Speaker 1: Some of the biggest players in AI reported earnings last week.
Speaker 1: Microsoft reported earnings right after the market closed, and we
Speaker 1: did have a revenue beating analyssessments, but they didn't see
Speaker 1: their shares jump on the good news. But the stock
Speaker 1: is falling in after hours trading. Google's parent company, Alphabet,
Speaker 1: saw its shares fall after it missed revenue expectations.
Speaker 2: Alphabet, parent of Google, is down severely.
Speaker 1: Alphabet less detail on really what contribution AI will have,
Speaker 1: particularly on search. We're in the midst of earning season
Speaker 1: for some of the biggest tech stocks, and falling shares
Speaker 1: in some of these companies suggest that investors may be
Speaker 1: disappointed by what these companies can deliver with AI.
Speaker 3: There are real questions about the business about how these
Speaker 3: new technologies, exciting as they are, cool as they are,
Speaker 3: are going to make these companies money.
Speaker 1: That's my colleague Max Chafkin, who reports on these companies.
Speaker 3: What I think people are starting to realize is that sure,
Speaker 3: you can be very bought in into the promise of AI,
Speaker 3: but even so, it's going to be a slog.
Speaker 1: On today's episode, we dig into the expectations for AI
Speaker 1: versus the reality why these companies are failing to meet
Speaker 1: some investors' hopes, and what it would take for these
Speaker 1: technologies to catch up to their promise. I'm your host,
Speaker 1: Sarah Holder, and this is big take from Bloomberg News.
Speaker 2: My name's Max Chafkin.
Speaker 3: I am a senior reporter with Bloomberg BusinessWeek and I
Speaker 3: cover technology, particularly kind of the intersection of technology and power,
Speaker 3: and I also co host the Elonink podcast.
Speaker 1: I know you've written a lot about sort of hype
Speaker 1: cycles in bitcoin and cryptocurrency, and so interested to talk
Speaker 1: about a new hype cycle today. Artificial intelligence has been
Speaker 1: making headlines for the supposedly vast potential of the technology.
Speaker 1: What kinds of expectations have technology leaders set about what
Speaker 1: they're going to do with this AI capacity.
Speaker 3: It's so funny you say the expectations. I mean, because
Speaker 3: the expectations are wild, like the craziest things you can
Speaker 3: possibly imagine. So Sam Altman, who is the founder a
Speaker 3: CEO of Open Ai, has talked about what his company
Speaker 3: is doing as you know, basically modern day you know, Oppenheimer,
Speaker 3: This is like as significant as potentially destructive as the
Speaker 3: atom bomb.
Speaker 2: It's a big part of why I'm here today and
Speaker 2: why we've been here in the past.
Speaker 1: Here's open AI CEO Sam Altman speaking before Congress in
Speaker 1: May of last year.
Speaker 3: I think if this technology goes wrong, it can go
Speaker 3: quite wrong, and we want to be vocal about that.
Speaker 2: We want to work with the government to prevent that
Speaker 2: from happening.
Speaker 3: What's interesting to me about these warnings, and I think
Speaker 3: it's worth taking, you know, any warning about technology.
Speaker 2: Seriously. Technologies can have unintended consequences, but.
Speaker 3: They also serve, of course as a sales pitch, because
Speaker 3: if you're going around saying that, hey, this thing I'm
Speaker 3: building is.
Speaker 2: So effective it could take.
Speaker 3: Over the world, it could render human beings irrelevant, that
Speaker 3: is another way of saying, like the thing I'm building works.
Speaker 3: And in technology, you know, over my career, I feel
Speaker 3: like a question like nobody asks enough, is like does
Speaker 3: it work? And one of the reasons is because technologists
Speaker 3: are very good at kind of obscuring that question and
Speaker 3: raising other questions, including does this technology potentially destroy the world.
Speaker 1: So even the terrifying expectations are inherently potentially overly positive
Speaker 1: about the technology itself.
Speaker 2: Absolutely, you know.
Speaker 3: On the Tesla earnings call, my favorite moment was when
Speaker 3: Elon Musk is sort of spinning this story about Tesla's
Speaker 3: AI investments. He's saying, optimists, their robot is going to
Speaker 3: be the greatest product of all time, better than any
Speaker 3: product in human history, and it's actually going to like
Speaker 3: usher in a world of unlimited productivity. Essentially, it's going
Speaker 3: to be like in the movie Wally, we can all
Speaker 3: just like sit back and let the robots.
Speaker 2: Do all our work.
Speaker 3: And while he's saying this, another executive on the Tesla
Speaker 3: call cuts in and says, the only issue is we
Speaker 3: have not found actual use for these robots to do
Speaker 3: where that it works. And you got to remember, like
Speaker 3: a lot of the people who buy software are not
Speaker 3: worried about apocalyptic scenarios. They're just trying to figure out
Speaker 3: is the return on investment for this going to be
Speaker 3: worth it? If I'm a chief technology offerer, chief information
Speaker 3: officer for a large company, I don't think I'm mostly
Speaker 3: thinking about like what's the long term societal impact of
Speaker 3: this giant check? I'm going to write Microsoft or open AI.
Speaker 3: I'm thinking, is it going to return? Am I going
Speaker 3: to look like a moron to my CEO for writing
Speaker 3: this check?
Speaker 2: Right?
Speaker 1: So Microsoft alphabet Apple, Meta have all been kind of
Speaker 1: riding high on these expectations. But can you talk about
Speaker 1: what we saw in earnings this past week?
Speaker 3: All right, So just take a step back for a second.
Speaker 3: There was kind of a hangover after the pandemic, right
Speaker 3: where a lot of technology company had invested huge amounts
Speaker 3: of money, sort of assuming that the growth that was
Speaker 3: happening because we're all sitting at home doing nothing, but
Speaker 3: like you know, scrolling our phones was going to go
Speaker 3: on forever, and they all kind of, in one way
Speaker 3: or another, went into a dip. And when open ai
Speaker 3: introduced chatch Ept, it essentially set the world on fire
Speaker 3: because it promised a new platform, a new thing to
Speaker 3: get people excited about. And all the companies that you
Speaker 3: mentioned in one way or another have investments in AI.
Speaker 3: And what's happened is, i'd say, just you know, a
Speaker 3: reality check. Essentially, Google reported earnings. It was not even
Speaker 3: like it was that bad. It just seems like the
Speaker 3: view from investors is like, WHOA, this stuff is going
Speaker 3: to take longer and be harder than maybe we had hoped.
Speaker 3: And you know, with Google, they were until very recently
Speaker 3: the market leader in AI, and then open Ai comes
Speaker 3: out kind of steals a step on them. There's this
Speaker 3: kind of sudden like, oh no, like maybe there's another player.
Speaker 3: But Google still has like all of these very impressive
Speaker 3: pieces of AI software.
Speaker 2: But the thing is that as impressive as.
Speaker 3: They are, they're all in one way or another kind
Speaker 3: of research projects. They're not really commercial projects yet. And
Speaker 3: what is a commercial product is you know, search advertising,
Speaker 3: this business that is getting mature. And this is kind
Speaker 3: of a dynamic with most of the big tech companies,
Speaker 3: where Google, Facebook, and Apple kind of dependent on a
Speaker 3: single mature or maybe a little long in the tooth product.
Speaker 3: Microsoft actually, you know, Microsoft reported earnings and did not
Speaker 3: see its stock crashed down and the same with the
Speaker 3: same kind of velocity as Google's. And that's because I
Speaker 3: think Microsoft is actually in the best position here because
Speaker 3: Microsoft has a way to turn in the AI into
Speaker 3: actual dollars, both because it's the market leader in selling
Speaker 3: cloud computing for AI, and Microsoft also has been very
Speaker 3: aggressively incorporating AI software in the form of open Ai,
Speaker 3: which has a partnership with into its office suite and
Speaker 3: charging actual money for it.
Speaker 1: This is the question at the heart of the disconnect
Speaker 1: between tech companies and investors. Can these companies make money
Speaker 1: off of the technology, and can they do it before
Speaker 1: investors sour on the project entirely.
Speaker 3: I mean, I think it's tough to read into any
Speaker 3: small stock movement and try to see something big about
Speaker 3: the future because in a lot of cases it's not
Speaker 3: necessarily that, oh, like Google stock went down. That doesn't
Speaker 3: mean that their AI doesn't work. It means two things.
Speaker 3: It means one is that investors maybe overestimated how quickly
Speaker 3: they were going to be able.
Speaker 2: To turn this technology into business.
Speaker 3: And also it says something about their legacy businesses right
Speaker 3: search ads, and their ability to continue to make money
Speaker 3: and their ability to cut costs. I do think the
Speaker 3: company that's worth paying the most attention to in this
Speaker 3: world is Microsoft. Microsoft actually has a plan, like there's
Speaker 3: a real clear sort of way that they are going
Speaker 3: to make money off of this, in contrast right to
Speaker 3: Facebook and Google, and what we're seeing with their business
Speaker 3: is that it's growing.
Speaker 2: It's very good.
Speaker 3: It's just not totally clear how fast it's going to
Speaker 3: grow or how big it's going to be. Large language models,
Speaker 3: which is what most of the stuff that everyone's excited
Speaker 3: about is they cost a lot of money to make,
Speaker 3: and every time you update them, they cost a lot
Speaker 3: of money.
Speaker 2: Every time you ask chat GPT, write an.
Speaker 3: Angry letter to a friend in the style of Chaucer,
Speaker 3: or like some fun thing you're costing Microsoft money and
Speaker 3: way more money, or if you ask them, if you
Speaker 3: ask them like a Google query, Hey, what's the best
Speaker 3: pizza restaurant in New York? Or something that costs a
Speaker 3: lot more money to answer than it does when you
Speaker 3: Google it, and there's not like an easy way to
Speaker 3: monetize it, Like they're not going to be as silly
Speaker 3: and little ads for pizza next to it. So I
Speaker 3: think there's like real questions, hard questions about the business.
Speaker 1: When we return one company in the AI space, and
Speaker 1: Vidia has bucked this trend, we dig into what makes
Speaker 1: Nvidia different and we'll get into what this all says
Speaker 1: about the maturity of the AI industry. Welcome back. I'm
Speaker 1: discussing the AI industry with my colleague BusinessWeek reporter and
Speaker 1: editor Max Chafkin. So Microsoft has this potential. We're going
Speaker 1: to see how it realizes it. Other companies are also
Speaker 1: kind of trying to make strides in the AI space,
Speaker 1: but Navidia seems to be one of the few that's
Speaker 1: actually making money on AI right now, what's going right
Speaker 1: for Navidia.
Speaker 3: First of all, this is a company that all these
Speaker 3: companies need. They make these GPUs that the entire industry
Speaker 3: depends on.
Speaker 2: A GPU is a graphical processing unit.
Speaker 3: You can use a CPU, a central processing unit, that's
Speaker 3: the kind of chip that your computer is running on,
Speaker 3: but it goes a lot slower and it uses a
Speaker 3: lot more power. So if you're using GPUs, you can
Speaker 3: do AI training much more efficiently and you can sort
Speaker 3: of answer AI queries more efficiently. When you look at
Speaker 3: how the other big like the big companies that are
Speaker 3: trying to develop AI, all they think about is how
Speaker 3: do we how do we have enough chip capacity. Elon
Speaker 3: Musk is developing his sort of like his own like
Speaker 3: AI computing solution.
Speaker 2: It's called Dojo, and on Tesla's earnings said, well, Dojo
Speaker 2: is a real long shot. It may not pay off.
Speaker 3: Which again, if Elon Muskay is somebody's not going to
Speaker 3: pay off, then like that that means that there's a
Speaker 3: real chance I don't want pay Because he's a very
Speaker 3: optimistic guy, and he said, you know, he's still betting
Speaker 3: on Nvidia, everybody is, and so in certain ways with Nvidia,
Speaker 3: it doesn't really matter at least for now, like how
Speaker 3: big a business AI is, because all these companies have
Speaker 3: decided they're in on the mania. There's this thought, you know,
Speaker 3: in a gold rush and this better shell sell shovels.
Speaker 3: Then you know, try to like pan for gold they
Speaker 3: need in Nvidia's shovels, and Nvidia is like right, selling
Speaker 3: equipment for this mania, and so that's always going to
Speaker 3: be a good business.
Speaker 1: I love how you put it that Navidia is selling
Speaker 1: shovels in a gold rush.
Speaker 3: You know.
Speaker 2: Funnily enough, you need GPUs to mind crypto.
Speaker 3: So like they rote that boom and now they've they've
Speaker 3: kind of moved on to this next one.
Speaker 1: I'm interested in what this conversation we're having about kind
Speaker 1: of the winners and the losers and the people in
Speaker 1: the middle of the race in the AI sphere says
Speaker 1: about where we are in the life cycle of the
Speaker 1: AI industry.
Speaker 2: Yeah, you know, it's funny. I've been thinking.
Speaker 3: I've written a lot about self driving cars and and
Speaker 3: because of that, I'm probably more skeptical or at least
Speaker 3: a little bit more cautious about these large language models.
Speaker 3: If you cast your mind back to twenty twelve or
Speaker 3: twenty thirteen or twenty fourteen, the conversation around.
Speaker 2: Cars was very similar.
Speaker 3: It was like, look at these things, they're like almost
Speaker 3: as good as a human and we kind of see
Speaker 3: that a similar conversation happening with large language models, where
Speaker 3: you ask them a question. You ask it to say.
Speaker 3: You can ask it to describe yourself. Right, you ask
Speaker 3: it like who is Max Chafkin? And and I haven't
Speaker 3: done this in a while, but right, it makes up
Speaker 3: a lot of stuff. Right, it's like pretty good. It's close,
Speaker 3: but it's.
Speaker 2: Not quite right.
Speaker 3: And it's got you know, it's got me going to
Speaker 3: a different college, and I watch with some on or
Speaker 3: these like beautiful photorealistic images and then you look closely,
Speaker 3: it's got six fingers. If you were talking to a human,
Speaker 3: you could just be like, hey, artist, actually human beings
Speaker 3: have five fingers, so in the future, just make the
Speaker 3: hands with five.
Speaker 2: But AI, these AI.
Speaker 3: Models, you cannot have that conversation with them. They don't
Speaker 3: understand what a finger is. They don't understand what a
Speaker 3: hand is, and no one knows. Just like with the cars,
Speaker 3: no one knows what it's going to take to cross
Speaker 3: that bridge. There are people on smart people think to
Speaker 3: really cross that bridge, you would need an entirely new technology,
Speaker 3: and that could mean new winners and new losers and
Speaker 3: you know and so on, new chips. And the other
Speaker 3: thing is, you know, cost really matters. Like weimo right now,
Speaker 3: the Google Driver's car company is doing all these tests
Speaker 3: in Phoenix and in other localities, and in a lot
Speaker 3: of ways like their cars are matching up to human drivers,
Speaker 3: but except in a very important way they're not, which
Speaker 3: is that these cars have you know, potentially hundreds of
Speaker 3: thousands of dollars of censors on them, and they're powered
Speaker 3: by armies of PhDs.
Speaker 2: And your Uber is a you know, twenty.
Speaker 3: Five thousand dollars Corolla and a gig worker, and you
Speaker 3: have some of the same dynamics with these large language
Speaker 3: models where like wow, like this sound this is almost
Speaker 3: as good as somebody in a call center could do
Speaker 3: on customer service, But we're not actually sure that it
Speaker 3: costs less than hiring a human being to do customer service.
Speaker 1: We saw a lot of AI companies pop up begin
Speaker 1: to open their products to the public. Are we on
Speaker 1: the brink of seeing contraction in the AI industry.
Speaker 3: I think, you know, it's it's very hard to like
Speaker 3: call a bubble, and like if you go back to
Speaker 3: if you like you go back to other example like
Speaker 3: Crypto right like, people were saying it was a bubble
Speaker 3: like for years before it became apparent. So it's it's
Speaker 3: a little hard to know how valuable these things are
Speaker 3: because it seems like they may be everywhere, you know.
Speaker 3: So the sort of like worst case scenario here is
Speaker 3: consumers just get kind of used to this, but they're
Speaker 3: not willing to pay for it because they're versions of
Speaker 3: it everywhere.
Speaker 2: They're not that different from one another.
Speaker 3: And no one thinks like, oh, you know know, like
Speaker 3: have you seen like Microsoft's latest spell check, Like just
Speaker 3: no one cares because it's because all spell checks are
Speaker 3: the same. And that would be the that would be
Speaker 3: the kind of worst case scenario, although again not necessarily
Speaker 3: a bad scenario for Nvidia because it's still gonna they're
Speaker 3: all gonna be still running these these these models and.
Speaker 2: Buying chips and so on.
Speaker 3: It's just that maybe, as it turns out, you just
Speaker 3: need to make a bunch of investments in AI to
Speaker 3: continue selling the same software that you've been selling.
Speaker 1: Well. Thank you so much, Max. Thanks Sarah, thanks for
Speaker 1: listening to Big Take. I'm Sarah Holder. If you have
Speaker 1: a moment to rate and review the show, we'd appreciate it.
Speaker 1: It helps other listeners find us. This episode was produced
Speaker 1: by Alex Suguira and Naomi Shaven. Our senior producers are
Speaker 1: Naomi Shaven and Jill de Decari. It was edited by
Speaker 1: Caitlin Kenny. It was fact checked by Adriana Tapia. It
Speaker 1: was mixed by Alex Uguira. We get editorial direction from
Speaker 1: Elizabeth Ponso. Nicole Beemster Bort is our executive producer. Saint
Speaker 1: Bauman is Bloomberg's head of podcasts. We'll be back tomorrow.