Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Bloomberg Tech is alive
Speaker 1: from coast to coast, with Caroline Hyde in New York
Speaker 1: and Eva Low in San Francisco.
Speaker 2: This is Bloomberg Tech coming up.
Speaker 3: Oracle reports strong sales and issues an outlook that suggests
Speaker 3: little lead up in demand for AI computing.
Speaker 2: We break down the results.
Speaker 4: Plus Meta plans to deploy four new generations of its
Speaker 4: in house AI chips by the end of twenty twenty
Speaker 4: seven to help power its rapidly expanding AI workloads.
Speaker 3: And Uber will let customers hail robo taxis from Amazon
Speaker 3: zekes starting.
Speaker 2: In Las Vegas this summer.
Speaker 3: We'll hear from both companies in an exclusive interview later
Speaker 3: this hour. There's one big mover in technology, and it's Oracle,
Speaker 3: on track for its best days since September, up around
Speaker 3: eleven twelve percent.
Speaker 2: The numbers are very clear.
Speaker 3: Sales of ninety billion dollars for the fiscal year starting
Speaker 3: in June, capital expenditures holding at fifty billion dollars. The
Speaker 3: backlog is rising, and so the market's looking at this
Speaker 3: and saying, okay, the AI demand storing story is intact.
Speaker 3: Now it's about execution risk and credit risk, and how
Speaker 3: is Oracle going to manage cash to fund all of this.
Speaker 2: There's a lot more behind the numbers.
Speaker 5: Carry there is, and we can dig into them.
Speaker 4: And the results of Bloomberg's Brodie Ford that they did
Speaker 4: enough to stay off at any anxiety. They're sticking to
Speaker 4: just that fifty billion in capital expenditure, and the numbers
Speaker 4: thus far, particularly in the offering of cloud and the
Speaker 4: infrastructure side, really strong.
Speaker 6: Exactly right now. It's all about, as you both said,
Speaker 6: that execution risk. I mean, we have to remember that
Speaker 6: Oracle was kind of the poster child for Wall Street
Speaker 6: getting scared about all these big data centers not going
Speaker 6: quite right or the margins being upside down. But the
Speaker 6: results last night gave a lot of comfort, right They
Speaker 6: showed that things are progressing, They're recognizing revenue, the margin
Speaker 6: is reasonable, and so these are kind of all the
Speaker 6: messages that investors were hoping to hear from Oracle.
Speaker 2: Brady, take us inside the earning school.
Speaker 3: What was it like, you know, going into it, we
Speaker 3: had broken this story about Abilene not expanding from what
Speaker 3: was a set one point two gigawatts to potentially two gigawatts,
Speaker 3: and the markets reacted as far as I can tell
Speaker 3: on the transcripts, it didn't come up, but there were
Speaker 3: lots of questions about like how.
Speaker 2: Oracle was going to pull this off.
Speaker 6: Yeah, for sure. I think the big question underlying a
Speaker 6: lot of it is how are the big builds going right?
Speaker 6: And the indicator that some of these are going well
Speaker 6: is that the revenue is getting recognized. That means capacity
Speaker 6: is getting handed over to big customers like Open AI.
Speaker 6: The line in the transcript I thought was interesting was
Speaker 6: that their new co CEO, Clay mcglurk, said that ninety
Speaker 6: percent of what we delivered this quarter was on time
Speaker 6: or ahead of schedule. I think that was probably a
Speaker 6: kind of response to a lot of anxiety out there
Speaker 6: about potential delays or things kind of going sideways that
Speaker 6: despite the noise, we're handing over capacity just like we
Speaker 6: certainly would.
Speaker 3: Brody's got the Tech and Depth newsletter out about how
Speaker 3: Oracle's pulling every lever in its AI cash crunch. Really
Speaker 3: worth reading, Brody, Thank you very much. That's the story
Speaker 3: right now, right in markets. What is happening on the
Speaker 3: demand side, what is happening in the supply side, and
Speaker 3: what is happening in the cash supporting all of it.
Speaker 3: Matthew Weird Goldman Sachs Managing director joins us. Now, it's
Speaker 3: really interesting because for a very long time we were
Speaker 3: zeroed in on capital expenditures and that was the be
Speaker 3: all and the end all. There are stories on the
Speaker 3: Bloomberg terminal today about the credit profile and CDs for
Speaker 3: example on Oracle. Our investors starting to look deeper and
Speaker 3: at different data sets to assess the health of what's
Speaker 3: going on.
Speaker 5: Yeah.
Speaker 7: Absolutely, we look though at the structure of the national
Speaker 7: economy and we look at CAPPAX as a share of GDP.
Speaker 7: What's interesting to note is that capital formation as a
Speaker 7: sharf GDP is the same level as a percent as
Speaker 7: it was in twenty eighteen. So we've seen a tremendous
Speaker 7: amount of investment, especially in the tech inn AI space.
Speaker 7: From a national standpoint, it doesn't feel imbalance. Now rightly,
Speaker 7: investors are starting to wonder what is the return going
Speaker 7: to be on all of the investment going into AI.
Speaker 7: We think that there has been a lot of excitement,
Speaker 7: perhaps over exuberance in places, but clearly, as you mentioned
Speaker 7: ed that there is a lot of demand still and
Speaker 7: that's something that we think can continue to go.
Speaker 3: You have a very clear line which is the primary
Speaker 3: determinant in equity markets and equity prices is earnings percent.
Speaker 3: We just had an astonishingly detailed earning sprint from Oracle.
Speaker 3: What did you learn about the world from it?
Speaker 7: Yeah, well, I think going back to what you just said,
Speaker 7: that demand for AI capex is still very very high.
Speaker 7: When we think about what determines equity returns, whether you're
Speaker 7: looking at history since World War Two or even the
Speaker 7: last year. For the s and P five hundred and
Speaker 7: eighty to ninety percent of the return comes from earnings,
Speaker 7: and so the outlook for prices is really predicated on
Speaker 7: do you expect earnings to grow and we do think so.
Speaker 7: We think earnings growth for the SMP five hundred will
Speaker 7: be about ten percent this year. That is largely predicated
Speaker 7: on an economy that we think will be growing above trend.
Speaker 5: We're expecting two point four percent real.
Speaker 7: GDP growth versus trend growth for the US of about
Speaker 7: two percent that we think will generate that ten percent
Speaker 7: earnings growth. We think, though, in our base case, that
Speaker 7: the total return though for a SMP five hundred, will
Speaker 7: be about seven percent, which means not all that earnings
Speaker 7: growth makes its way into the total return, primarily because
Speaker 7: we think multiples will come down a little bit from
Speaker 7: their currently elevated levels.
Speaker 8: Interesting.
Speaker 4: Interesting because you've sort of been talking about the tech
Speaker 4: sect to not being in a bubble math. You talking
Speaker 4: about how valuations are relatively reasonable and Sunny Oracles come
Speaker 4: down some in terms of its own.
Speaker 2: Pe ratios, but taught.
Speaker 4: Us a little bit about the anxiety and the software
Speaker 4: part of the business. Oracle spoke and pushed back against that.
Speaker 4: Have you pushed back against that more broadly?
Speaker 7: Yeah, we do think that there should be some concern
Speaker 7: about the software sector. This, though, is reminding us of
Speaker 7: a few episodes historically. Whether it was the global financial
Speaker 7: crisis and there were concerns about the US financial sector.
Speaker 5: And existential concerns.
Speaker 7: If we go back to COVID, there are concerns about
Speaker 7: the office real estate sector and whether it was dead.
Speaker 5: We don't think this time is different.
Speaker 7: Investors unfortunately, tend to move in mass, especially when pessimism
Speaker 7: takes over.
Speaker 5: We think this time is no different.
Speaker 7: Reality is probably somewhere in between what we've seen in
Speaker 7: terms of the status quo and the existential concerns that
Speaker 7: investors have. We think of this more as an evolution
Speaker 7: of the sector. There's certainly a competitive risk, but we
Speaker 7: certainly don't think that this is an existential one where
Speaker 7: there's so much focus on the terminal value. We think
Speaker 7: these are absolutely very competitive companies that will continue to
Speaker 7: evolve over time.
Speaker 4: There was a lot of anxiety in the market initially
Speaker 4: about software, and then look, there was the geopolitical risk
Speaker 4: that we're now combating day in, day out for twelve
Speaker 4: state drips straight day over with the Middle East and
Speaker 4: conflict with Iran. Matthew, does that change in any way
Speaker 4: your perspective on investing into this moment in where you
Speaker 4: are in the market.
Speaker 7: Yeah, We're very focused on your on and it's terrible
Speaker 7: from a humanity standpoint. I think though, we would focus
Speaker 7: though on from an investment standpoint, that this resembles what
Speaker 7: typically happens when there's a geopolitical shock over the near term,
Speaker 7: there is a reaction by financial markets. What we've seen
Speaker 7: historically and specifically we've looked at the last forty years,
Speaker 7: there are twenty one air strikes in terms of the
Speaker 7: US in the Middle East, and we do see some
Speaker 7: patterns emerge. The first is that the initial reaction in
Speaker 7: markets is at risky assets like equities initially sell off,
Speaker 7: but then we see the dollar appreciate, we see oil rise,
Speaker 7: we see bond prices rise. But after about eight weeks, fundamentals,
Speaker 7: whether economic or corporate fundamentals, reassert themselves and the prior
Speaker 7: trend in financial markets resumes such that after eight weeks
Speaker 7: ninety five percent of the time, US equities are actually
Speaker 7: above their pre strike levels by an average of about
Speaker 7: four percent. So as a consequence, we haven't changed our views.
Speaker 7: We have a key investment tended at Golden sachs that
Speaker 7: history is a useful guide. The words this time is different,
Speaker 7: we think are very dangerous. We think that history will
Speaker 7: repeat itself here and eventually markets will resume their work.
Speaker 3: Trend every day trying to understand the long term impact
Speaker 3: of AI yes on inflation, YEP and labor markets. And
Speaker 3: I know the Goldman House cool is that AI may
Speaker 3: display a million jobs a year, but there is also
Speaker 3: job creation happening in parallel. Just what you said about
Speaker 3: how one might approach that as an investor from your desk,
Speaker 3: in your perspective, what happens in the short, medium and
Speaker 3: long term.
Speaker 7: So we think technological innovation is a feature and not
Speaker 7: a bug of the US equity market. In the US economy,
Speaker 7: the US economy is by far the most innovative globally.
Speaker 7: It's a key reason that we recommend our clients be
Speaker 7: strategically overweight US assets. Now, certainly AI is going to
Speaker 7: boost productivity, and unfortunately it is going to displace workers.
Speaker 7: But as you mentioned ed, there's a tremendous amount of
Speaker 7: new job creation each year. Bear with me some numbers here.
Speaker 7: There are one hundred and seventy million jobs in the
Speaker 7: US each year, they are about twenty five to thirty
Speaker 7: million jobs either lost or destroyed. But on the other
Speaker 7: side of that, and this is creative destruction, new jobs
Speaker 7: are created, and there are more new jobs created than
Speaker 7: jobs destroyed.
Speaker 5: So as we put that one.
Speaker 7: Million number into context, we think that a lot, if
Speaker 7: not all, of those jobs lost to AI will be
Speaker 7: replaced by new jobs. Think about the investment we've seen
Speaker 7: so far, whether it's the construction of data centers, think
Speaker 7: about rising financial market prices. That's increased net worth, and
Speaker 7: it's increased consumer consumption. And so as we think about
Speaker 7: the net impact longer term, we're bullish and we think
Speaker 7: that this is something that will be additive, even though
Speaker 7: over the show there will be some tough adjustments.
Speaker 8: Matthew, I'm Goldman Sachs.
Speaker 4: Great to have your expertise today. Thanks for coming in.
Speaker 4: While our next Meta plans to deploy homegrown chips handle
Speaker 4: AI workloads. I'll have the details next.
Speaker 3: This is Bloomberg Tech. This is Meta's chip lab in Fremont, California.
Speaker 3: Inside the company is developing the next generations of MTIA,
Speaker 3: short for Meta Training and Inference Accelerator. It's in house
Speaker 3: AI chip program. It's a long term effort to build
Speaker 3: the most efficient architecture for Meta's internal workloads, with four
Speaker 3: new generations of chips planned over the next two years,
Speaker 3: from ranking and recommendations to large scale gen AI inferants.
Speaker 3: When chips come in from the fab, this is where
Speaker 3: they're validated, tested at the chip rack and workload level
Speaker 3: before deployment into metas data centers. MTIA three hundred is
Speaker 3: already in production, supporting ranking and recommendations, training helping decide
Speaker 3: what shows up in your social feed Those go into
Speaker 3: liquid called servers like these. MTIA four hundred is moving
Speaker 3: towards deployment, expanding into broader AI workloads, including Genai Future
Speaker 3: versions four fifty and five hundred push further into Genai inference,
Speaker 3: with deployments planned in twenty twenty seven. The effort hasn't
Speaker 3: always moved as quickly as Mark Zuckerberger Meta had hoped.
Speaker 3: Meta has made some acquisitions, it has tried to make
Speaker 3: some others in an effort to strengthen its in house
Speaker 3: chip talent and accelerate progress. AI models are evolving faster
Speaker 3: than traditional chip cycles, so Meta is speeding up the
Speaker 3: design process, aiming to improve performance, cost and power efficiency
Speaker 3: at scale. At the same time, the company is striking
Speaker 3: major supply deals with leading chip makers, securing gigawats of
Speaker 3: AI computing capacity. The strategy is by compute at scale
Speaker 3: from Nvidia and AMD, but also use custom silicon, where
Speaker 3: metas works are uniquely its own, because in the AI race,
Speaker 3: it isn't just about the models, It's about the compute
Speaker 3: behind them. Okay, so we broke the story on this
Speaker 3: one this morning. The reaction in Meta shares kind of muted.
Speaker 3: Remember Meta still a big buyer of Nvidia and AMD GPUs.
Speaker 3: Let's get more on Meta's own chip plans the in
Speaker 3: house and custom silicon with Bluemos Riley Griffin who came
Speaker 3: with me to Fremont, and it was an interesting experience, right,
Speaker 3: Like this is both they want to do custom silicon
Speaker 3: because they have a lot of internal workloads, lots of
Speaker 3: AI inference, but they're going to have to keep buying
Speaker 3: from the big names as well.
Speaker 9: Yeah, it's a strategy of more is more is more,
Speaker 9: and that is for the chips, that is for gigawatts.
Speaker 9: That is a total diversification approach to securing that compute
Speaker 9: given the insatiable demand.
Speaker 4: Ed right now for that, talk to us a little
Speaker 4: bit about the m and A that was involved Riley
Speaker 4: to beef up the team because Ed hinted at it
Speaker 4: in his piece there. But they were pushed back by
Speaker 4: one Japanese company. But then we're able to buy a
Speaker 4: local chip making focus.
Speaker 5: Yeah.
Speaker 9: So early last year, Meta had made an approach for
Speaker 9: furios Ai, a Korean chip maker that rebuked it. It
Speaker 9: turned down that eight hundred million dollar offer. We then
Speaker 9: reported in March. Later towards the end of the year,
Speaker 9: we also reported that Meta was successfully able to acquire
Speaker 9: Revos and with that it's bench of talent and that
Speaker 9: was really the key here. They were able to bring
Speaker 9: in more than four hundred employees who are now peppered
Speaker 9: across MTIA. This team, some of whom we met in Fremont,
Speaker 9: that are making these four different chips, and they're able
Speaker 9: to do that in parallel with that additional headcount.
Speaker 3: I think the specific is really interesting, Like MTIA three
Speaker 3: hundred is real. You know, Meta tells us it's in
Speaker 3: production and they still regard that as.
Speaker 2: AI right training the AI.
Speaker 3: That results in what shows in your timeline targeted ads,
Speaker 3: but actually the next step is LAMA running the model.
Speaker 3: So when you put a request into Meta AI, what
Speaker 3: else did you see there? I mean, the facility was
Speaker 3: large and from a dollar perspective, they seem very committed here.
Speaker 9: It seems like a massive investment. They wouldn't give us
Speaker 9: a specific figure to how much they're spending on their
Speaker 9: own in house chip work, but just seeing the products
Speaker 9: on the table, you got to believe there are billions
Speaker 9: at play here. One thing I would have loved to
Speaker 9: see was MTIA for fifty to five hundred. These are
Speaker 9: the next next generation, and.
Speaker 2: We asked they showed us the cads or the EDAs.
Speaker 9: But yeah, no, no finished product yet and that we're
Speaker 9: looking to twenty twenty seven for that. So two more
Speaker 9: being released next year and in six month increments, which
Speaker 9: is really notable.
Speaker 4: Yeah, it was a great deep dive. I'm looking forward
Speaker 4: to seeing the next chips. Roddy Griffin, thank you very much. Indeed,
Speaker 4: Now in other news, in Nvidia will invest two billion
Speaker 4: dollars into Nevius now as part of a strategic partnership
Speaker 4: to develop and build AI data centers. You guessed it
Speaker 4: will help Nevius deploy more than get this five gigawatts
Speaker 4: in video systems by the end of twenty thirty. Again,
Speaker 4: this age old circular financing question comes once more, and
Speaker 4: in video putting it in to the end customer for
Speaker 4: its chips. Nebe also has a fascinating history. Of course,
Speaker 4: it's the Dutch company. It was the holding company of
Speaker 4: what was once Yandex. In fact, it's still led by
Speaker 4: Yandex's co founder of the Russian business.
Speaker 3: And it's basically you know, it's one of the European names.
Speaker 3: The stock reaction, I guess not that surprising, right. In
Speaker 3: Video has made several multi billion dollar investments in one
Speaker 3: or another layer of what's happening in AI infrastructure, but
Speaker 3: at one point of eighteen percent on track for his
Speaker 3: biggest jobs since September. I guess a vote of confidence
Speaker 3: from the main market leader for GPUs that go into
Speaker 3: the data centers.
Speaker 8: Yeah, certainly.
Speaker 4: What we've just had news this week of in scale
Speaker 4: and the fact that they've got new funding got more
Speaker 4: people on the board. Nick klegg Sheryl Samberg. So there's
Speaker 4: a real heating up of those neo clouds over in
Speaker 4: Europe as well as cool we've hover in the US
Speaker 4: another day. It is another big bit of AI agent news.
Speaker 4: Data Bricks is launching Geni Code. It's a built in
Speaker 4: autonomous AI assistant for really technical talent. Joining us now
Speaker 4: in New York CEO Ali Goertzi, who's here for a
Speaker 4: big sales event that you host here in the city
Speaker 4: and Alik, what is this particular bit of code equipment
Speaker 4: going to do for your talent?
Speaker 8: Yeah?
Speaker 10: So what it is is, you know, we've heard a
Speaker 10: lot about cloud code and coding agents and so on,
Speaker 10: but what they do, they're kind of like interns that
Speaker 10: can write code for you. That's great, but how do
Speaker 10: you get that code into production? And for that you
Speaker 10: need to be sure that you know nothing's going to
Speaker 10: go wrong, that you can measure it.
Speaker 8: And so on.
Speaker 10: So that's gen code is really about, how do you
Speaker 10: actually get your code into production, how do you measure
Speaker 10: the data pipelines that power all of the dashboards that
Speaker 10: you see, and how do you even start building AI models.
Speaker 10: So what gen code can do is it can build
Speaker 10: a machine learning model for you that can predict prices
Speaker 10: or estimate, you know, how much you're going to sell
Speaker 10: of this, and it can do this you know itself.
Speaker 10: It just goes build machine learning models, iterates on them,
Speaker 10: does a lot of the things that data scientists previously
Speaker 10: had to do themselves.
Speaker 8: It just automates all of that for you, and.
Speaker 4: It's not getting things incorrect. As the work continues, there's
Speaker 4: still been a lot of concern that these things are
Speaker 4: hallucinating the longer term that they work.
Speaker 10: Yeah, this is a big problem in the industry. So
Speaker 10: with this, this is like one piece of the puzzle,
Speaker 10: is what I talked about. The other piece of the
Speaker 10: puzzle is we also acquired a company called Quotient, and
Speaker 10: these are the folks behind GitHub Copilot's quality measurement.
Speaker 8: So there are folks focused.
Speaker 10: On quality measurement making sure that they can do monitoring
Speaker 10: of how things are going. So this is the other
Speaker 10: piece of the puzzle, so that we can actually do that.
Speaker 10: So when you launch these things and they're running, how
Speaker 10: can we oversee to make sure that nothing is going
Speaker 10: wrong or it's sort of if it's going off.
Speaker 8: Track, we can stop it, restart it.
Speaker 10: So that's equally important, maybe even more important than the
Speaker 10: first part.
Speaker 2: Alian.
Speaker 3: It's great to have you back on on Bloomberg Tech,
Speaker 3: like taking in aggregate, launch of gene code, acquisition of quoting,
Speaker 3: and how much at this stage for data breaks. Is
Speaker 3: it about product diversity, you know, kind of broadening the
Speaker 3: offering or suite or platform for your customers.
Speaker 10: Yeah, I mean that's super important, right because the space
Speaker 10: is moving so fast, and you know it just you know,
Speaker 10: just I think six months ago we were talking about
Speaker 10: things that were out of completing code. To now everybody
Speaker 10: has agents that can write the code. So the question
Speaker 10: is how do we actually make these the code that
Speaker 10: has been written into production, make sure that they're powering
Speaker 10: up everything inside of the enterprise and making sure that
Speaker 10: we're monitoring it, making sure that we can actually start
Speaker 10: doing more interesting things with it. Can we build AI
Speaker 10: models automatically for the business that can predict the business?
Speaker 10: So you know, to complete that puzzle and have all
Speaker 10: the different puzzle pieces, you know, you have to expand.
Speaker 10: So it is about that and you know that's why
Speaker 10: Quotient and also gene code that have been added to this.
Speaker 3: How how defensive was that move? You know, I think
Speaker 3: about the interesting cursor right now, what Claude is offering
Speaker 3: on or anthropic through the coding side, your awareness of
Speaker 3: the battle field in AI.
Speaker 10: Yeah, I mean those are great partners and customers that
Speaker 10: we work with and we use actually cursor and we
Speaker 10: use cloud code internally at theater but.
Speaker 2: As well, Yeah we do.
Speaker 10: But those things they focus on how can we write
Speaker 10: how can we help software engineers? What gene code really
Speaker 10: can do is it brings it to the knowledge worker,
Speaker 10: the people that create your dashboards inside of an organization
Speaker 10: and they make sure that your revenue numbers are correct
Speaker 10: or the people that make sure that the data that's
Speaker 10: coming in every day into the organization is correct. Nothing breaks,
Speaker 10: you know, there's not an outage. You don't have like
Speaker 10: a blue screen. The dashboard is out and you can't
Speaker 10: see it, or the people that are building machine learning
Speaker 10: models that can predict your prices or your costs or
Speaker 10: you know, doing assessment with machine learning models. It automates
Speaker 10: that portion. So it's sort of very complementary to cloud
Speaker 10: code and you know, cursor and Report and.
Speaker 4: I'm interested in vibe coding like we're all talking about it.
Speaker 4: And indeed, you've got some slightly less technical talent in
Speaker 4: side your business who are using report and in fact,
Speaker 4: Data Break Ventures invested in Report.
Speaker 2: As of today.
Speaker 4: So how how you seeing the adoption of AI ah,
Speaker 4: So what does it mean actually for the strength of
Speaker 4: your data business?
Speaker 10: Yeah, so that's a great point. So geni code is
Speaker 10: really for data scientists, for data engineers. These are people
Speaker 10: that the knowledge worker that understands data. At Data Breaks,
Speaker 10: we have ten thousand employees about you know, three four
Speaker 10: thousand of them are in this category that they're quite
Speaker 10: technical and genicode will help them.
Speaker 8: But you know, we have five.
Speaker 10: Six thousand people that are sitting in marketing or in
Speaker 10: HR or in finance and they don't have those technical skills.
Speaker 8: Replet is excellent.
Speaker 10: They love it, you know, It's just that these are
Speaker 10: people that would never otherwise even touch code, and they're
Speaker 10: now using replet themselves and they're building things that actually work.
Speaker 10: And the best part of it is, you know, whenever
Speaker 10: you build a piece of software that works with Replet,
Speaker 10: it uses.
Speaker 8: A database behind the scenes.
Speaker 10: And actually we have very deep partnership with them, so
Speaker 10: it uses our late based offering behind the scenes, So
Speaker 10: it uses that database that we offer, which is perfect for.
Speaker 8: These kind of agentic tools.
Speaker 10: So I think repleit is really amazing for democratization to
Speaker 10: the broader masses, the people that you would never imagine
Speaker 10: we'd write a single line of code.
Speaker 3: Yeah, I'm sorry to interrupt, We just got thirty seconds.
Speaker 3: I've got to ask you, where are we on data
Speaker 3: bricks path to the public markets?
Speaker 10: Yeah, I knew you were going to ask. Look, I
Speaker 10: don't think right now is the best time to be public.
Speaker 10: So I'm pretty happy to be private to this very moment.
Speaker 10: Right with everything that's going on in the markets, we
Speaker 10: are really excited about what we can do with AI.
Speaker 10: As we can see, we're doing all these acquisitions, we're investing,
Speaker 10: we're hiring people. So we're focused on this long term
Speaker 10: revolution that's happening with AI, rather than having to be
Speaker 10: bogged down about you know, Ibita and what's happening in
Speaker 10: the market today. So we will be public, but I
Speaker 10: don't think now is a very good time.
Speaker 3: At Agusi Data Bricks CEO had to ask up next
Speaker 3: Uber and zeokes.
Speaker 2: This is Bloomberg Tech okay.
Speaker 3: China is moving to restrict state run enterprises and banks
Speaker 3: from using Openclaw AI apps, citing security concerns. According to sources,
Speaker 3: notices have been sent to various agencies in recent days
Speaker 3: that warned against installing open Claw software. Shares a Chinese
Speaker 3: AI and textoc slid on the report here with more
Speaker 3: Bloomberg Executive editor Peter Elstrom, I think explain the reporting
Speaker 3: a bit, you know, the sort of rule or notice
Speaker 3: that was sent out, and I guess also what openclaw
Speaker 3: is and how it's used in China in the context
Speaker 3: of that report.
Speaker 11: Sure, sure, Well, you guys have talked about openclaw a
Speaker 11: few times in the past. Of course, it's this agentic
Speaker 11: AI service that started here with Peter Steinberger who released
Speaker 11: it to the public, and it was kind of a
Speaker 11: hit a few months ago. What people may not know
Speaker 11: quite as well is that in the past couple of
Speaker 11: weeks there's just been a frenzy in China about open
Speaker 11: claw and the service and this idea that agentic ai
Speaker 11: can really begin to do things for you if you
Speaker 11: connect it to your messaging, your finances, to all sorts
Speaker 11: of other maybe company documents too. So it's supposed to
Speaker 11: be a plug in that you can use through your
Speaker 11: messaging service WhatsApp for we chat in China in particular,
Speaker 11: and there's been this huge uptake over the past couple
Speaker 11: of weeks. We've seen companies like ten Cent and Mini
Speaker 11: Max introduce plugins to be able to use open class
Speaker 11: services that are slightly modified, and their shares have really
Speaker 11: been soaring on this news. So consumers have begin to
Speaker 11: adopt it, some companies have begun to adopt it, and
Speaker 11: that leads us to where we are today, where this
Speaker 11: is an exclusive from Bloomberg where we found out that
Speaker 11: Beijing has told the state owned enterprises, the military government
Speaker 11: agencies that they can't adopt this kind of technology because
Speaker 11: there are risks you're giving tons of access to a
Speaker 11: lot of information that could be confidential through the service,
Speaker 11: and they don't want these organizations to do that.
Speaker 4: I mean, Austria developer Peter Steinberger who's now gone over
Speaker 4: to open Ai Peter.
Speaker 8: He was the first to admit.
Speaker 4: That security and safety wasn't his first focus when building
Speaker 4: out what has become a hugely popular tool, and so
Speaker 4: therefore it's China a little bit late to the party here,
Speaker 4: only adopting a Western made piece of tech here, an
Speaker 4: open source one, but also one that had been omittedly
Speaker 4: already pushed back by US institutions because for fear that
Speaker 4: it would go rowe.
Speaker 5: I think that's right.
Speaker 11: I think Steinberger was quite vocal about some of the
Speaker 11: risks here. I think it was more focused on trying
Speaker 11: to prove that agentic ai can be useful and that
Speaker 11: it can be easily accessible if you're able to tap
Speaker 11: into it through WhatsApp, for example, So I think it
Speaker 11: was more focused on that side of it. He was
Speaker 11: kind of deprecating about what the service could actually be
Speaker 11: used for. But now we're seeing it being taken up
Speaker 11: in a number of different places, certainly in the US
Speaker 11: and Europe, also in in Asia. With this China explosion
Speaker 11: of interest, I think the Chinese tech companies have been
Speaker 11: very quick to pick it up and see how they
Speaker 11: can adopt it for their own market too. And I
Speaker 11: think with those advances you've seen, these organizations take it
Speaker 11: on and some of these risks are coming to the fore.
Speaker 11: I think you're exactly right, though cybersecurity experts have been
Speaker 11: quite worried about agentic AI in general and open Claws specifically.
Speaker 4: Max Klaw relaunch. I mean, so many of them taking
Speaker 4: on the news. Peter Elstrom, great to have the breakdown.
Speaker 4: Thank you. Let's take another look at Uber shares right now,
Speaker 4: because as you see, they earlier in the session had
Speaker 4: spiked and now still holding on to three percent gains
Speaker 4: after the right Helth company announced it would offer users
Speaker 4: the chance to ride in robotaxis from Amazon Zoos Suning
Speaker 4: in Las Vegas this summer. Earlier this years of both
Speaker 4: companies sat down with Ed in an exclusive interview.
Speaker 12: That hasn't changed. We're doing that, and I've also told
Speaker 12: you that we'd be practical about our commercial deployment. This
Speaker 12: is a big market and so talk to Darra and
Speaker 12: in Las Vegas and LA We're really looking forward to
Speaker 12: bringing our experience and sort of this way to ride
Speaker 12: to the Uber platform, in addition to also being available.
Speaker 2: The way to write is interesting.
Speaker 3: You know, Uber is not a ride it is a platform,
Speaker 3: a marketplace for increasingly different kinds of rides. What is it,
Speaker 3: Dora that you think you can help z dukes do that?
Speaker 3: I guess they wouldn't otherwise be able to do on
Speaker 3: their own.
Speaker 13: Well, we bring enormous distribution on a global basis, and
Speaker 13: I think that there's this narrative of its either or
Speaker 13: you know, either you go direct to your customer or
Speaker 13: you go through a platform. And the fact is, if
Speaker 13: you look, for example, on Uber Eats, you have some
Speaker 13: of the best brands in the world, whether it's a
Speaker 13: McDonald's or a Starbucks or Chipotle, having a direct interaction
Speaker 13: with customers through their own apps, but also using the
Speaker 13: platform to expand their distribution. And I see the same
Speaker 13: thing with Zeus as well. You know, Zeus is on
Speaker 13: his way to create one of the best brands in
Speaker 13: the business and transportation.
Speaker 2: You experienced recently, right.
Speaker 8: It's a great experience. It's a purpose build vehicle.
Speaker 13: And to have that experience on Uber, we think will
Speaker 13: only magnify what Zeus is bringing to the market place.
Speaker 3: This is small scale in the first instance, right, you know,
Speaker 3: in Las Vegas, and then where you're already active, and
Speaker 3: then and then LA. The mechanics of it are interesting, right,
Speaker 3: because as a rider, you are not guaranteed in the
Speaker 3: first instance, that you can choose a zus. It's algorithmic.
Speaker 3: It depends on the circumstances for.
Speaker 2: You to have.
Speaker 3: I think the language uses the opportunity to be matched
Speaker 3: with the zokes. I guess supply in the first instance,
Speaker 3: is why you've pursued that plan at first?
Speaker 12: Yeah, but really no, because look, it's a platform, it's
Speaker 12: a global platform, it's available. You have all kinds of
Speaker 12: people who show up in Las Vegas. Some might know
Speaker 12: about Zoukes, some might not, and the ones who don't
Speaker 12: probably know about Uber, So why not take advantage of that?
Speaker 5: That's really all it is.
Speaker 3: This is early, but would you just explain I guess
Speaker 3: longer term what the business plan is. Is there a
Speaker 3: per ride revenue share agreement? How do you split the
Speaker 3: fare when you're able to charge a fair dara?
Speaker 13: I think for us now, the focus is going to
Speaker 13: be the customer experience. We want to make sure that
Speaker 13: whether obviously the Zukes direct experience is going to be
Speaker 13: best to breed, but also having the experience through Uber
Speaker 13: is going to introduce U see you to the differentiated
Speaker 13: experience on zekes as well. That's really going to be
Speaker 13: That's really going to be what Aisha and I are
Speaker 13: focused on. You know, I would say at the economics
Speaker 13: take care themselves over the long term.
Speaker 3: Data is going to be critically important here. So let's
Speaker 3: start a guess, Stara, with what data sets can Uber
Speaker 3: provide ZEKES that help them with scale and data sharing
Speaker 3: that you've got in place.
Speaker 13: Well, we are certainly going to work with zekes in
Speaker 13: terms of uh traffic patterns, and now ZEUS is going
Speaker 13: to have access to that data directly as well. But
Speaker 13: I think the combination of the traffic patterns that we
Speaker 13: see and the customers that we see, whether local or international,
Speaker 13: along with zekes's proprietary data, I think is going to
Speaker 13: help zekes be even more efficient as an entity going forward.
Speaker 12: Yeah, and I'll add to that also the experience of
Speaker 12: dealing with things. We're just starting to deal with large events,
Speaker 12: things that Uber knows how to do in their sleep
Speaker 12: and will essentially up our knowledge of doing that.
Speaker 3: That was Uber CEO Dara Kostra Shahi and zooks CEO
Speaker 3: Aisha Evans now coming out Michelle Voles, formerly of the
Speaker 3: American Dynamism Team at Andres and Horowitz joins us to
Speaker 3: discuss her new fund and it's focus on investing in
Speaker 3: technical superiority. What a conversation to come. This has been
Speaker 3: a big tech.
Speaker 4: The conflict with Iran. While it's highlighting a concern some
Speaker 4: investors and many others have been flagging for a while,
Speaker 4: expensive hard to replace weapons are being used to counter
Speaker 4: cheap Iranian missiles and drones. Now today Michelle Voles is
Speaker 4: joining us, who's a former member of the American Dynamism Team.
Speaker 4: Over Andresen Horowitz, you've been investing in startups looking to
Speaker 4: address the costly, the slow that sometimes missing parts of
Speaker 4: the US industrial supply chain. And Michelle, you've just launched
Speaker 4: a new firm, pax VC. You've raised fifty million dollars.
Speaker 4: You're looking at early stage. But I'm really interested in
Speaker 4: just this moment that we're in, of geopolitical tension of conflict.
Speaker 4: And you, as an investor in defense tech, but more
Speaker 4: broadly in supply chain and American dynamism, what do you
Speaker 4: make of this moment?
Speaker 14: Yeah, I think the conflict has really highlighted the gaps
Speaker 14: that we have in our industrial base. We've outsourced a
Speaker 14: lot of our manufacturing overseas to China, and now are
Speaker 14: realizing we need to be able to resupply our weapons systems,
Speaker 14: our components. We need to manufacture these things domestically in house.
Speaker 14: We need to shore up our critical mineral supply chain.
Speaker 14: And so many of the companies that I'm investing at
Speaker 14: and looking at, whether back at in recent Horowitz or now,
Speaker 14: are really focused on this industrial base and making sure
Speaker 14: that we can, in any sort of future conflict, have
Speaker 14: the capabilities domestically to be able to fight back.
Speaker 4: I mean, the name says it all, because packs we
Speaker 4: think of Packs Britannico or Romana or Packs Americana of
Speaker 4: like sort of the dominant of one particular global force
Speaker 4: and the stability that brings. But you've talked about how
Speaker 4: in this competitive area that we're currently in, maybe Packs
Speaker 4: American or isn't so solid.
Speaker 8: But you're looking at.
Speaker 4: PEX Technica, So where in particularly excited about which particular
Speaker 4: technology innovations have you found it worth to put your
Speaker 4: money in?
Speaker 2: Totally?
Speaker 14: And I mean I think the name packs, like you said,
Speaker 14: says it all. Where the eras of relative stability have
Speaker 14: been defined by who is in power and who has
Speaker 14: the most technological power. And I think for us in
Speaker 14: America to be able to ensure continued relative stability in
Speaker 14: the world, we need to make sure our robotics systems
Speaker 14: are incredible, our supply chain, and our ability to mine
Speaker 14: and refine critical minerals. I invest in a mining company
Speaker 14: called Marianna Minerals that's working to reshure all of our
Speaker 14: capacity to bring all these critical minerals to the industrial base.
Speaker 14: I think, look around corners, what should we prepare against
Speaker 14: for future threats. We should be looking at biosecurity and biodefense.
Speaker 14: We should be looking at all sorts of things on
Speaker 14: the AI and what that does for cybersecurity. And so
Speaker 14: I think there's an element of like how do we
Speaker 14: reshure and reindustrialize and how do we see around corners
Speaker 14: for what are the threats coming next.
Speaker 2: Michelle High zed, it's good to see you.
Speaker 3: Welcome to the show. Go back to basics just for seconds.
Speaker 3: You were in rec and Horowitz and you're part of
Speaker 3: this American dynamism team, and the thesis was like simply
Speaker 3: supporting the national interest, and you've decided to go out
Speaker 3: and do something almost solo, right, and I just wonder
Speaker 3: if you reflect a little bit on the decision making
Speaker 3: behind that, the pros and cons of going from such
Speaker 3: a big team within a big firm to doing this
Speaker 3: with packs totally and reeson.
Speaker 14: Horwitz is an incredible place, like some of the most
Speaker 14: brilliant people. Lots of scale, but I love the early
Speaker 14: stage and I love the sort of zero to one
Speaker 14: when companies are just getting off the ground and at
Speaker 14: that point you don't quite need the firepower of a
Speaker 14: big multi stage. You almost need a trusted partner in
Speaker 14: the trenches with you, helping like block and tackle for
Speaker 14: all the small things like getting a company off the ground.
Speaker 14: And then you can bring in these big multi stage
Speaker 14: firms that have all these resources to really pour gasoline
Speaker 14: on the fire that starts burning. And so it's just
Speaker 14: my favorite stage to be and I love being really
Speaker 14: really close with the founders there.
Speaker 3: Well, without putting words in your mouth, you talk about
Speaker 3: this idea that often the founders and themselves, they're kind
Speaker 3: of ahead of the vcs that backing them, even if
Speaker 3: they're like, you know, vcs that have the operator background
Speaker 3: or the founder background, how are you going to catch up?
Speaker 3: You know, how are you going to put yourself in
Speaker 3: a place where you are moving at the same rhythm
Speaker 3: or pace as the people you want to back.
Speaker 2: Totally.
Speaker 14: I would say my style of founder that I like
Speaker 14: is somebody that has a lot of experience in the
Speaker 14: dome they're going after and a sort of unique view
Speaker 14: or a unique right to win, even if that is
Speaker 14: a category that hasn't yet been mainstream or people have seen, Like,
Speaker 14: the founders should be the ones dictating what is the future.
Speaker 14: And if you start to see patterns of okay, a
Speaker 14: lot of smart people are working in this category, it
Speaker 14: feels like, even if I haven't seen it, like they
Speaker 14: are seeing something and I should really spend more time
Speaker 14: and get up to speed. But really it's all about
Speaker 14: like having a network of experts in these domains to
Speaker 14: be able to bounce things off of and get the ideas,
Speaker 14: you know, solidified but real. Like the founders should be
Speaker 14: the smartest people in the room. Venture capitalist should not.
Speaker 3: Be Michelle, Just really, really really quick, did you take
Speaker 3: any port codes with you? Like?
Speaker 2: How does that work? We just have fifteen seconds.
Speaker 14: I invested through my fund in some of my old
Speaker 14: and recent Horwitz port codes, which is a very big
Speaker 14: honor for them to want me back on their cap table.
Speaker 3: Appreciate that. We just wanted to check that one. Michelle Boles,
Speaker 3: founder and managing partner of pax VC. Thank you for
Speaker 3: your time. Carry got some more news.
Speaker 4: Yeah, it's time now for talking tech ed and first
Speaker 4: up anthropic. When it's told a judge it could lose
Speaker 4: billions of dollars in revenue this year after the Trump
Speaker 4: administration declared the company a US supply chain risk. Now,
Speaker 4: the startup made a case for urgency to the San
Speaker 4: Francisco judge a day after it was sued sued the
Speaker 4: Defense Department. Now a hearing is set for March twenty four.
Speaker 2: Plus.
Speaker 4: Grub Hub Well, it's going to test drone delivery of
Speaker 4: takeout orders for the first time. It's part of a
Speaker 4: limited three month pilot program in the area of green Brook,
Speaker 4: New Jersey, starting next week. Now, this is the company's
Speaker 4: latest experiment with automation technology. Is after using sidewalk robots
Speaker 4: on some US college campuses, and Nintendo shares gained as
Speaker 4: much as ten and a half percent in their biggest
Speaker 4: rally since April as a surprise. Success of the new
Speaker 4: Pokemon game helped offset those worries about rising memory costs.
Speaker 4: Physical copies of Pokemon Pacopia have sold out several major
Speaker 4: retailers in the US, with Amazon even raising its price
Speaker 4: to around.
Speaker 2: You tell us.
Speaker 3: Last year, Synopsis closed a thirty five billion dollar acquisition
Speaker 3: of Engineering Software and SIS. Now the focus turns from
Speaker 3: integration to execution. We spoke with CEO sisin Ghazi about
Speaker 3: how AI is transforming chit design and manufacturing.
Speaker 2: Take a listen what our customers are facing.
Speaker 15: Beside the supply chain challenges and the global stress that
Speaker 15: we're dealing with. There's a shortage of engineering. They cannot
Speaker 15: get enough engineers to deal with that complexity. So AI
Speaker 15: helps augment the existing engineers. The second aspect of it
Speaker 15: reducing the cost. You can reduce the cost by improving
Speaker 15: the yield. Memory is a fantastic example. No matter how
Speaker 15: much the memory companies try to expand capacity by building factories,
Speaker 15: you need to be able to have better yield. That's
Speaker 15: where we come in to help them design the actual
Speaker 15: physics of manufacturing with the design phase to improve the yield.
Speaker 15: So those are the components and reducing the design cycle. Traditionally,
Speaker 15: chip design was eighteen to twenty four months. Now you
Speaker 15: have customers like Nvidia and other talking about the design
Speaker 15: rhythm of twelve months. That is not possible without injecting
Speaker 15: AI everywhere in the flow. And when you go to
Speaker 15: manufacturing you're able to improve your yield.
Speaker 3: So see what conclusions did you reach in recent weeks
Speaker 3: after the selloff in software names about the long term
Speaker 3: definitive impact that AI will have on EDA. You're one
Speaker 3: of the biggest DA names, right, but you're not the
Speaker 3: only one.
Speaker 5: Yeah.
Speaker 15: Unfortunately, it was a naive approach to say that all
Speaker 15: software is the same. You're going to have some software
Speaker 15: where LLMS foundation models. Yes, is a perfect application to
Speaker 15: let me call it replace. But when you look at
Speaker 15: the engineering software, I'll go back to the physics aspect
Speaker 15: of it. We run, we code, and we deliver software
Speaker 15: to our customers, but that's not the most The key
Speaker 15: differentiation are the solvers to translate from an architecture design
Speaker 15: to an actual manufacturing product. So I believe it was
Speaker 15: absolutely in over the action by putting every company that
Speaker 15: delivers software in the same categorization.
Speaker 4: So to Seo sasine Ghazi there, let's turn our attention
Speaker 4: to Google now and it's aiming to battle AI slot
Speaker 4: videos on YouTube and YouTube kids. By backing companies it
Speaker 4: sees actually creating high quality AI content. This includes very
Speaker 4: small but focused one million dollars strategic investment in an
Speaker 4: AI animation studio, Animaje. Let's get more with Alexander Levine,
Speaker 4: who covers social media for Boomberg. Look, one million is
Speaker 4: but nothing to Google, but it's saying something about what
Speaker 4: content it wants to do.
Speaker 16: Back Exactly one million is nominal not only for Google
Speaker 16: but also for the company which has raised a ton
Speaker 16: of money over the last several years. But I think
Speaker 16: what is significant about Google's backing of this company, Aimage,
Speaker 16: is that it is really making a bet that AI
Speaker 16: can be used to make really strong quality content for
Speaker 16: children who are using both YouTube and YouTube kids. They
Speaker 16: have had a problem over at YouTube, especially over the
Speaker 16: last year or two, with AI slop and with content
Speaker 16: that a lot of parents and child experts think are
Speaker 16: is more exploitative than educational for children, and so this
Speaker 16: is really a swing to try.
Speaker 8: To take that on.
Speaker 3: Alex For the Brimotech audience, what is AI slop.
Speaker 16: A slop is what we are now often calling spammy, clickbaity, repetitive,
Speaker 16: low quality slimy looking content that you see not only
Speaker 16: on YouTube but also on pretty much every major social
Speaker 16: media platform at this point, and it is often repetitive,
Speaker 16: something that I think is particularly concerning for younger viewers
Speaker 16: because they are real developing their brains at early ages
Speaker 16: when they start watching watching a lot of this stuff.
Speaker 16: And so that's why there has been a bit more attention.
Speaker 16: An AI slot for toddler's babies and children.
Speaker 4: I mean, Adamage I found them back in twenty twenty
Speaker 4: two is an extraordinary like more billions of views. Why
Speaker 4: is it the first kids media business for Google to book?
Speaker 16: So it is not the only kids media business out there.
Speaker 16: I think the biggest, probably best known competitor of an
Speaker 16: homage is Moonbug, which Moonbug Entertainment, which is the company
Speaker 16: behind things like Coco Melon and Blippy. But I think
Speaker 16: that if you look at some of the Google AI
Speaker 16: accelerators other investments, you'll see that they are very much
Speaker 16: focused on other companies in the media space. This is
Speaker 16: the first, I think focused on the kids space, which
Speaker 16: I think says a lot about its priorities.
Speaker 3: For twenty twenty six, Bloomberg's Alex Leevin, thank you very much,
Speaker 3: jam packed. But that does it for this edition of
Speaker 3: Bloomberg Tech.
Speaker 4: Carrac Yeah, don't forget to check out the podcast.
Speaker 8: You can find it on the terminal.
Speaker 4: Can also find it online on Apple, Spotify, and iHeart.
Speaker 4: From New York and San Francisco, this is a blue
Speaker 4: leg Tech