WEBVTT - GM Joins Tesla and AI Investing

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<v Speaker 1>From Mahart that we're Innovation, Money and Power Collie in

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<v Speaker 1>Silicon Valley, NBN.

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<v Speaker 2>This is Bloomberg Technology with Caroline Hyde and Ed Ludlow.

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<v Speaker 3>I'm Caroline hide a Bloomberg's World headquarters in New York,

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<v Speaker 3>and I'm Ed Ludlow out here in San Francisco.

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<v Speaker 4>This is Bloomberg Technology coming up.

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<v Speaker 3>Ed will break down GM's decision to join Tesla's charging network.

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<v Speaker 5>This is the ev makerheads for a record setting rally.

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<v Speaker 6>Plus, we discuss all things AI, from its use cases

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<v Speaker 6>in the field of law to investing in the space

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<v Speaker 6>with venture capitalist Wesley.

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<v Speaker 3>Chat And for today's Bloomberg Big Take, we'll discuss how

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<v Speaker 3>artificial intelligence is taking racial and gender stereotypes to new extremes.

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<v Speaker 5>But first let's check in on these markets.

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<v Speaker 3>Ed, we're kind of muted ahead of the Federal Reserve

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<v Speaker 3>next week. This is a macro perspective on how much

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<v Speaker 3>the Federal Reserve will pause, will they then hike in July.

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<v Speaker 3>But at the moment, we've have seen the downward trajectory

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<v Speaker 3>for the dollar. On the Weekles's in the s and

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<v Speaker 3>P five hundred, managing to get into that ballmarket territory

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<v Speaker 3>twenty percent off of it's October lows. We're currently clinging

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<v Speaker 3>to gains only up two and a half. Now's that

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<v Speaker 3>one hundred up higher, up to tens percent, some big

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<v Speaker 3>tech names. When you're driving us higher, I know you'll

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<v Speaker 3>dig into that. Blubo Dollar Index actually just basically treading

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<v Speaker 3>water on the day, but actually has been selling off

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<v Speaker 3>over the course of the week as many start to

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<v Speaker 3>buy in on the fact that maybe the FED can.

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<v Speaker 5>Pause this hiking cycle.

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<v Speaker 3>Now's some a little look at what's happening in terms

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<v Speaker 3>of risk.

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<v Speaker 5>As said of.

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<v Speaker 3>Choice, we're currently seeing bitcoin having a pretty tortuous few

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<v Speaker 3>days five day run and we're off by two point

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<v Speaker 3>nine percent.

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<v Speaker 5>We're still clinging to that twenty six thousand there and thereabouts,

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<v Speaker 5>But all.

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<v Speaker 3>Of this comes on the back of some significant regulatory headwinds.

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<v Speaker 3>Look at that downward trajectory when we got the news

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<v Speaker 3>on binance and of course some coinbas when it comes

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<v Speaker 3>to what the SEC is doing. So notable that at

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<v Speaker 3>the moment we're not seeing that move into textocs being

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<v Speaker 3>reciprocated by moving in to bitcoin ed.

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<v Speaker 6>One big influence on this market is Tesla up five percent.

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<v Speaker 6>It's trading in its highest level since early October. End

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<v Speaker 6>of September, the news General Motors is adopting Tesla's North

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<v Speaker 6>America charging standard. It is also having upside impact for GM,

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<v Speaker 6>up two percent.

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<v Speaker 4>But look at the downside.

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<v Speaker 6>Movers Evego charge Point principally CCS standard offerings for charging

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<v Speaker 6>networks really taking a hit. The Tesla name has momentum.

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<v Speaker 6>Just quickly look at this terminal chart. We're up for

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<v Speaker 6>eleventh straight session. That matches the streak of games we

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<v Speaker 6>saw at the beginning of twenty twenty one, eleven days

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<v Speaker 6>that ended in early twenty twenty one. We don't often

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<v Speaker 6>get technical on this show, and I'm not showing it

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<v Speaker 6>in this chart, but I think about relative strength index.

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<v Speaker 6>This is a stock that's at an eighty six level,

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<v Speaker 6>very heavily and overbought territory. It is trading above its

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<v Speaker 6>two hundred, one hundred and fifty day moving average. I

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<v Speaker 6>raised that because it has momentum. But how long does

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<v Speaker 6>this last? Really impressive outperformance, but what about the details here?

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<v Speaker 6>We heard from GMCO Mary Barra on a Twitter spaces

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<v Speaker 6>with Elon Musk.

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<v Speaker 7>As we looked at this and realized we could really

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<v Speaker 7>address one of the biggest issues that customers are telling

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<v Speaker 7>us is, Hey, you know, I like evs, but if

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<v Speaker 7>it's going to be my only vehicle, I need to

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<v Speaker 7>know that there's a robust charging infrastructure. So I think

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<v Speaker 7>this gives us a huge opportunity to do something that's

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<v Speaker 7>better for customers and to drive it to be the standard.

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<v Speaker 6>Is this just the next domino to fall? Is this

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<v Speaker 6>something much bigger? Let's get the details with Bloomberg's Detroit

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<v Speaker 6>Bura G David Welch. David tell us how GM's going

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<v Speaker 6>to pull this off.

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<v Speaker 2>Well, they're basically going to equip their cars starting in

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<v Speaker 2>twenty twenty five with an import that can take Teslass chargers.

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<v Speaker 2>Before then they'll have the customers, the owners of those vehicles.

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<v Speaker 2>I have to use some kind of adapter so that

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<v Speaker 2>they can use Testla's vehicles. But basically it gives these

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<v Speaker 2>gives GM electric vehicle owners and forward for that matter,

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<v Speaker 2>because they announce this deal is a similar deal previously

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<v Speaker 2>gives them access to twelve thousand Tesla fast chargers and

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<v Speaker 2>some more of their regular chargers. So it gives these

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<v Speaker 2>people a lot of options with probably minimal impact on

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<v Speaker 2>their lives and not a lot of investment either.

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<v Speaker 5>Honest, David saying, another win for Tesla. How does this

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<v Speaker 5>actually help the revenue of Tesla? How does this well

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<v Speaker 5>help the business model of Tesla.

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<v Speaker 2>So, Tesla, they still are going to make all of

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<v Speaker 2>their money or most of their money selling cars, but

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<v Speaker 2>they do have this business of charging where they do

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<v Speaker 2>make some revenue. It's not huge money. Estimates are that

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<v Speaker 2>Tesla can make it anywhere from one and a half

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<v Speaker 2>billion to three billion by twenty thirty and even more

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<v Speaker 2>after that with all of these customers of other companies

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<v Speaker 2>cars that will use their charging network. And if you

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<v Speaker 2>think about it, Tesla has by far right now the

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<v Speaker 2>most reliable EV charging network and it's also the most

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<v Speaker 2>mature compared to this patchwork quilt of other EV companies,

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<v Speaker 2>ev Go, charge Point, others they either provide hardware services,

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<v Speaker 2>and JD Power says Tesla is the most reliable. So

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<v Speaker 2>I think more and more customers of other people's vehicles

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<v Speaker 2>will start using Tesla chargers because the apps as they're available,

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<v Speaker 2>they're actually available when the people get there, the charger

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<v Speaker 2>actually works. It's a big problem for EV drivers. So

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<v Speaker 2>with a better network, they'll went out and they will

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<v Speaker 2>make revenue off of their rivals customers in the same

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<v Speaker 2>way they've made revenue and profits off of regulatory credits

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<v Speaker 2>for years. All this kind of it may not be

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<v Speaker 2>huge money, but it's still helping Tesla at sort of

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<v Speaker 2>an opportunity cost to the other car companies who don't

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<v Speaker 2>have their own networks.

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<v Speaker 6>David on that point about that Tesla's networks reach and maturity,

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<v Speaker 6>We actually have this chart from Bloomberg and you Energy

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<v Speaker 6>Finance which shows the Tesla fast charger network relative to ccs,

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<v Speaker 6>and the chart tells the story, right, It is just

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<v Speaker 6>so much bigger here in the United States. But the

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<v Speaker 6>reason I bring up as well is the title of

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<v Speaker 6>the Bloomberg NIF research. GM cruels back to Twitter and

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<v Speaker 6>Tesla to save it's ev ambitions. You know GM better

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<v Speaker 6>than anyone on the planet pretty much. What did you

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<v Speaker 6>make of Mary barrr breaking her Twitter silence to make

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<v Speaker 6>this announcement?

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<v Speaker 8>Oh?

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<v Speaker 2>Look, first off, when Ford made the announcement, I went

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<v Speaker 2>to GM and said are you following next? And you

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<v Speaker 2>know they sort of hemmed in had I think they

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<v Speaker 2>realized once Ford did that, they had to go in.

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<v Speaker 2>And you know, Tesla and GM I've sort of traded

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<v Speaker 2>barbs over the years, subtly at times, not so subtly

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<v Speaker 2>at others. So I do think they didn't love having

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<v Speaker 2>to do this. But ultimately Mary Barr is an extremely

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<v Speaker 2>pragmatic CEO, and if this will help her sell electric vehicles,

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<v Speaker 2>if this will help her customers stay happy, I think

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<v Speaker 2>she knows she has to do it, and I think,

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<v Speaker 2>you know, going on Twitter to do it is probably

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<v Speaker 2>not their favorite way of putting news out, but it

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<v Speaker 2>would also also very very effective sitting on the stage,

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<v Speaker 2>all right. Rather it was done by Zoomer actually, I think,

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<v Speaker 2>but speaking with Elon Musk about this is going to

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<v Speaker 2>give them a lot of publicity for the fact that

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<v Speaker 2>they're changing their charging network.

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<v Speaker 6>You know, Caroline, this is clearly giving Tesla Antiema boost.

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<v Speaker 6>But again, let's go back to those movies to the

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<v Speaker 6>downside and some of the rivals out there in the network.

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<v Speaker 3>Yeah, Devid to that point, you mentioned EVgo, but also Pilot,

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<v Speaker 3>there was a joint venture already announced by GM as

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<v Speaker 3>you referenced. Who does this hurt perhaps more in the

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<v Speaker 3>startup space and the areas that you know, I think

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<v Speaker 3>of here in New York, the announcements that are being

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<v Speaker 3>made of all the EV charging that's going to be

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<v Speaker 3>put in the capacity, and it was helping players other

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<v Speaker 3>than Tesla, who are the other battery operators, I mean

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<v Speaker 3>the charging operators out there who perhaps stand to forfeit here.

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<v Speaker 2>Yeah, look, this is going to be tough on them,

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<v Speaker 2>at least in the near term, I think medium long

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<v Speaker 2>term they can adapt if Tussel's chargers are more reliable

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<v Speaker 2>and I on a Cadillac Lyric are a four one

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<v Speaker 2>fifty lightning electric vehicle, and I can use it six

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<v Speaker 2>months from now because I get an adapter. Why wouldn't

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<v Speaker 2>I do that if I know it's a better network.

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<v Speaker 2>So they're going to lose potential revenue on this thing,

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<v Speaker 2>and that's what the market's looking at. I think it's

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<v Speaker 2>going to be tough on all of these companies, and

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<v Speaker 2>they're already in a not great financial position, you know,

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<v Speaker 2>kind of speaking broadly as a charging industry, and the

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<v Speaker 2>stocks are not doing that great either, So it's not

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<v Speaker 2>like they have this amazing ability to raise capital because

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<v Speaker 2>they're not loved as investments.

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<v Speaker 3>David Welch, as you say, GM aficionado, EV aficionado charger Officionado,

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<v Speaker 3>We thank you so much for Detroit.

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<v Speaker 6>A New York lawyer told a judge he never meant

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<v Speaker 6>to fool anybody when he filed a court brief full

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<v Speaker 6>of phony legal precedents invented by chat GPT. The lawyer

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<v Speaker 6>who faces punishment to the brief are the US district

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<v Speaker 6>judge for leniency, claiming he had no idea the free

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<v Speaker 6>artificial intelligence school could create fake case citations and court opinions.

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<v Speaker 6>Generative AI tools promise to disrupt industries, but the technology

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<v Speaker 6>also has many risks associated with it, so staying in

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<v Speaker 6>the field of AI and the law. Even Up is

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<v Speaker 6>a generative AI startup focused on personal injury cases with

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<v Speaker 6>trained models using hundreds of thousands of pages of medical

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<v Speaker 6>records cases compiled into possibly the largest ever repository of

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<v Speaker 6>case data. The company just announced its series being round

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<v Speaker 6>led by Best Adventure Partners and joining us now is

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<v Speaker 6>Ramy Kareba bar even Up CEO and Samir de Lakia,

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<v Speaker 6>partner with Best Some Adventures.

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<v Speaker 4>And Sami, I'm going to start with you.

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<v Speaker 6>Sure a month ago you came on this program and

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<v Speaker 6>told Caroline and I, I have a billion dollars to

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<v Speaker 6>invest in artificial intelligence yep, And I said, well, how

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<v Speaker 6>are you going to do it? And you didn't fully

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<v Speaker 6>answer the question. This goes some way to answering the question.

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<v Speaker 9>In the there has been an outpouring of interest, of course,

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<v Speaker 9>by entrepreneurs around the world inbound coming in saying, hey,

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<v Speaker 9>this is exactly these are the folks to come and

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<v Speaker 9>help us launch our ideas, and this is what we

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<v Speaker 9>knew would happen. Whenever you have a paradigm shift as

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<v Speaker 9>profound as large language models and generative AI, the creative

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<v Speaker 9>juices of all the entrepreneurs around the world get going

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<v Speaker 9>and they try to solve novel problems in a novel way.

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<v Speaker 4>And that's what we've seen and even.

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<v Speaker 9>Up as one of the very first examples we've seen

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<v Speaker 9>coming out of this allocation of that billion.

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<v Speaker 3>Dollars, Robbie, that creative juices that got flowing. We're actually

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<v Speaker 3>started in twenty twenty, and I'm interested how much has

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<v Speaker 3>generative AI changed the game for you?

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<v Speaker 5>How much?

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<v Speaker 3>And already you been iterating on making legal work in

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<v Speaker 3>particular that much easier, and is sort of the momentum

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<v Speaker 3>around chatchipt just helping with a cell cycle.

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<v Speaker 1>For sure, we've been experimenting with generative models since the

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<v Speaker 1>founding of the company, and we also use discriminative models

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<v Speaker 1>as well.

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<v Speaker 8>And the way you can think about.

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<v Speaker 1>What we do is we turn raw case files, typically

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<v Speaker 1>medical reports and police reports, into AI generated legal documents

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<v Speaker 1>for injury attorneys. These legal documents value what these personal

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<v Speaker 1>injury cases are worth, enabling injury attorneys not only to

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<v Speaker 1>save time in legal drafting, but also maximize the value

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<v Speaker 1>of their claims and helping the many millions of Americans

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<v Speaker 1>they get injured every year in the US.

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<v Speaker 6>Romie, what is your response to the story that we

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<v Speaker 6>outlined at the beginning of this segment, a lawyer who

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<v Speaker 6>now has to defend himself because he relied on fake

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<v Speaker 6>legal precedence that chat GPT gave him. What is the

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<v Speaker 6>risk for your company in that context?

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<v Speaker 1>I think it really comes down to how you're using

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<v Speaker 1>generative models. The way our approach to generative AI is

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<v Speaker 1>very different than a lot of other companies were strictly

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<v Speaker 1>focused on one type of legal letter called the demand letter,

0:11:49.320 --> 0:11:51.400
<v Speaker 1>and this is where the way we use generative models

0:11:51.400 --> 0:11:51.600
<v Speaker 1>is in a.

0:11:51.600 --> 0:11:52.600
<v Speaker 8>Pretty confined way.

0:11:52.960 --> 0:11:55.280
<v Speaker 1>We feuded a handful of inputs and we're asking it

0:11:55.320 --> 0:11:56.760
<v Speaker 1>to summarize it in pros.

0:11:57.200 --> 0:11:57.680
<v Speaker 8>Since we're not.

0:11:57.760 --> 0:12:00.439
<v Speaker 1>Scaring the web or asking for or a lot of

0:12:00.520 --> 0:12:03.320
<v Speaker 1>different types of use cases for general models, the risk

0:12:03.360 --> 0:12:07.920
<v Speaker 1>of hallucinations is significantly less. And separately, our demands are

0:12:07.920 --> 0:12:10.680
<v Speaker 1>acuated by a team of legal professionals. We've done thousands

0:12:10.720 --> 0:12:13.720
<v Speaker 1>of these cases, and our clients haven't seen those types

0:12:13.760 --> 0:12:16.320
<v Speaker 1>of results of hallucinations impacting their business.

0:12:16.880 --> 0:12:19.679
<v Speaker 3>Submitted to that end, you know how, was even not

0:12:19.760 --> 0:12:24.160
<v Speaker 3>attractive because it is such a specific application of generative

0:12:24.200 --> 0:12:27.320
<v Speaker 3>AI and AI in general. Are you looking for those

0:12:27.360 --> 0:12:31.880
<v Speaker 3>specific areas, particularly in healthcare, particularly in legal work, or

0:12:31.920 --> 0:12:34.640
<v Speaker 3>are you seeing more broad applications that excite you to.

0:12:36.200 --> 0:12:38.559
<v Speaker 9>Certainly all the above, although I would say even up

0:12:38.760 --> 0:12:42.520
<v Speaker 9>is a perfect company for a Bessemer venture partners to

0:12:42.600 --> 0:12:45.280
<v Speaker 9>invest in. Bestmer has for the last ten or fifteen

0:12:45.360 --> 0:12:48.000
<v Speaker 9>years been I would argue, the leading venture capital firm

0:12:48.360 --> 0:12:51.840
<v Speaker 9>investing in vertical SaaS companies. So whether it's Toast in

0:12:51.880 --> 0:12:56.760
<v Speaker 9>the restaurant industry, Service Titan for plumbers and electricians, pro

0:12:56.920 --> 0:13:00.800
<v Speaker 9>Core and construction Shopify for e commerce, been focused on

0:13:00.880 --> 0:13:03.880
<v Speaker 9>investing in companies that solve the particular problem set of

0:13:03.920 --> 0:13:07.080
<v Speaker 9>problems for a vertical industry. And of course the legal

0:13:07.120 --> 0:13:11.640
<v Speaker 9>industry is the perfect vertical industry for this disruption in

0:13:11.720 --> 0:13:14.480
<v Speaker 9>large language models. All they do every day as lawyers

0:13:14.520 --> 0:13:18.960
<v Speaker 9>is deal in pros in language. And so this is

0:13:19.000 --> 0:13:22.200
<v Speaker 9>at the absolute intersection sweet spot of what Bessemer likes

0:13:22.240 --> 0:13:24.080
<v Speaker 9>to invest in Smith.

0:13:24.240 --> 0:13:26.400
<v Speaker 6>What was doing this deal? Like what does the cap

0:13:26.440 --> 0:13:29.040
<v Speaker 6>table look like at the moment as well? Like, yes,

0:13:29.120 --> 0:13:32.040
<v Speaker 6>you're on this, so it's Spain. So are many others. Yeah,

0:13:32.400 --> 0:13:35.880
<v Speaker 6>you know who's convincing? Who you convincing? Rammy, Remy convincing you.

0:13:36.679 --> 0:13:40.640
<v Speaker 6>It is a very competitive environment. Ronny is an incredible entrepreneur.

0:13:40.880 --> 0:13:43.640
<v Speaker 9>There were he could tell you how many venture capital

0:13:43.640 --> 0:13:46.840
<v Speaker 9>firms he had interest in in it. We were privileged

0:13:46.840 --> 0:13:49.080
<v Speaker 9>that he chose to work with us.

0:13:49.160 --> 0:13:51.520
<v Speaker 4>Very polite answer, and and we have many friends.

0:13:51.720 --> 0:13:55.200
<v Speaker 9>The folks of being capital ventures are friends of ours from.

0:13:55.320 --> 0:13:58.160
<v Speaker 4>A decade long. So you're seeing more and more of that.

0:13:58.240 --> 0:14:02.400
<v Speaker 9>I think partnering go on on, but in spirit of serving,

0:14:02.760 --> 0:14:05.199
<v Speaker 9>and I think you'll always hear that from from Investmer.

0:14:05.559 --> 0:14:07.920
<v Speaker 9>We're in service of our entrepreneurs and in service of

0:14:07.920 --> 0:14:08.440
<v Speaker 9>those companies.

0:14:08.480 --> 0:14:10.720
<v Speaker 6>So Romie, I'll give you the opportunity to give your

0:14:10.720 --> 0:14:11.480
<v Speaker 6>perspective on that.

0:14:11.720 --> 0:14:13.400
<v Speaker 4>I would note, you know, do not pay.

0:14:14.400 --> 0:14:16.240
<v Speaker 6>Josh Brown has been on the show with Carolina and

0:14:16.320 --> 0:14:19.760
<v Speaker 6>I they do something quite similar as well. So how

0:14:19.800 --> 0:14:22.160
<v Speaker 6>did you stand out to all these bench catalysts that

0:14:22.200 --> 0:14:23.080
<v Speaker 6>were fighting over you?

0:14:24.360 --> 0:14:24.720
<v Speaker 8>For sure?

0:14:24.800 --> 0:14:26.920
<v Speaker 1>And our approach was just to do one type of

0:14:26.960 --> 0:14:30.120
<v Speaker 1>document and more affordably, more accurately and faster than a

0:14:30.200 --> 0:14:32.560
<v Speaker 1>human can on their own. And knock on wood, we've

0:14:32.600 --> 0:14:35.160
<v Speaker 1>had some pretty good traction. It speaks volumes, just at

0:14:35.200 --> 0:14:37.560
<v Speaker 1>the value proposition, being able to deliver it to injury

0:14:37.560 --> 0:14:41.200
<v Speaker 1>attorneys and again, ultimately their clients were injured us. What

0:14:41.320 --> 0:14:43.360
<v Speaker 1>is most important is finding a partner that is online

0:14:43.360 --> 0:14:46.160
<v Speaker 1>in that mission, and I think Samor sold them a

0:14:46.200 --> 0:14:48.239
<v Speaker 1>little bit short there. But I think I think Investmor's

0:14:48.360 --> 0:14:51.280
<v Speaker 1>lead in Vertical SaaS, I think Samir had more general

0:14:51.280 --> 0:14:54.320
<v Speaker 1>to our investments in any other investor that we've spoken to.

0:14:54.440 --> 0:14:57.280
<v Speaker 1>Specifically with Vertical SaaS, it admitted an easy decision for

0:14:57.360 --> 0:14:58.240
<v Speaker 1>us to choose s Bestimer.

0:14:58.440 --> 0:15:01.240
<v Speaker 3>Robbie, you just mentioned there quicker than a human can.

0:15:01.760 --> 0:15:02.960
<v Speaker 3>Is this going to reduce jobs?

0:15:04.360 --> 0:15:05.320
<v Speaker 8>We have not seen that.

0:15:05.800 --> 0:15:08.160
<v Speaker 1>Ultimately, if you think about personal injury, you're talking about

0:15:08.200 --> 0:15:10.920
<v Speaker 1>some of the most vonderable times in individuals' lives. You're

0:15:11.000 --> 0:15:12.560
<v Speaker 1>living a normal life, then all of a sudden, something

0:15:12.560 --> 0:15:15.160
<v Speaker 1>really had happened to you. I can't imagine a human

0:15:15.240 --> 0:15:17.680
<v Speaker 1>not being there to help help these folks share through

0:15:17.680 --> 0:15:20.080
<v Speaker 1>the personal injury claims process. From what we've seen from

0:15:20.080 --> 0:15:22.720
<v Speaker 1>our clients, they're spending last time on document review and

0:15:22.760 --> 0:15:25.040
<v Speaker 1>document drafting, and they're spending more times with our clients.

0:15:25.240 --> 0:15:27.480
<v Speaker 1>We're taking on more clients that they've ever had before.

0:15:28.280 --> 0:15:32.240
<v Speaker 3>Ami Caravaba, thank you very much. Indeed, even up CEO Sama,

0:15:32.360 --> 0:15:34.000
<v Speaker 3>great to have some time with you as well. Sammy,

0:15:34.160 --> 0:15:36.920
<v Speaker 3>are lacking a partner at best in the venture, but partners,

0:15:37.400 --> 0:15:39.160
<v Speaker 3>that's so much for joining us. That was a really

0:15:39.240 --> 0:15:43.120
<v Speaker 3>interesting roundtable about all things generative applications. Meanwhile, coming up,

0:15:43.480 --> 0:15:46.120
<v Speaker 3>we're on the fallout actually from the Securities and Exchange

0:15:46.120 --> 0:15:50.040
<v Speaker 3>Commissions lawsuit against Finance. What the crypto exchange platform is

0:15:50.040 --> 0:15:51.119
<v Speaker 3>telling its customers.

0:15:51.480 --> 0:15:54.160
<v Speaker 5>From New York. From San Francisco, Disi Bloomberg.

0:16:08.600 --> 0:16:10.440
<v Speaker 4>It is time for talking tech.

0:16:10.480 --> 0:16:14.120
<v Speaker 6>First up, Taiwan semiconductor Manufacturing, seeing a dip in revenue

0:16:14.360 --> 0:16:16.800
<v Speaker 6>for a third straight month. Sales at the world's largest

0:16:16.800 --> 0:16:20.000
<v Speaker 6>supplier have made to order. Chip slid five percent, which

0:16:20.000 --> 0:16:23.560
<v Speaker 6>indicates that the sector's slump has yet to bottom. TSMC

0:16:23.640 --> 0:16:27.040
<v Speaker 6>executive has signaled a gradual recovery in the second half

0:16:27.080 --> 0:16:29.600
<v Speaker 6>of the year and a resumption of growth in twenty

0:16:29.640 --> 0:16:33.480
<v Speaker 6>twenty four. And Binance US is telling its customers to

0:16:33.520 --> 0:16:36.360
<v Speaker 6>take action in order to protect their assets following the

0:16:36.400 --> 0:16:39.520
<v Speaker 6>SEC's lawsuit. This comes as its banking partners seek to

0:16:39.600 --> 0:16:43.360
<v Speaker 6>pause US dollar channels for deposits and withdrawals as early

0:16:43.360 --> 0:16:45.960
<v Speaker 6>as June thirteenth. In an email to customers which was

0:16:46.000 --> 0:16:48.800
<v Speaker 6>also posted on Twitter, the company says, we are taking

0:16:48.840 --> 0:16:53.120
<v Speaker 6>these proactive steps as we transition to a crypto only exchange.

0:16:53.120 --> 0:16:56.600
<v Speaker 6>We will continue to vigorously defend ourselves, our customers, and

0:16:56.720 --> 0:17:00.000
<v Speaker 6>industry against the meritless attacks of the SEC.

0:17:00.320 --> 0:17:01.440
<v Speaker 4>End quote plus.

0:17:01.560 --> 0:17:04.560
<v Speaker 6>Crypto firms are reshaping the landscape with its come to

0:17:04.640 --> 0:17:07.240
<v Speaker 6>a banking system in the US. Firms are turning to

0:17:07.320 --> 0:17:11.160
<v Speaker 6>smaller regional lenders to open accounts. Swiss and Asian banks

0:17:11.160 --> 0:17:13.880
<v Speaker 6>were also playing a bigger role as to where assets

0:17:13.880 --> 0:17:17.640
<v Speaker 6>are placed. The changes come amid increased scrutiny from regulators

0:17:17.920 --> 0:17:19.400
<v Speaker 6>against that industry.

0:17:19.640 --> 0:17:24.160
<v Speaker 3>Caroline, in fact, cryptoregulation and all that was being discussed

0:17:24.359 --> 0:17:27.159
<v Speaker 3>in a venture capital conversation I had yesterday, Ed, I

0:17:27.200 --> 0:17:29.240
<v Speaker 3>got a chance to sit down with Rebeca Cayden of

0:17:29.760 --> 0:17:32.000
<v Speaker 3>Union Square Ventures based right here in New York. Dina

0:17:32.040 --> 0:17:34.600
<v Speaker 3>Shaker of Lux Capital as well, had flown in from

0:17:34.600 --> 0:17:37.160
<v Speaker 3>the West Coast where you are for the Bloomberg invest conference,

0:17:37.200 --> 0:17:40.159
<v Speaker 3>and actually when it turned to AI, they expressed some

0:17:40.640 --> 0:17:43.560
<v Speaker 3>optimism there the role in the shift that we're seeing

0:17:43.640 --> 0:17:44.640
<v Speaker 3>in fintech and healthcare.

0:17:44.720 --> 0:17:48.119
<v Speaker 10>Just ticul listen, I'm an optimist. I think I'm partly

0:17:48.160 --> 0:17:52.639
<v Speaker 10>an optimist by trade and overall, but particularly here. I

0:17:52.680 --> 0:17:57.440
<v Speaker 10>think the opportunities set to unmark and the efficiencies to create,

0:17:57.520 --> 0:18:01.439
<v Speaker 10>and the skills and abilities that we can't think of

0:18:01.520 --> 0:18:07.360
<v Speaker 10>yet are going to outweigh the risks which are there

0:18:07.400 --> 0:18:10.480
<v Speaker 10>and are are relevant, but are going to be manageable.

0:18:11.040 --> 0:18:13.439
<v Speaker 3>Okay, I like that they're manageable. Dan, Do you have

0:18:13.520 --> 0:18:16.480
<v Speaker 3>that same optimism that ultimately all the ringing of hands,

0:18:16.520 --> 0:18:20.560
<v Speaker 3>worries about job implications bias will ultimately be outweighed by

0:18:20.600 --> 0:18:23.120
<v Speaker 3>the positives and productivity and how it's going to help

0:18:23.200 --> 0:18:24.680
<v Speaker 3>your particular area of expertise, like.

0:18:24.640 --> 0:18:25.760
<v Speaker 5>Healthcare, absolutely.

0:18:25.760 --> 0:18:27.760
<v Speaker 11>I mean, we've been investing in AI for years, so

0:18:27.800 --> 0:18:30.080
<v Speaker 11>if certainly there is a lot that's new now, it

0:18:30.080 --> 0:18:32.560
<v Speaker 11>does feel like a paradigm shift. But we were early

0:18:32.600 --> 0:18:35.960
<v Speaker 11>investors in Hugging Face and Runway. In the healthcare space,

0:18:35.960 --> 0:18:37.720
<v Speaker 11>we actually sit on a board together of a company

0:18:37.720 --> 0:18:40.120
<v Speaker 11>called a Life Health, which is using AI to transform

0:18:40.240 --> 0:18:41.040
<v Speaker 11>fertility care.

0:18:41.680 --> 0:18:42.960
<v Speaker 7>There is still so much.

0:18:42.800 --> 0:18:45.560
<v Speaker 11>More and we're incredibly excited about the potential, of course,

0:18:45.640 --> 0:18:49.600
<v Speaker 11>notwithstanding the regulatory environment that we're in right now and

0:18:49.640 --> 0:18:52.879
<v Speaker 11>making sure that everything is done safely and also without bias.

0:18:53.320 --> 0:18:55.159
<v Speaker 5>Go to regulation in a moment.

0:18:55.320 --> 0:18:58.879
<v Speaker 3>But to the point of where AI is really disrupting?

0:18:58.960 --> 0:19:00.840
<v Speaker 5>Are you where's your rain space app?

0:19:00.920 --> 0:19:04.159
<v Speaker 3>Is it more inbound people wanting money, people cooling themselves,

0:19:04.240 --> 0:19:05.879
<v Speaker 3>ultificial intelligence companies you try to.

0:19:05.960 --> 0:19:06.919
<v Speaker 5>Sort wheat from chaff?

0:19:07.359 --> 0:19:09.000
<v Speaker 3>Or is it how do I ensure the port photio

0:19:09.040 --> 0:19:12.280
<v Speaker 3>Companies I have that aren't truly built on AI are

0:19:12.320 --> 0:19:13.240
<v Speaker 3>ready for that disruption?

0:19:13.480 --> 0:19:14.640
<v Speaker 5>I mean, I think it's a little of both.

0:19:14.680 --> 0:19:17.080
<v Speaker 11>Obviously, as a consumer I love using it for bedtime

0:19:17.119 --> 0:19:19.920
<v Speaker 11>stories for my kids and you know, recipes and so on.

0:19:20.119 --> 0:19:22.040
<v Speaker 11>But as an investor, we're looking at what the really

0:19:22.040 --> 0:19:24.400
<v Speaker 11>big platform shifts are going to be and I think

0:19:24.440 --> 0:19:26.280
<v Speaker 11>any tech company right now needs to take a really

0:19:26.280 --> 0:19:28.800
<v Speaker 11>close and deep look at how they're using AI to

0:19:28.840 --> 0:19:33.080
<v Speaker 11>make themselves more efficient, particularly in well, frankly, in every field.

0:19:33.080 --> 0:19:34.560
<v Speaker 11>But I would say when it comes to healthcare, there's

0:19:34.680 --> 0:19:38.640
<v Speaker 11>so much opportunity, so much latency, so much bureaucracy.

0:19:39.040 --> 0:19:41.159
<v Speaker 5>But it's a very different world when you're still.

0:19:40.880 --> 0:19:43.840
<v Speaker 11>Talking about fax machines in the same sentence.

0:19:43.520 --> 0:19:47.280
<v Speaker 3>As chat gpt Well said, And what about the fintech space, Rebecca,

0:19:47.320 --> 0:19:49.240
<v Speaker 3>and in this is an area that you'll particularly got

0:19:49.240 --> 0:19:50.240
<v Speaker 3>some expertise in as well.

0:19:50.280 --> 0:19:52.040
<v Speaker 5>Across the board as well as healthcare.

0:19:52.600 --> 0:19:55.159
<v Speaker 3>Some have said maybe fintech isn't the first era of

0:19:55.160 --> 0:19:55.960
<v Speaker 3>adoption for AI.

0:19:56.160 --> 0:19:57.040
<v Speaker 5>What would you make of that?

0:19:58.320 --> 0:20:02.239
<v Speaker 10>I think that the enco across industries and healthcare and

0:20:02.280 --> 0:20:07.359
<v Speaker 10>fintech and education have a big opportunity here, less because

0:20:07.359 --> 0:20:09.679
<v Speaker 10>of their industry specific nature that there's some of that,

0:20:09.760 --> 0:20:13.560
<v Speaker 10>and more than the idea that where there's rote tasks

0:20:13.640 --> 0:20:17.159
<v Speaker 10>or efficiencies to be created by people, it can be

0:20:17.200 --> 0:20:20.200
<v Speaker 10>a very powerful tool to create that. And that's cost savings,

0:20:20.200 --> 0:20:23.720
<v Speaker 10>that's time savings, that's adding value into the stacks that's

0:20:23.760 --> 0:20:26.199
<v Speaker 10>already there, and you know, you asked about kind of

0:20:26.240 --> 0:20:28.840
<v Speaker 10>existing companies versus new entrants. I think one of the

0:20:29.000 --> 0:20:32.560
<v Speaker 10>unique things about this technology shift that's somewhat different than

0:20:32.640 --> 0:20:35.280
<v Speaker 10>if you think about mobile is that the incumbents not

0:20:35.359 --> 0:20:39.520
<v Speaker 10>only have distribution advantage, but they have data advantage that's very,

0:20:39.640 --> 0:20:43.000
<v Speaker 10>very relevant, and that the technology is easier to use,

0:20:43.480 --> 0:20:46.840
<v Speaker 10>So integrating it into existing platforms is a lot easier

0:20:46.880 --> 0:20:49.680
<v Speaker 10>than in prior technology shifts, so they can do it faster,

0:20:49.840 --> 0:20:52.679
<v Speaker 10>get up to speed, and use multiple advantages. And I

0:20:52.680 --> 0:20:55.040
<v Speaker 10>think you're going to see that now and you know,

0:20:55.080 --> 0:20:58.680
<v Speaker 10>for a while across industries that give these big players,

0:20:59.000 --> 0:21:00.920
<v Speaker 10>you know, a real opportunity really if they can jump

0:21:00.960 --> 0:21:02.399
<v Speaker 10>on it.

0:21:02.400 --> 0:21:04.399
<v Speaker 5>It was such a great conversation. You can go online

0:21:04.400 --> 0:21:04.960
<v Speaker 5>to see more of it.

0:21:05.040 --> 0:21:10.480
<v Speaker 3>Rebecca Cayden of Union Square Ventures, Tina Shakir of Lux Capital.

0:21:18.480 --> 0:21:21.960
<v Speaker 6>Welcome back to Bloomberg Technology. I'm Med Ludlow in San Francisco.

0:21:21.520 --> 0:21:23.080
<v Speaker 5>And I'm Caroline Hyde in New York.

0:21:23.160 --> 0:21:26.119
<v Speaker 3>Let's check in on these markets because nasdak treading some

0:21:26.200 --> 0:21:29.240
<v Speaker 3>water today. We're up to eleven points, but remember it

0:21:29.240 --> 0:21:31.320
<v Speaker 3>had hit the higher since April twenty twenty two at

0:21:31.320 --> 0:21:34.359
<v Speaker 3>one point, so the SMP get into a ballmarket technical

0:21:34.400 --> 0:21:35.600
<v Speaker 3>ball market, twenty percent off.

0:21:35.560 --> 0:21:36.200
<v Speaker 5>Of its lows.

0:21:36.600 --> 0:21:39.560
<v Speaker 3>We're still worried, tentative about what happens with the Federal

0:21:39.600 --> 0:21:41.560
<v Speaker 3>Reserve next week here in the United States, So at

0:21:41.560 --> 0:21:43.359
<v Speaker 3>the moment, maybe just a little bit of caution as

0:21:43.400 --> 0:21:45.439
<v Speaker 3>we end up the trading week basically flat on the

0:21:45.440 --> 0:21:47.320
<v Speaker 3>week for the NASDA two year yield, though up seven

0:21:47.359 --> 0:21:49.800
<v Speaker 3>basis points as we think about where in which the

0:21:49.840 --> 0:21:52.040
<v Speaker 3>Federal Reserve has to go, how it has to tackle

0:21:52.080 --> 0:21:54.640
<v Speaker 3>interest rates, whether or not that's really priced in into

0:21:54.640 --> 0:21:55.440
<v Speaker 3>the front end of the curve.

0:21:55.480 --> 0:21:57.040
<v Speaker 5>So two year yield's just pushing up higher.

0:21:57.160 --> 0:22:00.320
<v Speaker 3>Bitcoin currently down by sixteen percent torrid week, int of

0:22:00.440 --> 0:22:03.600
<v Speaker 3>course the regulatory overhang for crypto in general, but actually

0:22:03.640 --> 0:22:05.719
<v Speaker 3>managed to be relatively resolute in the face of that.

0:22:05.840 --> 0:22:07.879
<v Speaker 3>Twenty six thousands is where we're currently trade. Moving on,

0:22:07.920 --> 0:22:10.400
<v Speaker 3>and let's go into some individual movers, because there are some.

0:22:10.280 --> 0:22:11.119
<v Speaker 5>Big moves going on.

0:22:11.240 --> 0:22:13.480
<v Speaker 3>I'm looking at its Heather, of course, up almost four

0:22:13.520 --> 0:22:15.320
<v Speaker 3>percent on the day as they sign that deal with

0:22:15.400 --> 0:22:19.080
<v Speaker 3>gm as look really to be leveraging the power of

0:22:19.440 --> 0:22:21.560
<v Speaker 3>their charging that they have at the United States at

0:22:21.560 --> 0:22:23.639
<v Speaker 3>the moment. But Adobe is on the higher side, up

0:22:23.640 --> 0:22:26.159
<v Speaker 3>more than four percent, going overweight in some analysts or

0:22:26.160 --> 0:22:27.760
<v Speaker 3>really thinking that this is a company to be leveraging,

0:22:27.800 --> 0:22:30.320
<v Speaker 3>whereas we look at generative AIAI applications.

0:22:30.720 --> 0:22:33.920
<v Speaker 5>Interesting that Peloton's at more than four percent. Who knows why.

0:22:34.160 --> 0:22:36.919
<v Speaker 3>Big volume at one point up nineteen percent on the

0:22:37.000 --> 0:22:39.679
<v Speaker 3>day at the moment, Bluemode not reporting any real reason

0:22:39.760 --> 0:22:42.600
<v Speaker 3>and catalysts into the Surgeon volume and actually the Surgeon shares.

0:22:42.600 --> 0:22:44.480
<v Speaker 3>But we're coming down to about four percent, but one

0:22:44.560 --> 0:22:47.800
<v Speaker 3>standard deviation move compared to their twenty day moving average.

0:22:47.840 --> 0:22:50.360
<v Speaker 3>Blue Apron as well. Look, it's a penny stock. Basically,

0:22:50.560 --> 0:22:52.879
<v Speaker 3>we're seeing it only what fifty million dollars worth in

0:22:52.920 --> 0:22:53.400
<v Speaker 3>market cap.

0:22:53.440 --> 0:22:55.440
<v Speaker 5>It used to be worth one hundred million, in.

0:22:55.400 --> 0:22:58.679
<v Speaker 3>Excess of perhaps sixty percent on the day. This is

0:22:58.680 --> 0:23:01.159
<v Speaker 3>a pay off some debts away from one to be

0:23:01.240 --> 0:23:03.320
<v Speaker 3>watching as well. But we want to be turning back

0:23:03.320 --> 0:23:05.639
<v Speaker 3>from public markets to private markets at the moment. Ed

0:23:05.720 --> 0:23:08.919
<v Speaker 3>and a business planning platform Pigment just reported in an

0:23:08.960 --> 0:23:11.280
<v Speaker 3>eighty eight million dollars Series C funding round led by

0:23:11.600 --> 0:23:14.240
<v Speaker 3>Iconic Growth as well as Felix Capital coming in on

0:23:14.280 --> 0:23:16.240
<v Speaker 3>the deal as well. Let's bring in the co CEO

0:23:16.480 --> 0:23:19.400
<v Speaker 3>co founder Eleanor Crespo now who's joining.

0:23:19.240 --> 0:23:21.840
<v Speaker 5>Us from France. But the focus that is funding around

0:23:21.960 --> 0:23:22.960
<v Speaker 5>is look.

0:23:22.760 --> 0:23:25.160
<v Speaker 3>Eleanor, you're going to be beefing up the business, going

0:23:25.160 --> 0:23:27.359
<v Speaker 3>to be pushing more globally here in the United States

0:23:27.400 --> 0:23:27.720
<v Speaker 3>as well.

0:23:27.760 --> 0:23:28.960
<v Speaker 5>What services do you offer?

0:23:30.320 --> 0:23:32.280
<v Speaker 8>Thank you very much for having me so.

0:23:32.440 --> 0:23:35.280
<v Speaker 12>First of all, just to reset this scene, to explain

0:23:35.320 --> 0:23:38.159
<v Speaker 12>for everybody who we are. Pigment is a business planning

0:23:38.200 --> 0:23:42.600
<v Speaker 12>platform that Eulu's business leader no materally signe finance if

0:23:42.600 --> 0:23:45.080
<v Speaker 12>you are a CEO, the CRO or the HRVP of

0:23:45.080 --> 0:23:49.560
<v Speaker 12>your company, to take better, faster and smarter decisions. The

0:23:49.600 --> 0:23:52.240
<v Speaker 12>other point of Pigment is to quickly adapt to change.

0:23:52.560 --> 0:23:54.719
<v Speaker 12>What we relate with my con friend de romaan Nicoli

0:23:55.320 --> 0:23:58.719
<v Speaker 12>is that basically, as of today, business leaders usually have

0:23:58.800 --> 0:24:02.639
<v Speaker 12>to take very strategic this decisions based on inaccurate, incomplete

0:24:02.720 --> 0:24:05.880
<v Speaker 12>or sided data. What we try to offer with Pigment

0:24:05.960 --> 0:24:08.560
<v Speaker 12>or you see, is to help these business leaders not

0:24:08.640 --> 0:24:12.200
<v Speaker 12>only take the most strategic decisions but also be able

0:24:12.240 --> 0:24:13.680
<v Speaker 12>to adapt quickly to the.

0:24:13.600 --> 0:24:15.240
<v Speaker 8>Current macroeconomic environment.

0:24:15.680 --> 0:24:17.480
<v Speaker 12>So to give you a concrete example of what we

0:24:17.600 --> 0:24:19.760
<v Speaker 12>do of today, as you can see or you see,

0:24:19.760 --> 0:24:22.880
<v Speaker 12>the macroeconomic environment is what it is. You have inflation

0:24:23.040 --> 0:24:26.520
<v Speaker 12>raising a different paces in different countries, and so any

0:24:26.560 --> 0:24:28.720
<v Speaker 12>business leader needs to be able to answer in the

0:24:28.800 --> 0:24:32.080
<v Speaker 12>very first fashion, for instance, if inflation raises even more

0:24:32.080 --> 0:24:35.040
<v Speaker 12>in North America, how that will impact my hiring, how

0:24:35.080 --> 0:24:36.720
<v Speaker 12>that will impact my growth margin.

0:24:36.680 --> 0:24:38.520
<v Speaker 8>How that will affect my self process?

0:24:38.720 --> 0:24:41.600
<v Speaker 12>And ultimately Stigment is here to offer you to help

0:24:41.640 --> 0:24:43.360
<v Speaker 12>you answer better these questions.

0:24:43.960 --> 0:24:46.480
<v Speaker 3>What's so fascinating is the problemly you're solving, but also

0:24:46.520 --> 0:24:48.680
<v Speaker 3>your own background, the fact that you were a vcy

0:24:48.800 --> 0:24:50.760
<v Speaker 3>yourself before an index, the fact that you were working

0:24:50.760 --> 0:24:52.720
<v Speaker 3>at Google for a long time. In terms of strategy,

0:24:53.320 --> 0:24:57.360
<v Speaker 3>what was the funding environment like for you? How easy

0:24:57.480 --> 0:24:59.040
<v Speaker 3>or hard was it to sell real vision and the

0:24:59.119 --> 0:25:01.800
<v Speaker 3>need for capital take a macroeconomic head wind moment, they

0:25:01.800 --> 0:25:02.840
<v Speaker 3>were just painting for us.

0:25:03.880 --> 0:25:07.239
<v Speaker 12>Yeah, So actually we were not raising these rounds and

0:25:07.320 --> 0:25:10.840
<v Speaker 12>what happened was very different. So we still had the

0:25:10.920 --> 0:25:13.080
<v Speaker 12>money from the past two runs, pretty much everything in

0:25:13.359 --> 0:25:15.879
<v Speaker 12>the bank. So here actually we got a lot of

0:25:15.920 --> 0:25:19.720
<v Speaker 12>interest and that came from a couple of reasons. First

0:25:19.760 --> 0:25:22.480
<v Speaker 12>one being that we are must have platform in the

0:25:22.520 --> 0:25:23.720
<v Speaker 12>current economic environment.

0:25:24.119 --> 0:25:26.159
<v Speaker 8>Everybody, every customer.

0:25:25.800 --> 0:25:28.760
<v Speaker 12>Across all industries need to plan on the two like hours.

0:25:28.960 --> 0:25:31.320
<v Speaker 12>That was the first thing, and the second thing was

0:25:31.440 --> 0:25:34.360
<v Speaker 12>I think the type of customers we managed to sign

0:25:34.440 --> 0:25:38.119
<v Speaker 12>across all industries. So we managed to actually get on

0:25:38.200 --> 0:25:41.040
<v Speaker 12>board and serve the most incredible customers in the likes

0:25:41.040 --> 0:25:45.800
<v Speaker 12>of Figma, of Mirror, of air Table, Mozilla, Kelvin, Klein

0:25:46.320 --> 0:25:49.720
<v Speaker 12>arp et cetera. And really what happened with them is

0:25:49.760 --> 0:25:53.040
<v Speaker 12>that not only they showed to investors that were they

0:25:53.040 --> 0:25:53.840
<v Speaker 12>were using us on.

0:25:53.800 --> 0:25:56.199
<v Speaker 8>A daily basis, but that also they loved us.

0:25:56.240 --> 0:26:00.040
<v Speaker 12>And what happened really with this round is that the

0:26:00.040 --> 0:26:02.320
<v Speaker 12>stores actually got some word of mass from all of

0:26:02.440 --> 0:26:05.320
<v Speaker 12>these customers that you know, this product was a total

0:26:05.359 --> 0:26:08.680
<v Speaker 12>revolution in the way they plan. And we actually got

0:26:08.720 --> 0:26:12.440
<v Speaker 12>this from preempted biobsisis leading firms that you mentioned iconically

0:26:12.480 --> 0:26:17.080
<v Speaker 12>dig the round alongside Felix, Capital IVPN, Meritech participating. And

0:26:17.320 --> 0:26:19.520
<v Speaker 12>really the reason was I think the attraction that we

0:26:19.600 --> 0:26:21.879
<v Speaker 12>got not only that, but we also managed to go

0:26:22.080 --> 0:26:25.080
<v Speaker 12>very deep in the organization. We actually grew by ten

0:26:25.280 --> 0:26:26.600
<v Speaker 12>x or customer based last.

0:26:26.440 --> 0:26:29.240
<v Speaker 4>Year and that elent know. Eleanor.

0:26:29.600 --> 0:26:31.480
<v Speaker 6>I want to jump in and just ask you because

0:26:31.520 --> 0:26:34.360
<v Speaker 6>I want to understand some of how you manage that.

0:26:34.480 --> 0:26:36.560
<v Speaker 6>First of all, you have runway right, so you raised

0:26:36.600 --> 0:26:39.520
<v Speaker 6>eighty eight million dollars in this series C two hundred

0:26:39.560 --> 0:26:43.600
<v Speaker 6>and fifty million today, where did this round value your company?

0:26:44.840 --> 0:26:47.240
<v Speaker 12>So, actually, we are not discussing valuation for a very

0:26:47.240 --> 0:26:48.919
<v Speaker 12>good reason is that, first, it's.

0:26:48.800 --> 0:26:51.040
<v Speaker 8>Not a focus at all. What we are very proud

0:26:51.040 --> 0:26:51.520
<v Speaker 8>of at.

0:26:51.359 --> 0:26:55.120
<v Speaker 12>Pigment is our customer base or partners and the fact

0:26:55.119 --> 0:26:57.160
<v Speaker 12>that we had the tremend destruction in the past year.

0:26:57.240 --> 0:26:59.080
<v Speaker 12>You know, we grew our revenue by seven x last

0:26:59.119 --> 0:27:01.520
<v Speaker 12>year and in north summer tenx in you know, the

0:27:01.600 --> 0:27:04.560
<v Speaker 12>number of users that are using Pigments. Really, I think

0:27:04.600 --> 0:27:06.480
<v Speaker 12>the valuation is not a metrix that we want to

0:27:06.520 --> 0:27:08.920
<v Speaker 12>follow because we think it's not really the real value.

0:27:08.920 --> 0:27:11.480
<v Speaker 12>But I can tell you it was a significant upront,

0:27:11.960 --> 0:27:14.119
<v Speaker 12>and I think you know, for us, it's more the

0:27:14.200 --> 0:27:16.760
<v Speaker 12>security cushion and the freedom to invest even more in

0:27:16.840 --> 0:27:19.040
<v Speaker 12>our product and the innovation than anything else.

0:27:19.840 --> 0:27:23.080
<v Speaker 6>So it's in response to growth, right, tenx, growth ten

0:27:23.359 --> 0:27:26.280
<v Speaker 6>x in the user base last year. How do you

0:27:26.440 --> 0:27:29.480
<v Speaker 6>deploy the capital to respond to that? You having to

0:27:29.600 --> 0:27:31.920
<v Speaker 6>manage headcount, infrastructure costs.

0:27:32.960 --> 0:27:35.280
<v Speaker 12>Yes, So actually I think we are going to do

0:27:35.400 --> 0:27:38.640
<v Speaker 12>two major investments this year, so obvious see one of them,

0:27:38.640 --> 0:27:41.160
<v Speaker 12>which is a continued investment, and the most important one

0:27:41.240 --> 0:27:44.359
<v Speaker 12>is in the product. So we are the most innovative

0:27:44.440 --> 0:27:46.760
<v Speaker 12>platform out there by far, and we want to remain

0:27:46.800 --> 0:27:49.000
<v Speaker 12>the most innovative platform. So there are a lot of

0:27:49.119 --> 0:27:52.119
<v Speaker 12>very exciting things coming in the roadmap, AI being one

0:27:52.200 --> 0:27:53.840
<v Speaker 12>of them. Of course, I'm very happy to tell you

0:27:53.960 --> 0:27:56.919
<v Speaker 12>more about that. The second being investment is our customer

0:27:57.000 --> 0:28:01.320
<v Speaker 12>support globally, because obviously we are serving the largest company

0:28:01.440 --> 0:28:04.399
<v Speaker 12>in the world in North America, in in media and

0:28:04.960 --> 0:28:06.160
<v Speaker 12>everywhere else and so forth.

0:28:06.240 --> 0:28:08.440
<v Speaker 8>What is important is to be able to bring the

0:28:08.520 --> 0:28:11.800
<v Speaker 8>most tremendous customer experiense to all of our customers. So

0:28:11.920 --> 0:28:13.840
<v Speaker 8>these are the main two areas where we are going

0:28:13.960 --> 0:28:16.240
<v Speaker 8>to completitles this year.

0:28:17.280 --> 0:28:21.160
<v Speaker 6>Caroline, I cannot resist the temptation. We have a startup

0:28:21.200 --> 0:28:25.440
<v Speaker 6>in the software space raising money. Let's go there and

0:28:25.520 --> 0:28:26.440
<v Speaker 6>talk about AI.

0:28:26.600 --> 0:28:28.119
<v Speaker 5>I mean, it got to you. Just let us there.

0:28:28.160 --> 0:28:31.280
<v Speaker 3>I don't know how, I mean artificial intelligence and just looking,

0:28:31.359 --> 0:28:33.919
<v Speaker 3>we saw some video of how you interact with your

0:28:33.960 --> 0:28:36.199
<v Speaker 3>product and it only looks like it sort of generator

0:28:36.320 --> 0:28:38.240
<v Speaker 3>AI and predictive AI in some way.

0:28:38.400 --> 0:28:40.120
<v Speaker 5>How are you folding that more into the business model?

0:28:41.040 --> 0:28:45.560
<v Speaker 12>Yes, so, actually AI is already something that obviously we

0:28:45.640 --> 0:28:47.680
<v Speaker 12>had thought about. It seemed the beginning first of Fuller

0:28:47.760 --> 0:28:50.959
<v Speaker 12>with my co founder Romanically, we started to integrate an

0:28:50.960 --> 0:28:54.320
<v Speaker 12>AI roonmap from day one and as of today we

0:28:54.400 --> 0:28:57.560
<v Speaker 12>see we have several new features in better with the

0:28:57.640 --> 0:28:58.920
<v Speaker 12>partnership with Chagpt.

0:28:59.120 --> 0:29:01.600
<v Speaker 8>And for us, that's what is critical about the way

0:29:01.640 --> 0:29:04.600
<v Speaker 8>we integrate AI within the platform is to make.

0:29:04.680 --> 0:29:08.520
<v Speaker 12>The most intreitive customer experience for pretty much any business

0:29:08.600 --> 0:29:10.960
<v Speaker 12>user on the platform. Again, our goal is to have

0:29:11.120 --> 0:29:14.320
<v Speaker 12>literally any business user within a specific company to use Pigment.

0:29:14.600 --> 0:29:17.160
<v Speaker 8>So for us, it's all about the simplicity of the platform.

0:29:17.160 --> 0:29:20.120
<v Speaker 12>I can give you a concrete example around natural language

0:29:20.120 --> 0:29:22.440
<v Speaker 12>that you can use with in Pigment to ask question

0:29:22.760 --> 0:29:25.200
<v Speaker 12>such as what was my revenue in North America asture

0:29:25.600 --> 0:29:27.720
<v Speaker 12>and that you can ask in natural language and the

0:29:28.040 --> 0:29:30.760
<v Speaker 12>platform will answer. So for us, the idea is really

0:29:31.080 --> 0:29:33.400
<v Speaker 12>to democratize even more the access to the platform.

0:29:34.560 --> 0:29:38.640
<v Speaker 6>Pigment co CEO co founder Eleanor Crespo, French entrepreneur, French

0:29:38.720 --> 0:29:39.640
<v Speaker 6>startup coming.

0:29:39.440 --> 0:29:42.120
<v Speaker 4>To the US market Carroy, Thank you for joining the show.

0:29:42.240 --> 0:29:46.000
<v Speaker 6>Now coming up All Things AI investing more with FPV

0:29:46.200 --> 0:29:48.440
<v Speaker 6>Ventures Wesley chan As.

0:29:48.560 --> 0:29:50.360
<v Speaker 4>Next, this is Plameberg.

0:30:03.880 --> 0:30:12.320
<v Speaker 13>The hype is absolutely remarkable and I've never seen anything

0:30:12.480 --> 0:30:16.120
<v Speaker 13>quite like it. AI has been having an impact for

0:30:16.280 --> 0:30:19.640
<v Speaker 13>decades and this stuff is into brand new.

0:30:20.040 --> 0:30:23.240
<v Speaker 14>If I'm right on AI and the impact on it,

0:30:23.480 --> 0:30:26.000
<v Speaker 14>I mean, it's already making the top coder seven eight

0:30:26.160 --> 0:30:30.160
<v Speaker 14>times seventy eight times more productive than they were five

0:30:30.240 --> 0:30:33.000
<v Speaker 14>months ago. If it's as big as I think it

0:30:33.160 --> 0:30:36.840
<v Speaker 14>is in Nvidia, is something we're going to want to

0:30:36.880 --> 0:30:38.920
<v Speaker 14>own for at least two or three years, not for

0:30:39.040 --> 0:30:39.560
<v Speaker 14>ten months.

0:30:39.720 --> 0:30:41.800
<v Speaker 15>We want to engage with the regulators to say, what's

0:30:41.840 --> 0:30:44.120
<v Speaker 15>the next generation of AI you going to do that's

0:30:44.160 --> 0:30:46.880
<v Speaker 15>going to make it even more effective. And therefore maybe

0:30:46.920 --> 0:30:49.760
<v Speaker 15>it's not totally explainable, but you understand that we're not

0:30:49.880 --> 0:30:51.560
<v Speaker 15>using it, we're using it for the right purpose, and

0:30:51.680 --> 0:30:55.840
<v Speaker 15>so changing the regulatory I would say process around the

0:30:55.920 --> 0:30:57.840
<v Speaker 15>use of AI is going to be an important except

0:30:57.880 --> 0:30:59.560
<v Speaker 15>for every industry.

0:31:00.320 --> 0:31:02.920
<v Speaker 16>More software, That's how I would see it. And we've

0:31:02.960 --> 0:31:07.280
<v Speaker 16>been bringing more software into finance for a really long time,

0:31:07.720 --> 0:31:10.160
<v Speaker 16>and there's been all kinds of breakthroughs, and there's been

0:31:10.520 --> 0:31:14.880
<v Speaker 16>all kinds of problems. It is statistical pattern matching. It's

0:31:14.960 --> 0:31:19.840
<v Speaker 16>extremely powerful and it's interesting, but I do not see

0:31:20.440 --> 0:31:23.800
<v Speaker 16>AI achieving what some would call the holy grail. Everybody

0:31:23.880 --> 0:31:25.880
<v Speaker 16>wants to know what's the S and P going to

0:31:25.960 --> 0:31:27.680
<v Speaker 16>be in six months?

0:31:27.720 --> 0:31:28.200
<v Speaker 4>Can you tell me?

0:31:28.360 --> 0:31:31.840
<v Speaker 16>And I can and neither can the AIS.

0:31:33.480 --> 0:31:35.600
<v Speaker 5>Ah some hype, maybe some reality there.

0:31:35.680 --> 0:31:38.000
<v Speaker 3>So speakers from Albertneberg invest some it this week giving

0:31:38.080 --> 0:31:41.440
<v Speaker 3>their thoughts on all things AI. The space of course

0:31:41.480 --> 0:31:43.200
<v Speaker 3>that we can't get enough of. Let's stick with it

0:31:43.400 --> 0:31:46.320
<v Speaker 3>for our VC Spotlight. Very pleased to welcome Wesley Chan

0:31:46.600 --> 0:31:48.520
<v Speaker 3>for more. Here's of course the co founder, management partner

0:31:48.600 --> 0:31:51.760
<v Speaker 3>and FPV Ventures. And I'm sure you have a view

0:31:51.840 --> 0:31:55.560
<v Speaker 3>or two whether we're hyped in terms of AI exuberance valuations.

0:31:55.600 --> 0:31:57.280
<v Speaker 5>Even know, it's.

0:31:57.200 --> 0:32:00.520
<v Speaker 17>Fascinating, right, I've watched this cycle happen so much many times.

0:32:00.600 --> 0:32:01.640
<v Speaker 4>You know, I've been an.

0:32:01.560 --> 0:32:05.600
<v Speaker 17>Investor for fifteen years and this happened when mobile phones

0:32:05.640 --> 0:32:07.200
<v Speaker 17>first came out. There was a hype there and you

0:32:07.240 --> 0:32:09.520
<v Speaker 17>know there were all these funds started on mobile and

0:32:09.680 --> 0:32:12.280
<v Speaker 17>today you know everything is mobile, right, Like can you

0:32:12.560 --> 0:32:15.240
<v Speaker 17>imagine trying to book an uber on your web browser.

0:32:15.280 --> 0:32:17.160
<v Speaker 17>This doesn't happen you do it on your phone. And

0:32:17.280 --> 0:32:19.280
<v Speaker 17>that hype sort of happened in two thousand and went

0:32:19.280 --> 0:32:22.800
<v Speaker 17>away because we had everybody incorporating uber I mean sorry

0:32:22.840 --> 0:32:26.520
<v Speaker 17>mobile into their products and into their apps. And today

0:32:26.600 --> 0:32:28.720
<v Speaker 17>we have AI and there's this massive hype where people

0:32:28.760 --> 0:32:30.960
<v Speaker 17>are saying, oh, we got to invest in all these

0:32:31.000 --> 0:32:33.440
<v Speaker 17>AI companies. But you know, ten years from now, five

0:32:33.480 --> 0:32:35.440
<v Speaker 17>years from now, we're going to be incorporating AI as

0:32:35.480 --> 0:32:37.480
<v Speaker 17>part of our products, part of our strategy. We've been

0:32:37.520 --> 0:32:38.760
<v Speaker 17>doing it for ten years and we're going to do

0:32:38.800 --> 0:32:41.800
<v Speaker 17>it for another ten years later. So it's an interesting cycle.

0:32:41.920 --> 0:32:43.960
<v Speaker 17>But you know, we've seen this happen over and over again.

0:32:44.040 --> 0:32:45.920
<v Speaker 3>Okay, So to take the long term view, managed to

0:32:45.960 --> 0:32:49.440
<v Speaker 3>sort of move out of the exuberants.

0:32:48.840 --> 0:32:49.680
<v Speaker 5>And the headlines.

0:32:50.280 --> 0:32:53.680
<v Speaker 3>How are you picking which companies in this current cohort

0:32:53.840 --> 0:32:56.640
<v Speaker 3>you want to be backing, because I know you're all

0:32:56.680 --> 0:33:00.160
<v Speaker 3>about the founder, but how are you ensuring that the

0:33:00.520 --> 0:33:02.960
<v Speaker 3>photio companies you have are resilient to the disruption and

0:33:03.080 --> 0:33:05.120
<v Speaker 3>how the founders that you back right here, right now

0:33:05.200 --> 0:33:05.800
<v Speaker 3>are the right ones?

0:33:06.680 --> 0:33:08.440
<v Speaker 4>Again, we take our lessons from history.

0:33:09.560 --> 0:33:12.560
<v Speaker 17>In twenty ten When I started at Google Ventures, we

0:33:12.680 --> 0:33:14.560
<v Speaker 17>invest in a lot of mobile companies, right. You know,

0:33:14.800 --> 0:33:16.440
<v Speaker 17>I was an investor in a company called Parse, which

0:33:16.440 --> 0:33:18.560
<v Speaker 17>wound up selling the Facebook, and you know that Facebook

0:33:18.560 --> 0:33:20.480
<v Speaker 17>wound up shutting it down. It made some money, the

0:33:20.560 --> 0:33:24.080
<v Speaker 17>founder did well, but we those mobile companies aren't around

0:33:24.080 --> 0:33:28.040
<v Speaker 17>anymore because again it starts being incorporated as table stakes

0:33:28.040 --> 0:33:29.560
<v Speaker 17>into every product. I think we're going to see some

0:33:29.560 --> 0:33:31.360
<v Speaker 17>of that with AI again. Right, this is a very

0:33:31.480 --> 0:33:33.480
<v Speaker 17>very contrary view. Every one of my colleagues is.

0:33:33.520 --> 0:33:34.240
<v Speaker 4>Rushing in AI.

0:33:34.720 --> 0:33:37.920
<v Speaker 17>Some firms are creating AI funds just specifically for looking

0:33:37.920 --> 0:33:40.720
<v Speaker 17>at AI, and we are not investing in AI companies

0:33:40.760 --> 0:33:43.040
<v Speaker 17>for the sake of AI. We're investing in companies because

0:33:43.240 --> 0:33:45.240
<v Speaker 17>there's going to be great business models that would be

0:33:45.280 --> 0:33:46.560
<v Speaker 17>created by the AI, and we don't even know what

0:33:46.600 --> 0:33:50.080
<v Speaker 17>those are yet. There were all these mobile infrastructure companies

0:33:50.080 --> 0:33:52.480
<v Speaker 17>in two thousand and ten when that happened. All those

0:33:52.560 --> 0:33:54.560
<v Speaker 17>no longer exists. There's very few of them. The companies

0:33:54.600 --> 0:33:57.040
<v Speaker 17>that wound up doing super well in that market had

0:33:57.120 --> 0:33:59.080
<v Speaker 17>yet to be invented. When that mobile praise came up,

0:33:59.120 --> 0:34:01.000
<v Speaker 17>it was Uber, it was Air, and it was these

0:34:01.000 --> 0:34:03.480
<v Speaker 17>companies that figured out a business model were mobile enabled

0:34:03.480 --> 0:34:04.240
<v Speaker 17>it to happen, and.

0:34:04.240 --> 0:34:05.560
<v Speaker 4>I think we're gonna see the same on AI.

0:34:05.920 --> 0:34:09.080
<v Speaker 17>There's gonna be new companies that will will change the

0:34:09.160 --> 0:34:11.000
<v Speaker 17>landscape of how we do business, how we do work,

0:34:11.080 --> 0:34:12.880
<v Speaker 17>how we buy products, how we book cars, how we

0:34:12.920 --> 0:34:15.320
<v Speaker 17>book flights, and we're looking for those companies where that

0:34:15.440 --> 0:34:17.799
<v Speaker 17>business model will be changed by AI rather than AI.

0:34:17.920 --> 0:34:19.560
<v Speaker 17>For the sake of it, right, there's a lot of

0:34:19.600 --> 0:34:21.640
<v Speaker 17>hype going into it, a lot of money going into it.

0:34:21.760 --> 0:34:25.120
<v Speaker 17>They're gonna you're going to see these wars between Google

0:34:25.239 --> 0:34:27.960
<v Speaker 17>and Microsoft and open Ai, and you know these companies

0:34:27.960 --> 0:34:30.600
<v Speaker 17>are well funded, buying outrageous amounts of compute power, and

0:34:30.840 --> 0:34:32.880
<v Speaker 17>you know, we want to be in the companies that

0:34:33.000 --> 0:34:35.520
<v Speaker 17>are enabled by AI, not because AI exists.

0:34:36.560 --> 0:34:40.359
<v Speaker 6>Wesley, it's been almost a year to the day since

0:34:40.440 --> 0:34:43.920
<v Speaker 6>you were on Bloomberg Technology because you'd raised and launched

0:34:43.920 --> 0:34:45.720
<v Speaker 6>your four hundred and fifty million dollar funds.

0:34:45.760 --> 0:34:45.880
<v Speaker 4>YEP.

0:34:47.600 --> 0:34:50.560
<v Speaker 6>Based on what you just said, have you not pivoted

0:34:50.640 --> 0:34:53.640
<v Speaker 6>at AOL in the first six months of this year

0:34:54.600 --> 0:34:57.360
<v Speaker 6>to respond to what's happening around you in terms of

0:34:57.400 --> 0:34:59.240
<v Speaker 6>strategy and where you deploy capital.

0:35:00.000 --> 0:35:03.080
<v Speaker 17>We are investing in mission driven founders that have a

0:35:03.200 --> 0:35:06.439
<v Speaker 17>unique insight on how to change the world. When Larry

0:35:06.480 --> 0:35:09.279
<v Speaker 17>and I spent my first fifteen years of my career

0:35:09.320 --> 0:35:11.359
<v Speaker 17>at Google, I joined very very early. There were very

0:35:11.400 --> 0:35:13.520
<v Speaker 17>few people at Google. When I joined, it was Sergei

0:35:13.560 --> 0:35:15.560
<v Speaker 17>Brin's chief of staff, and Sergey is one of the

0:35:15.640 --> 0:35:18.800
<v Speaker 17>co founders of Google. Google is an AI company today.

0:35:19.120 --> 0:35:20.799
<v Speaker 17>Back then, nobody saw Google as.

0:35:20.719 --> 0:35:21.319
<v Speaker 4>An AI company.

0:35:21.360 --> 0:35:23.160
<v Speaker 17>They saw it as an ADS company the search engine.

0:35:23.160 --> 0:35:25.719
<v Speaker 17>But Google has been using AI for fifteen years. We're

0:35:25.760 --> 0:35:28.640
<v Speaker 17>just investing in founders that get what new business models

0:35:28.680 --> 0:35:31.040
<v Speaker 17>get enabled because of AI. And We've been doing this

0:35:31.640 --> 0:35:33.719
<v Speaker 17>for the last year, and I've been doing this in

0:35:33.800 --> 0:35:35.800
<v Speaker 17>my career when I was at Google Ventures, in my

0:35:35.840 --> 0:35:38.799
<v Speaker 17>previous fund for the last fifteen years. Some of our

0:35:38.880 --> 0:35:42.160
<v Speaker 17>best companies are enabled by AI. Right, there's business models certainty,

0:35:42.239 --> 0:35:44.240
<v Speaker 17>and that's where we're investing. So I'll give you an example.

0:35:44.480 --> 0:35:48.600
<v Speaker 17>We have a company called stran Tx and they are

0:35:48.680 --> 0:35:51.360
<v Speaker 17>redesigning the future of MR and A drugs. We have

0:35:51.440 --> 0:35:54.000
<v Speaker 17>the mRNA vaccines that we got from Modernat and Pfizer,

0:35:54.040 --> 0:35:55.920
<v Speaker 17>and there's a new generation of drugs coming and they

0:35:56.000 --> 0:35:59.760
<v Speaker 17>redesign their MR and A sequences and they can figure

0:35:59.760 --> 0:36:03.160
<v Speaker 17>out how to target the cells that need the therapy,

0:36:03.239 --> 0:36:05.320
<v Speaker 17>that need the drugs. So they can target a cancer tumor,

0:36:05.360 --> 0:36:07.399
<v Speaker 17>they can target that. They can target your liver cells

0:36:07.440 --> 0:36:09.120
<v Speaker 17>if there's a defect in your lawyer of liver and

0:36:09.200 --> 0:36:11.000
<v Speaker 17>we have to deliver a drug in it. When they

0:36:11.120 --> 0:36:13.600
<v Speaker 17>use some AI to figure out how to create those

0:36:13.600 --> 0:36:16.080
<v Speaker 17>sequences so the drug is most effective and not toxic

0:36:16.120 --> 0:36:17.800
<v Speaker 17>at all, so you don't have any side effects. We

0:36:17.920 --> 0:36:21.000
<v Speaker 17>have another company called Inveda, for example, that is doing AI.

0:36:21.280 --> 0:36:24.239
<v Speaker 17>They use AI in the data on mass spectrometry to

0:36:24.400 --> 0:36:28.320
<v Speaker 17>find natural compounds that are able to be future drugs

0:36:28.360 --> 0:36:31.160
<v Speaker 17>for people to help here disease, skin disease, is cancer.

0:36:31.440 --> 0:36:33.160
<v Speaker 17>These natural compounds have been in use for a while,

0:36:33.239 --> 0:36:34.400
<v Speaker 17>but how do we turn them in the drugs so

0:36:34.440 --> 0:36:36.200
<v Speaker 17>that they're just not like weird things that people buy

0:36:36.520 --> 0:36:37.239
<v Speaker 17>at a drug store.

0:36:37.400 --> 0:36:38.839
<v Speaker 4>They're using AI to figure that out.

0:36:39.040 --> 0:36:41.160
<v Speaker 17>There's business model certainty in these businesses.

0:36:41.280 --> 0:36:41.360
<v Speaker 8>Right.

0:36:41.400 --> 0:36:43.200
<v Speaker 17>If it's a drug company and the AI helps them

0:36:43.320 --> 0:36:45.680
<v Speaker 17>get to a drug faster, then we know that the

0:36:45.760 --> 0:36:47.719
<v Speaker 17>drug is very valuable and the market values that. So

0:36:47.760 --> 0:36:49.399
<v Speaker 17>those are the type of companies we're investing in. They're

0:36:49.440 --> 0:36:51.680
<v Speaker 17>run by great founders, and the founders truly get that

0:36:51.760 --> 0:36:53.839
<v Speaker 17>AI is helping them get to the value faster rather

0:36:53.880 --> 0:36:55.160
<v Speaker 17>than just creating a new a AI.

0:36:55.239 --> 0:36:55.600
<v Speaker 4>Wesley.

0:36:55.680 --> 0:36:58.680
<v Speaker 6>Those are the types of companies, and I actually am

0:36:58.680 --> 0:37:01.759
<v Speaker 6>really interested in the mechanics of how deal making has

0:37:01.960 --> 0:37:04.160
<v Speaker 6>changed over the last twelve months. So you launched with

0:37:04.160 --> 0:37:06.920
<v Speaker 6>four hundred and fifty million last year because of the

0:37:07.160 --> 0:37:10.680
<v Speaker 6>inbound and activity around AI, are you having to write

0:37:10.960 --> 0:37:15.279
<v Speaker 6>larger checks or write checks with more frequency to keep

0:37:15.400 --> 0:37:17.160
<v Speaker 6>up with the energy of your industry.

0:37:17.480 --> 0:37:20.719
<v Speaker 17>Well, we've never, ever, ever invested into the hype when

0:37:20.800 --> 0:37:24.960
<v Speaker 17>there were grocery delivery companies where people were putting billions

0:37:24.960 --> 0:37:26.760
<v Speaker 17>of dollars. I remember betting pitched five with these grocery

0:37:26.800 --> 0:37:29.120
<v Speaker 17>delivery companies, you know, in my place in New York.

0:37:29.160 --> 0:37:31.359
<v Speaker 17>Every week I was getting a new postcard for thirty

0:37:31.400 --> 0:37:33.680
<v Speaker 17>percent off some fifteen minute grocery delivery company, and at

0:37:33.719 --> 0:37:35.759
<v Speaker 17>one point there were fifteen of them. Right, We didn't

0:37:35.760 --> 0:37:37.480
<v Speaker 17>invest in any of them. Same with the scooters. We

0:37:37.520 --> 0:37:40.040
<v Speaker 17>don't invest into the hype. So we're investing, for example,

0:37:40.120 --> 0:37:44.040
<v Speaker 17>into drug discovery, where AI enables it that ability to

0:37:44.080 --> 0:37:46.839
<v Speaker 17>get to the drug faster and to find a cure

0:37:46.920 --> 0:37:49.880
<v Speaker 17>for cancer faster, and there aren't many people competing in

0:37:49.920 --> 0:37:53.000
<v Speaker 17>that space for AI drug discovery companies.

0:37:53.040 --> 0:37:54.120
<v Speaker 4>In fact that you know, every time we.

0:37:54.120 --> 0:37:56.400
<v Speaker 17>Do a deal, there's probably like one or two other investors,

0:37:56.440 --> 0:37:59.680
<v Speaker 17>you know, looking at that space because everybody's chasing these

0:38:00.320 --> 0:38:02.719
<v Speaker 17>over hyped AI companies where we don't know which one's

0:38:02.760 --> 0:38:05.120
<v Speaker 17>the winter. So we're very very contraran in how we

0:38:05.160 --> 0:38:07.200
<v Speaker 17>look at companies. We do AI, but we don't do

0:38:07.480 --> 0:38:09.759
<v Speaker 17>AI for the sake of AI. Just like in twenty ten,

0:38:09.880 --> 0:38:12.080
<v Speaker 17>all the people that invested in mobile for the sake

0:38:12.120 --> 0:38:14.279
<v Speaker 17>of mobile, those companies are no longer around. We love

0:38:14.320 --> 0:38:16.719
<v Speaker 17>companies that are around for decades, in the centuries, if

0:38:16.719 --> 0:38:17.320
<v Speaker 17>that's possible.

0:38:19.360 --> 0:38:22.000
<v Speaker 6>Fpv A Ventures, co founder of managing partner WeSC Chann,

0:38:22.080 --> 0:38:22.960
<v Speaker 6>We fit a lot in there.

0:38:23.320 --> 0:38:24.560
<v Speaker 4>Thank you very much for your time.

0:38:33.080 --> 0:38:35.560
<v Speaker 6>By Dance, the parent company of TikTok, is testing its

0:38:35.600 --> 0:38:39.520
<v Speaker 6>own AI powered chatbot, joining China's AI Race. Not much

0:38:39.600 --> 0:38:42.600
<v Speaker 6>is known about Grace, the project's code name, but Bloomberg

0:38:42.680 --> 0:38:46.240
<v Speaker 6>sources say it's being tested internally among by Dance employees.

0:38:46.280 --> 0:38:49.279
<v Speaker 6>It's worth noting that TikTok is also experimenting with its

0:38:49.400 --> 0:38:53.160
<v Speaker 6>Taco chatbot, which appears as an instant messenger on the app.

0:38:53.880 --> 0:38:58.520
<v Speaker 6>Perpetuating harmful stereotypes and biases is one of humanity's biggest ills,

0:38:58.560 --> 0:39:01.520
<v Speaker 6>but a Bloomberg analysis of a text to image tool

0:39:01.600 --> 0:39:04.880
<v Speaker 6>found that bias from AI is even worse. It's today's

0:39:04.920 --> 0:39:07.239
<v Speaker 6>big take in joining us for more on this is

0:39:07.280 --> 0:39:10.520
<v Speaker 6>Bloomberg's Data Viz leader Leo Nicolette.

0:39:10.920 --> 0:39:13.480
<v Speaker 4>Okay, so Bloomberg.

0:39:13.120 --> 0:39:18.920
<v Speaker 6>Uses Stable Diffusion, It gives commands and generates thousands of images.

0:39:19.400 --> 0:39:21.759
<v Speaker 4>Talk us through what you did and why.

0:39:24.239 --> 0:39:29.080
<v Speaker 18>Yes, So we basically carried out an experiment with Stable Diffusion,

0:39:29.160 --> 0:39:32.080
<v Speaker 18>which is one of the biggest open source platforms for

0:39:32.239 --> 0:39:36.759
<v Speaker 18>AI generated images, and being open source makes it easy

0:39:36.840 --> 0:39:39.440
<v Speaker 18>to also analyze and experiment with, and we wanted to

0:39:39.560 --> 0:39:42.120
<v Speaker 18>understand how deeply ingrained.

0:39:41.960 --> 0:39:44.520
<v Speaker 4>Biases might be in this technology.

0:39:45.120 --> 0:39:48.160
<v Speaker 18>So we asked to create thousands of images of workers

0:39:48.320 --> 0:39:53.480
<v Speaker 18>for fourteen jobs and also different criminalized categories, and then

0:39:53.480 --> 0:39:55.680
<v Speaker 18>we analyzed the results and you know what we found

0:39:55.880 --> 0:40:00.480
<v Speaker 18>was a really systemic pattern of racial and gender that

0:40:00.680 --> 0:40:04.279
<v Speaker 18>doesn't just replicate stereotypes, but it actually makes them worse.

0:40:04.360 --> 0:40:07.920
<v Speaker 18>It stretches them to extremes worse than those found in

0:40:08.000 --> 0:40:11.960
<v Speaker 18>the real world. Women and people with darker skin tones

0:40:11.960 --> 0:40:16.160
<v Speaker 18>were underrepresented across images of high paying jobs and overrepresented

0:40:16.280 --> 0:40:17.960
<v Speaker 18>for low paying ones for example.

0:40:19.360 --> 0:40:23.759
<v Speaker 3>It's truly depressing, and I suppose ultimately the problem here

0:40:23.800 --> 0:40:27.880
<v Speaker 3>is society and stability AI and Stable Diffusion is just

0:40:28.040 --> 0:40:31.360
<v Speaker 3>emphasizing that even more when you're coming to your reporting,

0:40:31.440 --> 0:40:34.279
<v Speaker 3>when you're looking at the outcomes. How much was their

0:40:34.320 --> 0:40:38.239
<v Speaker 3>discussion about the fixes here? Is it about auditing some

0:40:38.360 --> 0:40:40.800
<v Speaker 3>of the algorithms, is there a way of ensuring that

0:40:40.840 --> 0:40:42.759
<v Speaker 3>we don't or is it about the prompting that's being

0:40:42.800 --> 0:40:43.120
<v Speaker 3>put in.

0:40:45.040 --> 0:40:48.279
<v Speaker 18>Well, one of the important things is about transparency. So

0:40:48.760 --> 0:40:51.680
<v Speaker 18>it is a good thing that Stable Diffusion is open

0:40:51.760 --> 0:40:57.239
<v Speaker 18>source because that enables people and you know, researchers to

0:40:58.000 --> 0:41:01.719
<v Speaker 18>actually improve the model and make it better. So that's

0:41:02.160 --> 0:41:04.399
<v Speaker 18>in a way it can be better than closed source

0:41:04.520 --> 0:41:07.239
<v Speaker 18>models like you know DAI for example, where we don't

0:41:07.280 --> 0:41:11.920
<v Speaker 18>really have that much knowledge into how they're being improved

0:41:12.680 --> 0:41:13.520
<v Speaker 18>and by how much.

0:41:14.719 --> 0:41:18.919
<v Speaker 6>So yeah, yeah, sorry, let me just jump in real quick,

0:41:18.960 --> 0:41:21.400
<v Speaker 6>and I would just point out that a spokesperson spinit

0:41:21.480 --> 0:41:24.240
<v Speaker 6>Ai points out that all AI models have this inherent

0:41:24.719 --> 0:41:27.280
<v Speaker 6>bias of them, but their point is that by open

0:41:27.360 --> 0:41:30.800
<v Speaker 6>sourcing it, Caroline, that they can work towards overcoming that

0:41:31.239 --> 0:41:33.160
<v Speaker 6>rather than close or non open source models.

0:41:33.239 --> 0:41:35.120
<v Speaker 3>I wish we could discuss this day and day out,

0:41:35.200 --> 0:41:37.319
<v Speaker 3>and Leo, I'm hoping you can come back and join

0:41:37.400 --> 0:41:39.600
<v Speaker 3>us a little bit more on but more imminently.

0:41:39.719 --> 0:41:40.719
<v Speaker 5>Go read his work.

0:41:40.840 --> 0:41:43.279
<v Speaker 3>It's with Dinabas as well. Leo Nicoletti, thank you for

0:41:43.360 --> 0:41:46.640
<v Speaker 3>staying late for us in Italy. It's absolutely fascinating peace

0:41:46.719 --> 0:41:50.120
<v Speaker 3>and ultimately a bit depressing as it stands, but we

0:41:50.160 --> 0:41:50.799
<v Speaker 3>don't want to end.

0:41:50.800 --> 0:41:53.080
<v Speaker 5>On a low note. Ed because the time is nigh.

0:41:53.120 --> 0:41:56.120
<v Speaker 3>We're wrapping up. It's the edition of Bloomberg Technology wrapped up,

0:41:56.200 --> 0:41:56.959
<v Speaker 3>but it can.

0:41:56.880 --> 0:41:59.959
<v Speaker 6>Get more incredible weak and I've forgotten that it starts

0:42:00.160 --> 0:42:03.480
<v Speaker 6>with Apple and WWDC. You can recap on the podcast

0:42:03.520 --> 0:42:06.400
<v Speaker 6>wherever you get yours, Apple, Spotify, iHeart and Don Bloomberg

0:42:06.640 --> 0:42:07.800
<v Speaker 6>from New York from SF.

0:42:08.160 --> 0:42:08.960
<v Speaker 4>This is Bloomberg