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Speaker 1: From Marhart where Innovation, Money and Power Collie in Silicon Valley, NBN.

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Speaker 2: This is Bloomberg Technology with Caroline Hyde and Ed.

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Speaker 3: Love Love.

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Speaker 4: And Caroline Hyde a Bloomberg's Weld headquarters in New York.

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Speaker 5: Annahmed Lovelow in San Francisco. This is Bloomberg Technology.

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Speaker 4: Coming up a conversation about the future of technology with

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Speaker 4: the CEO of Stability AI, the houp startup that has

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Speaker 4: just launched a chatbot to rival chat GBT.

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Speaker 5: Then Tesla raises prices of some models in the US

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Speaker 5: one day after CEO e Long Musk hinted at further

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Speaker 5: price cuts. This is more shareholders flag concerns that Musk

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Speaker 5: is stretched too thin.

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Speaker 4: An Earth Day is coming up and we'll break down

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Speaker 4: the pros the cons of carbon hatchare technology as more

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Speaker 4: companies to offset emissions. First says check in what's happening

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Speaker 4: in terms of public markets. Let's move it on, because

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Speaker 4: I wanted to show what was happening in terms of

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Speaker 4: some of the benchmarks. I don't want to steal Ed's thunder,

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Speaker 4: as I think this is one of his individual stocks

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Speaker 4: to watch, but we are seeing at the moment maybe

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Speaker 4: completely flat on the Nasdaq. Overall, we're still trying to

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Speaker 4: pass some of the overall view on the macro perspective here.

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Speaker 4: We actually had the PMI data, so business activity showing strength.

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Speaker 4: So does that mean the Federal zerv does have to hike?

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Speaker 4: We are flat on the NASDAK. The two year yield

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Speaker 4: just interest up four basis points. Has been far more

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Speaker 4: volatile than some of the benchmarks in terms of stocks recently,

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Speaker 4: I shine a light and crypto actually having its downward week.

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Speaker 4: We're off by seven percent on the OG in the

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Speaker 4: crypto space, just off by six tents of percent on

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Speaker 4: the day. Relatively muted volatility there. Let's moveing on and

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Speaker 4: have a little look on what's happening therefore in really

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Speaker 4: where we look towards earning z because I think this

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Speaker 4: is what is capturing traders' attention and why we're seeing

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Speaker 4: some caution in the market. We have overall seen well

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Speaker 4: perhaps well we'll move over into the micro. I think

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Speaker 4: we can't get that particular chart right now, but I

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Speaker 4: wanted to show just how much of the heavy lifting

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Speaker 4: technology is done, particularly in the S and P in

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Speaker 4: the first quarter ED.

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Speaker 5: It's why we continue to track the news cycle because

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Speaker 5: you look at points movers on the Nasdaq one hundred,

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Speaker 5: that is what is driving indexes in either direction right now. Amazon,

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Speaker 5: as you said, I was quite excited about this because

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Speaker 5: it's moving through.

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Speaker 6: The upside, up more than three percent.

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Speaker 5: The headline of the last twenty four hours is that

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Speaker 5: cuts are coming at the Whole Foods division, particularly.

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Speaker 6: In corporate roles.

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Speaker 5: We know about where Amazon's been trimming and that those

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Speaker 5: layoffs and ounts have started, but it's interesting that Whole

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Speaker 5: Foods as well is seeing some of that action.

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Speaker 6: Let's go back to Tesla.

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Speaker 5: Tesla's actually turned the corner in terms of its direction,

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Speaker 5: its momentum in the last hour or so. We're hired

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Speaker 5: by more than a percentage point, having been lower, markedly

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Speaker 5: lower actually earlier in the session. The emphasis overnight is

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Speaker 5: on price raises on model essenex here in the United States.

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Speaker 5: But we go back to earnings a couple of days ago,

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Speaker 5: the emphasis from elon musk more price cuts could come

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Speaker 5: because this is a company CARO that is willing to

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Speaker 5: sacrifice margin and profit to sustain that rate of growth

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Speaker 5: that it's targeting fifty p sent or so on an

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Speaker 5: annual basis going forward.

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Speaker 4: Yeah, and I think we're going to be diving much

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Speaker 4: more into the individual moves and into Tesla in a moment.

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Speaker 4: But first let's talk about some of the geopolitics at

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Speaker 4: play surrounding technology right now. President Biden, of course, he

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Speaker 4: has been saying that he's going to crack down even

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Speaker 4: more on US investment in key parts of China's economy,

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Speaker 4: including in Chip's AI quantum computing protu. Moost Josh Wingrove

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Speaker 4: covers this from DC and we sort of got hints

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Speaker 4: a great scoop coming from some of your team in

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Speaker 4: Washington about the move before the G seven summit. How

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Speaker 4: likely are we like to see allies actually agree with this?

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Speaker 7: Well, the move music isn't good right now that they're

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Speaker 7: all going to join hands and proceed on this, but

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Speaker 7: they might sort of give cover fire, if you will.

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Speaker 7: And Biden kind of wants a coalition of the willing

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Speaker 7: when it comes to sort of squeezing China's these sort

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Speaker 7: of key sectors and squeezing investment in China. So even

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Speaker 7: if for instance, Europe, Canada, Japan aren't enacting the same

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Speaker 7: type of restrictions, if they give either an explicit or

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Speaker 7: acid blessing, it looks like we'll see the US go

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Speaker 7: ahead with this with his executive order. Now, this is

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Speaker 7: our colleague Jenny Leonard reporting it. It will be sort

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Speaker 7: of aimed at a few sectors, and it is preliminary.

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Speaker 7: A the order itself is still in the works be

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Speaker 7: it would then lead to regulations which take a long time,

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Speaker 7: and you know, the devil is in the details. So

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Speaker 7: this is still very early stages. But what it looks

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Speaker 7: like is the US will proceed probably on its own

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Speaker 7: right now, according to our colleagues reporting, with the sort

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Speaker 7: of blessing of its allies. And this of course will

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Speaker 7: further risk inflaming tensions between Beijing and Washington at a

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Speaker 7: time where the sort of you know, wrangling over these

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Speaker 7: economic restrictions has really been on the upswing.

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Speaker 6: Josh.

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Speaker 5: The logic from the administration, whether it is semiconductor or

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Speaker 5: artificial intelligence related, has been the concern that China would

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Speaker 5: apply that technology for military or security purposes, going against

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Speaker 5: the security interests in the United States in the chip

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Speaker 5: sect to what's interesting is you kind of have Taiwan

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Speaker 5: caught right in the here TSMC, and that's actually an

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Speaker 5: example where the United States has got allies on side

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Speaker 5: the Netherlands Japan in terms of curbing chip exports to

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Speaker 5: China through the Taiwan straight What is the latest there.

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Speaker 7: Yeah, our colleagues are reporting that Taiwan is miffed a

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Speaker 7: little bit that the US is sort of lumping Taiwan

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Speaker 7: into its broader push, saying effectively, look, if you're trying

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Speaker 7: to reorient away from Taiwan as part of a reorientation

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Speaker 7: away from China, then in a way, you're kind of

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Speaker 7: distancing yourself from Taiwan, which nominally the US is looking

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Speaker 7: to support more broadly, and of course on the question

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Speaker 7: of even when it would ever come to a question

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Speaker 7: of military defense of Taiwan. So this is the kind

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Speaker 7: of the really the big question. The US is trying

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Speaker 7: to really steer those supply chains away, and that's got

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Speaker 7: people's backs up in Taiwan who are saying, look, you know,

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Speaker 7: like we were trying to be your friend here, maybe

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Speaker 7: just don't don't lump us all in, don't paint with

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Speaker 7: the same a big brush when it comes to dealing

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Speaker 7: with these issues. His attention to the US is trying

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Speaker 7: to do. Interestingly, in that story, they're particularly peeved at

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Speaker 7: the Commerce Secretary Gina Raymando, who they think has been

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Speaker 7: really ringing the bell on this one. And Raymondo has said, Look,

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Speaker 7: the US is not looking to make one hundred percent

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Speaker 7: of chips, for instance in the US, but they need

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Speaker 7: to make more. It's not just that question of spying, sponage,

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Speaker 7: military capability. It's also simply a question of availability. They

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Speaker 7: don't want to be left without these things.

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Speaker 5: Yes, certain the conversations I'm having around the world are

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Speaker 5: about the concentration of supply chain in Asia and not

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Speaker 5: necessarily bringing all on shoring one hundred percent to this nation.

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Speaker 5: Joshuaen Gerry about a DC, Thank you so much. Look,

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Speaker 5: another rival to open AI's chat GPT has hit the market.

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Speaker 5: Stability AI just launched Stable LM, a chat bottel designed

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Speaker 5: principally for researchers. But the model is already experiencing a

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Speaker 5: couple of issues and answering simple prompts.

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Speaker 6: Like what is five plus five?

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Speaker 5: By meandering into a meditation about square inches and feet?

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Speaker 6: What does all of that mean?

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Speaker 5: Will STABILITYI not that it's growing pains with this early technology.

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Speaker 5: We've also reported here at Bloomberg the company raising funds

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Speaker 5: at a four billion dollar valuation according to sources.

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Speaker 6: Let's get all of that with CEO Emmad Mustak for more.

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Speaker 5: Immad, Welcome to the program, Caroline, and I have been

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Speaker 5: looking forward to speaking with you. This model, it's targeted

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Speaker 5: at researchers, right, it's a little bit nascent. What is

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Speaker 5: it you think researchers will do with it?

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Speaker 2: So I think that you have two paradigms here. You

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Speaker 2: have the proprietary models, which includes open Ai, chatterpt and

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Speaker 2: the rapt bit, Google, et cetera. And then you have

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Speaker 2: the open paradigm where people are taking these models, they're

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Speaker 2: experimenting with them, and they're doing wonderful things. So last

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Speaker 2: August we collaborate and launch Stable Diffusion, this text image

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Speaker 2: model that drove Lensa and one of these avatar apps.

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Speaker 2: It was four of the top ten apps in the

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Speaker 2: app Store, and the developer community there overtook Bitcoin in

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Speaker 2: the theorem in cumulatative likes within two months. The ten

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Speaker 2: years of that and it's become almost as popular as

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Speaker 2: looks now in terms of.

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Speaker 8: All these people working on it.

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Speaker 2: So rather than basically take a gigantic model, put it

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Speaker 2: out there a keep it secret, this approach is stead

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Speaker 2: is iterative. You start with the seed and then you

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Speaker 2: build it up, and the iteration feedback loop means it's

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Speaker 2: going to get better and better live in front of

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Speaker 2: people as the whole world yes and hacks on it,

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Speaker 2: extends it, and it'll be super interesting to see what happens.

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Speaker 5: M do you take us an interesting place quite early,

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Speaker 5: which is everyone focused on the kind of billions of

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Speaker 5: parameters that open ai is using for its llms. Actually

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Speaker 5: a lot of what people are doing out there the

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Speaker 5: inputs are based on a few one hundred million parameters

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Speaker 5: at a lower scale, requiring less compute.

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Speaker 6: There's still a question.

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Speaker 5: About guard rails, and I'm interested in what guardrails you

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Speaker 5: put in place for stable LM.

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Speaker 8: So it was never our interest to build big models.

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Speaker 2: This came from a lot of the labs wanted to

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Speaker 2: build AGI generalized stages could do everything, so GPT four

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Speaker 2: can pass the bar exam and GRE and GMT and

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Speaker 2: everything like that.

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Speaker 8: It's an amazing piece of software.

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Speaker 2: But these big models are like really talented grads that

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Speaker 2: occasionally go off their mets and they've fed jug food

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Speaker 2: the whole internet because nobody's done the work on what

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Speaker 2: is the actual data you need optimally for these models.

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Speaker 2: What data do you need to feed these models so

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Speaker 2: you can use them inside Bloomberg or a hedge fund

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Speaker 2: or irregulated industry, so you have data transparency as well

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Speaker 2: with these things. So the guardrails that we've put in

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Speaker 2: place are trying to work with various parties. We've anounced

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Speaker 2: Amazon as a partner in many mores coming on setting

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Speaker 2: standards around open models because they are essential for private data.

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Speaker 2: We have done things like in our previous image model

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Speaker 2: that we released, we offered opt out and we're the

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Speaker 2: only company in the world to do that, because what

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Speaker 2: if artists don't want to be in the data sets

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Speaker 2: and we offered full transparency around that allowed them to

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Speaker 2: opt out. And I think these things will be interestingly

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Speaker 2: important as you need standardization and industry self regulation at

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Speaker 2: the big company level, at the anthropic open AI, cutting

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Speaker 2: edge big model level, and at the open level. And

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Speaker 2: we are looking specially there at the open level. That's

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Speaker 2: why I kind of signed the FLI letter a few

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Speaker 2: weeks ago, because I think now is the time for

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Speaker 2: self regulation to take a pause and do this right.

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Speaker 8: Yeah, because it's going around the world incredibly.

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Speaker 4: Quickly, many will know stability AI well because of well

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Speaker 4: already the image text to image that you've so put

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Speaker 4: out their stable diffusion and you say you gave the

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Speaker 4: opt out, but many it didn't stop some of the

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Speaker 4: lawsuits coming your direction. How do you win hearts and

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Speaker 4: minds of people that are worried about where the data

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Speaker 4: is coming from and ultimately actually who owns it and

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Speaker 4: who benefits from it.

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Speaker 2: I think the way that you do this is you

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Speaker 2: put it out in the open and have an open

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Speaker 2: discussion around it. They're working with multiple governments now and

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Speaker 2: helping them with national.

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Speaker 8: Models as well with open data sets.

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Speaker 2: And I think we should move away from web crawls

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Speaker 2: and these are random things to having really high quality

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Speaker 2: data that reflects the diversity of the world and is

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Speaker 2: open and interpretive.

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Speaker 8: That at Watertable and I.

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Speaker 2: Said, there's no regulated industry or government that can use

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Speaker 2: a close black box model. You need to have open

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Speaker 2: models that come to the data that you own. So

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Speaker 2: a lot of these companies, these really talented grads that

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Speaker 2: are these models. Effectively, you know, they're like hiring for McKinsey,

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Speaker 2: whereas you want to hire your own and have that

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Speaker 2: because you can't give away your private knowledge.

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Speaker 8: But again, there's so many standards and.

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Speaker 2: Questions that need to be done around this, and we

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Speaker 2: have to have those discussions now because the technology is

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Speaker 2: literally going everywhere.

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Speaker 8: I think it will be the number.

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Speaker 2: One topic on Earning Scores and then definitely every government

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Speaker 2: policy within the next few months.

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Speaker 4: Do you have confidence that either you can self regulate

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Speaker 4: or that some sort of multinational agreement can come to bear.

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Speaker 8: I think it will be both.

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Speaker 2: I think that you've got regulation, you have legislation, and

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Speaker 2: you're seeing legislation come to bear, like Italy banning CHATCHEPT

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Speaker 2: for example, and the EU looking at GDPR laws, whereas

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Speaker 2: Japan has a very different regime.

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Speaker 8: In the UK has to writing regime.

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Speaker 2: One of the reasons I created Stability has a headflow

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Speaker 2: manager before say to be on Alexis Show, was to

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Speaker 2: try and make an entity that could help organize this

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Speaker 2: that scale across the open area, even as this other

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Speaker 2: regulation in the cut edge area with giant models and

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Speaker 2: at the big company level as well.

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Speaker 5: Am I Bloomberg's reported you're raising funds at a four

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Speaker 5: billion dollar valuation.

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Speaker 6: Did you close that round?

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Speaker 8: I can't talk about things like that.

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Speaker 2: We're the only company in the world that can build

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Speaker 2: any model of any type for anyone.

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Speaker 8: So it's a very interesting time.

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Speaker 4: How about revenue generations, go ahead, current ad talk to

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Speaker 4: us about how you make money.

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Speaker 2: It's an open core model. So this is where I

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Speaker 2: saw the arbitrage again the head trad manager past. What

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Speaker 2: we're going to do is we're going to build the benchmark.

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Speaker 2: We are the one of the largest putters of open

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Speaker 2: source in the world, academia, others, because there's a big

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Speaker 2: gap there with our supercompute. And then we take that

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Speaker 2: and build on stable series of models that are the

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Speaker 2: benchmark models that we build from scratch with the data sets.

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Speaker 2: We have commercial partners that license it to financial variance,

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Speaker 2: insurance variance, healthcare variance, and the language models, et cetera.

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Speaker 2: And then national variants of that, and then through partners

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Speaker 2: such as Amazon with Bedrock, you can go on Amazon,

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Speaker 2: take that to your data and fully own your own model.

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Speaker 8: And you know, we share on.

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Speaker 2: The upside with our partners at Hyperscalar system integrators. And

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Speaker 2: then for a series of companies that will announce soon,

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Speaker 2: we're building them dedicated a item so they can keep

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Speaker 2: on top of this. And that's an incredibly great business

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Speaker 2: model because we offer stability at times has meant kind

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Speaker 2: of chaos because no one knows what's going to happen.

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Speaker 6: Am I do?

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Speaker 5: You tweeted about AWS and Bedrock there was interesting How

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Speaker 5: much of a differentiator is it to partner with tech

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Speaker 5: companies that have mL specialized silicon and are working to

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Speaker 5: improve the compute part of this process.

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Speaker 2: I think it's a huge differentiator because you're moving from

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Speaker 2: the GPU age to the asset age, just like you

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Speaker 2: saw with bitcoin. So we work closely with Amazon on

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Speaker 2: Inferentia too, which is an amazing diece of silicon that

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Speaker 2: can run these workloads much much cheaper, and you'll see

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Speaker 2: that being released an announcement.

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Speaker 8: I think it's just John ga.

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Speaker 2: The Bedrock service itself means that a company switches ours

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Speaker 2: as eighteen months old, one hundred and eighty people.

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Speaker 8: We do so much.

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Speaker 2: We do all these models and all the types of

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Speaker 2: video models, code models, language models. We can instead leverage

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Speaker 2: that infrastructure. Take there's two one hundred thousand companies at

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Speaker 2: stage Maker, and you know they can face Amazon.

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Speaker 8: Instead of us because that's who they trust.

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Speaker 2: And again, you want trust at a time like this,

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Speaker 2: You want to have customs silicon that can run this

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Speaker 2: incredibly cheaply, and you want standards around it. This is

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Speaker 2: why our stable diffusion model is the first model on

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Speaker 2: the Aerial Neural Engine at the start of December. Yeah,

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Speaker 2: and so the customization optimization is going to be key.

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Speaker 2: The key thing is distribution, because so many people want

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Speaker 2: this and no company can scale fast enough and trustworthy

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Speaker 2: manner enough to be able to support those slayers, et cetera.

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Speaker 4: Stability AI CEO M. D. Mustak. Great to have some

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Speaker 4: time with you. We thank you for working us into

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Speaker 4: your calendar. It's busy, We appreciate it.

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Speaker 5: Ev maker Tesla is raising the prices of its Model

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Speaker 5: S and X in the United States. Comes after steep

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Speaker 5: price earlier this year, which took a toll on profits,

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Speaker 5: and of course it's had an impact weighing down the shares.

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Speaker 5: It's just two days after test the lowered prices of

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Speaker 5: some of its other models. We're tracking all the moves.

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Speaker 5: Bloomberg Show and O came with us from Austin. The

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Speaker 5: narrative does not match the action, right Sean, So tell

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Speaker 5: us what we know about Model S and X in

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Speaker 5: the US versus what Musk has been saying this week

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Speaker 5: about strategy.

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Speaker 9: Yeah, I mean, I think what we're seeing here is,

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Speaker 9: you know, possibly a response to some of the pressure

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Speaker 9: on the company as far as all of these price

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Speaker 9: cuts that they've been sort of throwing at the Model

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Speaker 9: Y and the Model three. But also I think it's

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Speaker 9: important to remember that the first quarter of this year

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Speaker 9: was one of the lower quarters of deliveries for the

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Speaker 9: Model SNX over the last year and a half or so,

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Speaker 9: basically since Tesla relaunched the sort of semi redesigned version

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Speaker 9: of each of those cars about a year and a

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Speaker 9: half ago. So this is you know that sort of

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Speaker 9: almost counterintuitively, you think they would want to cut prices

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Speaker 9: to increase the demand on those cars.

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Speaker 8: But I think this is them saying, Hey, if.

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Speaker 9: Our numbers are already kind of low on these cars,

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Speaker 9: we might as well make back some of the margin here,

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Speaker 9: even though it's obviously not going to make up, you know,

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Speaker 9: for all of the money that they're losing by cutting

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Speaker 9: some drastically on the Model Why.

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Speaker 4: Yeah, this is where they've got to kind of square

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Speaker 4: the circle. They have a very luxury end that actually

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Speaker 4: is still way cheaper than it wasn't the start of

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Speaker 4: the year, but it insured up by two or three

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Speaker 4: percent on the day. But they're trying to keep the

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Speaker 4: allure of that luxury number the xcs well at the

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Speaker 4: same time making the threes and the ys just really

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Speaker 4: mass scale here. Can they do that? Can they keep

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Speaker 4: the allure of the higher end while still competing with

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Speaker 4: Runner and foward ultimately?

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Speaker 9: I mean, I think that's what we're really going to

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Speaker 9: see this year. And you know, I think one of

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Speaker 9: the things that's instructive when thinking about Tesla is that

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Speaker 9: some of these other companies that are looking to try

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Speaker 9: to replicate their success in the luxury space as far

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Speaker 9: as the startups go, are actually really struggling. Lucid Motors

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Speaker 9: has spent the last couple of months talking about how

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Speaker 9: they're actually really having trouble creating demand and they're throwing

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Speaker 9: more money into marketing to try to find customers for

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Speaker 9: their expensive cars. And I think Tesla's always been kind

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Speaker 9: of content ever since the model why especially took off

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Speaker 9: in popularity, to let the S and X sort of

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Speaker 9: be these sort of high end things that don't really

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Speaker 9: push too foreign volume. They've started selling versions of those

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Speaker 9: in China, these newer versions, and so I think they're

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Speaker 9: going to find some homes for it, but I don't

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Speaker 9: think they really think of it as as a big

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Speaker 9: driver for them, And it just sort of remains this

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Speaker 9: kind of like legacy halo thing that if you have

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Speaker 9: the money, it's probably nice to have. But people seeing

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Speaker 9: content with the Model Y and the Model three and

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Speaker 9: Tesla seems content to push volume on those models and

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Speaker 9: really just let that lead.

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Speaker 4: Yeah, sure to Kaine trying to weave the narrative for us,

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Speaker 4: as I'm sure you know Musk is trying to weave

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Speaker 4: the sales as well. We thank you in other Musk

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Speaker 4: related news to do it. Twitter stirring up all sorts

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Speaker 4: of confusion after the social media platform remove those legacy

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Speaker 4: check marks for verified accounts that aren't paying for its

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Speaker 4: Twitter Blue program. Yet not everyone had to say goodbye

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Speaker 4: to the blue checks? What was going on? Asia Counts

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Speaker 4: joins us now right here in New York to talk

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Speaker 4: us through it, and Asia seemingly Elon is paying for

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Speaker 4: some blue check marks himself. How does that make sense?

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Speaker 10: Yeah, it's a really weird situation. So, as you mentioned,

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Speaker 10: legacy blue check marks went away in April twentieth. That

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Speaker 10: means celebrities, athletes, anyone that's sort of notable under the

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Speaker 10: old verification process lost their check mark. And so Musk

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Speaker 10: has been very vocal about saying people are gonna have

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Speaker 10: to pay for them. But then he decided to pay

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Speaker 10: for a few celebrities, so Lebron, James, William Shatner and

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Speaker 10: then Stephen King because they had been sort of vocal

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Speaker 10: about not wanting to pay for the check So I

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Speaker 10: guess he felt like he wanted to pay for them

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Speaker 10: for some reason.

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Speaker 5: Asia you know, I elected to subscribe to Twitter Blue.

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Speaker 5: I did it the moment it was available in North

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Speaker 5: America for functionality experience. I wanted to see what the

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Speaker 5: edit button did. I wanted to see what the ten

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Speaker 5: eighty video quality was like. Longer form tweets is another

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Speaker 5: new function, But the verification process is really at the

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Speaker 5: core of this debate. What does it require for you

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Speaker 5: to technically be verified on Twitter from this point on?

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Speaker 10: Yeah, so interesting change from what it used to be

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Speaker 10: in the past. Essentially, you have to pay for Twitter Blue.

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Speaker 10: You have to subscribe, which is about eight dollars per month,

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Speaker 10: and then you have to have a phone number, and

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Speaker 10: then there's a few other requirements like having a display

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Speaker 10: photo and a name. But it's very very simple and

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Speaker 10: users I've talked to you so that they were verified

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Speaker 10: within twenty four or forty eight hours, so there's not

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Speaker 10: really much that you have to go through as you

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Speaker 10: counts ed.

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Speaker 5: Yeah, Bloomberg's age counts out of New York. Coming up,

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Speaker 5: We've got to get back to that story on lift.

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Speaker 5: The headline's rolling is the company announcing a restructuring plan.

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Speaker 6: We'll have all of those details for you. This is Bloomberg.

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Speaker 5: Caroline Lift out with a statement confirming it is doing

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Speaker 5: a restructuring plan which will include significantly lowering the size

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Speaker 5: of its team, although it does not give a specific number.

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Speaker 5: What jumps out of me though, is that there's no

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Speaker 5: change to its previously issued financial guidance. But look, this

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Speaker 5: is in line with what we're seeing across industry, a

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Speaker 5: cost saving exercise.

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Speaker 4: And interestingly, the CEO, the new CEO, only days into

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Speaker 4: the roles saying they'll use those savings to invest in

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Speaker 4: competitive pricing. This is about the competition, of course they

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Speaker 4: have so directly with Uber and the United States, but

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Speaker 4: this is a large amount of people and they already

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Speaker 4: of course had a previous helm of the founders had

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Speaker 4: slashed about seven hundred jobs late last year, we understand,

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Speaker 4: and this is Wall Street Journal reporting that it has to

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Speaker 4: cut one two hundred or more jobs. So that's what

441
00:21:00,480 --> 00:21:02,720
Speaker 4: thirty percent of their entire employee base.

442
00:21:03,359 --> 00:21:05,600
Speaker 5: Yeah, and I actually misspoke a little earlier. When CEO

443
00:21:05,680 --> 00:21:07,960
Speaker 5: Risher came on the show for the first time a

444
00:21:08,000 --> 00:21:10,520
Speaker 5: couple of weeks ago, he said the company is not

445
00:21:10,920 --> 00:21:13,399
Speaker 5: for sale. He then went to an all hands meeting

446
00:21:13,440 --> 00:21:16,040
Speaker 5: and told staff, I'll tell you what I told Bloomberg.

447
00:21:16,320 --> 00:21:18,280
Speaker 5: The company is not for sale, and that was something

448
00:21:18,320 --> 00:21:21,280
Speaker 5: the market thought might happen. But yeah, he's clearly acting

449
00:21:21,359 --> 00:21:23,479
Speaker 5: quickly to get the costs under control.

450
00:21:23,840 --> 00:21:25,760
Speaker 4: He is, and it is a sign of the times

451
00:21:25,840 --> 00:21:28,560
Speaker 4: as of we, of course see the macro environment continuing

452
00:21:28,600 --> 00:21:30,960
Speaker 4: to be tough for some of these technology focused companies.

453
00:21:30,960 --> 00:21:33,560
Speaker 4: A lot that did a lot of hiring during the pandemic.

454
00:21:33,840 --> 00:21:37,000
Speaker 4: We've seen, of course, the continue layoffs that continue to

455
00:21:37,000 --> 00:21:39,520
Speaker 4: be executed over at Meta of course this week, and

456
00:21:39,720 --> 00:21:42,600
Speaker 4: this hits morale. I wonder how the CEO really tries

457
00:21:42,640 --> 00:21:52,359
Speaker 4: to speak to that at this moment. Wellcome back to

458
00:21:52,400 --> 00:21:54,720
Speaker 4: Bluemow Technology. I'm Caroline Hyde in New York.

459
00:21:55,000 --> 00:21:57,120
Speaker 5: And I met Ludlow in San Francisco. Cary, Let's get

460
00:21:57,119 --> 00:21:58,600
Speaker 5: to check on these markets, because you look at the

461
00:21:58,640 --> 00:22:01,320
Speaker 5: nas that one hundred completely flat, but ro on course

462
00:22:01,320 --> 00:22:04,320
Speaker 5: for a weekly decline of around seven tenths of a percent.

463
00:22:04,440 --> 00:22:06,520
Speaker 5: Earning season starting to be a big factor, but we're

464
00:22:06,520 --> 00:22:09,240
Speaker 5: still passing the economic data and what that means for

465
00:22:09,240 --> 00:22:12,000
Speaker 5: the FED going before going forward under performance in the

466
00:22:12,040 --> 00:22:13,639
Speaker 5: chip space, look at the socks, we're soft by a

467
00:22:13,640 --> 00:22:16,040
Speaker 5: percentage point, a little push higher on the ten year

468
00:22:16,119 --> 00:22:18,960
Speaker 5: yield three point five percent, and as you talked about earlier,

469
00:22:19,000 --> 00:22:22,160
Speaker 5: some downward pressure this Friday on Bitcoin of just under

470
00:22:22,160 --> 00:22:25,159
Speaker 5: twenty eight thousand US dollars per token. A lot in

471
00:22:25,200 --> 00:22:27,439
Speaker 5: the new cycle is about artificial intelligence. We're going to

472
00:22:27,480 --> 00:22:29,399
Speaker 5: go big on one story in just a moment, but

473
00:22:29,480 --> 00:22:31,439
Speaker 5: you look at some of the specific movers on the

474
00:22:31,480 --> 00:22:34,400
Speaker 5: Nasdaq one hundred driving us in one direction or the other,

475
00:22:34,440 --> 00:22:37,720
Speaker 5: putting us in that flat position. Microsoft Alphabet, the parent

476
00:22:37,760 --> 00:22:40,399
Speaker 5: company of Google. That's a real focus for us, not

477
00:22:40,480 --> 00:22:43,120
Speaker 5: big trading in one direction or the other, bigger moves

478
00:22:43,119 --> 00:22:45,879
Speaker 5: into other AI names, C three AI to the downside,

479
00:22:45,920 --> 00:22:48,720
Speaker 5: Big Bear AI to the upside, What I would say

480
00:22:48,840 --> 00:22:51,800
Speaker 5: is we've kind of lost this kind of broad momentum

481
00:22:51,920 --> 00:22:55,280
Speaker 5: or logic when it comes to trading in AI related stocks.

482
00:22:55,440 --> 00:22:56,400
Speaker 6: Still fun to track.

483
00:22:56,280 --> 00:22:58,600
Speaker 4: Though it is, and it's also fund to try and

484
00:22:58,640 --> 00:23:00,639
Speaker 4: track what the companies are doing to se harness some

485
00:23:00,680 --> 00:23:03,200
Speaker 4: of the growth potential of this AI that they're using

486
00:23:03,240 --> 00:23:06,080
Speaker 4: internally or seeing other companies try and compete away. I

487
00:23:06,320 --> 00:23:08,320
Speaker 4: used to say in the house on this Friday's Megs

488
00:23:08,359 --> 00:23:11,480
Speaker 4: Davey Alba, who was just reported on Alphabet's changes to

489
00:23:11,560 --> 00:23:17,159
Speaker 4: combine its AI research units basically under DeepMind, which was

490
00:23:17,200 --> 00:23:19,760
Speaker 4: the big UK pin up, and when I was talking

491
00:23:19,880 --> 00:23:22,680
Speaker 4: about technology over there in Europe, DeepMind was this big

492
00:23:22,760 --> 00:23:26,200
Speaker 4: kind of loss when Google bought it. Interesting that's taken

493
00:23:26,280 --> 00:23:29,160
Speaker 4: so long to sort of put them on underneath deep Mind.

494
00:23:29,640 --> 00:23:31,879
Speaker 1: Yeah, I think it makes a lot of sense. You know,

495
00:23:32,040 --> 00:23:36,240
Speaker 1: Google has had these separate units in two different places,

496
00:23:36,760 --> 00:23:40,520
Speaker 1: Google Research on one side, and the brain unit under

497
00:23:40,640 --> 00:23:43,560
Speaker 1: Google Research was responsible for a lot of a tech

498
00:23:43,800 --> 00:23:47,960
Speaker 1: that is now in chatbots like chat GBT, even its

499
00:23:48,040 --> 00:23:53,760
Speaker 1: competitor by Open Ai and Google's bar So they've been

500
00:23:53,800 --> 00:23:55,320
Speaker 1: innovating sort of in the.

501
00:23:55,240 --> 00:23:57,880
Speaker 4: Mountain View office while deep.

502
00:23:57,800 --> 00:24:00,520
Speaker 1: Mind has been in London and sort of working on

503
00:24:00,640 --> 00:24:05,160
Speaker 1: loftier research concepts. There's been lots of questions about how

504
00:24:05,200 --> 00:24:08,360
Speaker 1: to consolidate those groups so that they can move forward

505
00:24:08,440 --> 00:24:10,879
Speaker 1: more efficiently with their research. And that's I think what

506
00:24:10,960 --> 00:24:13,560
Speaker 1: this reorganization does, David.

507
00:24:13,560 --> 00:24:15,240
Speaker 5: It also goes back to kind of the history of

508
00:24:15,280 --> 00:24:17,879
Speaker 5: Google and AI, right, and you go down to Mountain

509
00:24:17,960 --> 00:24:20,600
Speaker 5: View ten or so more years ago, you're kicking the

510
00:24:20,600 --> 00:24:24,080
Speaker 5: hacky sack around. It's an extracurricular activity where like lots

511
00:24:24,119 --> 00:24:26,920
Speaker 5: of different teams would get together and talk about machine

512
00:24:27,000 --> 00:24:31,040
Speaker 5: learning later AI. But actually what they're doing now is

513
00:24:31,359 --> 00:24:34,560
Speaker 5: getting some personnel changes as well. Some of the management

514
00:24:34,640 --> 00:24:37,399
Speaker 5: are moving into more kind of research focus roles. What

515
00:24:37,440 --> 00:24:38,679
Speaker 5: are the details in that respect?

516
00:24:39,040 --> 00:24:39,960
Speaker 4: Yeah, absolutely.

517
00:24:40,480 --> 00:24:44,560
Speaker 1: Jeff Dean, who is this like legendary researcher at Google,

518
00:24:45,320 --> 00:24:50,040
Speaker 1: is becoming chief scientist at the company and he is

519
00:24:50,080 --> 00:24:54,600
Speaker 1: not managing people anymore. You know, we've talked to sources

520
00:24:54,680 --> 00:24:57,200
Speaker 1: inside Google who think that this is actually a good

521
00:24:57,240 --> 00:25:00,720
Speaker 1: development that he, you know, is folks using more on

522
00:25:00,800 --> 00:25:03,840
Speaker 1: the research and he will work closely with Google Deep

523
00:25:03,880 --> 00:25:08,640
Speaker 1: Mind to just move the ball forward in terms of research.

524
00:25:09,800 --> 00:25:13,760
Speaker 1: And then you know, Google's CEO of deep Mind is

525
00:25:13,800 --> 00:25:15,960
Speaker 1: the one that is going to be ultimately in charge

526
00:25:16,040 --> 00:25:17,639
Speaker 1: of this consolidated unit.

527
00:25:17,840 --> 00:25:21,719
Speaker 4: So Demis, of course Demis has a buss Yep. Absolutely,

528
00:25:22,080 --> 00:25:25,600
Speaker 4: what about the anxiety levels in Alphabet, Can you just

529
00:25:25,640 --> 00:25:27,560
Speaker 4: give us a check in of what they feel. We've

530
00:25:27,560 --> 00:25:29,760
Speaker 4: seen a pitch I on sixteen minutes. We've seen a

531
00:25:29,800 --> 00:25:31,960
Speaker 4: lot of talk of trying to get back the narrative

532
00:25:32,080 --> 00:25:35,200
Speaker 4: that you know, AI is synonymous with Alphabet and Google,

533
00:25:35,240 --> 00:25:37,800
Speaker 4: not just chat GPT in open Ai. Yeah. Absolutely.

534
00:25:37,840 --> 00:25:41,280
Speaker 1: I mean Google has for so long called itself an

535
00:25:41,320 --> 00:25:44,800
Speaker 1: AI first company. As Sondhar Pachai took over as CEO,

536
00:25:44,920 --> 00:25:47,800
Speaker 1: that was his sort of line that he kept sort

537
00:25:47,840 --> 00:25:49,560
Speaker 1: of hammering away at.

538
00:25:50,040 --> 00:25:51,200
Speaker 4: And Google really.

539
00:25:50,960 --> 00:25:54,320
Speaker 1: Has something to prove here. It's been scrambling to catch

540
00:25:54,400 --> 00:25:57,760
Speaker 1: up to open ai and the innovations with chat GPT,

541
00:25:58,480 --> 00:26:01,600
Speaker 1: and also it's big competitive or in Microsoft as they've

542
00:26:01,600 --> 00:26:07,840
Speaker 1: integrated that tech into bing that scramble has come out

543
00:26:07,880 --> 00:26:10,520
Speaker 1: with a code red you know, under the company, and

544
00:26:11,880 --> 00:26:15,359
Speaker 1: they're really just from the top down telling folks to

545
00:26:15,920 --> 00:26:19,800
Speaker 1: hunker down and come out with these generative AI products.

546
00:26:20,680 --> 00:26:24,760
Speaker 1: So anxiety levels are absolutely high. At the same time,

547
00:26:24,840 --> 00:26:29,040
Speaker 1: you know there are ethical questions about the tech that

548
00:26:29,160 --> 00:26:32,439
Speaker 1: is being rolled out, and Google has a ton of

549
00:26:32,480 --> 00:26:36,240
Speaker 1: brilliant researchers who are also concerned with these things, and

550
00:26:36,560 --> 00:26:37,720
Speaker 1: there's that tension there.

551
00:26:39,080 --> 00:26:41,560
Speaker 6: All right, Bloomberg's debut, Albert and all things Google. Thank you.

552
00:26:41,680 --> 00:26:44,840
Speaker 5: Let's continue with AI and a coverage actually the Emerged

553
00:26:44,880 --> 00:26:48,119
Speaker 5: Tech conference out in Miami. Care and bring in Angel Bush,

554
00:26:48,200 --> 00:26:52,480
Speaker 5: founder of Black Women in Artificial Intelligence. An interesting group. Angel,

555
00:26:52,720 --> 00:26:56,440
Speaker 5: Let's start with Miami first. Why have you taken yourself

556
00:26:56,760 --> 00:26:59,480
Speaker 5: in your firm out to Emerge this week.

557
00:27:00,520 --> 00:27:03,520
Speaker 11: I am so excited to be here at Emerge because

558
00:27:03,520 --> 00:27:06,480
Speaker 11: there are so many innovative things that are happening, and

559
00:27:06,520 --> 00:27:09,600
Speaker 11: you have so many subject matter experts here that you

560
00:27:09,680 --> 00:27:13,200
Speaker 11: can connect with and really find out more information about

561
00:27:13,280 --> 00:27:16,760
Speaker 11: what's happening in the industry and what's going to happen

562
00:27:16,800 --> 00:27:18,159
Speaker 11: the future of AI.

563
00:27:19,960 --> 00:27:22,720
Speaker 5: Hey, Caro, I think you know for us, we're trying

564
00:27:22,720 --> 00:27:25,359
Speaker 5: to pass over some names and leaders in a field

565
00:27:25,400 --> 00:27:28,800
Speaker 5: of artificial intelligence, which everyone seems to be jumping on.

566
00:27:29,440 --> 00:27:33,080
Speaker 5: Like all tech subsectors, we also consider diversity right in

567
00:27:33,200 --> 00:27:34,640
Speaker 5: leadership in that space, and.

568
00:27:34,600 --> 00:27:37,359
Speaker 4: There's been a lot of anxiety, particularly around biases that

569
00:27:37,400 --> 00:27:40,399
Speaker 4: get automatically built within some of the II models. If

570
00:27:40,400 --> 00:27:42,160
Speaker 4: the right people aren't at the table, if the right

571
00:27:42,160 --> 00:27:45,000
Speaker 4: input something put in And Angel, we want to go

572
00:27:45,040 --> 00:27:48,159
Speaker 4: back to why you founded Black Women in AI. What

573
00:27:48,320 --> 00:27:50,480
Speaker 4: event were you at where you saw that it wasn't

574
00:27:50,520 --> 00:27:51,400
Speaker 4: speaking to you.

575
00:27:53,000 --> 00:27:55,400
Speaker 11: Well, I was at an event and I don't want

576
00:27:55,440 --> 00:27:58,840
Speaker 11: to name the event to be honest, but the purpose

577
00:27:59,000 --> 00:28:03,280
Speaker 11: of the event was AI and technology, and I did

578
00:28:03,320 --> 00:28:06,880
Speaker 11: not see a full representation of myself. And at that time,

579
00:28:06,920 --> 00:28:11,360
Speaker 11: in twenty nineteen, they continued to say that artificial intelligence

580
00:28:11,440 --> 00:28:14,840
Speaker 11: is the fourth Industrial Revolution, and again I didn't see

581
00:28:14,840 --> 00:28:17,880
Speaker 11: a representation of myself. And in those moments, I said, well,

582
00:28:17,960 --> 00:28:22,239
Speaker 11: surely you can't have a revolution without black women, and

583
00:28:22,359 --> 00:28:24,800
Speaker 11: that was the start of Black Women in AI.

584
00:28:25,880 --> 00:28:27,520
Speaker 6: Well, Angel, what work are you do it?

585
00:28:29,080 --> 00:28:31,399
Speaker 5: What is it the function that Black Women in AI

586
00:28:31,680 --> 00:28:32,760
Speaker 5: carries out each day?

587
00:28:33,920 --> 00:28:37,080
Speaker 11: Well, our mission is to educate, engage, embrace, and empower

588
00:28:37,160 --> 00:28:40,200
Speaker 11: black women and we've partnered with another a number of

589
00:28:40,360 --> 00:28:44,320
Speaker 11: companies to do just that by providing various initiatives that

590
00:28:44,560 --> 00:28:48,120
Speaker 11: educate our community on AI. What is data science? And

591
00:28:48,160 --> 00:28:50,360
Speaker 11: I know data science is not AI, but it's a

592
00:28:50,400 --> 00:28:54,840
Speaker 11: close cousin. What is autonomous vehicles, what is machine learning?

593
00:28:55,000 --> 00:28:59,120
Speaker 11: What is computer vision? What is AR ANDVR? We are

594
00:28:59,200 --> 00:29:02,440
Speaker 11: learning these things, but we're also learning how to ace

595
00:29:02,520 --> 00:29:05,920
Speaker 11: the case in essence when we're going to our interviews

596
00:29:05,960 --> 00:29:09,680
Speaker 11: and things of that nature. So we're educating the entire

597
00:29:10,040 --> 00:29:13,920
Speaker 11: process of AI, from being a creator to being a

598
00:29:13,960 --> 00:29:16,560
Speaker 11: consumer to being a part of the industry.

599
00:29:16,920 --> 00:29:20,400
Speaker 4: So Angel, is it more about ensuring that people coming

600
00:29:20,440 --> 00:29:23,360
Speaker 4: through education are being well versed in AI and therefore

601
00:29:23,400 --> 00:29:26,120
Speaker 4: able to join these companies in partner with or is

602
00:29:26,160 --> 00:29:29,160
Speaker 4: it more ensuring that people within these companies are having

603
00:29:29,240 --> 00:29:32,880
Speaker 4: a voice, able to take leadership roles. What part of

604
00:29:32,920 --> 00:29:35,960
Speaker 4: the stack are you're looking at To a certain extent, it's.

605
00:29:35,880 --> 00:29:39,520
Speaker 11: A combination of both. We want our community to be

606
00:29:39,600 --> 00:29:43,320
Speaker 11: engaged in AI because it affects all of us. AI

607
00:29:43,480 --> 00:29:46,680
Speaker 11: is global, it's not something local. It's not a fad.

608
00:29:46,960 --> 00:29:49,880
Speaker 11: It's going to be here and it is affecting all

609
00:29:49,920 --> 00:29:52,400
Speaker 11: of us. It has an impact on our community, and

610
00:29:52,440 --> 00:29:54,960
Speaker 11: I want to make sure that we're not only exposed

611
00:29:54,960 --> 00:29:58,040
Speaker 11: to it. But again, we're not just consumers, but we're

612
00:29:58,080 --> 00:30:02,040
Speaker 11: creators as well. You know, how to be a part

613
00:30:02,120 --> 00:30:04,880
Speaker 11: of the system in terms of AI and corporate America.

614
00:30:05,080 --> 00:30:07,640
Speaker 11: But we're also learning how to be creators in this

615
00:30:07,720 --> 00:30:08,800
Speaker 11: space as well.

616
00:30:08,800 --> 00:30:11,760
Speaker 4: And in a way, and this also speaks to of course,

617
00:30:11,800 --> 00:30:14,440
Speaker 4: it's about diversity thought, diversity of background, but it also

618
00:30:14,560 --> 00:30:17,480
Speaker 4: means that therefore the talent pools are so much richer,

619
00:30:17,480 --> 00:30:19,840
Speaker 4: and not just in Sullivon Valley, not just in New York.

620
00:30:19,880 --> 00:30:21,560
Speaker 4: They're in Miami, they're in Austin, They're in on the

621
00:30:21,560 --> 00:30:22,480
Speaker 4: parts of the United States.

622
00:30:22,560 --> 00:30:22,760
Speaker 12: Right.

623
00:30:23,560 --> 00:30:25,640
Speaker 5: I think Angel, you're interested in who you're speaking to

624
00:30:25,880 --> 00:30:27,840
Speaker 5: out in Miami this week. You yourself had a long

625
00:30:27,880 --> 00:30:30,480
Speaker 5: career in the oil and gas industry, You've made the

626
00:30:30,560 --> 00:30:33,520
Speaker 5: jump to AI. Are you seeing people make similar moves

627
00:30:33,560 --> 00:30:36,160
Speaker 5: to kind of jump on the momentum in this sector?

628
00:30:37,160 --> 00:30:39,520
Speaker 11: I am, Actually I'm seeing a lot of people.

629
00:30:39,800 --> 00:30:40,200
Speaker 4: I know.

630
00:30:40,320 --> 00:30:45,080
Speaker 11: The notion is that women are leaving technology and AI,

631
00:30:45,480 --> 00:30:48,600
Speaker 11: but we're seeing the opposite. We're seeing people from across

632
00:30:48,640 --> 00:30:52,840
Speaker 11: the globe. Again, we have members located on five continents,

633
00:30:53,080 --> 00:30:55,840
Speaker 11: so we're seeing women across the globe come back to

634
00:30:55,960 --> 00:30:58,600
Speaker 11: tech who may not have been encouraged when they were younger,

635
00:30:59,320 --> 00:31:01,760
Speaker 11: who may not have thought that there was space for them,

636
00:31:02,120 --> 00:31:05,480
Speaker 11: and we are providing that space. So again they can learn,

637
00:31:05,520 --> 00:31:08,360
Speaker 11: they can be educated and engaged, embraced and empowered in

638
00:31:08,440 --> 00:31:13,200
Speaker 11: this space. Would also feel that they can succeed in

639
00:31:13,240 --> 00:31:16,240
Speaker 11: this space. Again, whether they're a creator or a consumer,

640
00:31:16,560 --> 00:31:20,800
Speaker 11: we want them to know everything they can know about

641
00:31:20,960 --> 00:31:22,880
Speaker 11: artificial intelligence.

642
00:31:22,880 --> 00:31:26,000
Speaker 4: Black women and AI founder Angel Bush, we appreciate you

643
00:31:26,080 --> 00:31:27,960
Speaker 4: making the time while busy out there in Miami.

644
00:31:28,600 --> 00:31:30,920
Speaker 6: Ed I think, yeah, well, thanks to Angel and are

645
00:31:30,920 --> 00:31:31,200
Speaker 6: coming up.

646
00:31:31,200 --> 00:31:32,760
Speaker 5: We're actually going to take a closer look at the

647
00:31:32,800 --> 00:31:37,400
Speaker 5: Miami VC scene with Panoramic Ventures Paul Judd, which is

648
00:31:37,400 --> 00:31:39,400
Speaker 5: coming up next. So let's talk about what's happened in

649
00:31:39,480 --> 00:31:40,960
Speaker 5: the ground out at Emerge.

650
00:31:41,160 --> 00:31:42,400
Speaker 6: This is Bloomberg.

651
00:32:03,120 --> 00:32:03,640
Speaker 4: AI.

652
00:32:03,800 --> 00:32:07,120
Speaker 12: You've heard of it. It's probably a thing that's going

653
00:32:07,160 --> 00:32:11,640
Speaker 12: to grow, and you know, let's invest in that. So

654
00:32:11,840 --> 00:32:18,600
Speaker 12: semiconductors at a minimum and other software investments are probably

655
00:32:18,640 --> 00:32:20,880
Speaker 12: going to be key.

656
00:32:21,880 --> 00:32:25,000
Speaker 5: That was Kim Forrest, founder and CIO of Boque Capital Partners.

657
00:32:25,040 --> 00:32:25,280
Speaker 1: There.

658
00:32:25,560 --> 00:32:27,560
Speaker 5: Let's go from the public markets though to the private

659
00:32:27,600 --> 00:32:30,920
Speaker 5: markets and bring in Panoramic Ventures managing partner Paul Judge

660
00:32:31,080 --> 00:32:33,800
Speaker 5: for his take and where he's looking for the next

661
00:32:33,840 --> 00:32:36,680
Speaker 5: big thing. You're out there in Miami to Emerge, right,

662
00:32:36,720 --> 00:32:39,920
Speaker 5: you split your time between Atlanta and down in Florida.

663
00:32:40,760 --> 00:32:42,840
Speaker 5: Good hunting ground for a venture capitalist.

664
00:32:42,880 --> 00:32:47,840
Speaker 3: In Miami, It's been amazing. I've been based in Atlanta

665
00:32:47,880 --> 00:32:51,360
Speaker 3: for the last twenty years and always believe this view

666
00:32:51,400 --> 00:32:53,920
Speaker 3: that you know, innovation is not just in the traditional

667
00:32:53,920 --> 00:32:57,120
Speaker 3: techops of the valley, and we've done most of eighty

668
00:32:57,160 --> 00:32:59,440
Speaker 3: five percent of our investments have been in the Southeast

669
00:32:59,440 --> 00:33:02,320
Speaker 3: and the Midwest. And two years ago I started to

670
00:33:02,320 --> 00:33:05,360
Speaker 3: spend my time in Miami and split because of just

671
00:33:05,400 --> 00:33:07,240
Speaker 3: the growth of the tech ecosystem here.

672
00:33:08,000 --> 00:33:10,720
Speaker 5: We've did the sound sorry to interrupt you, Paul. The

673
00:33:10,720 --> 00:33:14,160
Speaker 5: SoundBite we just played from that that public market investor

674
00:33:14,240 --> 00:33:18,080
Speaker 5: is about your opportunity in AI and semiconductors. I'm interested,

675
00:33:18,200 --> 00:33:22,440
Speaker 5: you know, which sections of the technology industry are most appealing.

676
00:33:22,520 --> 00:33:25,240
Speaker 5: What's the good work that's going on out in Miami.

677
00:33:26,600 --> 00:33:28,600
Speaker 3: You know, there's a few things that are happening. If

678
00:33:28,600 --> 00:33:30,760
Speaker 3: you look at all the changes in finance right now,

679
00:33:31,440 --> 00:33:35,400
Speaker 3: you know, decentralization with blockchain, but then also kind of

680
00:33:35,480 --> 00:33:39,640
Speaker 3: the shortage of capital that's flowing through the markets now

681
00:33:40,040 --> 00:33:42,800
Speaker 3: right and then lastly what happened with SVB and others.

682
00:33:42,840 --> 00:33:44,560
Speaker 3: People are thinking about, you know, how do you have

683
00:33:44,600 --> 00:33:47,480
Speaker 3: safety of your treasury, but then how do you find

684
00:33:48,000 --> 00:33:49,600
Speaker 3: upside and where do you find outf and where do

685
00:33:49,680 --> 00:33:52,160
Speaker 3: you find returns? And so you know, individuals as well

686
00:33:52,200 --> 00:33:55,880
Speaker 3: as corporations are rethinking finance totally right, and so even

687
00:33:55,880 --> 00:33:59,040
Speaker 3: we just saw Apple get into savings accounts and so

688
00:33:59,120 --> 00:34:01,959
Speaker 3: we're investors and another number of companies that are kind

689
00:34:02,000 --> 00:34:05,520
Speaker 3: of rethinking finance. Another big trend where we're spending a

690
00:34:05,520 --> 00:34:09,520
Speaker 3: lot of time is is cybersecurity, and it's an evolving landscapehere.

691
00:34:09,520 --> 00:34:12,440
Speaker 3: You're always chasing the attackers when as they're figuring out

692
00:34:12,480 --> 00:34:15,880
Speaker 3: the next battleground to try to break into organizations. You

693
00:34:15,880 --> 00:34:17,719
Speaker 3: know a number of our investments are there. There's a

694
00:34:17,719 --> 00:34:21,120
Speaker 3: company called Loumu that's a cybersecurity company based in Miami

695
00:34:21,160 --> 00:34:24,120
Speaker 3: that helps you figure out you know, you must assume

696
00:34:24,200 --> 00:34:26,520
Speaker 3: that you have an attack on your network, but how

697
00:34:26,560 --> 00:34:28,200
Speaker 3: do you identify it and how do you figure out

698
00:34:28,200 --> 00:34:29,040
Speaker 3: what to then do next?

699
00:34:30,239 --> 00:34:32,520
Speaker 5: Caroline on BlueBag Technology, you and I are trying to

700
00:34:32,520 --> 00:34:35,640
Speaker 5: cover this industry globally. Look at what's happening not just

701
00:34:35,680 --> 00:34:37,719
Speaker 5: on the West coast or the East coast. But it

702
00:34:37,800 --> 00:34:40,600
Speaker 5: is interesting to look at where the megacat Tech names

703
00:34:40,640 --> 00:34:44,319
Speaker 5: are where the biggest startups are. Does size matter when

704
00:34:44,320 --> 00:34:46,759
Speaker 5: it comes to momentum in the sector.

705
00:34:46,719 --> 00:34:51,000
Speaker 4: And also where does the value originate? And it's interesting, Paul,

706
00:34:51,080 --> 00:34:54,399
Speaker 4: you talk about cyber which feels like a space that's

707
00:34:54,440 --> 00:34:58,000
Speaker 4: been more resilient to the macroeconomic headwinds because everyone still

708
00:34:58,040 --> 00:35:02,759
Speaker 4: needs to protect themselves. Seems to be managing to be

709
00:35:02,880 --> 00:35:06,239
Speaker 4: a silver lining amid this dark cloud of where valuations head.

710
00:35:06,280 --> 00:35:09,200
Speaker 4: From your perspective, where is some of the AI talent?

711
00:35:09,440 --> 00:35:13,759
Speaker 4: Where is the stack? Is it interesting to be investing

712
00:35:14,200 --> 00:35:15,480
Speaker 4: at the moment in terms of AI.

713
00:35:17,160 --> 00:35:17,759
Speaker 8: Absolutely.

714
00:35:18,640 --> 00:35:22,759
Speaker 3: AI is allowing computers who've always been chasing how to

715
00:35:22,760 --> 00:35:26,120
Speaker 3: have computers think like humans and operate faster. We knew

716
00:35:26,120 --> 00:35:28,960
Speaker 3: computers always had more storage than the humans, always had

717
00:35:29,000 --> 00:35:32,640
Speaker 3: faster compute, but giving it the ability to learn and

718
00:35:32,680 --> 00:35:36,160
Speaker 3: make deeper logical decisions. And what we're seeing with these

719
00:35:36,280 --> 00:35:38,960
Speaker 3: language models now is really becoming more of an on

720
00:35:39,040 --> 00:35:42,040
Speaker 3: rap to technology. It used to be to get into

721
00:35:42,080 --> 00:35:45,680
Speaker 3: technology you had to program cards and give them to

722
00:35:45,680 --> 00:35:47,799
Speaker 3: a computer. You have to learn cobol, and there's all

723
00:35:47,800 --> 00:35:50,440
Speaker 3: of these barriers. Now that you can just talk to

724
00:35:50,440 --> 00:35:53,920
Speaker 3: a computer normal language and have no code and create

725
00:35:53,960 --> 00:35:57,560
Speaker 3: websites and create software is opening up the playing field

726
00:35:57,719 --> 00:36:00,120
Speaker 3: to a lot of people that have been underrepresented, a

727
00:36:00,120 --> 00:36:02,719
Speaker 3: lot of people that have been overlooked, and so I

728
00:36:02,719 --> 00:36:05,279
Speaker 3: think we'll see even more of emergence of kind of

729
00:36:05,320 --> 00:36:08,880
Speaker 3: remote locations as people on board through through AI, and

730
00:36:08,880 --> 00:36:12,439
Speaker 3: then it's allowing us to disrupt some traditional I would

731
00:36:12,440 --> 00:36:15,640
Speaker 3: call them boring industries. Right there's companies that we've invested

732
00:36:15,680 --> 00:36:18,640
Speaker 3: in like Togo, who won the pitch competition here last

733
00:36:18,680 --> 00:36:22,360
Speaker 3: year that's taken a traditional, boring industry of construction and

734
00:36:22,480 --> 00:36:25,200
Speaker 3: applying software to it to help it be more efficient.

735
00:36:25,360 --> 00:36:28,320
Speaker 3: We'll see more of that AI to go into traditional

736
00:36:28,320 --> 00:36:30,600
Speaker 3: industries and helping them move faster and be more efficient.

737
00:36:30,880 --> 00:36:34,880
Speaker 4: Productivity is what it's all about. Paul, you yourself seasoned

738
00:36:35,040 --> 00:36:38,840
Speaker 4: founder and then now into the world of investing, ensuring

739
00:36:38,840 --> 00:36:42,520
Speaker 4: that it's more democratized. Are you seeing the amount of

740
00:36:42,520 --> 00:36:45,000
Speaker 4: founders you want to see from diverse backgrounds. Do you

741
00:36:45,000 --> 00:36:47,040
Speaker 4: think at the moment, with the macroheadwinds as they are,

742
00:36:47,080 --> 00:36:49,200
Speaker 4: they're more enticed to go out there build their own

743
00:36:49,239 --> 00:36:50,000
Speaker 4: company or not?

744
00:36:51,080 --> 00:36:54,000
Speaker 3: I believe that, you know, there's always been this desire

745
00:36:54,480 --> 00:36:59,239
Speaker 3: with minorities to be entrepreneurial right to carve out their

746
00:36:59,280 --> 00:37:03,960
Speaker 3: own path. And you've seen the numbers. Traditionally, the tech industry, unfortunately,

747
00:37:04,280 --> 00:37:06,040
Speaker 3: was based on a close network. It was based on

748
00:37:06,080 --> 00:37:09,200
Speaker 3: who do you know, and that wasn't very inviting if

749
00:37:09,200 --> 00:37:11,279
Speaker 3: you're coming from a different background, if you're coming from

750
00:37:11,280 --> 00:37:14,600
Speaker 3: a different geographic region. One of the things that is

751
00:37:14,680 --> 00:37:17,160
Speaker 3: a benefit of how the world is very distributed now

752
00:37:17,640 --> 00:37:19,560
Speaker 3: is investors have to hop on a plane and hop

753
00:37:19,560 --> 00:37:22,400
Speaker 3: on zoom. And I think we're seeing enough examples of

754
00:37:22,719 --> 00:37:26,359
Speaker 3: great companies founded by women and founded by minorities that

755
00:37:26,960 --> 00:37:29,200
Speaker 3: investors are now seeking them out. The numbers still have

756
00:37:29,320 --> 00:37:33,400
Speaker 3: so much further left to go. Right At Panoramic, over

757
00:37:33,680 --> 00:37:37,120
Speaker 3: half of our portfolio is founded by women, are founded

758
00:37:37,160 --> 00:37:40,160
Speaker 3: by minorities. We also partner with soft Bank on the

759
00:37:40,200 --> 00:37:43,600
Speaker 3: Opportunity Fund, which is a twillion dollar fund dedicated to

760
00:37:43,719 --> 00:37:45,560
Speaker 3: Latino and Black founders.

761
00:37:46,200 --> 00:37:48,320
Speaker 4: Yeah, that was a key player, certainly in bridging the

762
00:37:48,360 --> 00:37:52,520
Speaker 4: gap between America Latin America in particular. Panoramic Adventures Managing

763
00:37:52,520 --> 00:37:54,880
Speaker 4: partner Paul Judge, what a great person to have on today.

764
00:37:54,960 --> 00:37:56,600
Speaker 4: We thank you in it. It really is just this

765
00:37:56,680 --> 00:37:59,560
Speaker 4: focus on ensuring that we're looking at these pools of

766
00:37:59,600 --> 00:38:03,680
Speaker 4: capital that are being built by diverse vcs and attracting

767
00:38:03,719 --> 00:38:04,480
Speaker 4: diverse founders.

768
00:38:04,920 --> 00:38:06,880
Speaker 5: What he set out was really important the history of

769
00:38:06,920 --> 00:38:09,800
Speaker 5: Silicon Valley was you had Stanford, you had the talent,

770
00:38:09,920 --> 00:38:12,680
Speaker 5: They founded companies and the vcs move to them. What

771
00:38:12,719 --> 00:38:16,000
Speaker 5: he's just explained is why the capitol is going to

772
00:38:16,040 --> 00:38:19,560
Speaker 5: Miami because of what's there and the diversity of foundership.

773
00:38:19,600 --> 00:38:21,280
Speaker 6: That's on off a really interesting.

774
00:38:28,880 --> 00:38:31,120
Speaker 4: Time. It's running out to avoid the worst effects of

775
00:38:31,120 --> 00:38:33,600
Speaker 4: climate change. That's according to a research note of published

776
00:38:33,640 --> 00:38:37,319
Speaker 4: just yesterday by the International Energy Agency the IEA, which

777
00:38:37,360 --> 00:38:40,040
Speaker 4: outlines that that after a short drop during the pandemic

778
00:38:40,320 --> 00:38:43,200
Speaker 4: global carbon dioxide emissions, they rose to a new record

779
00:38:43,280 --> 00:38:46,319
Speaker 4: high of thirty six point eight billion metric tons in

780
00:38:46,360 --> 00:38:49,960
Speaker 4: twenty twenty two. And yet clean energy it's becoming more

781
00:38:50,000 --> 00:38:53,600
Speaker 4: and more affordable and could potentially lower global warming, according

782
00:38:53,600 --> 00:38:55,839
Speaker 4: to the same report. For more on this, let's talk

783
00:38:55,840 --> 00:38:58,440
Speaker 4: about the future of clean tech carbon capture, in particular,

784
00:38:58,719 --> 00:39:02,600
Speaker 4: Springing Claude, his president CEO of carbon capture company It's

785
00:39:02,640 --> 00:39:05,360
Speaker 4: funte It's just got fifteen million dollar investment from United

786
00:39:05,400 --> 00:39:08,600
Speaker 4: Airlines also scored the biggest carbon and emissions Tech deal

787
00:39:08,640 --> 00:39:11,200
Speaker 4: of twenty twenty two through on an eighteen million Series

788
00:39:11,239 --> 00:39:14,719
Speaker 4: E was around led by Chevron Technology Ventures Claude. It's

789
00:39:14,760 --> 00:39:16,440
Speaker 4: wonderful to have some time with these. We look towards

790
00:39:16,480 --> 00:39:19,480
Speaker 4: Earth Day coming up tomorrow, just how do you see

791
00:39:19,719 --> 00:39:24,080
Speaker 4: deals with United how do you see more private market

792
00:39:24,160 --> 00:39:26,799
Speaker 4: money allocation coming to solve what it is a very

793
00:39:26,840 --> 00:39:27,439
Speaker 4: public need.

794
00:39:27,840 --> 00:39:29,839
Speaker 6: I think we lost claud which is a shame.

795
00:39:29,880 --> 00:39:31,719
Speaker 5: I'll go back to something I discussed the other day

796
00:39:31,760 --> 00:39:34,680
Speaker 5: at Teconomy Climate and A did a panel and the

797
00:39:34,719 --> 00:39:37,040
Speaker 5: point is that if you're a bench capitalist, right, you

798
00:39:37,080 --> 00:39:39,360
Speaker 5: want to be investing in startups that can get the

799
00:39:39,440 --> 00:39:43,879
Speaker 5: money the dollars from a the government and B those

800
00:39:44,080 --> 00:39:46,680
Speaker 5: energy companies that have to diversify. And I think carry

801
00:39:46,719 --> 00:39:48,560
Speaker 5: we asked our own audience that exact question.

802
00:39:48,640 --> 00:39:50,880
Speaker 4: Right, Yes, let's go to what we put out to Twitter,

803
00:39:50,880 --> 00:39:53,640
Speaker 4: because it got people engaged with This is a global problem.

804
00:39:53,680 --> 00:39:55,520
Speaker 4: It's also one that we all consider on social media

805
00:39:55,520 --> 00:39:58,640
Speaker 4: an awful lot. And really the view was is research

806
00:39:59,120 --> 00:40:01,680
Speaker 4: in particular is the time running out for the worst

807
00:40:01,680 --> 00:40:04,120
Speaker 4: effects of climate change? And we don't want to politicize this,

808
00:40:04,320 --> 00:40:08,160
Speaker 4: but actually, like thirty five percent said, no support is

809
00:40:08,200 --> 00:40:13,279
Speaker 4: actually needed in terms of government focus VC focus. Yes,

810
00:40:13,840 --> 00:40:15,840
Speaker 4: we're on the right tracks as eleven percent, So that

811
00:40:15,880 --> 00:40:18,120
Speaker 4: felt to me a little bit pessimistic. Only eleven percent

812
00:40:18,160 --> 00:40:19,719
Speaker 4: out there think that we're on the right track in

813
00:40:19,760 --> 00:40:22,520
Speaker 4: terms of funding for clean tech companies, but actually the

814
00:40:22,880 --> 00:40:25,319
Speaker 4: large amount thing that no more support is needed. Does

815
00:40:25,320 --> 00:40:27,320
Speaker 4: that mean they just think that enough is there already

816
00:40:27,320 --> 00:40:28,960
Speaker 4: and the private sector can solve this.

817
00:40:30,239 --> 00:40:32,759
Speaker 5: I'm thinking about Bill Gates as an example. How long

818
00:40:32,800 --> 00:40:35,000
Speaker 5: has he been talking about the need to focus on

819
00:40:35,040 --> 00:40:37,759
Speaker 5: climate But one of the initiatives that he's doing out

820
00:40:37,760 --> 00:40:41,359
Speaker 5: of Washington State and Seattle is to make it commercially

821
00:40:41,400 --> 00:40:44,919
Speaker 5: attractive invest in things that make people money, so they're

822
00:40:44,920 --> 00:40:48,040
Speaker 5: incentivized to get into that space if they're in lives.

823
00:40:48,080 --> 00:40:50,840
Speaker 4: The argument of impact investing isn't it This isn't about

824
00:40:51,320 --> 00:40:53,880
Speaker 4: doing things without profit. It's about doing things for profit

825
00:40:53,960 --> 00:40:56,640
Speaker 4: and actually talking about profit. There are some companies out

826
00:40:56,680 --> 00:40:58,640
Speaker 4: there having to make some very difficult decisions right now.

827
00:40:59,239 --> 00:41:01,040
Speaker 5: Yeah, let's get back to Lift and look at shares

828
00:41:01,080 --> 00:41:04,239
Speaker 5: of the company. Actually really interesting move, you know, basically

829
00:41:04,280 --> 00:41:07,760
Speaker 5: flat now having spiked as much as five percent, issued

830
00:41:07,760 --> 00:41:12,680
Speaker 5: a statement confirming a restructuring plan carrow. They will reduce headcount,

831
00:41:12,680 --> 00:41:15,800
Speaker 5: but that's after the journal had reported twelve hundred jobs

832
00:41:15,800 --> 00:41:17,719
Speaker 5: to be cut. They didn't give that number. But the

833
00:41:17,800 --> 00:41:20,520
Speaker 5: key point, I think there will be no impact to

834
00:41:20,560 --> 00:41:23,360
Speaker 5: the prior financial guidance that they gave now negative a

835
00:41:23,440 --> 00:41:24,160
Speaker 5: tenth of a percent.

836
00:41:24,520 --> 00:41:26,600
Speaker 4: This is all about competition as well, isn't it. They're

837
00:41:26,640 --> 00:41:30,000
Speaker 4: talking about the need to use those cost savings for

838
00:41:30,480 --> 00:41:33,439
Speaker 4: basically competitive pricing for savings for you and I when

839
00:41:33,440 --> 00:41:35,320
Speaker 4: we take a lift. Now, that's going to be probably

840
00:41:35,400 --> 00:41:38,000
Speaker 4: quite difficult for the people who about to lose their

841
00:41:38,080 --> 00:41:41,560
Speaker 4: roles to swallow. But this is about continuing to ensure

842
00:41:41,560 --> 00:41:44,120
Speaker 4: that they can take uber on here in the United States.

843
00:41:44,400 --> 00:41:46,439
Speaker 4: But this is all about morale. How can a new

844
00:41:46,520 --> 00:41:49,040
Speaker 4: CEO to the business be able to study a ship

845
00:41:49,080 --> 00:41:50,880
Speaker 4: when also having to make such strassic cuts.

846
00:41:51,480 --> 00:41:53,560
Speaker 5: And when we spoke to the CEO a few weeks ago,

847
00:41:53,680 --> 00:41:57,360
Speaker 5: he said the company is not for sale, but clearly

848
00:41:57,440 --> 00:41:59,640
Speaker 5: he's putting a stamp on this company having just joined.

849
00:42:00,120 --> 00:42:02,520
Speaker 4: Meanwhile, that does it for this edition of Brittenbog technology yet?

850
00:42:03,440 --> 00:42:05,239
Speaker 6: Yeah, but stick with us.

851
00:42:05,280 --> 00:42:09,120
Speaker 5: Twitter spaces Caro coming up four minutes time. What a

852
00:42:09,239 --> 00:42:11,920
Speaker 5: week it has been this is Bloomberg