Speaker 1: Welcome to Tech Stuff, a production of iHeart Podcasts and Kaleidoscope.
Speaker 1: I'm mos Vloscian and today Cara Price and I will
Speaker 1: bring you the headlines this week, including the fashion models
Speaker 1: getting Ai twins. Then on Tech Support, we'll talk to
Speaker 1: Garrett da vinc a reporter at the Washington Post about
Speaker 1: all the news in AI from Open Ai, it's huge
Speaker 1: new fundraise, to Xai's acquisition of x formerly Twitter, all
Speaker 1: of that on the Weekend.
Speaker 2: Tech It's Friday, April fourth, Cara Price.
Speaker 3: As Volashan, you're looking like me today.
Speaker 1: Yeah, we are matching.
Speaker 3: We are very matchie matchie. I was at.
Speaker 1: McLaren f one's headquarters as you know, earlier this week
Speaker 1: and I sent you a picture that I was very
Speaker 1: proud of me and Zach Brown, the CEO of McLaren Racing,
Speaker 1: And your only comment was, oh, black trainers.
Speaker 3: Always black six sneakers. You can sponsor us, but yes,
Speaker 3: always black A six sneakers, even if he's wearing a tie.
Speaker 3: But it's, you know, for a tech podcast, it's very
Speaker 3: funny how much we talk about fashion. And it's not
Speaker 3: because I'm a woman.
Speaker 1: I mean, it is also ironic that we both have
Speaker 1: our own uniforms. We're wearing blue blue shirts, dark blue pants, shirt,
Speaker 1: blubby pants, and a cool hat.
Speaker 3: Though I am wearing a cool hat, I'm always wearing well,
Speaker 3: I wouldn't call it a cool hat, but if you
Speaker 3: want to call it a cool hat, it's an appropriated
Speaker 3: Yankees city by hat that is completely illegal and was
Speaker 3: taken off the internet the minute it went up, So
Speaker 3: I'm very proud to be wearing it now.
Speaker 1: The reason we're talking about this is not just because
Speaker 1: we're totally self indulgent, but because the topic of tech
Speaker 1: folks and their uniforms is pretty fascinating. Earlier this year,
Speaker 1: we talked about Mark Zuckerberg, who had this famous, weirdly
Speaker 1: shaped T shirt with the Latin phrase out zuk out
Speaker 1: nihil printed on it, which means all zook ll all
Speaker 1: nothing and is a is a reference to Julius Caesar.
Speaker 1: Of course I can't.
Speaker 3: It's just insane to me. The level of It's not narcissism.
Speaker 3: It's this sense of like an obsession with being cool that,
Speaker 3: while chased, is the antithesis of anything cool and fashionable.
Speaker 1: I thought that one of the genuinely coolest tech swipes
Speaker 1: of the year was when Jay Graber, who's the CEO
Speaker 1: of blue Sky, the kind of left leaning social media platform,
Speaker 1: wore a T shirt at south By Southwest that said
Speaker 1: in Latin, no more Caesars, and it was a real
Speaker 1: sort of shots fired at Zuck, I think, and.
Speaker 3: Also just again in Latin. I mean, these are the
Speaker 3: nerdiest people in human history.
Speaker 1: This was a huge best seller for Blue Sky. I
Speaker 1: saw that almost immediately.
Speaker 3: I bet it was, you know, it's tech Titans, as
Speaker 3: I was saying, just seemed to want to have a
Speaker 3: signature look. And I don't know if part of it
Speaker 3: is pathology, psychological pathology, I mean, certainly minus psychological pathology.
Speaker 3: I like to wear the same thing every day. I
Speaker 3: don't have to think about it. Like our friend Elizabeth Holmes,
Speaker 3: who also rocked the Black Turble.
Speaker 1: She flex hard on the Steve Jobs.
Speaker 3: She flexed and copped. She coughed something from Steve Jobs.
Speaker 3: And I guess it's sort of worked for her because
Speaker 3: we're still talking about it.
Speaker 1: But I think the most sort of talked about tech
Speaker 1: CEO of the moment is none other than Jensen Hwang
Speaker 1: of Invidia.
Speaker 3: The man who made a lot of people a lot
Speaker 3: of money and now is making a lot of people
Speaker 3: a little less money.
Speaker 1: And still still a lot, but little less exactly. He
Speaker 1: has a signature. Look, can you describe it.
Speaker 3: It's sort of a Marlon Brando, sort of on the
Speaker 3: waterfront black leather jacket that is I think Tom Ford.
Speaker 1: I think it is Tom forty, which is like a
Speaker 1: ten thousand dollars.
Speaker 3: That's an extremely expensive jacket.
Speaker 1: But there's a lot of Invidia stands. It turns out,
Speaker 1: and if you build it, they will come to buy
Speaker 1: a fake black leather jacket.
Speaker 3: If you build it, they will come and buy it
Speaker 3: a lot cheaper exactly.
Speaker 1: So our friends at four or for Media reported that
Speaker 1: there are all these websites popping up with knockoff Jensen
Speaker 1: Hwang in video jackets for which have creative titles like
Speaker 1: Jensen wang in Video Jacket totally optimized their CEO.
Speaker 3: People, They're going to find my phone and be like
Speaker 3: that was the last thing that she was looking at.
Speaker 1: But I think I don't sure if you saw it.
Speaker 1: But this week Jensen Hwang went to visit a company
Speaker 1: called x one Robots, and they had one of their
Speaker 1: robots come up to Jensen whanging humanoid robot and present
Speaker 1: him with a new black leather jacket, not a tom
Speaker 1: Ford one. This one was bedazzled with the Nvidia stock
Speaker 1: ticker on the front left pocket and the logo in
Speaker 1: kind of Swarovsky style crystals on.
Speaker 3: The back to imagine if the humanoid robot is also sentient,
Speaker 3: if the robot was like, I don't want to do this,
Speaker 3: like the people programming mean, please give me something better
Speaker 3: to do.
Speaker 1: But there's a more serious story about fashion AI here,
Speaker 1: and it comes from The Guardian. So according to their story,
Speaker 1: the clothing retailer H and M announced that they're going
Speaker 1: to create so called AI twins of thirty models, which
Speaker 1: they're going to use for social media marketing posts.
Speaker 3: The way, I wish I had this for a podcast.
Speaker 3: As much as I enjoy doing it with you, I wish.
Speaker 1: You could just have a I'd have a relic. Would
Speaker 1: you rather use your replica or my replica?
Speaker 3: Well, if we wanted me to be on time, I
Speaker 3: would use your replica. But sorry, keep going, because I
Speaker 3: do love the story.
Speaker 1: No, So these models, the real models, have given their
Speaker 1: permission to H and M to use their likeness with AI.
Speaker 1: You know again, as you said, sounds good, you know,
Speaker 1: why why sharp yourself you can send your digital twin.
Speaker 3: Well that and also I think there's something that's very
Speaker 3: interesting here that reminds me of the Atlantic sort of
Speaker 3: selling its data, which is these are people that, like
Speaker 3: in the film industry, are cannibalizing their own job.
Speaker 1: Well, of course, just like with the Atlantic and Open Ai,
Speaker 1: where open Ai actually compensates the Atlantic for using its archive,
Speaker 1: these models are also being compensated for their image. They
Speaker 1: own the rights of their twins, and their twins can
Speaker 1: work for other brands, not just H and M, and
Speaker 1: they'll get paid. One of the thirty models said, quote,
Speaker 1: she's like me without the jet lag.
Speaker 3: So the other thing is, and it's worth saying, the
Speaker 3: images in the business of fashion right up look amazingly similar.
Speaker 1: Like it's not distinguishable.
Speaker 3: Yeah, it's not like who was that person that we
Speaker 3: used to cover on Sleepwalkers. She was a model Lil Michayla.
Speaker 3: Oh yeah, like littl Michayla looked like AI. I think
Speaker 3: you know, after four years, we have now gotten to
Speaker 3: a place where it's just an AI replica, which is incredible.
Speaker 1: Yeah, and there's no sick finger. As you pointed out,
Speaker 1: it does raise concerns about the future of modeling in
Speaker 1: the fashion industry because although these models who are participation
Speaker 1: in the partnership will get paid every time that digital
Speaker 1: twin is used, you know, just like in the film industry,
Speaker 1: also raises questions about what happens to all the people
Speaker 1: who work on sets. I mean the hair siders, the
Speaker 1: makeup artist, the lighting designers.
Speaker 3: Yeah, you know, I was actually talking to our producer Eliza.
Speaker 3: Shout out to Eliza, I like to keep her in
Speaker 3: the mix. And she actually knows a hairstylist who works
Speaker 3: with a company that's testing out replacing many of their
Speaker 3: e commerce models with AI, and you know, work has
Speaker 3: slowed way down for her.
Speaker 1: So this is happening.
Speaker 3: I mean, think about it. You don't need to be
Speaker 3: doing a blood on a digital avatar.
Speaker 1: Yeah, and I mean even for models themselves, if I
Speaker 1: guess if you're you know, for these thirty who have
Speaker 1: gotten first through the door. It's one thing, but this
Speaker 1: may affect the kind of future job landscape for model
Speaker 1: as well as people work around modeling.
Speaker 3: But this is like a This is Zulander three where
Speaker 3: Derek realizes we're in jeopardy. But in all seriousness, you know,
Speaker 3: what what does it look like for models who are
Speaker 3: no longer in demand? And it might not seem important
Speaker 3: to like a lay person, but I do think it's
Speaker 3: a harbinger of things to come, which is like, if
Speaker 3: you can replace something that is so sort of ubiquitous
Speaker 3: for you know, a century, what does that look like?
Speaker 1: Yeah, I mean wile I find most interesting about these
Speaker 1: like AI digital twins or AI actors or whatever, or
Speaker 1: you know, chatbots of famous people from history is their
Speaker 1: effect is kind of to lock in the very few
Speaker 1: most famous people in the world as the only characters
Speaker 1: who are worth interacting with. I mean, you know, if
Speaker 1: you think about an action movie, why would you not
Speaker 1: make it with Tom Cruise? Or talking about doing a
Speaker 1: fashion shoot, why would you not make it with el McPherson.
Speaker 1: So kind of, I think there's a longer term kind
Speaker 1: of chilling effect on the pipeline and new talent in
Speaker 1: creative industries, which you know, which will pretty interesting, you know,
Speaker 1: and somewhat disturbing. To be fair to H and M,
Speaker 1: they're being very upfront about their use of AI. They're
Speaker 1: going to watermark the images of the AI twins in
Speaker 1: their ads so people will know they're the AI versions,
Speaker 1: And by doing this, the company will also be abiding
Speaker 1: with the EU's AI Act, which is coming into effect
Speaker 1: in twenty twenty six, and it will require all AI
Speaker 1: images to be labeled as AI images, to.
Speaker 3: Which I say, who's looking for that? Like, I you
Speaker 3: know what I mean? Like the role of AI and
Speaker 3: creative industries, which you know, is something I'm obsessed with
Speaker 3: and sort of how to regulate it is something that's
Speaker 3: obviously going to keep coming up, and I'm curious to
Speaker 3: see where it goes next. I wonder to what extent
Speaker 3: the lay person cares that, Like where they're buying a shirt,
Speaker 3: is either modeling that shirt with a fake twin or
Speaker 3: the real person.
Speaker 1: Probably not, Probably not. The next headline comes from the
Speaker 1: stuff of nightmares for anyone who's a frequent traveler, and
Speaker 1: it has to do me, and it has to do
Speaker 1: with an airport being shut down for twenty four hours.
Speaker 3: I heard it was Heathrow.
Speaker 1: It was London heath Throw Airport, where I flew out
Speaker 1: of just this week. It shut down, leading to over
Speaker 1: a thousand council flights after a fire caused a power outage,
Speaker 1: and Bloomberg reported that the outage could be traced back
Speaker 1: to a single point of failure, a burned transformer and
Speaker 1: twenty five thousand liters of transformer cooling oil that was
Speaker 1: a blaze for several hours. It's a fascinating story, and
Speaker 1: Bloomberg had such a great headline, which was the device
Speaker 1: throttling the world's electrified future. But Kara, do you know
Speaker 1: what a transformer is?
Speaker 3: I know what the movie Transformers is. I know it's
Speaker 3: a car that turns into a man.
Speaker 1: So most simply put, a transformer is something that changed
Speaker 1: is voltage. So when you create electricity in a power plant,
Speaker 1: you actually want to increase the voltage as much as
Speaker 1: you can because that allows it to travel far and
Speaker 1: fast with less loss of electricity along the way. So
Speaker 1: use a transformer on the way out of the power plant.
Speaker 1: But then when it gets to your home or the
Speaker 1: local electric grid or whatever it may be, you actually
Speaker 1: want to use a transformer to turn the voltage back
Speaker 1: down because otherwise it blows up your stuff.
Speaker 3: So ostensibly, it's sort of like when I'm staying at
Speaker 3: not the greatest hotel and I turn a blow dryer
Speaker 3: on and all of a sudden, the entire room short
Speaker 3: circuits and all of the electricity goes off because the
Speaker 3: voltage is too high.
Speaker 1: Yeah, exactly mean that is a short circuit where there's
Speaker 1: likely been a problem with the transformer being bypassed or
Speaker 1: not functioning correctly.
Speaker 3: Right. And I think that in a storm or a
Speaker 3: natural disaster, they can sometimes explode and it's very loud.
Speaker 1: Yeah, it sounds like fireworks or a bomb going off.
Speaker 1: And I mean during hurricanes and other kind of natural disasters,
Speaker 1: these things come under a lot of pressure when they
Speaker 1: do go out. Follow And so what happened to Heathrow
Speaker 1: Airport was this fire broke out in a substation which
Speaker 1: houses transformers, and it took the firefighters seven hours to
Speaker 1: get it under control. The airport, in order to come
Speaker 1: back online, was able to accept power from other substations,
Speaker 1: but even the time it took them to do that,
Speaker 1: many many flights were canceled. It was chaos.
Speaker 3: She made it, but it was not a pretty team.
Speaker 1: Yeah him. Back in twenty thirteen, there was actually a
Speaker 1: sniper attack on a substation in California which caused a
Speaker 1: fire that burned seventeen transformers and almost knocked out all
Speaker 1: of the power of Silicon Valley. Now This actually led
Speaker 1: to a lot more security around substations and even stockpiling
Speaker 1: of transformers, which of course sparked another problem, a shortage
Speaker 1: of transformers, transformer shortage and obviously with the supply chain
Speaker 1: issues recently, the lead time for delivering a new large
Speaker 1: transformer is now about three to five years for a
Speaker 1: single transformer, and bear in mind these can be huge. Nonetheless,
Speaker 1: the scale of transformer that was Heathrow, it takes a
Speaker 1: long hundre replace and they've got a much more expensive
Speaker 1: So you know, now we're living in the error of
Speaker 1: EVS and the AI boom powered by data centers and
Speaker 1: transformers are also required to bring renewable power onto the grid,
Speaker 1: and so you know, one of the interesting implications of
Speaker 1: this story, as Imaza, who's on the show, recently told
Speaker 1: me that actually the US's struggles to onboard new power
Speaker 1: to the grid maybe the reason why the US ultimately
Speaker 1: falls behind China in AI.
Speaker 3: So it's ironic actually that the increase in transformer prices
Speaker 3: could be further increased by President Trump's on again, off
Speaker 3: again relationship with imposing tariffs in Canada and Mexico, which
Speaker 3: is where we import a lot of our large transformers from.
Speaker 1: And that's why I love this Bloomberg story because it's
Speaker 1: fun think about all the sexy stuff like new chips
Speaker 1: and data center construction, you know, but there's still this
Speaker 1: one hundred year old technology that hasn't changed and that
Speaker 1: has to be imported and which is absolutely critical for infrastructure,
Speaker 1: digital and otherwise, you know.
Speaker 3: Speaking of the AI boom and things that use a lot,
Speaker 3: a lot, a lot of energy. This next story that
Speaker 3: I want to tell you is for those people who've
Speaker 3: been sitting here thinking about lms and not really understanding
Speaker 3: how they work. I have news for you. Much like
Speaker 3: Trump's decisions on tariffs, nobody knows how they work. And Anthropic,
Speaker 3: which is the same company that makes the AI model Claud,
Speaker 3: has been trying to figure out how the hell these things.
Speaker 1: This is to solve the so called black books.
Speaker 3: The black box problem. So Anthropic, which is the same
Speaker 3: company that makes the A model Claud, has been trying
Speaker 3: to figure out sort of what's under the hood, and
Speaker 3: they recently released two reports on how llms do things
Speaker 3: like complete sentences, solve math problems, and suppress hallucinations. And
Speaker 3: they use a technique called circuit tracing which let them
Speaker 3: track an LM's decision making process for ten different tasks
Speaker 3: by working back from the solution to the query.
Speaker 1: Huh okay, that makes sense. But I just think more broadly,
Speaker 1: as more and more decisions are taken for us by
Speaker 1: AI or outcomes kind of determined by AI, it's kind
Speaker 1: of remarkable that this huge elephant is still in the
Speaker 1: corner of the room, which is we can't understand how
Speaker 1: they make their decisions.
Speaker 3: But it also makes you think of like, I don't
Speaker 3: really know. I mean, I know how a car works,
Speaker 3: but like the I'm not an engineer, and yet increasingly,
Speaker 3: I mean, you know, since whenever the nineteen twenties, people
Speaker 3: have used cars more and more to get around, and
Speaker 3: we're just kind of like, well, this thing's gonna work
Speaker 3: until it blows up, you know. That's why I feel whatever.
Speaker 3: But it's important to note that lllm's, like Claude, which
Speaker 3: was the focus of this study, are trained, which I
Speaker 3: thought this was really interesting. They're trained, not programmed on
Speaker 3: a bunch of data. They create their own rules based
Speaker 3: on the data they ingest. But up until now we
Speaker 3: haven't been able to see into the models to know
Speaker 3: what those rules actually are. Let alone how the models
Speaker 3: generate them.
Speaker 1: Yeah, and I think what the work the anthropic is
Speaker 1: doing is all about basically understanding decision making. So then
Speaker 1: not yet at the stage of being able to understand
Speaker 1: how the models generate their own rules, But I think
Speaker 1: this story is all about how it's starting to become
Speaker 1: easier or possible to basically backfigure out how a model
Speaker 1: has made a decision.
Speaker 3: Yes, and it's not that simple. I think the researchers
Speaker 3: in this particular case were inspired by brain scan techniques
Speaker 3: that are used in neuroscience. They found that llms, which
Speaker 3: again I'm so interested in how we anthropomorphize lms, But
Speaker 3: they found that llms store different constellations of knowledge in
Speaker 3: different parts of their model. So, for example, the concept
Speaker 3: of smallness or the idea of a rabbit. Anthropic was
Speaker 3: actually able to identify and then turn off certain parts
Speaker 3: of the model. So like the idea rabbit and tune
Speaker 3: it down so that it couldn't be part of a
Speaker 3: queer result.
Speaker 1: And for exac like what eats carrots? It would be like.
Speaker 3: People right, a dog right exactly, And so the same
Speaker 3: query would have a different answer if the rabbit part
Speaker 3: of the model was dealt up or down, which you.
Speaker 1: Say said by contrast, you might you might say, you know,
Speaker 1: what's a mammal? It always asks rabbit if you dial
Speaker 1: it up, and if you if you dial it down,
Speaker 1: it would never own a rabbit.
Speaker 4: Correct.
Speaker 3: So I mean, and it's similar. I mean it's similar
Speaker 3: in human beings, which is like I don't drink anymore,
Speaker 3: so I go to a bar and I'm like water.
Speaker 3: It's a similar kind of thing.
Speaker 1: I mean that that actually clarifies for me, and I
Speaker 1: kind of We've done a bunch of coverage of AI
Speaker 1: and spoken to Jeffrey Hinton and others, but like that
Speaker 1: idea of a neural network is to me really clarified
Speaker 1: by this study.
Speaker 3: Well, and how could it not be. It's something that
Speaker 3: we've created, you know what I mean, it's going to
Speaker 3: reflect a sort of human centric way of working. I
Speaker 3: think this story actually came from the MIT Technology Review
Speaker 3: with the headline anthropic can now track the bizarre inner
Speaker 3: workings of a large language model, to which I said, Now,
Speaker 3: what really blew my mind, though, is the way that
Speaker 3: this thing solves math problems, because it's not the way
Speaker 3: that humans do math.
Speaker 1: Okay, tell me more.
Speaker 3: So, research asked it to solve the equation thirty six
Speaker 3: plus fifty nine, and while Claude came up with there,
Speaker 3: what's the correct answer? Well, well, Claude took a very
Speaker 3: circuitous route to get that answer. Anthropic found that Claude
Speaker 3: used multiple computation paths in parallel to get its final answer,
Speaker 3: unlike you, who just used your brain. So one path
Speaker 3: added a bunch of numbers. I love this. This is
Speaker 3: like nerd alert close to thirty six and fifty nine
Speaker 3: to approximate the total like thirty five and sixty, while
Speaker 3: another path focused on determining the last digit of the sum,
Speaker 3: so it actually added the last two digits of thirty
Speaker 3: six and fifty nine, six and nine to know that
Speaker 3: the answer had to be a multiple of five. So
Speaker 3: Claude used these two paths to come up with the
Speaker 3: correct answer, which is as you said.
Speaker 1: Ninety five. You know, I find particularly fascinating about this,
Speaker 1: so I always struggled to be with math. My dad,
Speaker 1: on the other hand, is a crazy math murder. He
Speaker 1: was like the under thirteen chess champion of Britain, etc.
Speaker 1: You know, he said to me about the most important
Speaker 1: thing about how to do math well, is how approximate
Speaker 1: the answer before you work it out. He basically told
Speaker 1: me to do exactly what this model does. Basically break
Speaker 1: it down to much simpler calculation, and then when you
Speaker 1: do the actual work, you will know whether or not
Speaker 1: you're in the range.
Speaker 4: Right.
Speaker 3: Well, Claude does math like your dad. When asked by
Speaker 3: a user how it got the answer ninety five, it
Speaker 3: claims to do it by the book, for example, simple addition,
Speaker 3: carrying the one and one of the explanations anthropic positive.
Speaker 1: Positive for the fact that, when asked how it got
Speaker 1: to the answer, did it shared a response that was not,
Speaker 1: in fact, how it got to the answer.
Speaker 3: Right because Claude's written answer and I quote may reflect
Speaker 3: the fact that the model learns to explain math by
Speaker 3: simulating explanations written by people. So when asked to do
Speaker 3: math without being taught how to do it, it may
Speaker 3: develop its own internal strategies to do so. Remember it's
Speaker 3: trade not programmed.
Speaker 1: We're going to take a quick break, but stick around
Speaker 1: and we'll be back with this week's tech support, all
Speaker 1: about AI competition among tech giants. For our next segment,
Speaker 1: we're going to be talking about all things AI. Surprise, Surprise,
Speaker 1: on tech stuff Rise tell surprise, But in all seriousness,
Speaker 1: there's just so much happening in AI all the time.
Speaker 1: I find it even though it's our job, but it's
Speaker 1: hard to keep up with.
Speaker 3: We know. I literally work on a podcast about technology
Speaker 3: and half the time other people are telling me what's
Speaker 3: going on in the world of technology. Because I've come
Speaker 3: I've become kind of like a sieve for technology news
Speaker 3: from my friends.
Speaker 1: Absolutely, So this week we're going to talk to somebody
Speaker 1: who can help navigate a whirlwind of headlines. And you've
Speaker 1: got the big open Ai fundraise, You've got elon integrating
Speaker 1: x into Xai, and then you've got the struggles at
Speaker 1: Apple with.
Speaker 2: Their AI products or lack thereof.
Speaker 1: Here to help us decode the current AI landscape is
Speaker 1: Garrett Devinc who's a technology reporter for The Washington Post. Garrett,
Speaker 1: Welcome to Tech Stuff.
Speaker 4: Happy to be here.
Speaker 1: Let's start with a big one. The volume of news
Speaker 1: around AI today feels quite overwhelming. I mean, also, it
Speaker 1: comes from so many different companies Open Ai, Google, Xai, Microsoft, Amazon, Apple,
Speaker 1: The list goes on and on. How do you keep
Speaker 1: up with all of this and also figure out how
Speaker 1: to sort the signal from the noise.
Speaker 5: Yeah, I mean, I think this is kind of a
Speaker 5: key question. I'm sure a lot of people are asking themselves,
Speaker 5: and I think first thing is, just take a deep breath,
Speaker 5: calm down. AI is not coming for your job. AI
Speaker 5: is not going to take over the world tomorrow, even
Speaker 5: if really smart people or powerful people or rich companies
Speaker 5: are saying that. Essentially, what we're seeing here is the
Speaker 5: tech industry is this huge conglomeration of a bunch of
Speaker 5: powerful people and billions and billions of dollars that are
Speaker 5: always looking for the next thing, always looking for the
Speaker 5: next way to make money. And they look back at
Speaker 5: tech trends over the years, the Internet, cloud computing, moving
Speaker 5: to mobile phones, and the tech industry has sort of
Speaker 5: collectively decided that AI is the next one of those stages, right,
Speaker 5: And so when the mobile phone came out, a bunch
Speaker 5: of new people were able to make money.
Speaker 1: Right.
Speaker 4: We didn't have Uber, we didn't have.
Speaker 5: Those kind of mobile first door Dash, those kinds of
Speaker 5: companies before the mobile phone came out, and people made
Speaker 5: huge amounts of money during that tech transition. And so
Speaker 5: what's happening now is the tech industry believes and is
Speaker 5: convincing themselves and trying to convince all of us that
Speaker 5: AI is that next step, and so they're pouring money
Speaker 5: into it, they're pouring marketing dollars into it, but at
Speaker 5: the same time, there's still trying to build the plane
Speaker 5: as it's taking off. And so that's why you might
Speaker 5: see a lot of products that maybe.
Speaker 4: Don't work very well.
Speaker 5: You don't know really how they fit into your life,
Speaker 5: and you're not sure whether you should be paying for
Speaker 5: them yet. And so I think the first thing is
Speaker 5: to say, you know, don't worry. You're not missing the
Speaker 5: boat if you're not using a million AI apps right now.
Speaker 5: And so, yes, this is happening. There's a lot of
Speaker 5: hype and interest here, but it's not as if AI
Speaker 5: is just sort of gonna change everything immediately.
Speaker 1: And one of the things that Karen and I sometimes
Speaker 1: talk about is like, is it speeding up or is
Speaker 1: it slowing down? Because like November December, all the headlines
Speaker 1: were slowing down CHACKGBT five is not coming, and now
Speaker 1: it feels like we're in a big speeding up moment. Again,
Speaker 1: is it even a relevant question? And where do you
Speaker 1: fall on it?
Speaker 4: Yeah?
Speaker 5: I mean I think it's a great question, and I
Speaker 5: think that analysis is correct, right. I mean, everyone is
Speaker 5: trying to either build something up or tear it down.
Speaker 4: That's how these things work.
Speaker 5: And so the reason we had this huge boost in
Speaker 5: interest was because chat gipt came out. It was definitely
Speaker 5: better than what most people had you used before and
Speaker 5: being able to experience directly, and they were able to
Speaker 5: put it in a format that regular people could actually
Speaker 5: understand and have a conversation with it. And so that's
Speaker 5: sort of what fired the starting gun. And then they said, okay,
Speaker 5: how do we make that better? And then the techniques
Speaker 5: they were using, which was essentially to use way more data,
Speaker 5: to just shove more data into these AI models and
Speaker 5: hope that they get smarter. That had been working up
Speaker 5: into a certain point, and then they kind of ran
Speaker 5: out of data and that method slowed down, and so
Speaker 5: they've now pivoted to using different techniques where they're actually
Speaker 5: spending a lot more time training the model to.
Speaker 4: Do different things.
Speaker 5: They're sort of doing more coding so that it can
Speaker 5: be a bit more efficient and strategic, and now they're
Speaker 5: seeing a boost in capability.
Speaker 4: From that technique.
Speaker 5: And if people remember the Chinese AI model called deep Seek.
Speaker 5: They really had a huge breakthrough where they were able
Speaker 5: to use a little bit less data and less computing
Speaker 5: power to come up with a model that was really
Speaker 5: quite capable. And so now everyone is saying, oh, we're
Speaker 5: speeding up again because we found new ways of increasing
Speaker 5: the capabilities.
Speaker 3: Which is I mean, ultimately a more long term effective
Speaker 3: way to get these things to work than having to
Speaker 3: rely on humongous data sets that might not be replenishable.
Speaker 4: Yeah, yeah, absolutely.
Speaker 5: And I mean the other aspect here is that AI
Speaker 5: is very compute intensive, which is essentially just a way
Speaker 5: of saying they need a lot of computers and a
Speaker 5: lot of computer chips in order to do AI in
Speaker 5: the first place. Takes a lot of energy, and so
Speaker 5: there's a lot of potential environmental concerns. Even here in
Speaker 5: the United States, coal power plants that were slated to
Speaker 5: be shut down have actually been ramped back up in
Speaker 5: order to serve all those AI data centers. So there
Speaker 5: is a lot of interest and pressure in making AI
Speaker 5: more efficient so that it's cheaper and more environmentally friendly.
Speaker 3: One of the biggest names in the industry is open AI,
Speaker 3: and for anyone who is living under a rock and
Speaker 3: wasn't on social media this week. Why is everybody talking
Speaker 3: so much about Studio Ghibli.
Speaker 5: Yeah, so, open Ai they released a new image generator, right.
Speaker 4: So one of the things that people have been able.
Speaker 5: To use AI for over the last couple of years
Speaker 5: is you make a short description and it spits out
Speaker 5: an image, and you know, it's been getting better and
Speaker 5: better over the months, and essentially they released a big
Speaker 5: update to THEIRS and people realize that they could use photos,
Speaker 5: upload photos and get the model to recreate that image
Speaker 5: and sort of the same design styles from iconic animators
Speaker 5: like you mentioned Studio Ghibli or even like the movie
Speaker 5: Wallace and Grommet or the Muppets, and.
Speaker 3: I like the Lego Family, the Lego Family or exactly.
Speaker 5: It was this moment where the technology allowed people to
Speaker 5: apply their creativity.
Speaker 4: In a new way, and that's why it went viral.
Speaker 5: And open Ai I think they did hope that this
Speaker 5: would happen. They themselves were using some of these Studio
Speaker 5: Ghibili examples early on. But also they can't really predict
Speaker 5: or control how this is going to go, and some
Speaker 5: of their releases have just kind of fallen flat. Everyone's
Speaker 5: been like boring we don't care, but.
Speaker 4: This one a lot of people.
Speaker 5: They found it really fun, they found it really interesting,
Speaker 5: and it definitely went viral, and it actually brought a
Speaker 5: lot more people who.
Speaker 4: Hadn't been using chat GPT before.
Speaker 5: But now, of course this raises all sorts of questions
Speaker 5: about art and copyright and big problems like that.
Speaker 3: What's interesting, though, is while they're trying to grow very quickly,
Speaker 3: we have someone like Sam Altman come out and say,
Speaker 3: please slow down with this image generation.
Speaker 1: He said that the GPUs are being melted by the demand, right.
Speaker 5: Yeah, I mean it's possible some GPUs actually did melt
Speaker 5: a little bit. I mean, you know, when you're using
Speaker 5: your laptop, you've got two hundred Chrome tabs open and
Speaker 5: you're trying to listen to YouTube, it gets hot, right,
Speaker 5: and so that's exactly what happens.
Speaker 3: My phone does get quite hot.
Speaker 5: Yeah, you know that same thing happens with AI, right.
Speaker 5: I mean, so this is still a very physical thing.
Speaker 5: Every single time everyone in the world says, hey, make
Speaker 5: an image of this. Hey write me a resume for this,
Speaker 5: Hey answer my test questions for me. That needs to
Speaker 5: go to a data center, It needs to be computed
Speaker 5: on these computer chips on GPUs, which is the technical
Speaker 5: term for the computer chips, and that heats them up
Speaker 5: and so I don't know if they were actually melting,
Speaker 5: But essentially what sam Altman was referring to there is
Speaker 5: just that so many people wanted to use this that
Speaker 5: it was becoming very expensive for open ai to run it.
Speaker 5: And this kind of gets to a central problem for
Speaker 5: them because the more people use it, the more it
Speaker 5: costs them in computer chip costs, and so they want
Speaker 5: people to use it, but at the same time they
Speaker 5: need to figure out, you know, how can we convince
Speaker 5: people force people to pay for these things so that
Speaker 5: we can actually grow as a business. And this is
Speaker 5: a huge question mark around open ai and other AI companies.
Speaker 1: As fun as it is making a studio ghibli portrait yourself,
Speaker 1: it's hard to measure it being a huge, huge business
Speaker 1: driver of people buying tokens to do that.
Speaker 5: That's sort of a question for open Ai, right, I mean,
Speaker 5: they've been able to have these viral moments. Chat GPT
Speaker 5: itself was a viral moment, and I do think people
Speaker 5: are using these technologies. I mean, in some ways open
Speaker 5: ai chatch GPT is one of the fastest growing, if
Speaker 5: not the fastest growing consumer Internet products ever. But we're
Speaker 5: not quite at that point yet where I think regular
Speaker 5: people are saying, oh, like, this is so important to
Speaker 5: my life, I need it so badly to write emails
Speaker 5: or to have fun like generating these images that I'm
Speaker 5: willing to pay two hundred dollars a month for it.
Speaker 5: I mean, I do think that the company is still
Speaker 5: in this world where they're trying to figure out how
Speaker 5: do we convince people that they need this so badly
Speaker 5: that they're willing to spend hundreds of dollars a year
Speaker 5: on it.
Speaker 1: So you may be somewhat skeptical, Garrett, but the market
Speaker 1: is not, or at least soft Bank is not. You
Speaker 1: talk a bit about that.
Speaker 5: Yeah, I mean open Ai raised forty billion dollars and
Speaker 5: at a three hundred billion dollar valuation, and I mean it's.
Speaker 1: The largest ever private financing of a US company that.
Speaker 3: A company that's not a private company a little while ago.
Speaker 5: Yeah, And the only private company that is actually worth
Speaker 5: more than three hundred billion dollars is SpaceX. So that's
Speaker 5: Elon Musk's Space company. But they build big physical rockets
Speaker 5: that cost hundreds of millions of dollars, So that company's
Speaker 5: worth three hundred fifty billion. Opening Eye is now, according
Speaker 5: to its investors, worth three hundred billion dollars, right, And
Speaker 5: so I think this mostly goes to the to that
Speaker 5: question about how expensive it is to do AI, right,
Speaker 5: and so they need that money in order to buy
Speaker 5: data centers, buy computer chips to keep helping people make
Speaker 5: these these studio ghibli images and all the other things
Speaker 5: that Opening Eye is working for.
Speaker 4: And so so it's.
Speaker 1: Just the uber model where you basically subsidized users and
Speaker 1: then once you get them hooked, you start to charge them,
Speaker 1: but at huge scale.
Speaker 4: Yeah.
Speaker 5: I mean that's a playbook that tech companies have used
Speaker 5: for years now, right, and get people hooked on something
Speaker 5: that is fun, cheap, easy, free work into their lives
Speaker 5: and so that they feel like they needed every single day,
Speaker 5: and then start to increase the costs. I mean Google
Speaker 5: has done this. I'm paying for my Gmail storage. I
Speaker 5: don't know if you guys are.
Speaker 3: I mean, yes, it's a question of when does something
Speaker 3: go from the gimmick phase to the business phase. I think,
Speaker 3: at least in terms of chat GBT.
Speaker 5: Yeah, And I think the other thing to point out
Speaker 5: here is that open ai does have a business where
Speaker 5: they sell access to their AI to other businesses, right,
Speaker 5: and so there's the consumer question, which is exactly what
Speaker 5: we're talking about. Then they are actually selling to businesses
Speaker 5: who want to put AI technology into their own apps
Speaker 5: and into their own technology, and that's also a big
Speaker 5: part of what open ai is trying to do here.
Speaker 5: But that's also, you know, a big question mark because
Speaker 5: we have these open source AI models as well. Deep Seek,
Speaker 5: the Chinese one we mentioned earlier is an example of that.
Speaker 5: Facebook also provides these tools where they just essentially put
Speaker 5: the AI out there for free for other businesses to
Speaker 5: take and use in their own ways. And so open
Speaker 5: ai is a very strong business. They have incredible technology,
Speaker 5: they have some of the smartest people in the world
Speaker 5: on AI, and they have huge funding and backing.
Speaker 4: From their investors.
Speaker 5: But at the same time, that doesn't guarantee that they're
Speaker 5: going to continue to grow or even be around in
Speaker 5: five years.
Speaker 1: When we come back, we'll hear about how other big
Speaker 1: tech companies like Amazon and Apple are fairing in the
Speaker 1: scramble to develop AI products. Yeah, we want to ask
Speaker 1: you a little bit about what Amazon and Apple are
Speaker 1: doing in the realm of AI. But just before we
Speaker 1: get there, there's another huge deal this week, also with
Speaker 1: a potentially dubious price tag.
Speaker 5: Yes, so I think you're referring to Elon Musk's merger
Speaker 5: of two of his companies little confusing.
Speaker 4: They're both kind of called XX.
Speaker 1: I couldn't even get through the read at the beginning
Speaker 1: of the shows. That's a tongue twist. Yeah, exactly.
Speaker 4: I mean I think that sort of speaks to Elon Musk.
Speaker 5: He has many companies at this point, and he's been
Speaker 5: known to sort of move assets around a little bit.
Speaker 5: And so when it comes to x which is formally Twitter,
Speaker 5: so that's the social media platform that he bought a
Speaker 5: couple of years ago for forty four billion dollars, he
Speaker 5: has now sold that company to his AI company, which
Speaker 5: is called Xai. And sort of formally, the social media
Speaker 5: company is now owned by the AI company. And so
Speaker 5: I say formally, because these companies had kind of been
Speaker 5: working together in a lot of ways. User data from
Speaker 5: the social media company was already being used to train
Speaker 5: the AI on the AI company. The AI company's main product,
Speaker 5: which is called Grock, which is a chat GPT competitor,
Speaker 5: was available through the social media company, and so in
Speaker 5: a lot of ways, these companies were already the same thing.
Speaker 5: And what he did here is he said, look, the
Speaker 5: AI company is able to raise a lot of money
Speaker 5: because everyone loves AI, everyone wants to boost AI. So
Speaker 5: I'm going to use that money that the AI company
Speaker 5: is able to raise to bail out and kind of
Speaker 5: give me more time when it comes to the social
Speaker 5: media company, which is very influential and maybe Elon Musk's
Speaker 5: most important company right now because of the political influence
Speaker 5: it gives him. But from a business perspective, the social
Speaker 5: media company has struggled and sort of been, you know,
Speaker 5: going through the wilderness a little bit since Elon Musk
Speaker 5: bought it because most of its users left, a whole
Speaker 5: bunch of new users came in, advertisers left.
Speaker 4: Maybe the advertisers are going.
Speaker 5: To come back, and so it's a way for Elon
Speaker 5: Musk to sort of use the AI hype to kind
Speaker 5: of help shore up the finances of his social media company.
Speaker 3: You know another major tech company that isn't mentioned as
Speaker 3: much in the AI raise, and that is Amazon. They
Speaker 3: recently unveiled Amazon Nova Act. They're AI agent. So what
Speaker 3: does the lay person need to know about this.
Speaker 4: So okay, a couple of things. I think AI agent
Speaker 4: is a term.
Speaker 5: That people are probably already hearing, and I guarantee you
Speaker 5: they're going to be hearing more about it in the
Speaker 5: coming months and years. An AI agent all that is
Speaker 5: is just you know, you can say, okay, well, if
Speaker 5: I can have a conversation with chat GPT, I can
Speaker 5: ask it things. Can I ask it to then go
Speaker 5: and read the internet for me? Can I ask it
Speaker 5: to go do things on the internet for me?
Speaker 1: Right?
Speaker 5: If chat GPT is able to read an e commerce website,
Speaker 5: can't I just tell chat GPT, hey, go buy me
Speaker 5: the cheapest sofa for my new furniture that you can
Speaker 5: find from my new apartment that's green and seats three people.
Speaker 4: Right.
Speaker 5: And so that's what an AI agent is, is essentially using
Speaker 5: AI to go and help people to do things on
Speaker 5: the internet for them. And so this is something a
Speaker 5: lot of AI companies are talking about. Obviously, there's a
Speaker 5: lot of problems. The technology is not quite there. The
Speaker 5: last thing you want is to say to your AI agent,
Speaker 5: go buy me a sofa for under one thousand dollars,
Speaker 5: and then suddenly eight sofas that cost ten thousand dollars
Speaker 5: each show up at your door a week later, right,
Speaker 5: I mean we need to be really careous. Is that?
Speaker 3: What is that the phase we're in right now?
Speaker 5: I mean a colleague of mine ran an experiment where
Speaker 5: he asked some of these AI agents to go and
Speaker 5: find him the cheapest eggs.
Speaker 4: This was sort of at the height of the egg panic,
Speaker 4: and eggs.
Speaker 5: Were selling for twenty dollars a dozen and the agent
Speaker 5: actually went and bought I think it was like thirty
Speaker 5: dollars eggs and had them delivered to his home before
Speaker 5: he even had the chance to say, like, yes, make
Speaker 5: that purchase.
Speaker 4: And so it's definitely in the experimental phase.
Speaker 5: But Amazon they sort of see that this is maybe
Speaker 5: the next frontier of the technology.
Speaker 1: Is that what the key interest is to basically do
Speaker 1: your shopping for you?
Speaker 4: I mean, I think that would make sense.
Speaker 5: I mean, they are the shopping company, and we all
Speaker 5: know Amazon Alexa.
Speaker 4: Was an early version of this.
Speaker 5: I mean you could ask Amazon Alexa to buy things
Speaker 5: for you on Amazon. You could ask it obviously to
Speaker 5: remind you about the weather, and people never really use
Speaker 5: it for more than that. And so people have been
Speaker 5: saying why was Amazon not ahead of this trend? Why
Speaker 5: is Amazon Alexa not smarter? Why can chat GPT do
Speaker 5: things that Amazon Alexa can't. So Amazon has been under
Speaker 5: a huge amount of pressure from its investors, from its
Speaker 5: own employees, from other people in the tech industry to
Speaker 5: show that they are riding this AI wave just like
Speaker 5: these other companies. And I think this Nova Act product,
Speaker 5: which is very new, it's still in the experimental phase,
Speaker 5: is a sign that they're trying to do that.
Speaker 1: All of this, of course brings us to Siri. I mean,
Speaker 1: the irony being that Amazon Alexa and Apple Siri were
Speaker 1: kind of ahead of the curve in terms of voice
Speaker 1: driven assistance, essentially agentic type features, and now Amazon feels
Speaker 1: like it's behind the curve a little bit, and certainly
Speaker 1: Apple does too when it comes to AI. What's going
Speaker 1: on there?
Speaker 5: Yeah, I mean, I think there's this dynamic that you're
Speaker 5: putting your finger on where these companies are really really
Speaker 5: invested in their own way of doing things and then
Speaker 5: there's a new way that kind of comes out of
Speaker 5: left field and to adjust right. This is the classic
Speaker 5: innovator's dilemma. And so I would say for both companies.
Speaker 5: Don't count them out. Amazon, Apple. They are both massive companies.
Speaker 5: They are bigger than pretty much anything we've seen in
Speaker 5: the history of business. They are in people's lives every minute,
Speaker 5: every day, and so I think, first of all, we
Speaker 5: should be careful not to just write them off and say, oh,
Speaker 5: they're behind, therefore they will fail. But I do think
Speaker 5: that this is really five alarm fire moment for them.
Speaker 1: So you, I mean, they've been having internal sort of
Speaker 1: ol hands meetings. What's the kind of internal drummer at
Speaker 1: apput on this?
Speaker 5: They are saying, Wow, we need to get on board
Speaker 5: with this. And Google had the same thing when chat
Speaker 5: GPT eventually came out.
Speaker 4: Right, Google is the AI company.
Speaker 5: A lot of this technology we're talking about was actually
Speaker 5: developed by Google and then just shared with the world
Speaker 5: because they didn't quite know what to do with it.
Speaker 5: And so for Apple, people have said, okay, well Series
Speaker 5: been around, are you going to make series smarter? This
Speaker 5: seems like an obvious application. They stayed quiet for about
Speaker 5: a year after chat GPT came out, and then they said.
Speaker 4: Yes, we're doing it. We're going all in.
Speaker 5: We're an AI company like everyone else. And now they've
Speaker 5: delayed some of those releases, right, they've said, oh, actually,
Speaker 5: we might need a bit more time to get it
Speaker 5: to the level where we want it. And we can
Speaker 5: see some of Apple's AI experiments have already failed pretty spectacularly.
Speaker 5: They had a bot that summarized some of the messages
Speaker 5: that were coming into your phone. Well I remember that, yeah,
Speaker 5: last year Apple Intelligence. And what they would do is
Speaker 5: they would see news alerts and they and said, oh,
Speaker 5: let me be helpful. Let me summarize the three or
Speaker 5: four news alerts you have. And the summaries were incorrect, right,
Speaker 5: And so that is upsetting as journalists. It's upsetting as
Speaker 5: a user because you want accurate information and Apple is
Speaker 5: muddling the waters.
Speaker 4: And so they had to pull that back.
Speaker 5: And people are starting to seriously ask the question, is
Speaker 5: this the moment where a company like Apple kind of
Speaker 5: falls from grace and loses its steam, loses its power.
Speaker 1: But here's the thing. Fifty percent of Apple's revenues come
Speaker 1: from setting the iPhone. And I can't imagine why do
Speaker 1: they need to be a monket eater in AI? Why
Speaker 1: can't they license other people they eye products?
Speaker 5: Yeah, I mean Apple doesn't really like doing that. They
Speaker 5: like to do everything on their own. They now build
Speaker 5: their own computer chips, they build their own software.
Speaker 4: And the whole point of Apple was.
Speaker 5: That it wasn't a PC, right, they had their own
Speaker 5: operating system. And so that is a huge part of
Speaker 5: Apple's value where they say, come into our walld garden.
Speaker 5: You're going to pay a lot of money, but you're
Speaker 5: going to be more secure, it's going to be cool,
Speaker 5: it's going to be more intuitive. We're doing things our way,
Speaker 5: and so that is their whole pitch to the consumer.
Speaker 5: And if they suddenly have to start saying, come into
Speaker 5: our world garden and use Google's AI, come into our
Speaker 5: world garden and use opening.
Speaker 3: Which I do now by the way, Yeah, I mean
Speaker 3: I use my Apple products to use chatchibt. But it's
Speaker 3: also like can you get everything from one guy? I
Speaker 3: don't know?
Speaker 5: Yeah, And I mean this is another big question because
Speaker 5: that's been the struggle over the last ten years between
Speaker 5: these giant tech companies to sort of box each other
Speaker 5: out and try to corral people within their own ecosystems.
Speaker 5: And maybe AI is the technology that kind of blows
Speaker 5: that up in a way.
Speaker 1: Gary, what a great place to end.
Speaker 4: Thank you so much for thank you so much, of course,
Speaker 4: it is my pleasure.
Speaker 2: That's it for this week for tech Stuff, I'm mos Vloshin.
Speaker 3: And I'm Kara Price. This episode was produced by Eliza Dennis,
Speaker 3: Victoria Dominguez, and Adriana Tapia. It was executive produced by
Speaker 3: me Oz Valashan and Kate Osbourne for Kaleidoscope and Katrina
Speaker 3: Norvel for iHeart Podcasts. Jess Crinchich is the engineer and
Speaker 3: Jack Insley mix the episode. Kyle Murdoch wrote our theme song.
Speaker 1: Join us next Wednesday for tech Stuff The Story when
Speaker 1: we'll share an in depth conversation with Reid Hoffmann, legendary
Speaker 1: founder of LinkedIn, venture capitalist at gray Lock, and author
Speaker 1: of the new book Super Agency. What could possibly go
Speaker 1: right with our AI future?
Speaker 3: Please rate, review, and reach out to us at tech
Speaker 3: Stuff podcast at gmail dot com. We want to hear
Speaker 3: from you.