February 5, 2024 • 16 mins
The seemingly vast profit potential of artificial intelligence has helped buoy the stock prices of tech behemoths like Alphabet, Apple and the rest of the Magnificent Seven. But last week’s earnings showed that for many of these companies going all-in on AI, lofty investor expectations are hard to meet. As advanced as AI applications like ChatGPT and GitHub Copilot may seem, it’s an open question as to whether tech companies can monetize them.
In today’s episode of The Big Take podcast, Bloomberg Businessweek technology reporter Max Chafkin explains the gap between investors' AI expectations and reality, and what it would take for these technologies to live up to their promise.
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Speaker 1 (00:01):
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.
Speaker 2 (00:20):
Alphabet, parent of Google, is down severely.
Speaker 1 (00:23):
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.
Speaker 3 (00:38):
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.
Speaker 1 (00:47):
That's my colleague Max Chafkin, who reports on these companies.
Speaker 3 (00:51):
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.
Speaker 1 (01:02):
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.
Speaker 2 (01:27):
My name's Max Chafkin.
Speaker 3 (01:28):
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.
Speaker 1 (01:40):
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.
Speaker 3 (02:03):
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,
(02:24):
This is like as significant as potentially destructive as the
atom bomb.
Speaker 2 (02:28):
It's a big part of why I'm here today and
why we've been here in the past.
Speaker 1 (02:32):
Here's open AI CEO Sam Altman speaking before Congress in
May of last year.
Speaker 3 (02:37):
I think if this technology goes wrong, it can go
quite wrong, and we want to be vocal about that.
Speaker 2 (02:43):
We want to work with the government to prevent that
from happening.
Speaker 3 (02:47):
What's interesting to me about these warnings, and I think
it's worth taking, you know, any warning about technology.
Speaker 2 (02:53):
Seriously. Technologies can have unintended consequences, but.
Speaker 3 (02:56):
They also serve, of course as a sales pitch, because
if you're going around saying that, hey, this thing I'm
building is.
Speaker 2 (03:02):
So effective it could take.
Speaker 3 (03:05):
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.
Speaker 1 (03:25):
So even the terrifying expectations are inherently potentially overly positive
about the technology itself.
Speaker 2 (03:33):
Absolutely, you know.
Speaker 3 (03:34):
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
(03:55):
to be like in the movie Wally, we can all
just like sit back and let the robots.
Speaker 2 (03:58):
Do all our work.
Speaker 3 (04:00):
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
(04:20):
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?
Speaker 2 (04:39):
Right?
Speaker 1 (04:40):
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?
Speaker 3 (04:52):
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
(05:16):
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
(05:36):
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
(05:58):
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.
Speaker 2 (06:15):
But the thing is that as impressive as.
Speaker 3 (06:18):
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
(06:40):
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
(07:01):
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.
Speaker 1 (07:18):
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.
Speaker 3 (07:32):
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
(07:53):
they were going to be able.
Speaker 2 (07:54):
To turn this technology into business.
Speaker 3 (07:57):
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
(08:18):
Facebook and Google, and what we're seeing with their business
is that it's growing.
Speaker 2 (08:24):
It's very good.
Speaker 3 (08:26):
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.
Speaker 2 (08:41):
Every time you ask chat GPT, write an.
Speaker 3 (08:45):
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
(09:07):
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.
Speaker 1 (09:16):
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
(09:39):
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
(10:00):
for Navidia.
Speaker 3 (10:01):
First of all, this is a company that all these
companies need. They make these GPUs that the entire industry
depends on.
Speaker 2 (10:10):
A GPU is a graphical processing unit.
Speaker 3 (10:13):
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
(10:34):
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.
Speaker 2 (10:46):
It's called Dojo, and on Tesla's earnings said, well, Dojo
is a real long shot. It may not pay off.
Speaker 3 (10:54):
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,
(11:16):
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.
Speaker 1 (11:29):
I love how you put it that Navidia is selling
shovels in a gold rush.
Speaker 3 (11:32):
You know.
Speaker 2 (11:33):
Funnily enough, you need GPUs to mind crypto.
Speaker 3 (11:36):
So like they rote that boom and now they've they've
kind of moved on to this next one.
Speaker 1 (11:40):
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.
Speaker 2 (11:53):
Yeah, you know, it's funny. I've been thinking.
Speaker 3 (11:55):
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.
Speaker 2 (12:14):
Cars was very similar.
Speaker 3 (12:15):
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.
Speaker 2 (12:34):
Not quite right.
Speaker 3 (12:35):
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
(12:55):
hands with five.
Speaker 2 (12:56):
But AI, these AI.
Speaker 3 (12:58):
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
(13:19):
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
(13:43):
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.
Speaker 2 (13:51):
And your Uber is a you know, twenty.
Speaker 3 (13:54):
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.
Speaker 1 (14:15):
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.
Speaker 3 (14:23):
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.
(14:46):
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.
Speaker 2 (14:55):
They're not that different from one another.
Speaker 3 (14:57):
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.
Speaker 2 (15:18):
Buying chips and so on.
Speaker 3 (15:19):
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.
Speaker 1 (15:30):
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
(15:51):
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.
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.
Speaker 2 (00:20):
Alphabet, parent of Google, is down severely.
Speaker 1 (00:23):
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.
Speaker 3 (00:38):
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.
Speaker 1 (00:47):
That's my colleague Max Chafkin, who reports on these companies.
Speaker 3 (00:51):
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.
Speaker 1 (01:02):
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.
Speaker 2 (01:27):
My name's Max Chafkin.
Speaker 3 (01:28):
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.
Speaker 1 (01:40):
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.
Speaker 3 (02:03):
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,
(02:24):
This is like as significant as potentially destructive as the
atom bomb.
Speaker 2 (02:28):
It's a big part of why I'm here today and
why we've been here in the past.
Speaker 1 (02:32):
Here's open AI CEO Sam Altman speaking before Congress in
May of last year.
Speaker 3 (02:37):
I think if this technology goes wrong, it can go
quite wrong, and we want to be vocal about that.
Speaker 2 (02:43):
We want to work with the government to prevent that
from happening.
Speaker 3 (02:47):
What's interesting to me about these warnings, and I think
it's worth taking, you know, any warning about technology.
Speaker 2 (02:53):
Seriously. Technologies can have unintended consequences, but.
Speaker 3 (02:56):
They also serve, of course as a sales pitch, because
if you're going around saying that, hey, this thing I'm
building is.
Speaker 2 (03:02):
So effective it could take.
Speaker 3 (03:05):
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.
Speaker 1 (03:25):
So even the terrifying expectations are inherently potentially overly positive
about the technology itself.
Speaker 2 (03:33):
Absolutely, you know.
Speaker 3 (03:34):
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
(03:55):
to be like in the movie Wally, we can all
just like sit back and let the robots.
Speaker 2 (03:58):
Do all our work.
Speaker 3 (04:00):
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
(04:20):
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?
Speaker 2 (04:39):
Right?
Speaker 1 (04:40):
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?
Speaker 3 (04:52):
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
(05:16):
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
(05:36):
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
(05:58):
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.
Speaker 2 (06:15):
But the thing is that as impressive as.
Speaker 3 (06:18):
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
(06:40):
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
(07:01):
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.
Speaker 1 (07:18):
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.
Speaker 3 (07:32):
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
(07:53):
they were going to be able.
Speaker 2 (07:54):
To turn this technology into business.
Speaker 3 (07:57):
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
(08:18):
Facebook and Google, and what we're seeing with their business
is that it's growing.
Speaker 2 (08:24):
It's very good.
Speaker 3 (08:26):
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.
Speaker 2 (08:41):
Every time you ask chat GPT, write an.
Speaker 3 (08:45):
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
(09:07):
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.
Speaker 1 (09:16):
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
(09:39):
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
(10:00):
for Navidia.
Speaker 3 (10:01):
First of all, this is a company that all these
companies need. They make these GPUs that the entire industry
depends on.
Speaker 2 (10:10):
A GPU is a graphical processing unit.
Speaker 3 (10:13):
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
(10:34):
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.
Speaker 2 (10:46):
It's called Dojo, and on Tesla's earnings said, well, Dojo
is a real long shot. It may not pay off.
Speaker 3 (10:54):
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,
(11:16):
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.
Speaker 1 (11:29):
I love how you put it that Navidia is selling
shovels in a gold rush.
Speaker 3 (11:32):
You know.
Speaker 2 (11:33):
Funnily enough, you need GPUs to mind crypto.
Speaker 3 (11:36):
So like they rote that boom and now they've they've
kind of moved on to this next one.
Speaker 1 (11:40):
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.
Speaker 2 (11:53):
Yeah, you know, it's funny. I've been thinking.
Speaker 3 (11:55):
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.
Speaker 2 (12:14):
Cars was very similar.
Speaker 3 (12:15):
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.
Speaker 2 (12:34):
Not quite right.
Speaker 3 (12:35):
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
(12:55):
hands with five.
Speaker 2 (12:56):
But AI, these AI.
Speaker 3 (12:58):
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
(13:19):
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
(13:43):
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.
Speaker 2 (13:51):
And your Uber is a you know, twenty.
Speaker 3 (13:54):
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.
Speaker 1 (14:15):
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.
Speaker 3 (14:23):
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.
(14:46):
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.
Speaker 2 (14:55):
They're not that different from one another.
Speaker 3 (14:57):
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.
Speaker 2 (15:18):
Buying chips and so on.
Speaker 3 (15:19):
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.
Speaker 1 (15:30):
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
(15:51):
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.
Hosts And Creators
Sarah Holder
Saleha Mohsin
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