June 19, 2025 • 18 mins
This week, Meta made its second-largest acquisition ever, buying a major stake in AI company Scale AI for $21 billion. But that's just the beginning of Silicon Valley's most extreme talent war, where tech giants are offering $100 million signing bonuses to poach AI experts from competitors. In today's podcast, Sam and Billi unpack why companies like Meta, Google and Microsoft are offering nine-figure salaries to lure the best minds in artificial intelligence, why AI is so expensive, and a housemate rivalry.
Hosts: Sam Koslowski and Billi FitzSimons
Producer: Elliot Lawry
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Already and this is this is the Daily This is
the Daily OS. Oh, now it makes sense.
Speaker 2 (00:14):
Good morning, and welcome to the Daily OS. It's Friday,
the twentieth of June. I'm Sam Kazowski.
Speaker 1 (00:19):
I'm Billy Fitzimon's.
Speaker 2 (00:21):
Meta just made its second largest acquisition ever, buying AI
company Scale Ai for twenty one billion dollars and sparking
what's being called Silicon Valley's most extreme talent wark. On
today's podcast, we're going to unpack why tech giants are
spending hundreds of millions of dollars on AI and offering
nine figure salaries to law the best minds in artificial intelligence.
Speaker 1 (00:48):
Sam, when you pitched this yesterday, you're basically like, the
tech bros are fighting and we need to cover it.
Speaker 2 (00:52):
The tech bros are fighting with money. They are really
fighting with money.
Speaker 1 (00:57):
So I want to understand all of this more. But
first I want to understand this twenty one billion dollar
deal that happened last week. So what exactly did Meta buy?
Speaker 2 (01:06):
So Meta bought a forty nine percent stake in this
company called scale Ai. It was fourteen billion US dollars,
So twenty one billion. Ossie and scale AI are basically
a company that specializes in human powered data collection, and
what that means in reality is they employ thousands of
humans both in physical workplaces but also lots of remote
(01:27):
workers to help train machine learning models. And they're the
systems that power AI.
Speaker 1 (01:33):
So how is that different to other AI companies?
Speaker 2 (01:37):
So they are employed by AI companies to help work
through information and basically tag the information as a good response,
a bad response, as particular data being a successful way
to frame something not successful, basically giving AI feedback. As
you might say, if you're talking to chat GBT, I
(01:58):
didn't like that response, Can you go again? These guys
do it on massive scale, and so they're essentially helping
AI companies with the quality of the information and their customers.
To give you a sense of who they're working with,
their customers include the US Army, Open AI, so they're
working with what we see at CHATBT pretty much every
major car brand, so working on internal car technology and meta.
(02:22):
So this is a case of a customer becoming a
part owner or nearly a majority owner because they obviously
see so much value in the product, so.
Speaker 1 (02:30):
They don't necessarily have a product though. It's more their
employees who are so well trained in AI.
Speaker 2 (02:37):
It's basically a service, so if you plug us into
your system, will make sure that your data is being
tagged and labeled, and think of it like almost a
Gmail inbox. Will make sure that the data is going
in the right folders so that when your customers you
and I ask CHATBT or whatever you're using for a response,
it's got a pretty good idea of the types of
(02:58):
answers it should be giving. And the founder is considered
one of the world leaders in AI at the tender
age of twenty eight. His name's Alexander Wang, and he's
now going to lead Meta's new AI division. And remember
this is Meta's second biggest acquisition ever. They own Facebook, Instagram,
(03:19):
and WhatsApp, so this is a massive investment for them.
Speaker 1 (03:21):
Just a quick side note. When we were looking at
this yesterday, we came across the very fun fact that
the founder, Alexander Wang, used to be the roommate of
the CEO of Open Ai, which owns chat GPT Yeh
Sama Altman Samultman.
Speaker 2 (03:36):
And he's going to play a part in this discussion
in a sec But can you imagine the discussions in
that college dorm.
Speaker 1 (03:42):
So two of the biggest names in AI used to
be roommates.
Speaker 2 (03:46):
It's phenomenal and it's really interesting to remember that as
we now discuss the talent war that's coming. Because Sam
Altman made some pretty big comments on a podcast this
week that's kind of inspiring the way that we're thinking
about this. He said that Meta are offering one hundred
million dollar signing bonuses, so one hundred and fifty four
(04:07):
million Aussie or thereabouts, to try and poach his company's employees.
And that's not a typo. One hundred million dollars just
to sign on and then you get paid.
Speaker 1 (04:17):
That's crazy. So Meta is trying to poach the employees
of open Ai with upwards of one hundred million dollars.
Speaker 2 (04:25):
Yeah, And to understand this from a Meta perspective, there's
been a lot of reporting that says that Mark Zuckerberg,
the founder of Meta, has stepped into what people call
founder mode, which is essentially he is on the tools
in the room with this new AI team as the
key priority for the company.
Speaker 1 (04:39):
Kind of making all of the calls exactly.
Speaker 2 (04:42):
One Meta employee told Reuters this week, Zuckerberg is calling
researchers directly, flying them to his house. Whatever it takes Wow.
Speaker 1 (04:49):
Let's take a step back here. Why are these companies
spending such astronomical amounts on AI?
Speaker 2 (04:56):
I think this is a really interesting point that we
don't spend enough time when we cover AI and actually
considering is we're talking about hundreds of billions here, hundreds
of billions of their Why like why is there so
much money flying around? And I think the costs can
really be broken down into three main areas. So the
first is data centers, the physical storerooms, warehouses, fields as
(05:18):
far as the eye can see of servers, and that's
a lot of computing power. Microsoft and open Ai have
announced plans for one hundred billion dollar data center project
called Stargage for twenty twenty eight, and that's going to
house thousands of chips that process all the data needed
to train AI systems.
Speaker 1 (05:36):
Can we pause for a second because this is something
that always confuses me, that the amount of technology, like
physical technology that is required to power even just something
like chap GPT, Like how can we kind of conceptualize
that You.
Speaker 2 (05:51):
Basically need to think about your laptop having one chip
in it, and when you put your laptop into a bag,
it's not does way nothing, it's got a bit of
weight to it. Now think of a million chips and
laptops with tiny chips, and servers with tiny chips stacked
on top of each other. And now think of the
fact that they can't be directly next to each other
(06:13):
because they might get too hot, so they need to
be spaced out a little bit. And now think about
the fact that as we get more and more ambitious
with what we want AI to do, win in more
of them, win in more brain power. There are some places,
particularly in the US, who are really investing a lot
into these data centers. There are some places which are
so big that you need cars to drive around inside
(06:33):
them that are just for the storage of data chips.
So it's a massive scale. And of course the other
part of this, though is the energy consumption, and that's
actually the second real cost to these companies. There is
enormous amounts of electricity and water needed. Water is needed
to cool down the systems. Microsoft's water usage jumped thirty
(06:54):
four percent between twenty twenty one and twenty twenty two,
and this was at the beginning of the AI revolution.
Speaker 1 (07:00):
And that's because Microsoft partly owns open ai, which as
we said, is a maker of chat GPT exactly.
Speaker 2 (07:06):
And to put that stat into perspective, that's two and
a half thousand Olympic swimming pools, just an increase three
years ago, So can you imagine what it is today.
There was one study I was reading from a university
in Amsterdam that said that AI could require the same
amount of energy as the entire country of the Netherlands
by twenty twenty seven. So we're talking almost like there's
(07:26):
another nation on the planet in terms of carbon emissions.
And then the third cost is cloud infrastructure. So I've
taken you through the hardware, the stuff on the ground,
then the power that's needed to power that stuff, but
we haven't even talked about the cloud yet. And building
the systems to deliver AI services to users takes a
lot of power in creating connections. I mean, you and
(07:46):
I don't have a data ten to run through our desk.
We tap into it wirelessly. That takes a lot of
power and energy and cost as well.
Speaker 1 (07:53):
And so that's why buying something like scale AI, which
Meta has done, is so expensive.
Speaker 2 (07:58):
Partly, I think there's a huge amount of the what
the company is required to spend to keep it operational
that contributes to that price tag. The other part of it, though,
is the talent and the intellectual property, and that at
the moment you almost can't put a price on.
Speaker 1 (08:15):
I think something that's interesting to talk about here is
kind of the historical context of all of this, because
you know, we're seeing technology kind of revolutions quite a bit,
especially over the past century. Sure is this different to that?
Speaker 2 (08:29):
Like?
Speaker 1 (08:29):
How important is this in the context of what we
have seen in terms of advancements of the last one
hundred years.
Speaker 2 (08:35):
I feel like we're at the stage where it's not
controversial to say this is the real deal. This is
really big, And if we just look at the behavior
of the world's biggest companies as an indicator, then we
can see that they think AI is the next big shift,
the next seismic shift in the way that we live
our lives. And I think what's really interesting about where
(08:57):
we are in the AI tech world is that there's
so much investment because we still don't know what AI
could be capable of. So it's not like there's a
benchmark that's been set that now everybody's trying to reach
and spend heaps of money trying to get to it's
that people are spending money because they don't know what
they don't know, and that's what's really interesting. There's this
idea called artificial general intelligence or AGI, and that's basically
(09:22):
AI that can do the same thing that you and
I can do. So it can write stuff, and it
can make decisions and all of that kind of stuff.
But there's also a thing called artificial superintelligence, which is
AI that is smarter than you and I. Not smarter
than you. You're the smartest person ever. AI that's smarter
than me. And in February, talking about artificial superintelligence, Zuckerberg said,
(09:43):
this year is going to set the course for the future.
AI is potentially one of the most important innovations in history.
Speaker 1 (09:49):
So what I'm hearing is they're kind of buying into
the potential.
Speaker 2 (09:52):
Of it exactly. They're buying into the potential of understanding
that the moves they make now could dictate their ponomic
future and how much proper they make for the next
fifty years.
Speaker 1 (10:03):
Yeah, and they don't know exactly what that potential is,
but they're pretty sure it's very big.
Speaker 2 (10:08):
Yeah. I've been thinking about the space race, for example,
Like we know that there was a race to get
to the moon you could see the moon. Whoever got
their first would win the race. This is a different
type of race because nobody actually knows where the finish
line is here, and so the amount of money being
spent is not spent on just trying to get to
that endpoint. It's what's beyond that.
Speaker 1 (10:26):
Sam, I feel like we've only just touched the surface.
But before we go on, just a quick message from
our sponsor. Okay, and so I want to go back
to this talent war that we spoke about, the race
to get people in the room who are the best
and brightest are working out the capabilities of AI in
real time. Sam, I'm most surprised that no one has
(10:47):
tapped you on the shoulder yet.
Speaker 2 (10:48):
But I'm sure.
Speaker 1 (10:49):
I'm sure it's coming. Mark Zuckerberg just a call away.
Speaker 2 (10:52):
I'm sure when I talk you through these salaries, you're
going to understand why I would do that.
Speaker 1 (10:57):
I'm sure you would. How extreme are we talking.
Speaker 2 (11:00):
We're talking nine figures, so we are talking salaries in
the hundreds of millions in US dollars. So I mean,
if you're at the top of the game in the
AI space, you could easily be looking at the salary
of four to five hundred million dollars a year.
Speaker 1 (11:14):
Just so hard to comprehend.
Speaker 2 (11:16):
That so hard to comprehend, especially with a sign on
bonus of one hundred million that I talked to you
about earlier. There was a line, a great line in
an article I was reading from a tech recruiter. He said,
we're seeing signing bonuses that exceed what CEOs made a
decade ago, and this is just the beginning of all
of this. There was another researcher from Stanford who said,
the key to winning isn't just computing power, it's the
(11:38):
people who know how to use it. And I think
this is the key to this talent war. It's that
anyone could buy warehouses of massive machinery and ships, and
by anyone, I mean anyone with that sort of money
at their disposal. But the people who know how to
harness that and quick they're going to be the really
valuable assets in Silicon Valley. And most of those people
(11:59):
already have jobs in AI. And that's why you're seeing
this bidding war with talent trying to drag people from
one place to another.
Speaker 1 (12:06):
And so when I mentioned at the start that you
kind of pitch this as the tech bros are fighting.
You also mentioned that Sam Oltman had some things to
say about Meta kind of stealing or poaching his employees.
What exactly did Sam Oltman say about this and about
the sign on bonuses?
Speaker 2 (12:21):
Well, that's the interesting bit we learned from Sam mommon
this week was we didn't know about these one hundred
million dollars sign on bonuses before an interview that Sam
Oltman gave on a podcast, and he didn't hold back.
He called these offers crazy. He said that Meta was
offering not just these signing bonuses, but these hundreds of
millions of dollars worth of salary. And the really interesting
(12:42):
part is that Oldman also said that despite these massive offers,
none of open AI's best people have taken Meta up
on them, at least not yet.
Speaker 1 (12:51):
Why would they not accept those offers?
Speaker 2 (12:53):
Well, according to Oltman, he said that it's about culture
and mission and you can start to see a bit
of competition really emerging between these tech As he said,
and I'm paraphrasing here, that when a company focuses so
heavily on upfront guaranteed compensation rather than the work and
the mission and the growth of the company. It doesn't
set up a great culture. Everyone's kind of there, according
(13:14):
to Altman, because they got a very sweet package to
land there, and when they're there, they don't really want
to be there. And he took a direct shot at
Meta's ability to innovate. He said that metas push into
AI is the same as when Google tried to push
into social media a decade or so ago, and it
was clear to people that Google weren't really going to
compete with an Instagram or a Facebook. And he said
(13:36):
that it feels a bit similar here with metas ai efforts,
that they're kind of in the realm that they don't
serve to be in, which is spicy.
Speaker 1 (13:44):
That is quite a big claiming and quite a big criticism.
Speaker 2 (13:47):
Yeah, and it's particularly a big criticism to give to
somebody who's offering your employees hundreds of millions of dollars
to go over there. But Altman raised the possibility that
even if they do join Meta, and even if they
do try and achieve this superintelligence that Mark Zuckerberg's talking about,
we aren't going to see AI change the world as
(14:09):
much as we think it could. I thought that was
so interesting from the head of open AI. He said
that people will live their lives pretty much in the
same way in two years time. But when we start
to have AI discovering new science, which is about five
to ten years away, that's when things could really change.
Speaker 1 (14:26):
What does that mean discovering new science?
Speaker 2 (14:28):
So right now, scientific discoveries are made by human researchers
who run experiments in a lab and form hypotheses, they
analyze results. They're using AI at the moment to analyze
results and to speed up their processes. But what Oltman
is suggesting is that AI could actually make scientific discoveries itself.
So if you think about it this way, AI could
potentially analyze all the data, spot patterns that humans can
(14:51):
miss and propose entirely new theories which could lead to
new materials for batteries, or new compounds that go into drugs,
or new physics theories that humans haven't thought of yet.
And there are examples of this. We've seen AI discover
new antibiotics. We've seen deep minds AI predict protein structures
(15:13):
that had stumped scientists for decades. So if AI is
starting to actually do the research itself and form its
own hypotheses, that's an entirely new wave.
Speaker 1 (15:22):
I feel like I've said so many times this podcast
already that it is kind of hard to wrap your
head around and to kind of understand what it all means.
Are companies doing anything else besides throwing money at individual researchers.
Speaker 2 (15:36):
Well, the interesting thing about the scale ai acquisition this
week by Meta is that clearly if they can't get
the person to come over and lead their AI project
and AI innovation, they're just going to buy the company
and bring everybody over. And so we've seen that with Microsoft,
as you mentioned earlier, they invested in open Ai to
get a seat at the table. Google acquired DeepMind for
(15:57):
five hundred million dollars, which seems kind of cheap in
the scheme of what we're talking about today. But we're
going to see more of this. We're going to see
more companies getting acquired when there's an individual at the
heart of the company they really want.
Speaker 1 (16:09):
You mentioned Google just then, and I also saw that
Google was one of the clients of Scale Ai, And
after this acquisition by Meta was announced, Google then dropped
Scale ai also they will no longer work with them.
Speaker 2 (16:22):
You've got to remember there's so much data and there's
so much under the hood information that's plugged into all
of these systems, and if a competitor buys something that
you're using, I don't think you're going to use it
for very much longer.
Speaker 1 (16:32):
Where does this go from.
Speaker 2 (16:34):
Here, Well, industry experts say that this talent war, these
sign on bonuses, these salaries are just going to get bigger.
I mean, a lot of the commentary I was reading
was saying that we are not too far away from
billion dollar salaries in the AI space, and the pool
of experts will grow as well. But so all the
demands and what's at stake for the companies to make
(16:56):
money from all of this. And the other thing to
remember is that the companies have placed their bets. They've
gone all in on AI. They've gone all in on
these innovations. They've told shareholders, they've told customers that this
is the next big thing, and so now they're almost
too far away from the start line to go backwards,
but they can't quite see the finish line yet, and
(17:16):
they just have no choice but to spend billions and
billions trying to push forward.
Speaker 1 (17:21):
As fast as possible just before we end. I do
think it is worth mentioning the impact that this has
on employees at these companies that aren't in the AI space.
We saw this week that Amazon has announced redundancies for
people who aren't in the AI space, and I think
that's a really important thing to acknowledge as well in
(17:42):
all of these conversations.
Speaker 2 (17:43):
Definitely, and there's a law floating around in amongst US
legislators proposing that employers have to declare when AI is
replacing the jobs of humans in any redundancy announcements that
they make, So you're right, this is a major as well.
Speaker 1 (18:00):
Sam. Thanks for coming on and explaining to us your
favorite topic.
Speaker 2 (18:04):
Thanks so much for having me what an indulgence.
Speaker 1 (18:07):
And thank you so much for listening to this episode
of The Daily Os. We'll be back this afternoon with
your evening headlines, but until then, have a great day,
See you later. My name is Lily Madden and I'm
a proud Arunda Bungelung Calcuttin woman from Gadighl Country. The
Daily oz acknowledges that this podcast is recorded on the
(18:28):
lands of the Gadighl people and pays respect to all
Aboriginal and Torres Strait Island and nations. We pay our
respects to the first peoples of these countries, both past
and present,
Already and this is this is the Daily This is
the Daily OS. Oh, now it makes sense.
Speaker 2 (00:14):
Good morning, and welcome to the Daily OS. It's Friday,
the twentieth of June. I'm Sam Kazowski.
Speaker 1 (00:19):
I'm Billy Fitzimon's.
Speaker 2 (00:21):
Meta just made its second largest acquisition ever, buying AI
company Scale Ai for twenty one billion dollars and sparking
what's being called Silicon Valley's most extreme talent wark. On
today's podcast, we're going to unpack why tech giants are
spending hundreds of millions of dollars on AI and offering
nine figure salaries to law the best minds in artificial intelligence.
Speaker 1 (00:48):
Sam, when you pitched this yesterday, you're basically like, the
tech bros are fighting and we need to cover it.
Speaker 2 (00:52):
The tech bros are fighting with money. They are really
fighting with money.
Speaker 1 (00:57):
So I want to understand all of this more. But
first I want to understand this twenty one billion dollar
deal that happened last week. So what exactly did Meta buy?
Speaker 2 (01:06):
So Meta bought a forty nine percent stake in this
company called scale Ai. It was fourteen billion US dollars,
So twenty one billion. Ossie and scale AI are basically
a company that specializes in human powered data collection, and
what that means in reality is they employ thousands of
humans both in physical workplaces but also lots of remote
(01:27):
workers to help train machine learning models. And they're the
systems that power AI.
Speaker 1 (01:33):
So how is that different to other AI companies?
Speaker 2 (01:37):
So they are employed by AI companies to help work
through information and basically tag the information as a good response,
a bad response, as particular data being a successful way
to frame something not successful, basically giving AI feedback. As
you might say, if you're talking to chat GBT, I
(01:58):
didn't like that response, Can you go again? These guys
do it on massive scale, and so they're essentially helping
AI companies with the quality of the information and their customers.
To give you a sense of who they're working with,
their customers include the US Army, Open AI, so they're
working with what we see at CHATBT pretty much every
major car brand, so working on internal car technology and meta.
(02:22):
So this is a case of a customer becoming a
part owner or nearly a majority owner because they obviously
see so much value in the product, so.
Speaker 1 (02:30):
They don't necessarily have a product though. It's more their
employees who are so well trained in AI.
Speaker 2 (02:37):
It's basically a service, so if you plug us into
your system, will make sure that your data is being
tagged and labeled, and think of it like almost a
Gmail inbox. Will make sure that the data is going
in the right folders so that when your customers you
and I ask CHATBT or whatever you're using for a response,
it's got a pretty good idea of the types of
(02:58):
answers it should be giving. And the founder is considered
one of the world leaders in AI at the tender
age of twenty eight. His name's Alexander Wang, and he's
now going to lead Meta's new AI division. And remember
this is Meta's second biggest acquisition ever. They own Facebook, Instagram,
(03:19):
and WhatsApp, so this is a massive investment for them.
Speaker 1 (03:21):
Just a quick side note. When we were looking at
this yesterday, we came across the very fun fact that
the founder, Alexander Wang, used to be the roommate of
the CEO of Open Ai, which owns chat GPT Yeh
Sama Altman Samultman.
Speaker 2 (03:36):
And he's going to play a part in this discussion
in a sec But can you imagine the discussions in
that college dorm.
Speaker 1 (03:42):
So two of the biggest names in AI used to
be roommates.
Speaker 2 (03:46):
It's phenomenal and it's really interesting to remember that as
we now discuss the talent war that's coming. Because Sam
Altman made some pretty big comments on a podcast this
week that's kind of inspiring the way that we're thinking
about this. He said that Meta are offering one hundred
million dollar signing bonuses, so one hundred and fifty four
(04:07):
million Aussie or thereabouts, to try and poach his company's employees.
And that's not a typo. One hundred million dollars just
to sign on and then you get paid.
Speaker 1 (04:17):
That's crazy. So Meta is trying to poach the employees
of open Ai with upwards of one hundred million dollars.
Speaker 2 (04:25):
Yeah, And to understand this from a Meta perspective, there's
been a lot of reporting that says that Mark Zuckerberg,
the founder of Meta, has stepped into what people call
founder mode, which is essentially he is on the tools
in the room with this new AI team as the
key priority for the company.
Speaker 1 (04:39):
Kind of making all of the calls exactly.
Speaker 2 (04:42):
One Meta employee told Reuters this week, Zuckerberg is calling
researchers directly, flying them to his house. Whatever it takes Wow.
Speaker 1 (04:49):
Let's take a step back here. Why are these companies
spending such astronomical amounts on AI?
Speaker 2 (04:56):
I think this is a really interesting point that we
don't spend enough time when we cover AI and actually
considering is we're talking about hundreds of billions here, hundreds
of billions of their Why like why is there so
much money flying around? And I think the costs can
really be broken down into three main areas. So the
first is data centers, the physical storerooms, warehouses, fields as
(05:18):
far as the eye can see of servers, and that's
a lot of computing power. Microsoft and open Ai have
announced plans for one hundred billion dollar data center project
called Stargage for twenty twenty eight, and that's going to
house thousands of chips that process all the data needed
to train AI systems.
Speaker 1 (05:36):
Can we pause for a second because this is something
that always confuses me, that the amount of technology, like
physical technology that is required to power even just something
like chap GPT, Like how can we kind of conceptualize
that You.
Speaker 2 (05:51):
Basically need to think about your laptop having one chip
in it, and when you put your laptop into a bag,
it's not does way nothing, it's got a bit of
weight to it. Now think of a million chips and
laptops with tiny chips, and servers with tiny chips stacked
on top of each other. And now think of the
fact that they can't be directly next to each other
(06:13):
because they might get too hot, so they need to
be spaced out a little bit. And now think about
the fact that as we get more and more ambitious
with what we want AI to do, win in more
of them, win in more brain power. There are some places,
particularly in the US, who are really investing a lot
into these data centers. There are some places which are
so big that you need cars to drive around inside
(06:33):
them that are just for the storage of data chips.
So it's a massive scale. And of course the other
part of this, though is the energy consumption, and that's
actually the second real cost to these companies. There is
enormous amounts of electricity and water needed. Water is needed
to cool down the systems. Microsoft's water usage jumped thirty
(06:54):
four percent between twenty twenty one and twenty twenty two,
and this was at the beginning of the AI revolution.
Speaker 1 (07:00):
And that's because Microsoft partly owns open ai, which as
we said, is a maker of chat GPT exactly.
Speaker 2 (07:06):
And to put that stat into perspective, that's two and
a half thousand Olympic swimming pools, just an increase three
years ago, So can you imagine what it is today.
There was one study I was reading from a university
in Amsterdam that said that AI could require the same
amount of energy as the entire country of the Netherlands
by twenty twenty seven. So we're talking almost like there's
(07:26):
another nation on the planet in terms of carbon emissions.
And then the third cost is cloud infrastructure. So I've
taken you through the hardware, the stuff on the ground,
then the power that's needed to power that stuff, but
we haven't even talked about the cloud yet. And building
the systems to deliver AI services to users takes a
lot of power in creating connections. I mean, you and
(07:46):
I don't have a data ten to run through our desk.
We tap into it wirelessly. That takes a lot of
power and energy and cost as well.
Speaker 1 (07:53):
And so that's why buying something like scale AI, which
Meta has done, is so expensive.
Speaker 2 (07:58):
Partly, I think there's a huge amount of the what
the company is required to spend to keep it operational
that contributes to that price tag. The other part of it, though,
is the talent and the intellectual property, and that at
the moment you almost can't put a price on.
Speaker 1 (08:15):
I think something that's interesting to talk about here is
kind of the historical context of all of this, because
you know, we're seeing technology kind of revolutions quite a bit,
especially over the past century. Sure is this different to that?
Speaker 2 (08:29):
Like?
Speaker 1 (08:29):
How important is this in the context of what we
have seen in terms of advancements of the last one
hundred years.
Speaker 2 (08:35):
I feel like we're at the stage where it's not
controversial to say this is the real deal. This is
really big, And if we just look at the behavior
of the world's biggest companies as an indicator, then we
can see that they think AI is the next big shift,
the next seismic shift in the way that we live
our lives. And I think what's really interesting about where
(08:57):
we are in the AI tech world is that there's
so much investment because we still don't know what AI
could be capable of. So it's not like there's a
benchmark that's been set that now everybody's trying to reach
and spend heaps of money trying to get to it's
that people are spending money because they don't know what
they don't know, and that's what's really interesting. There's this
idea called artificial general intelligence or AGI, and that's basically
(09:22):
AI that can do the same thing that you and
I can do. So it can write stuff, and it
can make decisions and all of that kind of stuff.
But there's also a thing called artificial superintelligence, which is
AI that is smarter than you and I. Not smarter
than you. You're the smartest person ever. AI that's smarter
than me. And in February, talking about artificial superintelligence, Zuckerberg said,
(09:43):
this year is going to set the course for the future.
AI is potentially one of the most important innovations in history.
Speaker 1 (09:49):
So what I'm hearing is they're kind of buying into
the potential.
Speaker 2 (09:52):
Of it exactly. They're buying into the potential of understanding
that the moves they make now could dictate their ponomic
future and how much proper they make for the next
fifty years.
Speaker 1 (10:03):
Yeah, and they don't know exactly what that potential is,
but they're pretty sure it's very big.
Speaker 2 (10:08):
Yeah. I've been thinking about the space race, for example,
Like we know that there was a race to get
to the moon you could see the moon. Whoever got
their first would win the race. This is a different
type of race because nobody actually knows where the finish
line is here, and so the amount of money being
spent is not spent on just trying to get to
that endpoint. It's what's beyond that.
Speaker 1 (10:26):
Sam, I feel like we've only just touched the surface.
But before we go on, just a quick message from
our sponsor. Okay, and so I want to go back
to this talent war that we spoke about, the race
to get people in the room who are the best
and brightest are working out the capabilities of AI in
real time. Sam, I'm most surprised that no one has
(10:47):
tapped you on the shoulder yet.
Speaker 2 (10:48):
But I'm sure.
Speaker 1 (10:49):
I'm sure it's coming. Mark Zuckerberg just a call away.
Speaker 2 (10:52):
I'm sure when I talk you through these salaries, you're
going to understand why I would do that.
Speaker 1 (10:57):
I'm sure you would. How extreme are we talking.
Speaker 2 (11:00):
We're talking nine figures, so we are talking salaries in
the hundreds of millions in US dollars. So I mean,
if you're at the top of the game in the
AI space, you could easily be looking at the salary
of four to five hundred million dollars a year.
Speaker 1 (11:14):
Just so hard to comprehend.
Speaker 2 (11:16):
That so hard to comprehend, especially with a sign on
bonus of one hundred million that I talked to you
about earlier. There was a line, a great line in
an article I was reading from a tech recruiter. He said,
we're seeing signing bonuses that exceed what CEOs made a
decade ago, and this is just the beginning of all
of this. There was another researcher from Stanford who said,
the key to winning isn't just computing power, it's the
(11:38):
people who know how to use it. And I think
this is the key to this talent war. It's that
anyone could buy warehouses of massive machinery and ships, and
by anyone, I mean anyone with that sort of money
at their disposal. But the people who know how to
harness that and quick they're going to be the really
valuable assets in Silicon Valley. And most of those people
(11:59):
already have jobs in AI. And that's why you're seeing
this bidding war with talent trying to drag people from
one place to another.
Speaker 1 (12:06):
And so when I mentioned at the start that you
kind of pitch this as the tech bros are fighting.
You also mentioned that Sam Oltman had some things to
say about Meta kind of stealing or poaching his employees.
What exactly did Sam Oltman say about this and about
the sign on bonuses?
Speaker 2 (12:21):
Well, that's the interesting bit we learned from Sam mommon
this week was we didn't know about these one hundred
million dollars sign on bonuses before an interview that Sam
Oltman gave on a podcast, and he didn't hold back.
He called these offers crazy. He said that Meta was
offering not just these signing bonuses, but these hundreds of
millions of dollars worth of salary. And the really interesting
(12:42):
part is that Oldman also said that despite these massive offers,
none of open AI's best people have taken Meta up
on them, at least not yet.
Speaker 1 (12:51):
Why would they not accept those offers?
Speaker 2 (12:53):
Well, according to Oltman, he said that it's about culture
and mission and you can start to see a bit
of competition really emerging between these tech As he said,
and I'm paraphrasing here, that when a company focuses so
heavily on upfront guaranteed compensation rather than the work and
the mission and the growth of the company. It doesn't
set up a great culture. Everyone's kind of there, according
(13:14):
to Altman, because they got a very sweet package to
land there, and when they're there, they don't really want
to be there. And he took a direct shot at
Meta's ability to innovate. He said that metas push into
AI is the same as when Google tried to push
into social media a decade or so ago, and it
was clear to people that Google weren't really going to
compete with an Instagram or a Facebook. And he said
(13:36):
that it feels a bit similar here with metas ai efforts,
that they're kind of in the realm that they don't
serve to be in, which is spicy.
Speaker 1 (13:44):
That is quite a big claiming and quite a big criticism.
Speaker 2 (13:47):
Yeah, and it's particularly a big criticism to give to
somebody who's offering your employees hundreds of millions of dollars
to go over there. But Altman raised the possibility that
even if they do join Meta, and even if they
do try and achieve this superintelligence that Mark Zuckerberg's talking about,
we aren't going to see AI change the world as
(14:09):
much as we think it could. I thought that was
so interesting from the head of open AI. He said
that people will live their lives pretty much in the
same way in two years time. But when we start
to have AI discovering new science, which is about five
to ten years away, that's when things could really change.
Speaker 1 (14:26):
What does that mean discovering new science?
Speaker 2 (14:28):
So right now, scientific discoveries are made by human researchers
who run experiments in a lab and form hypotheses, they
analyze results. They're using AI at the moment to analyze
results and to speed up their processes. But what Oltman
is suggesting is that AI could actually make scientific discoveries itself.
So if you think about it this way, AI could
potentially analyze all the data, spot patterns that humans can
(14:51):
miss and propose entirely new theories which could lead to
new materials for batteries, or new compounds that go into drugs,
or new physics theories that humans haven't thought of yet.
And there are examples of this. We've seen AI discover
new antibiotics. We've seen deep minds AI predict protein structures
(15:13):
that had stumped scientists for decades. So if AI is
starting to actually do the research itself and form its
own hypotheses, that's an entirely new wave.
Speaker 1 (15:22):
I feel like I've said so many times this podcast
already that it is kind of hard to wrap your
head around and to kind of understand what it all means.
Are companies doing anything else besides throwing money at individual researchers.
Speaker 2 (15:36):
Well, the interesting thing about the scale ai acquisition this
week by Meta is that clearly if they can't get
the person to come over and lead their AI project
and AI innovation, they're just going to buy the company
and bring everybody over. And so we've seen that with Microsoft,
as you mentioned earlier, they invested in open Ai to
get a seat at the table. Google acquired DeepMind for
(15:57):
five hundred million dollars, which seems kind of cheap in
the scheme of what we're talking about today. But we're
going to see more of this. We're going to see
more companies getting acquired when there's an individual at the
heart of the company they really want.
Speaker 1 (16:09):
You mentioned Google just then, and I also saw that
Google was one of the clients of Scale Ai, And
after this acquisition by Meta was announced, Google then dropped
Scale ai also they will no longer work with them.
Speaker 2 (16:22):
You've got to remember there's so much data and there's
so much under the hood information that's plugged into all
of these systems, and if a competitor buys something that
you're using, I don't think you're going to use it
for very much longer.
Speaker 1 (16:32):
Where does this go from.
Speaker 2 (16:34):
Here, Well, industry experts say that this talent war, these
sign on bonuses, these salaries are just going to get bigger.
I mean, a lot of the commentary I was reading
was saying that we are not too far away from
billion dollar salaries in the AI space, and the pool
of experts will grow as well. But so all the
demands and what's at stake for the companies to make
(16:56):
money from all of this. And the other thing to
remember is that the companies have placed their bets. They've
gone all in on AI. They've gone all in on
these innovations. They've told shareholders, they've told customers that this
is the next big thing, and so now they're almost
too far away from the start line to go backwards,
but they can't quite see the finish line yet, and
(17:16):
they just have no choice but to spend billions and
billions trying to push forward.
Speaker 1 (17:21):
As fast as possible just before we end. I do
think it is worth mentioning the impact that this has
on employees at these companies that aren't in the AI space.
We saw this week that Amazon has announced redundancies for
people who aren't in the AI space, and I think
that's a really important thing to acknowledge as well in
(17:42):
all of these conversations.
Speaker 2 (17:43):
Definitely, and there's a law floating around in amongst US
legislators proposing that employers have to declare when AI is
replacing the jobs of humans in any redundancy announcements that
they make, So you're right, this is a major as well.
Speaker 1 (18:00):
Sam. Thanks for coming on and explaining to us your
favorite topic.
Speaker 2 (18:04):
Thanks so much for having me what an indulgence.
Speaker 1 (18:07):
And thank you so much for listening to this episode
of The Daily Os. We'll be back this afternoon with
your evening headlines, but until then, have a great day,
See you later. My name is Lily Madden and I'm
a proud Arunda Bungelung Calcuttin woman from Gadighl Country. The
Daily oz acknowledges that this podcast is recorded on the
(18:28):
lands of the Gadighl people and pays respect to all
Aboriginal and Torres Strait Island and nations. We pay our
respects to the first peoples of these countries, both past
and present,
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