March 18, 2025 • 42 mins
Bloomberg’s Caroline Hyde and Tim Stenovec discuss Google’s plan to acquire cybersecurity startup Wiz. Plus, Sands Capital’s Daniel Pilling lays out what investors are expecting from Nvidia CEO Jensen Huang’s Keynote at GTC. And Chinese tech stocks and EV makers are having a moment.
See omnystudio.com/listener for privacy information.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
From the heart of where innovation, money and power collide
in Silicon Valley and beyond. This is Bloomberg Technology with
Caroline Hyde and Ed Ludlow.
Speaker 2 (00:27):
Live from New York. I'm Caroline Hyde Man.
Speaker 3 (00:30):
I'm Jim Steinbeck in San Francisco. This is Bloomberg Technology
Coming up.
Speaker 2 (00:34):
Alphabet is set to acquire cloud security firm Whiz for
thirty two billion dollars in cash. The acquisition is Alphabet's
largest to date. Plus all eyes on Invidios CEO Jensen
Wang is set to take the stage in a few
hours as investors seek clarity on Blackwell Ultra and the
next AI chip breakthrough, and Chinese car Make is a
gaining market share globally. This is BYD's souls. On the
(00:55):
news of its five minute charging system, Alphabet off by
more than three percent market fools, but also after a
huge acquisition is announced. It's biggest ever and it's going
to be leaning into cloud security with the help of Wiz.
This is just a five year old company, tim and
it's back by VCS. It's going to be a bit
of a payday if they can get it secured by
twenty twenty six. But phenomenal Ride for these group of
(01:18):
full founders who built once before and sold out to
Microsoft back in twenty fifteen. This time it's Google's chance.
Speaker 3 (01:24):
He had incredible track record and incredible what they've done
in five years. From more on Google's Whiz acquisition, Bloombergs
Katie Roof joins us. Now, Katie, you've been following this
for the last ten months. Wiz spurned the offer of
twenty three billion dollars over the summer, apart from the
additional nine billion dollars. What changed, that's right?
Speaker 4 (01:41):
Well, yes, obviously they had nine billion more reasons to
say yes. But also one thing that's changed is the
regulatory environment and at least the perception that it will
be better under this regulatory environment. Yes, we've been following
this for ten months. We started to hear rumors and
even two months before they leaked last year. The talks
(02:02):
first began at RSA, this big security convention last year,
so it's been going on for a while.
Speaker 2 (02:08):
What's so interesting, Yes, ar ours have increased significantly after
the last eight months, we understand, and that vindicates the
price point. But for a thirty two billion dollar price
tag and a company that's going to remain independent there's
actually still going to be multi cloud provision. It's not
just going to be selling into Alphabet customers. When it
comes to cloud, it's still going to be servicing aw
(02:28):
West and as you it's a pretty phenomenal deal.
Speaker 4 (02:32):
Absolutely, it's definitely a phenomenal deal for Whiz. But you know,
it just shows how threatened Google feels right now about
security and how you know, worried they are about competing
with Microsoft and Amazon growing AI related security threats, and
you know they're willing to pay the price. And granted,
you know, Google can easily afford this. It's a high
(02:55):
number for Wiz, but you know, these trillion dollar companies
can can make these multi you know, billion dollar deals
happen quite easily.
Speaker 3 (03:03):
Does it move the needle with competition in the cloud
space as we just talked about, you have AWS number one,
you have Microsoft Azure number two. Google Cloud is in
third place? Can it compete now that it has or
if the Whiz deal goes through?
Speaker 4 (03:17):
I mean that will be the question that remains to
be seen. But you know, certainly that's their goal here.
They really are hoping that you know, look, Whiz that's
only been around just a few years, quickly gained the
significant percentage of the fortune one hundred customers, and you
know they want to be able to sell this as
part of their offerings, you know, certainly, you know, as
(03:38):
Caroline noted, it can still work with the competitors, but
Google clearly has this on their team right now. I'm
sure Whiz will you know, maybe continue to work with
them and innovate, you know, under the umbrella of Google.
Speaker 2 (03:52):
What's interesting is they could have fundraised in the private
market still, they could have ide an IPO, but they've
gone this particular route. Who wins out? Remind us of
the vcs that have bat this from day one.
Speaker 4 (04:04):
Sure, so you've got Index, You've got Sequoia, You've got Cyberstarts,
which is a hot Israeli security firm. Those are all
on the board. You also have Insight partners on the
board as well. But they've also been backed by Green
Oaks and light Speed and Injrecing and they have such
a long list of you know, top tiers Silicon Valley
(04:24):
investors that were throwing money at this thing. It was
practically born a unicorn, was it was so highly valued
when it first launched several years ago, and so there
have been big believers in this company for a while.
They felt like, you know, just you know, hit the
ground running.
Speaker 2 (04:41):
Katie move, thanks so much on all angles on Wiz
and Google. Meanwhile, we turn our attention to win Video
the Global Artificial Intelligence Conferences upon us GtC expect you
to give developers and investors insight into the company's future
AI chip plans as well as of course we're awaiting
CEO Jensen Huang's keynote speech, Let's bringing Bloomberg Intelligence is
Man Deep Sing look earnings. Jensen couldn't quite get the
(05:05):
investor base to get excited about the new scaling law,
which is reasoning models. What is he going to be
able to convince us this time?
Speaker 5 (05:12):
Well, I assume the focus will be on AI agents
and inferencing, given the market has clearly made that pivot
post Deep Seek. Everyone is focused more on you know,
reasoning and inference time scaling. And even though we are
talking about scaling laws, I mean, it's fair to say
everyone isn't very excited about pre training anymore. Where you
(05:33):
are spending you know, ten billion dollars upfront and training
just the next version of your model. So I would
assume Jensen will try and convey that, you know, reasoning
models do require a lot of GPUs, and in video
GPUs are still the most popular when it comes to
inference time reasoning. But clearly, you know, Google spending thirty
(05:54):
two billion dollars on an acquisition takes away some of
that capex they could have spent acquiring and GPUs. So
you have to remember four customers contribute forty five percent
of Nvidia's revenue. Now, if they're doing more deals and
deals of this magnitude, that will play into the KPEX
expectations for next year.
Speaker 3 (06:13):
So Mandip is that your view that reasoning that inference
still does use as much compute as training these llms.
Speaker 5 (06:22):
So it changes the nature of compute. Remember you know
when you were training models with bigger clusters as Grock three,
did you know with two hundred thousand GPUs you required
those all as a cluster. Now, if you're doing more
inference time reasoning, you don't require one data center with
you know, giant cluster. You can have it distributed for
(06:43):
latency purposes. And that's where you know, if you are
a hyperscaler, you are looking at different options a different
custom A six because these AI agents are performing different tasks.
It's not all text based chat pots. It could be
you know, audio based image generation. So some ACX solutions
could work. And Broadcom did talk about, you know, adding
(07:04):
four new customers when it comes to their ACIX. So
clearly there are a lot of moving parts when it
comes to inferencing. But that's what I think Jensen would
focus on during his keynote.
Speaker 2 (07:15):
It is interesting actually reports today of how much Amazon
is thinking about the price point of its homegrown chips
at the moment and where it can serve an inference
and inferentia. But when you've got that sort of level
of competition, they have to keep saying we've got the
next thing. We've got the next thing, So talk about
the next thing. We've got Blackwell Ultra and then the
next iteration of the AI chip.
Speaker 5 (07:33):
I mean, look, in Vidia's value proposition is they can
do so much more with one chip in terms of
power efficiency and the compute they give versus the alternative.
So Amazon may have a chip, but it's not the
same price power performance that in Video is giving you
with one chip. And that's their value proposition that you
have the Kuda layer when it comes to you know,
the cluster that you're running on for in Vidia. So
(07:56):
all the standardization does make a difference. But hyperscalers worry
about you know, costs not there what they're paying this year,
but five years down line, ten years down line, so
they're thinking long term, and you want to move away
from Nvidia given the size of you know, the investments,
say I'm making with the compute right now.
Speaker 3 (08:14):
That was Bloomberg Intelligence is at Mandeep Sing joining us
in New York. Mande thanks so much. Well, let's talk
more about what investors will be looking for from GtC
and Jensen Wong's keynote later today. We're joined by Daniel Pilling,
portfolio manager and a senior research analyst at Sans Capital. Daniel,
good to see you. What do you want to hear
from Jensen today that will convince you, that will convince
(08:34):
other investors that earnings growth and indeed growth of the
company has not peaked.
Speaker 6 (08:41):
Good morning, Yes, So I think, first of all, I
agree with the reasoning pop process and the inference spright.
So in France is becoming much more compute intensive. It
can be up to one hundred two thousand times more
compute intensive. In Vidia is the biggest player in that
by far, and you know the hoote would be a
Jensen will share some of his long term strategy, a
(09:01):
sort of how does he capture and continue to gain
market share with any imprints. Now, the second thing though
I would say, is I actually think that the training
the scaling laws within training are not entirely dead yet
either both in pre training and post training. And my
guess would be that their company, I in Vidia, will
confirm that as well. Now this year we're going to
(09:23):
see the big black ball clusters scaling. That's going to
be a ten x improvement versus lausity for sure. And
the secondly, also we have this reinforcement learning without humans
in the loop that deep Seek has shown that seems
to describe another scaling law in post training, and as
far as we can see, that has driven a lot
of a ton of them training demat and which you
can see actually frankly on GitHub and hugging space in
(09:45):
terms of the number of deep Seek models. And then
the three and the third thing, if I may say,
there's going to be a lot of talk about physical
AI that's going through its s curve as we speak.
You can see that in Weimo right in San Francisco
there twenty percent market share, and I think there's going
to be a lot of talk about agents, but frankly,
that's a little bit further out. It's nice if they
(10:06):
talk about that, but I think it's more important to
talk about inference, scaling and trading.
Speaker 2 (10:10):
Skilling, really interesting points. He's often paid lip service to
Elon Musk and Tesla, for example, maybe a bit with Memo.
Maybe in the future of robotics as well, Daniel, I'm
interested in the rampant pace at which now we see
Jensen put out new products. We're going to hear a
lot about Blackwell Ultra the next iteration. We're also going
to hear about Rubin I'm sure, which is in the
next AI breakthrough. Can they really develop at this pace
(10:34):
because we saw the sort of supply chain hiccups with
back Well as it rolled out.
Speaker 6 (10:39):
Yes, So I think the answer is yes. And I'll
tell you why. They have the best people. They are
the most people's folkusing those product, and they have the
biggest R and D fools, and they have the most
capacity to Taiwan Seti And I think the interesting counter
question to this, though, is to say, can the others
even follow? Right? So for example, big hyperscalers, they all
try to develop their own ship. They're trying to do
(11:01):
this with a partner. They're not doing it all alone,
which means that they're trying to optimize the system by themselves.
And video is optimizing the systems by themselves, whereas the
others are doing with partners, which is going to make
it much, much, much more difficult to compete in a video.
So I think the answer is yes, and I think
that crais a lot of competitive differentiation versus anybody else.
Speaker 3 (11:20):
Daniel, you mentioned physical AI, and I'm curious what you
think is the realistic form factor when it comes to
physical AI and just how far out we are from that.
Speaker 6 (11:30):
Yeah, so we thought about this a lot as well.
So it's clear that the big, big, big physical AI
revenue growth driver in the next one to two years
is going to be self driving at leastent not in
our opinion. Why because you can see it in San
Francisco ready. The experience is amazing when you drive away
old people. They have twenty percent market share. They're moving
(11:50):
to many other cities and this is just the beginning, right,
They have a tiny, tiny market Shante with total milage.
Now the humanoids they're pretty interesting as well, but arguably
a little bit further away. So in our opinion, is
going to be self driving vehicles within big cities the
next one to two years. That should drive a massive
explosion in terms of influence. The van which benefits in video,
(12:12):
but many many other players on the supply Machaine as well.
Speaker 2 (12:15):
Daniel, how far away is quantum because we actually heard
as sort of a long time rage maybe up to
two decades coming from Jensen, but now he's actually sitting
on a panel around quantum and trying to make it
a quantum day.
Speaker 6 (12:28):
Yes, so we think quantum computing is sort of maybe
in the eighties in terms of where semiconductors work like
at that time here right, So it's a long time out.
The reason for that is, first of all, there's no
Kuda equivalent as of today at pcent for quantum computing,
so you can't actually really use it easily. And secondly,
the scaling of the cubas has proven to be quite
(12:48):
difficult now, so it's at least ten to fifteen twenty
years out in terms of a use case potentially, And
I would actually argue if in Video plays their cards well,
then quantum computing could be one of the parts of
utation they offer in the very very long term, alongside
the GPUs, CPUs and whatever else the PU You might
have a.
Speaker 2 (13:09):
We want to thank you, Daniel Pelling, always fascinating catching
up with your portfolio manager and senior research analyst at
Sam's Capital. Meanwhile, we have some breaking news for you,
because of course the phone call has begun. Russian President
Vladimir Putin and Donald Trump have been speaking on the
phone today. We understand that the White House confirmed talk
started at ten am Eastern time. Discussions are said to
(13:29):
be related, of course, to the end of the war
with Ukraine, a key objective being a thirty day truce
with Kiev. As already we understand Kiev has accepted. That
will bring you any more breaking news on that.
Speaker 3 (13:50):
Well, let's take a look at some China tech names,
starting with the ride hailing app d d Global. Think
of it as China's answer to Uber. It did swing
back to a quarterly loss and it's a blow to
the company that's exploring a Hong Kong listing this year.
You can see shares on the day lower by about
three percent. Dedi has had an inconsistent recovery since that
regulatory crackdown a few years ago. That was when it
(14:11):
was delisted from the New York Stock Exchange. They lost
much of its market value. This after Beijing cracked down
on data sharing practices among big tech companies, and Shallomi
shares are higher right now. Revenue in the fourth quarter
beat the average general's estimate. The company CEO also said
it's delivered two hundred thousand vehicles and its race its
target this year from three hundred thousand to three hundred
(14:33):
and fifty thousand vehicles. Shares hire right now by about
three point three percent, Caroline.
Speaker 2 (14:37):
Isn't that extraordinary that we know that Shaomi first got
into the EV market only in the summer of last year,
and now they're pumping out two hundred thousand and likely
to increase it to three hundred and fifty, all the
while that we're seeing just the Chinese EV sector go
from strength to strength. Also in innovation, I mean, what
did you make of BYD's pop today?
Speaker 3 (14:56):
Yeah, I mean it's pretty incredible. Five minute charging. I
mean that's really what led to that pop today, And
if you think about it from the perspective of somebody
who has an EV and maybe has range anxiety. That
is a game changer. If it takes just the same
amount of time for you to charge your vehicle as
it does to go to a gas station, then boom.
That's kind of the golden goose when it comes to
this tech caroline.
Speaker 2 (15:17):
But not everyone can get access to those coolden goose,
or at least those eggs, because that European countries can
buy the Hanel or the tang L, the latest that's
going to be coming from BYD with this new electric
charging technology, but not in the US. And let's just
talk about that, because of course President Trump is hoping
his tarifts are actually going to continue to combat cheaper
Chinese cars and stop them mentoring the US market. Already
(15:39):
you can't access BYD, for example, but outside of the US,
Chinese automakers have really established a global presence. It's today's
big take story. He discusses one of the authors of
the piece, Chester Dawson, And let's just take BYD for
as a case study here. Chester, they have just come
from behind to end up being well basically the world's
biggest EV maker, and they're doing it by accessing the
(16:00):
emerging markets, not of course.
Speaker 7 (16:02):
The US, that's right, Yeah, I mean it is somewhat unusual.
You know, typically when you have a fast rising new
entrant in the automotive business, the first thing they want
to do is get to the US because it's one
of the richest and biggest markets around the world. But
as you mentioned, you know, it's been kind of core
(16:24):
to US economic policy and national security policy to start,
you know, drawing up that drawbridge and protecting manufacture of cars.
You know, obviously the Detroit Three, but also companies like
Toyota that make a lot of cars in the US
are not facing that competition. But as you say, technology
doesn't stand still, and they're introducing some pretty impressive new developments.
(16:47):
They're going to show up in places like maybe Johannesburg
and you know, Bangkok before they get to the US.
Speaker 3 (16:56):
Do they ever get to the US? I mean, Chester
people love these vehicles. Ford CEO Jim Farley a few
months ago was quoted as really loving the Shallmi vehicle
that he had been driving. That was a sort of
big news in the US auto world. People love these cars.
What do they love about them?
Speaker 7 (17:15):
Well, I think the one thing they love is the price.
I mean they're very competitive, which is not to say
they're I mean they're cheap in terms of the price,
but they're not junk. They come with some pretty impressive
features and creature comforts. You know, as you noted at
the outset. You know Shaomi, which used to make cell
phones kind of like you know, the Apple on the iPhone,
(17:36):
but in China they quickly moved into cars and you know,
they're now projected to be making seven times what Rivian,
a US electronic electric vehicle startup, wants to make, and
that's in large part due to the fact that you know,
they're very user friendly. So yeah, it's it's no surprise.
They're good price and pretty good quality as well.
Speaker 3 (17:58):
Bluemberg's Chester, Dawson, Chester. Always good to see you, Thanks
so much for joining us. Coming up, we're going to
look at Google's impact in the health sector, all with AI.
This is Bloomberg. Google is expanding its health related AI
(18:25):
summaries and search to improve its influence in the health sector.
Answers will now cover thousands more health topics and expand
more countries and languages. The company is also adding a
separate feature in search called what People Suggest, which it
said aims to provide users with information from people with
similar lived medical experiences.
Speaker 2 (18:45):
Here on tim Yeah. Google also said it has been
updating a new AI system to help researchers speed up
the scientific en biomedical discoveries. For example Google's AI Coscientist tool.
It's an AI system meant to act basically as a
virtual collaborator for buy my scientists. I actually got to
sit down with Ane Lisa Polowski, founder and principal investigator
(19:05):
for the Accelerated Science Biochemistry and Molecular Biology Lab over
at Google. I asked her to walk us through what
this looks like. Take a listen.
Speaker 8 (19:14):
The idea is we've built a system that works together
with scientists. So, for instance, if it's a Friday afternoon,
I'm in my lab. I have all these ideas I
want to explore, but I have my two kids at home,
and so I can actually plug in a research goal
into the system, check the ideas it's creating and the
evolution over time, and by Monday morning, I have a
bunch of different approaches I can use for my research.
Speaker 2 (19:33):
Ultimately, there's been much said a Lisa about how JENATAI
can bring fruits to bear when it comes to healthcare,
when it comes to research, when it comes to tackling disease,
Is this the reality that you're currently thinking can be
the help with a co scientist an AI coscientist.
Speaker 8 (19:49):
We're at the early stages of the project. So in
our paper we show these three examples. So we've seen
that there is evidence in this direction and we're looking
forward to leaning in more and showing more keep abilities
as we extend this work.
Speaker 2 (20:01):
Has there been any friction or should I say just
reticence by the scientific community to allow models to juke
it out on their areas of focus over a weekend
while they sort of down tools.
Speaker 8 (20:14):
There can be skepticism, but as a scientist, skepticism is good, right.
It encourages that we push ourselves, that we create a
system that's useful and something that's impactful for the community.
Speaker 2 (20:24):
How have you stress tested it? For many the limitations
degenerative AI and for agents is well the fact that
they do sometimes get things wrong. When have you seen
that occur? And how have you managed to cab to
balance it right?
Speaker 8 (20:35):
So no large language model is perfect, but we try
to supplement this with novelty and checkness reviews. We both
use the literature that's available and web search. We add
in tools and databases, and we're learning to extend our
work into knowledge crafts and other strategies where we can
improve and refine on our techniques. The other thing is
we're looking to the community for feedback, so we created
(20:56):
a Trusted Tester program and we welcome and open those
different idea approaches to how we can improve AA coos.
Speaker 2 (21:02):
Scientists, Google research analyst and scientist and Alisa Pawski. They're
ahead of the key Google Health event in New York
coming up in Vidio's GtC has investors focused on the
AI outlook. Locks Capital's Grace is fruders with us and
to join to talk all about the startups and where
she's finding opportunities for the technology. This is Blue Meg Technology.
(21:30):
Welcome back to Blue Meg Technology. I'm Caroline Hide in
New York, Man.
Speaker 3 (21:33):
I'm Tim Stanovek in San Francisco.
Speaker 2 (21:35):
Quick check on these markets, Tim, because risk off at
the moment, many reasons for investors to be anxious. You've
got the FED later today. What do tariffs actually mean
for the US economy? What does Middle East and instability
once again mean in terms of a flight to Haven's
golds that are record but the nasdaqc's off by one
point eight percent. We're seeing Bitcoin another key risk, ASSEID
No Choice off by two point five percent. We're only
(21:55):
at eighty one thousand. Now move on some of the
individual names that have tried to keep above water but fail.
Nvidia is one of the key point strikes on the day.
Even as we look ahead to GtC and the keynote
coming of course from Jensen Huang at one pm Eastern time.
Can he reinject optimism into the need for his GPUs
for inference as well as training. I'm looking at alphabet
making a big purchase thirty two billion dollars for Whiz
(22:18):
down three and a half percent, as indeed the entire
market sinks. I'm looking at Meta though off by almost
five percent. This was the one magnificent seven name that
actually was in the green for the year, not anymore
when out the lowest since November the twenty nine to ten.
Speaker 3 (22:33):
Well, let's see what Katherine and runey Vera has to say.
She's stone X's group Chief Market Strategies and she joins us. Now, Catherine,
you saw what Caroline was talking about there with Tech
under pressure, the Nasdaq one hundred and only eight of
the one hundred socks in the green right. Now, what's
your outlook for the sector?
Speaker 9 (22:50):
Well, it depends on what happens with rates. This is
a very rate sensitive sector.
Speaker 10 (22:54):
So with treasury yields high three and a half two,
sorry for four thirty to fifty, that squeezes long duration
growth stocks, and that's unwinding some of these more crowded trades.
Speaker 9 (23:06):
I think that's probably one of the biggest drivers of
Tech's moved to the downside. Then, of course we have
any type of broad market correction, given that TECH is
about thirty percent of the SMP's market cap, if it
is vulnerable as well to any risk off trade which
could be precipitated by trade tensions or anything of the like.
I think it's also entering a mature phase so that
(23:28):
anything AI related no longer gets this immediate bump up.
But now the market is starting to be more discerning
and more aware of massive capital spending and the potential
for less capital spending going forward, especially with the introduction
of lower cost chips and competition.
Speaker 2 (23:50):
Within a video though for example, let's just use that
as the pin up now trading at twenty five times
future earnings. A lot of floth has come out of
the valuations. At what point do we start to get
dip buying, do you think, Catherine.
Speaker 9 (24:02):
I suspect we soon will. A ten percent correction is something,
but it certainly doesn't unwind the frothiness that we've seen
in the run up for AI stocks in general over
the past several years. And I'll add one point, which
I think is that what you're seeing is a resetting
of value expectations and a move out of tech and
(24:24):
into second derivative AI plays such as industrials and even energy.
You're seeing financials catching a bit. My top pick for
this year is healthcare, and the reason for that is
both because it's defensive, so it's a nice hedge of
your bets, but also because it was the worst performing
sector last year. And if effectively we do get a
(24:45):
purchase of the dip and a resumption to the upside
of the S and P by year end, then my
suspicion is that the most beaten up sector from last
year will become one of the better performing sectors as
those value investors come in.
Speaker 2 (24:58):
I mean, even right thus far, this year, healthcare has
been the winning formula and financials text languishing near the bottom.
I use another case study in Tesla because last year,
since the election results, Tesla didn't trade off fundamentals, it
traded on vibes and an association with the White House.
When we go back to vibes again, if ever, I
mean does it? Is it a case in point and
(25:19):
once yields stopped, we do start to see the upside
and risk hunger just returns like that or are we
in a totally different paradigm right now?
Speaker 9 (25:28):
I think you make a really good point. I think
the vibe shift now is less focused on inflation and
the FED and it's more focused on tariff. So we
have to ask ourselves the biggest component of the S
and P how vulnerable is it to tariff's and is
this tariff fear a real threat to economic growth? And
(25:48):
what we did was we looked back at President Trump
one point zero and.
Speaker 11 (25:52):
Even the Federal Reserve came out with a recent analysis
quantifying caroline the impact of core PCE from Donald Trump
one point oh twenty seventeen, twenty eighteen to twenty nineteen tariffs,
and they quantified it at zero point five percentage points
addition to core PCE.
Speaker 9 (26:10):
Now, the impact on final goods back then was a
one year effect before it rolled off, and then we
saw intermediate goods have a longer lasting impact to the
upside on prices. This time is slightly different because supply
chains have already adjusted, so that means that new tariffs
would hit margins and earnings rather than spark inflation. So
(26:32):
my concern is that, you know, additional tariffs will directly
impact market sentiment and that will bring additional downside to tech.
So my call has paid off pretty well, which is
stay long tech, buyputs on tech now that trade is
looking really awesome. I put it in when tech was
at all time highs, but now what we see is
(26:52):
that put volumes are at all time record highs. So
now it's expensive to do, but it has been it
has proven to be a very good.
Speaker 8 (27:00):
Trade, Catherine.
Speaker 3 (27:01):
Up to now, we've seen the main beneficiaries of the
AI boom be the chip makers and the hyper scalers.
Right to what extent are we seeing other companies and
other sectors within the S and P five hundred for example,
adopt this technology and has it hit their margins?
Speaker 9 (27:15):
Yet Yeah, you're right, and this has been a kind
of a buzzword more than wholesale adoption, and the market
is looking for results at this point, so enough talking,
and we want to see deployment, not only deployment, but
full execution and earnings. So there's been massive investments in
(27:36):
this space. I think at this point, as I said before,
I think it's we're in a mature face. We want
to look for second order effects, so second order investment
opportunities that are indirect beneficiaries or perhaps the second wave
beneficiaries of this large scale, massive capex in AI. Last year,
I recommended utilities. Utilities was one of the top performing
(27:56):
sectors last year, and my recommendation was because it's, you know,
second derivative to AI. Now, I think we need to
be looking at data center operators, cloud infrastructure, medical, real estate, financials.
I think there are second order effects. That's where the
bang is for the coming for coming years in a
structural way.
Speaker 3 (28:18):
Which sector do you think will take advantage of it
the most?
Speaker 9 (28:22):
Well, I still like healthcare. I think there's going to
be a lot of deployment of AI in the in
the sector, and I like it one for that reason.
And two also because it's a nice defensive sector. So
that's going to be my top pick for this year,
just as utilities was last year. So they're nice because
they're both defensive and they could potentially benefit from additional upside.
Speaker 2 (28:42):
Katherin Rooney Vera of stone X Group. Always great to
catch up. Thank you, Tim Well, So I'm now for
talking tech.
Speaker 3 (28:50):
Starting into space. Two NASA astronauts our returning home in
a SpaceX craft after being stuck in orbit for nine months.
The astronauts undocked from the International Space Station last night
and are expected to splash down off the Florida coast
later today, where they will undergo medical checks and be
reunited with their families. Plus Apple lost it fed at
(29:11):
Germany's top civil court to overturn a regulator's decision to
put it under tighter antitrust scrutiny alongside other US tech giants.
The court cited apples a vertically integrated products and services,
it's importance for third party access to markets, and its
potential to harm competition as reasons for the decision. And
Cognition Ai, the developer of an AI powered coding assistant,
(29:34):
has raised hundreds of millions of dollars at evaluation close
to four billion dollars. That's, according to sources, around doubles
the previous valuation of the startup, which released its generative
AI coding tool a year ago.
Speaker 2 (29:46):
Caroline, let's stick more on general to AI, THEAI sector,
Written Large and VC Spotlight. Chray Isford is with us
panner at Lux Capital, focusing on emerging opportunities in artificial intelligence.
You're based here in New York and you're on the
board or focus in and helping with the investments in
certain pinups. I think of Runway and I think of
together AI for example. How are we seeing the application
(30:08):
there AI starting to return real value because at the
moment everyone has been so focused on the foundational and
the infrastructure layer. Are we starting to read rewards?
Speaker 12 (30:15):
For the last few years, we've really seen enterprises focusing
on foundational AI. Now we're seeing that shift, as you say,
to the applications applications of AI and doubling down into
the twenty twenty five year year of the agents. Actually,
we've been talking a lot about AI agents in enterprise production.
We just talked about where are we actually seeing that impact.
We're seeing it across our portfolio in really three main areas,
(30:38):
one encoding, two in customer support, and three in sales.
And I'll give you one quick example on that. On
the real ROI and impact we're seeing. There's a company
called niven Agi. They're an AI agent for customer support.
They are seeing real enterprise impact, integrating with those legacy
systems and working with public companies to actually solve these
(30:58):
tickets in a fraction of the time, at a fraction
of the cost, driving that real value.
Speaker 2 (31:04):
What's interesting is we just had Daniel Pilling Sance Capital
on who's an investor in someone Nvidia. His theory is
actually sort of physical AI, which I know you've also
invested in. The physical AI is actually in many ways
closer than a agentic AI in terms of the real rewards.
What do you make of that sort of time frame
and when we start to see more bountiful effects of agentic.
Speaker 12 (31:24):
AI, Well, it depends how you define AA agents. Then
that's actually part of the confusion, right, everyone has their
own definition, right for my purposes, For engineers I talk
to across our portfolio, it's really an autonomous system where
an LM is directing its own actions across a series
of steps, and these agents are getting better and better
or not really seeing that truly verifiable loop in fields
(31:45):
that are not kind of coding customer support sales. It's
because in fact, they're actually difficult to integrate with right
And that's kind of a funny kind of the irony
in the space right now. If you look at the
physical world, we've seen text and image highly repeatable, highly
verifiable tasks, whether it's in the sciences or whether it's
in math. But as you start to get to these
proprietary data trops I'm talking robotics, I'm talking warehouses, I'm
(32:08):
talking data and manufacturing, where you have to tap into
this proprietary workflow of data, even for a biologist or chemists,
and you have to not just get that data, but
also integrate it with the workflow. That's where we're seeing
really exciting advancements. But it's still early from seeing that
real enterprise ROI grace.
Speaker 3 (32:24):
It does seem like the opportunities that you just spoke
about are really all enterprise focused. Really, you know, thinking
about it B to B, what does it look like
for consumers if this technology is adopted on a wide scale.
Speaker 12 (32:36):
Well, I'll give a simple example on that. You know,
I tried to piog a flight from New York to
San Francisco using open a eyes operator a few weeks ago.
Simple tasks in theory, sure of flight lots of us do.
In reality, it's pretty complex. You know, you need to
get the right airline, you need to get the right airport,
you need to get the right time, and all my preferences.
And so that's a common issue we're seeing. These AI
(32:57):
agents are complex systems with compound I need errors. So
even if there's one little error like not getting access
to that one criteria, it compounds across that fifty steps
in the agentic system. So from a consumer experience perspective,
it can be pretty frustrating when you don't book that
flight exactly the way you want it.
Speaker 3 (33:15):
Are you seeing crowding out come from big companies right now?
You have the hyper scaler's valued at multi trillion dollars?
Is there an opportunity for small companies to come in
and actually provide solutions and complement these hyper scalers complement
these large chip companies most definitely.
Speaker 12 (33:35):
In fact, we're seeing it across our own portfolio. I'll
give another example. They are called together AI. They're an
open source AI cloud. They actually help you run and
productize these fantastic open source models, and you can use
them not using a cloud provider in tandem or working
in tandem with your own existing suite of tools. And
so you're seeing the emergence of this new AI infrastructure stack,
which is still nascent but exciting, where you're using these
(33:57):
cheaper chips, you're actually using these open source and even
smaller models to achieve that same outcome.
Speaker 2 (34:02):
We're looking at your portfolio companies and some of them
have international flavors and certainly hugging face I think a
climb over there running that business to in Sikana AI
as well Tokyo based. At this moment where we're almost
putting up this race of China versus the US, what
are the talent pools are you looking to for AI
expertise and excellence?
Speaker 12 (34:22):
Well, AI expertise is everywhere, and I think if you
focus on China for a second, right, China has fantastic
engineers and researchers, and I think Deep Seek was a
lot of a wake up call for the US whe
were saying, wow, you know whatever you thought deep Sea
cost to train it was a true feat of hardware
efficiency and engineering efficiency, and I actually think that's a
blueprint we're going to see us internationally, So not just
in Japan with companies like Sicana AI using those smaller
(34:44):
agent based models, also in France with companies like Mistral
building really really strong AI open source models as well.
But how do you actually leverage and do more with less?
How do you use less compute? How do you actually
use more efficient chips in fact cheaper chips and actually
have a more effe AI infrastructure stack using that open
source technology grace here.
Speaker 3 (35:05):
In the US to change an administration not even two
months ago. I'm curious about how you view the landscape
differently now that somebody like David Sachs, for example, whom
Caroline and Jackie spoke to just a couple of weeks ago,
is in the position as ais are. Do you see
a difference in the landscape here.
Speaker 12 (35:21):
I think it's early to know, but we're really seeing
a huge excitement for the AI industry and really the
industry being invigorated. Right we just talked about deepseek as
that being a wake up call to invest more heavily
in engineering startups in awesome AI startups who are building
right here on US soil and who are actually leveraging
this open source and transparent innovation to ultimately create really
exciting applications. I'm really excited for new AI applications in
(35:45):
some of those physical AI areas we talked about earlier.
So robotics, space, defense, and manufacturing all have those proprietary
data sources and that unique workflow you need to understand
the workflow of a defense contractor if you're actually building
a fantastic application. So that mix of kind of public
and private is something that's really exciting.
Speaker 3 (36:04):
Melox Capitals Grace Isford, Grace, thanks so much for joining us.
Do appreciate it well. Coming out a lawsuit alleges that
Google and the startup character Ai are to blame for
the death of a fourteen year old boy. Who will
discuss the claims and the implications for Silicon Valley. This
is Bloomberg. In February of last year, fourteen year old
(36:38):
Sewell Setzer took his own life. His mother blames the
chatbot he spent months messaging with. Now she issuing character Ai.
It's the company behind the bot and Google, which has
a partnership with the startup. Let's bring in Bloomberg's Malathi Nayak.
She joins us here in San Francisco, Malafi. This is
just a heart wrenching story. We're going to get to
the legal implications in just a minute, but what happened here.
Speaker 13 (37:02):
So this is a story about a young boy who
began using this chatpot technology and he got romantically involved
with the chat pot that he created himself, sort of
inspired by a character from Game of Thrones, and he
got really sort of caught up in this chatbot. You know.
His mother says that he began to sort of withdraw
(37:23):
from his friends, was having trouble at school, you know.
After a few months, and at some point the parents
decided to confiscate his phone, and while looking for the phone,
he happened to find his stepfather's gun, which was hidden
in their home in compliance with Florida law where the
family lived, and he sort of, you know, Goddess phone,
(37:44):
had this last conversation where he was talking about sort
of coming home to this chat pot and then he
you know, he just sort of took the gun to
his head and shot himself. So it's a tragic story,
and there is a lawsuit now which the mother has
filed against Character Ai, which created this chat pot technology,
as well as Google, which has an interesting sort of
(38:06):
deal with this AI startup, and it sort of brings
into question these sort of new deal structures in AI
where companies technology companies are not really buying these startups outright,
but they are instead of buying the assets, they're sort
of investing in these technologies in a very sort of
(38:28):
unusual way where they're hiring the talent and they're just
licensing the technology. So there's no real full blown acquisition here.
And it's possible that, yeah, it's possible that these deals
were sort of structured this way because last year with
the Biden administration, we saw a crackdown and mergers and
acquisitions where you know, big companies were not allowed to
scoop up their smaller rivals. So it's possible that these
(38:50):
deals were structured that way. But it's sort of interesting
that the lawsuit sort of brings these deals into the
spotlight and sort of questions the legality of the sort
of relationship between these big dear companies and these smaller
startups right multi.
Speaker 2 (39:03):
Garcia's lawyers say in the complaint that Google in particular
contributed financial resources, personnel, intellectual property, and AI technology to
the design and development of character AI's chatbots. We must
be clear that both Google and of course character Ai
dispute any responsibility in terms. They have come back and
(39:24):
certainly stated that Google and character ai are completely separate
and unrelated companies, so says Google. What has been the response?
When do we see it play out? Briefly?
Speaker 13 (39:33):
So in a few months, we'll have a hearing on
the motion to dismiss where Google and a character ai are.
We'll try to convince the Florida Fredrikrourt to actually throw
out this case and sort of so that it won't
proceed further. So we'll see that play out in a
few months. But we've seen what Google and character I
have said. They've said they're separate companies, that they're not
(39:54):
related to each other. And Google says that it's sort
of involvement as an investor, as the mother calls it,
or through a cloud services partnership, that bet that they
both had or the fact that they had this deal
doesn't sort of, you know, implicate it in any way
or sort of is sort of tenuously connected to the
(40:14):
harm that was caused here, and in a lot of
these cases, yes, as we've seen with say Autopilot and
Tesla too, it's very hard to sort of, you know,
connect the harm to the technology. So you know, this
whole question about who's to blame here is sort of
a big one and still sort of unraveling in courts
when it comes to different technologies. And in this case,
you know, AI is coming to the forefront in terms
(40:36):
of these new up and coming AI technologies that are
getting really popular. And you know who is to blame
when things go wrong? And you know, when there are
unexpected turns like this one, blame.
Speaker 2 (40:45):
Magis malt nayak. It's a story that people must go
and read. We thank you so much. Sticking with all
things AI and Vidia CEO Johnsen Wang is set to
deliver his keynote addressed in just a few hours time
at GtC. What can we expect when most common ranicky
is here and well the shares are selling off in anticipation,
So what can you do to study people's nerves?
Speaker 14 (41:07):
Yeah, so what people are really looking for here is
some near term visibility and optimism in what Invidia has coming.
So some of the biggest things that they're looking for
are comments on Blackwell Ultra, which is expected in the
second half of the year, rubin other updates in next
gen GPUs. I think the other thing that people are
really looking for from Jensen is countering the bear case here,
(41:28):
which was really sparked by deep Seek earlier this year,
and that is really just saying that you know, there
won't be a cyclical downturn in spending from these big
AI companies, that they don't have too much AI compute
going right now, and that Invidia will still be sort
of the top of mind, you know, chip maker place
that they're buying for you know, much time to come.
Speaker 2 (41:48):
Have we seen any dip buying ahead of this, you know.
Speaker 14 (41:52):
Dip buying and Invidia has been really interesting this year.
Usually we see people rushing back in so quickly to
buy the dip, and this year we've seen investors really
let the stock draw down much more than it has
in the past. So, I mean it's down for the
last two days that you know, is after having a
big sell off, So we really haven't seen a ton
of diffying two.
Speaker 2 (42:11):
Point eight trillion dollar company only come in Ryanikey, thank
you so much for joining us. That does it from
this edition of Blueberg Technology. Don't forget to check out
our podcast. You can find it on the terminal as
well as online on Apple, Spotify, and iHeart This is
Bluemberg Technology
From the heart of where innovation, money and power collide
in Silicon Valley and beyond. This is Bloomberg Technology with
Caroline Hyde and Ed Ludlow.
Speaker 2 (00:27):
Live from New York. I'm Caroline Hyde Man.
Speaker 3 (00:30):
I'm Jim Steinbeck in San Francisco. This is Bloomberg Technology
Coming up.
Speaker 2 (00:34):
Alphabet is set to acquire cloud security firm Whiz for
thirty two billion dollars in cash. The acquisition is Alphabet's
largest to date. Plus all eyes on Invidios CEO Jensen
Wang is set to take the stage in a few
hours as investors seek clarity on Blackwell Ultra and the
next AI chip breakthrough, and Chinese car Make is a
gaining market share globally. This is BYD's souls. On the
(00:55):
news of its five minute charging system, Alphabet off by
more than three percent market fools, but also after a
huge acquisition is announced. It's biggest ever and it's going
to be leaning into cloud security with the help of Wiz.
This is just a five year old company, tim and
it's back by VCS. It's going to be a bit
of a payday if they can get it secured by
twenty twenty six. But phenomenal Ride for these group of
(01:18):
full founders who built once before and sold out to
Microsoft back in twenty fifteen. This time it's Google's chance.
Speaker 3 (01:24):
He had incredible track record and incredible what they've done
in five years. From more on Google's Whiz acquisition, Bloombergs
Katie Roof joins us. Now, Katie, you've been following this
for the last ten months. Wiz spurned the offer of
twenty three billion dollars over the summer, apart from the
additional nine billion dollars. What changed, that's right?
Speaker 4 (01:41):
Well, yes, obviously they had nine billion more reasons to
say yes. But also one thing that's changed is the
regulatory environment and at least the perception that it will
be better under this regulatory environment. Yes, we've been following
this for ten months. We started to hear rumors and
even two months before they leaked last year. The talks
(02:02):
first began at RSA, this big security convention last year,
so it's been going on for a while.
Speaker 2 (02:08):
What's so interesting, Yes, ar ours have increased significantly after
the last eight months, we understand, and that vindicates the
price point. But for a thirty two billion dollar price
tag and a company that's going to remain independent there's
actually still going to be multi cloud provision. It's not
just going to be selling into Alphabet customers. When it
comes to cloud, it's still going to be servicing aw
(02:28):
West and as you it's a pretty phenomenal deal.
Speaker 4 (02:32):
Absolutely, it's definitely a phenomenal deal for Whiz. But you know,
it just shows how threatened Google feels right now about
security and how you know, worried they are about competing
with Microsoft and Amazon growing AI related security threats, and
you know they're willing to pay the price. And granted,
you know, Google can easily afford this. It's a high
(02:55):
number for Wiz, but you know, these trillion dollar companies
can can make these multi you know, billion dollar deals
happen quite easily.
Speaker 3 (03:03):
Does it move the needle with competition in the cloud
space as we just talked about, you have AWS number one,
you have Microsoft Azure number two. Google Cloud is in
third place? Can it compete now that it has or
if the Whiz deal goes through?
Speaker 4 (03:17):
I mean that will be the question that remains to
be seen. But you know, certainly that's their goal here.
They really are hoping that you know, look, Whiz that's
only been around just a few years, quickly gained the
significant percentage of the fortune one hundred customers, and you
know they want to be able to sell this as
part of their offerings, you know, certainly, you know, as
(03:38):
Caroline noted, it can still work with the competitors, but
Google clearly has this on their team right now. I'm
sure Whiz will you know, maybe continue to work with
them and innovate, you know, under the umbrella of Google.
Speaker 2 (03:52):
What's interesting is they could have fundraised in the private
market still, they could have ide an IPO, but they've
gone this particular route. Who wins out? Remind us of
the vcs that have bat this from day one.
Speaker 4 (04:04):
Sure, so you've got Index, You've got Sequoia, You've got Cyberstarts,
which is a hot Israeli security firm. Those are all
on the board. You also have Insight partners on the
board as well. But they've also been backed by Green
Oaks and light Speed and Injrecing and they have such
a long list of you know, top tiers Silicon Valley
(04:24):
investors that were throwing money at this thing. It was
practically born a unicorn, was it was so highly valued
when it first launched several years ago, and so there
have been big believers in this company for a while.
They felt like, you know, just you know, hit the
ground running.
Speaker 2 (04:41):
Katie move, thanks so much on all angles on Wiz
and Google. Meanwhile, we turn our attention to win Video
the Global Artificial Intelligence Conferences upon us GtC expect you
to give developers and investors insight into the company's future
AI chip plans as well as of course we're awaiting
CEO Jensen Huang's keynote speech, Let's bringing Bloomberg Intelligence is
Man Deep Sing look earnings. Jensen couldn't quite get the
(05:05):
investor base to get excited about the new scaling law,
which is reasoning models. What is he going to be
able to convince us this time?
Speaker 5 (05:12):
Well, I assume the focus will be on AI agents
and inferencing, given the market has clearly made that pivot
post Deep Seek. Everyone is focused more on you know,
reasoning and inference time scaling. And even though we are
talking about scaling laws, I mean, it's fair to say
everyone isn't very excited about pre training anymore. Where you
(05:33):
are spending you know, ten billion dollars upfront and training
just the next version of your model. So I would
assume Jensen will try and convey that, you know, reasoning
models do require a lot of GPUs, and in video
GPUs are still the most popular when it comes to
inference time reasoning. But clearly, you know, Google spending thirty
(05:54):
two billion dollars on an acquisition takes away some of
that capex they could have spent acquiring and GPUs. So
you have to remember four customers contribute forty five percent
of Nvidia's revenue. Now, if they're doing more deals and
deals of this magnitude, that will play into the KPEX
expectations for next year.
Speaker 3 (06:13):
So Mandip is that your view that reasoning that inference
still does use as much compute as training these llms.
Speaker 5 (06:22):
So it changes the nature of compute. Remember you know
when you were training models with bigger clusters as Grock three,
did you know with two hundred thousand GPUs you required
those all as a cluster. Now, if you're doing more
inference time reasoning, you don't require one data center with
you know, giant cluster. You can have it distributed for
(06:43):
latency purposes. And that's where you know, if you are
a hyperscaler, you are looking at different options a different
custom A six because these AI agents are performing different tasks.
It's not all text based chat pots. It could be
you know, audio based image generation. So some ACX solutions
could work. And Broadcom did talk about, you know, adding
(07:04):
four new customers when it comes to their ACIX. So
clearly there are a lot of moving parts when it
comes to inferencing. But that's what I think Jensen would
focus on during his keynote.
Speaker 2 (07:15):
It is interesting actually reports today of how much Amazon
is thinking about the price point of its homegrown chips
at the moment and where it can serve an inference
and inferentia. But when you've got that sort of level
of competition, they have to keep saying we've got the
next thing. We've got the next thing, So talk about
the next thing. We've got Blackwell Ultra and then the
next iteration of the AI chip.
Speaker 5 (07:33):
I mean, look, in Vidia's value proposition is they can
do so much more with one chip in terms of
power efficiency and the compute they give versus the alternative.
So Amazon may have a chip, but it's not the
same price power performance that in Video is giving you
with one chip. And that's their value proposition that you
have the Kuda layer when it comes to you know,
the cluster that you're running on for in Vidia. So
(07:56):
all the standardization does make a difference. But hyperscalers worry
about you know, costs not there what they're paying this year,
but five years down line, ten years down line, so
they're thinking long term, and you want to move away
from Nvidia given the size of you know, the investments,
say I'm making with the compute right now.
Speaker 3 (08:14):
That was Bloomberg Intelligence is at Mandeep Sing joining us
in New York. Mande thanks so much. Well, let's talk
more about what investors will be looking for from GtC
and Jensen Wong's keynote later today. We're joined by Daniel Pilling,
portfolio manager and a senior research analyst at Sans Capital. Daniel,
good to see you. What do you want to hear
from Jensen today that will convince you, that will convince
(08:34):
other investors that earnings growth and indeed growth of the
company has not peaked.
Speaker 6 (08:41):
Good morning, Yes, So I think, first of all, I
agree with the reasoning pop process and the inference spright.
So in France is becoming much more compute intensive. It
can be up to one hundred two thousand times more
compute intensive. In Vidia is the biggest player in that
by far, and you know the hoote would be a
Jensen will share some of his long term strategy, a
(09:01):
sort of how does he capture and continue to gain
market share with any imprints. Now, the second thing though
I would say, is I actually think that the training
the scaling laws within training are not entirely dead yet
either both in pre training and post training. And my
guess would be that their company, I in Vidia, will
confirm that as well. Now this year we're going to
(09:23):
see the big black ball clusters scaling. That's going to
be a ten x improvement versus lausity for sure. And
the secondly, also we have this reinforcement learning without humans
in the loop that deep Seek has shown that seems
to describe another scaling law in post training, and as
far as we can see, that has driven a lot
of a ton of them training demat and which you
can see actually frankly on GitHub and hugging space in
(09:45):
terms of the number of deep Seek models. And then
the three and the third thing, if I may say,
there's going to be a lot of talk about physical
AI that's going through its s curve as we speak.
You can see that in Weimo right in San Francisco
there twenty percent market share, and I think there's going
to be a lot of talk about agents, but frankly,
that's a little bit further out. It's nice if they
(10:06):
talk about that, but I think it's more important to
talk about inference, scaling and trading.
Speaker 2 (10:10):
Skilling, really interesting points. He's often paid lip service to
Elon Musk and Tesla, for example, maybe a bit with Memo.
Maybe in the future of robotics as well, Daniel, I'm
interested in the rampant pace at which now we see
Jensen put out new products. We're going to hear a
lot about Blackwell Ultra the next iteration. We're also going
to hear about Rubin I'm sure, which is in the
next AI breakthrough. Can they really develop at this pace
(10:34):
because we saw the sort of supply chain hiccups with
back Well as it rolled out.
Speaker 6 (10:39):
Yes, So I think the answer is yes. And I'll
tell you why. They have the best people. They are
the most people's folkusing those product, and they have the
biggest R and D fools, and they have the most
capacity to Taiwan Seti And I think the interesting counter
question to this, though, is to say, can the others
even follow? Right? So for example, big hyperscalers, they all
try to develop their own ship. They're trying to do
(11:01):
this with a partner. They're not doing it all alone,
which means that they're trying to optimize the system by themselves.
And video is optimizing the systems by themselves, whereas the
others are doing with partners, which is going to make
it much, much, much more difficult to compete in a video.
So I think the answer is yes, and I think
that crais a lot of competitive differentiation versus anybody else.
Speaker 3 (11:20):
Daniel, you mentioned physical AI, and I'm curious what you
think is the realistic form factor when it comes to
physical AI and just how far out we are from that.
Speaker 6 (11:30):
Yeah, so we thought about this a lot as well.
So it's clear that the big, big, big physical AI
revenue growth driver in the next one to two years
is going to be self driving at leastent not in
our opinion. Why because you can see it in San
Francisco ready. The experience is amazing when you drive away
old people. They have twenty percent market share. They're moving
(11:50):
to many other cities and this is just the beginning, right,
They have a tiny, tiny market Shante with total milage.
Now the humanoids they're pretty interesting as well, but arguably
a little bit further away. So in our opinion, is
going to be self driving vehicles within big cities the
next one to two years. That should drive a massive
explosion in terms of influence. The van which benefits in video,
(12:12):
but many many other players on the supply Machaine as well.
Speaker 2 (12:15):
Daniel, how far away is quantum because we actually heard
as sort of a long time rage maybe up to
two decades coming from Jensen, but now he's actually sitting
on a panel around quantum and trying to make it
a quantum day.
Speaker 6 (12:28):
Yes, so we think quantum computing is sort of maybe
in the eighties in terms of where semiconductors work like
at that time here right, So it's a long time out.
The reason for that is, first of all, there's no
Kuda equivalent as of today at pcent for quantum computing,
so you can't actually really use it easily. And secondly,
the scaling of the cubas has proven to be quite
(12:48):
difficult now, so it's at least ten to fifteen twenty
years out in terms of a use case potentially, And
I would actually argue if in Video plays their cards well,
then quantum computing could be one of the parts of
utation they offer in the very very long term, alongside
the GPUs, CPUs and whatever else the PU You might
have a.
Speaker 2 (13:09):
We want to thank you, Daniel Pelling, always fascinating catching
up with your portfolio manager and senior research analyst at
Sam's Capital. Meanwhile, we have some breaking news for you,
because of course the phone call has begun. Russian President
Vladimir Putin and Donald Trump have been speaking on the
phone today. We understand that the White House confirmed talk
started at ten am Eastern time. Discussions are said to
(13:29):
be related, of course, to the end of the war
with Ukraine, a key objective being a thirty day truce
with Kiev. As already we understand Kiev has accepted. That
will bring you any more breaking news on that.
Speaker 3 (13:50):
Well, let's take a look at some China tech names,
starting with the ride hailing app d d Global. Think
of it as China's answer to Uber. It did swing
back to a quarterly loss and it's a blow to
the company that's exploring a Hong Kong listing this year.
You can see shares on the day lower by about
three percent. Dedi has had an inconsistent recovery since that
regulatory crackdown a few years ago. That was when it
(14:11):
was delisted from the New York Stock Exchange. They lost
much of its market value. This after Beijing cracked down
on data sharing practices among big tech companies, and Shallomi
shares are higher right now. Revenue in the fourth quarter
beat the average general's estimate. The company CEO also said
it's delivered two hundred thousand vehicles and its race its
target this year from three hundred thousand to three hundred
(14:33):
and fifty thousand vehicles. Shares hire right now by about
three point three percent, Caroline.
Speaker 2 (14:37):
Isn't that extraordinary that we know that Shaomi first got
into the EV market only in the summer of last year,
and now they're pumping out two hundred thousand and likely
to increase it to three hundred and fifty, all the
while that we're seeing just the Chinese EV sector go
from strength to strength. Also in innovation, I mean, what
did you make of BYD's pop today?
Speaker 3 (14:56):
Yeah, I mean it's pretty incredible. Five minute charging. I
mean that's really what led to that pop today, And
if you think about it from the perspective of somebody
who has an EV and maybe has range anxiety. That
is a game changer. If it takes just the same
amount of time for you to charge your vehicle as
it does to go to a gas station, then boom.
That's kind of the golden goose when it comes to
this tech caroline.
Speaker 2 (15:17):
But not everyone can get access to those coolden goose,
or at least those eggs, because that European countries can
buy the Hanel or the tang L, the latest that's
going to be coming from BYD with this new electric
charging technology, but not in the US. And let's just
talk about that, because of course President Trump is hoping
his tarifts are actually going to continue to combat cheaper
Chinese cars and stop them mentoring the US market. Already
(15:39):
you can't access BYD, for example, but outside of the US,
Chinese automakers have really established a global presence. It's today's
big take story. He discusses one of the authors of
the piece, Chester Dawson, And let's just take BYD for
as a case study here. Chester, they have just come
from behind to end up being well basically the world's
biggest EV maker, and they're doing it by accessing the
(16:00):
emerging markets, not of course.
Speaker 7 (16:02):
The US, that's right, Yeah, I mean it is somewhat unusual.
You know, typically when you have a fast rising new
entrant in the automotive business, the first thing they want
to do is get to the US because it's one
of the richest and biggest markets around the world. But
as you mentioned, you know, it's been kind of core
(16:24):
to US economic policy and national security policy to start,
you know, drawing up that drawbridge and protecting manufacture of cars.
You know, obviously the Detroit Three, but also companies like
Toyota that make a lot of cars in the US
are not facing that competition. But as you say, technology
doesn't stand still, and they're introducing some pretty impressive new developments.
(16:47):
They're going to show up in places like maybe Johannesburg
and you know, Bangkok before they get to the US.
Speaker 3 (16:56):
Do they ever get to the US? I mean, Chester
people love these vehicles. Ford CEO Jim Farley a few
months ago was quoted as really loving the Shallmi vehicle
that he had been driving. That was a sort of
big news in the US auto world. People love these cars.
What do they love about them?
Speaker 7 (17:15):
Well, I think the one thing they love is the price.
I mean they're very competitive, which is not to say
they're I mean they're cheap in terms of the price,
but they're not junk. They come with some pretty impressive
features and creature comforts. You know, as you noted at
the outset. You know Shaomi, which used to make cell
phones kind of like you know, the Apple on the iPhone,
(17:36):
but in China they quickly moved into cars and you know,
they're now projected to be making seven times what Rivian,
a US electronic electric vehicle startup, wants to make, and
that's in large part due to the fact that you know,
they're very user friendly. So yeah, it's it's no surprise.
They're good price and pretty good quality as well.
Speaker 3 (17:58):
Bluemberg's Chester, Dawson, Chester. Always good to see you, Thanks
so much for joining us. Coming up, we're going to
look at Google's impact in the health sector, all with AI.
This is Bloomberg. Google is expanding its health related AI
(18:25):
summaries and search to improve its influence in the health sector.
Answers will now cover thousands more health topics and expand
more countries and languages. The company is also adding a
separate feature in search called what People Suggest, which it
said aims to provide users with information from people with
similar lived medical experiences.
Speaker 2 (18:45):
Here on tim Yeah. Google also said it has been
updating a new AI system to help researchers speed up
the scientific en biomedical discoveries. For example Google's AI Coscientist tool.
It's an AI system meant to act basically as a
virtual collaborator for buy my scientists. I actually got to
sit down with Ane Lisa Polowski, founder and principal investigator
(19:05):
for the Accelerated Science Biochemistry and Molecular Biology Lab over
at Google. I asked her to walk us through what
this looks like. Take a listen.
Speaker 8 (19:14):
The idea is we've built a system that works together
with scientists. So, for instance, if it's a Friday afternoon,
I'm in my lab. I have all these ideas I
want to explore, but I have my two kids at home,
and so I can actually plug in a research goal
into the system, check the ideas it's creating and the
evolution over time, and by Monday morning, I have a
bunch of different approaches I can use for my research.
Speaker 2 (19:33):
Ultimately, there's been much said a Lisa about how JENATAI
can bring fruits to bear when it comes to healthcare,
when it comes to research, when it comes to tackling disease,
Is this the reality that you're currently thinking can be
the help with a co scientist an AI coscientist.
Speaker 8 (19:49):
We're at the early stages of the project. So in
our paper we show these three examples. So we've seen
that there is evidence in this direction and we're looking
forward to leaning in more and showing more keep abilities
as we extend this work.
Speaker 2 (20:01):
Has there been any friction or should I say just
reticence by the scientific community to allow models to juke
it out on their areas of focus over a weekend
while they sort of down tools.
Speaker 8 (20:14):
There can be skepticism, but as a scientist, skepticism is good, right.
It encourages that we push ourselves, that we create a
system that's useful and something that's impactful for the community.
Speaker 2 (20:24):
How have you stress tested it? For many the limitations
degenerative AI and for agents is well the fact that
they do sometimes get things wrong. When have you seen
that occur? And how have you managed to cab to
balance it right?
Speaker 8 (20:35):
So no large language model is perfect, but we try
to supplement this with novelty and checkness reviews. We both
use the literature that's available and web search. We add
in tools and databases, and we're learning to extend our
work into knowledge crafts and other strategies where we can
improve and refine on our techniques. The other thing is
we're looking to the community for feedback, so we created
(20:56):
a Trusted Tester program and we welcome and open those
different idea approaches to how we can improve AA coos.
Speaker 2 (21:02):
Scientists, Google research analyst and scientist and Alisa Pawski. They're
ahead of the key Google Health event in New York
coming up in Vidio's GtC has investors focused on the
AI outlook. Locks Capital's Grace is fruders with us and
to join to talk all about the startups and where
she's finding opportunities for the technology. This is Blue Meg Technology.
(21:30):
Welcome back to Blue Meg Technology. I'm Caroline Hide in
New York, Man.
Speaker 3 (21:33):
I'm Tim Stanovek in San Francisco.
Speaker 2 (21:35):
Quick check on these markets, Tim, because risk off at
the moment, many reasons for investors to be anxious. You've
got the FED later today. What do tariffs actually mean
for the US economy? What does Middle East and instability
once again mean in terms of a flight to Haven's
golds that are record but the nasdaqc's off by one
point eight percent. We're seeing Bitcoin another key risk, ASSEID
No Choice off by two point five percent. We're only
(21:55):
at eighty one thousand. Now move on some of the
individual names that have tried to keep above water but fail.
Nvidia is one of the key point strikes on the day.
Even as we look ahead to GtC and the keynote
coming of course from Jensen Huang at one pm Eastern time.
Can he reinject optimism into the need for his GPUs
for inference as well as training. I'm looking at alphabet
making a big purchase thirty two billion dollars for Whiz
(22:18):
down three and a half percent, as indeed the entire
market sinks. I'm looking at Meta though off by almost
five percent. This was the one magnificent seven name that
actually was in the green for the year, not anymore
when out the lowest since November the twenty nine to ten.
Speaker 3 (22:33):
Well, let's see what Katherine and runey Vera has to say.
She's stone X's group Chief Market Strategies and she joins us. Now, Catherine,
you saw what Caroline was talking about there with Tech
under pressure, the Nasdaq one hundred and only eight of
the one hundred socks in the green right. Now, what's
your outlook for the sector?
Speaker 9 (22:50):
Well, it depends on what happens with rates. This is
a very rate sensitive sector.
Speaker 10 (22:54):
So with treasury yields high three and a half two,
sorry for four thirty to fifty, that squeezes long duration
growth stocks, and that's unwinding some of these more crowded trades.
Speaker 9 (23:06):
I think that's probably one of the biggest drivers of
Tech's moved to the downside. Then, of course we have
any type of broad market correction, given that TECH is
about thirty percent of the SMP's market cap, if it
is vulnerable as well to any risk off trade which
could be precipitated by trade tensions or anything of the like.
I think it's also entering a mature phase so that
(23:28):
anything AI related no longer gets this immediate bump up.
But now the market is starting to be more discerning
and more aware of massive capital spending and the potential
for less capital spending going forward, especially with the introduction
of lower cost chips and competition.
Speaker 2 (23:50):
Within a video though for example, let's just use that
as the pin up now trading at twenty five times
future earnings. A lot of floth has come out of
the valuations. At what point do we start to get
dip buying, do you think, Catherine.
Speaker 9 (24:02):
I suspect we soon will. A ten percent correction is something,
but it certainly doesn't unwind the frothiness that we've seen
in the run up for AI stocks in general over
the past several years. And I'll add one point, which
I think is that what you're seeing is a resetting
of value expectations and a move out of tech and
(24:24):
into second derivative AI plays such as industrials and even energy.
You're seeing financials catching a bit. My top pick for
this year is healthcare, and the reason for that is
both because it's defensive, so it's a nice hedge of
your bets, but also because it was the worst performing
sector last year. And if effectively we do get a
(24:45):
purchase of the dip and a resumption to the upside
of the S and P by year end, then my
suspicion is that the most beaten up sector from last
year will become one of the better performing sectors as
those value investors come in.
Speaker 2 (24:58):
I mean, even right thus far, this year, healthcare has
been the winning formula and financials text languishing near the bottom.
I use another case study in Tesla because last year,
since the election results, Tesla didn't trade off fundamentals, it
traded on vibes and an association with the White House.
When we go back to vibes again, if ever, I
mean does it? Is it a case in point and
(25:19):
once yields stopped, we do start to see the upside
and risk hunger just returns like that or are we
in a totally different paradigm right now?
Speaker 9 (25:28):
I think you make a really good point. I think
the vibe shift now is less focused on inflation and
the FED and it's more focused on tariff. So we
have to ask ourselves the biggest component of the S
and P how vulnerable is it to tariff's and is
this tariff fear a real threat to economic growth? And
(25:48):
what we did was we looked back at President Trump
one point zero and.
Speaker 11 (25:52):
Even the Federal Reserve came out with a recent analysis
quantifying caroline the impact of core PCE from Donald Trump
one point oh twenty seventeen, twenty eighteen to twenty nineteen tariffs,
and they quantified it at zero point five percentage points
addition to core PCE.
Speaker 9 (26:10):
Now, the impact on final goods back then was a
one year effect before it rolled off, and then we
saw intermediate goods have a longer lasting impact to the
upside on prices. This time is slightly different because supply
chains have already adjusted, so that means that new tariffs
would hit margins and earnings rather than spark inflation. So
(26:32):
my concern is that, you know, additional tariffs will directly
impact market sentiment and that will bring additional downside to tech.
So my call has paid off pretty well, which is
stay long tech, buyputs on tech now that trade is
looking really awesome. I put it in when tech was
at all time highs, but now what we see is
(26:52):
that put volumes are at all time record highs. So
now it's expensive to do, but it has been it
has proven to be a very good.
Speaker 8 (27:00):
Trade, Catherine.
Speaker 3 (27:01):
Up to now, we've seen the main beneficiaries of the
AI boom be the chip makers and the hyper scalers.
Right to what extent are we seeing other companies and
other sectors within the S and P five hundred for example,
adopt this technology and has it hit their margins?
Speaker 9 (27:15):
Yet Yeah, you're right, and this has been a kind
of a buzzword more than wholesale adoption, and the market
is looking for results at this point, so enough talking,
and we want to see deployment, not only deployment, but
full execution and earnings. So there's been massive investments in
(27:36):
this space. I think at this point, as I said before,
I think it's we're in a mature face. We want
to look for second order effects, so second order investment
opportunities that are indirect beneficiaries or perhaps the second wave
beneficiaries of this large scale, massive capex in AI. Last year,
I recommended utilities. Utilities was one of the top performing
(27:56):
sectors last year, and my recommendation was because it's, you know,
second derivative to AI. Now, I think we need to
be looking at data center operators, cloud infrastructure, medical, real estate, financials.
I think there are second order effects. That's where the
bang is for the coming for coming years in a
structural way.
Speaker 3 (28:18):
Which sector do you think will take advantage of it
the most?
Speaker 9 (28:22):
Well, I still like healthcare. I think there's going to
be a lot of deployment of AI in the in
the sector, and I like it one for that reason.
And two also because it's a nice defensive sector. So
that's going to be my top pick for this year,
just as utilities was last year. So they're nice because
they're both defensive and they could potentially benefit from additional upside.
Speaker 2 (28:42):
Katherin Rooney Vera of stone X Group. Always great to
catch up. Thank you, Tim Well, So I'm now for
talking tech.
Speaker 3 (28:50):
Starting into space. Two NASA astronauts our returning home in
a SpaceX craft after being stuck in orbit for nine months.
The astronauts undocked from the International Space Station last night
and are expected to splash down off the Florida coast
later today, where they will undergo medical checks and be
reunited with their families. Plus Apple lost it fed at
(29:11):
Germany's top civil court to overturn a regulator's decision to
put it under tighter antitrust scrutiny alongside other US tech giants.
The court cited apples a vertically integrated products and services,
it's importance for third party access to markets, and its
potential to harm competition as reasons for the decision. And
Cognition Ai, the developer of an AI powered coding assistant,
(29:34):
has raised hundreds of millions of dollars at evaluation close
to four billion dollars. That's, according to sources, around doubles
the previous valuation of the startup, which released its generative
AI coding tool a year ago.
Speaker 2 (29:46):
Caroline, let's stick more on general to AI, THEAI sector,
Written Large and VC Spotlight. Chray Isford is with us
panner at Lux Capital, focusing on emerging opportunities in artificial intelligence.
You're based here in New York and you're on the
board or focus in and helping with the investments in
certain pinups. I think of Runway and I think of
together AI for example. How are we seeing the application
(30:08):
there AI starting to return real value because at the
moment everyone has been so focused on the foundational and
the infrastructure layer. Are we starting to read rewards?
Speaker 12 (30:15):
For the last few years, we've really seen enterprises focusing
on foundational AI. Now we're seeing that shift, as you say,
to the applications applications of AI and doubling down into
the twenty twenty five year year of the agents. Actually,
we've been talking a lot about AI agents in enterprise production.
We just talked about where are we actually seeing that impact.
We're seeing it across our portfolio in really three main areas,
(30:38):
one encoding, two in customer support, and three in sales.
And I'll give you one quick example on that. On
the real ROI and impact we're seeing. There's a company
called niven Agi. They're an AI agent for customer support.
They are seeing real enterprise impact, integrating with those legacy
systems and working with public companies to actually solve these
(30:58):
tickets in a fraction of the time, at a fraction
of the cost, driving that real value.
Speaker 2 (31:04):
What's interesting is we just had Daniel Pilling Sance Capital
on who's an investor in someone Nvidia. His theory is
actually sort of physical AI, which I know you've also
invested in. The physical AI is actually in many ways
closer than a agentic AI in terms of the real rewards.
What do you make of that sort of time frame
and when we start to see more bountiful effects of agentic.
Speaker 12 (31:24):
AI, Well, it depends how you define AA agents. Then
that's actually part of the confusion, right, everyone has their
own definition, right for my purposes, For engineers I talk
to across our portfolio, it's really an autonomous system where
an LM is directing its own actions across a series
of steps, and these agents are getting better and better
or not really seeing that truly verifiable loop in fields
(31:45):
that are not kind of coding customer support sales. It's
because in fact, they're actually difficult to integrate with right
And that's kind of a funny kind of the irony
in the space right now. If you look at the
physical world, we've seen text and image highly repeatable, highly
verifiable tasks, whether it's in the sciences or whether it's
in math. But as you start to get to these
proprietary data trops I'm talking robotics, I'm talking warehouses, I'm
(32:08):
talking data and manufacturing, where you have to tap into
this proprietary workflow of data, even for a biologist or chemists,
and you have to not just get that data, but
also integrate it with the workflow. That's where we're seeing
really exciting advancements. But it's still early from seeing that
real enterprise ROI grace.
Speaker 3 (32:24):
It does seem like the opportunities that you just spoke
about are really all enterprise focused. Really, you know, thinking
about it B to B, what does it look like
for consumers if this technology is adopted on a wide scale.
Speaker 12 (32:36):
Well, I'll give a simple example on that. You know,
I tried to piog a flight from New York to
San Francisco using open a eyes operator a few weeks ago.
Simple tasks in theory, sure of flight lots of us do.
In reality, it's pretty complex. You know, you need to
get the right airline, you need to get the right airport,
you need to get the right time, and all my preferences.
And so that's a common issue we're seeing. These AI
(32:57):
agents are complex systems with compound I need errors. So
even if there's one little error like not getting access
to that one criteria, it compounds across that fifty steps
in the agentic system. So from a consumer experience perspective,
it can be pretty frustrating when you don't book that
flight exactly the way you want it.
Speaker 3 (33:15):
Are you seeing crowding out come from big companies right now?
You have the hyper scaler's valued at multi trillion dollars?
Is there an opportunity for small companies to come in
and actually provide solutions and complement these hyper scalers complement
these large chip companies most definitely.
Speaker 12 (33:35):
In fact, we're seeing it across our own portfolio. I'll
give another example. They are called together AI. They're an
open source AI cloud. They actually help you run and
productize these fantastic open source models, and you can use
them not using a cloud provider in tandem or working
in tandem with your own existing suite of tools. And
so you're seeing the emergence of this new AI infrastructure stack,
which is still nascent but exciting, where you're using these
(33:57):
cheaper chips, you're actually using these open source and even
smaller models to achieve that same outcome.
Speaker 2 (34:02):
We're looking at your portfolio companies and some of them
have international flavors and certainly hugging face I think a
climb over there running that business to in Sikana AI
as well Tokyo based. At this moment where we're almost
putting up this race of China versus the US, what
are the talent pools are you looking to for AI
expertise and excellence?
Speaker 12 (34:22):
Well, AI expertise is everywhere, and I think if you
focus on China for a second, right, China has fantastic
engineers and researchers, and I think Deep Seek was a
lot of a wake up call for the US whe
were saying, wow, you know whatever you thought deep Sea
cost to train it was a true feat of hardware
efficiency and engineering efficiency, and I actually think that's a
blueprint we're going to see us internationally, So not just
in Japan with companies like Sicana AI using those smaller
(34:44):
agent based models, also in France with companies like Mistral
building really really strong AI open source models as well.
But how do you actually leverage and do more with less?
How do you use less compute? How do you actually
use more efficient chips in fact cheaper chips and actually
have a more effe AI infrastructure stack using that open
source technology grace here.
Speaker 3 (35:05):
In the US to change an administration not even two
months ago. I'm curious about how you view the landscape
differently now that somebody like David Sachs, for example, whom
Caroline and Jackie spoke to just a couple of weeks ago,
is in the position as ais are. Do you see
a difference in the landscape here.
Speaker 12 (35:21):
I think it's early to know, but we're really seeing
a huge excitement for the AI industry and really the
industry being invigorated. Right we just talked about deepseek as
that being a wake up call to invest more heavily
in engineering startups in awesome AI startups who are building
right here on US soil and who are actually leveraging
this open source and transparent innovation to ultimately create really
exciting applications. I'm really excited for new AI applications in
(35:45):
some of those physical AI areas we talked about earlier.
So robotics, space, defense, and manufacturing all have those proprietary
data sources and that unique workflow you need to understand
the workflow of a defense contractor if you're actually building
a fantastic application. So that mix of kind of public
and private is something that's really exciting.
Speaker 3 (36:04):
Melox Capitals Grace Isford, Grace, thanks so much for joining us.
Do appreciate it well. Coming out a lawsuit alleges that
Google and the startup character Ai are to blame for
the death of a fourteen year old boy. Who will
discuss the claims and the implications for Silicon Valley. This
is Bloomberg. In February of last year, fourteen year old
(36:38):
Sewell Setzer took his own life. His mother blames the
chatbot he spent months messaging with. Now she issuing character Ai.
It's the company behind the bot and Google, which has
a partnership with the startup. Let's bring in Bloomberg's Malathi Nayak.
She joins us here in San Francisco, Malafi. This is
just a heart wrenching story. We're going to get to
the legal implications in just a minute, but what happened here.
Speaker 13 (37:02):
So this is a story about a young boy who
began using this chatpot technology and he got romantically involved
with the chat pot that he created himself, sort of
inspired by a character from Game of Thrones, and he
got really sort of caught up in this chatbot. You know.
His mother says that he began to sort of withdraw
(37:23):
from his friends, was having trouble at school, you know.
After a few months, and at some point the parents
decided to confiscate his phone, and while looking for the phone,
he happened to find his stepfather's gun, which was hidden
in their home in compliance with Florida law where the
family lived, and he sort of, you know, Goddess phone,
(37:44):
had this last conversation where he was talking about sort
of coming home to this chat pot and then he
you know, he just sort of took the gun to
his head and shot himself. So it's a tragic story,
and there is a lawsuit now which the mother has
filed against Character Ai, which created this chat pot technology,
as well as Google, which has an interesting sort of
(38:06):
deal with this AI startup, and it sort of brings
into question these sort of new deal structures in AI
where companies technology companies are not really buying these startups outright,
but they are instead of buying the assets, they're sort
of investing in these technologies in a very sort of
(38:28):
unusual way where they're hiring the talent and they're just
licensing the technology. So there's no real full blown acquisition here.
And it's possible that, yeah, it's possible that these deals
were sort of structured this way because last year with
the Biden administration, we saw a crackdown and mergers and
acquisitions where you know, big companies were not allowed to
scoop up their smaller rivals. So it's possible that these
(38:50):
deals were structured that way. But it's sort of interesting
that the lawsuit sort of brings these deals into the
spotlight and sort of questions the legality of the sort
of relationship between these big dear companies and these smaller
startups right multi.
Speaker 2 (39:03):
Garcia's lawyers say in the complaint that Google in particular
contributed financial resources, personnel, intellectual property, and AI technology to
the design and development of character AI's chatbots. We must
be clear that both Google and of course character Ai
dispute any responsibility in terms. They have come back and
(39:24):
certainly stated that Google and character ai are completely separate
and unrelated companies, so says Google. What has been the response?
When do we see it play out? Briefly?
Speaker 13 (39:33):
So in a few months, we'll have a hearing on
the motion to dismiss where Google and a character ai are.
We'll try to convince the Florida Fredrikrourt to actually throw
out this case and sort of so that it won't
proceed further. So we'll see that play out in a
few months. But we've seen what Google and character I
have said. They've said they're separate companies, that they're not
(39:54):
related to each other. And Google says that it's sort
of involvement as an investor, as the mother calls it,
or through a cloud services partnership, that bet that they
both had or the fact that they had this deal
doesn't sort of, you know, implicate it in any way
or sort of is sort of tenuously connected to the
(40:14):
harm that was caused here, and in a lot of
these cases, yes, as we've seen with say Autopilot and
Tesla too, it's very hard to sort of, you know,
connect the harm to the technology. So you know, this
whole question about who's to blame here is sort of
a big one and still sort of unraveling in courts
when it comes to different technologies. And in this case,
you know, AI is coming to the forefront in terms
(40:36):
of these new up and coming AI technologies that are
getting really popular. And you know who is to blame
when things go wrong? And you know, when there are
unexpected turns like this one, blame.
Speaker 2 (40:45):
Magis malt nayak. It's a story that people must go
and read. We thank you so much. Sticking with all
things AI and Vidia CEO Johnsen Wang is set to
deliver his keynote addressed in just a few hours time
at GtC. What can we expect when most common ranicky
is here and well the shares are selling off in anticipation,
So what can you do to study people's nerves?
Speaker 14 (41:07):
Yeah, so what people are really looking for here is
some near term visibility and optimism in what Invidia has coming.
So some of the biggest things that they're looking for
are comments on Blackwell Ultra, which is expected in the
second half of the year, rubin other updates in next
gen GPUs. I think the other thing that people are
really looking for from Jensen is countering the bear case here,
(41:28):
which was really sparked by deep Seek earlier this year,
and that is really just saying that you know, there
won't be a cyclical downturn in spending from these big
AI companies, that they don't have too much AI compute
going right now, and that Invidia will still be sort
of the top of mind, you know, chip maker place
that they're buying for you know, much time to come.
Speaker 2 (41:48):
Have we seen any dip buying ahead of this, you know.
Speaker 14 (41:52):
Dip buying and Invidia has been really interesting this year.
Usually we see people rushing back in so quickly to
buy the dip, and this year we've seen investors really
let the stock draw down much more than it has
in the past. So, I mean it's down for the
last two days that you know, is after having a
big sell off, So we really haven't seen a ton
of diffying two.
Speaker 2 (42:11):
Point eight trillion dollar company only come in Ryanikey, thank
you so much for joining us. That does it from
this edition of Blueberg Technology. Don't forget to check out
our podcast. You can find it on the terminal as
well as online on Apple, Spotify, and iHeart This is
Bluemberg Technology
Popular Podcasts
Crime Junkie
Does hearing about a true crime case always leave you scouring the internet for the truth behind the story? Dive into your next mystery with Crime Junkie. Every Monday, join your host Ashley Flowers as she unravels all the details of infamous and underreported true crime cases with her best friend Brit Prawat. From cold cases to missing persons and heroes in our community who seek justice, Crime Junkie is your destination for theories and stories you won’t hear anywhere else. Whether you're a seasoned true crime enthusiast or new to the genre, you'll find yourself on the edge of your seat awaiting a new episode every Monday. If you can never get enough true crime... Congratulations, you’ve found your people. Follow to join a community of Crime Junkies! Crime Junkie is presented by audiochuck Media Company.
24/7 News: The Latest
The latest news in 4 minutes updated every hour, every day.
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.