0:01
I’m Ian seesaw like and I’m the host of bands playing a show where we explain cult bands and iconic artists by going deep into their histories and discographies.
We’re back with a brand new season at our brand-new home, the ringer podcast Network, tackling a whole new batch of artists from grunge Gods to Powerpuff Pioneers to new metal Legends and many many more.
0:22
Listen, a new episodes, every Thursday, only on Spotify.
Today it is the 100th episode of plain English.
I don’t know how that’s possible.
Thank you to.
0:38
All of you who’ve listened and said you love the show said you hated the show.
I’ve never done this before, hosting a Talk podcast.
Is something I have never done.
It has been a ton of work and a ton of fun.
I’m still figuring things out.
We’re still figuring out what this show is how to balance news and Tech gossip and Future.
0:58
Science and big Society questions and War coverage.
And to be totally honest, there are days.
I think I know exactly what I’m doing and there are days.
I think I know even less than when I started out and the truth is that I think this is just a good moment.
Episode 100 to say to all the folks who have on any medium offered negative feedback or positive feedback, email, Twitter, Reddit, I am reading you.
1:22
I am trying to make the show better and I am really sincerely grateful to all of you for everything that you’ve ever.
You’ve written even the route stuff because you know what sometimes rude people are right.
If you like the show, if you enjoy or have learned from any of the first 100 episodes you can do us one very specific solid.
1:40
You can give us five stars on Spotify or if you listen on a podcast.
Leave a five star review.
I would really appreciate that.
So this show launched in mid November, 20, 21.
Do you remember mid-november 2021?
1:55
It was an entirely different world.
The literal peak of the stock Market, the literal peak of crypto valuations inflation.
Nope, interest rate increases, not really no.
And if T Buzz was huge, I did this show with the New York Times, Kevin ruse my very first podcast episode called the future is going to be weird as hell.
2:15
And it was all about the future Technologies, like crypto and the metaverse that were all the rage last November.
And if you go back and listen, I think you’ll find that our analysis holds up pretty well.
We were, I think Dooley skeptical, that cryptocurrencies and metaverse She is as they existed, we’re going to make a big impact on the world even though we retained a certain amount of curiosity about their long-term potential.
2:38
Well today Kevin reaches back and this episode could have also been called the future is going to be weird as hell or the future, still going to be weird as hell.
But for a different reason, I think the most important story of 2022, more important than anything happening.
Maybe in Ukraine, where the economy is the rise of AI tools.
2:59
That sounds crazy because there are people dying in Ukraine.
There is war, there is famine.
There’s Rising costs for Essentials, like energy and food.
But War’s end inflation, Peters out, interest rates, go up and down.
3:17
But technology can leave a mark on the world.
That doesn’t go away.
The invention of cars in the 1880s, is more with us today, then the Empire struggles of the late 19th century.
3:32
And if you look around the world right now, you could the organization open a.i., they released two of the most important tools, G, PT 3, which synthesizes language responses to prompts and Dolly which creates original art based on any text if this episode is successful, I think you’ll come away with an almost odd sense of how Bizarre and strange and incredible and potentially scary.
3:58
The future could be in a world where these kind of Technologies grow and become more a part of our everyday life.
In today’s episode, we talked about how these AI tools work in the first place, why this summer was a coming out party for this technology why it should thrill us?
4:15
Why it should terrify Us and how a I could change our relationship to the internet to society to truth.
Ourselves for the 100th time.
I’m Derek Thompson.
This is plain English.
4:52
Kevin ruse.
Welcome back to the podcast.
Wow, what a journey it has been.
I am so happy to be here and close the circle on 100 episodes.
Congratulations.
Third episode.
You were my first guest, you are my hundredth guessed.
5:08
It is an honor to have you booked and at the first hundred episodes, if you recall, our first episode was called the future will be weird as hell.
And we talked about implications for crypto and the metaverse since then at the time Bitcoins price was 69,000.
5:23
Now it’s 19,000.
Crypto is crashed.
The metaverse is somewhere, not here.
The markets melted down inflation took over interest rates, soared growth, stocks plunged.
Netflix, It’s on Zoom.
All the pandemic.
Darlings fell down to earth, and also musk, Twitter happened.
5:41
And so, that’s kind of where I want to start the musk Twitter acquisition.
To me, is kind of like a television show that I enjoyed for a few weeks then I was like, wait the showrunners, like haven’t really thought this one through, you know?
Like when you watch a season of TV and it’s like a 15 to 15 like episode Arc but you’re like, this has to admit pitched as a three-hour movie.
6:01
There’s not more than three hours of like actual plot here.
That’s kind of where I got.
To with you on Twitter, but we had this during season finale last week, Elon announced that he will likely go ahead with the deal.
And by Twitter for forty, four billion.
Kevin is someone who tuned out of the show for the last six eight weeks or so.
6:18
What did I miss?
Where Do We Stand now?
Yeah, so we are in this very strange endgame now of sort of phase one of this store.
So sees it, you can think of it as a multi-season Prestige TV show.
6:34
We are now I believe coming to the end of season 1 which is the latest that’s happened is that Elon Musk sent a letter to Twitter.
Very unexpectedly, saying that he intended to close this deal that he was willing to pay.
6:55
The original price, forty four billion dollars fifty four dollars and twenty cents a share for Twitter and that he was going to attempt to close this deal.
Twitter and, and, you know, that happened days before a trial was supposed to begin in the Delaware Chancery Court, where tutor was going to force him to live up to his original intent and by the company for forty four billion dollars.
7:18
So now in the last couple of days, what we’ve seen is that these sides.
There’s obviously a lot of distrust between Twitter and Elon Musk because of you’ll on spending months, trying to get his way out of this deal.
And so Twitter has said, essentially, we’re We’re not, you know, calling off the trial until we get our money.
7:39
And until you buy the company and just yesterday, the trial was stayed, the delery Delaware Chancery Court stayed, the trial and gave the, the two parties Elon Musk and Twitter until October 28th to officially close this deal.
7:56
So either that will happen.
Elon Musk will pay forty four billion dollars and get control of Twitter before October 28.
Or they’ll go to trial and and that process will happen sometime in November.
So three parts.
8:12
Part 1 E Line says I’m going to buy you Twitter part. 2, he says never mind.
I don’t want to buy you part 3.
He says never mind.
Never mind.
I’m actually going to buy you.
It’s very obvious to me why we went from part. 1 2 part to the market crashed, the expected value of Twitter.
If you lined it up with similar companies that were social media companies, Advertising based companies like Snapchat should have been much lower than the Billion that he had pledged to pay.
8:36
He was trying to either wriggle out of the deal or negotiate some kind of lower price.
It became very clear at some point that the Delaware Chancery Court was not going to allow that.
Do you have a good sense of why we went from part to part 3 in the story?
Why did he say never mind to the never mind?
8:53
Well, yeah, that’s the big obvious question.
Here is what caused this sort of capitulation?
He’s not known for, you know, giving up and rolling over especially when it comes to this deal.
And and my best guess, at this point, and we’ll need more reporting to confirm.
This is basically that he just got ‘told.
9:10
Look, you’re going to lose, you’re going to lose this case.
It’s going to drag you into the public.
You know, I more of your texts and emails and phone calls, and Etc are going to be dragged out in court.
It’s going to be embarrassing to you and your friends and at the end of the day, the courts going to make you pay for forty four billion dollars anyway.
9:27
So you might as well do it now.
Frame it as a victory try to sort of make lemonade out of lemons and get this Or with, right?
It’s like you’re going to be 44 but you know, there’s any way you can either pay twenty million dollars in lawyer fees and embarrass all your friends and then pay forty four billion dollars or you cannot pay 20 million dollars in lawyer fees and just pay the forty four billion.
9:46
Now, that seems to me to be, as you said, we’re going to get more reporting on this, but that seems to me to be likely, why he, ultimately capitulated just want to talk a little bit briefly about, like, what happens to Twitter.
Now, I have three doors for you to choose from when it comes to a Twitter owned by Elon door.
10:03
Number one, There is nothing much changes, but everyone is so aware of Elon musk’s ownership of Twitter that we pretend like everything bad about.
The app is elon’s fault.
So we’re like I saw an anti vax tweet and it’s like, yeah maybe that’s elon’s thumb on the button, trying to make anti-vaxxer tweets, go a little bit more viral or maybe it’s just that people who are anti-vaxxer been on the site for many, many months.
10:24
So door, one is nothing changes but we blame Elon for the pre-existence of Twitter’s Badness anyway.
Door number two is what a lot of I think liberal journalists are worried about.
It’s like we Nari Valhalla like all the forces of far-right May hammer and least under the world.
Twitter becomes proud boys City every day in the app.
10:42
Feels like a men’s rights rally.
That’s what a lot of people are afraid of Twitter becoming door.
Number three is what Ilan.
Says, Twitter might become under his ownership and becomes the everything hap Ilan doesn’t.
He turns Twitter and has some joyous, incredibly useful combination of a town, square, and a new platform and an all-purpose messaging app.
11:00
In a venmo thing on top, like what we chat is for China, Twitter, Becomes for America.
So here are three doors that you can choose from or invent some other door because all these doors are totally fake, everyone conventions about the same old shit.
Number 12, proud boy City 3, the everything app.
11:16
What do you think is the future of Twitter?
My likeliest guess and I don’t have super high confidence in this guess because I’ve been wrong about this deal and an Elon Musk at you know many points is some combination of doors one and two.
I think that there will be turmoil.
11:33
Certainly I think that Going to clean house at the company, there will be lots and lots of turnover among Executives and employees.
Just people who don’t want to work for him and that that will result in not just like, lots of, you know, coverage of the turmoil there.
11:49
But like an actual product failures, like there will be, you know, Twitter is not super like they don’t have a super stable infrastructure.
A lot of the site is like held together with bubble gum and Scotch tape and so if you have like a second number of employees departing like stuff is going to start breaking and that’s going to happen at the same time.
12:11
As all of these people who have been banned from Twitter in the past, our let back on.
That is one thing that like Elon has said very clearly in public and in his private text messages that he is going to do is to reverse all permanent bands, or most permanent bands of Twitter users including Donald Trump including you know, including people, you know, right wing figures.
12:34
I’ve been banned for hate speech and harassment and misinformation and things like that.
So I think there will be those two things happening simultaneously.
I also think the first door is very likely which is that no matter what like he is going to become the de facto customer service department.
12:51
And like, I don’t think he understands how annoying that is going to be for him because it’s not just going to be people on the left who are saying like this sucks and and you know, it’s elon’s fault.
It’s also going to be his friends who Are saying you know, oh I didn’t get verified or you know my I’m being Shadow band because my tweets aren’t getting as much reach like.
13:12
That is absolutely going to happen and I think it’s going to make him pretty miserable.
The Known Unknown here is who’s actually going to run Twitter, Elon Musk, does not have time to actually on an hour-to-hour basis.
Be the chief executive of Twitter.
He’s a CEO of SpaceX.
13:28
He’s running Tesla, he’s running a bunch of other stuff.
He’s trying to create brain.
My brain computer interfaces with neurolink, he’s trying to create robots at Tesla.
There’s a lot going on in his sort of weird Emporium of future Tech science stuff.
13:45
He’s not going to have that much time to worry about Shadow band.
So do you have any insight into who?
From the Elan metaverse will end up as being de facto in charge of Twitter?
I don’t I mean what we saw in the text messages with him and his friends that came out of this.
14:00
As part of this court proceeding, is that there are a lot of people.
Who would gladly stick their hands up in his orbit to be the CEO of Twitter and Jason calacanis, the podcaster and investor volunteered to be the CEO.
Twitter, very magnanimously Mattias Dufner the owner of Politico.
14:20
Also volunteered to run it for him.
So I’m sure there will be any number of people who will be glad to take that on.
The question is like do any of them have actual expertise, and a vision for this company?
Or are they just trying to suck up to you on Moss?
Use his ownership as a way to increase their own power.
14:38
I have no idea who’s going to run it.
I have no idea what’s going to happen on it.
I have you kidding.
I’ve absolutely no interest in ruining my life, the only more efficient way I could possibly ruin.
My life is by running for political office.
Like those are two of the more like high Prestige ways to absolutely without a doubt, fuck over your life and everyone who you love like become the customer service department and Lead executive of the most famously hated David global app in the world or run for political office and make sure that like your entire life is just taking hate from everybody.
15:13
It’s actually the same.
It is the same job in a weird way like the future of being CEO of Twitter is a kind of political appointment.
It is in part because a lot of the incoming hates is going to be explicitly.
Political oh why is this group not getting more attention?
15:30
Why is this group getting too much attention?
I mean what a friggin mess.
Do you wanna run it this case?
When I run it.
No, I’m not volunteering.
I can’t think of anything that would make me more miserable, but I think we should say in the interest of seriousness.
Like, I actually think that there will be people.
15:48
I mean, it’s it can be hard to see from our like, little media bubble butt.
Like, you know, Elon Musk has plenty of people who work for him at his other companies, very talented Engineers like they build Rockets they build cars like clearly.
There are talented people who are going to work for you on Musk.
16:04
Has, he’s Elon Musk.
So I actually like I’ve talked to some people, this way you can basically have said, like you know they’ve told me like look this is recruiting is not an issue that we’re worried about.
So someone will step in to run this company for him and you know, Engineers will sign up to replace the ones who leave because they don’t want to work for you on musk.
16:22
I actually I think that’s like that’s something that is probably overstated in the media is you know.
Clearly there are a lot of people who already desperately want to work for Elon Musk and I think there will be more people who want to do it at Twitter.
Yeah.
And I think I think to a certain extent Elon will de facto run Twitter.
16:37
The way that like the pope de facto ran the Holy Roman Empire.
Like not overseeing day-to-day decisions, not overseeing actual politics and policy but having the ear of whoever is actually running it so that if anything starts to break or goes the wrong way.
Edelen says, actually maybe you should do this for me and then that thing will be accomplished by the deputy.
16:56
Anyway, that’s definitely enough.
He’ll on Twitter news.
Will evolve their or as it’s happened in the last two months, not really evolved.
There and we’ll just stay in some bizarre.
Delaware Chancery Purgatory.
I want to talk about AI because I think that far more than Elon and Twitter the story of the year when we look back, 20 years is going to be this year and the summer in particular was an extraordinary inflection point in the frontier of AI last year.
17:24
We’ve seen the emergence of two incredible categories of artificial intelligence.
You’ve got language models.
They G PT 3, where you basically ask a, an app, a program, a Question or give it a statement and like some kind of disembodied linguistic Super Brain.
17:39
It can answer complicated questions about the world or take a sliver of text and write a full story in the voice of someone.
It can synthesize books it is this kind of like I said, disembodied linguistic Super Brain that’s really extraordinary and has awesome implications and then maybe even more famously and more remarkably is this explosion of text to image a I like dolly And stable diffusion and these are programs that take prompts and turn them into extraordinary art.
18:10
So, for example, if you say, I want a picture of a cat, wearing a cowboy hat holding a scepter riding a unicorn on Venus in the post-impressionists van Gogh’s style, it can produce not just one, not just two, but several images in precisely that mode depicting precisely, that bizarre description of things.
18:34
To me first, like, like, I’m five and a high level.
How these AI image generators work?
What is the general concept behind the magic?
Yeah, it’s a great question and it’s something that, you know, in order to properly, explain it, we would probably need an hour and several computer science phds here with us to explain how it works.
18:57
It’s very, very complicated, but I’ll boil it down into, like, a very simplified like thing that, you know, people who actually understand this stuff will probably.
We write in and say like oh you took out, you know, 17 of the most important steps.
But like here is my like maybe not explain like I’m 5 but maybe like explain like I’m 15 version of it.
19:14
So basically it’s a two-step process.
So first there’s an AI model.
That would the one that runs dolly is called clip and it has been trained on hundreds of millions of image text pairs.
19:30
So essentially they went out, they licensed a bunch of data.
And, and scraped a bunch of images from the open web, and these would be like things like, you know, social media images that have captions, or stock photos that have captions or newswire photos that have captions.
19:45
So they take, I think it was hundreds of millions of images and captions shove them into this giant AI model.
And then, basically tell it to figure out all of the relationships between not just these images and these texts examples.
20:01
But all figure out the relationships between all All text and all images.
It’s a little more complicated than that, but basically that’s how it works.
And then once that model, is sufficiently good and trained.
Then anything, any text that you throw at it like, you know, monkey riding a bicycle on.
20:20
Mars will give you something that’s reasonably, good.
And by the way, it’s not just like taking elements from images that exist.
It’s not taking a monkey, from one photo and a motorcycle.
From one photo, it’s actually like, generating these things Entirely a new and so they are their unique creations.
20:40
So okay.
That’s the first step step 1, this thing called clip.
And then there’s another model a second model that’s called a diffusion model and this one is pretty cool actually.
So basically the way it works is it takes images and then it adds random noise to them.
20:59
So like you can imagine like turning up, you know, the static on a TV screen or something and it, Does that gradually and eventually it’s all noise and then it reverses that process.
So it takes the image from noise back to an image that is that is you know photo quality and it eventually learns how to take images that are static and make them into images.
21:25
It would be like you can kind of compare it to like if it learned to build a car by watching tons and tons of As people dismantling cars and then reversing those things.
So it learns to make images out of noise.
21:41
So basically, you know, you put in your text prompt into the first model, monkey riding a motorcycle on Mars and then it generates something.
And then the second model, the diffusion model kind of refines that image and turns it into the thing that you ultimately get on your computer screen.
21:59
It’s pretty cool.
That’s lovely.
I one thing that you reminded me of is I remember in my favorite college English class.
I learned about the story of ariadne’s thread and the Minotaur, and the way that the Labyrinth of the Minotaur was solved was that ariadne carried the thread, through the Labyrinth, to the center of the labyrinth, with the Minotaur live so that the thread could be followed back out in order to escape from the Labyrinth.
22:28
And so that’s like the the metaphor that occurred to me as you were talking about how you can figure out how to do.
Inge generatively by becoming an expert in its opposite in a weird way.
Like that’s it’s such a, it’s such a lovely and interesting, interesting thought experiment.
22:44
And I just want to double down on one thing that you said, because I do think it’s misunderstood, these are original Creations.
This is not Pastiche, like when I whatever might stupid.
Introductory example.
Was the rodent Cowboy had?
It’s not as if a dolly or stable diffusion is taking a rodent that they found on Google image, then putting it with the hat that I found on like you know being image search.
23:04
Church.
It’s literally drawing or drawing is actually to antropomorphic.
It’s in its synthesizing.
These things uniquely for my prompt which is spooky and weird and kind of wonderful one question.
I have for you that I haven’t seen explored as much as I want it to be is that I think it’s underrated Lee amazing.
23:26
That all these AI tools came out around the same time like gbt 3 comes out and then like Lambda comes out from Google and they do very similar things.
Dolly comes out in a text image world of from open Ai and then stable, diffusion comes out and this is an example of something in Tech History.
23:43
That’s called simultaneous invention, like Newton and leibnitz basically invented calculus the same time, Alexander Graham Bell and Elijah gray invented or submitted their patent for the telephone and the exact same day.
Do you have any explanation for why?
So many different groups seem to have reached the frontier at the exact same time?
24:01
I mean, it’s like, is this one summer was the Cambrian explosion.
Ian of all of these different tools.
Like why would they all secretly at the same place at the same time?
Yeah, it’s a great question and I think there are a couple explanations for it.
Sort of a few things that kind of converged with, in the past, really the past two years.
24:23
So, one thing is that Hardware just got a lot better.
So this is sort of a, you know, a classic Moore’s Law phenomenon in the in the, you know, in the tech industry like processors gpus, these Is get better over time, but specifically for this kind of AI, it relies on things called gpus, which are the basically the graphics cards inside computers.
24:46
That’s that’s what runs these models.
And ten years ago, if you wanted to build an AI, like a neural network, you’d go out and you’d literally by just got a bunch of graphics cards like the same ones that you buy.
If you were like a building, a high-end gaming PC and then you use those to run your AI models and it worked pretty well.
25:03
But Now, like 10 years later companies like Nvidia, these chip companies they make Hardware that is specifically designed to run a i models.
And that has made these models much faster, much more capable and brought the cost down to be able to train one of these models.
25:21
The other thing.
So, another thing that has happened so Hardware is one piece of it.
There’s also just the, the, what they call the hyper scalars.
So, these companies like Google, like open a, I like Facebook.
They got Richer because of things like search ads for Google and you know, targeted, social media ads for Facebook and they used those profits from their main business has to essentially do a land grab in AI.
25:47
So Google, you know, use the profits from search ads to hire like hundreds of, you know, maybe thousands of AI experts like the top people in the field.
Gave them essentially unlimited budgets and access to their massive troves of data.
26:04
And compute power, Facebook did the same thing.
And so they were all kind of racing armed with like billions and billions of dollars in capital and all trying to like work on some of the same problems.
So that’s that’s another structural piece of it.
The third and I think actually the most important thing that happened in the last few years is that computers figured out language.
26:29
So in 2019 open a, I came out with this model called EPT to, which was the predecessor to GPT 3.
And it was basically, as you said this, like disembodied, linguistic superbrain.
It was massively trained on tons and tons of examples.
26:47
Using one of the biggest computers in the world and it just became extremely good at predicting and transforming text.
And that started, what’s been called the large language, model era of AI and Then once G Pt 2 into PT 3, then came out, people started realizing, wait a minute, there are all these other things that can be turned into language problems.
27:14
So instead of asking the large language model to like, finish a sentence, what if you asked it to finish a line of code?
What if you asked it to finish a song or draw a picture or predict the 3D structure of a protein or the most recent example that Literally happened in the last week meta and Google both came out with text to video generators.
27:41
So the same principle.
The same thing you can do with dolly for static photos and images.
You can now do with movies.
So basically everything once once computers, had kind of figured out language, then they researchers started realizing wait a minute, everything can be a language problem.
28:00
There’s even this one really amazing paper and that just came out of Google.
Where the researchers took a robot like a physical Hardware, robot and traditionally the way these things are programmed is like, you know, in sort of, sort of if-then statements, if they’re, you know, is a drink that’s bills on the counter, then you go over this many inches to the sink and you grab, you know, the sponge and you come back and you wipe down the user robot arm and such and such an angle and instead of programming it.
28:34
Way.
They turned to the robot into a language model.
They started asking it.
How would you clean up a spill on the counter?
Is it A B C or D?
The robot chose and then it executes those instructions.
So the robot basically became the body of the large language model.
28:54
So this is an incredible breakthrough and it’s something that has under lied.
A lot of the last couple years of progress, now is a fantastic answer and it did better than I I could possibly do to explain why.
I think he’s AI.
Breakthroughs might be the single most important story of 2022.
29:12
Inflation is going to go away.
Interest rates will go up and then they’ll come down these Tech breakthroughs could be the opening Innings, I think of just an absolutely wild Revolution and unless you questions I want to choose to be excited about this Revolution and then scared, I just sort of like you know, bifurcate my emotions.
29:30
Like let’s be excited first and then it’s like that.
Save all of our fear For the next question.
All right, let’s be excited.
Like, holy fucking shit.
Are you telling me that I could just like, tell a machine to like make an animated scene of a movie and it’s going to do it, but I can just say, I want a song in the key of C sharp that sounds like early Coldplay, but actually the voice is a female and it can like begin to put together a truly novel.
29:57
Again, these are synthetic generative products.
They are new not borrowed, it can create a new song.
And therefore, help me as a songwriter, like what are you?
Kevin most excited about in terms of actually using one of these like AI.
30:13
Disembodied, superbrain assistance to help you be more creative in your life.
Yeah, I mean there’s unlimited possibilities, right?
So I am a writer and a podcaster and you know I also enjoy art.
30:29
So I’ve made some stuff in in Dali and these other image models that I’m very excited about.
I actually had him having one of my Creations like printed and framed to put up on the wall of my office, you know.
I I also like have some ideas about things that I would like to do with AI when it comes to, you know, for example, making videos, I’ve talked to a number of I’ve interviewed a number of people who are already using this stuff in their day.
30:56
Jobs, one person is a, is a sort of filmmaker and visual special effects designer, who is using this stuff.
If already in storyboarding in, you know, in doing some sort of primitive special effects with it.
31:11
So I mean everyone is now going to be able to make not just art but movies they’re going to be able to write in ways that they’ve never been able to write.
You know, I used G PT 3 based model last year to write a book review for the New York Times book review and it was a little bit of a stunt because the book was about Ai and I just closed it.
31:32
But like if I hadn’t told my editors that this is what I was doing.
Like I’m pretty sure they would have had no idea, so that’s the exciting part.
We’re all going to get not just better at this stuff but faster, I mean, one of the most interesting pieces of data about the effects of these programs.
31:50
So far, was with something called copilot which came out of GitHub.
And it’s basically this idea of using a large language model to complete lines of code and so it doesn’t replace the programmer but it basically goes inside The programming environment and it sort of autocompletes, when it, when it, you know, it sort of Senses.
32:09
What you’re trying to do.
It says like would you like to just have us do the rest and they’ve done some initial tests on that and they found that it makes programmers about 50% faster so just imagine that extrapolated to every creative industry and you get a sense for like just the pace and the scale that this is going to enable as far as trying out new ideas.
32:34
You know, making New Creations, it should, we’re just going to have sort of a Cambrian explosion of new creativity.
I, my Daydream is that I have this parallel life on Earth to where rather than go into journalism.
I tried to become a musician and I’m not very good at playing piano and I can’t play guitar and I’ve written a few songs.
32:59
But I haven’t really shared them with more than like, one or two people.
But sometimes, you know, No, I am not trying to be a wise guy here, but sometimes I like playing like a like a song that my wife says she likes like learn.
Like the really simple like Triad chord structure and then like sing it back to her.
33:15
What did they really didn’t say?
Excites me as you know, someone who has this sort of like parallel life Daydream as musician.
Well, my wife really likes Billy eilish and Taylor Swift.
So for her birthday I could ask like the dolly or G BT. 3 of original music writing Write me a song that goes no higher than F sharp because I’m a, you know, I say 10 or 2 that goes no higher than F sharp and is played or orchestrated in the style of Taylor Swift and and Billy eilish and is happy but wistful and like it could actually help me write an original song.
33:53
Like there’s little things like this that are just like so exciting to me and they’re exciting.
Not only for me, you know, look I’m 36.
I’m never going to become a professional musician ever pure with that attitude.
No.
Not with that attitude you want but like an 11 year old who has the same interest in music, he or she can now spend 10 years of their life, becoming a professional musician with these tools.
34:19
Like does it excite you to think, like how these tools are going to be used in education were again, you’re going to be able to have this superbrain alongside you to become a better writer, you know, write my essay in the style of, who knows John up.
Like right, if you like John Updike, you when I write with really beautiful, crystalline metaphors, rewrite my SNS, Tyler John Updike.
34:40
And in seeing your own words, translated through this algorithm, you might be able to become a better writer a better music, better musician, a better artist.
I mean, it’s these sort of educational benefits that actually have me really, really thrilled?
Yeah, it’s super exciting and it’s, it’s, you know, it’s really going to expand.
35:03
Not just the the The work that we’re able to do but but our ability to focus on the parts of our jobs that we actually like so like one, you know, early example of a I kind of Transforming Our industry is that, you know, I used to spend when I was a young journalist, 10 years ago, I used to spend a lot of time transcribing interviews.
35:20
It was like, kind of a drag and, and then this software came along that could just do it.
And I think the same thing goes for podcasts.
I mean, they’re used to have to be a producer if you want.
We’re working on a certain kind of of podcast who would sit in the in the, you know, meeting and type out everything that everyone said and then, you know, now that’s all instantaneous basically, and it happens through a, I so I think there’s just a ton of potential, not just to allow for new forms of creativity, but to sort of take away the parts of the job that no one actually likes doing.
35:57
Now, let’s be a little bit scared.
Um, I think there was a prediction of AI and algorithms and automation that said that the kind of jobs that are most likely to be replaced.
By this next Frontier, technology are jobs that are routine based the jobs that were considered safe for the jobs that were creative jobs.
36:14
Like, for example, writing making music drawing images being a video game designer being an artist, but it is precisely these higher-level creative tasks that.
This new crop of AI is potentially impinging on.
36:31
Are you worried about the employment or disemployment effects of Technology like this in the workforce?
Absolutely.
I mean, I think anyone who says that this is not going to displace workers in Creative Industries is is insane.
36:48
Or is selling something or is, you know, willfully ignorant.
You know, I’m already hearing from game studios that say, you know, we Now do things that we would have had to hire, 50 people to do, you know, this is and your right is a total failure of our predictive capabilities.
37:10
I mean, I remember just just, you know, a few years ago you had this sort of narrative and Andrew Yang was, you know, out there talking about how automation was going to, you know, get rid of truckers and Retail cashiers and warehouse workers industrial workers.
And the whole narrative was that, That basically Ai and automation, we’re a working class problems.
37:32
And instead, what happened is that it came for the creative class first.
And so, I, you know, I’m revising a lot of my own predictions about that.
I think that will have fully automated game and movie studios before, will have fully automated Amazon warehouses.
37:48
Why I think that an AI generated song will reach number one on the billboard charts before a car makes it from New York to LA with no.
Assistance.
I think that we are we are radically changing our idea of who is at risk of Automation and I think a lot of that is based on this misunderstanding of creative work.
38:09
A lot of what we call Creative work turns out to just be pattern recognition, you know, fast following.
You know, if you’re a fashion designer it’s like oh what are other fashion houses?
You know, doing this year?
What you know what, what hasn’t come around in?
This many seasons what’s do for you know?
38:27
A resurgent.
It’s just pattern recognition and prediction and turning ideas into media objects.
So I think that’s that’s like sort of the the reasoning flaw at the heart of this misunderstanding is that a lot of what we call Creative work.
38:43
Actually is just sort of gussied-up prediction and and pattern recognition.
I’m so fascinated by and motivated by the second thing that you said, I’m not exactly sure of the right way to think about it, but there’s this idea called more Paradox which says that there’s this irony in algorithms and Ai and automation that so-called hard tasks like say building an algorithm that’s smarter than the greatest test.
39:10
The greatest chess player in the world that seems hard.
But it’s actually like one of the first things that IBM could do meanwhile.
Like walking down the stairs, organizing a stack of paper.
These things seem really complicated like a five year old can do them but it’s very difficult to build a robot that Does them fluidly and maybe the most poetic, and possibly bullshitty explanation.
39:32
For more of X Paradox is that we are reverse running the history of evolution.
Our brains are among the last part, or our sophisticated creative minds are among the last part of the human body to evolve potentially and so, our creativity is younger than our ability to walk evolutionarily speaking.
39:57
And so as we sort of design through technology, the full capacity of the embodied, human were moving Against Time and checking the box of creative Brain.
Before we fully check the box of, you know, manual skills and perfect walking ability.
40:15
Have you ever heard any?
I don’t know if you have strong opinions about more of X Paradox or whether you’ve heard other explanations for why we’ve apparently we’ve been able to at least partially solve these problems.
Eames of AI art before.
We’ve clearly solve problems that seemed easier like self-driving, cars or robots that can do things other than just a fix the steering wheel.
40:37
Yeah, I mean, I think there are a lot of possible, explanations of it.
I agree that it’s a real phenomenon.
One possible explanation is just that it’s the difference between bits and atoms, right?
That, you know, things like Hardware robots, have to contend with like the physical world, right?
They get Dusty, you know, things aren’t where they’re supposed to be, the power goes out.
40:57
Like they’re all these kinds of edge cases that can, you know, if you’re talking about self-driving cars, like famously like you know, people will say like we’re 99 percent of the way there.
But that last 1% is all that anyone cares about because those are the the strange edge cases.
The you know, Snowball gets stuck on the sensor.
41:15
The you know that we’re there’s that that’s where people crash and die.
And so you really have to be like 99.99999% there before anyone’s going to get in a self-driving car.
So there’s There’s more fault tolerance in some of these creative Pursuits but it’s also purely software-based so you don’t have to like exist, you know, nothing has to come out of the computer screen basically to do that.
41:39
And so this is really like I think related to a point that I’ve been thinking and talking about four years which is that I think the most susceptible kinds of jobs to a I are not just the creative jobs but the remote jobs, right?
Because remote jobs are are are Mostly software-based a lot of them and if you can make a job remote, if you can make it as as abstract and and sort of task oriented as, as a as a fully remote job has to be in order to be able to be done remotely.
42:14
You can also give that set of tasks to an AI.
A lot of the time.
It’s a really powerful idea that remote ability is a proxy for replace ability in some version of the future that might It becoming obviously there’s lots of jobs that I think are remote, that are hard to replace, we don’t get have ai that can do perfect software programming.
42:37
We don’t, yet have a, i that can write full articles for the New York Times.
But one way that I think about technology is that it doesn’t automate jobs so much as it ought to mates tasks within that job.
So, for example, if you’re a podcast maker one, of your tasks and say, you know, Circa 2009, 2010 was to transcribe interviews so that you could edit them and you know, help you produce your edit, the tape.
43:03
Now that job can just be outsourced to rev.com, it just be purely outsourced to a machine but that doesn’t change the necessity of a job of a podcast producer podcast host.
It just automate the tasks within it and so optimistically one would hope that while this might have certain pressures in the white collar economy would it mostly does is akin to very first example, it makes creative job.
43:27
Jobs more fun because it amplifies creativity and accelerates the process by which you go from zero to final product.
Whether you’re making a video game or making a piece of art or making a piece of music, it just speedruns the creativity process, a little bit.
43:43
If you get to the end faster, that is certainly an optimistic way of looking at it.
And I hope that you are right.
I think that what we’ve seen throughout history and, and I wrote a book about this, a few years Years ago.
But today I and and as part of that book, I went back and and read a bunch of studies and books about previous waves of technological change and it turns out that a lot of work is atomized so it is just a collection of tasks and as soon as the people who are running the company, the CEO or the CTO or whatever, can can automate those tasks they generally do.
44:24
So you know, if you think about how many tasks are involved in your my job, you know, there’s probably there’s probably like, you know, seven or eight main ones and some of those can be automated probably today and some of them, you know, may not be automated for 10 years, but I do think that that we sort of law, salal ourselves into a false sense of security.
44:46
When we say that like, you know, it’s just going to help us, it’s just going to be our sort of assistant or amanuensis or whatever and that it’s not going to replace us.
Because I think what we’ve seen is just that that’s sort of a Of that we apply.
And then you know, the people in charge are the ones who are really deciding.
45:01
Okay.
It’s not Maybe not, maybe it, maybe it can only do so maybe the a, I can only do five of the seven things that Derek or Kevin currently doing their jobs.
But actually, that’s probably good enough.
Knowing what I know now.
I feel like it’s one of these situations where the, the stage of take off that were in with AI is kind of similar to where say, like, you know, fintech ABS apps might have been in. 2011 or like Bitcoin into 2010 like a part of me is like are we idiots for not seeing that?
45:33
This is the moment that like if you want to like do something that is hugely influential that like is a lever on the world and that could make you super damn rich.
Like get into a I now because like that plane is really starting to take off if you were given the opportunity to solve one problem with this.
45:57
Sweet of AI tools that are coming into view.
It could be in the disciplines of Art and culturally talked about, it can be in using AI to come up with interesting new drug.
Molecule combinations that can solve this eases in ways that humans previously, previously, hadn’t synthesized.
46:16
What is the space?
Where if you were going to jump head, shoulders and feet into a.
I where would you jump?
That’s a great question.
I mean I’m a journalist so naturally like that’s where my expertise is and and I think they’re going to be some interesting attempts to use these language models.
46:37
I mean, I’m sort of amazed that there’s no fully automated, G PT, 3 based news Outlet already.
It’s not something that has just just because I think I think maybe you know, 15% of people listening, might understand exactly what that is.
But like just paint a brief picture, what would a fully G, PT, 3 automated New sites kind of feel like, well, it could look a couple different ways.
46:59
I mean, the sort of the sort of light version of that would be, you know, instead of coming up with, you know, story ideas and then pitching them to an editor and then, you know, painstakingly reporting and writing them.
And then having a photo editor who selects the art that goes on the story and then, you know someone who hits publish and then, you know, team of people who are promoting it like that.
47:20
Could, you know, most are all that could be automated.
So I could come in one day and, and I could have already you know said these are the 10 most important stories of the in the world.
We’ve generated versions of them, three versions for each story, which one of these do you want to publish slap some headlines on them automatically Choose, You Know, generate the art automatically hit publish automatically promote on social media automatically that this would all just be seamless and is easy and you could basically run, you know, an entire Newsroom with one person.
47:48
That’s that’s one of the Visions for this.
The other Vision that I’ve spent a lot of time thinking about recently, is what I would call sort of dynamically Dynamic sort of generated content.
So if you go to a website right now, if you go to the newyorktimes.com or the Atlantic.com, you know, no matter who you are, where in the world, you are looking at that website, you’re going to see the same stories, right?
48:14
You might see them in a slightly different order or you might see, you know, different, you know, different mix of stories depending on, you know, what browser you’re using, or mobile or whatever, but you’re going to see the same story.
Is you’re going to open them up?
They’re going to look the same again.
Have the same Arts are going to the same words with AI and and large language models, what you could theoretically do is to generate content on the Fly for an audience of one.
48:39
So, when you go to a News website, it could figure out, you know, who you are, like, where in the world you’re located, it could make some predictions based on user behavior and pull from, you know your social media profiles or whatever and it could say this is the version.
48:54
A of a Ukraine story that Derek would that would most resonate with Derek and it could generate that spontaneously on the fly a new brand new story that no one’s ever seen before, choose a piece of art, generate a piece of art that is calculated to your specific tastes and it could do that all in the time that it takes you to load or news site today.
49:16
So you can effectively have billions of people each reading versions reading and experiencing and watching versions.
Of reality that are specifically tailored to them dude.
That is terrifyingly plausible.
And you know what company is obviously best suited to make this a reality?
49:34
Think about all the questions we already asked Google.
I asked you the news questions right?
What just happened in the UK, who’s the new q?
K prime minister.
I was doing a podcast that we could go about sort of some of the Domino effects of Federal Reserve policy on Southern Asian currencies, okay?
49:51
They know the Google questions that I asked for that, and the tabs I opened up for that.
But more than that, they know my consumer questions.
I just decided that I want an Apple Watch.
So, I went around looking for which Apple watch, I should buy as a first-time.
Apple watch customer, I need new slippers.
50:07
Hers.
So I went looking for slippers.
All right.
What’s really annoying is having a bunch of ads that really have nothing to do with what I’m interested in or actually just like boxes for.
It’s like here’s a picture of slippers.
What’s more interesting is to have the wire cutter article or something like it presented to me.
So that I already have this organization of, oh, here are the best kind of apple watches for someone like you, who’s buying their first, Apple watch and doesn’t swim a lot and doesn’t like go for long runs while that same news site.
50:37
Just serving me Dynamic advertisements.
In the form of content, is also serving the content that is fully written out.
It’s an article in G, PT 3.
But based on the prompts that I’ve asked Google, it’s like ask Jeeves but turned into a dynamic website paid for by the advertisement.
50:56
That’s also based on my search Behavior.
I mean, I’m not, but I’m sure there’s thousand problems with this.
Maybe in terms of privacy, that I’m sure there’d be some kind of blowback because it’s a little bit creepy to have a website.
Site that so very clearly holds up a mirror to your online Google’s, but something like this seems horrifyingly plausible to me.
51:15
Oh, I’m sure there are startups that are building this right now.
I’m sure there are, you know, someone will come out with us and I think that’s going to be a really interesting inflection point.
Not just in the digital media business, but in like kind of the history of society as a self conceiving thing.
51:35
So I have a A person, I talked a lot about this is Jack Clark, who’s a former journalist who then went into Ai and now is running a big AI company.
And he is very, very smart about this stuff.
51:50
And he sort of talks about this coming reality collapse, what?
He calls the route reality collapse, which is that basically, you know, up until this point Society.
You know, it’s at least since Gutenberg has had sort of a centralized idea about What is not only happening in the world but like what is society?
52:11
What what our institutions doing, who are we?
And so we’re moving from that he believes and I tend to agree with him into a world in which will all have these kind of like hyper personalized dynamically generated kind of like like sort of Choose Your Own Adventure realities, that’s all going to be sort of overseen and and conducted by AI.
52:36
So No, it’s not just that right-wing.
People will read white, right wing news, sites and left-wing people will read left-wing news sites.
It’s like everyone’s sense of reality will be individually tailored to an AI as prediction of what will generate the most clicks or watch time or product buys or anything from them.
52:56
And I think that’s a really important moment and it’s really, you know, this is sort of the scary part is that we actually have never run that experiment before and it will be very, very interesting and and I think there’ll be a lot of trouble that comes with that.
Yeah, here’s three categories of content that I just thought of a magazine is human content, edited by humans.
53:15
Tick-Tock is human content.
Organized by algorithm what’s box.
Number three, algorithmic content.
Al garage.
Organized by algorithm.
We’re all the tick-tocks or all the Articles, or all the images.
As if it’s an algorithmic, Instagram are all produced by these a eyes that were talking about.
53:34
And also, there’s a metal Ai and Orchestra conductor.
Derp.
That’s organizing the process by which or organization of which we confront.
I mean, that’s a really fascinating and weird idea that we will have at that point moved from a Content ecosystem that is for humans and by humans to a Content ecosystem that is for humans by entirely a is written by humans such that each individual’s.
54:02
Experience of the world and news and maybe even culture is so unique to them because all of these articles that they’re seeing and adds that they’re seeing our algorithms are algorithmically invented for an audience of one.
54:18
It’s bizarre and I agree like rather destabilizing because it cuts against I think certain assumptions of what a society is a kind of meshed shared.
Experience of similar inputs, everyone listening to the same Taylor Swift song.
54:37
Everyone watching the Superbowl everyone watching the same presidential debate and reading about it in the news.
What if it all becomes a little bit different and mediated by AI that is designed for one so we will end and just to it’s a very weird thought and just to add another layer of weird thought on top of it.
54:57
Because I think this all sounds kind of futuristic and like sci-fi when we talk about it but like Okay.
I think this is going to happen very soon, this sort of inflection point where Most of the things that we see and encounter on the internet are going to be a i generated.
55:17
I think we may be there like within the year for images.
I think that it’s entirely plausible if current trends hold that by the end of this year, the the majority of new original images that are produced on the internet, every day will be generated by Dolly mid-journey stable, diffusion just as a matter of just pure numbers, I think there’s a possibility that’s already happened.
55:45
It’s funny because episode 1 was the future will be weirder than you think the future weird as hell based on crypto in the metaverse.
And now, we’re like the future is going to be weird as hell.
But actually, because of AI into were right about the big picture, but every year I need you to come back because a different animating, technology will prove the point that the future is going to be weird as hell.
56:02
Like next year is going to be like, you know, micro drones dotting.
The skies is actually the big thing, the big breakout technology of 20. 83 and that’s why the future is going to be weird as help.
But if only there were a podcast devoted to the idea that the future is going to be weird as hell.
That would track.
These developments on a weekly basis.
56:19
God, I wish someone would start a podcast like that.
Kevin are you starting a podcast like this with the New York Times?
Derek as it turns out I am, I have a new podcast called hard Fork.
It came out the first episode, just came out, and it’s a me and Casey Newton, another fabulous, fabulous tech journalist and friend of mine.
56:39
And we, We are going to do weekly sort of chat shows about this very thing about the fact that the future is weird as hell and we’re going to look deeply into things like a i generated art.
We’re also going to look at other technological phenomena and and it’s going to be really fun.
56:56
So thank you.
I can’t believe we ended up circling all the way back to around to a promotion of my new podcast.
But, you know, the future is going to be weird as hell.
That’s the last note that I had on my notepad, so, I’m Very glad that you yourself built the on-ramp to it.
57:12
Kevin ruse.
The podcast is called hard Fork.
Hard Fork hard for a quick Kevin ruse and Casey Newton.
Kevin.
Thank you so much.
Thanks for having me.
Thank you for listening.
Plain English is produced by Devon manzi.
If you like the show, please go to Apple podcast or Spotify.
57:28
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