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Speaker 1: From Mahard where Innovation of Money and Power Collie in

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Speaker 1: Silicon Valley, NBN. This is Bloomberg Technology with Caroline Hyde

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Speaker 1: and Ed Ludlove.

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Speaker 2: I'm Caroline Hyde at Bloomberg's Weld headquarters in New York

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Speaker 2: and am Ed Ludlow in San Francisco.

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Speaker 3: This is Bloomberg Technology.

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Speaker 2: Coming up. We break down the market action as equities

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Speaker 2: trade in the red and inn video drops for the

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Speaker 2: first time since it's a blowout earnings report.

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Speaker 4: Plus, we'll talk artificial intelligence as the Biden administration weighs

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Speaker 4: how aggressively to regulate new artificial intelligence tools like chat, GPT, and.

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Speaker 2: A mid recent termoil in the US banking system. One

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Speaker 2: fintech startup saw revenue surge. We'll discuss why, plus so

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Speaker 2: much more throughout the show, including where public markets are

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Speaker 2: currently trading ed. We're seeing a little bit of a

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Speaker 2: dismal feel to the NASDAC today. We're dropping about seven

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Speaker 2: tenths of a percent nervousness. Nervousness out there that maybe

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Speaker 2: some of the economic data in the US is just

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Speaker 2: too strong. The Federal Serve is still going to have

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Speaker 2: to call this economy the Jolts data. Basically job openings

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Speaker 2: looking much better than any single economist out there had anticipated.

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Speaker 2: But what's interesting is you weave in therefore perhaps the

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Speaker 2: sell off, well, the buying that we see in two

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Speaker 2: year years actually just dipping a little bit. That's all

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Speaker 2: around where we go in terms of the debt ceiling

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Speaker 2: as well, whether we get some sort of agreement finally

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Speaker 2: push forward. We're hearing from Kanie Lynes just at the

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Speaker 2: end of the previous show on that. But what's notable

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Speaker 2: is also the fact that yes, economic data in the

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Speaker 2: US perhaps signing too much on the positive side, and

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Speaker 2: over in China and Europe that PMI and manufacturing data

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Speaker 2: looking weak. So we sell off once again in terms

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Speaker 2: of those US listed Chinese stocks. Let's move on and

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Speaker 2: see what's happening elsewhere in the world of technology. I'm

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Speaker 2: looking at what's happening in the world of crypto, because

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Speaker 2: what a dire amount month. We end up the month

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Speaker 2: of May down more than eight percent. This is the

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Speaker 2: worst since November when FTX collapsed. Of course, so there's

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Speaker 2: significant perhaps pullback and some of the games we've seen

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Speaker 2: going on in the crypto world. But dig into some

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Speaker 2: of the microne names that we're seeing on a move today.

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Speaker 4: Yeah, some of that nervousness you're talking about certainly coming

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Speaker 4: from earnings. Hewlett Packard Enterprise, the maker of networking or

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Speaker 4: office infrastructure tools, down seven percent, pretty big decline. It's

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Speaker 4: forecasts of the current period really coming in below expectations.

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Speaker 2: HP Inc.

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Speaker 4: Not to be confused with the maker of laptops missing

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Speaker 4: sales in the court, had just gone the fiscal second

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Speaker 4: quarter that giving some concern around demand for PC. But

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Speaker 4: Bloomberg Intelligence our colleagues, they're saying, actually there was evidence

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Speaker 4: that the market for PC's bottoming out. Based on what

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Speaker 4: HP Inc. Had to say, tut the down two percent.

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Speaker 4: Elon Musk over in China, giving back some of its

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Speaker 4: recent games, coming off a two month high. Elon Musk

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Speaker 4: photographed having dinner at a restaurant in China. Heats at

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Speaker 4: restaurants just like the rest of us.

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Speaker 3: Not sure me. I'm having much waiting on the stock.

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Speaker 4: But a lack of news for us mere mortals driving

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Speaker 4: Tessa action in the chip space. Actually really interesting, Intel

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Speaker 4: making significant gains now up four percent, had been up

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Speaker 4: significantly higher after the CFO said revenue in the current

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Speaker 4: period will come at the upper half of its previously

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Speaker 4: guided projection or range, that giving investors some confidence, and

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Speaker 4: then in Vidia down three point seven percent is actually

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Speaker 4: it's biggest drop since February. But remember we're coming off

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Speaker 4: of record close Tuesday evening, and of course twenty four

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Speaker 4: hours after Nvidia touched a one trillion dollar market cap

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Speaker 4: for the first time.

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Speaker 3: Some profit taking.

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Speaker 2: Perhaps, yeah, maybe, just maybe. And we've got to think

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Speaker 2: about the profit taking more broadly, because an awful lot

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Speaker 2: of the rally that we've seen so far this year

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Speaker 2: has been thanks to just a very few names, and

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Speaker 2: that's highlighted by the recent report coming from Joanne Feenim

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Speaker 2: with Police to welcome her to the show. Ed partner

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Speaker 2: and portfolio and manager over at Advisor's Capital Management. And

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Speaker 2: I read your note with real interest as we were,

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Speaker 2: of course still trying to muddy through the macro what's

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Speaker 2: happening in terms of debt ceilings and the like. But

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Speaker 2: talk to us are just how important a certain few

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Speaker 2: names of ultimately tech stocs have been to the rally

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Speaker 2: so far this year.

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Speaker 5: Yeah, Caroline, you know, we got a lot of inquiries

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Speaker 5: from our clients about how much the market has gone

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Speaker 5: up this year and is this a good time to

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Speaker 5: get into equities. And when you break down, there are

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Speaker 5: really two stock markets. There's the super seven, the biggest

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Speaker 5: tech and consumer companies like Amazon, Tesla, Microsoft, Apple, Google,

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Speaker 5: obviously in Nvidia that have served on average as of

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Speaker 5: Friday's closed, they are up sixty seven percent year to date,

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Speaker 5: whereas the rest of the market is only a zero

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Speaker 5: point three percent.

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Speaker 6: And that really does tie into two narratives.

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Speaker 5: One the growth opportunity related to AI on the one hand,

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Speaker 5: and then the rest of the market, which is all

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Speaker 5: about recession risks, intrast rate risk, inflation risk as we're

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Speaker 5: seeing materialized today.

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Speaker 2: Let's dig deeper into the AI equation because yes, in

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Speaker 2: Nvidia now back below that one trillion, but still basically

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Speaker 2: what a surge. Are we putting too many eggs in

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Speaker 2: just a very few baskets? Are you on the sort

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Speaker 2: of Kafy Woods side of the equation that there are

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Speaker 2: other companies that are going to win here and you

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Speaker 2: should start looking at better valuations out.

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Speaker 5: Yeah, Caroly, it's hard to tell how much we're going

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Speaker 5: to get out of AI over the long term, but

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Speaker 5: it's pretty clear that the companies that are enabling AI,

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Speaker 5: whether it's through the chips like Nvidea provides, or whether

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Speaker 5: it's the software and deployment like Amazon Web Services, Google,

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Speaker 5: Microsoft are enabling. What I think investors recognize is that

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Speaker 5: even though we don't know all the details, we know

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Speaker 5: that AI and it's spread across the economy is going

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Speaker 5: to provide years of growth for these companies. And that's

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Speaker 5: a step change in what we expected these companies to

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Speaker 5: be able to do, you know, just some months ago,

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Speaker 5: and so I think that's warranted. It isn't to say

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Speaker 5: that there aren't opportunities out there, and you know, investors

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Speaker 5: can look for those, but we think these are solidly

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Speaker 5: in the middle of this and it's a less risky

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Speaker 5: way I think to play the AI move over time,

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Speaker 5: and then you can look beyond that into companies adopting

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Speaker 5: AI in industrials, in healthcare. There are plenty of opportunities

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Speaker 5: out there. We'll learn more about them as time goes on.

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Speaker 4: Johan Kaffee Wood our programming our colleagues in Asia. Overnight

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Speaker 4: she's moved her focus on from Nvidia Tesla to the

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Speaker 4: next big bet.

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Speaker 3: Have listened to what she had said.

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Speaker 7: We are really happy that investors who own benchmarks like

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Speaker 7: the Nasdaq and QQQ still own Nvidia.

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Speaker 8: They own it in some of our portfolios. But we're

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Speaker 8: onto the next thing. Nvidia is a hardware stock, and

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Speaker 8: sure it has some software, but the history of hardware

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Speaker 8: and software is the bigger beneficiary.

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Speaker 7: Over time is going to be software, is.

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Speaker 4: Going to be software. I think you note that investors

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Speaker 4: are looking to move money into software, which can mean

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Speaker 4: a broad range of things, right cloud, SaaS enterprise. What

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Speaker 4: are the corners of that sub sector that interests you?

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Speaker 6: Oh yeah, there are going to be many. I mean

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Speaker 6: the adoption of AI.

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Speaker 5: Into software tools across multiple and markets I think is significant.

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Speaker 5: You know, we own a company, for example, called Slunk

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Speaker 5: in our growth strategy. They are a big data analytics company,

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Speaker 5: and AI is going to play an increasingly important role there.

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Speaker 5: From Nvidia's perspective, you know, the tying of hardware to

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Speaker 5: software is actually pretty critical to what they're enable enabling

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Speaker 5: their customers to do. It's also why their gross margins

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Speaker 5: are going up, and so you know, we see in

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Speaker 5: hardware and software to be more and more tied over time,

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Speaker 5: which I.

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Speaker 6: Think is why you know, in Vidia is such an

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Speaker 6: interesting opportunity here.

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Speaker 5: Not to say one should potentially take a little bit

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Speaker 5: of profits at this time, which we did for clients

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Speaker 5: actually after they recorded. But going forward, yeah, there are

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Speaker 5: going to be plenty of opportunities in software as well.

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Speaker 3: Yeah, Karry.

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Speaker 4: The point that I make as well is like this

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Speaker 4: obsession with a small group of US domicile names, right

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Speaker 4: the megacap tech, But if you look at the commentary

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Speaker 4: of the broader market, particularly the bomb market, when we

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Speaker 4: think about this economy and global inflation, everyone's got an

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Speaker 4: eye on China.

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Speaker 2: And well, Elon Musk is there at the moment, isn't

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Speaker 2: he as well that we were just highlighting a bit earlier?

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Speaker 2: I mean, joan to that point, how much when you're

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Speaker 2: looking at the macro, when you're looking at what's happening

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Speaker 2: here in the US and everyone seeing the seven winners

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Speaker 2: the rest leaving behind. When we think about where inflation's at,

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Speaker 2: how much are you looking for your investor base at oh, China,

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Speaker 2: Europe being opportunities or being just there as you need

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Speaker 2: to stay well away from at the moment.

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Speaker 5: You know, there's always a role for international exposure in portfolios.

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Speaker 5: We have actually separate international strategies in addition to some

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Speaker 5: strategies owning some international names.

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Speaker 6: China clearly has become more of a concern.

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Speaker 5: I think investors were overly optimistic about how much the

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Speaker 5: reopening we'll be able to drive demand in the near term,

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Speaker 5: Which isn't to say that China is not going to

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Speaker 5: be a very important source of conservat demand. They have

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Speaker 5: massively moved a big chunk of their population into the

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Speaker 5: middle class and that's going to be a foundation for

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Speaker 5: buying over many, many years.

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Speaker 6: So you know, overall, there are some attractive valuations.

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Speaker 5: Our approach is to look for companies in countries as

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Speaker 5: opposed to investing in countries as a whole, and that

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Speaker 5: way we can hand select, as we do in all

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Speaker 5: of our strategies. You know, back to the hardware side,

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Speaker 5: the advantage we're seeing that Ed mentioned the focus on

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Speaker 5: US I think comes from the deep modes right that

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Speaker 5: our companies have created around this technology. Now as a

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Speaker 5: semiconductor analyst for ten years, and one of the key

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Speaker 5: things there was digging into the technology advantage that companies

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Speaker 5: like Nvidia and Google teaming up with Broadcom to develop

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Speaker 5: an alternative big chip to be able to do these

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Speaker 5: AI training computations shows that mode is very deep.

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Speaker 6: It supports their margins. So I think it's not wrong

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Speaker 6: for investors.

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Speaker 5: To recognize that the source, right of a lot of

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Speaker 5: the gains are going to be in these US companies.

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Speaker 4: You know, we learn that one morning a market does

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Speaker 4: not make But it is interesting that there's more emphasis

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Speaker 4: right now on current risks rates recession. How much is

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Speaker 4: that going to impact the technology sector going forward? Do

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Speaker 4: you think from this moment right now?

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Speaker 9: Yeah?

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Speaker 5: Great, great thought there. As I said, we have two

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Speaker 5: stock markets right now. We have the investor focus on

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Speaker 5: which companies out there can actually provide years of growth

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Speaker 5: with or without a recession. Right, that's secular growth and

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Speaker 5: now it's been revealed in AI that that's going to

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Speaker 5: be significant. And then you have the rest of the

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Speaker 5: market ed and I think that's the market where they're

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Speaker 5: going to be really focused on those recession risks, and

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Speaker 5: rightly so. Right the jolt state of this morning suggests

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Speaker 5: that the Fed's going to have to raise rates again

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Speaker 5: instead of pausing, and it really throws into doubt the

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Speaker 5: FED being able to cut rates towards the end of

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Speaker 5: the year, and that makes investors worry that those higher

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Speaker 5: rates are going to constrain lending that's actually going to

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Speaker 5: trigger a recession.

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Speaker 6: The FED obviously would like to avoid that.

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Speaker 5: So there's just a massive amount of uncertainty right now,

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Speaker 5: and we're seeing it in the prices of stocks in

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Speaker 5: the rest of the market, where secular growth isn't as

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Speaker 5: big a driver, where the sickle could concerns are far

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Speaker 5: more important.

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Speaker 6: But the patient investors should really look beyond this.

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Speaker 5: Equities do a terrific job over the long term of

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Speaker 5: delivering the ability to build wealth. So it's an opportunity

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Speaker 5: to get some of those companies in the rest of

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Speaker 5: the in the rest of the market and build those

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Speaker 5: positions now while.

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Speaker 6: Risks are high, and if you're a long term investor, you.

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Speaker 5: Can look beyond that and build really solid, diversified portfolios.

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Speaker 2: And take profit in the seven names that have driven

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Speaker 2: us thus far.

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Speaker 6: We've done some of that absolutely.

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Speaker 5: You know, we're actually slightly now underweight and video we've

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Speaker 5: been overweight for the last year. And I think it's

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Speaker 5: appropriate to make sure that the risk exposure you have

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Speaker 5: in a portfolio is balanced against you know, how the

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Speaker 5: potential return from here is now confronting the level of

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Speaker 5: risk that we see, you know, across the global economy.

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Speaker 4: Jo Am Feenie Partner, Profolio Manager Advisor's Capital Management one morning.

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Speaker 4: A market does not make, but it's good to get

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Speaker 4: a snapshot of view.

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Speaker 3: Thank you very much. Credit Card starts up.

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Speaker 4: Brex has seen a surge and usage of its products

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Speaker 4: following this year's regional banking term or the think tech

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Speaker 4: company on track to reach roughly five hundred million in

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Speaker 4: recurring revenue over the next twelve months of it's if

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Speaker 4: its current pace of growth continues. Joining us now to

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Speaker 4: discuss is Brex co founder Enriq dubigraph in Rik what's

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Speaker 4: driving this. That's a pretty big build up in forward

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Speaker 4: looking revenue.

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Speaker 10: Right, Yeah, the first thank you so much for having

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Speaker 10: me here. So we're actually announcing that there's two products

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Speaker 10: that crossed.

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Speaker 11: Over one hundred million in revenue.

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Speaker 10: Which is our empower product, which is replacing concur for

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Speaker 10: travel and expense for most businesses. And also our business

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Speaker 10: Accounts right which can do operational payments, deposit checks, all

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Speaker 10: the kind of like traditional banking activities for our business

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Speaker 10: in one place.

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Speaker 11: And both of them crossed over one hundred million.

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Speaker 10: So now we have three products that crossed one hundred

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Speaker 10: million revenue.

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Speaker 11: That's something that we're like really really excited about.

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Speaker 4: So the one hundred million in annual recurring revenue for

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Speaker 4: the business account, it's unit and Enpower Bloomberg sources saying

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Speaker 4: that company wide and you're recurring revenue on track for

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Speaker 4: about five hundred million.

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Speaker 3: What is it that makes bres so.

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Speaker 4: Attractive to startups that you've done business with since the

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Speaker 4: fallout of SVB.

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Speaker 11: So, I think there are two kind of main growth drivers.

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Speaker 10: The first one is actually our enterprise segment of the business.

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Speaker 10: So Empower, which is our spend management solution, has been

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Speaker 10: serving customers such as door Dash, Coinbase, indeed that has

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Speaker 10: glassed or Elimonade, et cetera truck and I think both startups,

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Speaker 10: mid sized companies and a large enterprises have been attracted

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Speaker 10: for a product because it's super easy to use for employees.

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Speaker 10: You know, you can just slip your car and your

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Speaker 10: expenses are done. It's extremely global that works all over

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Speaker 10: the world for all your subsidiaries all over the world,

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Speaker 10: and that's been like growing a lot as remote work

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Speaker 10: grows and people are hiring internet a lot of focus

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Speaker 10: on experience. The second one is our banking product, and

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Speaker 10: I would say that, yes, the regional banking crisis definitely

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Speaker 10: had We didn't know if it was going to be

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Speaker 10: a net negative for Brax.

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Speaker 11: We got that question a lot. Now we know it

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Speaker 11: wasn't a positive.

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Speaker 10: Customers did come and stay with Brax and that also

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Speaker 10: drove a lot of growth for the company.

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Speaker 4: You know, Caroline, I think you and I will never

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Speaker 4: forget that Friday where the SVB story unfolded.

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Speaker 3: We did that special program.

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Speaker 4: But we learned so much about how startups manage their finances,

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Speaker 4: the type of funding that they seek beyond just raising

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Speaker 4: capital from vcs.

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Speaker 2: And what was interesting was Silicon Valley Bank was so

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Speaker 2: intertwined with the startup sector because not only was it

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Speaker 2: offering lending, but offering menu ways, venture, debt and enriquey.

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Speaker 2: That's an area that you've been working in as well.

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Speaker 2: How much are you seeing startups getting into cash burn

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Speaker 2: needing to get further money to deploy but don't want

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Speaker 2: to raise equity? Is venture that's still a big They're

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Speaker 2: part of your business.

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Speaker 10: I would say that vengjur Donna is a very small

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Speaker 10: part of you know, it's definitely an experiment that we

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Speaker 10: try it and continue to do it in small amounts,

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Speaker 10: but it's not mainly why customers come to us. The

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Speaker 10: main way our business account customers come to us is

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Speaker 10: because of their operational banking, so running payroll wires, they

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Speaker 10: ch checks, making their kind of day to day banking.

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Speaker 11: It's a lot of startups.

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Speaker 10: They keep up a lot of their funds in some

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Speaker 10: of the big banks, but they keep you know, one, two, three,

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Speaker 10: four months of money with us to be their day

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Speaker 10: to day partner that really understands them.

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Speaker 2: And where are these companies coming from. You're obviously focused

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Speaker 2: on allowing these companies to be global, to travel, to grow,

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Speaker 2: but are they largely US based companies? Are you seeing

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Speaker 2: this becoming much more of an international play?

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Speaker 11: These are a lot of them are global companies.

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Speaker 10: So over fifty percent of the companies that we serve

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Speaker 10: have global employees.

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Speaker 11: All of them have US employees.

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Speaker 10: We only serve you if you have some sort of

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Speaker 10: US operation for a bunch of regulatory reasons. But a

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Speaker 10: lot of them are global companies and we serve as

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Speaker 10: I said, from startups just were born to you know,

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Speaker 10: customers that were from SEB also to kind of like

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Speaker 10: larger enterprise is like indeed, door adacting point base.

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Speaker 2: I have a sninky suspicion as a few more AI

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Speaker 2: players looking to bank or being born at the moment

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Speaker 2: and Ed I mean every day with discussing the exuberance

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Speaker 2: around that.

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Speaker 4: Yeah, it reflects the conversation we had twenty four hours ago, right,

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Speaker 4: Rex Salisbury. It's about what AI can do for existing

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Speaker 4: fintech players And I guess, Enrica, that's my question for you.

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Speaker 4: How is AI going to boost your offering? Where are

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Speaker 4: you investing to jump on the bandwagon, so to speak.

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Speaker 10: I think AI is going to completely transform our industry.

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Speaker 10: And I would say there's a couple different places.

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Speaker 11: Right.

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Speaker 10: You can think about it something the employee experience. How

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Speaker 10: can you make it even easier for employee to do

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Speaker 10: their expenses or not have to do them at all,

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Speaker 10: you know, make all of that automated. We also announced

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Speaker 10: earlier this year kind of like you know, a CFO

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Speaker 10: for everyone where if anyone has any questions, you know,

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Speaker 10: you can just ask this chatbot and they will respond

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Speaker 10: to you, so you don't have to waste the finance

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Speaker 10: team time of questions. They already know the answer to

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Speaker 10: all the way to like accounting and operations. I think

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Speaker 10: it's going to completely revolutionize and transform our industry.

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Speaker 11: And we're investing very, very heavily into the.

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Speaker 10: Development of new capabilities, and you know, I think we're

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Speaker 10: over the next few months you'll see a lot of

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Speaker 10: very innovative things coming out.

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Speaker 12: Of Rex Henrique. When does Brex go public? We don't

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Speaker 12: have a timeline yet. We're not against being a public company,

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Speaker 12: you know, in any shape or form.

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Speaker 10: That being said, I would love to be a low

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Speaker 10: volatility public company. We're not yet profitable, so we'll probably

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Speaker 10: wait for that to happen before we go public. Other

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Speaker 10: things to remember is we're a six year old company.

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Speaker 10: We were started in twenty seventeen, so I would say,

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Speaker 10: you know, don't we don't have any pressure to go public. Yes,

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Speaker 10: we're still quite young for a startup.

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Speaker 2: And we understand you don't have any pressure to raise

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Speaker 2: new bench capital yourself. I'm interested though, where you are

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Speaker 2: in terms of the focus on being profitable, being CAPEX

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Speaker 2: positive and all this sort of good stuff. You still

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Speaker 2: trying to hire because you seem to be talk about

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Speaker 2: developing leaning into AI. How are you managing those costs?

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Speaker 10: Yeah, I would say that, you know, we're still investing

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Speaker 10: a lot in one our go to market organization because

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Speaker 10: the product is having so much traction that you know,

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Speaker 10: I think it is higher or y for us to

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Speaker 10: invest in sales and marketing to acquire you know, more

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Speaker 10: customers faster. So we are investing there and our investors

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Speaker 10: are really excited about it. And then in the second area,

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Speaker 10: as are. I do believe this is going to be

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Speaker 10: transformation on a step function change. So I think if

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Speaker 10: you know, if you're a company and you're not willing

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Speaker 10: to invest in AI, you know, you're probably missing the.

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Speaker 11: Next big shift.

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Speaker 10: That being said, we do have a good business of

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Speaker 10: a good business model. I think we can we have

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Speaker 10: a plan to being you know, cashul positive without raising

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Speaker 10: any more money.

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Speaker 2: Ry kay, great test in time with you again, Thank you.

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Speaker 2: Enrique Ubrogress is the bex Coach CEO. Meanwhile, coming up,

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Speaker 2: we're going to talk about a guest the email master

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Speaker 2: tests the CEO's been making the rounds Beijing. More on

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Speaker 2: who he met with and any projects that he's mulling.

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Speaker 2: That's next. Meanwhile, we've got to remember we've still got

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Speaker 2: earnings coming dripping through the system salesforce. Investor is going

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Speaker 2: to be watching that one closely today after the Bell

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Speaker 2: Company gonna update us on its own cost cutting campaign. Look,

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Speaker 2: how is it going to provide us with the long

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Speaker 2: run revenue goals that it's set itself. This is Bloomberg.

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Speaker 2: It's time now for talking tech. First up iPhone maker

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Speaker 2: fox Con Technology says it expects to more than double

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Speaker 2: its revenue in the second half of the year thanks

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Speaker 2: in large part two sales and guess what, artificial intelligence

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Speaker 2: and the servers thereof. It's part of a move to

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Speaker 2: generate revenue from other fields such as electric cars and

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Speaker 2: AI on high that's the listed vehicle for fox Con

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Speaker 2: Technology also working with Nvidia on autonomous driving applications, and

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Speaker 2: Baidu is looking to beef up China's AIC and the

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Speaker 2: internet company has set aside roughly one hundred and forty

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Speaker 2: million dollars to fund Chinese startups. Has specialized in generative

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Speaker 2: AI by Doo and its VC partners will accept pitches

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Speaker 2: from prospective founders who will use its ernie bot to

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Speaker 2: build their own large language model before Baydu determines well

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Speaker 2: who gets the seed funding. Plus day two of Youla

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Speaker 2: Musks visits China and he's already met with more government officials.

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Speaker 2: That's as he looks to bolster opportunities in the country.

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Speaker 2: Musk met earlier with China's Minister for Industry and Information

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Speaker 2: Technology and Scott by the Ministry of Commerce. He also

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Speaker 2: met with the head of the Battery Giant's ATL, sparking

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Speaker 2: rumors of potential collaboration, and Musk arrived in Beijing Tuesday,

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Speaker 2: where he met with Foreign Minister Quinn Gang. And I mean, overall,

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Speaker 2: this is a focus another CEO. We've heard from Mercedes,

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Speaker 2: We've heard from GM, We've heard from well Jamie Diamond,

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Speaker 2: really having to talk up that. Look, these are still

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Speaker 2: focused on Chinese despite some of the trade tensions, the

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Speaker 2: tech tensions.

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Speaker 4: Yeah, and in the statement issued by the Chinese government,

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Speaker 4: Elon Musk is quoted as saying that he is against

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Speaker 4: the decoupling from China. They'll continue to invest in China.

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Speaker 4: It's a really interesting two way situation. Fifty percent of

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Speaker 4: Tesla's output was in out of Shanghai globally last year.

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Speaker 4: And you know, the Chinese government have given Tesla the

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Speaker 4: freedom to operate there as an independent US company, which

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Speaker 4: is rare.

442
00:21:21,160 --> 00:21:23,880
Speaker 3: But again it's a chorus of names. As you point out, and.

443
00:21:24,000 --> 00:21:29,040
Speaker 2: Interesting that potential rumoring around the big battery maker, because

444
00:21:29,400 --> 00:21:32,199
Speaker 2: that's of course a big part of trying to have

445
00:21:32,280 --> 00:21:34,680
Speaker 2: supply chain in the US rather than depending on the

446
00:21:34,760 --> 00:21:36,200
Speaker 2: Chinese made goods.

447
00:21:36,680 --> 00:21:38,560
Speaker 4: Right well, when Ford announced that it was going to

448
00:21:38,600 --> 00:21:42,240
Speaker 4: make batteries in Michigan licensing Coatl Technology, there was a

449
00:21:42,240 --> 00:21:45,920
Speaker 4: big fallout, particularly from the Republican Party. That relationship really

450
00:21:45,920 --> 00:21:57,000
Speaker 4: closely watched. Welcome back to Bloomberg Technology. I med love

451
00:21:57,040 --> 00:21:58,040
Speaker 4: Vo in San Francisco.

452
00:21:58,160 --> 00:21:59,760
Speaker 2: I'm Caroline hid in New York. Let's check in on

453
00:21:59,800 --> 00:22:01,920
Speaker 2: these markets. Halfway through the show, halfway through the training

454
00:22:02,000 --> 00:22:04,560
Speaker 2: day therein and we're looking at slightly from lower and

455
00:22:04,600 --> 00:22:06,159
Speaker 2: now's that one hundred At the moment, we're off by

456
00:22:06,240 --> 00:22:09,639
Speaker 2: eight ten seven percent. Bit of a concern profit taking

457
00:22:09,680 --> 00:22:11,680
Speaker 2: call it after the massive run up that we've seen

458
00:22:11,720 --> 00:22:13,960
Speaker 2: in this particular benchmark, but also the warriors that basically

459
00:22:13,960 --> 00:22:15,840
Speaker 2: we're running too hot in the US. The Federal Reserve's

460
00:22:15,880 --> 00:22:18,440
Speaker 2: still going to have to tame the growth. The jolt

461
00:22:18,440 --> 00:22:20,960
Speaker 2: stata showing that people are still really hiring. We see

462
00:22:20,960 --> 00:22:22,560
Speaker 2: a little bit of movement in the two year yield

463
00:22:22,600 --> 00:22:24,320
Speaker 2: on the back of that data that came roaring through

464
00:22:24,320 --> 00:22:26,879
Speaker 2: but remember tepid growth coming from China in terms of

465
00:22:26,880 --> 00:22:30,119
Speaker 2: PMIS and manufacturing data there. Europe to interestingly taking the

466
00:22:30,119 --> 00:22:32,119
Speaker 2: window of the sales of bitcoin once again. We're actually

467
00:22:32,119 --> 00:22:33,679
Speaker 2: off by more than eight percent over the course of

468
00:22:33,720 --> 00:22:36,680
Speaker 2: this month, the worst month since when FTX collapsed. Let's

469
00:22:36,680 --> 00:22:38,480
Speaker 2: moving on and have a little look at what's happening

470
00:22:38,480 --> 00:22:40,399
Speaker 2: in terms of individual movers, because we do still have

471
00:22:40,440 --> 00:22:42,680
Speaker 2: earnings we used to have real news, but we also

472
00:22:42,760 --> 00:22:46,000
Speaker 2: have perhaps the first fall in Nvidia since it's waring

473
00:22:46,040 --> 00:22:48,440
Speaker 2: earnings that we saw last week. We're off by about

474
00:22:48,480 --> 00:22:50,480
Speaker 2: five percent, called it profit taking after it hit that

475
00:22:50,480 --> 00:22:53,320
Speaker 2: one trillion dollar mark. We also, though, see Intel on

476
00:22:53,359 --> 00:22:55,720
Speaker 2: the back seeing an uptick a more point three point

477
00:22:55,720 --> 00:22:57,879
Speaker 2: eight percent higher as we see the CFO speaking at

478
00:22:57,880 --> 00:23:00,879
Speaker 2: a td count event really discussing how maybe they're going

479
00:23:00,920 --> 00:23:02,240
Speaker 2: to manage to be at the top end of the

480
00:23:02,280 --> 00:23:05,400
Speaker 2: range when it comes to their forward looking guidance Packet enterprises,

481
00:23:05,440 --> 00:23:07,439
Speaker 2: though that disappoints on the back of its numbers ed

482
00:23:07,600 --> 00:23:10,560
Speaker 2: down by some seven percent. Are we worried about compute

483
00:23:10,600 --> 00:23:12,640
Speaker 2: spending at the moment. We are still in a very

484
00:23:12,640 --> 00:23:15,639
Speaker 2: difficult macroeconomic environment. We're going to remember that.

485
00:23:16,400 --> 00:23:19,360
Speaker 3: But AI is still a big factor in those markets.

486
00:23:19,400 --> 00:23:19,560
Speaker 11: Now.

487
00:23:19,560 --> 00:23:22,000
Speaker 4: The US and EU are meeting up this week at

488
00:23:22,000 --> 00:23:26,080
Speaker 4: the USU Trade and Technology Council gathering in Sweden, where

489
00:23:26,119 --> 00:23:29,639
Speaker 4: the EU is discussing plans to subject generative AI to

490
00:23:29,720 --> 00:23:30,480
Speaker 4: additional rules.

491
00:23:31,680 --> 00:23:34,320
Speaker 13: This is bigger than Europe. US is important, but it

492
00:23:34,400 --> 00:23:36,879
Speaker 13: is bigger than the US. But if the two of

493
00:23:36,960 --> 00:23:39,679
Speaker 13: us take the leads with close friends, I think we

494
00:23:39,720 --> 00:23:42,480
Speaker 13: can push something that will make us all much more

495
00:23:42,520 --> 00:23:45,680
Speaker 13: comfortable with the fact that generality of AI is now

496
00:23:45,760 --> 00:23:49,000
Speaker 13: in the world and is developing at amazing speed.

497
00:23:50,560 --> 00:23:54,520
Speaker 4: Meanwhile, Biden administration officials have been divided over how aggressively

498
00:23:54,600 --> 00:23:57,879
Speaker 4: new AI tools should be regulated, a dissonance that has

499
00:23:57,960 --> 00:24:01,320
Speaker 4: left the US without a coherent response to the EUS

500
00:24:01,680 --> 00:24:05,200
Speaker 4: proposals that it's bringing Bloombergs and eguiton to break it down.

501
00:24:05,240 --> 00:24:07,440
Speaker 4: And I thought we were in a place where the

502
00:24:07,560 --> 00:24:10,320
Speaker 4: US and EU were on the same page. You regulate

503
00:24:10,400 --> 00:24:14,840
Speaker 4: the use case the tool rather than the underlying technology itself.

504
00:24:14,840 --> 00:24:15,680
Speaker 3: Where have we gone wrong?

505
00:24:16,680 --> 00:24:18,320
Speaker 1: Well, we were kind of on the same page, you know.

506
00:24:18,440 --> 00:24:21,200
Speaker 14: The last TTC meeting came out with this joint roadmap

507
00:24:21,240 --> 00:24:24,280
Speaker 14: that kind of agreed to pursue this risk based approach,

508
00:24:24,359 --> 00:24:27,240
Speaker 14: like you said, focusing on the use of the technology

509
00:24:27,560 --> 00:24:30,600
Speaker 14: rather than the development of the technology. So AI used

510
00:24:30,600 --> 00:24:33,679
Speaker 14: for things like critical infrastructure would be subject to higher

511
00:24:33,760 --> 00:24:37,880
Speaker 14: levels of compliance. Now Chat GPT changed everything, and that

512
00:24:38,000 --> 00:24:41,040
Speaker 14: really showed us how a general purpose AI product, you know,

513
00:24:41,080 --> 00:24:44,720
Speaker 14: something that's not inherently high risk, can become very risky

514
00:24:44,760 --> 00:24:46,639
Speaker 14: on a societal level when it's used by one hundreds

515
00:24:46,640 --> 00:24:49,560
Speaker 14: of millions people every month for everything from you know,

516
00:24:50,119 --> 00:24:53,560
Speaker 14: helping with homework to know more nefarious purposes. So that's

517
00:24:53,600 --> 00:24:56,560
Speaker 14: what policymakers are grappling right with right now, is how

518
00:24:56,560 --> 00:25:00,000
Speaker 14: to treat these generative AI products and how to class

519
00:25:00,040 --> 00:25:01,280
Speaker 14: sify the risk of those.

520
00:25:01,560 --> 00:25:04,920
Speaker 2: At the moment, though, Anna doesn't even really matter if

521
00:25:04,920 --> 00:25:08,160
Speaker 2: the US, for examples, and parts of the administration are divided,

522
00:25:08,280 --> 00:25:10,439
Speaker 2: because to me, it feels like the EU is going

523
00:25:10,480 --> 00:25:12,880
Speaker 2: to be the first mover here with the AI Act.

524
00:25:13,640 --> 00:25:16,560
Speaker 14: Well, and that's why the the EU's AI Act is

525
00:25:16,600 --> 00:25:19,640
Speaker 14: most important, is because US companies are going.

526
00:25:19,480 --> 00:25:21,040
Speaker 9: To have to comply with this if they want to

527
00:25:21,080 --> 00:25:21,919
Speaker 9: operate in Europe.

528
00:25:21,960 --> 00:25:25,320
Speaker 14: So much like the GDPR did for privacy, the AI

529
00:25:25,440 --> 00:25:28,879
Speaker 14: Act is going to set a defacto floor for compliance.

530
00:25:29,000 --> 00:25:31,800
Speaker 14: So that's what US companies are worried about is as

531
00:25:31,840 --> 00:25:36,320
Speaker 14: they develop these foundation models that underpin this generative AI technology,

532
00:25:36,600 --> 00:25:38,439
Speaker 14: if they want to operate in Europe, they're going to

533
00:25:38,480 --> 00:25:40,840
Speaker 14: have to comply with those rules, whether or not the

534
00:25:41,000 --> 00:25:43,840
Speaker 14: US has passed its own policy. And these companies are

535
00:25:43,840 --> 00:25:47,040
Speaker 14: really looking for a champion in the Biden administration to

536
00:25:47,119 --> 00:25:49,320
Speaker 14: go to the EU and say, listen, we don't think

537
00:25:49,359 --> 00:25:52,160
Speaker 14: this policy is going to work the way it's imagined

538
00:25:52,200 --> 00:25:56,520
Speaker 14: when it's implemented eventually. So the reason why it matters

539
00:25:56,520 --> 00:25:59,160
Speaker 14: if the Biden administration is divided is that these companies

540
00:25:59,400 --> 00:26:01,840
Speaker 14: don't have that champion. They don't have anyone going to

541
00:26:01,920 --> 00:26:05,720
Speaker 14: these multi latteral discussions and really expressing their point of view.

542
00:26:06,480 --> 00:26:08,760
Speaker 4: There was a time where we thought that Vice President

543
00:26:08,840 --> 00:26:11,560
Speaker 4: Kamala Harris was the champion, right she hosted that meeting

544
00:26:11,760 --> 00:26:16,720
Speaker 4: with Sam Altman's statues at Nadella. The administration has made

545
00:26:16,800 --> 00:26:20,000
Speaker 4: noises about they want to do something. But I'll go

546
00:26:20,080 --> 00:26:22,840
Speaker 4: back to one point you made policy. We haven't really

547
00:26:22,880 --> 00:26:26,880
Speaker 4: heard any arguments for I side of where to focus regulation,

548
00:26:26,920 --> 00:26:28,680
Speaker 4: particularly from this White House.

549
00:26:29,720 --> 00:26:32,320
Speaker 14: That's right, Well, I will give the administration credit for

550
00:26:32,640 --> 00:26:36,040
Speaker 14: putting forward some very well received frameworks. We have the

551
00:26:36,520 --> 00:26:39,480
Speaker 14: Risk Management Framework from the National Institute of Standards and

552
00:26:39,520 --> 00:26:42,199
Speaker 14: Technology that had a lot of input from industry, from

553
00:26:42,240 --> 00:26:44,800
Speaker 14: civil society as a very well thought out document. You

554
00:26:44,880 --> 00:26:48,399
Speaker 14: had the White House putting forth their Bill of Rights

555
00:26:48,440 --> 00:26:51,840
Speaker 14: for people who use AI products. But we don't have

556
00:26:52,040 --> 00:26:55,119
Speaker 14: binding policy in the United States, and that's because the

557
00:26:55,240 --> 00:26:59,600
Speaker 14: US Congress is just now starting to hold their initial

558
00:26:59,640 --> 00:27:02,520
Speaker 14: hearing to educate while makers and what AI even is.

559
00:27:02,960 --> 00:27:05,359
Speaker 14: So while the Biden administration has done a lot of

560
00:27:05,400 --> 00:27:10,120
Speaker 14: work to kind of set non binding guidelines, actually binding

561
00:27:10,200 --> 00:27:12,600
Speaker 14: regulation is going to come from Europe.

562
00:27:12,600 --> 00:27:16,000
Speaker 15: Once again, Anna Juton, I mean thank you giving us

563
00:27:16,359 --> 00:27:20,159
Speaker 15: well the global perception of AI risks, let's dive into

564
00:27:20,200 --> 00:27:22,720
Speaker 15: some of the perceptions here in the United States, because

565
00:27:22,960 --> 00:27:25,560
Speaker 15: there's been another open letter signed by a group of

566
00:27:25,560 --> 00:27:26,720
Speaker 15: industry leaders.

567
00:27:26,480 --> 00:27:30,520
Speaker 2: Warning of this time the risks of extinction due to

568
00:27:30,880 --> 00:27:33,040
Speaker 2: artificial intelligence. So it was published by the Center for

569
00:27:33,080 --> 00:27:34,200
Speaker 2: AI Safety.

570
00:27:34,119 --> 00:27:40,400
Speaker 16: Which has mission to reduce societal scale risks from artificial intelligence,

571
00:27:40,480 --> 00:27:42,240
Speaker 16: joining us now to deeper dive on all of this

572
00:27:42,320 --> 00:27:44,320
Speaker 16: and explain the center zone thinking.

573
00:27:44,440 --> 00:27:47,520
Speaker 2: This is Executive director Dan Hendrix it's great to have you.

574
00:27:48,200 --> 00:27:52,080
Speaker 2: Thank you, Dan. I'm just interested in how this first started,

575
00:27:52,240 --> 00:27:55,240
Speaker 2: this whole mission statement that you put out there about

576
00:27:55,440 --> 00:27:59,960
Speaker 2: summer mitigating the risks in line with potentially a nuclear

577
00:28:00,359 --> 00:28:04,760
Speaker 2: war or indeed a global pandemic. How did you have

578
00:28:04,880 --> 00:28:08,880
Speaker 2: first off conversations with Sam Altman and demis of deep Mind.

579
00:28:09,040 --> 00:28:12,600
Speaker 2: Do they come to you, you go to them.

580
00:28:13,040 --> 00:28:17,040
Speaker 17: So we created the letter largely because I knew that

581
00:28:17,119 --> 00:28:20,320
Speaker 17: many people had concerns that AI could lead to extinction

582
00:28:20,480 --> 00:28:22,720
Speaker 17: and that we should not just be treating it like

583
00:28:22,760 --> 00:28:25,760
Speaker 17: every other tech issue, but instead as a global priority.

584
00:28:26,280 --> 00:28:30,000
Speaker 17: So we created this letter to succinctly convey the shared concern,

585
00:28:30,280 --> 00:28:32,360
Speaker 17: and then we disseminated it among.

586
00:28:32,160 --> 00:28:34,600
Speaker 9: Some profits and from there it spread organically.

587
00:28:34,640 --> 00:28:37,840
Speaker 17: And so there are some surprises in that open Ai

588
00:28:38,080 --> 00:28:41,120
Speaker 17: signed it, Google, deep Mind signed it, Microsoft signed it,

589
00:28:41,120 --> 00:28:43,760
Speaker 17: as well as many of the scientists that built the

590
00:28:43,880 --> 00:28:46,360
Speaker 17: current wave of artificial intelligence.

591
00:28:47,440 --> 00:28:50,200
Speaker 2: What's interesting is I'm trying to understand also how you

592
00:28:50,240 --> 00:28:54,400
Speaker 2: were born as a center for AI safety, you funded

593
00:28:54,440 --> 00:28:57,360
Speaker 2: by any of these businesses. What is the ultimate way

594
00:28:57,400 --> 00:28:59,960
Speaker 2: in which you continued to deliver and do your research.

595
00:29:02,280 --> 00:29:05,560
Speaker 17: So we started, we started a while ago and We're

596
00:29:05,640 --> 00:29:08,880
Speaker 17: largely funded by a philansphy. We're not funded by Elon

597
00:29:09,000 --> 00:29:12,240
Speaker 17: Musk or things like that. So we're just trying to

598
00:29:12,360 --> 00:29:15,640
Speaker 17: reduce the risks from AI. I've been when I was

599
00:29:16,240 --> 00:29:19,719
Speaker 17: as a graduate student at Berkeley, I'd been researching safety

600
00:29:19,720 --> 00:29:21,680
Speaker 17: for a very long time, So this has been a

601
00:29:21,760 --> 00:29:24,960
Speaker 17: concern that I've had for many years. And now finally

602
00:29:25,200 --> 00:29:27,880
Speaker 17: the AI technologies are getting to the point where it's

603
00:29:27,880 --> 00:29:30,680
Speaker 17: becoming a lot more obvious to the scientific community that

604
00:29:30,760 --> 00:29:33,520
Speaker 17: this is a large concern. A letter like this probably

605
00:29:33,520 --> 00:29:36,000
Speaker 17: could not have existed six months ago, but given the

606
00:29:36,080 --> 00:29:39,280
Speaker 17: rapid developments, a lot of people are changing their minds,

607
00:29:39,280 --> 00:29:41,160
Speaker 17: a lot of experts are changing their minds of just

608
00:29:41,200 --> 00:29:43,560
Speaker 17: about how severe the risks can be done.

609
00:29:43,560 --> 00:29:46,080
Speaker 4: I actually want to go back to Caroline's previous question quickly.

610
00:29:46,120 --> 00:29:49,840
Speaker 4: Have you taken money from any AI executives that were

611
00:29:49,880 --> 00:29:54,280
Speaker 4: signatories to that letter? The previous letter basically key names

612
00:29:54,720 --> 00:29:55,680
Speaker 4: in the field of AI.

613
00:29:57,280 --> 00:30:02,000
Speaker 17: There, I'd have to check if we're having any funding

614
00:30:02,040 --> 00:30:04,720
Speaker 17: from any employees that are also concerned about safety.

615
00:30:04,760 --> 00:30:06,920
Speaker 9: The AI community is particularly large.

616
00:30:07,200 --> 00:30:09,960
Speaker 17: Ninety plus percent of our funding is from Open Philanthropy,

617
00:30:10,040 --> 00:30:11,840
Speaker 17: which is an independent.

618
00:30:11,360 --> 00:30:15,800
Speaker 4: Philanthropy One question that arose quite quickly when you put

619
00:30:15,840 --> 00:30:19,280
Speaker 4: out the statement twenty four hours ago, is the rationale

620
00:30:19,360 --> 00:30:22,360
Speaker 4: or motivations behind the signatories?

621
00:30:22,520 --> 00:30:23,680
Speaker 3: Why are they doing this?

622
00:30:24,040 --> 00:30:27,760
Speaker 4: I get it there's a long term, broad concern about

623
00:30:27,800 --> 00:30:31,400
Speaker 4: the risks from AI. But why do you think these

624
00:30:31,560 --> 00:30:35,320
Speaker 4: leading names who want the field to advance signed the

625
00:30:35,440 --> 00:30:36,600
Speaker 4: letter that you organized.

626
00:30:38,120 --> 00:30:40,960
Speaker 17: Well, so many of them were on the voter. We

627
00:30:41,000 --> 00:30:43,560
Speaker 17: get out a move as quickly as possible. Developing I

628
00:30:43,600 --> 00:30:46,040
Speaker 17: will be a generally good thing, But a lot of

629
00:30:46,080 --> 00:30:49,520
Speaker 17: the scientists who signed this have recently changed their tune.

630
00:30:49,560 --> 00:30:54,280
Speaker 17: Jeff Hinton, of course helped create artificial intelligence modern artificial intelligence,

631
00:30:54,320 --> 00:30:57,600
Speaker 17: so did Yashchu A Benjio. But in recent months now

632
00:30:57,600 --> 00:31:01,400
Speaker 17: they're substantially more concerned about there being rogue behavior, that

633
00:31:01,480 --> 00:31:05,800
Speaker 17: these AIS could potentially lead to human extinction as they.

634
00:31:05,720 --> 00:31:06,600
Speaker 9: Get more advanced.

635
00:31:07,160 --> 00:31:10,960
Speaker 17: So for that reason, I think that it's just because

636
00:31:11,000 --> 00:31:13,880
Speaker 17: people are starting to see that this is moving extremely quickly.

637
00:31:13,960 --> 00:31:17,120
Speaker 17: We don't understand how these technologies work. It's difficult to

638
00:31:17,120 --> 00:31:20,320
Speaker 17: steer them, and so that could potentially.

639
00:31:19,960 --> 00:31:21,000
Speaker 9: They could be misused.

640
00:31:21,280 --> 00:31:25,000
Speaker 17: Those things could potentially lead to catastrophic risks or potentially,

641
00:31:25,040 --> 00:31:26,800
Speaker 17: in the longer term extinction.

642
00:31:27,920 --> 00:31:28,480
Speaker 3: Very quickly.

643
00:31:28,520 --> 00:31:30,520
Speaker 4: In response to your letter, there were those that know

644
00:31:31,320 --> 00:31:34,760
Speaker 4: present and much more near term basic risks have listened

645
00:31:34,800 --> 00:31:37,480
Speaker 4: to SASHALUCCIONI of hugging face you joined us yesterday.

646
00:31:38,880 --> 00:31:41,120
Speaker 18: I personally see it a bit as a magic trick,

647
00:31:41,200 --> 00:31:46,520
Speaker 18: as misdirection right. I care very very strongly about, for example,

648
00:31:47,120 --> 00:31:52,720
Speaker 18: data consent, for example, disclosing data sources, disclosing transparency and

649
00:31:52,720 --> 00:31:56,520
Speaker 18: model documentation. And instead of focusing on that we're being

650
00:31:56,600 --> 00:32:00,200
Speaker 18: directed towards these unsolvable risks.

651
00:32:01,040 --> 00:32:03,680
Speaker 4: Are we paying enough attention to more near term and

652
00:32:03,720 --> 00:32:05,040
Speaker 4: immediate risks from AI.

653
00:32:05,840 --> 00:32:08,160
Speaker 17: I should certainly hope that the risk of extinction is

654
00:32:08,200 --> 00:32:11,280
Speaker 17: not unsolvable, or else we're in big trouble. So I

655
00:32:11,320 --> 00:32:15,360
Speaker 17: think it's important as a society to manage multiple different risks.

656
00:32:15,520 --> 00:32:16,880
Speaker 9: I think we can certainly do it.

657
00:32:17,600 --> 00:32:20,240
Speaker 17: I also think that many of the risks from that

658
00:32:20,520 --> 00:32:24,040
Speaker 17: currently affect us can take on more extreme forms later on.

659
00:32:24,320 --> 00:32:27,920
Speaker 17: For instance, if the people developing these AI technologies have

660
00:32:28,000 --> 00:32:31,560
Speaker 17: decisive control over them, that gives them extremely high power

661
00:32:31,600 --> 00:32:34,560
Speaker 17: and power inequality with respect AI is a concern that

662
00:32:34,600 --> 00:32:37,520
Speaker 17: could potentially get out of control where a few people

663
00:32:37,560 --> 00:32:40,560
Speaker 17: are calling the shots in society. So I think that

664
00:32:40,600 --> 00:32:44,360
Speaker 17: it's important any competent form of risk management will address

665
00:32:45,240 --> 00:32:47,160
Speaker 17: current ongoing arms as well.

666
00:32:46,960 --> 00:32:49,400
Speaker 9: As tail risks. So I think we need to have

667
00:32:49,480 --> 00:32:50,680
Speaker 9: a complementary approach.

668
00:32:51,000 --> 00:32:56,440
Speaker 2: Yeah, and Hendricks, the outcome from this, You've certainly stirred

669
00:32:56,520 --> 00:32:59,880
Speaker 2: a lot of interest people, perhaps either aligning them to

670
00:33:00,280 --> 00:33:02,880
Speaker 2: thinking that this is too much or indeed that we

671
00:33:02,920 --> 00:33:05,480
Speaker 2: need to shine a light on this. How do you

672
00:33:05,520 --> 00:33:08,440
Speaker 2: think we ultimately take on these risks? You're very good

673
00:33:08,440 --> 00:33:11,600
Speaker 2: at highlighting what the risks are. Is it regulation, is

674
00:33:11,640 --> 00:33:14,160
Speaker 2: it self regulation? What do you think the outcome will be?

675
00:33:14,880 --> 00:33:17,840
Speaker 17: Well, I think that self regulation would be a useful start,

676
00:33:17,880 --> 00:33:21,560
Speaker 17: But we wouldn't trust something like a pandemic or nuclear

677
00:33:21,600 --> 00:33:24,920
Speaker 17: technology just to the scientists. We wouldn't say solve the

678
00:33:24,960 --> 00:33:27,240
Speaker 17: AI or the nuclear arms race scientists.

679
00:33:27,280 --> 00:33:29,440
Speaker 9: It's not necessarily completely their purview.

680
00:33:29,560 --> 00:33:31,640
Speaker 17: There are technical aspects to this problem, but there are

681
00:33:31,640 --> 00:33:34,120
Speaker 17: also social aspects to this problem. So we wanted to

682
00:33:34,160 --> 00:33:37,560
Speaker 17: just make sure that the public is aware that many

683
00:33:37,560 --> 00:33:40,120
Speaker 17: of the AI scientists, we have AI scientists from all

684
00:33:40,200 --> 00:33:42,760
Speaker 17: the top universities, and many of the people who created it.

685
00:33:42,760 --> 00:33:44,880
Speaker 9: Are concerned that it could even lead to extinction.

686
00:33:45,080 --> 00:33:48,720
Speaker 17: So then policy tends to be more of a negotiation process,

687
00:33:48,760 --> 00:33:52,200
Speaker 17: and so hopefully we can get those conversations started recognizing

688
00:33:52,200 --> 00:33:55,239
Speaker 17: that there are severe effects on the horizon, such as

689
00:33:55,360 --> 00:33:58,160
Speaker 17: potentially extinction. So I think we need to treat as

690
00:33:58,160 --> 00:34:00,000
Speaker 17: a global priority, and we need to work toward cold

691
00:34:00,120 --> 00:34:04,120
Speaker 17: operating domestically but also internationally so that we're not caught

692
00:34:04,160 --> 00:34:05,480
Speaker 17: in an AI arms.

693
00:34:05,280 --> 00:34:07,040
Speaker 9: Race between different countries.

694
00:34:07,360 --> 00:34:10,080
Speaker 17: The nuclear arms rate brought us to the brink of catastrophe,

695
00:34:10,280 --> 00:34:12,879
Speaker 17: and we don't want another arms race where we build

696
00:34:12,920 --> 00:34:16,160
Speaker 17: extremely powerful technologies that could potentially.

697
00:34:15,600 --> 00:34:18,439
Speaker 9: Destroy us all and we just keep stockpiling them.

698
00:34:18,760 --> 00:34:21,080
Speaker 17: That was not a good outcome for humanity that could

699
00:34:21,080 --> 00:34:23,680
Speaker 17: have gone substantially worse. I don't want the same to

700
00:34:23,680 --> 00:34:27,200
Speaker 17: happen with AI. So hopefully we can cooperate starting today.

701
00:34:27,480 --> 00:34:31,239
Speaker 2: Have you seen decent self regulation thus far on some

702
00:34:31,360 --> 00:34:35,080
Speaker 2: of the more near term, immediate societal damage we're seeing,

703
00:34:35,200 --> 00:34:38,680
Speaker 2: as many have referenced the bias already baked into some

704
00:34:38,719 --> 00:34:41,200
Speaker 2: of the data that it's being built upon, the worries

705
00:34:41,239 --> 00:34:44,120
Speaker 2: that the wrong people are being potentially stoped by police,

706
00:34:44,160 --> 00:34:48,000
Speaker 2: the worries that the wrong misinformation is already circulating. Have

707
00:34:48,080 --> 00:34:50,080
Speaker 2: you seen that tackle before we even get onto the

708
00:34:50,160 --> 00:34:53,200
Speaker 2: much larger and more significant concern that you have about extinction.

709
00:34:54,239 --> 00:34:55,719
Speaker 9: Yes, so I think we ought to be.

710
00:34:55,840 --> 00:34:58,520
Speaker 17: If we can't address many of those current risks, then

711
00:34:58,560 --> 00:35:00,759
Speaker 17: I don't have much help us addressing some of these

712
00:35:01,320 --> 00:35:02,440
Speaker 17: larger risks as well.

713
00:35:03,000 --> 00:35:03,880
Speaker 9: I should say.

714
00:35:03,640 --> 00:35:09,120
Speaker 17: That there's a bit of a mix in whether companies

715
00:35:09,200 --> 00:35:13,200
Speaker 17: have been successful at making their EI technologies safe. There's

716
00:35:13,800 --> 00:35:18,160
Speaker 17: some notable failures, like with Being's initial rollout where it

717
00:35:18,239 --> 00:35:22,640
Speaker 17: was threatening users, but some other technologies have been useful

718
00:35:22,920 --> 00:35:28,000
Speaker 17: in being relatively reliable and mitigating their amount of bias.

719
00:35:28,080 --> 00:35:32,200
Speaker 17: So there are fortunate signs that there's some progress being made.

720
00:35:32,320 --> 00:35:35,440
Speaker 17: I'm concerned though, that since the AI companies are competing

721
00:35:35,480 --> 00:35:37,960
Speaker 17: with each other and are locked in an arms race

722
00:35:38,080 --> 00:35:41,000
Speaker 17: with each other, that there won't be enough time for safety,

723
00:35:41,000 --> 00:35:46,200
Speaker 17: that they're going to prioritize development over safety, because if

724
00:35:46,239 --> 00:35:49,440
Speaker 17: they don't prioritize development and making it as powerful as

725
00:35:49,560 --> 00:35:52,759
Speaker 17: quickly as possible, then we won't be able to make

726
00:35:52,800 --> 00:35:55,319
Speaker 17: it safe and bring the risks down to a negligible level.

727
00:35:55,480 --> 00:35:58,880
Speaker 2: Is it a risk that by regulating, self regulating or

728
00:35:58,920 --> 00:36:02,080
Speaker 2: otherwise we start to stifle out the new competitors, the

729
00:36:02,120 --> 00:36:05,000
Speaker 2: smaller players, the academics that you're talking to within labs,

730
00:36:05,080 --> 00:36:07,600
Speaker 2: rather than the large players such as a being or

731
00:36:07,640 --> 00:36:09,319
Speaker 2: a bard or an open AI.

732
00:36:10,600 --> 00:36:13,920
Speaker 17: I think that, well, if we're concerned about academics, I

733
00:36:13,960 --> 00:36:16,600
Speaker 17: think that that would largely be the government to be

734
00:36:16,680 --> 00:36:19,640
Speaker 17: taken care of that. I think that we can definitely

735
00:36:19,680 --> 00:36:24,239
Speaker 17: target some of these catastrophic risks for models that are

736
00:36:24,280 --> 00:36:27,040
Speaker 17: at a certain level of capability. That seems like a

737
00:36:27,120 --> 00:36:32,400
Speaker 17: possible proposal. So we'll basically have to see with these negotiations.

738
00:36:32,520 --> 00:36:35,480
Speaker 17: I'm largely just pushing for us and initiating this process

739
00:36:35,560 --> 00:36:40,440
Speaker 17: and trying to have broader societal cooperation, including international cooperation

740
00:36:40,560 --> 00:36:41,240
Speaker 17: on these issues.

741
00:36:41,719 --> 00:36:46,279
Speaker 4: Dan, how does the public reconcile that the signatories to

742
00:36:46,360 --> 00:36:49,800
Speaker 4: your petition, your initiative were the leaders in the field

743
00:36:49,800 --> 00:36:53,719
Speaker 4: of AI, the AI evangelists for want of a better expression,

744
00:36:53,719 --> 00:36:57,319
Speaker 4: who are now apparently the AI doomers. How have we

745
00:36:57,680 --> 00:37:01,040
Speaker 4: moved so quickly from leadership to cann in that field.

746
00:37:01,560 --> 00:37:03,759
Speaker 17: I think that this is a natural part in any

747
00:37:03,840 --> 00:37:07,680
Speaker 17: industry where initially it's move fast and break things, be

748
00:37:07,880 --> 00:37:11,040
Speaker 17: risk seeking, tinker, throw stuff at the wall, see what sticks.

749
00:37:11,160 --> 00:37:13,120
Speaker 17: But then we start shifting over to a more risk

750
00:37:13,120 --> 00:37:16,880
Speaker 17: averse regime when the technology starts affecting society on a

751
00:37:16,960 --> 00:37:20,560
Speaker 17: larger scale. Now people are actually using these AI technologies,

752
00:37:20,600 --> 00:37:22,920
Speaker 17: and so we ought to be substantially more cautious with

753
00:37:22,960 --> 00:37:25,840
Speaker 17: their potential outcomes as well. It's getting a lot closer

754
00:37:25,920 --> 00:37:29,640
Speaker 17: to human level intelligence. So earlier two years ago, they

755
00:37:29,760 --> 00:37:32,920
Speaker 17: really weren't intelligent at all. But it's quite conceivable that

756
00:37:32,960 --> 00:37:36,080
Speaker 17: in the next few years that in many cognitive domains

757
00:37:36,080 --> 00:37:38,680
Speaker 17: we have AIS that are as good as us or better.

758
00:37:38,960 --> 00:37:40,120
Speaker 9: So I think that's one.

759
00:37:40,000 --> 00:37:43,520
Speaker 17: Of the main things shifting this underlying change in tone,

760
00:37:43,719 --> 00:37:46,440
Speaker 17: Because this air arms race is making it move so quickly,

761
00:37:46,880 --> 00:37:50,000
Speaker 17: people are starting to think we need to rethink our priorities.

762
00:37:50,440 --> 00:37:54,480
Speaker 4: Center for AI Safety Executive and research director Dan Hendrix,

763
00:37:54,560 --> 00:37:57,480
Speaker 4: thank you for your time now. Elizabeth Holmes turned herself

764
00:37:57,520 --> 00:38:00,440
Speaker 4: in at a minimum security prison in Texas, yes today

765
00:38:00,680 --> 00:38:04,200
Speaker 4: to begin her eleven year, three month sentence. The disgraced

766
00:38:04,200 --> 00:38:07,400
Speaker 4: tech founder had been convicted of defrauding investors at her

767
00:38:07,400 --> 00:38:10,720
Speaker 4: failed blood testing startup Pharrhos, and she and her former

768
00:38:10,760 --> 00:38:14,480
Speaker 4: business partner Ramesh Bahwani must together pay four hundred and

769
00:38:14,520 --> 00:38:18,239
Speaker 4: fifty two million dollars in restitution to investors.

770
00:38:18,360 --> 00:38:21,920
Speaker 2: Cara, Well, let's get back to it, ed, because we've

771
00:38:21,920 --> 00:38:24,239
Speaker 2: got more to discuss on artificial intelligence, if you can

772
00:38:24,320 --> 00:38:25,680
Speaker 2: believe it. But we're going to talk about it on

773
00:38:25,719 --> 00:38:28,200
Speaker 2: Wall Street. How hedge funds are actually dipping their toes

774
00:38:28,239 --> 00:38:31,120
Speaker 2: into the pool of AI tools to handle all the

775
00:38:31,120 --> 00:38:33,839
Speaker 2: boring stuff the grant work or on that next there's

776
00:38:33,840 --> 00:38:52,799
Speaker 2: a Bloomberg Wall Street. It's already filled with quants with

777
00:38:52,840 --> 00:38:55,920
Speaker 2: computer wizards. But because not stopping hedge funds from utilizing

778
00:38:55,960 --> 00:38:59,319
Speaker 2: the powers of CHATCHBT for the basics, be better to

779
00:38:59,320 --> 00:39:02,320
Speaker 2: discuss all of this reg news Will Street repolitician atibaseconds.

780
00:39:02,360 --> 00:39:05,759
Speaker 2: So what is it, junior hedge funds analysts?

781
00:39:05,960 --> 00:39:06,240
Speaker 9: Really?

782
00:39:06,560 --> 00:39:09,120
Speaker 2: Allelujah? You don't have to do the basics the grumble

783
00:39:09,120 --> 00:39:09,520
Speaker 2: work here.

784
00:39:09,680 --> 00:39:11,400
Speaker 19: Yeah, there's a reason that it only really works for

785
00:39:11,440 --> 00:39:13,960
Speaker 19: the basics. I don't normally like to use personal examples,

786
00:39:14,000 --> 00:39:16,279
Speaker 19: but I had gotten my degree in quant finance and

787
00:39:16,360 --> 00:39:19,200
Speaker 19: down over at NYU for the NBA program. What they

788
00:39:19,200 --> 00:39:21,680
Speaker 19: do is they take all the students, they have them

789
00:39:21,880 --> 00:39:24,640
Speaker 19: run through all this data. They put it through code

790
00:39:24,680 --> 00:39:27,480
Speaker 19: under different market scenarios, and what they show you is

791
00:39:27,520 --> 00:39:30,760
Speaker 19: that the code the models don't always work.

792
00:39:31,080 --> 00:39:32,640
Speaker 1: The point here being is two things.

793
00:39:32,680 --> 00:39:36,000
Speaker 19: Once, sometimes the data doesn't work, sometimes it's just wrong,

794
00:39:36,040 --> 00:39:39,319
Speaker 19: and the other times the market is just unpredictable. It's

795
00:39:39,360 --> 00:39:41,799
Speaker 19: why there's limitations to how chat GBT.

796
00:39:41,520 --> 00:39:43,960
Speaker 1: Could be used. But Caroline, there are things about.

797
00:39:43,760 --> 00:39:47,759
Speaker 19: Chat GBT that make this kind of rendition of quantitative

798
00:39:47,760 --> 00:39:51,360
Speaker 19: finance different than before, and that is natural language processing

799
00:39:51,440 --> 00:39:54,640
Speaker 19: generated AI that I'll make it easier to read company.

800
00:39:54,320 --> 00:39:56,360
Speaker 1: Reports, research reports.

801
00:39:56,239 --> 00:40:00,799
Speaker 19: News filings, and even company transcripts to hopefully make it

802
00:40:00,800 --> 00:40:04,640
Speaker 19: easier to purse through investing signs down to a third

803
00:40:04,760 --> 00:40:07,560
Speaker 19: minutes to get signals on how devish or hark is

804
00:40:07,680 --> 00:40:08,120
Speaker 19: something is.

805
00:40:08,320 --> 00:40:11,360
Speaker 2: So it feels for now it's about productivity and workflow

806
00:40:11,480 --> 00:40:13,480
Speaker 2: rather than perhaps getting an edge when actually comes to

807
00:40:13,520 --> 00:40:16,719
Speaker 2: your investing. Shenani bassa short sweep but always to the point.

808
00:40:16,760 --> 00:40:26,480
Speaker 2: We love it. And what is going viral? It's the

809
00:40:26,480 --> 00:40:30,640
Speaker 2: finale z ed of Succession and Ted Lasso Warner Brothers Discovery,

810
00:40:30,680 --> 00:40:33,320
Speaker 2: which calls owns HBO. It's so close to three million

811
00:40:33,320 --> 00:40:36,120
Speaker 2: people watch the Succession finale on TV and it's streaming

812
00:40:36,120 --> 00:40:39,320
Speaker 2: app HBO Max. In the US sixty eight percent improvement

813
00:40:39,360 --> 00:40:41,800
Speaker 2: over the last episode of season three, according to the company,

814
00:40:42,160 --> 00:40:44,680
Speaker 2: And meanwhile, it's the top trending topic on Google Trends.

815
00:40:44,719 --> 00:40:48,120
Speaker 2: Ted Lasso's own finale Tealasa has been born a boon,

816
00:40:48,200 --> 00:40:50,880
Speaker 2: of course, to Apple TV, breaking into the Nielsen weekly

817
00:40:50,920 --> 00:40:53,360
Speaker 2: streaming ranking in sixth place during the last week at

818
00:40:53,360 --> 00:40:56,680
Speaker 2: April with this third and last season. Meanwhile, though our

819
00:40:56,680 --> 00:40:58,719
Speaker 2: Brits aren't watching it much.

820
00:40:58,520 --> 00:40:59,480
Speaker 3: Right, it's unbelievable.

821
00:40:59,480 --> 00:41:02,560
Speaker 4: I mean, Apple doesn't disclose numbers, but it's all everyone's

822
00:41:02,600 --> 00:41:05,799
Speaker 4: talking about on social succession is on fin Twitch. Three

823
00:41:05,880 --> 00:41:07,680
Speaker 4: million doesn't seem like a lot in a country of

824
00:41:07,760 --> 00:41:11,000
Speaker 4: three thirty three hundred and thirty million people, but as

825
00:41:11,040 --> 00:41:13,120
Speaker 4: you know, we're both big fans. That does it for

826
00:41:13,120 --> 00:41:16,520
Speaker 4: this edition. Kara of Bloomberg Technology, don't forget so much

827
00:41:16,560 --> 00:41:19,000
Speaker 4: to recap and what's been a short week packed with

828
00:41:19,120 --> 00:41:22,359
Speaker 4: news on our podcast, Apple, Spotify, iHeart, wherever you get

829
00:41:22,480 --> 00:41:25,080
Speaker 4: your podcast from New York and San Francisco.

830
00:41:25,680 --> 00:41:27,080
Speaker 3: This is Bloomberg