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