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Speaker 1: From Marhard where Innovations, Money and Power Collie in Silicon Valley, NBN.

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Speaker 1: This is Bloomberg Technology with Caroline Hyde and Ed Ludlove.

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Speaker 2: I'm Caroline Hyde and Bloomberg's world headquarters in New York

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Speaker 2: and Ourmed Ludlow in San Francisco.

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Speaker 3: This is Bloomberg Technology.

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Speaker 4: Coming up in Vidio.

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Speaker 2: Boy Knock said out of the park with earnings that

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Speaker 2: blow past estimates.

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Speaker 4: Shares they're still at that all time high. We'll bring

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Speaker 4: you the details.

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Speaker 5: Class will have full market coverage and cover the other

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Speaker 5: tech names reporting results like Snowflake and China's may Twine,

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Speaker 5: and we.

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Speaker 2: Talk all things artificial intelligence with CEO's of Twilio and

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Speaker 2: Hugging Face. But we start with the AI story that

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Speaker 2: is pervasive for the entire.

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Speaker 4: Market there and in Vidio.

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Speaker 2: Even though it comes off of its highs that we're

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Speaker 2: trading what in excess of six percent higher earlier in

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Speaker 2: the day, we're still at a record. We're up more

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Speaker 2: than one, almost at two percentage points higher. But extraordiny

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Speaker 2: where some of these price targets have been moved to

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Speaker 2: one one hundred dollars one price target from Rosenblack.

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Speaker 4: We're looking at four hundred seventy nine.

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Speaker 2: This could still double Edits an amazing set of numbers.

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Speaker 5: Yeah, let's get right to the numbers, right. This is

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Speaker 5: the third consecutive quarter where Nvidia's guidance for the current

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Speaker 5: fiscal quarter was way ahead of expectations.

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Speaker 3: Revenue in the.

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Speaker 5: Fiscal third will be sixteen billion dollars plus or minus

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Speaker 5: two percent, as you see on your screen.

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Speaker 3: In the second quarter just gone.

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Speaker 5: Data center is everything ten point three billion, way past

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Speaker 5: the expectations of almost eight billion. And if you look

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Speaker 5: at the commentary Carrow, the growth is coming from the

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Speaker 5: United States. It's the hyperscalers in the enterprise company. But

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Speaker 5: it was also interesting to note that China accounted for

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Speaker 5: twenty to twenty five percent of data center revenue, which

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Speaker 5: is well within its historic range.

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Speaker 2: I mean, the fact that we're seeing it firing on

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Speaker 2: all these cylinders, whether or not we're seeing it at

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Speaker 2: being a data center story. I thought what was interesting

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Speaker 2: in a Morgan Stanley note just saying the pervasiveness of

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Speaker 2: the small companies, small cloud providers that want to be

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Speaker 2: in on an AI, not just the huge hyperscalers as well.

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Speaker 5: Yes, it's a really good point because for example, AMD,

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Speaker 5: a competitor, right opened up two percent in this morning's open.

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Speaker 5: It's now down almost eight percent, a swing of ten percent.

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Speaker 5: The market sees yields rising ahead of Jackson Hole, but

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Speaker 5: how much of that is the strength in response on Nvidia?

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Speaker 5: You know they are direct competitors for GPUs in the

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Speaker 5: service space. In Vidia, You're right, it's off session highs.

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Speaker 5: What's so interesting in that Morgan Stanley note, By Morgan

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Speaker 5: Stanley's estimation, in Vidia is only meeting fifty percent of

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Speaker 5: the demand that's out there. So when they talk up

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Speaker 5: supply improving as well, it gives confidence that the outlook

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Speaker 5: longer term is strong.

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Speaker 2: And I think you've raised this point and it's one

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Speaker 2: worth dwelling on the analyst reaction the price targets one

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Speaker 2: after another being raised or.

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Speaker 4: No single sell on this stock. Just look at that graphic.

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Speaker 5: Values tripled beyond three a trillion dollars of market cap.

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Speaker 5: The final sell final analyst throwed in through in the

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Speaker 5: tow which was morning Star really quick. They did give

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Speaker 5: us a note of caution and said on China that

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Speaker 5: even if US technology export restrictions are increased in the

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Speaker 5: near term, there won't be an immediate effect, why because

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Speaker 5: demand around the world for the HGX server designs is

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Speaker 5: so great. But long term they called it basically the

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Speaker 5: removal of opportunity and what is the world's key market

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Speaker 5: for data center and also electronics, So China is a

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Speaker 5: long term question.

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Speaker 2: Yeah, yeah, and is for armholdings as we approach that

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Speaker 2: particular IPO, and it is for this particular company as well,

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Speaker 2: the exposure to the demand there.

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Speaker 3: Yeah.

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Speaker 5: Well, we got the numbers out the way, but I

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Speaker 5: think we have to look forward. Let's bring in JP Scandalios,

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Speaker 5: Franklin Equity Group portfolio manager one hundred and twenty five

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Speaker 5: billion dollars in assets, and you guys hold and video

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Speaker 5: across multiple funds. Let's just start with the simple question,

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Speaker 5: what was your take from this?

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Speaker 3: In Nvidia earnings print.

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Speaker 6: Certainly and thank you for having me. As you guys

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Speaker 6: alluded to, you went through the numbers that kind of growth,

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Speaker 6: we just we just don't see eighty eight percent sequential

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Speaker 6: growth and one hundred and forty one percent quench growth

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Speaker 6: in that all important data center business that you referenced,

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Speaker 6: which is now seventy six percent of total revenues five

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Speaker 6: ten years ago, and I've been covering the group for

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Speaker 6: about thirty years.

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Speaker 3: Five ten years.

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Speaker 6: Ago in the video was a gaming company and now,

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Speaker 6: as you said, it's all about data center. So there's

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Speaker 6: two things here, right, fundamentals outstanding, huge beat huge rays.

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Speaker 6: It's like The Godfather too, the sequel was as good

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Speaker 6: as the predecessor. So nothing there, and I think Abigail

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Speaker 6: alluded to it. There was a lot of you if

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Speaker 6: you had been paying attention, I'm sure you were during

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Speaker 6: the quarter. Expectations we're so so high, and there's a

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Speaker 6: lot of people who get uncomfortable with parabolic growth like this.

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Speaker 6: We're much more comfortable on Wall Street with steady or

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Speaker 6: linear growth, and this kind of growth. People optically right

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Speaker 6: away say well, this can't continue, and certainly there is

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Speaker 6: a law of large numbers. But they are so well

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Speaker 6: positioned for the foreseeable future. I suspect fundamentals will remain

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Speaker 6: very strong, and ed you mentioned it, they conveyed that

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Speaker 6: they're getting additional capacity and they have more demand than

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Speaker 6: they know than they can address.

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Speaker 2: I'm interested more broadly with the fact that the competition.

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Speaker 2: Is this why we're seeing the likes of MD fall off.

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Speaker 2: Are people feeling that really this is the winner that

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Speaker 2: takes all or is that more a sign of market

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Speaker 2: and gyrations that's happening on the day.

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Speaker 7: I think it's the former.

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Speaker 8: I think.

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Speaker 6: Lisas who has done an excellent job at AMD, and

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Speaker 6: I think AMD, given their limited resources, went after the

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Speaker 6: CPU and especially the data center CPU market first, and

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Speaker 6: they're doing really nicely. They're a relative to Intel, especially

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Speaker 6: when you compare it to again five or ten years ago.

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Speaker 6: I think now they've just gotten with their I three hundred.

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Speaker 6: They're coming into the data center GPU market and the

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Speaker 6: issue is can they gain some share? And I think

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Speaker 6: there was some expectation throughout the quarter as that product

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Speaker 6: comes to market that they would gain share. They're not,

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Speaker 6: you know, the killer of Nvidia, but they're going to

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Speaker 6: gain some share. And I think with such strong results

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Speaker 6: from Nvidia, and not just the results but the guidance

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Speaker 6: of course, but also their product lineup, they're just canvassing

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Speaker 6: between inferencing and training, between the networking on the Finneban

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Speaker 6: side and Ethernet that I think some investors who are

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Speaker 6: saying this is going to be a real uphill battle

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Speaker 6: climb for AMD, that'd be my guess.

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Speaker 2: I mean, and it feels as though many a betting

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Speaker 2: in video is the only picks and shovels out there

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Speaker 2: at the moment, but only from a product perspective of offering,

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Speaker 2: but just the breadth of where their customers are coming from.

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Speaker 5: That's why the level of Intel's so key. Caroline right,

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Speaker 5: just I'm really sorry to interrupt JP. Just point out

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Speaker 5: one thing, you know, loads analysts ask collect cress, give

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Speaker 5: us detail which product, right, is it H one hundred.

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Speaker 5: It's broadly HGX server tip right, And I think Jensen

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Speaker 5: made the point that, you know, H one hundred is

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Speaker 5: not just a single chip, it has a trillion transistors

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Speaker 5: in combination. You don't just sort of hand it over

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Speaker 5: to the customers. But did you hear enough from them

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Speaker 5: that the pipeline of future products maintains that technology lead?

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Speaker 8: JP? You know I did.

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Speaker 6: I think HGX is just getting started, right, and Grace

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Speaker 6: on its own, and Grace Hopper CPUGPU combination is just

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Speaker 6: coming to market now. There are Ethernet product Spectrum just

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Speaker 6: coming to market, Bluefield their DPU Bluefield three kind of

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Speaker 6: just getting going. And then you add on top of

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Speaker 6: that all their software offerings. So yes, I don't think

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Speaker 6: this was like, Okay, that's the game over and now

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Speaker 6: time to move on. And Carolyn brings up a great point.

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Speaker 6: To some degree, there's just not many AI companies, especially

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Speaker 6: generated A companies that are monetizing this robust trend today,

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Speaker 6: and so there is a scarcity value there.

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Speaker 8: JP.

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Speaker 5: I think we need to look at what collect Cress

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Speaker 5: had to say about China long term that if you

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Speaker 5: know US X technology export controls are in place long term,

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Speaker 5: she's calling it a lots of opportunity in that market

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Speaker 5: the US semiconductor maker is how seriously do you take

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Speaker 5: that warning?

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Speaker 6: I take it very seriously. I've done several meetings with

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Speaker 6: in a podcast with Chris Miller, who wrote the book

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Speaker 6: Chapurs that has often cited, and there's public funds we

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Speaker 6: own GSMC as well. It's one of my major concerns

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Speaker 6: just because very difficult to analyze the political back and

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Speaker 6: forth between China and the US and how far our

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Speaker 6: government will go and how far their government will go.

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Speaker 6: And so to your point, I try to address it

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Speaker 6: with adjusting my terminal growth rate, because as you say,

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Speaker 6: if you take out that economy and that market, certainly

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Speaker 6: it will be a lower terminal growth rate over time,

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Speaker 6: and it's just a we will see what happens, but

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Speaker 6: certainly something to follow, as if we don't have enough

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Speaker 6: to follow. In the semiconductor industry, another thing to watch

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Speaker 6: very closely.

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Speaker 4: Well said.

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Speaker 2: One thing that many an investor was following was the

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Speaker 2: buyback enormous Where else could they should they be allocating

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Speaker 2: that money at these sorts of price points to be

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Speaker 2: buying back their stock? Is it because they believe the

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Speaker 2: stock has got yet further to run, as some analysts do.

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Speaker 4: Why not more in R and D?

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Speaker 6: So they spend they already spend a lot hot well,

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Speaker 6: a tremendous amount in R and D, And so I'm

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Speaker 6: willing to give them a pass there if you will.

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Speaker 6: I think what it says. One thing it says is

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Speaker 6: M and A again going back to China, that M

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Speaker 6: and A is going to be very difficult to get

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Speaker 6: approved through all these regulatory bodies. And so you turn

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Speaker 6: around and you say, well, what else can we do

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Speaker 6: with it? Well, we have four billion left in our

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Speaker 6: buy back. Let's add twenty five billion to that. I

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Speaker 6: think it's sending a message to the market that they're

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Speaker 6: very comfortable even at these prices, that their prospects are

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Speaker 6: so strong and they outlook so positive that they're willing

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Speaker 6: to buy back their shares, And you're right. It's again,

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Speaker 6: it's like the results. The sticker tag is a massive number,

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Speaker 6: but you're talking about a one point one trillion dollar

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Speaker 6: market cap company. So at the end of the day,

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Speaker 6: the number of shares that they'll actually buy back, it's

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Speaker 6: great to see, don't get me wrong, but put it

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Speaker 6: in a little context, I guess, yeah, hey.

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Speaker 5: I have the stocks undervalue, or they got nothing to

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Speaker 5: spend on JP scandalios the Frank collectority. Great to have

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Speaker 5: you here on bloem. It's analogy.

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Speaker 2: So further news, regarding, of course, the plane crash that

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Speaker 2: presumably killed the Wagner founder Progosion, we're hearing from Putin

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Speaker 2: himself saying an investigation into Progosion's death will be completed. Overall,

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Speaker 2: he's saying overall that there are concerns and indeed an

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Speaker 2: investigation will be completed, expressing condonencis over the Progosion jet

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Speaker 2: crash and saying he was a talented man and a businessman.

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Speaker 4: More on that global news throughout the day.

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Speaker 2: But meanwhile, let's just return to technology and China's mag

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Speaker 2: one in particular seeing its fastest pace of sales growth

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Speaker 2: two years. That's like it's a large part to a

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Speaker 2: rebound in dining, in travel demand. Bloomberg's Isabel Lee is

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Speaker 2: here with more and for many that's a hard thing

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Speaker 2: to swallow around all the narrative that China is slowing,

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Speaker 2: the consumers weak, the housing crisis, the wealth crisis.

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Speaker 1: It's kind of a good report and it takes some

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Speaker 1: analysis because yes, the Chinese economy is slowing down, but

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Speaker 1: because it's slowing down, consumers are shying away from big

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Speaker 1: ticket items expensive so far something like that, and they're

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Speaker 1: leaning more into the little pleasures of eating out or traveling.

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Speaker 1: May to One is a giant in the delivery business,

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Speaker 1: and this bounce back and travel and eating out really

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Speaker 1: help its earnings dis quarter, especially after the COVID pandemic

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Speaker 1: when we know that China had years of stringent lockdowns.

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Speaker 1: So salesloras are better than expected at thirty three percent

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Speaker 1: and may to One swung from an operating loss earlier

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Speaker 1: a year earlier to an operating profit. So it's really

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Speaker 1: good news for this report. But again we also focus

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Speaker 1: on the outlook and the outlook is kind of shaky.

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Speaker 1: Investors are saying that, you know, maybe we won't see

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Speaker 1: the same growth.

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Speaker 4: We are expecting a slowdown.

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Speaker 1: So it's still kind of a double edged sword when

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Speaker 1: it comes to these Chinese companies, But for now it's

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Speaker 1: kind of good news, so we'll take it.

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Speaker 5: Yeah, there's so much analogous with this May to One

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Speaker 5: report and what we saw really playing up AI as well,

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Speaker 5: while the macro conditions improve our big banks. To Bloomberg's

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Speaker 5: Isabel Lee out in New York, another company we are watching,

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Speaker 5: Cloud Software provide a snowflake displaying signs of caution after

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Speaker 5: its sales out looked for the third quarter came in

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Speaker 5: line with expectations. Bloomberg's Brodie Ford has everything on the

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Speaker 5: software beat. Bradie, what's the story with Snowflake.

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Speaker 9: Snowflake's one of those really interesting companies where it was

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Speaker 9: just growing like crazy a year or two. We're talking,

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Speaker 9: you know, doubling every year in terms of revenue. But

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Speaker 9: of course, like every other software company out there, I

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Speaker 9: hit this point where all of its customers said, WHOA,

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Speaker 9: our budgets with you have.

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Speaker 8: Grown insanely and so what's really.

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Speaker 9: Been hitting Snowflake is something that's been hitting AWS or

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Speaker 9: Microsoft Azure is that their customers are saying, how can

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Speaker 9: we trim our bill a little bit? And that's really

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Speaker 9: what's been impacting Snowflake. You know, they're still growing pretty heavily, right,

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Speaker 9: I mean we're talking like about around thirty percent revenue growth,

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Speaker 9: but at the end of the day, they're really having

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Speaker 9: trouble with those big customers because they want to trim

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Speaker 9: their budgets.

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Speaker 2: It's interesting reading through the analyst reactions and Barkley is saying, look,

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Speaker 2: after two quarters of life, we're down your guidance. The

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Speaker 2: fact that's unchanged, yep, it is actually a pretty good sign.

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Speaker 2: Why are we there for seeing a set off? Why

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Speaker 2: are people seeing more caution around this than optimism instability?

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Speaker 8: It's a good question because there's a funny dynamic.

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Speaker 9: Where as you said, it's stabilized, and that's what Amazon said,

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Speaker 9: and people loved it. They said, Okay, it's not getting worse,

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Speaker 9: but it's stabilized. I think people just want to see

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Speaker 9: a reacceleration. But something very funny that happened last night

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Speaker 9: is the results came out.

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Speaker 8: People said that I don't know about this.

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Speaker 9: They were selling off then in video hit in vidio hit,

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Speaker 9: and they had an insane result, and everyone said, oh man,

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Speaker 9: I mean that's a good sign for AI.

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Speaker 8: And you know what, Snowflake, they'll use that for AI.

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Speaker 8: So it's all good.

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Speaker 9: And so there's a lot of different factors here. Investors

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Speaker 9: want to see growth come back, but sometimes this AI

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Speaker 9: demand might be enough to bring Snowflake up with everybody else.

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Speaker 5: Brady just having a great time after the ending spread track.

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Speaker 3: In the aftermark.

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Speaker 5: I mean, to your point, we opened four percent higher

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Speaker 5: in this morning session of Snowflake, we're down three and

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Speaker 5: a half percent and one point down, a big swing.

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Speaker 5: A lot of that is the market broadly. Snowflake is

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Speaker 5: like this layer on top of the hyper scalers, right

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Speaker 5: on top of cloud, and they've been trying to tell

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Speaker 5: an AI story that the market wants to make sense of.

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Speaker 3: Have you made sense of it?

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Speaker 9: So yeah, there's an interesting thing. Where As you said,

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Speaker 9: Snowflake is a company that helps people organize their data

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Speaker 9: and prepare it for multiple uses.

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Speaker 8: One of those uses could be AI.

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Speaker 9: Of course, every company that's back end data management right

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Speaker 9: now is saying, hey.

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Speaker 8: Hey, we'll help you with AI. Don't worry.

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Speaker 9: We got you, And I think a lot of investors

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Speaker 9: are saying, well, you can't all do this, And so

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Speaker 9: that's kind of the question with Snowflake, right, I mean,

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Speaker 9: do they have a really unique positioning as it relates

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Speaker 9: to AI.

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Speaker 8: Some people say they do. Some others would say no, no, no,

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Speaker 8: we'll do it instead. So I think the jury is

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Speaker 8: still all out on who really will be.

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Speaker 9: Able to own this data organizational business as it relates

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Speaker 9: to AI and training large language models, the.

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Speaker 4: Pigs and shovels.

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Speaker 2: The infrastructure is well and truly owned by one, but

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Speaker 2: it feels as though the rest of being scribbled over

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Speaker 2: when it comes to the software application prodi forward brilliant,

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Speaker 2: Thank you so much. Meanwhile, coming up, look the impact

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Speaker 2: of the SEC's crypto crackdown on one of the oldest

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Speaker 2: digital asset exchanges.

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Speaker 4: All the details next from San Francisco. I'm from New York.

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Speaker 10: I'm sprinkling in Washington. This is bringing back technology.

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Speaker 3: Time for talking tech.

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Speaker 5: First up, Walmart is planning its longest distance drone deliveries

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Speaker 5: yet by teaming up with Alphabet's Wing. It's going to

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Speaker 5: be at two Dallas areas stores. They've got FDA approval

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Speaker 5: to drop off food and other household essentials as far

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Speaker 5: as six miles away from that store and vin Fast Auto.

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Speaker 5: It is seeing a head scratching surge, giving the money

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Speaker 5: losing ev startup a bigger market cap this time than

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Speaker 5: City Group. By the way, fame shortshare seller Jim Chainos

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Speaker 5: is even calling the stocks valuation quote insane. Plus Bitstamp,

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Speaker 5: one of the oldest digital asset exchanges, will discontinue staking

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Speaker 5: services in the US following a crackdown on their products

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Speaker 5: by the SEC. The move takes effect on September twenty fifth.

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Speaker 2: Caroline all eyes of course on that story and let's

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Speaker 2: just dwell on it Frommtionnani Basek is here for more

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Speaker 2: on that latest story and what's the read from market

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Speaker 2: on it.

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Speaker 11: The region of the market is that this is just

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Speaker 11: another player here that you're seeing not able to engage

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Speaker 11: in the staking service when we are watching a coinbase

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Speaker 11: clearly fight the issue, and we've seen the volatility kind

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Speaker 11: of a hover over the market when it comes to

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Speaker 11: of course, this means more for ethereum than it does

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Speaker 11: for say Bitcoin.

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Speaker 4: However, without staking.

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Speaker 11: You don't have that proof of stake model working out

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Speaker 11: the way that the market was so excited for. Remember

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Speaker 11: the exuberants that surrounded this kind of new model that

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Speaker 11: was supposed to be less energy dependent than the mining history,

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Speaker 11: that was supposed to potentially have more favorable economics for

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Speaker 11: a wider array of people, given that the control would

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Speaker 11: be changing in terms of the way that it is

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Speaker 11: held over time. So a lot of questions for the

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Speaker 11: model itself. But remember coinbase has been still sticking to

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Speaker 11: the staking model. While we have seen now Kraken as

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Speaker 11: well as Bitsdams start to throw on a towel.

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Speaker 5: The other piece of news than headlines that we've seen

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Speaker 5: across this morning is FTX court filings. What's the latest

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Speaker 5: with FTX Shanami.

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Speaker 11: Now, this is really interesting because you were watching them

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Speaker 11: look to Galaxy. Remember this is still a bankruptcy process

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Speaker 11: in which the FTX and John Jay had John Ray

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Speaker 11: I'm sorry, had looked over and looked to Galaxy Asset Management.

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Speaker 11: This is a business run by a fellow named Steve

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Speaker 11: Kerz that would oversee a certain amount of assets. What

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Speaker 11: happens from here, so a certain amount of these assets

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Speaker 11: will be essentially liquidated and sold so that they could

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Speaker 11: shore up money to pay the creditors back over at FTX.

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Speaker 11: There is a fee that Galaxy would take, although from

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Speaker 11: now until the court approval, things could definitely change from here.

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Speaker 11: But there is an aspect of this as well where

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Speaker 11: they don't want to get rid of that bitcoin and

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Speaker 11: ether right away as well because the value in the

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Speaker 11: price could change. So while a certain number of these

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Speaker 11: tokens can be sold into the market, there will be

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Speaker 11: a number here that are going to be held managed

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Speaker 11: and hedged in order to maintain the most value for

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Speaker 11: the firm.

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Speaker 2: Remind us there have been some credits of frustrations, to

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Speaker 2: say the least, about the fear that they were losing

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Speaker 2: money because some of these assets weren't being.

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Speaker 4: Sold off in a meticulous amount.

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Speaker 11: Yeah, and the idea here is that they would give

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Speaker 11: it to somebody who would have the ability to manage

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Speaker 11: these assets in the most profitable way. At the end

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Speaker 11: of the day, remember time value of money. If you're

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Speaker 11: not getting your money back today, how long do you

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Speaker 11: have to wait and what happens to those assets? It

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Speaker 11: would Bitcoin, ether and some of these tokens continue to

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Speaker 11: decline in value. That is of course a concern for

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Speaker 11: the creditors, which is why they are holding some of

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Speaker 11: that Bitcoin and ether and hedging the exposure rather than

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Speaker 11: selling that off right away as they are looking to

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Speaker 11: do with some of these other tokens.

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Speaker 5: To mind ourselves, sorry, Carol Bloomberg Technology that the whole

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Speaker 5: point of this story is about whether people get their

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Speaker 5: money back or not. You know, people that had assets

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Speaker 5: with not just the US, but multiple jurisdictions. We at

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Speaker 5: a place where we can answer that question. Shanale, it's early.

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Speaker 11: I think it's early. And the reason it's early is

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Speaker 11: even with these assets in itself, like I said, it's

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Speaker 11: a court process, So it's not even that Galaxy is

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Speaker 11: taking over those assets tomorrow and selling them off into

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Speaker 11: the market and giving them money back to the creditors

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Speaker 11: right away. It will take time and there is friction

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Speaker 11: as they look. It's not the only assets that FTX has,

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Speaker 11: so it will take time to see what ultimately we'll

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Speaker 11: be recouped. Now, if they do sell these assets at

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Speaker 11: a decent price, there could be some money, of course,

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Speaker 11: and a certain degree of funds reclaimed for creditors, but

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Speaker 11: even the creditors would expect that it would be materially

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Speaker 11: less than what they walked in.

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Speaker 2: With Shanale all over that story, all over the chrystosphere

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Speaker 2: for us today.

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Speaker 4: Welcome back to Brimog Technology. I'm Caaren Hide in.

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Speaker 5: New York at our Meed Ludlow in San Francisco. Carry

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Speaker 5: Let's check on the markets and Videa's the big story

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Speaker 5: and the market from a technology sector perspective of opened

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Speaker 5: higher and a's that one hundred has swung from a

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Speaker 5: pretty modest gain in the first hour of trading, we're

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Speaker 5: now down one point three percent. The story's kind of

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Speaker 5: evolving towards Jackson whole Friday when we hear from fed

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Speaker 5: chair Pale yields to creeping higher and Vidia pretty much

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Speaker 5: one of the only names now in the technology sector

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Speaker 5: that is still in the green. Earning's a big part

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Speaker 5: of it. As we've covered right we had Snowflake now

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Speaker 5: disappointing to the downside. There is one stock that is

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Speaker 5: frankly on a hyperbolic trajectory and we're not really sure

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Speaker 5: what's going on.

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Speaker 3: But let's quickly talk about.

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Speaker 5: Vin Fast because at current gains, the company listing via

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Speaker 5: spack just a week ago has a market cap and

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Speaker 5: more than one hundred billion. At one point in the session,

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Speaker 5: it had a market cap and more than one hundred

440
00:21:51,480 --> 00:21:54,080
Speaker 5: and twenty billion. So if you took four General motors

441
00:21:54,119 --> 00:21:57,480
Speaker 5: and throwing Rivian for good measure, and combined them, you

442
00:21:57,560 --> 00:22:00,119
Speaker 5: have vin Fast that's only delivered a thousand vehicles here

443
00:22:00,160 --> 00:22:02,040
Speaker 5: in the US, and then record all of them. I

444
00:22:02,119 --> 00:22:04,560
Speaker 5: have a lot of experience covering ev companies went public

445
00:22:04,640 --> 00:22:07,000
Speaker 5: vice back. This one makes no sense to me either.

446
00:22:07,240 --> 00:22:08,800
Speaker 5: And then I guess finally, in the name we're looking

447
00:22:08,840 --> 00:22:11,080
Speaker 5: at is Twilio. Twilio out with a new kind of

448
00:22:11,080 --> 00:22:13,640
Speaker 5: suite of AI powered products on the data management side.

449
00:22:13,640 --> 00:22:16,000
Speaker 5: I think what's interesting about this story is they're kind

450
00:22:16,000 --> 00:22:18,960
Speaker 5: of responding to what is customer demand. The news was

451
00:22:18,960 --> 00:22:21,000
Speaker 5: out twenty four hours ago, the stock closed around two

452
00:22:21,000 --> 00:22:23,920
Speaker 5: percent higher. It's now down about two percent. But again,

453
00:22:24,200 --> 00:22:26,199
Speaker 5: consider the broader market and where we're going in the

454
00:22:26,200 --> 00:22:27,800
Speaker 5: tech sector in particular.

455
00:22:27,520 --> 00:22:31,679
Speaker 2: And so many ed have said the amount of productivity gains,

456
00:22:31,720 --> 00:22:34,119
Speaker 2: the real value is going to be in proprietary data,

457
00:22:34,320 --> 00:22:36,840
Speaker 2: and well, maybe you access it through Twilio. We're priased

458
00:22:36,880 --> 00:22:38,680
Speaker 2: to say, we've got the CEO, Jeff Lawson, to talk

459
00:22:38,720 --> 00:22:41,320
Speaker 2: about the roll out of your new products and ultimately

460
00:22:41,400 --> 00:22:44,280
Speaker 2: that end user demand. What are your customers wanting to

461
00:22:44,359 --> 00:22:46,879
Speaker 2: have when it comes to accessing and making the most

462
00:22:46,880 --> 00:22:48,720
Speaker 2: of their customer data.

463
00:22:49,000 --> 00:22:50,960
Speaker 12: Well, look, I think we all know that you know,

464
00:22:51,240 --> 00:22:54,040
Speaker 12: AI is going to transform so many parts of business,

465
00:22:54,080 --> 00:22:55,680
Speaker 12: and it all comes down to the quality of the

466
00:22:55,800 --> 00:22:58,240
Speaker 12: data that you're going to bring in feed AI. That's

467
00:22:58,280 --> 00:23:01,560
Speaker 12: going to provide proprietory outcomes for companies. And so the

468
00:23:01,600 --> 00:23:03,920
Speaker 12: way we look at it is if companies are able

469
00:23:03,920 --> 00:23:06,879
Speaker 12: to take all the information they know about their customers,

470
00:23:07,040 --> 00:23:09,520
Speaker 12: write every click, every scroll, every propensity to buy all

471
00:23:09,520 --> 00:23:12,040
Speaker 12: that information and feed it to an AI, it can

472
00:23:12,040 --> 00:23:16,040
Speaker 12: make those companies ten times better at serving their customers

473
00:23:16,200 --> 00:23:18,080
Speaker 12: and do it at a tenth of the price. That's

474
00:23:18,080 --> 00:23:21,600
Speaker 12: because you can take make your marketers ten times as

475
00:23:21,600 --> 00:23:25,359
Speaker 12: effective by designing and executing marketing campaigns automatically. You can

476
00:23:25,400 --> 00:23:29,160
Speaker 12: make your contact center ten times as effective by automating

477
00:23:29,200 --> 00:23:30,320
Speaker 12: so many of those conversations.

478
00:23:30,320 --> 00:23:31,520
Speaker 7: You make your sales team.

479
00:23:31,480 --> 00:23:34,080
Speaker 12: Ten times as effective because you can automate so many

480
00:23:34,080 --> 00:23:36,600
Speaker 12: of those early conversations. And so because of this, we

481
00:23:36,640 --> 00:23:40,159
Speaker 12: think companies that combine their customer data with advanced AI

482
00:23:40,480 --> 00:23:42,080
Speaker 12: are going to be able to get ten times better

483
00:23:42,160 --> 00:23:43,080
Speaker 12: at a tenth of the cost.

484
00:23:43,640 --> 00:23:46,040
Speaker 2: Jeff, I hear you, and I hate to sound in

485
00:23:46,080 --> 00:23:49,720
Speaker 2: any way jaded around this AI euphoria, but I feel

486
00:23:49,720 --> 00:23:53,200
Speaker 2: like everyone's making an announcement about how they can supercharge

487
00:23:53,280 --> 00:23:55,400
Speaker 2: their clients using adswercial.

488
00:23:55,000 --> 00:23:58,359
Speaker 4: Intelligence and generative AI. Why is your offering different?

489
00:24:00,000 --> 00:24:01,639
Speaker 7: It really all comes down to the data.

490
00:24:01,800 --> 00:24:04,560
Speaker 12: And we have a segment which is the leading real

491
00:24:04,680 --> 00:24:05,560
Speaker 12: time CDPs.

492
00:24:05,600 --> 00:24:07,040
Speaker 7: CDP is a customer.

493
00:24:06,840 --> 00:24:10,360
Speaker 12: Data platform and what it does we work with leading companies.

494
00:24:10,359 --> 00:24:12,920
Speaker 12: It allows them to ingest all the signals that they

495
00:24:12,920 --> 00:24:14,840
Speaker 12: get from their customers. Now, mind you, this is all

496
00:24:14,880 --> 00:24:18,560
Speaker 12: first party data, but every website, visit, every click, every scroll, everything,

497
00:24:18,600 --> 00:24:20,560
Speaker 12: people buy everything they don't buy right, and turn that

498
00:24:20,600 --> 00:24:23,919
Speaker 12: into a profile. That's an understanding of that customer. And

499
00:24:24,000 --> 00:24:26,040
Speaker 12: when you take an understanding of your customer and then

500
00:24:26,080 --> 00:24:28,240
Speaker 12: feed it to AI. And the reason why I am

501
00:24:28,320 --> 00:24:32,280
Speaker 12: so bullish on AI is that you look at chat GPT, right,

502
00:24:32,359 --> 00:24:33,880
Speaker 12: you look at a lot of these applications are getting

503
00:24:33,920 --> 00:24:37,800
Speaker 12: built viz our computers that can reason like you can

504
00:24:37,840 --> 00:24:40,240
Speaker 12: look at chat GPT and it will reason its way

505
00:24:40,320 --> 00:24:43,040
Speaker 12: through solving a hard problem in the way that a

506
00:24:43,160 --> 00:24:45,879
Speaker 12: human would do it. Now you feed it knowledge of

507
00:24:45,920 --> 00:24:48,840
Speaker 12: your customers you can say, reason through how to target

508
00:24:48,920 --> 00:24:51,760
Speaker 12: my customers to get better offers in their hands, reason

509
00:24:51,800 --> 00:24:54,199
Speaker 12: through how to design a marketing campaign that's going to

510
00:24:54,200 --> 00:24:56,639
Speaker 12: do better than maybe one that a human would design.

511
00:24:56,800 --> 00:24:58,680
Speaker 12: And I think we are going to see great success

512
00:24:58,680 --> 00:25:00,560
Speaker 12: with those as being able to do those amazing things.

513
00:25:00,880 --> 00:25:02,240
Speaker 3: Like Caro, I'm jaded too.

514
00:25:02,440 --> 00:25:04,360
Speaker 5: You know, we handle a lot of headlines in this way,

515
00:25:04,480 --> 00:25:06,240
Speaker 5: the way that Caroline has been describing. If you take

516
00:25:06,240 --> 00:25:08,879
Speaker 5: in video, it's like the picks and shovels of the

517
00:25:08,920 --> 00:25:12,920
Speaker 5: AI chain. You and I've discussed this before, but if

518
00:25:12,920 --> 00:25:16,320
Speaker 5: you think about the large language model underlying algorithms, you're

519
00:25:16,320 --> 00:25:20,160
Speaker 5: basically technology agnostic because you're working with both open ai

520
00:25:20,160 --> 00:25:23,160
Speaker 5: and Google. Why is that beneficial to you to say, oh,

521
00:25:23,160 --> 00:25:25,440
Speaker 5: we'll work with anyone where we can benefit.

522
00:25:25,640 --> 00:25:28,320
Speaker 12: Yeah, well, it's the right model for the right use case,

523
00:25:28,640 --> 00:25:30,560
Speaker 12: and so we have partnered. We partner with Google for

524
00:25:30,600 --> 00:25:33,679
Speaker 12: certain use cases. We've partnered with open ai yesterday around

525
00:25:33,720 --> 00:25:36,639
Speaker 12: our AI personalization engine, and we're going to partner with

526
00:25:36,680 --> 00:25:39,040
Speaker 12: others as well. And the key thing is taking the

527
00:25:39,119 --> 00:25:41,400
Speaker 12: right model, the right technology to go solve.

528
00:25:41,240 --> 00:25:41,959
Speaker 7: The right problem.

529
00:25:41,960 --> 00:25:43,920
Speaker 12: But I do believe this is day zero of AI,

530
00:25:44,280 --> 00:25:46,080
Speaker 12: and so it is at the infrastructure stage.

531
00:25:46,119 --> 00:25:48,600
Speaker 7: That's why you see in video right posting the results

532
00:25:48,600 --> 00:25:48,960
Speaker 7: they are.

533
00:25:49,200 --> 00:25:51,760
Speaker 12: Well, we look at it as companies today, they are

534
00:25:51,800 --> 00:25:55,720
Speaker 12: getting their data AI ready because they know this rule

535
00:25:55,880 --> 00:25:57,640
Speaker 12: is coming and they need to get all of their

536
00:25:57,680 --> 00:26:01,080
Speaker 12: customer data into a format where and these use cases

537
00:26:01,119 --> 00:26:03,359
Speaker 12: become a reality, they're able to combine it with the

538
00:26:03,400 --> 00:26:05,680
Speaker 12: powers of large language models and actually get these kinds

539
00:26:05,680 --> 00:26:06,080
Speaker 12: of outcomes.

540
00:26:06,240 --> 00:26:07,800
Speaker 5: I actually wanted to ask you if you could walk

541
00:26:07,880 --> 00:26:10,960
Speaker 5: us through how Twilio is a management team came to

542
00:26:11,000 --> 00:26:14,280
Speaker 5: this decision because in the second line of our story,

543
00:26:14,400 --> 00:26:17,560
Speaker 5: in the first paragraph, it says Twilio is responding to

544
00:26:17,680 --> 00:26:20,320
Speaker 5: customer demand. So is this just a case of customers

545
00:26:20,400 --> 00:26:23,360
Speaker 5: kicking down your Doorjef and saying we need this, make

546
00:26:23,400 --> 00:26:24,240
Speaker 5: it for us please.

547
00:26:24,480 --> 00:26:26,879
Speaker 12: Well, if you think about it, if AI is able

548
00:26:26,920 --> 00:26:29,320
Speaker 12: to get ten times better results, which I firmly believe

549
00:26:29,480 --> 00:26:33,000
Speaker 12: at one tenth of the cost of prior solutions right

550
00:26:33,000 --> 00:26:34,920
Speaker 12: where you needed humans to go do all the work,

551
00:26:35,160 --> 00:26:38,800
Speaker 12: well guess what the economic incentives for companies to invest

552
00:26:39,040 --> 00:26:41,959
Speaker 12: in this technology are going to be undeniable? Right if

553
00:26:42,000 --> 00:26:44,360
Speaker 12: you can get a roughly one hundred x outcome by

554
00:26:44,359 --> 00:26:47,360
Speaker 12: investing in a technology. Every company is going to need

555
00:26:47,359 --> 00:26:49,679
Speaker 12: it if they're going to survive and thrive in this

556
00:26:49,720 --> 00:26:52,879
Speaker 12: AI powered world. And so that's why every conversation that

557
00:26:52,960 --> 00:26:57,080
Speaker 12: I have with customers today is all about AI, because

558
00:26:57,240 --> 00:26:59,840
Speaker 12: companies are asking the questions, how is this going to

559
00:27:00,240 --> 00:27:04,880
Speaker 12: radically transform my business in my industry in the coming one, two, five,

560
00:27:04,920 --> 00:27:07,520
Speaker 12: and ten years, and they're building in their starting those

561
00:27:07,560 --> 00:27:08,240
Speaker 12: investments today.

562
00:27:08,280 --> 00:27:11,080
Speaker 5: Caroline, the conversations that you and I have every single

563
00:27:11,160 --> 00:27:16,000
Speaker 5: day are all about AI, but with investors principally.

564
00:27:15,560 --> 00:27:18,600
Speaker 2: Yeah, and on that note, a more sensitive question I

565
00:27:18,640 --> 00:27:21,040
Speaker 2: suppose for Hujef, and that point is you've got customers

566
00:27:21,080 --> 00:27:23,200
Speaker 2: knocking down your door, have you got investors knocking down

567
00:27:23,200 --> 00:27:25,720
Speaker 2: your door asking how you're managing to capitalize on this moment?

568
00:27:25,760 --> 00:27:28,439
Speaker 2: In particular, I ask because we are the lookout for

569
00:27:28,520 --> 00:27:32,640
Speaker 2: activist investor interaction. Of course, some of your expire that's

570
00:27:32,680 --> 00:27:34,879
Speaker 2: happened in terms of your founder's shares means perhaps you're

571
00:27:34,880 --> 00:27:35,840
Speaker 2: a little bit more exposed.

572
00:27:37,160 --> 00:27:40,520
Speaker 12: Well, look, without dual class shares, we are in the

573
00:27:40,600 --> 00:27:43,119
Speaker 12: category of nearly every public company that exists. So I

574
00:27:43,160 --> 00:27:45,879
Speaker 12: don't think it's really a newsworthy story. But we have

575
00:27:45,920 --> 00:27:49,600
Speaker 12: been very active participants with our investors, understanding what they

576
00:27:49,640 --> 00:27:52,520
Speaker 12: want from us as a management team, and doing a

577
00:27:52,560 --> 00:27:55,800
Speaker 12: substance of actions representing the needs and the desires of

578
00:27:55,800 --> 00:27:59,480
Speaker 12: our shareholders. And so we've taken a major actions. We've

579
00:27:59,600 --> 00:28:03,000
Speaker 12: taken the company from really focused on growth and never

580
00:28:03,080 --> 00:28:06,120
Speaker 12: making a substantive profit to a company that's now throwing off,

581
00:28:06,280 --> 00:28:08,920
Speaker 12: you know, more than ten percent non gap operating margins

582
00:28:09,040 --> 00:28:10,520
Speaker 12: in just the course of six months. And so I

583
00:28:10,520 --> 00:28:13,440
Speaker 12: think that is from amazing progress that we've made listening

584
00:28:13,440 --> 00:28:17,200
Speaker 12: to the needs of investors, responding and taking substantive actions

585
00:28:17,480 --> 00:28:20,320
Speaker 12: while we're investing in this exciting new roadmap around AI

586
00:28:20,400 --> 00:28:21,359
Speaker 12: and customer AIA.

587
00:28:21,480 --> 00:28:23,119
Speaker 2: Jeff, I like that you take us there, that you

588
00:28:23,160 --> 00:28:25,640
Speaker 2: almost remind us of the macro picture that you're trying

589
00:28:25,640 --> 00:28:27,720
Speaker 2: to navigate right now. It is one where people want

590
00:28:27,760 --> 00:28:30,480
Speaker 2: to see not growth at any cost, but growth at

591
00:28:30,520 --> 00:28:34,280
Speaker 2: profitable costs here and I'm interested in what you're seeing

592
00:28:34,320 --> 00:28:37,400
Speaker 2: from clients. Yes, there's AI euphoria, but are people pulling back?

593
00:28:37,440 --> 00:28:39,520
Speaker 2: Are people reticent, worried, nervous to spend?

594
00:28:41,080 --> 00:28:41,320
Speaker 7: Well?

595
00:28:41,400 --> 00:28:44,040
Speaker 12: Companies are rightly looking at their investments and saying these

596
00:28:44,040 --> 00:28:46,480
Speaker 12: are the right investments. Have we invested in the right

597
00:28:46,560 --> 00:28:49,400
Speaker 12: software and are we getting an ROI from these investments?

598
00:28:49,600 --> 00:28:51,200
Speaker 7: And I think in the cases where they're not.

599
00:28:51,080 --> 00:28:53,320
Speaker 12: Seeing an ROI, they may be pulling back or they

600
00:28:53,320 --> 00:28:54,920
Speaker 12: may be reducing think abouts.

601
00:28:54,920 --> 00:28:56,600
Speaker 7: SAD SaaS is seat.

602
00:28:56,400 --> 00:28:59,040
Speaker 12: Based for most companies, and so if you know, with

603
00:28:59,080 --> 00:29:00,600
Speaker 12: all the layoffs that have gone on and a lot

604
00:29:00,600 --> 00:29:02,760
Speaker 12: of companies, you need fewer seats, right, So there's those

605
00:29:02,800 --> 00:29:06,040
Speaker 12: things happening. But at the same time, companies are looking

606
00:29:06,080 --> 00:29:08,800
Speaker 12: at this moment with technology still continuing to grow, the

607
00:29:08,800 --> 00:29:11,640
Speaker 12: economy is still doing fairly well, and AI right around

608
00:29:11,640 --> 00:29:13,440
Speaker 12: the corner. They're saying, how do I make sure I

609
00:29:13,480 --> 00:29:16,520
Speaker 12: have the right investments to fuel our growth going forward

610
00:29:16,560 --> 00:29:18,480
Speaker 12: while looking at some of the older investments and saying

611
00:29:18,680 --> 00:29:20,280
Speaker 12: are those still the ones that we need? And so

612
00:29:20,320 --> 00:29:21,440
Speaker 12: I think we're seeing a lot of that. And the

613
00:29:21,520 --> 00:29:24,080
Speaker 12: nice thing about our business model is that we're usage based, right.

614
00:29:24,080 --> 00:29:26,400
Speaker 12: We're not selling seats and we're not selling there's no

615
00:29:26,440 --> 00:29:29,840
Speaker 12: such thing as shelfware in a usage based model, and

616
00:29:29,880 --> 00:29:31,920
Speaker 12: so all of the money that companies pay.

617
00:29:31,880 --> 00:29:33,720
Speaker 7: Us today is value that they are getting.

618
00:29:33,760 --> 00:29:36,120
Speaker 12: And I think companies that sell seats or companies that

619
00:29:36,160 --> 00:29:38,120
Speaker 12: have had a history of selling shelfware i e.

620
00:29:38,280 --> 00:29:39,800
Speaker 7: Software the company didn't really need.

621
00:29:40,240 --> 00:29:43,360
Speaker 12: Those companies are struggling because those clients are pulling back.

622
00:29:43,200 --> 00:29:43,920
Speaker 8: In their investments.

623
00:29:44,160 --> 00:29:46,320
Speaker 3: Jeff, it's great to see you in person. Have you

624
00:29:46,360 --> 00:29:47,280
Speaker 3: here on set.

625
00:29:48,560 --> 00:29:51,720
Speaker 5: AI in San Francisco seems like a big story to me.

626
00:29:52,520 --> 00:29:54,760
Speaker 5: You seem to be spending more time with CEOs of

627
00:29:54,760 --> 00:29:57,480
Speaker 5: other companies doing things. Can you talk a little bit

628
00:29:57,520 --> 00:30:00,360
Speaker 5: about whether or not what's happening with the an has

629
00:30:00,400 --> 00:30:03,080
Speaker 5: actually brought some of the tech industry here back together

630
00:30:03,120 --> 00:30:04,320
Speaker 5: a little bit absolutely.

631
00:30:04,320 --> 00:30:07,040
Speaker 12: I mean, look, San Francisco has become the center of

632
00:30:07,080 --> 00:30:10,400
Speaker 12: the AI universe. You've got open Ai, their headquarters, has

633
00:30:10,400 --> 00:30:12,320
Speaker 12: done in the mission right, You've got a lot of

634
00:30:12,360 --> 00:30:15,440
Speaker 12: companies participating in this AI revolution right in this area

635
00:30:15,520 --> 00:30:18,280
Speaker 12: and in the broader tech community. And look, I think

636
00:30:18,720 --> 00:30:21,920
Speaker 12: that the advent of what we're seeing with large language models,

637
00:30:21,960 --> 00:30:26,280
Speaker 12: computers that can reason, this is going to be bigger

638
00:30:26,320 --> 00:30:29,120
Speaker 12: than the mobile revolution we saw, give or take fifteen

639
00:30:29,200 --> 00:30:32,400
Speaker 12: years ago. This is probably about as big as the

640
00:30:32,480 --> 00:30:35,080
Speaker 12: advent of the internet on what it means for business,

641
00:30:35,120 --> 00:30:36,320
Speaker 12: what it means for society.

642
00:30:36,320 --> 00:30:37,800
Speaker 7: And so that's why you see a lot of excitement,

643
00:30:37,840 --> 00:30:38,160
Speaker 7: a lot.

644
00:30:38,080 --> 00:30:40,560
Speaker 12: Of energy, a lot of developers going to work, building,

645
00:30:40,600 --> 00:30:43,480
Speaker 12: exploring what's now possible. And that's the energy that I

646
00:30:43,480 --> 00:30:46,000
Speaker 12: feel sitting here in San Francisco and inside of Silicon Valley.

647
00:30:46,440 --> 00:30:49,400
Speaker 5: Jeff Lawson, Twilio CEO, you were the first ed board

648
00:30:49,400 --> 00:30:51,520
Speaker 5: I did when I moved here from London. I think

649
00:30:51,520 --> 00:30:53,440
Speaker 5: it was five and a half years ago. Now, it's

650
00:30:53,480 --> 00:30:55,520
Speaker 5: good to have you back here in the seat. Carrie

651
00:30:55,560 --> 00:30:58,560
Speaker 5: coming up here on Bloomberg Technology Salesforce another name here

652
00:30:58,560 --> 00:31:01,240
Speaker 5: in SSET. Get me in on with a funding round

653
00:31:01,240 --> 00:31:03,480
Speaker 5: for Hugging Face. We're gonna have more details on that

654
00:31:03,560 --> 00:31:06,640
Speaker 5: next and Hugging Faces CEO Clem DeLong joins us Sunset.

655
00:31:06,640 --> 00:31:08,120
Speaker 3: I think you have some more breaking news as well.

656
00:31:08,200 --> 00:31:08,440
Speaker 8: We do.

657
00:31:08,520 --> 00:31:12,080
Speaker 2: Let's talk about document management. Boy, we've done data management.

658
00:31:12,160 --> 00:31:14,520
Speaker 2: We've of course done all the areas in which companies

659
00:31:14,520 --> 00:31:18,120
Speaker 2: are optimizing their data. But let's talk about managing your documents.

660
00:31:18,440 --> 00:31:19,000
Speaker 3: Dot Box.

661
00:31:19,080 --> 00:31:21,160
Speaker 2: Actually Singling could be charging you a little bit more

662
00:31:21,160 --> 00:31:23,160
Speaker 2: for it. New cloud storage plan fees are going to

663
00:31:23,200 --> 00:31:25,280
Speaker 2: be announced later Thursday. We understand they're going to be

664
00:31:25,440 --> 00:31:29,120
Speaker 2: ending their unlimited cloud document storage for customers at one

665
00:31:29,160 --> 00:31:31,800
Speaker 2: point we went positive and then we sat back down again.

666
00:31:32,400 --> 00:31:34,920
Speaker 10: From New York and from San Francisco, this is blame

667
00:31:34,960 --> 00:31:52,440
Speaker 10: Meg Technology, all right.

668
00:31:52,560 --> 00:31:54,640
Speaker 5: Some big funding news in the world of AI, with

669
00:31:54,760 --> 00:31:58,400
Speaker 5: Salesforce leading a financing round in Hugging Face, giving the

670
00:31:58,440 --> 00:32:01,640
Speaker 5: company a four point five billion dollar valuation. The startup,

671
00:32:01,880 --> 00:32:05,320
Speaker 5: which helps companies store and use AI software, has risen

672
00:32:05,360 --> 00:32:08,000
Speaker 5: as one of the main players in the field. CEO

673
00:32:08,080 --> 00:32:10,720
Speaker 5: clemmed Along joins us now for more on that round,

674
00:32:10,720 --> 00:32:13,960
Speaker 5: along with Bloomberg's Rachel Metz. Rachel leads our AI coverage

675
00:32:14,000 --> 00:32:15,640
Speaker 5: and reported on this round.

676
00:32:15,720 --> 00:32:16,520
Speaker 3: Rachel, take it away.

677
00:32:17,240 --> 00:32:18,960
Speaker 13: Good morning, Clem.

678
00:32:19,040 --> 00:32:20,920
Speaker 4: Why don't you start off by telling us.

679
00:32:20,760 --> 00:32:23,200
Speaker 13: A little bit about this funding round. Why did you

680
00:32:23,240 --> 00:32:27,160
Speaker 13: go with this group of investors which includes as at Salesforce,

681
00:32:27,240 --> 00:32:30,080
Speaker 13: There's Google in there, Amazon, a bunch of big name

682
00:32:30,120 --> 00:32:30,800
Speaker 13: tech companies.

683
00:32:32,040 --> 00:32:34,360
Speaker 14: Yeah, we're super excited about the secret system around. I

684
00:32:34,360 --> 00:32:36,440
Speaker 14: think it's pretty unique. I haven't seen in the past

685
00:32:36,440 --> 00:32:39,640
Speaker 14: around with so many big players around around the table.

686
00:32:39,680 --> 00:32:42,920
Speaker 14: We have Salesforce, we have Google, we have Amazon, we

687
00:32:42,960 --> 00:32:48,520
Speaker 14: have Nvidia, Intel, md IBM, quad Com. I think it's

688
00:32:48,600 --> 00:32:52,440
Speaker 14: a good signal for Hugging Face. Obviously, with this CAF

689
00:32:52,480 --> 00:32:55,280
Speaker 14: like collabority platform that all these people and most of

690
00:32:55,320 --> 00:32:59,120
Speaker 14: the AI builders are using, but also for open source

691
00:32:59,160 --> 00:33:01,720
Speaker 14: AI in in general, which is kept like a trend

692
00:33:01,800 --> 00:33:05,280
Speaker 14: that we've seen developing for the past few months.

693
00:33:07,640 --> 00:33:10,080
Speaker 13: One of the things that I'm curious about is how

694
00:33:10,360 --> 00:33:14,200
Speaker 13: your company pivoted from its initial product. Initially, when I

695
00:33:14,240 --> 00:33:16,360
Speaker 13: first met you years ago, you guys were working on

696
00:33:16,440 --> 00:33:18,880
Speaker 13: a chatbot. So how did you go from chatbot to

697
00:33:19,320 --> 00:33:22,400
Speaker 13: an AI platform that tons and tons of companies and

698
00:33:22,480 --> 00:33:25,360
Speaker 13: individuals used to share their AI models to a lot

699
00:33:25,360 --> 00:33:25,680
Speaker 13: of people.

700
00:33:27,240 --> 00:33:30,720
Speaker 14: It was quite quite a journey. It's really kept like

701
00:33:30,800 --> 00:33:36,680
Speaker 14: the community, the companies, the users that really drove us right.

702
00:33:37,040 --> 00:33:41,120
Speaker 14: We were building this conversational AI, and we were lucky

703
00:33:41,160 --> 00:33:43,560
Speaker 14: to have like work for quite a long time on

704
00:33:43,600 --> 00:33:48,080
Speaker 14: the underlying infrastructure for this conversational AI, which including the

705
00:33:48,080 --> 00:33:51,240
Speaker 14: ability to like have a lot of different models, a

706
00:33:51,240 --> 00:33:54,040
Speaker 14: lot of different data sets, and build a system that

707
00:33:54,120 --> 00:33:54,600
Speaker 14: makes sense.

708
00:33:55,360 --> 00:33:56,000
Speaker 8: And when we.

709
00:33:55,920 --> 00:33:58,520
Speaker 14: Started to kind of like share some of that with

710
00:33:58,720 --> 00:34:03,080
Speaker 14: the community, something really wonderful happened, which is that open

711
00:34:03,120 --> 00:34:07,080
Speaker 14: source contributors started to come in and kept contribute to

712
00:34:07,120 --> 00:34:11,120
Speaker 14: the platform that we released. In open source researchers started

713
00:34:11,160 --> 00:34:13,799
Speaker 14: to camp like share their models. So most of the

714
00:34:13,960 --> 00:34:19,120
Speaker 14: open AI models that you've heard of today have been

715
00:34:19,160 --> 00:34:25,160
Speaker 14: added to the platform, like Bloom for example, Lama to Lama,

716
00:34:25,280 --> 00:34:30,640
Speaker 14: Lama two, Lama code that has been released today, Stable diffusion.

717
00:34:31,160 --> 00:34:33,319
Speaker 14: All these models have been added by the community, and

718
00:34:33,360 --> 00:34:35,920
Speaker 14: that's really kept like what drove us to go from

719
00:34:36,200 --> 00:34:39,719
Speaker 14: this camp like end use kate to providing the platform

720
00:34:39,800 --> 00:34:41,920
Speaker 14: for all ail builders.

721
00:34:41,560 --> 00:34:44,560
Speaker 15: Time Also interesting though, is it feels like there's this

722
00:34:44,680 --> 00:34:48,920
Speaker 15: inherent sort of tension. You've got a community, an open

723
00:34:49,040 --> 00:34:51,800
Speaker 15: source community, and at the same time trying to build

724
00:34:52,160 --> 00:34:56,759
Speaker 15: an enterprise business that makes millions and millions of dollars investors.

725
00:34:57,040 --> 00:34:59,920
Speaker 15: How do you treat your community within this sudden moment.

726
00:35:01,320 --> 00:35:03,560
Speaker 14: The good thing is that we can look at other examples. Right,

727
00:35:03,600 --> 00:35:07,120
Speaker 14: if you look at GitHub, they've done that quite well

728
00:35:07,160 --> 00:35:10,120
Speaker 14: at much larger scale than we are now. Right they

729
00:35:10,160 --> 00:35:13,560
Speaker 14: have over one hundred million users. They are kept like

730
00:35:13,600 --> 00:35:18,239
Speaker 14: the main platform for software engineers, similar to us being

731
00:35:18,280 --> 00:35:22,160
Speaker 14: the main platform for AIAI builders. The way we approach

732
00:35:22,239 --> 00:35:26,799
Speaker 14: that is by kind like setting a clear boundary and

733
00:35:26,920 --> 00:35:29,799
Speaker 14: campt like a public and camp like.

734
00:35:29,760 --> 00:35:34,080
Speaker 3: A strategy that when users.

735
00:35:33,640 --> 00:35:38,200
Speaker 14: Are contributing for example, publishing models in the open for

736
00:35:38,360 --> 00:35:41,520
Speaker 14: others to use. It's free and it's always going to

737
00:35:41,560 --> 00:35:44,759
Speaker 14: stay free, right, But when it's a company that is

738
00:35:44,880 --> 00:35:49,800
Speaker 14: using for the private commercial gain without contributing to the community,

739
00:35:50,480 --> 00:35:53,640
Speaker 14: it's fair that we make money out of that. And

740
00:35:53,719 --> 00:35:56,520
Speaker 14: the money that we make there is funding all the

741
00:35:56,560 --> 00:35:58,880
Speaker 14: open source and all the free products that we can

742
00:35:58,920 --> 00:36:01,800
Speaker 14: offer to develop the commun do clem.

743
00:36:01,880 --> 00:36:03,680
Speaker 5: I want to go back to the investors again and

744
00:36:03,760 --> 00:36:07,560
Speaker 5: talk about not just the money but the strategy. I

745
00:36:07,640 --> 00:36:10,800
Speaker 5: understand this is an all cash round, but you share

746
00:36:10,880 --> 00:36:15,680
Speaker 5: investors with some pretty big AI companies taken VideA for example,

747
00:36:15,680 --> 00:36:19,239
Speaker 5: which is backed Inflection AI. When the stuffer, Sallyman came

748
00:36:19,239 --> 00:36:21,680
Speaker 5: on to beat Bloomberg Technology of Me, he didn't care

749
00:36:21,680 --> 00:36:24,520
Speaker 5: about the cash. He cared about all the GPUs that

750
00:36:24,560 --> 00:36:28,360
Speaker 5: he'd secured from Nvidia. Is there any strategic or compute

751
00:36:28,360 --> 00:36:31,000
Speaker 5: advantage to this group you've assembled.

752
00:36:31,840 --> 00:36:34,600
Speaker 14: So all these companies they are using massively hugging Face

753
00:36:34,640 --> 00:36:38,080
Speaker 14: already and contributing massively to hugging Face. Collectively, they have

754
00:36:38,160 --> 00:36:42,280
Speaker 14: over ten thousand team members using the platform and they've

755
00:36:42,280 --> 00:36:47,080
Speaker 14: shared over one thousand open models on bugging Face. So

756
00:36:47,120 --> 00:36:51,000
Speaker 14: we plan to complict double down on that. However, something

757
00:36:51,040 --> 00:36:53,280
Speaker 14: that we wanted to do during this round is to

758
00:36:53,320 --> 00:36:57,440
Speaker 14: make sure that there's no strings attached. There's no commitment

759
00:36:58,040 --> 00:37:04,160
Speaker 14: on our sides in exchange to this this investment, so

760
00:37:04,160 --> 00:37:04,920
Speaker 14: it's a pure.

761
00:37:04,719 --> 00:37:08,080
Speaker 3: Cash deal, really separated.

762
00:37:07,560 --> 00:37:10,719
Speaker 14: To some of the other commercial collaborations that we can

763
00:37:10,760 --> 00:37:14,239
Speaker 14: have with these with these companies, of course, having them

764
00:37:14,400 --> 00:37:17,959
Speaker 14: aligned with us on the cap table is a good

765
00:37:18,000 --> 00:37:22,360
Speaker 14: strategic advantage to keep developing our partnerships with these companies.

766
00:37:24,239 --> 00:37:26,719
Speaker 13: I'm really curious to know what you're planning to do

767
00:37:26,840 --> 00:37:28,680
Speaker 13: with some of this money. We spot you and I

768
00:37:28,680 --> 00:37:30,560
Speaker 13: spoke the other day. You said you're going to put

769
00:37:30,600 --> 00:37:31,399
Speaker 13: some of it in the bank.

770
00:37:31,719 --> 00:37:32,520
Speaker 8: What are you going to do with.

771
00:37:32,440 --> 00:37:33,080
Speaker 3: The rest of it?

772
00:37:34,520 --> 00:37:37,799
Speaker 14: Yeah, we really think that, you know, we're on the

773
00:37:37,920 --> 00:37:41,960
Speaker 14: long term technology trend here. Even if AI is all

774
00:37:42,000 --> 00:37:46,160
Speaker 14: the rage right now, we're pretty early. Ultimately, we believe

775
00:37:46,160 --> 00:37:49,080
Speaker 14: it's going to be the default paradigm to build or tech.

776
00:37:50,160 --> 00:37:52,680
Speaker 14: So in this perspective, we're still early. So we want

777
00:37:52,719 --> 00:37:54,880
Speaker 14: to make sure that you know we're here at building

778
00:37:54,880 --> 00:37:57,440
Speaker 14: on the long term right in the next ten years.

779
00:37:58,560 --> 00:37:59,319
Speaker 7: However, we're going.

780
00:37:59,320 --> 00:38:01,879
Speaker 14: To use it of this money to keep growing the team.

781
00:38:02,080 --> 00:38:05,600
Speaker 14: We're one hundred and seventy team members right now and

782
00:38:05,640 --> 00:38:09,600
Speaker 14: we're planning to keep hiring quite a lot. And as

783
00:38:09,640 --> 00:38:13,480
Speaker 14: you know, hiring in AI right now is very competitive,

784
00:38:13,560 --> 00:38:17,400
Speaker 14: especially for the best people in science and engineering that

785
00:38:17,840 --> 00:38:19,880
Speaker 14: we're looking for taking place.

786
00:38:20,600 --> 00:38:22,800
Speaker 2: Really great to have some time with you, Clem DeLong.

787
00:38:22,920 --> 00:38:25,000
Speaker 2: We want to thank you hugging face CEO on the

788
00:38:25,040 --> 00:38:27,200
Speaker 2: latest round, and of course the person reporting on all

789
00:38:27,239 --> 00:38:29,880
Speaker 2: of that and asking brilliant questions, as Rachel Metz, we

790
00:38:29,960 --> 00:38:31,520
Speaker 2: thank her for all of her work. And look, let's

791
00:38:31,520 --> 00:38:34,080
Speaker 2: just stick with AI. We're just talking about Lama. Of course,

792
00:38:34,160 --> 00:38:36,120
Speaker 2: let's talk about Lara a little bit more. Meta just

793
00:38:36,200 --> 00:38:39,800
Speaker 2: launching a new artificial intelligence coding tool called code Lama

794
00:38:40,160 --> 00:38:43,279
Speaker 2: and uses Generator AI surprise surprize to help developers work

795
00:38:43,360 --> 00:38:46,279
Speaker 2: faster by suggesting lines of software code. One of those

796
00:38:46,360 --> 00:38:49,080
Speaker 2: ASH accounts joins us now, So it's a monetization coming

797
00:38:49,120 --> 00:38:49,719
Speaker 2: in here.

798
00:38:51,160 --> 00:38:52,759
Speaker 8: Yeah, exactly right.

799
00:38:52,800 --> 00:38:55,799
Speaker 16: They're taking Comma and they're allowing people to actually use

800
00:38:55,840 --> 00:38:59,040
Speaker 16: it for commercial use, which is an interesting play because

801
00:38:59,040 --> 00:39:00,640
Speaker 16: you have some companies that will be able to use

802
00:39:00,640 --> 00:39:02,759
Speaker 16: it for free and then make money off of it,

803
00:39:03,080 --> 00:39:05,600
Speaker 16: and then Meta might also charge some of the larger companies,

804
00:39:05,600 --> 00:39:07,359
Speaker 16: so there's a couple different ways that they can make money.

805
00:39:07,400 --> 00:39:11,000
Speaker 16: But it's a really good opportunity for companies that want

806
00:39:11,120 --> 00:39:14,480
Speaker 16: us a cheap or free sort of tool to be able.

807
00:39:14,320 --> 00:39:15,200
Speaker 8: To make money for free.

808
00:39:16,360 --> 00:39:18,720
Speaker 5: Caro Asha, I'm just going to say it. This looks

809
00:39:18,840 --> 00:39:21,920
Speaker 5: very similar to Microsoft's get hub. In fact, they're basically

810
00:39:21,920 --> 00:39:22,320
Speaker 5: the same.

811
00:39:22,680 --> 00:39:24,440
Speaker 3: Is that fair? Asia?

812
00:39:24,640 --> 00:39:27,520
Speaker 16: I think that's fair, and that's part of I think

813
00:39:27,560 --> 00:39:30,000
Speaker 16: the play here, right. I know Meta has talked about

814
00:39:30,000 --> 00:39:32,600
Speaker 16: wanting to make things open source and available to sort

815
00:39:32,600 --> 00:39:35,520
Speaker 16: of like democratize access, but you can't help but avoid

816
00:39:35,520 --> 00:39:37,600
Speaker 16: that comparison to gethub, right, like, this could be a

817
00:39:37,600 --> 00:39:41,680
Speaker 16: way to undercut GitHub if companies can use code Lama

818
00:39:41,800 --> 00:39:43,440
Speaker 16: and then not have to pay for gethub. I mean

819
00:39:43,680 --> 00:39:47,000
Speaker 16: you have to take a look at that too, all.

820
00:39:46,960 --> 00:39:49,160
Speaker 3: Right, Blomberg's Asia counts and all things matter.

821
00:39:57,120 --> 00:40:00,880
Speaker 11: I've had enough already tonight of a guy who sounds

822
00:40:00,960 --> 00:40:04,320
Speaker 11: like Chatchy BT standing up here.

823
00:40:06,080 --> 00:40:11,280
Speaker 2: Ool chat ChiPT getting a shout out presidential hopeful there,

824
00:40:11,360 --> 00:40:13,680
Speaker 2: Chris Christy. I mean he was attacking at the time

825
00:40:13,760 --> 00:40:16,480
Speaker 2: the other fellow candidate, vik Gramaswami, and this was more

826
00:40:16,520 --> 00:40:19,120
Speaker 2: about what he well, the way in which he says

827
00:40:19,160 --> 00:40:22,480
Speaker 2: that climate change is a hoax, for example. But interesting

828
00:40:22,480 --> 00:40:26,160
Speaker 2: that technology was so ingrained in last night's Republican predessidential debate.

829
00:40:26,920 --> 00:40:28,480
Speaker 5: That was the big takeaway for me. I think, you know,

830
00:40:28,520 --> 00:40:30,680
Speaker 5: he said that he sounded like chat GPT, but I

831
00:40:30,719 --> 00:40:32,280
Speaker 5: think you and I have both been over the search

832
00:40:32,400 --> 00:40:35,320
Speaker 5: data on Google Trends, right, So, Ramaswami, I think was

833
00:40:35,320 --> 00:40:40,080
Speaker 5: the most searched of the participants overnight. There's the data

834
00:40:40,120 --> 00:40:40,920
Speaker 5: that's astonishing.

835
00:40:41,280 --> 00:40:41,480
Speaker 3: You know.

836
00:40:41,840 --> 00:40:44,239
Speaker 5: I think he's kind of come into the public consciousness

837
00:40:44,239 --> 00:40:46,279
Speaker 5: because of last night. And at the same time, Caro,

838
00:40:46,600 --> 00:40:49,759
Speaker 5: you have a completely different on air discussion happening on

839
00:40:49,800 --> 00:40:50,640
Speaker 5: the platform X.

840
00:40:51,040 --> 00:40:54,480
Speaker 2: Yeah, I mean the rub for Fox, I suppose, having

841
00:40:54,800 --> 00:40:57,520
Speaker 2: said goodbye to Tucker Carlson and then at exactly the

842
00:40:57,520 --> 00:41:00,400
Speaker 2: same time he's busy interviewing Donald Trump f used to

843
00:41:00,400 --> 00:41:03,040
Speaker 2: come on the overall debates in an interview on X.

844
00:41:03,080 --> 00:41:06,160
Speaker 2: It was sort of counterprogramming, which of course is still

845
00:41:06,200 --> 00:41:07,320
Speaker 2: being fought out at the moment.

846
00:41:07,960 --> 00:41:08,160
Speaker 3: Yeah.

847
00:41:08,200 --> 00:41:11,160
Speaker 5: And from X's perspective, you know, video on that platform,

848
00:41:11,280 --> 00:41:13,200
Speaker 5: it raised a lot of questions. When I last looked

849
00:41:13,200 --> 00:41:15,880
Speaker 5: at the tweet, the videos post to it said two

850
00:41:16,000 --> 00:41:18,680
Speaker 5: hundred and ten million views, But does that really mean

851
00:41:18,719 --> 00:41:20,840
Speaker 5: two hundred and ten million people watch the full forty

852
00:41:20,840 --> 00:41:21,600
Speaker 5: six minute thing.

853
00:41:22,080 --> 00:41:23,120
Speaker 3: We have to find out, And I.

854
00:41:23,160 --> 00:41:25,359
Speaker 2: Wonder how Ronda Santas felt about it all of course,

855
00:41:25,400 --> 00:41:29,280
Speaker 2: having announced his run on X well, it was formerly

856
00:41:29,280 --> 00:41:31,399
Speaker 2: known as Twitter at that point, right, and it well

857
00:41:31,640 --> 00:41:35,600
Speaker 2: wasn't the most slick introduction as it happened, full of

858
00:41:35,640 --> 00:41:36,719
Speaker 2: tech issues at that point.

859
00:41:36,719 --> 00:41:39,120
Speaker 4: But meanwhile, no tick issues here. That does it for

860
00:41:39,160 --> 00:41:40,520
Speaker 4: this edition of Bloomberg Technology.

861
00:41:41,040 --> 00:41:43,520
Speaker 5: Yeah, massive week, massive show, all about in video, So

862
00:41:43,520 --> 00:41:46,920
Speaker 5: don't forget to recap on our podcast, Apple, Spotify, iHeart

863
00:41:46,960 --> 00:41:49,799
Speaker 5: and our Bloomberg platforms. We have one day in a

864
00:41:49,840 --> 00:41:52,040
Speaker 5: mega week to go from over in New York and

865
00:41:52,080 --> 00:41:53,120
Speaker 5: here in San Francisco.

866
00:41:53,480 --> 00:41:54,839
Speaker 3: This is Bloomberg Technology