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Speaker 2: This is Bloomberg Business Week, insight from the reporters and
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Speaker 2: global business, finance and tech news as it happens. Bloomberg
Speaker 2: Business Week with Carol Masser and Tim Stenovek on Bloomberg Radio.
Speaker 3: All right, everybody, yes, indeed, this is Bloomberg Business Week.
Speaker 3: I want to talk a little bit about Apple. Apple
Speaker 3: shares have had a challenging start to twenty twenty five,
Speaker 3: investors freighting over weakness in the critical Chinese market, the
Speaker 3: shares falling for five strade sessions before Monday's bounce, in
Speaker 3: the longest losing streak since April. They've been pressured by
Speaker 3: China's smartphone data, which Alice said pointed to weaker iPhone
Speaker 3: shipments and a router's report that Apple is offering discounts
Speaker 3: in China. A lot going on. Noticing it all and
Speaker 3: weighing in Moffatt Nathanson, which today downgraded the iPhone maker
Speaker 3: to sell, citing high valuation, antitrust, overhanging weakening position in China,
Speaker 3: the firm setting a price target of one hundred and
Speaker 3: eighty eight dollars a share, which before today's open implied
Speaker 3: a twenty three percent decrease. Craig Moffatt, founding partner and
Speaker 3: senior Alset Moffat Nathanson, joining us in New York City. Craig,
Speaker 3: so great to have you here with us. We kind
Speaker 3: of laid out what your call was all about, but
Speaker 3: walk us through a little bit more deep more deeply
Speaker 3: the specifics around that downgrade, because not a lot of
Speaker 3: people do cell ratings, especially on a name like Apple.
Speaker 1: You're right, Hi, Carol, thank you for having me. I
Speaker 1: think you put it right. This is largely a evaluation call,
Speaker 1: so I want to be clear Apple. We wouldn't argue
Speaker 1: with anyone who says Apple is a great company. We
Speaker 1: think that their AI strategy is a very sound strategy.
Speaker 1: I love the appeal of the fact that Apple has
Speaker 1: a low capital intensity approach to AI, that they have
Speaker 1: the trust of customers, and on and on. They have
Speaker 1: a lot of advantages. No one would dispute that. But
Speaker 1: it's trading at thirty three and a half time's earnings,
Speaker 1: and one way to think about it is just think
Speaker 1: about the PEG ratio, if you will, the ratio of
Speaker 1: growth rate of the multiple to growth rate. It's trading
Speaker 1: at a PEG ratio north of three. The rest of
Speaker 1: the MAG seven trades at a PEG ratio of around two.
Speaker 1: So you're talking about a fifty percent premium even to
Speaker 1: the peers within the MAG seven. That just doesn't make sense.
Speaker 1: Given what are some very material risks, and you mentioned
Speaker 1: some of them. China is a very real risk, not
Speaker 1: just because the Chinese government, for obvious reasons, is going
Speaker 1: to be disinclined to allow Western language models in China,
Speaker 1: so you're going to have to have a domestic partner
Speaker 1: and share economics with now, but also because the Chinese
Speaker 1: handset competitors have gotten much stronger. Another risk that is
Speaker 1: not discounted in the stock rice at all is the
Speaker 1: anti trust risk you and here I'm not talking about
Speaker 1: the anti trust risk in the cases against Apple, although
Speaker 1: there are plenty of them. The real anti trust risk
Speaker 1: that's very approximate is the risk in the Google case, where.
Speaker 4: Explain explain that risk to Apple? How is that? How
Speaker 4: is Apple at risk?
Speaker 1: Because twenty five percent of apples operating income comes from
Speaker 1: payments from Google, and the judge in the Google case
Speaker 1: has specifically said those payments are illegal. Now, you can
Speaker 1: imagine that there are outcomes where where they still pay
Speaker 1: some or all of that, but you can't pretend that
Speaker 1: there's no risk at all when the judge has already
Speaker 1: said that Google is guilty and the payments they make
Speaker 1: to Apple are illegal. I mean, that's twenty five percent
Speaker 1: of Apples operating income. It's it's twenty five billion dollars
Speaker 1: a year. And so the point we were making in
Speaker 1: this morning's downgrade, and again, I love app just as
Speaker 1: much as everybody else, but there are very real risks
Speaker 1: that are simply not appropriately discounted in the multiple that
Speaker 1: Apple is being awarded right now in the market.
Speaker 4: I want to zero in on the China risk and
Speaker 4: what exactly is because this is a story we've been
Speaker 4: talking about for years. At this point, Craig like concern that,
Speaker 4: you know, Chinese consumers are not going to buy as
Speaker 4: many Apple devices as they have previously. Switching costs are
Speaker 4: pretty switching barriers are pretty low in China because of
Speaker 4: these you know, over the top third party apps. People
Speaker 4: don't get locked into the iOS ecosystem like they do
Speaker 4: in other countries because of we Chat, et cetera. What's
Speaker 4: going on in China in your view.
Speaker 1: Well, I think the biggest thing that's going on in
Speaker 1: China is that the other handset manufacturers, Huawei, Honor and
Speaker 1: so on have gotten much better. If you went back
Speaker 1: to the last big iPhone cycle, which was the five
Speaker 1: G cycle in circa twenty twenty one, the Apple competitors
Speaker 1: at the time were significantly weaker than they are today.
Speaker 1: And interestingly, Huawei in particular, because the government of the
Speaker 1: US put sanctions on Huawei and limited Huawei's access to
Speaker 1: chipsets and that sort of thing, Huawe has had to
Speaker 1: sort of reinvent itself with domestic technology, and they've done
Speaker 1: a really good job. The handsets that they make are
Speaker 1: now fully competitive. They are not a replacement for Apple.
Speaker 1: I'm not suggesting that they are, but they have a
Speaker 1: really competitive offering, and so the market share opportunity for
Speaker 1: Apple is simply not what it was in the last
Speaker 1: big cycle. But you also have this time the government's
Speaker 1: thumb on the scales, and there's a couple of ways
Speaker 1: for that. One is, as I said, you're simply not
Speaker 1: going to have the Chinese Communist Party saying it is
Speaker 1: acceptable to have Western lms answering questions like what happened
Speaker 1: at Tianneman Square. So you're going to have to have
Speaker 1: domestic partners or in order to be able to offer AI,
Speaker 1: and that means a different margin structure and what have you.
Speaker 1: You also have a very real risk from tariffs. And
Speaker 1: it's not just in fact, not even primarily that Apple
Speaker 1: itself imports almost all of its components, and in fact
Speaker 1: today imports all of its handsets mostly from China because
Speaker 1: they're assembled there. Even if you even if they are
Speaker 1: exempted as they were during the last Trump administration, you
Speaker 1: have the risk of retaliatory tariffs in response to American
Speaker 1: tariffs that could be related to the auto market or
Speaker 1: completely unrelated categories, but where foreign governments respond by putting
Speaker 1: tariffs on a company that is obviously quite highly identified
Speaker 1: with the US. So again I don't want to suggest
Speaker 1: that any of these risks are base case risks. It's
Speaker 1: not like there's a sixty or seventy percent chance that
Speaker 1: any one of them will happen. But if you have
Speaker 1: all of these potential risks, all of which are not
Speaker 1: shouldn't be described as sort of a tail risks. These
Speaker 1: are really real possibilities. In all of these cases that
Speaker 1: there are negative outcomes and they ought to be reflected
Speaker 1: in the multiple of the stock with a more sober multiple,
Speaker 1: and they simply are not.
Speaker 4: Craig our Apple device is not seen as having the
Speaker 4: same allure to the Chinese consumer as they once did.
Speaker 1: You know, I don't know that that's the case. There
Speaker 1: are still Apple is still a brand with real cachet
Speaker 1: in China. But you know, remember you now have Huawei
Speaker 1: introduced a trifold phone for where you have Samsung devices
Speaker 1: that have had foldable screens for a long time coming
Speaker 1: in from Korea. So the gap between Apple and its
Speaker 1: competitors is not what it once was. There's just there's
Speaker 1: simply no way to argue that that's not the case.
Speaker 3: You know your report, You know, pundents you have watched
Speaker 3: Apple stockgrind higher of the last several months have shrugged
Speaker 3: off the appreciation as a melt up on known news.
Speaker 3: But that's not quite right. And you go, there has
Speaker 3: been a lot of news, right, it's just all been bad.
Speaker 3: Why them melt up? Then? Why does everybody I don't know,
Speaker 3: maybe it's a stupid question, but why does everybody continue
Speaker 3: to chase it and send it higher? When there are
Speaker 3: some real threats in terms of, as you said, the
Speaker 3: DOJ case against Google, that would really take out a
Speaker 3: big chunk of kind of the financial or the balance
Speaker 3: sheet right of that company, of Apple specifically. So why
Speaker 3: does everybody kind of continue to support it, Why do
Speaker 3: they chase it? Why is there the melt up?
Speaker 1: You know, it's a great question. Now, first I should
Speaker 1: be clear, you know, although the stock has climbed higher
Speaker 1: and higher over the last few months, it's only traded
Speaker 1: in line with the market. So to some degree, this
Speaker 1: is an indictment of the way the market overall is
Speaker 1: pricing equities right now, particularly in the face of rising
Speaker 1: interest rates in therefore appropriately higher discount rates. It's but
Speaker 1: I suspect Carol, that the real issue here is is
Speaker 1: that there is this narrative that Apple is the quote
Speaker 1: unquote safe choice among the mag seven, and that it
Speaker 1: is such a strong consumer brand, has such an unassailable
Speaker 1: franchise that it's the safe place to be. And while
Speaker 1: that's absolutely true operationally, it's not a safe place to
Speaker 1: be if it's trading at thirty three times earnings. It's
Speaker 1: a safe place to be if it's appropriately valued, but
Speaker 1: if it's if it's already being awarded an extraordinarily high multiple,
Speaker 1: nothing safe about that, even if it is a very
Speaker 1: strong business model.
Speaker 3: So if it gets down to one eighty eight that's
Speaker 3: your price target, do you change your rating and do
Speaker 3: you say, okay, now you can come in and buy,
Speaker 3: or do you kind of still wait and see.
Speaker 1: Well, we would certainly say that what you want to
Speaker 1: get to is you want to get to a price
Speaker 1: where it's fairly valued, and if it gets below that,
Speaker 1: then there's actually an opportunity to buy it. And I
Speaker 1: certainly wouldn't argue that Apple doesn't deserve an attractive valuation.
Speaker 1: It's a great company. It's just that when you start
Speaker 1: to get to thirty three times earnings and again a
Speaker 1: peg ratio of north of three, what that implies is
Speaker 1: that the market has already priced in an enormous amount
Speaker 1: of potential good news, and so you'd have to see
Speaker 1: something quite extraordinary to be able to say that over
Speaker 1: the next call it, one, two, three years, that the
Speaker 1: stock can actually deliver attractive returns given this starting point.
Speaker 4: Okay, Craig, while we have you don't just cover Apple,
Speaker 4: you also cover Comcast, Echo Star, Charter, Altie, Verizon and
Speaker 4: more Charter Communications. It's your top pick for twenty twenty five,
Speaker 4: despite cord cutting accelerating, despite what's happening to PATV, Why
Speaker 4: is it your top pick?
Speaker 1: You know, it's funny in some ways, it is the
Speaker 1: exact antithesis of what we were just talking about with Apple.
Speaker 1: Charter and the cable stocks have been given up for dead,
Speaker 1: and so they are extraordinarily attractive valuations. And a company
Speaker 1: like Charter, remember, Charter was one of the best performing
Speaker 1: stocks in the S and P for a decade. It
Speaker 1: was out twenty threefold, I think over a decade when
Speaker 1: it was buying back a ton of stock and had
Speaker 1: a really attractive levered equity return strategy. They suspended that
Speaker 1: strategy for a while in order to invest really aggressively
Speaker 1: in rural buildouts, and the market has seen the free
Speaker 1: cash flow yield go down during that capital investment cycle.
Speaker 1: You could argue that those are actually pretty good investments,
Speaker 1: but the market hasn't found them as exciting as the
Speaker 1: old strategy of buying back stock. Well, they've already said
Speaker 1: they're largely getting to the end of that strategy, and
Speaker 1: so they will as CAPEC starts to come down, they're
Speaker 1: going to resume their stock buybacks in a very big way.
Speaker 1: And it's a company that we see growing in the
Speaker 1: load to mid single digits. Not sexy, but by the way,
Speaker 1: that's what Apple is growing as well, but growing in
Speaker 1: the load of call it low single digits. But it's
Speaker 1: priced for negative perpetual growth. And by our twenty twenty
Speaker 1: eight free cash flow estimate, we have this thing trading
Speaker 1: at a something like thirty three percent free cash flow
Speaker 1: yield on twenty twenty eight. That's an absurd, absurd valuation,
Speaker 1: and so I just think it's way too cheap and
Speaker 1: a really exciting opportunity at this price.
Speaker 3: Hey Craig, just one last question. You know, it's funny
Speaker 3: coming off the Golden Globes. I was kind of like,
Speaker 3: oh my god, it's just content coming from just all
Speaker 3: these different places and streaming and regular teeth. It's just
Speaker 3: kind of nuts. But I am curious as we look
Speaker 3: at where content's coming, in particular streaming other than Netflix,
Speaker 3: do you think the legacy media companies can navigate the
Speaker 3: change towards streaming.
Speaker 5: In a clear way.
Speaker 3: And unfortunately just got about forty five seconds.
Speaker 1: Well, it depends what you mean. By can they navigate it,
Speaker 1: Will they survive? Of course? Will it ever be as
Speaker 1: good a business as the old legacy cable network business
Speaker 1: was and it's heyday. No, it's just not going to
Speaker 1: be as attractive a business because it's so much easier
Speaker 1: for customers to churn. That creates a less attractive revenue profile,
Speaker 1: a less attractive cost profile. But you will still have winners,
Speaker 1: and they may not be winners on the scale of Netflix.
Speaker 1: But Disney is still going to be all right in
Speaker 1: the in the streaming business. Again, it's just not going
Speaker 1: to be as good as the business that they are
Speaker 1: leaving behind.
Speaker 3: Thank you, thank you so much. Really really enjoyed this,
Speaker 3: and happy New Year to you, really appreciate it. Craig Moffatt,
Speaker 3: founding partner and senior analyst, Debret Moffatt Nathanson joining us
Speaker 3: right here in New York City. If you missed any
Speaker 3: of it, be sure to check out our podcast feed
Speaker 3: a little bit later on. You can find it at
Speaker 3: Bloomberg dot com or on the Bloomberg Well.
Speaker 4: Big news out of meta Platforms Today, the company is
Speaker 4: going to end a third party fact checking on its
Speaker 4: social media platforms in the US, letting users comment on
Speaker 4: post accuracy with a community note system that it said
Speaker 4: it will promote.
Speaker 3: Free expression that could go wrong.
Speaker 4: Look over to x formerly known as Twitter, for example.
Speaker 4: A lot of questions around this name.
Speaker 3: Well say, what could go wrong?
Speaker 4: Well, okay, there are a lot of questions around this. Yeah,
Speaker 4: political questions, what it's doing ahead of the next administration
Speaker 4: in the wake of yesterday's announcements with the board and
Speaker 4: new folks in public policy, but also what it's going
Speaker 4: to do to engagement. And the reason I'm talking about
Speaker 4: engagement is because that's what it's the core of meta
Speaker 4: platforms business model. Using the services we use them, then
Speaker 4: the services know who we are. They learn who we are,
Speaker 4: Advertisers can better target ads to us. It's data, it's algorithms,
Speaker 4: it's big data. That's the currency.
Speaker 3: Yeah. This is Sandra at Mats's Business and Her World.
Speaker 3: She's Associate Professor of business at Columbia Business School, the
Speaker 3: author of a new book out today, mind Masters, The
Speaker 3: Data Driven Science of Predicting and Changing Human Behavior. She
Speaker 3: joins us in our Bloomberg Interactive Booker studio. I'm so
Speaker 3: glad to have you here with us. Welcome, happy New year.
Speaker 3: Before we get into the book. I mean, when you
Speaker 3: hear this development from meta platforms, I don't know what's
Speaker 3: your reaction and what confidence do you have that we
Speaker 3: don't have a lot of misinformation and some problems as
Speaker 3: a result out of this.
Speaker 6: I mean, I think I share your concerns of getting
Speaker 6: rid of content moderation, because, right, I think we've seen
Speaker 6: over the last couple of years what happens with misinformation,
Speaker 6: and just frankly, I think misinformation is just such a
Speaker 6: small piece of the entire puzzle because a lot of
Speaker 6: the information that's out there is not necessarily fake and
Speaker 6: not true, but it's just slant in right, So it's
Speaker 6: not necessarily that it's actually inaccurate, but it has like
Speaker 6: a certain angle and a certain spin that speaks to
Speaker 6: what we want to hear. And for me, that's even
Speaker 6: a bigger problem because it's much harder to regulate, right,
Speaker 6: it's much harder to take down from content moderators or
Speaker 6: as metas trying to do now just from a consensus
Speaker 6: among users. Right, that's an even higher bow when we
Speaker 6: think about these news that are maybe not actually inaccurate
Speaker 6: but certainly tailored to a certain opinion.
Speaker 4: Is this the way of the future, though, Are we
Speaker 4: sort of moving past this idea of a fact based
Speaker 4: society and one that's more based on interpreting what you
Speaker 4: see that's thrown at you. And I think it takes
Speaker 4: on a different The question takes on something different when
Speaker 4: you know, as I'm asking, I'm thinking about AI generated
Speaker 4: content and what we do know is real and what
Speaker 4: we don't know is not real.
Speaker 6: Yeah, I think it's a risky gamble to think about
Speaker 6: this moving post or past what I think of shared reality, right,
Speaker 6: it's the idea that as a society, people living together, frankly,
Speaker 6: we can make sense of the world in a way
Speaker 6: that at least offers us the opportunity to have a discussion.
Speaker 6: And I think the moment that we entered this world
Speaker 6: where I have completely a completely different reality than you
Speaker 6: and we're not even discussing it because we're cut into
Speaker 6: in these echo chambers online right where I don't even
Speaker 6: see what you see, I think that's a really risky
Speaker 6: gamble because it's it's not even out there anymore. We
Speaker 6: don't have the public square anymore where we can say, well,
Speaker 6: this is something that you've seen, I've seen this, Well,
Speaker 6: what do you think is actually true? How should we
Speaker 6: be moving forward? I think that is what is what
Speaker 6: is missing?
Speaker 3: Well, it's so funny, I think we thought social media.
Speaker 3: It's been great sparking all these conversations, but it's really
Speaker 3: kind of a one one you know, pathway dump if
Speaker 3: you will, right, and then you kind of have to
Speaker 3: deal with it. What I want to ask you is
Speaker 3: your book Mind Masters, The Data Driven Science of Predicting
Speaker 3: and Changing Human Behavior. I want to get into the predictability.
Speaker 3: Do you say that if we had really monitored social
Speaker 3: media we would have been able to predict the January
Speaker 3: sixth uprising? Could we have predicted some other things, you know,
Speaker 3: in terms of what has happened in society?
Speaker 6: Yeah, So what I'm mostly interested in is actually trying
Speaker 6: to understand individuals. So what you're talking about is like
Speaker 6: predicting these macro level trends, right, It's like how is
Speaker 6: the collective acting? And I do think that you might
Speaker 6: have been able to predict that, right, because it's this
Speaker 6: is all coming down to sentiment. It's like people are angry,
Speaker 6: people want to change, So the fact that people matters hands.
Speaker 6: It's like what we call social listening. It's trying to
Speaker 6: see what is it that people care about?
Speaker 3: And you talk specifically about something called psychological targeting, which
Speaker 3: I don't know that, tim have we kind of really,
Speaker 3: I don't know that I've heard that terminology. What is that? Yeah?
Speaker 6: So psychological targeting is the way in which algorithms can
Speaker 6: translate your data into psychological constructs. So it's really trying
Speaker 6: to make sense of you as a person. Right if
Speaker 6: I told you what, I can get access to everything
Speaker 6: you post on social media to maybe your credit card spending,
Speaker 6: your Google searches, the sensors that I'm betted in your
Speaker 6: smartphone which keep track youration via the GPS.
Speaker 5: Sense of for example.
Speaker 6: It doesn't seem that intimate, but once I can tell you, well,
Speaker 6: I can translate these traces into an understanding of whether
Speaker 6: you might be extroverted, impulsive, neurotic, whether you vote for
Speaker 6: a certain candidate, your political orientation, your sexual identity. Those
Speaker 6: are all really intimate insights. And that is the cracks
Speaker 6: of psychological targeting. It's making use of AIM machine learning
Speaker 6: to translate data into human understandable profile.
Speaker 4: Do you have to use AI and data? Because your
Speaker 4: book opens up talking about your own personal experience. The
Speaker 4: first eighteen years of your life spent in a very
Speaker 4: small village of what five hundred people, and an accident
Speaker 4: that you had that then immediately everybody knew about what's
Speaker 4: the connection between that and where technology is today and
Speaker 4: what tech can do?
Speaker 6: Yeah, So I think it's absolutely right, and that the
Speaker 6: fact that we oftentimes observe people's behavior and make inferences
Speaker 6: about who they are isn't new. Right in this village
Speaker 6: that I grew up and people observe what I was doing,
Speaker 6: They saw me running to the bus every morning, and
Speaker 6: they probably figured out that I wasn't the most organized
Speaker 6: person in the world. And so the incident that you
Speaker 6: were referring to is essentially I had this motorcycle crash
Speaker 6: and the whole village knew about it.
Speaker 5: Right, So in an instance, those.
Speaker 6: New spread and it's not just like an isolated well
Speaker 6: this is what happened. It's like, Okay, I'm gonna now
Speaker 6: understand who Sandra is, and I'm going to use that
Speaker 6: knowledge moving forward to maybe push her in certain directions.
Speaker 6: But maybe she if she's not organized, maybe she's not
Speaker 6: the one that I want to rely on when I
Speaker 6: have to move my stuff and when I have to
Speaker 6: organize something that has to be perfectly done. So I
Speaker 6: think that the shift to the digital world just means
Speaker 6: that we have neighbors all around us, and they're not
Speaker 6: really physical neighbors. They're neighbors that are algorithms and they
Speaker 6: observe everything we do based on the data and then
Speaker 6: make very similar inferences.
Speaker 3: Yeah, all right, So so I don't know that, you know,
Speaker 3: the horse is out of the barn, out of the
Speaker 3: social media barn. So I'm just like, I don't know
Speaker 3: where do we need to go or how do we
Speaker 3: then need to think about social media? I mean, I
Speaker 3: think I read that is it something that you did
Speaker 3: where you put I think all your social media down?
Speaker 3: I think it was a calumn for the times or
Speaker 3: something where you stepped away, Like, what is it that
Speaker 3: we need to do to maybe have a better way
Speaker 3: forward and maybe understand society at large.
Speaker 6: I think it's a great question, and to me, it
Speaker 6: actually comes back to this analogy of the village, right
Speaker 6: because the fact that my neighbors knew everything about me,
Speaker 6: they knew my innovations, preferences, dreams, hopes, and so on,
Speaker 6: actually allow them to give the best advice ever because
Speaker 6: they knew me right, They really understood what I wanted,
Speaker 6: but it was also like a lot of room for manipulation.
Speaker 6: So what I've been thinking a lot about is like,
Speaker 6: how do we actually push towards this.
Speaker 5: How do we use these insights to help people?
Speaker 6: And that could range anywhere from well, can I use
Speaker 6: insights to help you become healthy and happier by helping
Speaker 6: with mental I'm wondering about the and I should.
Speaker 3: Say, you took a decision holiday and put AI in
Speaker 3: charge of my life. Forgive me, I misread it, but
Speaker 3: it's like we could talk about that later.
Speaker 4: Incentives the idea of incentives, and I'm wondering about mis
Speaker 4: aligned incentives right now because if every time I pick
Speaker 4: up my phone, every app I'm looking at, the incentive
Speaker 4: is to keep me in that app by feeding me
Speaker 4: information that will keep me engaged. Are the companies that
Speaker 4: create these programs do they have our interests at heart?
Speaker 4: Do they have our best interests at heart? Or are
Speaker 4: the incentives totally misaligned?
Speaker 5: Yeah?
Speaker 6: So I think that the ones that you're talking about,
Speaker 6: which is like really attention economy, there the incentives are
Speaker 6: not in your favor. But if the only thing that
Speaker 6: I'm trying to do is engage you. What I'm going
Speaker 6: to show you is content that's negative, that's morally outraging,
Speaker 6: because that's what keeps us there and that's just playing
Speaker 6: into human nature. I do think, however, that there's a
Speaker 6: lot of companies outside of that space, right, So there's
Speaker 6: a lot of mental health applications, for example, that are
Speaker 6: popping up now, and the other are people people are
Speaker 6: using and it's a totally different way of commercializing because
Speaker 6: it's not necessarily grabbing your attention, but it's trying to
Speaker 6: help you make the most of the service. But you're
Speaker 6: absolutely right, the classic social media platforms that are the
Speaker 6: current big players incentives are really difficult to aligned.
Speaker 3: Soandra, maybe we just have calling it social media because
Speaker 3: if anything social Yeah, no, I'm serious, Like, you know,
Speaker 3: we kind of buy into this whole idea that look,
Speaker 3: it's going to reduce you know, reduce the gaps in
Speaker 3: the world and you know, make us all closer. And
Speaker 3: I think during I think about the Middle East uprising,
Speaker 3: like initially the Arab spring that we thought, oh my god,
Speaker 3: look at the good of social media, right, and that
Speaker 3: was years ago already. But I do think, you know,
Speaker 3: we by constantly saying social media are kind of buying
Speaker 3: into this and reinforcing the branding.
Speaker 6: That's absolutely true, and it's also I think it almost
Speaker 6: strects from the entire narrative, right because we're only talking
Speaker 6: about social media.
Speaker 5: So the only thing that's top.
Speaker 6: Of mindful people when they think about data that is
Speaker 6: intrusive is social media. But then think of about all
Speaker 6: of the other stuff. Again, It's like your Google searches
Speaker 6: is what you buy with your credit card. It's the
Speaker 6: data that gets captured by your smartphone that is really
Speaker 6: if you think about the analog comparison, that's a person
Speaker 6: walking behind you twenty four to seven, your smartphone knows
Speaker 6: exactly where you're right at any given.
Speaker 3: Point in the ultimate stalker exactly.
Speaker 4: So have you changed your behavior based on your research?
Speaker 6: It's a great I think actually, just observing my own
Speaker 6: behavior has made me a lot more pessimistic about the
Speaker 6: idea that we can just ask consumers to take care
Speaker 6: of their own data, right, because I don't have twenty
Speaker 6: four seven to read all of the terms and conditions.
Speaker 5: I'd much rather spend a meal.
Speaker 7: Oh, come on, piece the case, there's no way if
Speaker 7: we want to exactly if we want to change something,
Speaker 7: I think we just need systemic changes and that could
Speaker 7: be regulation which changes the default.
Speaker 3: How likely is that?
Speaker 5: Well, you do see.
Speaker 6: Regulation across the globe, right, so if you look to Europe,
Speaker 6: if you look at California, but it's still focused on
Speaker 6: this notion of like, well we just give control to people,
Speaker 6: and I think that's a that's a tricky gamble.
Speaker 3: Aren't you freaked out?
Speaker 7: My kids?
Speaker 4: And yeah, you know, because it's.
Speaker 3: Only going to get worse, and schools like you have
Speaker 3: to be on these social media platforms to also different
Speaker 3: social media platforms. Sondra, thank you so much, Thank you
Speaker 3: so much. Sondra Matts. Her book is mind Masters, The
Speaker 3: Data Driven Science of Predicting and Changing Behavior.
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