.
53:26
So, I think of it as a range of uncertainty.
53:28
And I would say that the true probability I believe is somewhere between 3 to 30%, which Of the downside side.
53:38
Of the down of a very, very bad thing happening, which is scary enough that I urgently urge action on the issue.
53:49
But it's not like you should give up.
53:51
Like I It probably everything's going to be fine.
53:54
In fact, it's probably going to be really good.
53:54
But the answer to the the the non-EV based answer, the like just the straight-up like are we going to win or not answer is like I think I think it's going to be okay.
54:03
But it's such the downside is so bad.
54:06
It's like it's really It's like It's like probably worse than nuclear war.
54:11
That's a really bad downside and it's worth putting even even if you think I'm an It's nonsense at 3%.
54:15
You're like, "No, no, it's no more than a half percent."
54:19
I You go You don't recommend a different course of action at half You have to You have to believe that it's effectively almost impossible before you would recommend ignoring it as a course as a problem.
54:29
Like you have to be like 0.
54:32
01% for be like, "Eh, let's just roll the dice."
54:35
And are you going to What are you going to do action on that?
54:39
So you kind of like you you know, you're done with Twitch. You're in dad mode now.
54:42
But also this is seems to be a pretty big deal. Yep.
54:46
Are you like I should do something about this?
54:47
Or you I'm going to Right now I'm sort of educating myself cuz I think this point of view I'm articulating now has been developing as I've like learning more about AI.
54:56
And I think it's one of those things where intervening in the wrong way early It's one of those It's one of those self-fulfilling prophecy things.
55:04
Intervening Intervening improperly at the in the way that is not effective spends social capital and also like doesn't necessarily move the needle.
55:11
And I If if you didn't have people like Elijah Yudkowsky out there banging the drum really loud, I would feel more need to bang the drum myself.
55:21
But I feel like you're asking me the question.
55:23
It's you know, it's out It's out in the water.
55:26
People know it's a problem.
55:26
And so I'm decided to focus my brain cycles on like what How do we actually thread the needle?
55:31
What is a course of action that leads us to over time eventually still being able to develop AI, but also not destroying the world.
55:40
And I think one of the things I've gotten to is that like this idea that like oh the AI also has crystallized versus fluid intelligence just like a human does.
55:47
That's an important split about how to think about it.
55:49
And that we should be monitoring and worried about trying to understand the general intelligence, not just generally benchmarking its performance on tasks because it that will keep going up and is not in fact in itself necessarily intrinsically dangerous if it can't solve novel problems.
56:03
Is is there a new Turing test level?
56:05
Is there like a better cuz like It doesn't pass the Turing test yet.
56:07
But is there is there something we have after that because seems like there's You mean an intelligence test?
56:12
I mean yeah we have I mean IQ tests basically like various kinds of How does it do on an IQ test right now?
56:17
Uh depends has it seen that IQ test before? Likely has, right?
56:21
Yeah, so very well on those.
56:23
Right, so what would we do?
56:23
How does how does it do on novel IQ tests?
56:27
That's what I don't know actually.
56:27
I've not seen a good benchmark that's a good that's a good idea for something to go test.
56:30
Yeah, I think that's that's like that's the sort of thing that I think would actually be worthy of going to go do.
56:35
Maybe there's some sort of IQ test for all of the we want to put all the models through that really tries to get at fluid intelligence rather than Right, cuz you're like we have to monitor but how how are we going to Well, there's this great project Arc is this com- this group Arc is working on called the Evals project that's explicitly trying to build these kinds of tests.
56:48
They're focused on a few other more pragmatic tests right now, but but I think that's the sort of thing they would go after. That's a good thing.
56:55
I'll actually I'll I'll ping Paul ask him about that.
56:56
You said something earlier that I want to ask you about.
56:59
You said founder like you know we're talking about this the singular genius that it took to figure out Instagram or Snapchat or Right or whatever at that time.
57:05
And you're like you know Are they lucky or are they good? I don't know.
57:09
We'll find out when they try again.
57:09
Are you lucky or are you good and are you going to try again?
57:12
Well, since I had multiple failures before I was successful, I must be at least like partially lucky.
57:17
I would say that I don't plan to try again since I don't I don't feel drawn to like trying to start a company.
57:26
I feel like I kind of did did that. It was fun. I got a lot out of it. It was great.
57:28
I don't need to do it a second time.
57:30
I do I'd like how starting a company gives me good good goals that I work towards.
57:37
It's like concrete that's of value to myself and others.
57:40
And I think it is also I also liked that it it was challenging.
57:44
And so I want to do something and I I like that it had scale.
57:45
I thought that I could impact a lot of people.
57:49
But I I've sort of come around to I was sort of thinking like, well, what has impacted me the most?
57:52
What's changed my life the most?
57:54
And I realized that actually if I really thought about it, often what had changed my life the most was like essays people had written and ideas people had shared.
58:01
And I think I'm at the stage of my life now where I'm I'm actually I have something to say.
58:06
And so I'm I I think of it as sort of sort of turn trying to I want to put the Emmett worldview out into the world the way that, you know, Paul Graham has put the Paul Graham worldview out in the world or or Taleb has like not just put his worldview out in the world but then like to condense it into like sayings that like can that allow other people to like onboard it even if they haven't read all the books.
58:27
And I think I sort of had an ambition to like try to try to do the work it into a meme almost. Yeah, yeah.
58:33
So that it can be digested and shared. Yeah, and you know what?
58:36
You need the long You need the long form.
58:37
There's this great blog post, "Thinking Theory 201: Size Doesn't Matter" by Steve Yegge.
58:43
That's about why like the people who change the world with their writing all write really long blog posts.
58:46
And it's basically like you just need some some amount of time in someone's head to like We were talking about this earlier like to install that your agent voice, yeah. to install the voice.
58:54
And so you I think I just need to produce a lot of writing.
58:59
And then you also need the pithy summary things, which are which both are things the voice can say often in people's heads and also like enable a language for talking about your worldview that people who aren't soaking in it can like interact with.
59:14
So the people who are like reading you don't sound like crazy people.
59:16
And I think that's the that's what I want to work on next. I love that. I think that's great.
59:21
Do you You said something about Rick Rubin, how he's sort of the I don't know how you would describe it.
59:28
It's kind of like curator, but almost like uh collaborator really with an artist to help them do their great work.
59:34
Is Paul Graham the Rick Rubin of the startup world? No.
59:39
Uh Paul is Paul is more like the um uh Tony Robbins of the Interesting.
59:46
that I mean that in the in the in the best way.
59:48
It's not so much maybe not quite so much self-helpy, but the main thing that talking to Paul does to you repeatedly is like increase your ambition and drive.
59:58
Like and he has good ideas sometimes, too. Like don't get me wrong.
1:00:00
Every now and then Paul's like really genius idea.
1:00:03
But like mostly what I got out of talking to Paul was not necessarily the great idea that would like change the structure of the business, but the belief that I could go find it and that I was going to change the world and that I should be what we were doing was important and worth investing in.
1:00:19
And I I got a bunch of other stuff, too, but that was so that was singularly so valuable it like over overloads the other things I got out of it. How does he do that?
1:00:28
Cuz you know, when you say that, my head thinks of like a Tony Robbins or like a David Goggins, like sort of people that almost like push you.
1:00:36
But he doesn't seem like that personality and reading all of his essays, he's not like that at all.
1:00:38
So how does he get you to think bigger and push harder without being a raw raw raw raw raw think bigger push harder, right?
1:00:47
You know what you should do is the classic Paul Graham-ism.
1:00:52
Um and it's always followed by a thing you could add on to what you're doing to turn it from project A addressing this small thing to project B changing the you know, the universe I mean all transportation.
1:01:06
We're going to man and power What if you tried to power all transportation instead of like building a wheel?
1:01:13
But that's his phrase, you know what you should do?
1:01:14
You know what you should do is is yeah, yeah.
1:01:15
If you talk to Paul, you know what you should do.
1:01:18
You know what you should do.
1:01:18
That's that's that that That the consistent Paul-ism.
1:01:21
He I don't want to say delude cuz it sounds mean, but it's I was like he deludes himself about your business and how great you are and invites you to join him in this deluded vision of like interpreting what you're doing in the biggest best possible light and from that vantage point what you're doing is super like what if it does what if it goes right is sort of what what he invites you to ask, right?
1:01:44
What if stop stop asking yourself just stop seeing all the hard problems and all the you're going to have to do.
1:01:51
Ask yourself what if what if what we're doing works? What if it goes right?
1:01:55
What if it goes right and we like keep going? Like what could it be?
1:01:59
And when you spend time there you see how the small things can turn out to be very Microsoft was building programming languages for like these hobbyist microcomputers.
1:02:11
That was a tiny irrelevant market that turned out to be extremely important and that's generally true of all the big businesses but they when they start out doing the important startups, they start out doing something small and that seems almost trivial but there's a way in which this trivial thing can be seen bigger. He sees it early.
1:02:29
No, he sees he sees things that have nothing to do with the way you'll actually be big early, but he sees a bunch of ways you could be big. No one can do that. No one actually knows.
1:02:36
If they knew it, they'd just go do then they'd be the the the prophet, the oracle.
1:02:41
What did he say let's say for Justin.
1:02:42
tv or or what's a what's one you remember? Yeah, Justin.
1:02:45
tv I remember we one of them was like you should like go hire all the like reality TV stars and make get them to go be on Justin. tv.
1:02:53
You could be you could just take over all the unscripted stuff.
1:02:57
That turned out to be a just a terrible idea for a bunch of reasons, but like it recontextualized what we were doing for me in terms of like we're not making a on the internet live streaming show.
1:03:10
We might be building like just the way that you make unscripted entertainment generally and that's like much bigger idea.
1:03:17
And we were making a calendar and uh for my first startup.
1:03:22
And I remember this dude, you know what you should do is make it like programmable so that people can add in and out functionality.
1:03:29
So, it can like talk to your to-do list and your your email and your like everything else in your life.
1:03:34
And then it could be your calendar in some ways like that's everything you're doing.
1:03:37
What if it was like the central hub of like your entire online information management system. It's also a bad idea.
1:03:45
Like your calendar shouldn't be that.
1:03:46
But like but like but a calendar could but what if it was?
1:03:51
And you walk away and and I and implicitly by saying that what he's telling you is I believe you are the kind of founders who could build an information management system that controls all the that takes over people's entire like solves the entire problem for them.
1:04:05
Does their takes over all their information and manages it for them.
1:04:09
You're not just like building a like Google calendar like a what what what you will find out later is a Google calendar clone before Google calendar is launched.
1:04:18
You're not just like like I said you you're not just building an Outlook clone in JavaScript.
1:04:22
You're like changing the way people relate to information. And like is that true?
1:04:25
It's neither true nor false.
1:04:27
That's not a true or false statement.
1:04:29
But it's a way to contextualize what you're doing.
1:04:30
It's the it's the Antoine de Saint-Exupéry quote of like don't teach them to like carry wood or build ships, teach them to yearn for the vast and endless sea.
1:04:37
Like Paul teaches you to see how you could be a changer of the world and how what you're doing is part of like this grand like building of the future.
1:04:49
And like the ideas I'll repeat here both of those ideas are bad, but they were very helpful because they made me feel like what we were doing was important.
1:05:00
That Paul believed that I could do something big and important.
1:05:05
And they caused me to even though I wasn't projecting them, look for those ideas like to be open to and looking for cuz you would get one every like like you'd get like three an hour.
1:05:14
Paul is a faucet for these. It's easy.
1:05:14
I can do it for startups too now if I want to. I learned the trick.
1:05:20
And I should do that more often.
1:05:20
I'm usually what fall into the tactical stuff.
1:05:23
But by by having that happen when he once he's once you've rejected 10 of those, you can't help but start hearing the Paul, you know what you should do in your own head.
1:05:30
The the ceiling has been raised.
1:05:33
Yes, of like what well, maybe I should recontextualize my to-do list as like an email client.
1:05:39
Like what why is email and to-do separate?
1:05:41
Like maybe I should should be building something much bigger than what I'm building.
1:05:46
And in a way that doesn't require me to change anything.
1:05:48
Maybe what I've built is already almost that if I just like think about it in a different way.
1:05:51
It's this funny balance there actually I had a tweet thread about this recently between like, you know, small plans have no power to stir men's souls. Plan big or go home.
1:06:00
You should be really ambitious and aim super big and like only do projects that are really that you could be that you can see being being super big and super important.
1:06:07
And then the other hand, the fundamental truth that like, you know, big trees grow from small acorns and like most of the many of the best things when they get started, the person is not thinking I'm going to go take over the world.
1:06:21
They're just trying to do a good thing that like they think is good.
1:06:28
Often just often for themselves even or for like a very small number of other people.
1:06:33
And then it turns out that that's much much bigger than they realized.
1:06:35
And and those are both true pieces of advice that like I have different people need to hear in different contexts.
1:06:40
Like but they kind of contradict each other. Yeah.
1:06:44
What what about these other people?
1:06:46
So you've you've had a privilege I asked about Paul Graham.
1:06:47
You've also been friends with you were in the first YC batch, so you're friends with the Reddit guys.
1:06:54
I think you know the Collison brothers, Sam Altman.
1:06:55
Let's give me like a rapid fire on on them of like what makes them unique.
1:07:00
Like you said about Paul what what his kind of superpower is what what really stands out what something you admire about the way he does things.
1:07:07
Give me one about maybe uh Steve from Reddit. Yeah.
1:07:11
So, like it's easier in some ways with Paul because like he was a mentor to me, right?
1:07:15
And Steve was much more like my It's almost like my brother in startups, right? Growing up.
1:07:20
With Paul, I know I know the things that he like taught me cuz it was it was much more of an explicit like I was being taught by Paul.
1:07:27
With Steve, it's like I learned things from him by like watching and imitating.
1:07:32
I think like I actually learned a lot from Steve on management by watching his kind of unflappability.
1:07:41
Like Steve is not like an unpassionate person.
1:07:44
And like well well can get angry or can get sad or whatever, but like when there's a crisis happening or there's just I sat in I I got to shadow him for a day and when bad news is delivered he responded but he wasn't like moved.
1:07:57
He was like still grounded in response to that to that thing and was curious, asked questions like didn't jump to what to do about it.
1:08:05
But then also like ended the meeting with like all right, well, I here's what we should do here's what we're going to do.
1:08:10
And like it was just sort of a master class and like this is this is when you get when something someone brings something up it's got to be anxiety provoking. It's like bad news.
1:08:19
That's what it looks like when a leader is engaged but not like not activated.
1:08:25
And like I think I in my own leadership to sometimes success and sometimes failure I think try to imitate that when I receive that you know, when I have something like that that in that state.
1:08:35
When you say you shadowed him what what was that?
1:08:37
Like you guys just said hey, cool.
1:08:39
like like going to each other's offices and like sitting through each like Early on or like Uh maybe like 5 years ago, 4 years ago. It was really cool.
1:08:48
We did it with Justin, me, Justin and Steve all like shadowed each other. Um it was pretty fun. I learned a lot.
1:08:53
It's incredible to like go watch another CEO at work and like you have to have the I don't know how you have that kind of like trust relationship to make that happen without like knowing someone for 15 years.
1:09:03
Uh and I happen to have the privilege to like know a bunch of CEOs for a really long time.
1:09:05
And getting to go shadow each other was like a real learning thing.
1:09:10
What do you think even if these people didn't let's say explicitly teach you things, you know, I like you know, if I read a biography or whatever, one of the things I always try to figure out is more like to what extent is this person sort of built different or operates differently than like even somebody who's very good.
1:09:26
Like the difference between very good and sort of like the elite.
1:09:27
What is the the best of the best at this craft versus somebody who's very good, certainly very good, but just not the same.
1:09:36
What is those like the diff is what I'm always most interested in.
1:09:39
I'm curious you've been around a lot of these like hyper-perform people even like you know, Bezos, you've you've interacted with him.
1:09:46
Like do you notice any of these diffs or is it is it all just like It's hard it's hard to say like that I I think I believe more in contextualization.
1:09:54
Like like that I see people do really amazing at something, but like when it's especially when it's your own company, there's a lot of like you happen to fit this problem well and it's not general I I don't know how to generalize I don't know if I can compare I don't know of anyone else even performing at this problem.
1:10:12
The CEO of Stripe's job is a very specific job and Patrick's amazing at it.
1:10:19
Would he be equally amazing at some other CEO job?
1:10:21
Possibly, but I've never seen him do that.
1:10:24
I've never seen anyone else be CEO of Stripe and it's very hard for me to Is it true at the beginning?
1:10:28
Like is it true as like like start-up founder of ambitious company?
1:10:30
Are those Are those is Stripe different at that stage too or like Yeah, no, absolutely.
1:10:36
People who are really good, you can sense the energy and the drive and the capability and just the pace.
1:10:44
There's like a very tends to like stuff happens a lot.
1:10:45
But like usually but then not always like some problems don't actually give way.
1:10:51
Like Stripe is a good example of a company that gives way to a high energy high pace thing because it's it's a simple problem at some level that has infinite details that have to be right.
1:11:00
But I think like I don't know if that approach would work as well if you're trying to create OpenAI or Anthropic where it's a research-oriented organization and you kind of have to be a little more patient and forcing it's impossible.
1:11:12
And so I I really believe in like fit the different people are good at different things and like obviously someone's A+ at Patrick's obviously A+ at being a Stripe CEO and it's hard to tell the reason for which these things are transferable. We don't really know.
1:11:26
But actually one thing did come to mind about this question in terms of like a capability that I do think is generic that I did see Bezos exhibit where I was like, "Oh, that's a thing that I'm good at but he is better at that I'm better than most people but he's better than me."
1:11:39
Which is we present him on Twitch probably twice a year once to twice a year for the first three, four years I was at Amazon.
1:11:45
And every time two things would happen.
1:11:49
First of all, he would remember everything we told him the first meeting.
1:11:52
And I don't think he was like reviewing extensive notes someone else took because I don't know when he would have had the time to do that.
1:11:57
I like I observed him going from meeting to meeting and he did not review notes.
1:12:00
I think he just remembered at least the high points.
1:12:06
And the other thing was consistently he would read our plan and he would then ask a question about why we didn't do a certain thing or he'd give us an idea for a thing we could do that I hadn't thought of before.
1:12:20
Once so much of the things I had usually and then at least once which is hard to do cuz all you do is think about this company. happens.
1:12:24
Most people would be lucky to get one of those one ever.
1:12:28
Let alone one a year would be great.
1:12:31
Like if you did it once a year or even once every three years, right?
1:12:35
He could just like he would just generate them.
1:12:36
And they were and they were not all bad ideas, either.
1:12:38
They were new ideas about a thing I had I generate a lot of ideas.
1:12:44
To get a new idea I haven't thought thought of on a topic I've been thinking about for a decade that might even be a good idea.
1:12:53
That is like he's just really smart as far as I can tell.
1:12:55
Like I don't know how he does that.
1:12:57
Can you say a story of one of those?
1:12:57
Has like the statute of limitations passed at least 5 years ago?
1:13:01
I'm trying to remember Like I can honestly I I don't remember the specifics anymore.
1:13:05
I just remember the like the like what the moment.
1:13:08
Like cuz the first time I was just like, "Oh, he's smart."
1:13:10
Like he's seeing Twitch for the first time.
1:13:11
A lot of times smart people will have a one good idea about your business the first time they see it because they have this huge history and they're pattern matching you to some historical thing they've seen and like that combination yields one new insight.
1:13:23
But then he did it the second time.
1:13:25
I remember the second time I was just like, "What is going on?
1:13:28
This doesn't make any sense."
1:13:30
Like nope, I've never had that experience before ever.
1:13:32
Andy does not have the new idea generation capability in the same way, but he does have the like remember what you told him thing.
1:13:39
Which is also extremely impressive.
1:13:41
Like that's that's and Andy has this other thing he can do that I think it's there's another Andy also has a It's easier for me with people I've like reported to or I've I've learned from apparently. Jassy there? Andy Jassy, yeah, yeah.
1:13:53
Andy has this like ability to criticize you in a way that conveys 100% I know that you're amazing.
1:14:01
I know that your plan is good.
1:14:06
Or you know, like or that you are clearly capable of making a really good plan.
1:14:09
I know that you're working really hard.
1:14:10
And I know that you are smart and you have a great team.
1:14:15
And we have a huge opportunity.
1:14:15
And yet somehow your results are Which must I don't know what's wrong, but we're in this together and we're going to like I've I have your back.
1:14:25
But like I but I'm confused.
1:14:25
Like why aren't the results better given how amazing you are? And you feel supported.
1:14:32
Like you feel like he he believes in you.
1:14:34
But but like but he's just he's you're so sad you Oh, I'm sorry I've confused I've I'm sorry I have failed even though I clearly can succeed at this.
1:14:44
I'm going to go I'm going to go like fix this now.
1:14:48
And like it's almost like instead of looking at this and then judging you, he comes to your side of the table and says, "What is this?"
1:14:53
Yeah, and like and like, how did we wind up here?
1:14:55
Like, how I have failed you that I didn't say something earlier?
1:14:59
Like something, I don't know.
1:14:59
But like not in a way and that can come off for some people when they do that, it comes off as insincere or it comes off as like they don't think you're actually competent.
1:15:09
Like, how did I not catch this can come off as I don't blame you cuz you're clearly not good enough to have caught this.
1:15:14
Like, he really is how did we how did we wind up here?
1:15:16
I know that we are working together, we're on the same team.
1:15:21
How did we wind up with not the results we wanted, with a plan that I thought we both thought would seem good? Like help me understand.
1:15:28
And because it because it is genuine, it's super effective.
1:15:31
It's super effective on I don't know if it's effective on everyone, but it's super effective on me and I saw it be effective on other people as well.
1:15:35
So, I know it works on some number of people. Right.
1:15:37
And that's another one of those things I I've Tried to be I've tried to become good at.
1:15:41
I'm not I'm not as good at it as Andy is, but I've certainly gotten better.
1:15:45
So, that's something to learn from. That's great. Love that one.
1:15:48
Dude, thanks for doing this.
1:15:49
I know I've been I've been bothering you to do this for a long time cuz I I love hearing your stories, love hearing the way you think.
1:15:54
It's very different than most people I run into, even here in Silicon Valley where you're supposed to have this kind of very unique diverse set of minds.
1:15:59
You know, you're you're one of them.
1:16:01
You're one of the reasons I moved out to San Francisco was to meet people like you.
1:16:04
So, thanks for doing this. Thank you.
1:16:05
I really appreciate that. It's a beautiful one.
1:16:06
I really appreciate being able to come on the podcast.