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Speaker 1: From Mahart. We're Innovation of Money and Power Collie in

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Speaker 1: Silicon Valley, NBN. This is Bloomberg Technology with Caroline Hyde

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Speaker 1: and Ed lud Love.

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Speaker 2: I'm Caroline Hyde live from San Francisco, Ed Ludlow, He's

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Speaker 2: on location in Palo Alto, and this.

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Speaker 3: Is Bloomberg Technology. Coming up.

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Speaker 2: We go live to Palanteer headquarters. That's where Ed is

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Speaker 2: sitting down with the CEO, Alex Krk to.

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Speaker 3: Dive into Guess What AI.

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Speaker 2: Their full conversation later this hour, plus we'll push ahead

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Speaker 2: to the earnings out after the bell. Broadcom is the

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Speaker 2: latest company to test if this AI rally is a

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Speaker 2: reality and the EU's Digital Markets Act it takes hold today,

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Speaker 2: hitting big tech firms with a list of dos and don'ts.

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Speaker 3: We'll break down.

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Speaker 2: The epic battles already raging for Apple and so much

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Speaker 2: more throughout this hour. But first look at what's happening

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Speaker 2: with Apple. They cannot really catch a break. We're still

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Speaker 2: lower and that's as we try to dive cell for

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Speaker 2: really digest what's happening with the EU and how it's

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Speaker 2: going to impact some of these big gatekeepers as their name.

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Speaker 2: Let's dive into it they e use Digital Markets Act.

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Speaker 2: It is coming into force today, hitting big tech firms

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Speaker 2: with a broad list of dos and don'ts, and well,

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Speaker 2: really they're going to face some threats of significant finds

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Speaker 2: that they don't live.

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Speaker 3: Up to the regulation. The aim, of course, to create a.

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Speaker 2: More competitive online field and posting changes on these six

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Speaker 2: so called gatekeepers. Apple is one of them, but so

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Speaker 2: is Amazon, so is Google owner Alphabet, So it's tiptop Pairent, Bike, Dance, Meta, Microsoft.

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Speaker 3: Today. Apple already faces questions from the EU, the.

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Speaker 2: Regulators over there wanting to know about the accusations that

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Speaker 2: is barring Epic Games from opening its own app store

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Speaker 2: for iPhone customers in Europe.

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Speaker 3: That is what these new rules are meant to be allowing.

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Speaker 2: Blue Meg's Mark German has more and the battles still

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Speaker 2: rages on Mark. Just give us the inside track of

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Speaker 2: why we're seeing Apple not allowed this third party app

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Speaker 2: store to come to light.

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Speaker 4: Caroline, Apple has a law on its plate right now.

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Speaker 4: It's looking for its next big thing today. Like you said,

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Speaker 4: the Digital Markets Act is coming into place. There is

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Speaker 4: a lawsuit coming from the United States Department of Justice

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Speaker 4: before the end of the month. So They're already dealing

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Speaker 4: with a lot. This latest bit completely unnecessary reading between

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Speaker 4: the lines. Essentially, Apple's app store chief Phil Schiller and

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Speaker 4: other people at Apple, perhaps we're upset about Tim Sweeney's

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Speaker 4: tweets and response to the DMA rollout. That's Apple's way

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Speaker 4: of complying with the new E regulation. Sweeney tweeted that

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Speaker 4: Apple's response and their new policies are quote hot garbage.

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Speaker 4: He called it malicious compliance. He had a few other

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Speaker 4: choice words, or, as Phil Schiller called it in an

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Speaker 4: email to him, colorful language. And so Apple decided they're

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Speaker 4: going to yank Epic Game's new developer account in the

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Speaker 4: European Union in Sweden that would allow Epic Games to

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Speaker 4: roll out its own third party at marketplace for the

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Speaker 4: iPhone in the twenty seven countries that make up the EU.

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Speaker 4: Apple pulls that developer account. Now the EU is asking

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Speaker 4: more questions on the launch date for these new rules.

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Speaker 4: Not great, and this has the potential for the EU

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Speaker 4: to continue to find Apple. Apple I think, in the

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Speaker 4: mind of the EU probably doesn't have a great argument

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Speaker 4: for why they did this, pulling their account because they

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Speaker 4: didn't like their response on Twitter about this. It's not

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Speaker 4: a great look for Apple, I would say, and it's

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Speaker 4: a little childish on one hand. On the other hand,

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Speaker 4: you know, there's the perspective that Apple believes that Epic

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Speaker 4: Games is going to break the rules again and they

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Speaker 4: to protect their users. So certainly not a great development

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Speaker 4: for Apple.

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Speaker 2: And we've been hearing the course, Spotify also taking issue

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Speaker 2: with some of Apple's ways of working around the DMA

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Speaker 2: and feeling that they're still going to be charged an

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Speaker 2: untoward amount of money ultimately if you do get more

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Speaker 2: than million downloads on your apps. As you say, Apple

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Speaker 2: faces so many issues right now. And the key piece

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Speaker 2: of reporting, of course, that you brought us last week

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Speaker 2: was this almost ten billion dollar effort of a car

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Speaker 2: over years of expenses that then they've retracted on. They're

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Speaker 2: no longer going to be building the so called Apple car,

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Speaker 2: and they're putting the money into general to AI, you've

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Speaker 2: got a real deep dive on what really went wrong here.

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Speaker 3: Just take us through your big take today, Mark.

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Speaker 4: Yeah, I actually think that's why the stock has been

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Speaker 4: down lately. Initially you saw the stock jump up when

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Speaker 4: Apple pulled out of the car a week or so ago,

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Speaker 4: But now I think investors are realizing what's coming next.

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Speaker 4: Investors always want to buy into what the future of

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Speaker 4: the company is, and that future is not so clear,

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Speaker 4: and it's less clear now after pulling out of the

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Speaker 4: car project. They had gigantic ambitions to build essentially what

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Speaker 4: amounted to a private jet or living room on wheels.

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Speaker 4: You had club style seating, recliners, you had footrests, gigantic

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Speaker 4: TVs and state of the art audio system the ability

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Speaker 4: to watch TV and FaceTime and game from your car

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Speaker 4: while it's driving itself. From what I'm told, some of

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Speaker 4: the designs of the Apple car were absolutely beautiful. It

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Speaker 4: looked like a canoe EV with white wall tires, a

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Speaker 4: white exterior, dark black tinted windows that could adjust their

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Speaker 4: tint levels, like a Boeming Dreamline or airplane, some state

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Speaker 4: of the art autonomy systems. Everything in there was state

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Speaker 4: of the art. It looked nothing like you'd ever seen

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Speaker 4: on the road before in mass quantities. But there were

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Speaker 4: major problems from indecision on what to do, what levels

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Speaker 4: of autonomy to go for. From the very top of

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Speaker 4: the company. There were issues, of course about the technology

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Speaker 4: and when autonomy or full autonomy would be ready. There

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Speaker 4: were manufacturing concerns, competitive issues, and of course the real

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Speaker 4: nature of the competitive car business and the profitability of

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Speaker 4: what a car would bring and the challenges in that business.

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Speaker 4: But one thing I'll say, I spoke to someone involved

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Speaker 4: in the project yesterday, and you know, they really pushed

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Speaker 4: back on that idea of their being profitability concerns, which

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Speaker 4: there absolutely were, But this person disagreed with those concerns, saying,

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Speaker 4: you know what business is harder than the automotive business,

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Speaker 4: the consumer electronics business. And Apple's doing a spine there.

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Speaker 2: I said, Mark German, such great reporting. We thank you

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Speaker 2: for it. Let's switch gears a little bit now and

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Speaker 2: go to where the optimism in the market has been.

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Speaker 2: Chip makers, Broadcom earnings. They're coming after the bell today.

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Speaker 2: Investors are waiting with bait in breath. I want to

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Speaker 2: say if the continue is really going to be there

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Speaker 2: for the AI frenzy across hardware, across chip stocks. Most common,

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Speaker 2: Ryan Nicky is with us. We're pleased to welcome comment.

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Speaker 2: We are anticipating big numbers, are we.

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Speaker 5: Yeah, so investors are really looking at these earnings as

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Speaker 5: the next gut check for as you said, the chip

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Speaker 5: making stocks, the sort of hardware the pigs and shovels,

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Speaker 5: if you would call them that, of the AI rally.

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Speaker 5: And so what Wall Street is expecting here is for

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Speaker 5: Broadcom to report about eleven point eight billion in revenue

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Speaker 5: for the quarter, which is a thirty percent jump year

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Speaker 5: over year.

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Speaker 3: Most of that is from AI.

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Speaker 5: And what they're also looking for is that the total

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Speaker 5: chip sales, which in the last quarter was about fifteen

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Speaker 5: percent of total chip sales, will go to about twenty

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Speaker 5: five percent for the total year. So they'll be looking

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Speaker 5: at an update on that is their progress. Are we

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Speaker 5: moving from that fifteen percent number towards that twenty five

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Speaker 5: percent number. The stock is looking really good up into

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Speaker 5: the earnings. We're up about three percent today, twenty five

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Speaker 5: percent year to date, and fifty percent from its last

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Speaker 5: earnings release.

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Speaker 3: And this has been huge.

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Speaker 5: Broadcom is a little bit of an unsung AI hero,

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Speaker 5: I think, because it's the tenth biggest stock in the

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Speaker 5: S and P five hundred right now. It doesn't get

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Speaker 5: the same shine as the star of the show in video,

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Speaker 5: but it is in a similar realm. One thing investors

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Speaker 5: are also looking at here is that sort of unlike

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Speaker 5: in video or AMD, Broadcom is a little bit less

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Speaker 5: of an AI peer place. So though AI is the

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Speaker 5: biggest thing that investors will be looking at, there are

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Speaker 5: other pieces of the puzzle here. So on one hand,

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Speaker 5: some people say that could be really beneficial if AI

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Speaker 5: is indeed a bubble, there might be some insulation for

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Speaker 5: broadcome investors. But on the flip side, it could kind

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Speaker 5: of hold back some of the growth that some of

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Speaker 5: the AI names are seeing. So there's a lot that

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Speaker 5: investors will be looking at here. If the last release

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Speaker 5: was any indication of what's coming on the call and

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Speaker 5: the future guidance from CEO Hockey tand is going to

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Speaker 5: be really important. Last quarter, we actually shares dip a

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Speaker 5: little bit and then go back up when the CEO

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Speaker 5: said that AI is going to be a bright spot

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Speaker 5: for them going forward.

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Speaker 2: Tom and Ronicky will please to say you're going to

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Speaker 2: be all across the story when the numbers come out

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Speaker 2: after the bill.

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Speaker 3: We really appreciate you on the show. Thank you.

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Speaker 6: AI is going to create tremendous wealth and value shareholders,

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Speaker 6: for stakeholders, for employees of these companies, for the people

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Speaker 6: and the businesses that utilize AI. And we can't leave

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Speaker 6: behind women in minorities in this value creation moment.

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Speaker 2: Salesforce executive Claris she there on just how diverse workforces

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Speaker 2: are key to building artificial intelligence models and creating the

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Speaker 2: policies in fact, for responsible AI.

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Speaker 3: We want to continue that conversation. It's a hot topic.

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Speaker 7: We know.

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Speaker 2: Narna Sing's with us, founder and CEO of Credo AI

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Speaker 2: is a responsible AI governance company which allows businesses to

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Speaker 2: ensure that their AI is compliant, transparent, auditable. And my

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Speaker 2: perspective would be that your business is just up into

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Speaker 2: the right people desperate to get their governance in place.

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Speaker 8: Is that really happening, Caroline, Good to see you, you

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Speaker 8: know right now. Governance has a very interesting connotation in

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Speaker 8: the world of AI. Many see it as actually a

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Speaker 8: deterrent to innovation, but that's actually not the right way

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Speaker 8: to think about it. We have seen that organizations that

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Speaker 8: embed and start doing governance early on in the AI

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Speaker 8: journey are the ones who are.

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Speaker 3: Actually showing up a inner.

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Speaker 8: So I think this is a very interesting interplay between

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Speaker 8: governance and innovation, and I think we're going to see

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Speaker 8: that drastically changed this year.

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Speaker 3: What's interesting is, I mean the headlines keep coming.

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Speaker 2: I think in my colleague who's Jackiedavloss writing that there's

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Speaker 2: been sort of a whistleblower over at Microsoft at the

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Speaker 2: moment coming forward, saying that there are concerns within the

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Speaker 2: copilot of image generation with Dali, and they feel that

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Speaker 2: there's not enough governance in guardrails in place this safe

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Speaker 2: image generation when it comes to these large.

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Speaker 3: Language models, and we know what's happened.

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Speaker 2: With Gemini where almost to correct one bias, they ended

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Speaker 2: up bringing us completely ahistorical images. How could governance have

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Speaker 2: perhaps fixed this ahead of time before release?

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Speaker 3: Yeah, great question.

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Speaker 8: So you know, GENERA and AI by itself is a

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Speaker 8: very complex technology. One of the things that I think

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Speaker 8: most people don't understand is it's not just a technical problem.

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Speaker 8: It's a social technical set of problems that are getting

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Speaker 8: introduced in artificial intelligence. So what that rule means is

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Speaker 8: how could geminize problems be solved or what we are

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Speaker 8: seeing with Microsoft, it actually needs oversight across your organization.

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Speaker 8: It needs oversight across your systems, your AI models as

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Speaker 8: well as your data sets. But more importantly, it needs

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Speaker 8: stakeholder involvement. And that stakeholder involvement really comes from everyone

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Speaker 8: in the organization. Your risk and compliance and data science teams,

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Speaker 8: but also your users, right, So what are we actually

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Speaker 8: providing these A applications for and how these users are

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Speaker 8: getting impacted is really critical. And I think that's what's

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Speaker 8: really critical for organizations to understand is before putting systems

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Speaker 8: out in the market, not only thorough testing an evaluation,

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Speaker 8: but making sure the right diverse set of eyes have

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Speaker 8: been on those systems to make sure that they're actually

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Speaker 8: meeting the end goal is really critical.

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Speaker 2: Can you just tow me through it as if I

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Speaker 2: was a customer? Yeah, what do you come in and do?

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Speaker 2: How do I put this governance in place?

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Speaker 3: Yeah? Absolutely? So the key thing.

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Speaker 8: Is there are three core pillars of AI governance. First

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Speaker 8: and foremost is alignment, Carolyn, As you can imagine, what

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Speaker 8: does good look like for your organization really varies organization

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Speaker 8: by organization, and that's why alignment on what are you

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Speaker 8: going to measure, how often are you going to measure,

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Speaker 8: how are you going to report against that alignment is

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Speaker 8: really critical. So our software basically helps you with alignment

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Speaker 8: as a service as step one. Now, once you've aligned,

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Speaker 8: the second step is really critical is what are you testing?

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Speaker 8: And as I mentioned, given the sociotechnical nature of artificial intelligence.

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Speaker 8: You have to test everything from your technical AI systems. Two,

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Speaker 8: how your organization is set up to actually provide that

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Speaker 8: oversight and accountability. And then the last thing is how

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Speaker 8: do you actually make the outcomes understandable by the diverse stakeholders.

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Speaker 8: Not everyone is an AI expert, right, So how do

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Speaker 8: you actually create the rist boards, the transparency reports, the

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Speaker 8: audit reports so that all these different stakeholders can understand one,

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Speaker 8: what are the risk within your system? But more importantly,

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Speaker 8: what mitigations are you going to put in place and

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Speaker 8: the accountability structures you're going to put in place to

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Speaker 8: make sure this AI works for.

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Speaker 3: All We've always been here before.

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Speaker 2: You look at the eregulation coming in today is a

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Speaker 2: highlight that many regulators feel innovation was put before safety

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Speaker 2: or indeed level playing fields competition. When you're saying innovation

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Speaker 2: is being seen as the offset to guard rails, do

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Speaker 2: you think more regulation is.

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Speaker 3: Needed to ensure that companies are.

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Speaker 2: Using CREATIOAI are bringing in their guard rails.

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Speaker 8: At the same time, I would say that more intentional

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Speaker 8: regulation is needed. I think more guardrails and enforcement mechanisms

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Speaker 8: are needed. So as an example, just think about twenty

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Speaker 8: twenty four, two billion people across fifty countries are going

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Speaker 8: to be an election and they're going to be voting,

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Speaker 8: and majority of that is going to be powered by

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Speaker 8: artificial intelligence.

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Speaker 3: So when you have artificial law, is that majority going

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Speaker 3: to be powered by artificial intelligence.

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Speaker 8: Because they are going to get access to information, They're

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Speaker 8: going to get access to text media ads which are

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Speaker 8: all going to be built around artificial intelligence. And we've

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Speaker 8: seen examples of defix already showing up. Right, So the

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Speaker 8: key question that we should be asking ourselves is one

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Speaker 8: not only what the bills and policies are in place,

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Speaker 8: but how are they going to enforce it so that

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Speaker 8: a normal, you know, voter, a citizen who's actually going

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Speaker 8: and showing up to vote, actually has the right information

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Speaker 8: before they make that very important decision for our democracy.

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Speaker 2: We've already had an executive voter when it comes to AI.

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Speaker 2: You advise the White House on AI. And indeed, I'm

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Speaker 2: sure policymaking is government doing enough.

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Speaker 3: We're seeing Congress moves swiftly enough.

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Speaker 8: I would say this is the first time in the

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Speaker 8: past couple of decades we've actually seen government action trying

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Speaker 8: to keep pace with technology. But we are in a

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Speaker 8: very different era of technology innovation where it is moving

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Speaker 8: so fast, and so this is where adaptive policymaking becomes

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Speaker 8: really critical. And Executive Order is a fantastic example. There

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Speaker 8: were key goalposts that were put in place in Executive Order,

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Speaker 8: including OMB guidance. How does federal government procure third party

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Speaker 8: commercial AI? What are the safety standards that we need

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Speaker 8: to establish? NIST's role in the US Safety Institute. We

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Speaker 8: are a proud funding member, but I think there is

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Speaker 8: more work needs that needs to happen. For example, Congress

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Speaker 8: just passed the budget and this funding was.

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Speaker 3: Cut by twelve percent.

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Speaker 8: So what does that signal If AI safety and benchmarking

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Speaker 8: is critical, I think we need to show investment dollars

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Speaker 8: in those initiatives, and that's not happening.

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Speaker 3: I'm imagining you're a global company. Yes, what are other

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Speaker 3: companies other countries doing?

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Speaker 8: Yeah, you know you mentioned Europe EUAI Act goes into

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Speaker 8: effect this month, and that is a big step forward

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Speaker 8: for a country that is putting European citizens rights first

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Speaker 8: through a very risk base approach. The key is going

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Speaker 8: to be how is that going to be enforced over

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Speaker 8: the next sixteen months where each and every organization, multinational

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Speaker 8: organization will have to play by the EU rules. So

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Speaker 8: EUAI act is truly going to have that Brussels effect

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Speaker 8: that we saw with GDPR, but at a magnitude that

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Speaker 8: we can't even imagine. And this is the time that

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Speaker 8: organizations need to start getting ready for it.

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Speaker 3: Better thought, it are getting in ahead of time. Neverena

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Speaker 3: saying we thank you so much for lending us your expertise.

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Speaker 2: Today, CEO and founder of Credo Ai coming up. Sk Heinex,

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Speaker 2: it's investing more than a billion dollars in South Korea

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Speaker 2: this year to expand and improve the final steps in

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Speaker 2: its chip manufacturing. We're going to discuss why. That's also

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Speaker 2: an hang house story too. This is bringing back technology

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Speaker 2: time now for talking tech. Sk Heinex rumming up. It

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Speaker 2: spending on advanced chip packaging in hopes to capturing more

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Speaker 2: of the burgeoning demand for the crucial component in artificial

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Speaker 2: intelligence development, high bandwidth memory. Now, the firm is investing

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Speaker 2: more than a billion dollars in South Korea this year

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Speaker 2: to expand and improve the final steps of its chip manufacturing. Plus,

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Speaker 2: China's Foreign Minister Wang Yi called the level of trade

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Speaker 2: curbs that the US is imposed on the country, bewildering

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Speaker 2: as his President Biden's efforts to block Beijing from advanced

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Speaker 2: tech undermine a steadying of ties between the.

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Speaker 3: World's two largest economies. Meanwhile, the US government is.

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Speaker 2: Pressing its allies to further tighten those restrictions on China's

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Speaker 2: access to semiconductor technology.

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Speaker 3: The Byern administration's.

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Speaker 2: Latest push, it aims to plug holes and export controls

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Speaker 2: that it's levied over the past two years, basically to

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Speaker 2: restrain China's progress and developing domestic chip capabilities. Let's just

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Speaker 2: stick on well, the tension with US and China right now,

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Speaker 2: because today the House Energy and Commerce Committee plans to

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Speaker 2: act on legislation introduced by a bipartisan group of House

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Speaker 2: members that would give TikTok's Chinese parent Dance just one

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Speaker 2: hundred and sixty five days.

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Speaker 3: To sell it for more.

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Speaker 2: Let's bring in Bloomberg's Dan Flatley and Dan. That of course,

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Speaker 2: seems to be where we're seeing the push going get

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Speaker 2: rid of the Chinese ownership of TikTok.

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Speaker 9: Yeah, you know, we've sort of been here before. It's interesting,

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Speaker 9: this is the latest push and it does seem to

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Speaker 9: have some momentum behind it in the sense that you know,

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Speaker 9: there's a markup today as you mentioned in the House

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Speaker 9: Energy and Commerce Committee. There could be potentially some Senate action,

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Speaker 9: although we don't have any indication yet as to you know.

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Speaker 1: How that chamber will act.

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Speaker 9: But you know, this still has to go to the

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Speaker 9: House floor, it still has to go to the Senate potentially,

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Speaker 9: it still has to go to the President. So we're

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Speaker 9: a long way from from actually seeing this happen. And

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Speaker 9: you know, the TikTok has been under some form of

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Speaker 9: national security review since twenty eighteen, and Trump, when he

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Speaker 9: was president, tried to ban it in twenty twenty that

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Speaker 9: ended up in the courts, So you know, there's still

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Speaker 9: a lot of tape to play out on this, but

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Speaker 9: certainly some action plan today in the House.

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Speaker 2: Meanwhile, TikTok's response is, well, this is curbing free speech,

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Speaker 2: a right to access to such a platform. Do you

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Speaker 2: think that their argument carries weight?

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Speaker 8: Yeah.

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Speaker 9: I think the reason that they're making that argument is

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Speaker 9: because the First Amendment challenges are where they're on their

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Speaker 9: strongest legal footing. You know, there's broad exceptions and a

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Speaker 9: lot of US national security laws for free speech, and

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Speaker 9: so obviously that's where they intend to mount their challenge.

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Speaker 9: You know, the sponsors of this bill, or at least

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Speaker 9: one of them, Mike Gallagher, the chairman of the China

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Speaker 9: Committee here in DC, is saying this is not a band.

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Speaker 9: This is essentially just trying to excize the Chinese ownership

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Speaker 9: of this company. But you know that's in some ways

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Speaker 9: not really for him to say. I mean that the

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Speaker 9: courts will have their say on this at some point.

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Speaker 9: Certainly he has his argument to make, and he's making

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Speaker 9: it right.

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Speaker 2: Now down fatly brilliant that you bring us up to

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Speaker 2: speed with illegal ramifications. We thank you so much. Welcome

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Speaker 2: back to New Meg Technology. I'm Caroline Hyde right here

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Speaker 2: in San Francisco. Now it's twenty twenty three. Venture deal

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Speaker 2: making declined, you know that across the board for a

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Speaker 2: second con sective year.

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Speaker 3: It's pretty ugly out there.

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Speaker 2: Female founders, of course, they were impacted, but interestingly they

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Speaker 2: still manage to secure a record share of total deal

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Speaker 2: value as according to the latter's report in Female Founders in.

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Speaker 3: The VC ecosystem.

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Speaker 2: It's all put together by Pitchbook and they put the

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Speaker 2: data out today. Let's dig in with Amriy Donaghan just

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Speaker 2: the analyst behind this research. Oh, twenty eight percent doesn't

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Speaker 2: look too bad. But can you put that into actual numbers?

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Speaker 2: What sort of.

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Speaker 3: Money is being raised by female founders? Yeah, that's right.

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Speaker 10: We're looking at a population of companies that have grown

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Speaker 10: significantly over the past decade, raising tens of billions of

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Speaker 10: dollars collectively. But then when you can textualize that, look

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Speaker 10: at the amount of funding for companies that have no

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Speaker 10: women representation on the founding teams. You're seeing companies pull

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Speaker 10: in one hundreds.

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Speaker 3: Of billions of dollars.

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Speaker 10: So quite a bit of a difference there, but definitely

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Speaker 10: seeing some resilience among those companies in twenty twenty three,

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Speaker 10: as broader activity has come down, those companies are hanging

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Speaker 10: onto their share of the total.

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Speaker 3: Okay, So is there a number for that total?

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Speaker 10: Tens of billions in the network of thirty billion about okay,

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Speaker 10: So if we look at thirty billion, why is that

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Speaker 10: number heading up?

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Speaker 2: Is that because there's more just share a scale of

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Speaker 2: numbers of individual founders and individual companies coming for money,

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Speaker 2: or is it that the companies are starting to get

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Speaker 2: such a size that they're able to raise a decent clip.

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Speaker 10: Yeah, so we're seeing fewer companies engaging because the conditions

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Speaker 10: are so difficult. So it's not a matter of the

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Speaker 10: individual company numbers, it's more about the quality of those companies.

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Speaker 10: So standards are getting higher for investors. They're getting more

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Speaker 10: discerning with the amount of capital that they deployed to

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Speaker 10: certain companies. So we're seeing larger best position companies hang

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Speaker 10: on to that share, and when we look at female

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Speaker 10: founded companies, we see the proportion rise, and that's indicating

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Speaker 10: that those companies are already at such a high standard

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Speaker 10: that they're able to pull in additional capital and have

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Speaker 10: some resiliency on the downside there.

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Speaker 2: So I think some of the unicorns that were inked

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Speaker 2: very early in twenty twenty three that was sort of

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Speaker 2: maven for example, a healthcare company run like Kate Ryder

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Speaker 2: able to raise money. Is there a sector that does well?

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Speaker 2: I mean, I think of healthcare being a key area

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Speaker 2: of focus for female founders, or is this like more

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Speaker 2: broad Are we seeing the AI revolution being driven by

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Speaker 2: women too?

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Speaker 10: Of course, it would not be a conversation about VC

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Speaker 10: these days without mentioning AI. A lot of women really

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Speaker 10: at the forefront of that. Companies like Anthropics, SCALEAI creator

440
00:22:35,000 --> 00:22:38,119
Speaker 10: who we heard from earlier, really involved in this movement

441
00:22:38,160 --> 00:22:39,720
Speaker 10: for sure. We're seeing that's pretty much one of the

442
00:22:39,760 --> 00:22:42,680
Speaker 10: only verticals in twenty twenty three that managed to pull

443
00:22:42,680 --> 00:22:45,200
Speaker 10: in more capital than the year prior. So most other

444
00:22:45,240 --> 00:22:48,439
Speaker 10: sectors seeing a decline across the broader VC space, but

445
00:22:48,840 --> 00:22:51,880
Speaker 10: seeing some resiliency in healthcare, like you mentioned climate tech

446
00:22:51,920 --> 00:22:54,280
Speaker 10: as well, some areas that have been able to hang

447
00:22:54,320 --> 00:22:55,680
Speaker 10: on to that value.

448
00:22:55,840 --> 00:22:58,560
Speaker 2: It's interesting that you bring up Anthropic. We had done

449
00:22:58,600 --> 00:23:00,600
Speaker 2: in on the show just earlier. But she is a

450
00:23:00,640 --> 00:23:03,520
Speaker 2: co founder. Are you looking at numbers that they are

451
00:23:03,800 --> 00:23:06,440
Speaker 2: within the founding team diverse there is at least one

452
00:23:06,520 --> 00:23:08,800
Speaker 2: female founder, or is it more that they have to

453
00:23:08,800 --> 00:23:10,480
Speaker 2: solely be lit by women.

454
00:23:10,720 --> 00:23:12,440
Speaker 3: We look at both the main methodology.

455
00:23:12,440 --> 00:23:15,000
Speaker 10: We're looking at companies that have at least one female founder,

456
00:23:15,040 --> 00:23:17,320
Speaker 10: so they may have a male co founder, they may not.

457
00:23:17,920 --> 00:23:20,359
Speaker 10: We also look at a smaller section of that total,

458
00:23:20,400 --> 00:23:23,520
Speaker 10: which is just those companies with only women founders. That

459
00:23:23,560 --> 00:23:26,920
Speaker 10: population much smaller, if only pulling in about a single

460
00:23:26,920 --> 00:23:28,600
Speaker 10: digit percentage of the total dollars.

461
00:23:29,240 --> 00:23:30,960
Speaker 2: What about the people that they're sat on the other

462
00:23:31,000 --> 00:23:32,560
Speaker 2: side of the table to who are.

463
00:23:32,440 --> 00:23:33,520
Speaker 3: They going to for money?

464
00:23:33,560 --> 00:23:36,280
Speaker 2: Are they more diverse vcs that are tending to write

465
00:23:36,320 --> 00:23:37,360
Speaker 2: the checks right?

466
00:23:37,400 --> 00:23:39,520
Speaker 10: Well, that's a big part of the equation. You know,

467
00:23:39,560 --> 00:23:41,600
Speaker 10: when you have founders that are looking to enter the

468
00:23:41,680 --> 00:23:43,920
Speaker 10: space and break in, you need an investor that really

469
00:23:44,200 --> 00:23:47,480
Speaker 10: can see the value of that founder and the company itself.

470
00:23:47,880 --> 00:23:50,119
Speaker 3: So big part of the equation there when we look at.

471
00:23:49,960 --> 00:23:53,080
Speaker 10: The breakdown of gender check writers at BC firms, So

472
00:23:53,400 --> 00:23:55,560
Speaker 10: these are the people that ultimately determine whether or not

473
00:23:55,600 --> 00:23:58,320
Speaker 10: a company gets funded. We're looking at another area that's

474
00:23:58,359 --> 00:24:01,760
Speaker 10: heavily male dominated, about seventeen percent of checkerators or women

475
00:24:01,800 --> 00:24:04,159
Speaker 10: in the US. So in most cases you're going to

476
00:24:04,160 --> 00:24:06,720
Speaker 10: have a situation where as a female founder, you're pitching

477
00:24:06,720 --> 00:24:10,560
Speaker 10: to mostly men investors. So there are definitely some implications

478
00:24:10,560 --> 00:24:11,119
Speaker 10: there as well.

479
00:24:11,440 --> 00:24:13,720
Speaker 2: It's a great report, thanks for bringing it to us,

480
00:24:13,840 --> 00:24:16,560
Speaker 2: Amriy Donagan. Just of course a pitch book a research

481
00:24:16,640 --> 00:24:20,840
Speaker 2: analyst there. Next up we talk that AI flavor with Palenteer.

482
00:24:20,840 --> 00:24:32,480
Speaker 2: This is Blue med Technology.

483
00:24:37,400 --> 00:24:37,919
Speaker 3: Welcome back to.

484
00:24:37,880 --> 00:24:40,320
Speaker 2: Bluem Med Technology and Caroline Hide and San Francisco and

485
00:24:40,840 --> 00:24:43,480
Speaker 2: well we have got to get down to Palo Alto.

486
00:24:43,600 --> 00:24:47,280
Speaker 2: There's a big conversation being had around AI and Ed Ludlow.

487
00:24:47,320 --> 00:24:49,200
Speaker 3: You're sat there, how has it been going?

488
00:24:51,880 --> 00:24:54,800
Speaker 11: Well, We're at aip COM where the focus for Palenteer

489
00:24:55,000 --> 00:24:58,600
Speaker 11: is its commercial customers, but frankly, the near term catalyst

490
00:24:58,680 --> 00:25:02,080
Speaker 11: in this stock has been the contracts win around Titan.

491
00:25:02,520 --> 00:25:05,119
Speaker 11: I did sit down for an extended conversation with Alex

492
00:25:05,160 --> 00:25:08,600
Speaker 11: carp the palente CEO, where quite clearly he is showing

493
00:25:08,680 --> 00:25:12,679
Speaker 11: progress on going to a wider commercial customer base, but

494
00:25:12,760 --> 00:25:15,560
Speaker 11: also frustration about how business is done in this country.

495
00:25:15,840 --> 00:25:16,479
Speaker 8: Have listened.

496
00:25:18,800 --> 00:25:22,800
Speaker 1: I'm very exquisitely happy about how well we're doing, both

497
00:25:22,840 --> 00:25:26,199
Speaker 1: in the government and commercially. But the most important change

498
00:25:26,240 --> 00:25:28,120
Speaker 1: in the US government has nothing to do with pound here.

499
00:25:28,359 --> 00:25:31,280
Speaker 1: When we got to we started building this company, the

500
00:25:31,359 --> 00:25:35,280
Speaker 1: idea that software would power intelligence were fighting general health

501
00:25:35,280 --> 00:25:40,199
Speaker 1: issues was viewed as something esoteric, scandalous, obviously questionable. The

502
00:25:40,240 --> 00:25:44,520
Speaker 1: idea that America's primary advantage, premier advantage would be software

503
00:25:44,840 --> 00:25:49,240
Speaker 1: was viewed as also esoteric, academic, self serving, and every

504
00:25:49,240 --> 00:25:52,720
Speaker 1: institution in America and especially depending on have begun to

505
00:25:52,760 --> 00:25:56,600
Speaker 1: come to terms with the idea. The reality that hardware

506
00:25:57,000 --> 00:26:01,880
Speaker 1: driven systems purely are going inferior to software driven hardware systems.

507
00:26:02,080 --> 00:26:05,160
Speaker 1: And beyond that, our adversaries are as good or better

508
00:26:05,160 --> 00:26:08,800
Speaker 1: at building hardware systems and have a deficit in building software.

509
00:26:08,880 --> 00:26:12,000
Speaker 11: What is different about Titan as compared to say, Maven,

510
00:26:12,480 --> 00:26:16,280
Speaker 11: is that you are entering new relationships with other hardware providers,

511
00:26:16,359 --> 00:26:20,159
Speaker 11: right like Anderil is one example. Explain how that is

512
00:26:20,240 --> 00:26:21,280
Speaker 11: working in this case.

513
00:26:21,440 --> 00:26:25,320
Speaker 1: Actually, I see this as a commonality. America needs to

514
00:26:25,480 --> 00:26:30,560
Speaker 1: establish dominance on the battlefield. Maven. What's publicly known about

515
00:26:30,560 --> 00:26:33,800
Speaker 1: Mavin is one of these projects that actually took what

516
00:26:33,880 --> 00:26:37,040
Speaker 1: America is the best at in the world software and

517
00:26:37,119 --> 00:26:39,280
Speaker 1: put it in the hands of our warfighter. By the way,

518
00:26:39,480 --> 00:26:42,240
Speaker 1: at enormous costs. You're sitting in pal Alto. I had

519
00:26:42,240 --> 00:26:46,520
Speaker 1: people protesting here, hundreds, putting up change in front of

520
00:26:46,560 --> 00:26:50,040
Speaker 1: our office, calling us Nazis because we were dedicated to

521
00:26:50,119 --> 00:26:52,920
Speaker 1: serving the American people, because we had the sense God

522
00:26:52,960 --> 00:26:54,679
Speaker 1: gave a goat and we realized that if you're going

523
00:26:54,720 --> 00:26:57,160
Speaker 1: to do really important things in this country, you should

524
00:26:57,200 --> 00:27:00,560
Speaker 1: defend this country with every asset we have. What really

525
00:27:00,600 --> 00:27:03,800
Speaker 1: happened on the Silicon Valley side is that you've got

526
00:27:03,880 --> 00:27:06,000
Speaker 1: because of that success, because of the power of it,

527
00:27:06,040 --> 00:27:09,239
Speaker 1: and quite frankly, because of our success, people realize this

528
00:27:09,280 --> 00:27:12,000
Speaker 1: is a place where you should invest and make America

529
00:27:12,040 --> 00:27:14,840
Speaker 1: even stronger. And then what happened is you've got a

530
00:27:14,880 --> 00:27:19,600
Speaker 1: whole ecosystem of defense startups and an ecosystem of people

531
00:27:19,600 --> 00:27:22,439
Speaker 1: inside the Pentagon who are ready to embrace that that

532
00:27:22,480 --> 00:27:25,359
Speaker 1: are doing things, by the way, that are very similar

533
00:27:25,359 --> 00:27:27,200
Speaker 1: to what's happening in the commercial space. And what are

534
00:27:27,240 --> 00:27:29,600
Speaker 1: those things. We're going to look at software, not off

535
00:27:29,640 --> 00:27:31,480
Speaker 1: power points. We're going to look at We're going to

536
00:27:31,480 --> 00:27:34,479
Speaker 1: buy software from people have actually sold software commercially. And

537
00:27:34,520 --> 00:27:37,960
Speaker 1: what's unique about Titan is not the difference. It's that

538
00:27:38,240 --> 00:27:40,960
Speaker 1: it's the logical extension. And what is that logical extension?

539
00:27:41,320 --> 00:27:43,840
Speaker 1: People who've built software products that have been used on

540
00:27:43,840 --> 00:27:47,359
Speaker 1: the battlefield and used commercially. You have to ask yourself

541
00:27:47,400 --> 00:27:50,280
Speaker 1: a question. If your software is so good, why have

542
00:27:50,359 --> 00:27:53,080
Speaker 1: you not sold it commercially and made yourself billions of dollars.

543
00:27:53,280 --> 00:27:56,960
Speaker 1: So that simple insight, which you see in the battlefield

544
00:27:57,000 --> 00:27:59,600
Speaker 1: in Ukraine, which you see in Israel, is something that

545
00:27:59,800 --> 00:28:04,479
Speaker 1: is hard for institutions to internalize. The Pentagon. This step

546
00:28:04,560 --> 00:28:06,679
Speaker 1: is one of the most historic steps ever because what

547
00:28:06,760 --> 00:28:09,600
Speaker 1: it basically says is we're going to fight for real.

548
00:28:09,800 --> 00:28:11,800
Speaker 1: We're going to put the best on the battlefield. And

549
00:28:11,840 --> 00:28:13,399
Speaker 1: what is the best. The best is not just some

550
00:28:13,760 --> 00:28:17,760
Speaker 1: not one company. It's a team of people led by

551
00:28:18,040 --> 00:28:21,760
Speaker 1: the most prominent software provider in defense in the world, talented.

552
00:28:21,800 --> 00:28:24,680
Speaker 11: If there's something you said there that inside the Pentsagon

553
00:28:24,760 --> 00:28:28,040
Speaker 11: people are ready for this. Bloomberg did some quite deep

554
00:28:28,080 --> 00:28:32,240
Speaker 11: reporting on the use of Maven specifically in twenty twenty

555
00:28:32,280 --> 00:28:35,840
Speaker 11: four so far, and the complaint from operators in the

556
00:28:35,880 --> 00:28:39,080
Speaker 11: context that it's used for targeting is that it's still

557
00:28:39,120 --> 00:28:42,200
Speaker 11: not quite there. It's still this is I'm just offering

558
00:28:42,200 --> 00:28:43,640
Speaker 11: you an opportunity to respond to it.

559
00:28:43,640 --> 00:28:43,760
Speaker 4: Now.

560
00:28:43,800 --> 00:28:45,680
Speaker 1: I'm not going to respond because I'd have to tell

561
00:28:45,680 --> 00:28:48,400
Speaker 1: you all sorts of things. There is no one. By

562
00:28:48,440 --> 00:28:50,680
Speaker 1: the way, there was a long and very important article.

563
00:28:50,680 --> 00:28:52,800
Speaker 1: Everyone should read it. What the way I read the

564
00:28:52,880 --> 00:28:55,360
Speaker 1: article was, this is the most important thing, one of

565
00:28:55,400 --> 00:28:57,800
Speaker 1: the most important things the Pentagon has done in decades.

566
00:28:58,280 --> 00:29:00,640
Speaker 1: I can tell you the way our adversaries ma even

567
00:29:00,920 --> 00:29:03,800
Speaker 1: and our friends is like, what the f how do

568
00:29:03,960 --> 00:29:06,120
Speaker 1: they actually produce this? And I tell you what the

569
00:29:06,160 --> 00:29:10,120
Speaker 1: average citizen read O recurs like, thank God, we're spending

570
00:29:10,120 --> 00:29:12,560
Speaker 1: the money on things that are more valuable than what

571
00:29:12,640 --> 00:29:15,600
Speaker 1: we're investing in. And to go into more detail, if

572
00:29:15,600 --> 00:29:18,360
Speaker 1: to go to all sorts of classiz, that program is

573
00:29:18,400 --> 00:29:20,560
Speaker 1: one of the shining stars of what this country has

574
00:29:20,600 --> 00:29:24,040
Speaker 1: done and serves as a template for we're going on

575
00:29:24,080 --> 00:29:26,680
Speaker 1: the offense. We are going to shirve dominance, and we're

576
00:29:26,680 --> 00:29:28,240
Speaker 1: going to negotiate after we're the best.

577
00:29:28,600 --> 00:29:30,720
Speaker 11: Alex I host the Technology Show, and I want to

578
00:29:30,760 --> 00:29:34,680
Speaker 11: talk about the technology, its current capabilities, and where it

579
00:29:34,680 --> 00:29:39,480
Speaker 11: can go. Is that platform ready to move from assisting

580
00:29:39,520 --> 00:29:44,680
Speaker 11: in targeting, which is intelligence basically, to giving more information.

581
00:29:44,800 --> 00:29:48,200
Speaker 11: The artic will also look to the idea that there

582
00:29:48,280 --> 00:29:50,720
Speaker 11: is a hope from intelligence services in the US government

583
00:29:50,760 --> 00:29:53,600
Speaker 11: that it can be one day in a position to

584
00:29:53,640 --> 00:29:58,680
Speaker 11: recommend which weapon to use to give more tactical.

585
00:29:58,440 --> 00:30:00,360
Speaker 1: Let me give you, let me give you commercial examples.

586
00:30:00,360 --> 00:30:02,800
Speaker 1: Because I can't I can't go into I can't go

587
00:30:02,880 --> 00:30:04,360
Speaker 1: into what it can do and what I can't do.

588
00:30:04,400 --> 00:30:06,600
Speaker 1: I can tell you what we're doing commercially. Right now,

589
00:30:06,680 --> 00:30:10,480
Speaker 1: you are going to see a normal non engineer sitting

590
00:30:10,680 --> 00:30:16,600
Speaker 1: at their terminal tasking satellites, exporting a logic inside the

591
00:30:16,680 --> 00:30:21,080
Speaker 1: security model of the company to figure out which satellite

592
00:30:21,080 --> 00:30:25,080
Speaker 1: should be over which part of their agricultural assets, and

593
00:30:25,120 --> 00:30:28,240
Speaker 1: what should happen based on weather conditions. Now you can

594
00:30:28,400 --> 00:30:31,280
Speaker 1: just imagine how you could do that with a weapon system.

595
00:30:31,320 --> 00:30:36,120
Speaker 1: This is exactly what palach your commercial not Palenteer highly

596
00:30:36,160 --> 00:30:40,520
Speaker 1: classified environment, Palenteer with somebody that has been hired five

597
00:30:40,600 --> 00:30:44,080
Speaker 1: days ago, that can't write code, that's very smart, may

598
00:30:44,120 --> 00:30:46,760
Speaker 1: not speak English, and as just enter the enterprise is

599
00:30:46,800 --> 00:30:50,120
Speaker 1: doing that workflow that is happening right now, and that

600
00:30:50,280 --> 00:30:54,880
Speaker 1: is why the thing that this revolution which is highly confusing.

601
00:30:54,920 --> 00:30:58,320
Speaker 1: It's highly confusing. Yeah, it's confusing because a lot of

602
00:30:58,320 --> 00:31:01,000
Speaker 1: the stuff is BS. Then there's the poetry side of it.

603
00:31:01,080 --> 00:31:03,680
Speaker 1: I love poetry. If I could go read more poetry,

604
00:31:03,720 --> 00:31:07,360
Speaker 1: I would. Enterprises don't need more poth Yes, you mean, well,

605
00:31:07,400 --> 00:31:10,320
Speaker 1: it's like I don't know somebody's delivers PowerPoint. We're gonna

606
00:31:10,360 --> 00:31:12,880
Speaker 1: give you a you know. It's like, look, everybody has

607
00:31:12,920 --> 00:31:14,560
Speaker 1: to try to sell something. If you don't have something

608
00:31:14,560 --> 00:31:17,440
Speaker 1: to sell, you sell words. Right now, so you're selling

609
00:31:17,520 --> 00:31:19,760
Speaker 1: something that doesn't work. Can't work. You're explaining to your

610
00:31:19,840 --> 00:31:22,120
Speaker 1: enterprise you can't have the car you want, which is

611
00:31:22,160 --> 00:31:24,160
Speaker 1: honestly pound here. But you can have the car you

612
00:31:24,160 --> 00:31:26,360
Speaker 1: don't want because this and this and this and this,

613
00:31:26,400 --> 00:31:26,760
Speaker 1: and you have.

614
00:31:26,720 --> 00:31:27,000
Speaker 3: To buy it.

615
00:31:27,040 --> 00:31:29,400
Speaker 1: And that's that's by the way, that that is a

616
00:31:29,440 --> 00:31:32,480
Speaker 1: plague on many societies, less so America. There is this

617
00:31:32,560 --> 00:31:35,960
Speaker 1: problem in Europe that there's really no high end software venders.

618
00:31:36,200 --> 00:31:38,120
Speaker 1: Luckily our adversaries have this problem.

619
00:31:38,200 --> 00:31:41,160
Speaker 11: And you've spoken about your frustrations with Europe not being

620
00:31:41,320 --> 00:31:42,760
Speaker 11: more adopted.

621
00:31:42,960 --> 00:31:44,960
Speaker 1: Well, I'm pro I spent half my life in Europe.

622
00:31:45,000 --> 00:31:47,760
Speaker 1: I want the West to win, so I want. But

623
00:31:47,800 --> 00:31:50,400
Speaker 1: it's a confusing revolution. If you're sitting there and you're

624
00:31:50,440 --> 00:31:53,800
Speaker 1: sitting in society that's lead industrial evolutions for hundreds of years,

625
00:31:53,880 --> 00:31:56,080
Speaker 1: and all of a sudden, the industrial evolution is happening

626
00:31:56,120 --> 00:31:59,080
Speaker 1: basically in one place, and that's right here. That's confusing.

627
00:31:59,280 --> 00:32:02,160
Speaker 1: It's confusing because three vendors are saying they're going to

628
00:32:02,160 --> 00:32:04,400
Speaker 1: offer the same thing. One thing is like, you know,

629
00:32:04,480 --> 00:32:06,040
Speaker 1: I'm going to explain to you why it doesn't work

630
00:32:06,080 --> 00:32:08,160
Speaker 1: you have to buy or bs thing. The others like, oh,

631
00:32:08,160 --> 00:32:10,320
Speaker 1: it does work, but it's only poetry. And then there's

632
00:32:10,360 --> 00:32:13,400
Speaker 1: a third category, which judges by the fruits we provide,

633
00:32:13,400 --> 00:32:15,600
Speaker 1: which is exactly what we're doing, which is like, great,

634
00:32:15,680 --> 00:32:18,040
Speaker 1: We're not going to argue about this part of our product.

635
00:32:18,040 --> 00:32:19,720
Speaker 1: That part of our product. I'm happy to explain it

636
00:32:19,720 --> 00:32:21,880
Speaker 1: to somebody who's technical. We're going to show you what

637
00:32:22,040 --> 00:32:25,640
Speaker 1: happens in four to six hours as opposed to what

638
00:32:25,680 --> 00:32:28,600
Speaker 1: happened in your whole enterprise over the last two years.

639
00:32:28,640 --> 00:32:31,040
Speaker 11: We will talk about the commercial business, we will talk

640
00:32:31,080 --> 00:32:33,160
Speaker 11: about bootcoms, but let me just say.

641
00:32:33,080 --> 00:32:34,480
Speaker 1: I know we can talk about whatever you want.

642
00:32:35,280 --> 00:32:38,560
Speaker 11: A final point, you talked about the confusion of the revolution. Okay,

643
00:32:39,200 --> 00:32:43,120
Speaker 11: Today probably will be the first time that a president

644
00:32:43,440 --> 00:32:47,400
Speaker 11: says artificial intelligence in a State of the Union speech.

645
00:32:47,760 --> 00:32:50,160
Speaker 11: So it's a very simple question, what is your summary

646
00:32:50,640 --> 00:32:55,200
Speaker 11: of this administration's leadership so to speak of the US

647
00:32:55,240 --> 00:32:56,240
Speaker 11: in the context of AI.

648
00:32:57,160 --> 00:32:59,680
Speaker 1: You know, it's very helpful if you spend a lot

649
00:32:59,680 --> 00:33:02,400
Speaker 1: of time abroad, because like, if you look at this internally,

650
00:33:02,520 --> 00:33:05,320
Speaker 1: like internally in America, there's a long list of criticisms

651
00:33:05,520 --> 00:33:09,200
Speaker 1: that you could make of anyone. This country is the

652
00:33:09,280 --> 00:33:12,760
Speaker 1: dominant country with no second country in the world. So

653
00:33:12,960 --> 00:33:16,840
Speaker 1: whatever we're doing, it's working out pretty damn well. So

654
00:33:16,880 --> 00:33:19,160
Speaker 1: it's like, you know, yeah, could we be better, could

655
00:33:19,160 --> 00:33:21,520
Speaker 1: we have better regulation, could we understand these things better?

656
00:33:21,600 --> 00:33:25,280
Speaker 1: But again, we are dealing with a revolution. That's one

657
00:33:25,320 --> 00:33:28,040
Speaker 1: of the really confusing things again for Americans is like,

658
00:33:28,320 --> 00:33:31,440
Speaker 1: normally you have a revolution and multiple countries are participating.

659
00:33:31,600 --> 00:33:34,280
Speaker 1: This is a revolution where the technology is pretty being

660
00:33:34,280 --> 00:33:36,520
Speaker 1: produced in America, mostly in Silicon Valle.

661
00:33:36,680 --> 00:33:39,160
Speaker 11: You do have multiple customers. To just bear with me

662
00:33:39,240 --> 00:33:41,880
Speaker 11: on this one. Take for example, Israel, where you are

663
00:33:41,880 --> 00:33:46,480
Speaker 11: doing some work with that country. The administration as an example,

664
00:33:46,600 --> 00:33:49,240
Speaker 11: is pushing for a ceasefire in that region, but you

665
00:33:49,320 --> 00:33:52,400
Speaker 11: are working with Israel. How do you manage that? Because

666
00:33:52,400 --> 00:33:55,920
Speaker 11: it sounds like your first priority is the United States?

667
00:33:57,440 --> 00:34:00,240
Speaker 1: How do we manage Look, we I'm very happy, very

668
00:34:00,240 --> 00:34:04,200
Speaker 1: happily supply our products to our allies, including Israel. I

669
00:34:04,240 --> 00:34:07,920
Speaker 1: don't like Israel. What's going on here is does America

670
00:34:07,960 --> 00:34:10,040
Speaker 1: provide Israel with more aid? I don't think there's any

671
00:34:10,120 --> 00:34:12,160
Speaker 1: question of does Israel have the right to buy the

672
00:34:12,160 --> 00:34:15,960
Speaker 1: world's best technologies, assess them, and implement them. Israel, I

673
00:34:15,960 --> 00:34:19,000
Speaker 1: think has decided we have some of the world's best technologies.

674
00:34:19,280 --> 00:34:22,799
Speaker 1: They've implemented many of them and publicly discussed some of them,

675
00:34:23,080 --> 00:34:26,920
Speaker 1: and I will palent here. I think the really orthogonal

676
00:34:26,960 --> 00:34:29,000
Speaker 1: may be more question was why do we say in

677
00:34:29,080 --> 00:34:32,920
Speaker 1: public what everyone else believes in private? We should defend

678
00:34:32,960 --> 00:34:35,680
Speaker 1: the West, we should not apologize for fighting terrorism, and

679
00:34:35,719 --> 00:34:38,080
Speaker 1: we are going to provide our sharp tools to our allies.

680
00:34:38,520 --> 00:34:39,960
Speaker 1: Let's talk about the commercial business.

681
00:34:40,040 --> 00:34:43,359
Speaker 11: Okay, you told my colleague Lazett Chapman one month ago,

682
00:34:43,400 --> 00:34:46,680
Speaker 11: almost of the day quote, we don't know what to

683
00:34:46,719 --> 00:34:49,680
Speaker 11: do with the onslaught of demand in the commercial context.

684
00:34:49,680 --> 00:34:52,080
Speaker 11: Do you know one month on what to do now?

685
00:34:52,600 --> 00:34:52,680
Speaker 3: No?

686
00:34:53,080 --> 00:34:54,920
Speaker 1: I mean, if you're going to see a boot camp

687
00:34:55,120 --> 00:34:57,480
Speaker 1: here a series of things we've had to you know,

688
00:34:57,520 --> 00:34:59,439
Speaker 1: we haven't been able to meet demand. We've had to

689
00:34:59,480 --> 00:35:03,120
Speaker 1: tell people we couldn't accommodate them. We have hundreds of

690
00:35:03,160 --> 00:35:05,920
Speaker 1: people coming, not just people but leaders of industry. And

691
00:35:06,040 --> 00:35:08,320
Speaker 1: if you just look at it from the internal dynamics

692
00:35:08,320 --> 00:35:10,320
Speaker 1: of how do you deal with the contracting, how do

693
00:35:10,360 --> 00:35:13,080
Speaker 1: you deal with the implementation, It's true these things have

694
00:35:13,160 --> 00:35:15,520
Speaker 1: gone from taking us three months to four hours.

695
00:35:16,040 --> 00:35:18,200
Speaker 11: But it's also true, but the idea is you cram

696
00:35:18,320 --> 00:35:20,480
Speaker 11: four months work worth of work into the day in

697
00:35:20,520 --> 00:35:21,320
Speaker 11: these boot camps.

698
00:35:21,360 --> 00:35:25,000
Speaker 1: Right, It is not even a day. It's hours and

699
00:35:25,040 --> 00:35:25,920
Speaker 1: so and so.

700
00:35:26,040 --> 00:35:29,799
Speaker 11: Why why is that importance, Palenteer, Why did you go

701
00:35:29,920 --> 00:35:30,720
Speaker 11: down that route?

702
00:35:30,800 --> 00:35:33,319
Speaker 1: Well, the most important reason it's important to palent here

703
00:35:33,440 --> 00:35:35,960
Speaker 1: is we can fight with people about power points and

704
00:35:36,000 --> 00:35:37,520
Speaker 1: their ability to do Why.

705
00:35:37,480 --> 00:35:39,719
Speaker 11: Do you keep bringing up powerpoints? Is the point you're

706
00:35:39,760 --> 00:35:42,640
Speaker 11: making that your competitors go in with a debt. Absolutely,

707
00:35:42,680 --> 00:35:44,640
Speaker 11: this is what we'll do. But they don't have a product.

708
00:35:45,840 --> 00:35:48,080
Speaker 1: Well, I'm not saying anything. What I'm really saying is

709
00:35:48,120 --> 00:35:49,719
Speaker 1: if you have it, what did you said, power co

710
00:35:49,840 --> 00:35:52,360
Speaker 1: It's many times exactly. So what I'm telling to everyone

711
00:35:52,400 --> 00:35:55,640
Speaker 1: there is like they may have a product, we're showing

712
00:35:55,680 --> 00:35:57,960
Speaker 1: you our product. Okay, I can't comment about where they

713
00:35:57,960 --> 00:36:00,520
Speaker 1: have a product. I can't tell you they're bare buttoned

714
00:36:00,600 --> 00:36:03,560
Speaker 1: up and not showing any leg We show our product.

715
00:36:03,840 --> 00:36:06,040
Speaker 1: And why is it important? Yes, why it's important. I'm

716
00:36:06,040 --> 00:36:08,640
Speaker 1: telling you why it's important because a people fight us,

717
00:36:08,840 --> 00:36:11,160
Speaker 1: or fight enterprises that are doing the most important work

718
00:36:11,360 --> 00:36:14,480
Speaker 1: with slick power points and great steak dinners. We're bad

719
00:36:14,520 --> 00:36:16,760
Speaker 1: at slick power points or even worse at steak dinners.

720
00:36:17,040 --> 00:36:19,440
Speaker 1: We don't play golf. What we do do is we

721
00:36:19,480 --> 00:36:23,040
Speaker 1: play software. We will put if you want to actually compete,

722
00:36:23,239 --> 00:36:26,919
Speaker 1: compete on your product and what's very special? And yes,

723
00:36:26,960 --> 00:36:29,880
Speaker 1: do I enjoy humiliating people who have better steak dinners

724
00:36:29,920 --> 00:36:32,239
Speaker 1: and sharp ourn eyes and better at golf? So yes,

725
00:36:32,280 --> 00:36:34,440
Speaker 1: I do you know what, I really I really like

726
00:36:34,520 --> 00:36:36,480
Speaker 1: that we win in that way. It makes me very happy,

727
00:36:36,560 --> 00:36:39,319
Speaker 1: and it makes our clients happy because let me sorry,

728
00:36:39,400 --> 00:36:40,040
Speaker 1: let me finish.

729
00:36:39,840 --> 00:36:42,840
Speaker 11: Saying, but we're getting closed great well the people.

730
00:36:43,080 --> 00:36:46,959
Speaker 1: And why are our clients happy Because American industry knows

731
00:36:46,960 --> 00:36:49,560
Speaker 1: that this is a structural advantage and that needs the

732
00:36:49,600 --> 00:36:52,920
Speaker 1: best products. And why is the boot camp overrun? Because

733
00:36:53,080 --> 00:36:55,880
Speaker 1: the clients themselves are tired of these damn steak dinners

734
00:36:56,040 --> 00:36:58,919
Speaker 1: and the golf. They want to see products that actually work,

735
00:36:59,120 --> 00:37:01,200
Speaker 1: that actually live up to what people are saying. What

736
00:37:01,640 --> 00:37:04,440
Speaker 1: are people saying? You will transform your enterprise. You'll make

737
00:37:04,480 --> 00:37:06,640
Speaker 1: it cheaper to run your enterprise, that you'll make it

738
00:37:06,680 --> 00:37:08,959
Speaker 1: safer to run the enterprise. You'll be able to track

739
00:37:09,000 --> 00:37:11,400
Speaker 1: what you're doing, and you'll be able to uplift workers

740
00:37:11,440 --> 00:37:13,920
Speaker 1: who formerly only could be engineers, and now they can

741
00:37:14,000 --> 00:37:16,200
Speaker 1: be everywhere. And by the way, you can do all

742
00:37:16,280 --> 00:37:19,080
Speaker 1: this in America. You can manufacture like you or manufacturing

743
00:37:19,160 --> 00:37:22,279
Speaker 1: Japan and Taiwan. In America, you can use workers that

744
00:37:22,440 --> 00:37:25,000
Speaker 1: used to have to be engineers right here in this country.

745
00:37:25,280 --> 00:37:27,959
Speaker 1: And why is it? It's also fun for Poundier because

746
00:37:28,000 --> 00:37:30,560
Speaker 1: we are winning, Alex, I've got to ask you before

747
00:37:30,560 --> 00:37:31,240
Speaker 1: I lose you.

748
00:37:31,520 --> 00:37:34,480
Speaker 11: Actually, the most common question that I get to ask

749
00:37:34,520 --> 00:37:37,080
Speaker 11: you from the audience I post on social media coming

750
00:37:37,120 --> 00:37:40,759
Speaker 11: on is when will there be a direct to consumer

751
00:37:41,320 --> 00:37:44,360
Speaker 11: or a I don't even know what we would call it,

752
00:37:44,080 --> 00:37:48,359
Speaker 11: but a publicly available version of AIP And if that would, let.

753
00:37:48,280 --> 00:37:48,839
Speaker 3: Me tell you.

754
00:37:48,880 --> 00:37:51,240
Speaker 11: Because they see you as a leader in the space

755
00:37:51,440 --> 00:37:55,480
Speaker 11: the Palenteer, not necessarily you as an individual whatever.

756
00:37:55,520 --> 00:37:59,719
Speaker 1: They see this happy But okay, look, Palier, you are

757
00:37:59,760 --> 00:38:02,000
Speaker 1: seeing the tip of the iceberg when you are buying

758
00:38:02,000 --> 00:38:04,120
Speaker 1: our product. Now we've been working on these things.

759
00:38:04,120 --> 00:38:06,279
Speaker 11: You can't buy it if you're a person off the street.

760
00:38:06,760 --> 00:38:08,600
Speaker 1: You are seeing the tip of the iceberg of our

761
00:38:08,600 --> 00:38:11,200
Speaker 1: product development. And we are going to show more and

762
00:38:11,239 --> 00:38:13,520
Speaker 1: more and more of what we have and I think people,

763
00:38:13,680 --> 00:38:15,239
Speaker 1: and I would also like a lot of those people

764
00:38:15,320 --> 00:38:17,359
Speaker 1: asking the question, by the way, are non academic. They

765
00:38:17,360 --> 00:38:20,439
Speaker 1: are investors in Palenteer and they have supported us when

766
00:38:20,440 --> 00:38:22,080
Speaker 1: we were down on the ropes, and those are the

767
00:38:22,120 --> 00:38:23,239
Speaker 1: people that we are fighting for.

768
00:38:24,239 --> 00:38:28,400
Speaker 2: Anyone else thinking about steak Dinners right now? Talenteer CEO

769
00:38:28,440 --> 00:38:32,560
Speaker 2: co founder Alex Kupp what an interview ed Ludlowe.

770
00:38:32,800 --> 00:38:34,560
Speaker 3: Great work. Meanwhile, look, we've.

771
00:38:34,440 --> 00:38:37,160
Speaker 2: Got to pivot and think about AI and ultimately getting

772
00:38:37,200 --> 00:38:40,960
Speaker 2: into the hands of customers right more, How are people.

773
00:38:40,760 --> 00:38:41,759
Speaker 3: Using generative AI?

774
00:38:42,120 --> 00:38:45,560
Speaker 2: Amazon Web Services has just launched a new GENI competency

775
00:38:45,600 --> 00:38:49,279
Speaker 2: program for partners, aiming to basically help customers figure out

776
00:38:49,440 --> 00:38:52,320
Speaker 2: which partner to use, what product to be actually using

777
00:38:52,400 --> 00:38:56,719
Speaker 2: within their business and have generative AI suit them and

778
00:38:56,800 --> 00:38:58,080
Speaker 2: not just something that they aspire to.

779
00:38:58,200 --> 00:38:59,919
Speaker 3: For more about this program and what it means.

780
00:39:00,080 --> 00:39:03,200
Speaker 2: It's launch partners including the likes of Accentiakahir, Moga, dB,

781
00:39:03,440 --> 00:39:05,880
Speaker 2: Hugging Face, and Video to name but a few, us

782
00:39:05,920 --> 00:39:10,000
Speaker 2: bringing Amazon Web Services Vice President Worldwide Channels and Alliances

783
00:39:10,280 --> 00:39:10,920
Speaker 2: Rubert Borno.

784
00:39:11,360 --> 00:39:12,680
Speaker 3: Rubert I love.

785
00:39:12,560 --> 00:39:16,080
Speaker 2: This focus basically on the fat that you're saying.

786
00:39:15,840 --> 00:39:18,480
Speaker 3: This can't be an aspiration. People are going to be able.

787
00:39:18,280 --> 00:39:21,200
Speaker 2: To use it practically in their businesses. How are they

788
00:39:21,320 --> 00:39:24,680
Speaker 2: using aws and the offerings of large language models.

789
00:39:24,719 --> 00:39:26,480
Speaker 3: You have to change their businesses.

790
00:39:27,760 --> 00:39:30,040
Speaker 7: Caroline First of all, thank you so much for having

791
00:39:30,040 --> 00:39:32,640
Speaker 7: me today. I'm really excited to be coming to you

792
00:39:32,719 --> 00:39:35,920
Speaker 7: live from Seattle, where we're hosting two hundred of a

793
00:39:36,200 --> 00:39:41,160
Speaker 7: WUS's most strategic partners, and we've launched just this week,

794
00:39:41,320 --> 00:39:44,640
Speaker 7: the Generative AI Competency for our partners that you mentioned.

795
00:39:44,880 --> 00:39:47,880
Speaker 7: I'm so excited that we have over forty five launch

796
00:39:47,960 --> 00:39:51,439
Speaker 7: partners and they're doing exactly what you talked about, which

797
00:39:51,480 --> 00:39:56,280
Speaker 7: is demonstrating technical expertise and generative AI technologies. We're talking

798
00:39:56,280 --> 00:40:00,480
Speaker 7: about technologies like Amazon Bedrock, Amazon Stagemaker, and joh Start

799
00:40:00,480 --> 00:40:01,200
Speaker 7: in Amazon Q.

800
00:40:01,800 --> 00:40:02,920
Speaker 4: And why is that important?

801
00:40:03,280 --> 00:40:08,279
Speaker 7: Because these partners have demonstrated not only experience and knowledge

802
00:40:08,280 --> 00:40:13,120
Speaker 7: of those technologies, but customer success, customer success with multiple

803
00:40:13,200 --> 00:40:17,400
Speaker 7: production ready use cases. So we're excited to showcase that

804
00:40:17,440 --> 00:40:20,960
Speaker 7: to customers because customers right now are thinking, I've heard

805
00:40:21,000 --> 00:40:24,360
Speaker 7: about generator AI. I know it can transform my business,

806
00:40:24,560 --> 00:40:26,239
Speaker 7: but who can I work with and what are the

807
00:40:26,280 --> 00:40:27,640
Speaker 7: right use cases I can use?

808
00:40:29,200 --> 00:40:32,760
Speaker 2: Give us some standouts where we're already seeing use cases.

809
00:40:32,880 --> 00:40:37,760
Speaker 2: How are people using Claud Threethropic's latest set of models

810
00:40:37,840 --> 00:40:39,640
Speaker 2: within their business to change things?

811
00:40:40,920 --> 00:40:43,600
Speaker 7: So the Anthropic Claud three announcement that we made this

812
00:40:43,640 --> 00:40:47,319
Speaker 7: week is really exciting. Claude Three's Opus model is the

813
00:40:47,320 --> 00:40:50,759
Speaker 7: most intelligent model on the market today and it runs

814
00:40:50,800 --> 00:40:55,320
Speaker 7: on AWS Trainium and Inferentia, giving it the best price performance.

815
00:40:55,560 --> 00:40:58,799
Speaker 7: But why is this interesting? What it allows customers to do.

816
00:40:59,000 --> 00:41:02,480
Speaker 7: Customers like crowd Strike, which is a leading cybersecurity customer

817
00:41:02,600 --> 00:41:05,759
Speaker 7: also an AWS partner, they use it to power their

818
00:41:05,760 --> 00:41:09,840
Speaker 7: own generative AI capabilities and they're able to then handle

819
00:41:09,920 --> 00:41:13,879
Speaker 7: things like large prompts and provide scale and safety. That's

820
00:41:13,920 --> 00:41:18,080
Speaker 7: something really critical to how AWS views generative AI. And

821
00:41:18,120 --> 00:41:20,520
Speaker 7: I'll give you a couple of other examples. One of

822
00:41:20,600 --> 00:41:24,720
Speaker 7: our competency launch partner's Mission Cloud worked with a company

823
00:41:24,760 --> 00:41:28,200
Speaker 7: called Magell and TV and this company has a large

824
00:41:28,239 --> 00:41:32,240
Speaker 7: library of documentaries that they make accessible to a global audience,

825
00:41:32,600 --> 00:41:34,680
Speaker 7: and what they wanted to do was to enable that

826
00:41:34,840 --> 00:41:39,200
Speaker 7: audience to achieve to be able to watch those documentaries

827
00:41:39,280 --> 00:41:44,480
Speaker 7: in every language. Both have transcribed the translations and also

828
00:41:44,680 --> 00:41:48,320
Speaker 7: foreign language dubbing. Now that's a really tough problem because

829
00:41:48,320 --> 00:41:50,680
Speaker 7: you've got to have an address slang, you've got to

830
00:41:50,719 --> 00:41:54,240
Speaker 7: address profanity, you've got to address inflections for the voice dubbing.

831
00:41:54,520 --> 00:41:57,400
Speaker 7: It was cost prohibitive to do the old way, it

832
00:41:57,400 --> 00:42:00,960
Speaker 7: costs about twenty dollars per minute. With AI, they were

833
00:42:01,000 --> 00:42:03,240
Speaker 7: able to reduce that down to less than a dollar

834
00:42:03,280 --> 00:42:05,959
Speaker 7: per minute. That allowed Magel and TV to now reach

835
00:42:06,160 --> 00:42:10,520
Speaker 7: a global audience nearly instantly with that technology.

836
00:42:10,560 --> 00:42:12,759
Speaker 3: But I love that tangible example.

837
00:42:13,120 --> 00:42:15,000
Speaker 2: What is also at the forefront of people's minds, and

838
00:42:15,000 --> 00:42:17,520
Speaker 2: we've only got a minute left, is safety. How do

839
00:42:17,560 --> 00:42:21,120
Speaker 2: you ensure that the offerings you're providing through bedrock aren't

840
00:42:21,360 --> 00:42:23,480
Speaker 2: going to be making things up, but also more broadly

841
00:42:23,560 --> 00:42:24,959
Speaker 2: have ethical implications.

842
00:42:26,120 --> 00:42:29,040
Speaker 7: It's a great question, and responsible AI is core to

843
00:42:29,239 --> 00:42:34,200
Speaker 7: every service from AWS. We have been designing security integrated

844
00:42:34,239 --> 00:42:36,960
Speaker 7: into the products from the beginning, and it is more

845
00:42:36,960 --> 00:42:39,520
Speaker 7: important with generative AI than it has ever been before.

846
00:42:39,880 --> 00:42:43,959
Speaker 7: We've heard customers and partners state concerns about generative AI.

847
00:42:44,360 --> 00:42:47,440
Speaker 7: Customers have the ability to aggregate data and insights from

848
00:42:47,480 --> 00:42:50,799
Speaker 7: different sources, and so that's why we designed security in

849
00:42:50,840 --> 00:42:53,319
Speaker 7: from the get go, to make sure there are guardrails

850
00:42:53,520 --> 00:42:56,160
Speaker 7: so that individuals who should have access to the data

851
00:42:56,400 --> 00:43:00,640
Speaker 7: think financial data and other proprietary information have access to it,

852
00:43:00,680 --> 00:43:02,960
Speaker 7: and those that shouldn't can still make use of the

853
00:43:03,000 --> 00:43:06,320
Speaker 7: general of AI tools, but have those guardrails based on

854
00:43:06,400 --> 00:43:09,200
Speaker 7: the data they're allowed to have access to. So providing

855
00:43:09,320 --> 00:43:12,960
Speaker 7: policy and governance to ensure that enterprises are able to

856
00:43:12,960 --> 00:43:15,600
Speaker 7: protect their information. I think this is critical for every

857
00:43:15,719 --> 00:43:18,799
Speaker 7: enterprise to think through and it's going to be the

858
00:43:18,840 --> 00:43:21,080
Speaker 7: way that we're able to ensure that gener of AI

859
00:43:21,160 --> 00:43:22,520
Speaker 7: is adopted in mass.

860
00:43:23,800 --> 00:43:26,239
Speaker 2: Ruber great to have time speaking with you on the

861
00:43:26,320 --> 00:43:29,120
Speaker 2: day that you're announcing this, of course, RUBERBORNO of Amazon

862
00:43:29,160 --> 00:43:32,239
Speaker 2: Web Services, we appreciate it. Meanwhile, that does it for

863
00:43:32,280 --> 00:43:34,840
Speaker 2: this addition of bloom bag technology. Do not forget to

864
00:43:34,920 --> 00:43:36,800
Speaker 2: check out our podcast. You want to go and listen

865
00:43:36,800 --> 00:43:42,400
Speaker 2: back to that Alex Karp conversation. This is bloombag technology.