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Speaker 1: Bloomberg Audio Studios, Podcasts, radio news from Mahart where innovation,

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Speaker 1: money and power.

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Speaker 2: Collie in Silicon Vallet Nbon.

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Speaker 3: This is Bloomberg Technology with Caroline Hyde and Ed Ludlove.

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Speaker 4: Live from New York. This is Blouebag Technology. Coming up.

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Speaker 5: China opens a probe into Nvidia regarding anti monopoly laws.

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Speaker 4: That's the global tech.

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Speaker 5: War escalates this adding to broader geopolitical risk.

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Speaker 4: Weighing on US tech stocks.

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Speaker 5: We get the market impact and Alphabet reveals new details

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Speaker 5: on the progress it's made in quantum computing.

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Speaker 4: But first we check in on Invidio. Top story for

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Speaker 4: the day.

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Speaker 5: We're currently shaving off a cool one hundred billion dollars

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Speaker 5: in terms of market capitalization for what is the most

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Speaker 5: valuable company out there and listed in the United States,

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Speaker 5: down three percent.

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Speaker 4: Why Moores about.

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Speaker 5: An investigation, a probe being opened up by China about

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Speaker 5: a deal and acquisition.

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Speaker 4: It made back in twenty twenty.

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Speaker 5: But this really fits into a context of global tit

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Speaker 5: for tap between China and the US. Peter Elstrom joins us.

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Speaker 5: Now just go into the intricacies of this particular investigation

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Speaker 5: regarding a deal made years ago.

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Speaker 6: Yeah, this was a bit of a surprise. We got

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Speaker 6: this news last night from state television in China, where

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Speaker 6: CCTV disclosed that the Agency SAMR has opened this probe

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Speaker 6: into Nvidia and what it calls potentially anti monopolistic behavior.

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Speaker 6: It's a little curious because, as you say, they're focused

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Speaker 6: in or They mentioned specifically a deal that they did

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Speaker 6: four years ago to buy a company called Melanox. It's

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Speaker 6: a company in Israel that makes networking equipment. China had

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Speaker 6: to approve that deal when Nvidia decided to do it,

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Speaker 6: and they did approve it, as you mentioned in twenty twenty,

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Speaker 6: but they did with seven different conditions about invidious behavior

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Speaker 6: going forward. That included giving the customers in China access

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Speaker 6: to product information from Melanox and product access for Invidia's

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Speaker 6: own chips. Now it's not exactly clear what they're going

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Speaker 6: to dig into in this case, but as you noted,

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Speaker 6: in Vidia has been this company at the heart of

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Speaker 6: the US China conflicts over technology. Washington has cut off

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Speaker 6: in Vidia's ability to sell its most advanced chips into China,

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Speaker 6: so they're really at the heart of this conflict between

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Speaker 6: the two biggest economies.

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Speaker 7: In the world.

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Speaker 5: Now it's worth noting that the US Justice Department itself

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Speaker 5: is looking for information to in video and potentially violating

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Speaker 5: anti trust laws. It's not the only country investigating in video,

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Speaker 5: but set the context of what other companies are being

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Speaker 5: focused in on on this tip for tap, because Micron,

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Speaker 5: for example, is having difficulty with its relationship with China.

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Speaker 5: This just does seem to be around the context of

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Speaker 5: China trying to be one up on what respond to

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Speaker 5: the US limitations of technology into its country.

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Speaker 6: Yeah, as you say, the anti monopoly concerns around in

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Speaker 6: Vidia are quite broad. It's not just China, it's also

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Speaker 6: the United States. France here has taken a look at it.

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Speaker 6: That's because they have essentially a monopoly one hundred percent share,

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Speaker 6: almost one hundred percent share in the most advanced chips

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Speaker 6: to train AI models. But you're exactly right. Also, there's

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Speaker 6: this broader trade conflict that we've seen that creates all

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Speaker 6: sorts of problems for companies in between the US and China.

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Speaker 6: In Vidia still sells a lot of chips into China.

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Speaker 6: It had sold more in the past, but two years

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Speaker 6: ago we got this news that Washington was going to

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Speaker 6: cut off their ability to sell the most high end

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Speaker 6: AI chips used to train AI models. Those restrictions have

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Speaker 6: been steadily tightened since then, so in Vidia has suffered

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Speaker 6: because of that.

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Speaker 4: But it's beyond that too.

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Speaker 6: It's the chip equipment makers that have been restrained, ASML

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Speaker 6: here in Europe has been restrained from selling their most

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Speaker 6: advanced gear. So in response, Beijing has taken a number

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Speaker 6: of actions that we're seeing them retaliate in kinds of

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Speaker 6: ways where they cut off a couple of minerals that

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Speaker 6: are very important to the chip industry, in the defense industry,

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Speaker 6: in particular Gallium and Germanium. So they're beginning to show

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Speaker 6: that they also have some cards to play some lovers

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Speaker 6: the poll in this broader trade conflict.

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Speaker 4: Peter Elstrom always bringing us the context.

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Speaker 5: Thank you so much, and look, let's just stick with

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Speaker 5: China and the US. Because President elect Donald Trump said

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Speaker 5: he did have an exchange with Chinese leader Jijingping in

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Speaker 5: recent days, the first clear indication of direct contact between

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Speaker 5: the two men since Trump's reelection in November. Here he

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Speaker 5: is in an interview with NBC's Meet the Press with

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Speaker 5: Kristin Walker.

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Speaker 4: Have a listen, I.

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Speaker 3: Had any agreement with President she who I got along

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Speaker 3: with very well, we've had communication as recently as this week,

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Speaker 3: and I had communication with him where they were going

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Speaker 3: to give the death penalty to anybody sending drugs into

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Speaker 3: the United States.

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Speaker 5: Let's get out to Mike Shepherd in Washington, and that

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Speaker 5: was notable. The fact that conversation has already started in

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Speaker 5: the environment where we're seeing actually China having to respond

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Speaker 5: to its own economic issues, particularly as they have to

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Speaker 5: look to what Trump might do to their economy come

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Speaker 5: twenty twenty five.

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Speaker 8: Well highlights the uncertainty surrounding the US China relationship as

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Speaker 8: we enter this change over here in Washington. On the

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Speaker 8: one hand, Trump is coming in with very much a

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Speaker 8: hawkish agenda towards China when you look at his promises

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Speaker 8: during the campaign to impose tariffs of as much as

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Speaker 8: sixty percent on Chinese goods and his follow on promise

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Speaker 8: to put ten percent tariffs on Chinese goods over his

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Speaker 8: complaint is mentioned in that interview over the flow of

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Speaker 8: fentanel from China into this country. But at the same time,

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Speaker 8: Beijing is sort of looking to the change in administration

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Speaker 8: as possibly a way to engage with an incoming president

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Speaker 8: who has shown a tendency to be a little bit

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Speaker 8: more transactional. Maybe they can engage with him in a

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Speaker 8: way they have been able to with President Joe Biden

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Speaker 8: and his team. So they have signaled that. And in fact,

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Speaker 8: when you were talking just now with Peter about some

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Speaker 8: of the restrictions that China recently imposed on some of

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Speaker 8: those sensitive critical minerals like gallium and germanium, their statement

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Speaker 8: also included an overture to the US that we look

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Speaker 8: forward to engaging more positively at some point. So there

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Speaker 8: is sort of a we don't know which exactly which

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Speaker 8: way this is going to go, but the direction of travel,

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Speaker 8: based on the people in Trump's team, looks to be pretty.

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Speaker 5: Tough, tough, and particularly around tariffs. Just take listen to

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Speaker 5: what he said in that particular interview on tariffs too.

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Speaker 3: I can't guarantee anything. I can't guarantee tomorrow, but I

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Speaker 3: can say that if you look at my just pre COVID,

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Speaker 3: we had the greatest economy of the history of our country,

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Speaker 3: and I had a lot of tariffs on a lot

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Speaker 3: of different countries, but in particular China. We took in

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Speaker 3: hundreds of billions of dollars and we had no inflation.

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Speaker 5: Finding that to consumer impact in terms of inflationary pressure.

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Speaker 4: But businesses are eyeing this very closely.

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Speaker 8: To Mike, they should be, because businesses really are consumers.

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Speaker 8: A lot of those goods that are coming in from

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Speaker 8: China as key inputs to their industries and key components,

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Speaker 8: and an increase in tariffs would not just hit consumers

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Speaker 8: at the end of the very end of the supply chain,

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Speaker 8: it would also hit businesses and they would have to

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Speaker 8: think about.

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Speaker 6: Well, what do we do with our costs?

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Speaker 8: Do we pass those along? And from that interview you

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Speaker 8: really get the sense that Trump is not inclined to

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Speaker 8: back away from any of those pledges. He was pressed

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Speaker 8: during the interview about the impact on consumers and he

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Speaker 8: insisted that no, there was no cost to consumers. He

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Speaker 8: was asked that he said so twice during the interview

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Speaker 8: that there is no cost, and he actually tried to

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Speaker 8: tout the benefits of it, and moreover, he actually pointed

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Speaker 8: to tariffs as a way to gain in other areas

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Speaker 8: when it comes to non economic policy as well. So

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Speaker 8: it'll be interesting to see how he approaches this not

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Speaker 8: only as an economic tool, but maybe as a way

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Speaker 8: to gain leverage in other policy areas, including immigration and

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Speaker 8: who knows what else.

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Speaker 5: Who knows what else Mike sheppin, We appreciate it now,

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Speaker 5: all of this geopolitical risk, but we haven't even mentioned

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Speaker 5: Syria yet. There's also this week the likelihood of another

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Speaker 5: sticky inflation print here in the United States. All of

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Speaker 5: it is sending US tech stocks lower on the day, despite,

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Speaker 5: of course, what had been a bit of optimism around

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Speaker 5: stimulus potential from China.

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Speaker 4: That's going to market take.

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Speaker 5: Brian Cosman is with US portfolio manager at GQG.

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Speaker 4: You'll read on the.

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Speaker 5: Market trends, look any of these latest issues in video

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Speaker 5: China giving you pause, Brian.

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Speaker 2: Yeah, So further starts in terms of technology. We did

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Speaker 2: have a very heavy waiting to a lot of technology

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Speaker 2: names earlier in the year about in terms of our

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Speaker 2: US secuity portfolio, more than fifty percent of the portfolio

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Speaker 2: was in it and also communications services types of names.

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Speaker 2: And we have seen a lot of hype kind of

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Speaker 2: build up in a lot of these technius. In recent months,

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Speaker 2: we've adjusted and gotten a little bit more of a

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Speaker 2: neutral posture at this point in time. So, you know,

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Speaker 2: coming back to in Vidia specifically, they do seem to

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Speaker 2: be still garnering the majority of economics within sort of

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Speaker 2: this capital cycle and its tech cycle that we're seeing

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Speaker 2: at this point in time. And I think you're seeing

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Speaker 2: that in terms of the power and their ability to

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Speaker 2: sort of you know, capture the market and sort of

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Speaker 2: you know, circle of wagons, if you will, in that sense.

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Speaker 2: With regards to China, they do have a slightly reduced

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Speaker 2: exposure to China then they've had in recent years and months,

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Speaker 2: and I think that's because you're just seeing the overall

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Speaker 2: strength in the other areas that they're selling through to

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Speaker 2: the hyperscalers and things like that. So China's now fifteen

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Speaker 2: sixteen percent of their revenue at this point in time.

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Speaker 2: So I think this is a little bit less of

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Speaker 2: an issue than maybe what we saw a couple of

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Speaker 2: years ago when you saw some of those US restrictions

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Speaker 2: sort of pulling back you know, in video's ability to

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Speaker 2: sell into China.

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Speaker 5: So ultimately, the valuations when we're still seeing in video

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Speaker 5: and excess of three trillion dollars that's been dedicat by

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Speaker 5: the extend to the market opportunity despite some curtailment to

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Speaker 5: China for example.

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Speaker 2: Yeah, so when I think you look at in video,

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Speaker 2: you look at sort of the hardware implication and sort

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Speaker 2: of the technology that they have, and they have a

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Speaker 2: better mouse trap in terms of the GPUs that they're

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Speaker 2: selling into folks, and I think you're seeing that in

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Speaker 2: terms of the strength of their business. But I think

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Speaker 2: goes underappreciation is the software element of the thesis as well.

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Speaker 2: That gives them a lot longer tail in terms of

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Speaker 2: the ability to continue to sell through that software angle.

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Speaker 2: The thesis pulls up the margin structure of the video

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Speaker 2: over the course of time. People talk about kudos or

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Speaker 2: being that base software layer, and then these software stacks

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Speaker 2: that they're building on top of this. This all creates

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Speaker 2: a very system wide approach and it's hard to sort

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Speaker 2: of disrupt. That creates really good economics for them, and

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Speaker 2: I think there's a plenty of hetero for them to

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Speaker 2: continue to grow on an ongoing basis. I think it

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Speaker 2: gives a little more stickiness than maybe some of their peers,

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Speaker 2: even within the GPU space.

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Speaker 5: Interesting, Brian take us there for to software more broadly,

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Speaker 5: we have seen the shift out of perhaps chip stocks

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Speaker 5: semi conductors into the software space. Thinking how generative AI

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Speaker 5: is going to make their valuation suddenly ever more inflated.

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Speaker 4: Is that the right way to go?

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Speaker 2: Yeah, So, in terms of the software names, we have

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Speaker 2: seen so multiples really inflate and sort of expectations go up.

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Speaker 2: In terms of the ability for these folks to sort

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Speaker 2: of capture the economics from GENAI and things like that,

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Speaker 2: I think we're still in early stages. I think we

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Speaker 2: need to see where some of the impacts are going

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Speaker 2: and where folks are able to sort of monetize the

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Speaker 2: economics of how generative AI work. I think enterprise is

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Speaker 2: a little bit slower to adopt and it's going to

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Speaker 2: take some time to get there. Where we see the

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Speaker 2: impact more immediately is on the advertising space. So we

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Speaker 2: are seeing that by using sort of GENAI, using this

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Speaker 2: unstructured data to predict what people want to see, what

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Speaker 2: people want to sort of buy in terms of the

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Speaker 2: advertising and things along those lines, matching up that media

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Speaker 2: content with those advertising components. That's an area where we're

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Speaker 2: seeing real returns at this point in time. The economics

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Speaker 2: are actually going in that direction. So that's more of where

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Speaker 2: our focus has been on a your to day basis

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Speaker 2: and where we're positioned in the portfolio at the moment.

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Speaker 5: Interesting of course, we've seen Meta outperform on the back

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Speaker 5: of that alphabet as well.

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Speaker 4: Brian, What was so interesting about what you said.

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Speaker 5: Earlier was that the fact that you have gone more neutral,

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Speaker 5: and I'm interested as to therefore are you putting allocation

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Speaker 5: into other AI tangential industry groups if it's not just

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Speaker 5: all about semiconductors and software right now.

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Speaker 2: Yeah, So when I describe sort of the neutral posturing,

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Speaker 2: I think that's the overall sort of exposure that you've

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Speaker 2: seen in the portfolio has come down. We still have

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Speaker 2: higher conviction calls in those select names like an Nvidia,

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Speaker 2: like a Meta, even like a Microsoft, a little bit

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Speaker 2: lesser extent in our portfolio. You see some fairly decent

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Speaker 2: sizing in those anes. Lean knows is a tech show

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Speaker 2: and a tech program that we're talking about, But we

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Speaker 2: are seeing some more interesting areas and some of the

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Speaker 2: more boring areas of the economy as well. So consumer

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Speaker 2: staples to some extent utilities, not necessarily because they're defensive

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Speaker 2: by nature, but quite frankly because they're a little bit

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Speaker 2: or less trend. The multiples look more attractive and you're

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Speaker 2: getting a little bit more in a bank for your buck,

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Speaker 2: so to speak. In terms of the returns, you get

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Speaker 2: to a high single digit, low double digit type of

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Speaker 2: return with a divid and eel on top of it

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Speaker 2: for a reasonable multiple. You don't have to worry about

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Speaker 2: the higher So it's almost like a barbell approach, if

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Speaker 2: you will, having some of these punchier names, some of

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Speaker 2: these higher conviction names within the tech ecosystem where we

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Speaker 2: are seeing the economics coming through, and then you're seeing

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Speaker 2: some of these other things where we can get some

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Speaker 2: really good quality compounding at a reasonable price.

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Speaker 4: Diversification key no matter who the audience is.

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Speaker 5: Brian Kersman, we thank you, GQG portfolio manager there, appreciate it. Meanwhile,

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Speaker 5: let's just check in on super micro shares for a moment.

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Speaker 5: Jumping earlier in the training session, we're now up just

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Speaker 5: two percent.

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Speaker 4: But all of this after the battle.

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Speaker 5: Server makers said that the Nasdaq has granted the firm

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Speaker 5: more time to become compliant with listing rules. Ultimately, this

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Speaker 5: is all regarding whether or not they've got time to

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Speaker 5: serve their teen filings.

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Speaker 4: Take whose filings.

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Speaker 5: We're seeing that the company has now been given till

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Speaker 5: February the twenty fifth the file. It's financial report for

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Speaker 5: the fiscal year ended June thirtieth alphabet, and it's revealing

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Speaker 5: new details on the progress it's made in quantum computing.

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Speaker 5: The exponential advancements are being done with a new Willow

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Speaker 5: quantum chip that is helping overcome some of the biggest

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Speaker 5: challenges in the field. Now the next step is of

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Speaker 5: course coming up with real world use for all the

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Speaker 5: theoretical power. Here to talk about all of it, hartman

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Speaker 5: Naven here's the vice president of Engineering at Google as

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Speaker 5: well as a founder and manager of the Quantum Artificial

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Speaker 5: Intelligence Lab, And the biggest challenge thus far has been.

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Speaker 4: Ultimately error correction. Can you just talk us through what

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Speaker 4: you have just achieved.

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Speaker 9: Yeah, The biggest news for today is that we developed

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Speaker 9: a new very high quality quantum ship and this enabled

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Speaker 9: to break through computations. And one is an error correction

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Speaker 9: where we showed for the first time that as we

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Speaker 9: use more cubits, the error rate comes down, and it

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Speaker 9: comes down exponentially. The second important achievement breakthrough achievement, is

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Speaker 9: that we did benchmark computation took just five minutes on

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Speaker 9: our new chip that would take the top supercomputer ten

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Speaker 9: septillion years to perform, so ten septillion audience may not

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Speaker 9: know that's one with twenty five zero. So it's a

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Speaker 9: mind bogglingly long time and it.

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Speaker 4: Took five minutes.

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Speaker 5: What I want to understand is what this benchmark test

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Speaker 5: is benchmark random circuit sampling?

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Speaker 4: What does this prove?

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Speaker 9: Yes, So this benchmark problem we often say, it's not

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Speaker 9: a problem that people on mainstream have yet, but it's

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Speaker 9: a suitable benchmark to compare different quantum processors to each other,

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Speaker 9: or to compare quantum processors quantum chips against classical computers.

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Speaker 5: What's so mind boggling about quantum more generally is that

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Speaker 5: we have been talking about it for decades, but it

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Speaker 5: always feels another five, another ten years off.

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Speaker 4: Is it different this time? Hartlett?

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Speaker 9: Yeah, Queen computing is definitely not that field. There's a

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Speaker 9: steady progress, and our new chips that we just released

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Speaker 9: a Willow will form the basis to start doing useful

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Speaker 9: computations that will enable or solve problems that humankind has.

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Speaker 4: Let's talk about those problems.

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Speaker 5: Then everyone immediately goes to healthcare drug discovery. Is that

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Speaker 5: the most obvious place with which this sort of computing

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Speaker 5: is going to be helpful.

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Speaker 9: So the well understood killer application of Queen computers is

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Speaker 9: to model systems where quantum effects are important, and that

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Speaker 9: is more often the case than you may think. For example,

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Speaker 9: if you want to understand how drugs pharmaceuticals bind to cells,

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Speaker 9: or if you want to design a nuclear fusion reactor

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Speaker 9: or something as mundane as improving the batteries for an

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Speaker 9: electric are all these engineering challenges involve quantum problems, and

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Speaker 9: their quantum processes are just the tool of choice to

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Speaker 9: solve those problems.

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Speaker 5: Put it in the perspective of generative AI, the hype

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Speaker 5: that our audience that we have had over the past

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Speaker 5: couple of years.

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Speaker 4: What does quantum mean for that? Is it?

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Speaker 5: Is it superior even in its effects versus generative AI,

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Speaker 5: and does itself the same issues in terms of energy

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Speaker 5: and infrastructure.

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Speaker 9: I wouldn't compare it in those terms. AI as well

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Speaker 9: as quantum computing will be the most transformative technologies of

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Speaker 9: our time. But there's a lot of cross fertilization between

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Speaker 9: AI and quan computing. So AI can help work for

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Speaker 9: quantum ships to work better and vice versas. There are

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Speaker 9: many key computational tasks in AI where a quantum ship

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Speaker 9: will just be the tool of choice to handle laws.

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Speaker 4: It's a phenomenal step.

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Speaker 5: Please come back as you start to apply the significance

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Speaker 5: of this computing. Heartment name and he's the VP of

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Speaker 5: Engineering at Google and founder of the Quantum Malt Official

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Speaker 5: Intelligence Lab.

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Speaker 4: Meanwhile, coming up, we're going to.

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Speaker 5: Be discussing the limits of power grids and how they're

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Speaker 5: running up against the ambitions of both Big Tech and

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Speaker 5: Wall Street when it comes to generator of AI. What

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Speaker 5: we're just finishing on conversation there on. Meanwhile, just check

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Speaker 5: in on some key stories today. Comcast one of those,

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Speaker 5: off by seven percent, key drops on the SMP at

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Speaker 5: the moment and the Nasdaq shares sliding, and this after

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Speaker 5: the Comcast Cable president's CEO David Watson is saying is

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Speaker 5: projects broadband subscribing losses and more than one hundred thousand

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Speaker 5: in the fourth quarter, hurricans Helene and Milton causing around

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Speaker 5: ten thousand broad band losses. We're also looking at Vivendi

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Speaker 5: Big News out of France, as this company does look

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Speaker 5: to break itself apart, saying it's very likely though it

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Speaker 5: would drop out of the CAC forty after it did

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Speaker 5: indeed approve that three spin off listing December the sixth,

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Speaker 5: Steek investors approving that and plans spin off of three

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Speaker 5: multi billion euro units.

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Speaker 4: This is bluembg technology time now for talking tech.

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Speaker 5: First up, Hello Fresh, But it's facing allegations that one

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Speaker 5: of its facilities in Illinois used migrant teen labor chars

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Speaker 5: fell after ABC News reported it is under investigation by

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Speaker 5: the US Labor Department along with a staffing agency that

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Speaker 5: hired employees for the facility, plus Finance Holding CEO Richard

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Speaker 5: Teng told Bloomberg that quote, the future is bright for

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Speaker 5: crypto and encouraging other countries to follow the lead in

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Speaker 5: the US just.

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Speaker 4: Take a listen.

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Speaker 10: Yeah, Now, very crypto friendly president in the States and

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Speaker 10: extremely smart strategy appointing AI in crypto za on that

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Speaker 10: front because this tool represents the most innovative technology for

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Speaker 10: the future.

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Speaker 5: And Jack Maher made a rare public appearance Sunday to

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Speaker 5: Mark and Group's twentieth anniversary, spoke optimistically about the future

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Speaker 5: of the company while acknowledging its challenges and also the

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Speaker 5: hype around the company's AI powered products. Let's just talk

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Speaker 5: about AI a little bit more because Blackstone has been

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Speaker 5: big data center investor and its ambitions are huge, but

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Speaker 5: not everyone is into it. After it bought data center

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Speaker 5: developer QTS in twenty twenty one, it's turned it into

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Speaker 5: a major profit driver for the firm.

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Speaker 4: But around Fairttville, Georgia.

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Speaker 5: The data center has stirred discord in the community over

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Speaker 5: its unprecedented power needs. No most Josh Saul is here

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Speaker 5: with more to deliver. This sort of infrastructure impacts real people,

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Speaker 5: real citizens', real ground.

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Speaker 11: Yes, people in Fairville were unhappy when the local power

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Speaker 11: company came around and started knocking on the door saying

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Speaker 11: they wanted to buy easements. Basically wanted to run big

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Speaker 11: transmission lines through their yards. And that's because these data

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Speaker 11: centers take so much power. The one that QTS is

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Speaker 11: building in Georgia will take about as much power as

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Speaker 11: a million horns.

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Speaker 5: Wow, what does this mean in terms of scaling that

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Speaker 5: across different states, different towns, Because there's not only just

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Speaker 5: one data center in.

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Speaker 4: Georgia right now.

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Speaker 11: Exactly, Georgia isn't even one of the hotspots. It's not.

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Speaker 11: It's not data center Alley, it's not it's not in Arizona.

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Speaker 11: So it means that there'll be more and more of

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Speaker 11: this more power infrastructure being built all over the US

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Speaker 11: and people unhappy when power lines runs run across. But

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Speaker 11: at the same time, data centers in AI we all

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Speaker 11: use all the time, so there's certainly no indication that

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Speaker 11: it's going to slow down.

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Speaker 5: So is there a price point or is there just

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Speaker 5: money recompense what happens?

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Speaker 11: It seems like pretty much all the systems are set

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Speaker 11: up for more data centers to get built faster and faster.

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Speaker 11: Power power companies have a lot of power sorry to

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Speaker 11: build transmission lines across in order to provide electricity, So

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Speaker 11: it seems like most of our systems and most of

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Speaker 11: the profit and in centers are set up to have more

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Speaker 11: data centers and more transmission lines at a pretty rapid

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Speaker 11: pace and.

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Speaker 5: More discord perhaps among communities. It's great reporting. I urge

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Speaker 5: you to go and read the story in full.

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Speaker 4: Welcome back to Blue Megtechnology and Karen Hide in New York.

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Speaker 5: As the course of the show continues, we've actually seen

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Speaker 5: a build up in risk a version surrounding tech. At

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Speaker 5: the moment, we're off by seven tenths of a percent

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Speaker 5: all fixation on the CPI print that inflationary print here

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Speaker 5: on Wednesday in the United States. What does that mean

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Speaker 5: for FED policy going forward? What about the geopolitical risks

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Speaker 5: that are currently affecting in video for example, once again

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Speaker 5: a new probe being announced by China that tip for

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Speaker 5: tach continues to build anxiety.

445
00:22:34,800 --> 00:22:36,720
Speaker 4: We're seeing though, Crypto off by about two percent.

446
00:22:36,840 --> 00:22:39,359
Speaker 5: No, we're still in a ninety eight thousand level close

447
00:22:39,400 --> 00:22:41,920
Speaker 5: to that one hundred, but then too seeing a little

448
00:22:41,920 --> 00:22:43,200
Speaker 5: bit of a sell off as we get out of

449
00:22:43,200 --> 00:22:45,400
Speaker 5: those riskier assets. Move on to some of the individual

450
00:22:45,440 --> 00:22:47,359
Speaker 5: movers that I want to shine a light on. I mean,

451
00:22:47,440 --> 00:22:50,399
Speaker 5: PDD don't remember there is some optimism, particularly around in

452
00:22:50,480 --> 00:22:54,239
Speaker 5: China and its own monetary policy, fiscal policy stimulus, some

453
00:22:54,320 --> 00:22:57,239
Speaker 5: of the most positive that we've seen in more than

454
00:22:57,240 --> 00:22:59,560
Speaker 5: a decade. According to Morgan Stanley, we're up some eleven

455
00:22:59,600 --> 00:23:01,960
Speaker 5: percent as Chinese names do well off the.

456
00:23:01,920 --> 00:23:03,080
Speaker 4: Hints coming from the politboro.

457
00:23:03,320 --> 00:23:05,240
Speaker 5: We're looking at work Day up six percent this as

458
00:23:05,280 --> 00:23:07,719
Speaker 5: we get new announcements of who and who is in

459
00:23:07,880 --> 00:23:09,160
Speaker 5: and out of the next S.

460
00:23:09,119 --> 00:23:09,959
Speaker 4: And P five hundred.

461
00:23:10,080 --> 00:23:12,439
Speaker 5: It's a real and we're seeing Workday software company up

462
00:23:12,440 --> 00:23:14,119
Speaker 5: six percent because it will make the S and p

463
00:23:14,160 --> 00:23:16,119
Speaker 5: five hundred everyone, or rather I hope app love and

464
00:23:16,160 --> 00:23:19,040
Speaker 5: it's Investibatis thought it might, but it's down fourteen percent

465
00:23:19,080 --> 00:23:21,159
Speaker 5: as it doesn't make the cut. Meanwhile, let's just talk

466
00:23:21,200 --> 00:23:24,119
Speaker 5: about Reddit as well. Currently testing a new AI powered

467
00:23:24,200 --> 00:23:27,280
Speaker 5: chatbot called Reddit Answers to help users find information and

468
00:23:27,359 --> 00:23:30,440
Speaker 5: surface discussions from across the platform's forums. That's why I

469
00:23:30,440 --> 00:23:33,080
Speaker 5: am Bluemokes Asia Council. What's so interesting is I feel

470
00:23:33,119 --> 00:23:36,720
Speaker 5: as a user, you and I use Generator AI of Googles.

471
00:23:36,840 --> 00:23:40,119
Speaker 5: I often get Reddit citations and I'm being pushed to

472
00:23:40,119 --> 00:23:41,960
Speaker 5: Reddit more and more. But this is about keeping people

473
00:23:41,960 --> 00:23:44,919
Speaker 5: within the platform absolutely.

474
00:23:44,960 --> 00:23:47,440
Speaker 12: Like you said, right, you search on Google and you

475
00:23:47,520 --> 00:23:50,200
Speaker 12: end up on Reddit. Sometimes I search Google specifically, so

476
00:23:50,240 --> 00:23:53,000
Speaker 12: if I'm trying to find la versus San Francisco, I

477
00:23:53,080 --> 00:23:55,240
Speaker 12: might search for that and then add Reddit at the end,

478
00:23:55,800 --> 00:23:57,520
Speaker 12: and that's how I find stuff on the site. Because

479
00:23:57,560 --> 00:23:59,520
Speaker 12: sometimes on Reddit site it can be a little bit

480
00:23:59,520 --> 00:24:02,879
Speaker 12: difficult to find things. They have like one hundred thousand communities,

481
00:24:02,920 --> 00:24:07,119
Speaker 12: billions of posts, and so with this new chatbot, you

482
00:24:07,200 --> 00:24:09,199
Speaker 12: can just search on the Reddit site and find all

483
00:24:09,200 --> 00:24:10,840
Speaker 12: the things that you're looking for. It or give you

484
00:24:10,880 --> 00:24:13,080
Speaker 12: a summary or give you links, and it just makes

485
00:24:13,119 --> 00:24:14,199
Speaker 12: the experience a lot easier.

486
00:24:14,560 --> 00:24:16,639
Speaker 5: What's so interesting is Reddit, of course have been signing

487
00:24:16,640 --> 00:24:19,680
Speaker 5: deals with generative AI companies Google for example, and getting

488
00:24:19,680 --> 00:24:24,919
Speaker 5: money for its own website and data to be used

489
00:24:25,000 --> 00:24:27,240
Speaker 5: by these generative AI large language models.

490
00:24:27,880 --> 00:24:29,840
Speaker 4: What then are they doing in terms of their own

491
00:24:30,040 --> 00:24:32,879
Speaker 4: lms to build its products? Are they agnostic? Are they

492
00:24:32,920 --> 00:24:33,920
Speaker 4: using one or a couple?

493
00:24:34,920 --> 00:24:37,320
Speaker 12: They're using a few, so they have their own models.

494
00:24:37,359 --> 00:24:40,280
Speaker 12: They also use models from Open AI and Google. So

495
00:24:40,280 --> 00:24:42,840
Speaker 12: they have really interesting partnerships with these companies where they're

496
00:24:42,960 --> 00:24:46,320
Speaker 12: using their large language models. But then, like we mentioned,

497
00:24:46,320 --> 00:24:48,200
Speaker 12: if you go on Google and you find Reddit results,

498
00:24:48,240 --> 00:24:50,280
Speaker 12: that's because of a partnership that they have with Google

499
00:24:50,320 --> 00:24:53,280
Speaker 12: that allows Reddit posts to appear in Google searches. So

500
00:24:53,359 --> 00:24:55,760
Speaker 12: it's a mixture of all those different things. But they're

501
00:24:55,800 --> 00:24:57,600
Speaker 12: definitely building their own models.

502
00:24:57,280 --> 00:24:59,280
Speaker 4: Internally, and then they're using some from some of these

503
00:24:59,280 --> 00:25:01,960
Speaker 4: other companies, becoming evan more integral to the business model

504
00:25:02,119 --> 00:25:04,960
Speaker 4: generator of aiish accounts. Great to have me, thank you.

505
00:25:05,040 --> 00:25:06,720
Speaker 5: Now let's just stick with social media a little bit

506
00:25:06,760 --> 00:25:09,480
Speaker 5: more because TikTok's Chinese parent company, bite Dance now faces

507
00:25:09,520 --> 00:25:12,280
Speaker 5: of course a ban in the US next month as

508
00:25:12,320 --> 00:25:15,439
Speaker 5: a result of a federal appeals court decision Friday, But

509
00:25:15,520 --> 00:25:19,360
Speaker 5: the ruling has sparked opposition from civil liberties groups and users,

510
00:25:19,359 --> 00:25:21,679
Speaker 5: with only thirty two percent in fact of US adults

511
00:25:21,680 --> 00:25:24,720
Speaker 5: supporting the ban according to a recent Pew survey. Let's

512
00:25:24,720 --> 00:25:29,080
Speaker 5: bring in Annapam Chandra is professor of law at Georgetown University.

513
00:25:29,359 --> 00:25:31,200
Speaker 5: And what's so interesting is this is of course ban

514
00:25:31,400 --> 00:25:35,239
Speaker 5: or divest. You have written at length prior to this

515
00:25:35,400 --> 00:25:37,760
Speaker 5: talking about some of the legal issues, perhaps with the

516
00:25:37,800 --> 00:25:41,840
Speaker 5: government arguing, highlighting in particular, two key sentences in the

517
00:25:41,880 --> 00:25:45,080
Speaker 5: briefing to the DC circuit that you thought basically cast

518
00:25:45,119 --> 00:25:47,200
Speaker 5: doubt on the national security arguments.

519
00:25:47,200 --> 00:25:48,800
Speaker 4: Didn't you just go through them for us?

520
00:25:49,760 --> 00:25:50,080
Speaker 10: Sure?

521
00:25:50,600 --> 00:25:52,800
Speaker 13: So, I think some of the key issues in the

522
00:25:52,840 --> 00:25:56,359
Speaker 13: case revolve around whether or not there is in fact

523
00:25:56,960 --> 00:26:00,440
Speaker 13: national security risk from the app, and the nationaleus risk,

524
00:26:00,480 --> 00:26:03,919
Speaker 13: as the government tells us, is that the Chinese government

525
00:26:04,000 --> 00:26:08,840
Speaker 13: might manipulate the app to push Chinese propaganda, or that

526
00:26:08,920 --> 00:26:13,399
Speaker 13: it might manipulate the app to surveil Americans. And in

527
00:26:13,480 --> 00:26:18,640
Speaker 13: order to avoid this, the United States government has negotiated

528
00:26:18,760 --> 00:26:21,320
Speaker 13: has been negotiated for the last handful of years with

529
00:26:21,440 --> 00:26:25,200
Speaker 13: TikTok to kind of locate the data entirely within Texas

530
00:26:25,280 --> 00:26:29,680
Speaker 13: essentially and also put all these controls over that data.

531
00:26:29,760 --> 00:26:32,639
Speaker 13: But the United States government has been dissatisfied with the

532
00:26:32,760 --> 00:26:37,320
Speaker 13: level of protection. But the US government says that it

533
00:26:37,359 --> 00:26:41,800
Speaker 13: has no information that there has in fact been any

534
00:26:41,960 --> 00:26:46,680
Speaker 13: Chinese government manipulation of the app. So it's all speculative,

535
00:26:46,800 --> 00:26:49,119
Speaker 13: and that's part of the difficulty for the government in

536
00:26:49,160 --> 00:26:49,600
Speaker 13: this case.

537
00:26:50,840 --> 00:26:55,760
Speaker 5: But is potential and speculation enough ultimately to argue that

538
00:26:55,800 --> 00:26:58,440
Speaker 5: we're protecting citizens here exactly?

539
00:26:58,600 --> 00:27:03,480
Speaker 13: The Circuit Court of Appeals believed it was that the

540
00:27:03,520 --> 00:27:09,159
Speaker 13: government had was uncomfortable, did not trust Bitedance, did not

541
00:27:09,280 --> 00:27:13,440
Speaker 13: trust TikTok, and therefore could move to ban it from

542
00:27:13,480 --> 00:27:17,360
Speaker 13: the country if it's so decided. And so I think

543
00:27:17,400 --> 00:27:20,399
Speaker 13: that is in fact the question now that we'll be

544
00:27:20,440 --> 00:27:23,160
Speaker 13: posed to the Supreme Court of the United States. Should

545
00:27:23,240 --> 00:27:24,960
Speaker 13: the Supreme Court take the case.

546
00:27:25,520 --> 00:27:28,760
Speaker 5: Can you give us context ugus examples in the past

547
00:27:28,800 --> 00:27:32,520
Speaker 5: where we've really seen court side with a government in

548
00:27:32,560 --> 00:27:35,439
Speaker 5: this way around national security that even though it is

549
00:27:35,520 --> 00:27:39,600
Speaker 5: theoretical in nature, it's important enough to overcome any well

550
00:27:39,880 --> 00:27:40,920
Speaker 5: rights to free speech.

551
00:27:42,359 --> 00:27:46,280
Speaker 13: So this is a very unusual case. There's nothing that

552
00:27:46,400 --> 00:27:49,200
Speaker 13: is on all fours with this case. The closest case

553
00:27:49,240 --> 00:27:51,879
Speaker 13: involving foreign propaganda in the United States is a case

554
00:27:52,000 --> 00:27:57,480
Speaker 13: from the nineteen sixties involving literally Chinese communist propaganda, and

555
00:27:57,520 --> 00:28:00,560
Speaker 13: the Supreme Court unanimously in that case said Americans have

556
00:28:00,560 --> 00:28:04,120
Speaker 13: a right to receive Chinese communist propaganda if they so want.

557
00:28:04,440 --> 00:28:04,560
Speaker 8: So.

558
00:28:04,600 --> 00:28:07,879
Speaker 13: There's not an ideal case. I think if cases around

559
00:28:07,960 --> 00:28:11,480
Speaker 13: national security, like the Pentagon Papers case, where the US

560
00:28:11,560 --> 00:28:14,120
Speaker 13: government said it's too dangerous for the New York Times

561
00:28:14,280 --> 00:28:18,320
Speaker 13: to reveal the secret history of the Vietnam War, and

562
00:28:18,600 --> 00:28:22,160
Speaker 13: the Supreme Court said, actually, no, we don't believe that

563
00:28:22,240 --> 00:28:25,360
Speaker 13: it's not a kind of immediate risk that you are

564
00:28:25,640 --> 00:28:29,320
Speaker 13: pointing to. And that's what the TikTok's argument is going

565
00:28:29,359 --> 00:28:30,479
Speaker 13: to be to the Supreme Court.

566
00:28:30,560 --> 00:28:32,920
Speaker 5: Do you think that it's argument will carry weight if

567
00:28:33,040 --> 00:28:35,200
Speaker 5: indeed it does get to the level of the Supreme Court.

568
00:28:36,240 --> 00:28:38,480
Speaker 13: Well, if it goes to the Supreme Court, I think

569
00:28:38,480 --> 00:28:42,360
Speaker 13: the same question is going to be before those judges

570
00:28:42,360 --> 00:28:46,080
Speaker 13: as was before the DC Circuit. Do you defer to

571
00:28:46,520 --> 00:28:52,400
Speaker 13: the judgment of the political branches of Congress and the

572
00:28:52,440 --> 00:28:55,440
Speaker 13: President when they insist that there is a national security

573
00:28:55,520 --> 00:28:59,040
Speaker 13: risk and there's no other way to protect Americans than

574
00:28:59,040 --> 00:28:59,880
Speaker 13: to ban the app.

575
00:29:01,840 --> 00:29:05,760
Speaker 5: What's so interesting is it feels as though perhaps the

576
00:29:05,840 --> 00:29:09,880
Speaker 5: worries coming from the user base have diminished significantly, and

577
00:29:09,920 --> 00:29:12,520
Speaker 5: as we're just saying just a third of US users

578
00:29:12,600 --> 00:29:15,880
Speaker 5: or in the US population supports such a ban, So

579
00:29:16,400 --> 00:29:18,720
Speaker 5: is there context as to how that might need to change.

580
00:29:18,760 --> 00:29:19,440
Speaker 4: I mean, you're no.

581
00:29:19,440 --> 00:29:21,520
Speaker 5: Marketing expert, but it almost feels as though the government

582
00:29:21,560 --> 00:29:22,760
Speaker 5: needs to go out and make its case.

583
00:29:22,640 --> 00:29:23,280
Speaker 4: A little louder.

584
00:29:24,000 --> 00:29:27,040
Speaker 13: The government tried to make its case for the last

585
00:29:27,080 --> 00:29:30,480
Speaker 13: six months. So the ban has been in place, has

586
00:29:30,560 --> 00:29:33,520
Speaker 13: been you know, the law has been in place threatening

587
00:29:33,520 --> 00:29:38,600
Speaker 13: this ban since April, and so since that time, we've

588
00:29:38,640 --> 00:29:42,360
Speaker 13: seen the government say that China controls this app and

589
00:29:42,400 --> 00:29:46,120
Speaker 13: therefore it's unsafe. But Americans have continued using the app,

590
00:29:46,440 --> 00:29:49,160
Speaker 13: so they you know, Americans have decided that they don't

591
00:29:49,160 --> 00:29:54,000
Speaker 13: feel unsafe despite the government's use. In fact, Donald Trump

592
00:29:54,080 --> 00:29:57,200
Speaker 13: started using the app. In fact, Joe Biden started using

593
00:29:57,200 --> 00:30:01,040
Speaker 13: the app, Kamala Harris started using the app. Every politician

594
00:30:01,120 --> 00:30:03,880
Speaker 13: is now using the app, despite it's the fact that

595
00:30:03,880 --> 00:30:10,160
Speaker 13: it's apparently possibly prone to foreign manipulation, though the government

596
00:30:10,200 --> 00:30:14,400
Speaker 13: again has said we have no evidence of that thus far.

597
00:30:15,280 --> 00:30:16,200
Speaker 4: January in the nineteenth.

598
00:30:16,240 --> 00:30:20,200
Speaker 5: Therefore it gets banned, it has to be sold during

599
00:30:21,320 --> 00:30:23,000
Speaker 5: foresee that being the outcome.

600
00:30:22,680 --> 00:30:27,880
Speaker 13: Here, I think that's becoming closer and closer to the

601
00:30:28,080 --> 00:30:31,760
Speaker 13: likely outcome that TikTok goes dark on January nineteenth.

602
00:30:32,680 --> 00:30:34,640
Speaker 4: And it's great to have some time with you. Thank you.

603
00:30:34,680 --> 00:30:37,560
Speaker 4: Anamam Chandra is of Georgetown University Law Center.

604
00:30:38,040 --> 00:30:41,920
Speaker 5: Meanwhile, let's talk about a network of Facebook accounts posting

605
00:30:41,960 --> 00:30:46,040
Speaker 5: more than forty one hundred political ads against Romania's pro

606
00:30:46,320 --> 00:30:50,560
Speaker 5: EU presidential candidate while also promoting far right figures. Digital

607
00:30:50,600 --> 00:30:53,720
Speaker 5: threat research groups reset tech and check First say the

608
00:30:53,720 --> 00:30:54,280
Speaker 5: posts were.

609
00:30:54,280 --> 00:30:56,440
Speaker 4: Viewed by nearly two hundred million times.

610
00:30:56,480 --> 00:30:59,200
Speaker 5: Facebook's parent company Meta did not comment on the report,

611
00:30:59,240 --> 00:31:01,520
Speaker 5: but says it has not seen any evidence of major

612
00:31:01,520 --> 00:31:05,080
Speaker 5: incidents on its platforms in Romania. Now coming up, Lily

613
00:31:05,160 --> 00:31:07,480
Speaker 5: Lyman underscore VC Managing Partner.

614
00:31:07,160 --> 00:31:09,880
Speaker 4: Is here with a look VC funding where it's heading next.

615
00:31:09,960 --> 00:31:29,960
Speaker 4: In twenty twenty five, this Bloomberg technology.

616
00:31:22,840 --> 00:31:27,080
Speaker 5: Investment strategies of major multi billion dollar firms and then

617
00:31:27,120 --> 00:31:30,760
Speaker 5: small specialized VC funds, Well, they're showing that venture the

618
00:31:30,800 --> 00:31:33,480
Speaker 5: asset class is kind of bierfurcating.

619
00:31:32,760 --> 00:31:34,240
Speaker 4: Here heading into the new year.

620
00:31:34,760 --> 00:31:37,840
Speaker 5: According to Linny Lyman, who is managing partner at Underscore VC,

621
00:31:38,000 --> 00:31:40,760
Speaker 5: joining us now from Boston and nearly the theory here

622
00:31:40,840 --> 00:31:43,240
Speaker 5: is that you're getting two very different strategies, not both

623
00:31:43,280 --> 00:31:44,480
Speaker 5: of particularly complementary.

624
00:31:45,880 --> 00:31:48,000
Speaker 1: That's right, Carolin, It's great to be here. Thanks for

625
00:31:48,040 --> 00:31:51,760
Speaker 1: having me again. I'm Lily Lyman. I'm at Underscore BC.

626
00:31:52,000 --> 00:31:54,800
Speaker 1: We are a community driven early stage venor from BC

627
00:31:54,840 --> 00:31:57,760
Speaker 1: here in Boston. We invested the preceed and seed stages

628
00:31:57,800 --> 00:32:00,959
Speaker 1: in the world of B to B software. In your questions,

629
00:32:01,640 --> 00:32:03,720
Speaker 1: the right one. We're seeing the emergence of two very

630
00:32:03,760 --> 00:32:07,000
Speaker 1: different strategies in venture, and it's bifurcating the market in

631
00:32:07,000 --> 00:32:09,840
Speaker 1: a way that almost looks like a barbell. On one hand,

632
00:32:09,920 --> 00:32:13,280
Speaker 1: you have these big, multi billion dollar multi stage venture

633
00:32:13,320 --> 00:32:16,480
Speaker 1: firms that our megafirms. They're really sort of financial institutions

634
00:32:16,520 --> 00:32:19,120
Speaker 1: at this point, and I hear LPs refer to these

635
00:32:19,160 --> 00:32:22,160
Speaker 1: as venture beta. On the other side of the spectrum,

636
00:32:22,160 --> 00:32:26,040
Speaker 1: you have smaller funds, more specialized targeted strategies. That's where

637
00:32:26,040 --> 00:32:28,760
Speaker 1: we at Underscore play and it's because our that's our

638
00:32:28,800 --> 00:32:31,040
Speaker 1: belief of where you can still get venture alpha that

639
00:32:31,160 --> 00:32:34,640
Speaker 1: three to five even ten x return. What's happened over

640
00:32:34,640 --> 00:32:36,520
Speaker 1: the last eighteen months or so, though, is we're seeing

641
00:32:36,520 --> 00:32:41,160
Speaker 1: a heavy concentration of LP dollars going into the megafunds.

642
00:32:41,240 --> 00:32:44,160
Speaker 1: So eighty percent of capital rais last year went to

643
00:32:44,240 --> 00:32:48,040
Speaker 1: established managers of firm sizes over five hundred million, and

644
00:32:48,080 --> 00:32:51,360
Speaker 1: the representation of these firms is increased, so it's gone

645
00:32:51,400 --> 00:32:53,640
Speaker 1: from about two percent to twenty five percent in the

646
00:32:53,720 --> 00:32:57,320
Speaker 1: venture market. And that overrepresentations has a lot of implications

647
00:32:57,320 --> 00:32:59,560
Speaker 1: for LPs, for managers and for founders.

648
00:33:00,000 --> 00:33:03,640
Speaker 5: Okay, let's talk about what means for onto school VC. Ultimately,

649
00:33:03,720 --> 00:33:07,520
Speaker 5: you're going to see more consolidation aman among the seed

650
00:33:07,600 --> 00:33:11,080
Speaker 5: the pre seed stage or actually do need the breadth

651
00:33:11,600 --> 00:33:13,960
Speaker 5: of more focused funds, and it's actually in consolidation that

652
00:33:14,000 --> 00:33:14,880
Speaker 5: you have in the middling.

653
00:33:16,320 --> 00:33:18,200
Speaker 1: I think what it means is for I think we're

654
00:33:18,200 --> 00:33:21,320
Speaker 1: going to see, you know, more specialization at the earliest stages,

655
00:33:21,400 --> 00:33:23,360
Speaker 1: so firms like ours that you need to really lean

656
00:33:23,400 --> 00:33:27,160
Speaker 1: into differentiation and get to founders early and also back

657
00:33:27,240 --> 00:33:30,880
Speaker 1: perhaps non consensus, non not obvious both founders and ideas

658
00:33:31,160 --> 00:33:34,240
Speaker 1: because that's how you can capture true alpha. What's happening

659
00:33:34,240 --> 00:33:36,440
Speaker 1: with the multi stage strategy at the at the seed

660
00:33:36,480 --> 00:33:38,800
Speaker 1: stage is that they play more of an index game

661
00:33:39,160 --> 00:33:41,040
Speaker 1: and putting out a lot of a lot of checks

662
00:33:41,080 --> 00:33:43,800
Speaker 1: and then backing the ones that are that are winners.

663
00:33:44,120 --> 00:33:45,880
Speaker 4: But the implications of that is that.

664
00:33:45,800 --> 00:33:47,720
Speaker 1: They're not always very pre sensitive in that and it

665
00:33:47,760 --> 00:33:50,920
Speaker 1: can dilute the entire early stage class if you can't

666
00:33:51,440 --> 00:33:53,720
Speaker 1: know counter that and and really dig in and go

667
00:33:53,880 --> 00:33:56,760
Speaker 1: earliest and non consensus. So that's how we've been thinking

668
00:33:56,840 --> 00:34:00,000
Speaker 1: about it in order to counteract some of the implications

669
00:34:00,080 --> 00:34:02,800
Speaker 1: of the multi stage indexing model at seed stage.

670
00:34:02,960 --> 00:34:06,520
Speaker 5: It kind of takes me back to when Softbad first unveiled.

671
00:34:07,760 --> 00:34:10,840
Speaker 5: There are one hundred billion dollar fund and everyone feeling

672
00:34:10,840 --> 00:34:13,800
Speaker 5: that the vision FuMB was just going to give enormous

673
00:34:13,840 --> 00:34:16,799
Speaker 5: amounts of money to individual founders or companies that sort

674
00:34:16,800 --> 00:34:20,000
Speaker 5: of drown out the rest of the competition. Is that

675
00:34:20,040 --> 00:34:22,400
Speaker 5: what happens or is it just more that it inflicts valuations.

676
00:34:22,440 --> 00:34:25,160
Speaker 5: What is a real impact on those very early stage bets.

677
00:34:26,440 --> 00:34:28,279
Speaker 1: I think it can have multiple impacts. I think, on

678
00:34:28,320 --> 00:34:32,520
Speaker 1: one hand, the indexing can impact valuations for everyone, and

679
00:34:32,600 --> 00:34:37,400
Speaker 1: so it can actually dilute the returns across the board.

680
00:34:37,160 --> 00:34:39,160
Speaker 1: And but on the other hand, if you can get

681
00:34:39,160 --> 00:34:41,320
Speaker 1: in early, or if you can do you know, invest

682
00:34:41,360 --> 00:34:43,239
Speaker 1: in sort of non consensus, I think you still can

683
00:34:43,360 --> 00:34:44,240
Speaker 1: generate that.

684
00:34:44,000 --> 00:34:44,880
Speaker 4: That real alpha.

685
00:34:45,360 --> 00:34:49,800
Speaker 1: I think part of the implications are questions for founders

686
00:34:49,840 --> 00:34:52,799
Speaker 1: as well. I think we're seeing founders think a lot

687
00:34:52,840 --> 00:34:54,960
Speaker 1: about who do they bring around their table at which

688
00:34:55,000 --> 00:34:57,640
Speaker 1: stages of company building. You know, in the in the

689
00:34:57,680 --> 00:35:00,640
Speaker 1: market peak during the zerp era, there's a lot speed dating,

690
00:35:00,680 --> 00:35:02,000
Speaker 1: there's a lot of capital that went out of the

691
00:35:02,000 --> 00:35:04,719
Speaker 1: door very quickly, and then two years later we had

692
00:35:04,760 --> 00:35:07,160
Speaker 1: a much tougher environment and it was hard for founders.

693
00:35:07,200 --> 00:35:09,560
Speaker 1: And so I think we're seeing founders, both new founders

694
00:35:09,600 --> 00:35:12,360
Speaker 1: and sophisticated founders, think about, you know, what kind of

695
00:35:12,400 --> 00:35:15,759
Speaker 1: investments and investors do they want around their table for

696
00:35:15,840 --> 00:35:18,239
Speaker 1: each stage of company building, and thinking more about this

697
00:35:18,320 --> 00:35:20,600
Speaker 1: question of alignment. You know, what does that check mean

698
00:35:20,640 --> 00:35:23,399
Speaker 1: to that firm and how does that infer how they'll

699
00:35:23,400 --> 00:35:26,560
Speaker 1: show up during the company company building process.

700
00:35:27,320 --> 00:35:29,759
Speaker 5: I'm assuming that most founders you're seeing are having to

701
00:35:30,120 --> 00:35:33,319
Speaker 5: lean into generative AI, even if they are not an

702
00:35:33,320 --> 00:35:34,640
Speaker 5: AI specific company.

703
00:35:34,680 --> 00:35:37,319
Speaker 4: But what does that mean, in terms of trying to.

704
00:35:37,360 --> 00:35:41,480
Speaker 5: Uncover the least covered areas or the particularly non consensus

705
00:35:42,000 --> 00:35:43,600
Speaker 5: builders and founders you want to find.

706
00:35:44,800 --> 00:35:48,120
Speaker 1: We're certainly seeing a ton of opportunity in AI and

707
00:35:48,040 --> 00:35:50,680
Speaker 1: and you know, over thirty percent of venture capital this

708
00:35:50,680 --> 00:35:53,400
Speaker 1: past year went into AI companies, and we expect that

709
00:35:53,480 --> 00:35:56,280
Speaker 1: number to increase next year. But what we're seeing is

710
00:35:56,280 --> 00:35:59,160
Speaker 1: is companies that are AI native, both in their products

711
00:35:59,160 --> 00:36:00,960
Speaker 1: that they're offering, but so in terms of how they're

712
00:36:01,000 --> 00:36:02,799
Speaker 1: being built. I think one of the things that makes

713
00:36:02,800 --> 00:36:04,960
Speaker 1: this time particularly exciting to be an investor at the

714
00:36:05,000 --> 00:36:08,600
Speaker 1: earliest stages is companies are being built with a new model.

715
00:36:08,680 --> 00:36:10,520
Speaker 1: I think with a new capital structure you can do

716
00:36:10,560 --> 00:36:13,799
Speaker 1: a lot more things more efficiently. Engineering can be much

717
00:36:13,840 --> 00:36:16,560
Speaker 1: more efficient, sales and marketing can be much more efficient,

718
00:36:16,840 --> 00:36:19,239
Speaker 1: and so I think there's a mentality of you do

719
00:36:19,360 --> 00:36:21,799
Speaker 1: more with less, achieve more with less, and so in

720
00:36:21,840 --> 00:36:24,200
Speaker 1: many ways, you know, outside of a certain bucket of

721
00:36:24,480 --> 00:36:28,040
Speaker 1: types of investments like foundational models and infrastructure, I think

722
00:36:28,080 --> 00:36:30,720
Speaker 1: where we invest a lot of the applied AI vertical

723
00:36:30,719 --> 00:36:34,279
Speaker 1: AI applications, I actually think you can build pretty big

724
00:36:34,280 --> 00:36:37,880
Speaker 1: companies with a totally different capital structure given the increased

725
00:36:37,920 --> 00:36:39,760
Speaker 1: efficiencies of building with AI itself.

726
00:36:39,960 --> 00:36:42,320
Speaker 4: How many of them are in Boston? Are you looking

727
00:36:42,800 --> 00:36:43,680
Speaker 4: across the US?

728
00:36:44,880 --> 00:36:47,640
Speaker 1: We invest across the US, but we're very bullish on Boston.

729
00:36:47,760 --> 00:36:51,120
Speaker 1: So over half of our capital is deployed here here

730
00:36:51,120 --> 00:36:53,600
Speaker 1: in Boston. And it's because this is an ecosystem that

731
00:36:54,000 --> 00:36:57,520
Speaker 1: continuously produces the top talent in the world. It continuously

732
00:36:57,560 --> 00:37:00,440
Speaker 1: produces some of the top research and development the world,

733
00:37:00,840 --> 00:37:04,480
Speaker 1: and increasing you know, it consistently solves really hard problems,

734
00:37:04,480 --> 00:37:07,960
Speaker 1: so things like healthcare and biotech and life sciences and

735
00:37:08,000 --> 00:37:12,880
Speaker 1: financial services, and so cybersecurity and energy and climate. So

736
00:37:13,000 --> 00:37:16,319
Speaker 1: you know, these three factors are you know, are part

737
00:37:16,360 --> 00:37:18,680
Speaker 1: of what gets us very excited to invest in the

738
00:37:18,680 --> 00:37:21,280
Speaker 1: bus and ecosystem, particularly in this age of applied.

739
00:37:20,920 --> 00:37:25,120
Speaker 5: Ailey Lyman, great to have you, managing partner of Underscore VC,

740
00:37:25,680 --> 00:37:28,440
Speaker 5: Wishing well for the rest of the year. Meanwhile, Elsewhere

741
00:37:28,480 --> 00:37:30,960
Speaker 5: and VC, a startup founded by the former lead researcher

742
00:37:30,960 --> 00:37:33,840
Speaker 5: of Open AI, is raised forty million dollars in funding.

743
00:37:33,920 --> 00:37:34,759
Speaker 4: Waveforms AI.

744
00:37:35,040 --> 00:37:38,920
Speaker 5: It makes AI audio software that improves verbal interactions with machines,

745
00:37:39,160 --> 00:37:42,040
Speaker 5: and is one of several AI companies developing.

746
00:37:41,560 --> 00:37:44,839
Speaker 4: More human length voice features. Bloomberg's Rachel Mets is here

747
00:37:44,840 --> 00:37:46,240
Speaker 4: with more. It's come out of stealth.

748
00:37:46,360 --> 00:37:48,520
Speaker 5: There's only five people there, but it's got quite a

749
00:37:48,520 --> 00:37:49,720
Speaker 5: head evaluation already.

750
00:37:50,680 --> 00:37:53,200
Speaker 14: Yeah, this is It's still a pretty small company. They

751
00:37:53,239 --> 00:37:56,359
Speaker 14: don't have a demo to share publicly yet, but they

752
00:37:56,480 --> 00:38:00,640
Speaker 14: are working on software to improve the kind of interactions

753
00:38:00,640 --> 00:38:03,680
Speaker 14: that people have with computers when we're speaking to computers.

754
00:38:04,360 --> 00:38:08,040
Speaker 5: And this is all around Alexis Kono, who was very

755
00:38:08,080 --> 00:38:10,839
Speaker 5: senior at open Ai. So I assume that even though

756
00:38:10,880 --> 00:38:14,399
Speaker 5: small and without a demo, people are betting on him

757
00:38:14,440 --> 00:38:16,120
Speaker 5: his expertise in his background.

758
00:38:16,880 --> 00:38:20,640
Speaker 14: Yeah, he has quite a long tale of experience working

759
00:38:20,800 --> 00:38:25,960
Speaker 14: on voice computer human interactions and he was pretty instrumental

760
00:38:26,040 --> 00:38:29,520
Speaker 14: in helping Opening Eye build its advanced voice mode. This

761
00:38:29,600 --> 00:38:32,680
Speaker 14: is the more recent version of the voice mode that

762
00:38:32,719 --> 00:38:35,319
Speaker 14: works with chat GPT that is meant to feel more

763
00:38:35,360 --> 00:38:38,880
Speaker 14: lifelike than it had in the past, more feeling like

764
00:38:38,880 --> 00:38:41,120
Speaker 14: you're having a conversation with an actual person than with

765
00:38:41,160 --> 00:38:43,280
Speaker 14: a computer business model.

766
00:38:43,440 --> 00:38:45,240
Speaker 5: I mean the fact that we don't have a demo,

767
00:38:45,400 --> 00:38:47,800
Speaker 5: but ultimately, how is this company going to be returning

768
00:38:47,880 --> 00:38:48,880
Speaker 5: value to its vcs?

769
00:38:49,680 --> 00:38:52,279
Speaker 14: That is a great question. What they told me is

770
00:38:52,320 --> 00:38:56,200
Speaker 14: they are going to be releasing at some point products

771
00:38:56,280 --> 00:38:59,920
Speaker 14: that are both B to B and also consumer oriented.

772
00:39:00,120 --> 00:39:05,120
Speaker 14: So you might imagine something like an app perhaps that

773
00:39:05,239 --> 00:39:07,239
Speaker 14: would be for consumers from the company to kind of

774
00:39:07,239 --> 00:39:09,120
Speaker 14: give people a sense of what their technology is and

775
00:39:09,120 --> 00:39:12,439
Speaker 14: how it works. But then also perhaps they might work

776
00:39:12,480 --> 00:39:15,240
Speaker 14: with other companies to let other companies use their software

777
00:39:15,239 --> 00:39:15,640
Speaker 14: as well.

778
00:39:15,920 --> 00:39:18,880
Speaker 4: And the San Francisco based yes, that is true. They

779
00:39:18,880 --> 00:39:20,040
Speaker 4: are based in San Francisco.

780
00:39:20,640 --> 00:39:23,759
Speaker 5: Rachel Matz all things open ai and ex open ai.

781
00:39:23,840 --> 00:39:25,279
Speaker 4: We appreciate it, thank you very much.

782
00:39:25,280 --> 00:39:37,399
Speaker 5: Indeed, Oracle stock having its best year since nineteen ninety nine.

783
00:39:37,560 --> 00:39:39,640
Speaker 5: It's all thanks to the momentum of its cloud business.

784
00:39:40,040 --> 00:39:42,239
Speaker 5: We'll find out if that momentum will continue when the

785
00:39:42,280 --> 00:39:44,799
Speaker 5: company releases its earnings today after the closing bell.

786
00:39:45,000 --> 00:39:47,719
Speaker 4: Luberg's Brodie Ford with a preview. I mean, what an

787
00:39:47,719 --> 00:39:48,640
Speaker 4: extraordinary chart.

788
00:39:48,719 --> 00:39:50,879
Speaker 5: It's just done so well because people are now like, oh,

789
00:39:50,880 --> 00:39:54,440
Speaker 5: the cloud thing, it's actually relatively doing well compared to competitors.

790
00:39:54,600 --> 00:39:57,279
Speaker 7: There were many years that people would make jokes to

791
00:39:57,320 --> 00:39:59,719
Speaker 7: me about Oracle's cloud about how we know, will this

792
00:39:59,760 --> 00:40:03,040
Speaker 7: ever actually be a serious player, And within the last

793
00:40:03,080 --> 00:40:05,560
Speaker 7: two years, the answer has become yes, right, And a

794
00:40:05,680 --> 00:40:08,640
Speaker 7: large part of that is because the AI workloads are

795
00:40:08,680 --> 00:40:12,280
Speaker 7: so demanding that the traditional giants like AWS and Azure

796
00:40:12,760 --> 00:40:15,840
Speaker 7: just don't have the capacity, right, And so it's become

797
00:40:15,920 --> 00:40:18,719
Speaker 7: that Oracle's cloud is seriously taken as the kind of

798
00:40:18,840 --> 00:40:21,480
Speaker 7: fourth hyperscaler, and it's been able to grow in such

799
00:40:21,520 --> 00:40:25,520
Speaker 7: an accelerated way that very few other software companies can

800
00:40:25,920 --> 00:40:26,839
Speaker 7: you know, compete with.

801
00:40:27,400 --> 00:40:30,000
Speaker 5: It's got some I wouldn't call it a key Man risk,

802
00:40:30,120 --> 00:40:32,960
Speaker 5: but certainly I'm talking about Uber as a client, but

803
00:40:33,000 --> 00:40:35,799
Speaker 5: also TikTok, and we've been talking a lot about some

804
00:40:35,840 --> 00:40:37,480
Speaker 5: concerns around the deal around TikTok.

805
00:40:37,640 --> 00:40:39,799
Speaker 7: Absolutely right. I mean, yeah, we saw the court ruling

806
00:40:39,880 --> 00:40:42,680
Speaker 7: that the there's increased odds that TikTok could go ahead

807
00:40:42,719 --> 00:40:46,920
Speaker 7: and go poof, But there is you know, at the

808
00:40:47,000 --> 00:40:49,720
Speaker 7: end of the day, if anybody buys TikTok, the odds

809
00:40:49,760 --> 00:40:52,120
Speaker 7: that you say, hey, let's go ahead and switch out

810
00:40:52,120 --> 00:40:55,319
Speaker 7: our cloud infrastructure. Nobody wants to do that, right, I mean,

811
00:40:55,320 --> 00:40:57,920
Speaker 7: so it is a risk for Oracle, But you know,

812
00:40:58,120 --> 00:41:00,600
Speaker 7: when TikTok was one of their only marque customers two

813
00:41:00,680 --> 00:41:03,520
Speaker 7: years ago for OCI, it was a bigger deal today,

814
00:41:03,560 --> 00:41:05,560
Speaker 7: they have a lot of customers, and I think the

815
00:41:05,719 --> 00:41:07,960
Speaker 7: risk isn't quite as acute as it once was.

816
00:41:08,719 --> 00:41:11,480
Speaker 5: What about other things in infrastructure and cloud provision, i

817
00:41:11,520 --> 00:41:13,360
Speaker 5: mean software, other services.

818
00:41:13,360 --> 00:41:15,279
Speaker 4: What else are they managing to do well on?

819
00:41:15,560 --> 00:41:15,799
Speaker 12: Yeah.

820
00:41:15,880 --> 00:41:18,239
Speaker 7: Well, one that's really interesting is that for a long

821
00:41:18,320 --> 00:41:20,480
Speaker 7: time they really tried to get you to run their

822
00:41:20,800 --> 00:41:24,920
Speaker 7: key iconic database software on prem and kind of control that.

823
00:41:24,960 --> 00:41:27,560
Speaker 7: And they finally said, you know what, you could run

824
00:41:27,600 --> 00:41:29,560
Speaker 7: it on Azure, you could run it on AWS. And

825
00:41:29,600 --> 00:41:32,239
Speaker 7: that's kind of been a pretty big exciting thing for

826
00:41:32,280 --> 00:41:35,200
Speaker 7: investors that you know, a lot of the world is

827
00:41:35,239 --> 00:41:37,239
Speaker 7: on AWS or Azure and they want to use the

828
00:41:37,320 --> 00:41:40,080
Speaker 7: Oracle database and so being able to kind of update

829
00:41:40,120 --> 00:41:43,560
Speaker 7: that customer segment, you know, breathe some growth into the

830
00:41:43,640 --> 00:41:47,239
Speaker 7: database segment, which you know dates back to the late seventies.

831
00:41:47,320 --> 00:41:49,800
Speaker 7: That is another thing that's been exciting folks about a Oracle.

832
00:41:50,719 --> 00:41:52,359
Speaker 5: But a lot of it is probably priced in when

833
00:41:52,400 --> 00:41:54,400
Speaker 5: you're looking at an eighty percent run up and the shares.

834
00:41:54,440 --> 00:41:57,600
Speaker 5: So what do you tend to hear in these calls

835
00:41:57,680 --> 00:41:59,080
Speaker 5: post earnings.

836
00:41:58,840 --> 00:42:01,920
Speaker 7: Oh god, we're building data centers across the world, you know,

837
00:42:01,960 --> 00:42:04,600
Speaker 7: no one's ever seen a data center build out like this, right,

838
00:42:04,640 --> 00:42:05,320
Speaker 7: I mean that's.

839
00:42:05,120 --> 00:42:07,920
Speaker 4: The general energy informative. Yes, absolutely, I.

840
00:42:07,920 --> 00:42:10,040
Speaker 7: Mean the amount of hype right now about the AI

841
00:42:10,160 --> 00:42:13,080
Speaker 7: fueled data center buildout is massive, right. You see you

842
00:42:13,080 --> 00:42:15,799
Speaker 7: sat pricing the shares. You know they are near an

843
00:42:15,880 --> 00:42:18,399
Speaker 7: all time high. And so the question is so much

844
00:42:18,480 --> 00:42:21,319
Speaker 7: that how quickly are they going to be able to

845
00:42:21,320 --> 00:42:26,000
Speaker 7: convert demand into actually offering these services, because you know

846
00:42:26,239 --> 00:42:29,319
Speaker 7: they just like Microsoft and Amazon, they can't get these

847
00:42:29,360 --> 00:42:31,400
Speaker 7: centers up quick enough. They can't get enough chips to

848
00:42:31,400 --> 00:42:32,200
Speaker 7: deal with the demand.

849
00:42:32,760 --> 00:42:34,920
Speaker 5: Well, despite the run up, they've got twenty five buys,

850
00:42:35,000 --> 00:42:37,280
Speaker 5: fifteen holds and not a single cell rating.

851
00:42:37,400 --> 00:42:38,960
Speaker 4: Bodie Ford, all things are coole.

852
00:42:39,000 --> 00:42:41,759
Speaker 5: We'll be checking in on him tomorrow, I'm sure, after

853
00:42:41,760 --> 00:42:43,880
Speaker 5: those earning drop. But meanwhile, that does it for this

854
00:42:43,880 --> 00:42:46,000
Speaker 5: addition of Blomberg technology. You don't want to forget a

855
00:42:46,080 --> 00:42:48,760
Speaker 5: podcast find out on the terminal as well as online

856
00:42:48,760 --> 00:42:52,520
Speaker 5: on Apple, Spotify, and iHeart this is Bloomberg technology.