WEBVTT - Lordstown's Bankruptcy and AI Regulation

0:00:01.480 --> 0:00:05.760
<v Speaker 1>From Mahard where Innovation, Money and Power Collie in Silicon

0:00:05.880 --> 0:00:06.760
<v Speaker 1>Valley NBN.

0:00:07.120 --> 0:00:11.600
<v Speaker 2>This is Bloomberg Technology with Caroline Hyde and Ed Ludlow.

0:00:25.360 --> 0:00:28.120
<v Speaker 1>And Ed Ludlow here in San Francisco. Caroline Hyde is

0:00:28.160 --> 0:00:32.519
<v Speaker 1>off today. This is Bloomberg Technology coming up. Lordstown files

0:00:32.640 --> 0:00:36.080
<v Speaker 1>for bankruptcy, shares plunge as the evmakers deal with fox

0:00:36.120 --> 0:00:40.360
<v Speaker 1>Con unravels. We'll bring you the details on that developing story. Plus,

0:00:40.360 --> 0:00:43.960
<v Speaker 1>we'll talk AI and regulation with an MIT researcher who

0:00:44.040 --> 0:00:47.320
<v Speaker 1>recently sat down with President Biden on his trip to

0:00:47.360 --> 0:00:50.080
<v Speaker 1>San Francisco. Will get her takeaways on how the government

0:00:50.159 --> 0:00:54.080
<v Speaker 1>plans to tackle the booming technology. Plus, we sit down

0:00:54.120 --> 0:00:57.480
<v Speaker 1>with famed venture capitalist Gary Town of y Combinator for

0:00:57.560 --> 0:01:01.400
<v Speaker 1>an exclusive interview talking investing the generative AI and how

0:01:01.440 --> 0:01:04.839
<v Speaker 1>his firm is investing in building back the Bay Area.

0:01:05.000 --> 0:01:07.360
<v Speaker 1>When it comes to names in the technology sector, we

0:01:07.400 --> 0:01:12.000
<v Speaker 1>are thinking about lord Town, the news a Chapter eleven filing.

0:01:12.040 --> 0:01:14.720
<v Speaker 1>We're down thirty seven percent right now, we are trading.

0:01:14.880 --> 0:01:18.319
<v Speaker 1>The stock had been halted. The relationship with fox Con,

0:01:18.560 --> 0:01:20.840
<v Speaker 1>which was the contract manufacturer right that was going to

0:01:20.840 --> 0:01:25.640
<v Speaker 1>build Lordstown's electric vehicles. That relationship is disintegrating. Let's get

0:01:25.680 --> 0:01:28.880
<v Speaker 1>the details with Bloomberg Shaun o'caine. He covers everything Evy

0:01:29.080 --> 0:01:31.679
<v Speaker 1>and auto start up for us at Bloomberg News. Sean,

0:01:31.720 --> 0:01:33.640
<v Speaker 1>what is the latest when it comes to Lord's Town.

0:01:35.480 --> 0:01:38.000
<v Speaker 3>Well, we've known this fight was coming for a while

0:01:38.880 --> 0:01:42.800
<v Speaker 3>and now it's here. We have Lordstown Motors basically saying

0:01:42.840 --> 0:01:45.920
<v Speaker 3>that from a minute that they started inking these contracts

0:01:45.920 --> 0:01:48.800
<v Speaker 3>and Fox Hunt to build the vehicles that Lordstown has

0:01:48.800 --> 0:01:51.080
<v Speaker 3>spent the last couple of years trying to build. There,

0:01:51.120 --> 0:01:53.720
<v Speaker 3>it went south that Fox COHNN started renegging on some

0:01:53.800 --> 0:01:56.200
<v Speaker 3>of its promises, that it wasn't living up to its

0:01:56.200 --> 0:02:00.160
<v Speaker 3>funding milestones, that they were basically not showing up to meeting,

0:02:00.400 --> 0:02:03.320
<v Speaker 3>that the Lordstown CEO went to Taiwan to try to

0:02:03.360 --> 0:02:05.760
<v Speaker 3>get some information on the vehicles that they wanted to

0:02:05.760 --> 0:02:08.760
<v Speaker 3>build together and was just basically ghosted at a meeting there.

0:02:08.880 --> 0:02:13.600
<v Speaker 3>So this is a relationship that's been deteriorating pretty quickly

0:02:13.639 --> 0:02:16.800
<v Speaker 3>over the last half year or so, and we've started

0:02:16.800 --> 0:02:18.840
<v Speaker 3>to see signs of that in May when Lordstown really

0:02:18.840 --> 0:02:22.280
<v Speaker 3>started making some noise about this in regulatory filings. But

0:02:22.560 --> 0:02:25.120
<v Speaker 3>now it's really out there in full view, you.

0:02:25.080 --> 0:02:27.800
<v Speaker 1>Know, The next question is is this the end for

0:02:27.880 --> 0:02:31.840
<v Speaker 1>Lord's Town. You know, they've kind of relied on fox

0:02:31.919 --> 0:02:35.520
<v Speaker 1>Con to build their endurance on their behalf. Do they

0:02:35.520 --> 0:02:37.359
<v Speaker 1>have other options to move forward?

0:02:38.800 --> 0:02:42.160
<v Speaker 3>They've been saying for months that they need another outside partner,

0:02:42.720 --> 0:02:46.640
<v Speaker 3>which I always looked at as a pretty big claim,

0:02:46.680 --> 0:02:50.040
<v Speaker 3>considering that Foxcom was supposed to contract manufacturer these vehicles

0:02:50.080 --> 0:02:51.560
<v Speaker 3>for them, and for Lordstown to come out a couple

0:02:51.639 --> 0:02:53.919
<v Speaker 3>months ago and say, hey, we actually still need more

0:02:53.919 --> 0:02:56.960
<v Speaker 3>help from somebody else, another OEM that it was a

0:02:56.960 --> 0:02:59.720
<v Speaker 3>pretty big sign that Foxcom was probably not up to snuff.

0:02:59.760 --> 0:03:02.520
<v Speaker 3>So Bardstown still thinks that it can find a way through.

0:03:02.520 --> 0:03:05.239
<v Speaker 3>It's still going to try to operate, you know, sort

0:03:05.280 --> 0:03:08.000
<v Speaker 3>of the core aspects of the business is going to

0:03:08.000 --> 0:03:09.520
<v Speaker 3>try to keep the lights on while it goes through

0:03:09.520 --> 0:03:13.000
<v Speaker 3>this Chapter eleven proceeding and uses that restructuring process to

0:03:13.000 --> 0:03:14.560
<v Speaker 3>give it a bit more of a solid footing to

0:03:14.639 --> 0:03:17.360
<v Speaker 3>fight fox Con in this lawsuit that it filed.

0:03:17.080 --> 0:03:17.800
<v Speaker 1>Today as well.

0:03:18.600 --> 0:03:19.280
<v Speaker 2>But we'll see.

0:03:19.320 --> 0:03:21.560
<v Speaker 3>I mean, I think the question is have they actually

0:03:21.639 --> 0:03:25.720
<v Speaker 3>developed anything over the last three four years of value

0:03:26.200 --> 0:03:29.640
<v Speaker 3>that would either attract a buyer or attract a partner

0:03:29.680 --> 0:03:31.280
<v Speaker 3>that they've like they've been looking for over the last

0:03:31.280 --> 0:03:33.080
<v Speaker 3>couple of months. Obviously they haven't found anybody.

0:03:33.160 --> 0:03:36.600
<v Speaker 1>So it's a great point. I mean I started this

0:03:36.680 --> 0:03:39.080
<v Speaker 1>year at CES in Las Vegas. As you know, I

0:03:39.160 --> 0:03:42.480
<v Speaker 1>borrowed one of the Endurance pickups. I drove the CEO

0:03:42.600 --> 0:03:46.160
<v Speaker 1>around for about an hour. There was a truck at least,

0:03:46.200 --> 0:03:50.160
<v Speaker 1>you know, an early prototype. What's the widest story here.

0:03:50.240 --> 0:03:52.760
<v Speaker 1>You know, we've always kind of viewed Lord's Town as

0:03:52.800 --> 0:03:55.560
<v Speaker 1>one of the pile of eb startups that we're kind

0:03:55.560 --> 0:03:58.000
<v Speaker 1>of not really sure about, like the viability of their

0:03:58.080 --> 0:03:59.680
<v Speaker 1>business and their technology.

0:04:01.680 --> 0:04:04.360
<v Speaker 3>I mean, the history of the company and the story

0:04:04.360 --> 0:04:06.920
<v Speaker 3>that they were trying to tell was always just very tangled.

0:04:07.120 --> 0:04:10.120
<v Speaker 3>I mean you think back to twenty nineteen and the

0:04:10.160 --> 0:04:12.920
<v Speaker 3>fact that they bought the company from General Motors, or

0:04:12.960 --> 0:04:15.520
<v Speaker 3>the factory from General Motors that they were supposed to

0:04:15.560 --> 0:04:18.320
<v Speaker 3>be operating in. It was announced by President Trump in

0:04:18.360 --> 0:04:20.360
<v Speaker 3>a tweet, and he sort of got it wrong. Without

0:04:20.400 --> 0:04:22.560
<v Speaker 3>the gate. He said that Workhorse, which is another startup

0:04:22.600 --> 0:04:25.320
<v Speaker 3>that was going to be buying the factory, and this

0:04:25.360 --> 0:04:28.320
<v Speaker 3>company was really spun out of Workhorse. There was always

0:04:28.360 --> 0:04:31.400
<v Speaker 3>this weird relationship between it and Workhorse. It was licensing

0:04:31.440 --> 0:04:33.720
<v Speaker 3>ip from Workhorse to build this vehicle. It was always

0:04:33.760 --> 0:04:37.280
<v Speaker 3>this sort of tortured product. And yeah, they were able

0:04:37.320 --> 0:04:38.839
<v Speaker 3>to get to the point where they were making a

0:04:38.839 --> 0:04:40.839
<v Speaker 3>few of them with fox gone, but they were never

0:04:40.880 --> 0:04:42.599
<v Speaker 3>able to get to the point where they were able

0:04:42.600 --> 0:04:46.560
<v Speaker 3>to get that cost under the MSRP that they were

0:04:46.560 --> 0:04:48.719
<v Speaker 3>trying to promise, and even the MSRP had gone up

0:04:48.760 --> 0:04:52.080
<v Speaker 3>over time. They were originally targeting a lower MSRP, and

0:04:52.640 --> 0:04:54.320
<v Speaker 3>they just got to the point where, yeah, they had

0:04:54.320 --> 0:04:56.080
<v Speaker 3>a vehicle, but it was really not one that was

0:04:56.080 --> 0:04:58.880
<v Speaker 3>ever going to scale by their own admission. So that's

0:04:58.880 --> 0:05:01.000
<v Speaker 3>why they started looking for out i'd help, and they

0:05:01.040 --> 0:05:04.359
<v Speaker 3>clearly needed that outside help because Foxcom wasn't able or

0:05:04.360 --> 0:05:06.400
<v Speaker 3>willing to live up to its end of the bargain.

0:05:06.520 --> 0:05:09.400
<v Speaker 3>So I think now looking forward, aside from what's going

0:05:09.440 --> 0:05:11.440
<v Speaker 3>to happen with Lordstown, this just really raises a lot

0:05:11.440 --> 0:05:13.880
<v Speaker 3>of questions about what fox Conn is going to try

0:05:13.880 --> 0:05:16.560
<v Speaker 3>to do for EVS in the US. It's got big

0:05:16.600 --> 0:05:19.599
<v Speaker 3>ambitions for that factory in Ohio, but right now we

0:05:19.640 --> 0:05:22.640
<v Speaker 3>see Lordstown filing for Chapter eleven bankruptcy. One of the

0:05:22.640 --> 0:05:24.679
<v Speaker 3>other tenants that's supposed to be in there working. Fox

0:05:24.760 --> 0:05:28.800
<v Speaker 3>Cohn had its back merger dissolved earlier this month, and

0:05:28.839 --> 0:05:31.960
<v Speaker 3>Fisker has already delayed its second vehicle with fox con

0:05:32.000 --> 0:05:34.479
<v Speaker 3>in that building. So that's three out of four partners

0:05:34.520 --> 0:05:36.920
<v Speaker 3>already that are seeing trouble with foxtun in that building.

0:05:37.839 --> 0:05:40.160
<v Speaker 1>All right, Bloomberg Sean o'caine on the ev beat. One

0:05:40.200 --> 0:05:44.479
<v Speaker 1>of the top stories on Bloomberg technology this Tuesday. Thank you.

0:05:44.520 --> 0:05:47.080
<v Speaker 1>The other top story and kind of activity we're seeing

0:05:47.120 --> 0:05:50.040
<v Speaker 1>is in the crypto space. US based payments and crypto

0:05:50.080 --> 0:05:54.560
<v Speaker 1>firm Circle closely watching regulatory developments in Hong Kong after

0:05:54.600 --> 0:05:57.520
<v Speaker 1>the territories new crypto rules went into effect. We sat

0:05:57.560 --> 0:06:02.520
<v Speaker 1>down with its chairman and CEO to discuss.

0:06:00.960 --> 0:06:04.840
<v Speaker 4>All around the world, every major market, stable coin laws

0:06:04.839 --> 0:06:07.440
<v Speaker 4>are coming into place, and I think what that signifies

0:06:07.560 --> 0:06:11.279
<v Speaker 4>is that this kind of digital currency, these fiat linked

0:06:11.320 --> 0:06:14.880
<v Speaker 4>digital currencies, are about to become a part of the

0:06:14.920 --> 0:06:17.440
<v Speaker 4>mainstream global financial system.

0:06:18.040 --> 0:06:21.680
<v Speaker 1>Elsewhere, we're seeing activity in bitcoin and bitcoin cash. And

0:06:21.760 --> 0:06:25.360
<v Speaker 1>guess who's here Bloomberg Shnali Bassk with the crypto beat.

0:06:25.560 --> 0:06:30.440
<v Speaker 1>Let's start with Bitcoin gap cash, right, it's basically doubled

0:06:30.480 --> 0:06:32.760
<v Speaker 1>in value over a seven day period. It's the kind

0:06:32.760 --> 0:06:35.719
<v Speaker 1>of spin off. Why what's happening?

0:06:35.960 --> 0:06:38.080
<v Speaker 5>There are a few things. One, remember bitcoin cash is

0:06:38.120 --> 0:06:40.680
<v Speaker 5>in fixed supply and it is now being traded on

0:06:40.760 --> 0:06:44.479
<v Speaker 5>the new exchange that was started by the backing of

0:06:44.640 --> 0:06:48.320
<v Speaker 5>Citadel Securities and also a fidelity in large players on

0:06:48.360 --> 0:06:51.000
<v Speaker 5>Wall Street, And so you do seem some activity here

0:06:51.040 --> 0:06:54.039
<v Speaker 5>and they are trading only really four assets here, it's bitcoin,

0:06:54.120 --> 0:06:55.719
<v Speaker 5>either lightcoin, and bitcoin cash.

0:06:55.880 --> 0:06:57.120
<v Speaker 6>So oere Bitcoin in.

0:06:57.080 --> 0:06:59.920
<v Speaker 5>The last seven days ed has risen less than thirteen percent.

0:07:00.400 --> 0:07:04.400
<v Speaker 5>Bitcoin cash has risen almost one hundred and thirteen percent,

0:07:04.760 --> 0:07:07.719
<v Speaker 5>so a very meaningful rise there. But remember it is

0:07:07.760 --> 0:07:12.440
<v Speaker 5>not traded as widely as Bitcoin is in particular, so

0:07:12.520 --> 0:07:14.400
<v Speaker 5>you would see a bigger jump there.

0:07:14.600 --> 0:07:16.360
<v Speaker 1>Then you would see a bitcoin.

0:07:16.000 --> 0:07:19.120
<v Speaker 5>For example, that is a largest cryptocurrency and is trading

0:07:19.440 --> 0:07:23.600
<v Speaker 5>at more than thirty thousand dollars. And remember that thirty

0:07:23.640 --> 0:07:26.240
<v Speaker 5>thousand level. We have been there for about a week

0:07:26.320 --> 0:07:29.480
<v Speaker 5>or so now pretty much since we have seen that

0:07:29.600 --> 0:07:33.280
<v Speaker 5>kind of black Rock filing for a spot ETF giving

0:07:33.320 --> 0:07:36.520
<v Speaker 5>a lot more institutional support to some of these crypto assets.

0:07:37.160 --> 0:07:39.520
<v Speaker 1>Well, speaking of the headline, which caught my eye and

0:07:39.800 --> 0:07:42.360
<v Speaker 1>moved the needle in terms of the energy we saw

0:07:42.400 --> 0:07:45.960
<v Speaker 1>in bitcoin in Tuesday's US morning session was the block

0:07:46.040 --> 0:07:50.120
<v Speaker 1>reporting that Fidelity is preparing to submit a spot big

0:07:50.200 --> 0:07:52.160
<v Speaker 1>Bitcoin ETF filing. What do we know about that?

0:07:52.320 --> 0:07:53.800
<v Speaker 5>Something that's interesting if you take a look at the

0:07:53.800 --> 0:07:57.240
<v Speaker 5>movement here. You saw a pretty significant rise in Bitcoin

0:07:57.280 --> 0:08:00.440
<v Speaker 5>after the black Rock ETF filing, which coin basically the

0:08:00.480 --> 0:08:02.840
<v Speaker 5>custodian of the bitcoin assets as well. But now the

0:08:02.880 --> 0:08:07.240
<v Speaker 5>block reporting Fidelity gives you another big investing giant behind

0:08:07.480 --> 0:08:10.520
<v Speaker 5>the weight of crypto and this idea here that there

0:08:10.520 --> 0:08:13.200
<v Speaker 5>could be support even from US regulators to get this

0:08:13.400 --> 0:08:16.360
<v Speaker 5>done in fuller form. We have plenty of ETFs when

0:08:16.360 --> 0:08:19.080
<v Speaker 5>it comes to the future markets of futures markets in

0:08:19.120 --> 0:08:22.840
<v Speaker 5>the crypto world, but to have this much institutional support

0:08:23.120 --> 0:08:26.840
<v Speaker 5>behind a spot ETF shows you that perhaps the biggest

0:08:26.960 --> 0:08:30.720
<v Speaker 5>of Wall Street giants are getting on the bandwagon here

0:08:30.760 --> 0:08:33.280
<v Speaker 5>when it comes to crypto and the idea of getting

0:08:33.320 --> 0:08:35.920
<v Speaker 5>regulatory support behind it. We know that there are many

0:08:35.920 --> 0:08:39.720
<v Speaker 5>many applications in the sidelines here, and the question is

0:08:39.800 --> 0:08:42.160
<v Speaker 5>at what point do those become reality and how much

0:08:42.160 --> 0:08:43.600
<v Speaker 5>more money can that really draw.

0:08:43.480 --> 0:08:44.160
<v Speaker 6>Into the system.

0:08:45.200 --> 0:08:49.000
<v Speaker 1>Bloomberg Shnali Basek on the crypto beat here on Bloomberg Technology,

0:08:49.040 --> 0:08:51.920
<v Speaker 1>Thank you so much. Now coming up, the Biden administration

0:08:52.040 --> 0:08:56.200
<v Speaker 1>is taking steps to better understand and regulate artificial intelligence.

0:08:56.240 --> 0:08:59.560
<v Speaker 1>We're going to talk to AI researcher doctor joy Bola

0:08:59.640 --> 0:09:02.800
<v Speaker 1>Weeni about the panel and get her view on the

0:09:02.920 --> 0:09:06.600
<v Speaker 1>risks and rewards of using AI. Just really interesting reaction

0:09:06.679 --> 0:09:09.760
<v Speaker 1>to her conversation with President Briden. As we had to break.

0:09:09.800 --> 0:09:13.480
<v Speaker 1>We're also watching shares of Snowflake, the software company announcing

0:09:13.480 --> 0:09:17.200
<v Speaker 1>an AI related partnership with in Nvidia that will enable

0:09:17.240 --> 0:09:21.800
<v Speaker 1>businesses to create customized generatorve AI apps using their own

0:09:21.840 --> 0:09:25.040
<v Speaker 1>proprietary data. Having a positive effect to the upside up

0:09:25.080 --> 0:09:27.560
<v Speaker 1>three percent is we had to break. Here's what RBC's

0:09:27.600 --> 0:09:32.200
<v Speaker 1>laur Calvacina had to say about the NASDAK earlier today, thinkingvaluations,

0:09:32.240 --> 0:09:35.720
<v Speaker 1>this is Bloomberg, I would still stick with tech names.

0:09:35.800 --> 0:09:38.840
<v Speaker 7>I do think that NASDAK is getting a little bit overbought.

0:09:39.440 --> 0:09:41.679
<v Speaker 7>If you look at the CFTC data, we're hitting new

0:09:41.720 --> 0:09:45.200
<v Speaker 7>highs on NASDAK mini positioning that Beings said, I don't

0:09:45.200 --> 0:09:47.040
<v Speaker 7>think you have to sort of throw all the growth

0:09:47.040 --> 0:09:50.040
<v Speaker 7>stocks out at this point. A sector like communication services

0:09:50.080 --> 0:09:52.400
<v Speaker 7>where a lot of the Internet names are those earnings

0:09:52.440 --> 0:09:55.120
<v Speaker 7>revisions are starting to weaken. They're actually staying stronger in

0:09:55.160 --> 0:10:06.960
<v Speaker 7>tech property.

0:10:07.000 --> 0:10:10.760
<v Speaker 1>Oars official intelligence startups stability AI has lost at least

0:10:10.760 --> 0:10:14.200
<v Speaker 1>two top executives in recent weeks. That includes its head

0:10:14.240 --> 0:10:17.520
<v Speaker 1>of research and chief operating officer. The departure has come

0:10:17.840 --> 0:10:22.359
<v Speaker 1>the same month that an article Informs criticize the startups.

0:10:22.520 --> 0:10:25.600
<v Speaker 1>Stability joined us to discuss more is the person that

0:10:25.679 --> 0:10:28.880
<v Speaker 1>broke that story, Bloomberg's Rachel met Rachel.

0:10:28.920 --> 0:10:29.320
<v Speaker 2>What do we know?

0:10:30.720 --> 0:10:34.079
<v Speaker 8>Well, we know that David ha who is stability AI's

0:10:34.120 --> 0:10:38.480
<v Speaker 8>head of research, left recently, and we also know that

0:10:39.080 --> 0:10:42.319
<v Speaker 8>Reren Itto, who had been the company's chief operating officer,

0:10:42.760 --> 0:10:43.439
<v Speaker 8>also left.

0:10:43.760 --> 0:10:48.920
<v Speaker 1>Stability told me that David had left. Sources had told me.

0:10:48.880 --> 0:10:52.240
<v Speaker 8>That David had left on his own, as he had resigned.

0:10:53.000 --> 0:10:58.880
<v Speaker 8>Stability CEO Emad Mustak told me that Wren actually had been,

0:10:58.960 --> 0:10:59.960
<v Speaker 8>in his words, leg.

0:11:02.200 --> 0:11:06.599
<v Speaker 1>Interesting. You very recently spoke with an ad at the

0:11:06.600 --> 0:11:11.040
<v Speaker 1>Bloomberg Technology Summit. I give our audience a sense of

0:11:11.080 --> 0:11:13.240
<v Speaker 1>what's going on with this company. They're kind of seen

0:11:13.280 --> 0:11:16.480
<v Speaker 1>as a leader in the field. But you know, despite

0:11:16.480 --> 0:11:19.920
<v Speaker 1>the name stability AI, there are some issues that investors

0:11:19.920 --> 0:11:24.000
<v Speaker 1>as well are concerned about. Yeah, that's true.

0:11:24.200 --> 0:11:27.640
<v Speaker 8>The name has inspired unfortunately a few jokes on Twitter

0:11:27.679 --> 0:11:30.840
<v Speaker 8>in the last twenty four hours. Is it stable or

0:11:30.920 --> 0:11:33.520
<v Speaker 8>it's not so stable as some people are saying. I mean,

0:11:33.559 --> 0:11:37.120
<v Speaker 8>the fact is that some people have had really short

0:11:37.120 --> 0:11:41.480
<v Speaker 8>tenures at the company, and you could see how that

0:11:41.679 --> 0:11:44.720
<v Speaker 8>might concern people on the outside. I'm aren't sure what's happening.

0:11:45.040 --> 0:11:47.200
<v Speaker 8>On the other hand, it is true that I'm often

0:11:47.440 --> 0:11:50.439
<v Speaker 8>with a fast growing, early stage company, which this is.

0:11:50.840 --> 0:11:53.319
<v Speaker 8>You might have some executive turnover for a bunch of

0:11:53.400 --> 0:11:56.679
<v Speaker 8>different reasons, right Perhaps you want to bring in more

0:11:56.679 --> 0:12:00.600
<v Speaker 8>experienced leadership or different leadership, and sometimes things just work out.

0:12:01.400 --> 0:12:05.120
<v Speaker 8>So right now, you know, we're looking into, well, what

0:12:05.240 --> 0:12:06.960
<v Speaker 8>really happened here and what's going.

0:12:06.840 --> 0:12:07.760
<v Speaker 6>To happen going forward?

0:12:08.600 --> 0:12:11.280
<v Speaker 1>All right, Bloomberg's Rachel Met the latest reporting there from

0:12:11.280 --> 0:12:14.800
<v Speaker 1>Bloomberg Technology and what's happening in the AI space. Let's

0:12:14.800 --> 0:12:18.440
<v Speaker 1>stick with AI and talk about regulation. Doctor Joy boll

0:12:18.480 --> 0:12:21.640
<v Speaker 1>And Weeny is the founder of the Algorithmic Justice League

0:12:21.920 --> 0:12:25.000
<v Speaker 1>and an AI researcher at MIT. Just last week, she

0:12:25.120 --> 0:12:29.120
<v Speaker 1>sat with President Biden to discuss artificial intelligence and joins

0:12:29.160 --> 0:12:31.760
<v Speaker 1>US now from New York. We'll get to that conversation

0:12:31.840 --> 0:12:34.600
<v Speaker 1>with President Biden in a moment. You listen there to

0:12:34.640 --> 0:12:37.640
<v Speaker 1>what Rachel had to say. How do you see the

0:12:37.760 --> 0:12:41.360
<v Speaker 1>pace of innovation right now in AI startups? And does

0:12:41.400 --> 0:12:44.199
<v Speaker 1>it trouble you how quickly some of these companies are

0:12:44.240 --> 0:12:46.160
<v Speaker 1>moving and then changing direction.

0:12:47.280 --> 0:12:50.600
<v Speaker 9>Yes, I am extremely troubled with how quickly we are

0:12:50.679 --> 0:12:55.560
<v Speaker 9>releasing generative AI systems without the transparency to know where

0:12:55.600 --> 0:12:59.720
<v Speaker 9>the data is coming from. And also we continue to

0:12:59.760 --> 0:13:05.360
<v Speaker 9>see data being taking without consent, without compensation, and you're

0:13:05.400 --> 0:13:11.520
<v Speaker 9>going to continue to see increasing lawsuits against companies that

0:13:11.679 --> 0:13:15.440
<v Speaker 9>are built on what some would say is stolen data.

0:13:15.679 --> 0:13:19.679
<v Speaker 9>You had Meta Facebook settle six hundred and fifty million

0:13:19.840 --> 0:13:24.080
<v Speaker 9>dollars when it came to violating the bip UP Biometric

0:13:24.200 --> 0:13:28.000
<v Speaker 9>Information Privacy Act of Illinois. So I think it's extremely

0:13:28.679 --> 0:13:32.640
<v Speaker 9>risky for companies to be building on unstable foundations.

0:13:33.559 --> 0:13:36.560
<v Speaker 1>Doctor Joy, What were the specifics of your discussion with

0:13:36.640 --> 0:13:40.520
<v Speaker 1>President Biden? Yes, artificial intelligence, but what did he want

0:13:40.559 --> 0:13:40.839
<v Speaker 1>to know?

0:13:42.000 --> 0:13:42.120
<v Speaker 8>So?

0:13:42.440 --> 0:13:46.560
<v Speaker 9>I was very encouraged by how engaged President Biden was

0:13:46.800 --> 0:13:50.640
<v Speaker 9>at the roundtable, and we started off discussing the possibilities

0:13:50.720 --> 0:13:56.040
<v Speaker 9>of AI for healthcare for education, but quickly focused on

0:13:56.280 --> 0:14:00.440
<v Speaker 9>real world harms of AI, and so I focused on

0:14:00.640 --> 0:14:05.960
<v Speaker 9>racial bias, gender bias, and known false matches that have

0:14:06.120 --> 0:14:09.760
<v Speaker 9>led to false arrests, such as the case of Robert Williams,

0:14:09.760 --> 0:14:12.880
<v Speaker 9>who was arrested in front of his two young daughters

0:14:13.200 --> 0:14:16.840
<v Speaker 9>and his wife due to faulty AI system. So that

0:14:17.000 --> 0:14:20.200
<v Speaker 9>was certainly a top of mine. I also think there's

0:14:20.200 --> 0:14:24.040
<v Speaker 9>an opportunity for the US to lead on biometric rights,

0:14:24.080 --> 0:14:28.480
<v Speaker 9>but right now we're going in the opposite direction. Last

0:14:28.480 --> 0:14:33.800
<v Speaker 9>week we had EU lawmakers push forward the EUAI Act,

0:14:33.880 --> 0:14:37.000
<v Speaker 9>which has a provision that bans the use of live

0:14:37.120 --> 0:14:41.560
<v Speaker 9>facial recognition in public places. While this is happening here

0:14:41.600 --> 0:14:45.320
<v Speaker 9>in the US, we have the TSA rolling out domestic

0:14:45.440 --> 0:14:49.280
<v Speaker 9>facial recognition, and most people don't even know that they

0:14:49.320 --> 0:14:52.080
<v Speaker 9>can opt out. So that's why we're doing the TSA

0:14:52.200 --> 0:14:56.200
<v Speaker 9>scorecard fly dot AJL dot org to actually hear from travelers.

0:14:56.680 --> 0:15:00.400
<v Speaker 9>Was their notice, was their consent, was their signage? Happened

0:15:00.400 --> 0:15:02.480
<v Speaker 9>if you try to opt out? Could you actually opt

0:15:02.520 --> 0:15:06.920
<v Speaker 9>out without consequences? So we certainly discussed where the US

0:15:07.000 --> 0:15:10.360
<v Speaker 9>could lead when it comes to biometrics rights.

0:15:12.160 --> 0:15:16.040
<v Speaker 1>Dr Joy It's interesting the comparison between the European Union

0:15:16.080 --> 0:15:19.480
<v Speaker 1>and what we see in the United States. Did President

0:15:19.480 --> 0:15:22.520
<v Speaker 1>Biden give you any sort of indicational pledge that the

0:15:22.640 --> 0:15:25.920
<v Speaker 1>US will kind of be more active in regulating or

0:15:25.920 --> 0:15:29.160
<v Speaker 1>at least acting on a framework in terms of god

0:15:29.240 --> 0:15:31.840
<v Speaker 1>rails for artificial intelligence technology.

0:15:32.280 --> 0:15:35.160
<v Speaker 9>Well, something that I was encouraged to see last year

0:15:35.200 --> 0:15:37.960
<v Speaker 9>from the Biden Harris administration was the release of a

0:15:38.000 --> 0:15:40.800
<v Speaker 9>blueprint from an AI Bill of Rights. And I think

0:15:40.920 --> 0:15:46.200
<v Speaker 9>that's exactly the way the US should approach regulating AI right,

0:15:46.240 --> 0:15:48.960
<v Speaker 9>which is a rights based framework, and so here you

0:15:48.960 --> 0:15:54.920
<v Speaker 9>would have protection. You would have specific protections against algorithmic discrimination.

0:15:55.040 --> 0:15:58.760
<v Speaker 9>There would be notice and explanation, there would be privacy

0:15:58.880 --> 0:16:02.680
<v Speaker 9>when it comes today, and consent, and these systems would

0:16:02.680 --> 0:16:05.360
<v Speaker 9>have to be shown to be safe and effective. In

0:16:05.400 --> 0:16:09.200
<v Speaker 9>the case of Robert Williams being falsely arrested, the system

0:16:09.280 --> 0:16:12.640
<v Speaker 9>wasn't even effective. But even if we had more effective

0:16:12.960 --> 0:16:17.240
<v Speaker 9>biometric systems, this then can lead to mass state surveillance,

0:16:17.280 --> 0:16:19.760
<v Speaker 9>which is not the society we want to have here

0:16:19.800 --> 0:16:20.840
<v Speaker 9>in the United States.

0:16:22.400 --> 0:16:25.560
<v Speaker 1>At MIT, tell me about your research. What are you

0:16:25.600 --> 0:16:28.280
<v Speaker 1>focused on. So my research.

0:16:27.880 --> 0:16:32.520
<v Speaker 9>Looks into bias in various types of AI systems, So

0:16:32.680 --> 0:16:35.960
<v Speaker 9>probably most known for the gender Shades paper, which showed

0:16:36.240 --> 0:16:40.520
<v Speaker 9>racial bias and gender bias in commercially sold projects products

0:16:40.600 --> 0:16:46.800
<v Speaker 9>from IBM, Microsoft. Later on we also did Amazon as well.

0:16:47.080 --> 0:16:49.960
<v Speaker 9>Right now, with the Algorithmic Justice League, we're focused on

0:16:50.240 --> 0:16:55.080
<v Speaker 9>real world harms. How are people experiencing AI systems being

0:16:55.200 --> 0:16:59.680
<v Speaker 9>used for access to government services? So, for example, we

0:16:59.720 --> 0:17:03.720
<v Speaker 9>know that the IRS put on board id ME as

0:17:03.760 --> 0:17:08.679
<v Speaker 9>a way of accessing basic tax information. But we launched

0:17:08.680 --> 0:17:11.520
<v Speaker 9>a campaign and we heard from many people saying that

0:17:11.560 --> 0:17:14.480
<v Speaker 9>they were having so many different issues, not just from

0:17:14.520 --> 0:17:17.879
<v Speaker 9>the technical side, but also from a privacy side. And

0:17:17.960 --> 0:17:21.000
<v Speaker 9>when you have a company that says to use our system,

0:17:21.200 --> 0:17:24.359
<v Speaker 9>you waive your right to sue, but there's no other

0:17:24.400 --> 0:17:27.800
<v Speaker 9>way to access that government service. We are moving in

0:17:27.880 --> 0:17:30.640
<v Speaker 9>the wrong direction, but it's not too late to course.

0:17:30.440 --> 0:17:35.600
<v Speaker 1>Correct, doctor Joy. The core of your research and your

0:17:35.720 --> 0:17:39.439
<v Speaker 1>role is looking at the societal risks of AI. But

0:17:39.520 --> 0:17:43.440
<v Speaker 1>how do you yourself use artificial intelligence tools? Would you say

0:17:43.440 --> 0:17:47.800
<v Speaker 1>that you are pro AI is as a tool to

0:17:47.920 --> 0:17:49.000
<v Speaker 1>advance mankind?

0:17:50.320 --> 0:17:55.520
<v Speaker 9>I am optimistic about using ethnical AI systems, and what

0:17:55.560 --> 0:17:58.600
<v Speaker 9>I try to do is say are the AI systems

0:17:58.680 --> 0:18:01.159
<v Speaker 9>that I would like to see use built on an

0:18:01.240 --> 0:18:04.680
<v Speaker 9>ethical pipeline, and if not, are there ways we can

0:18:05.160 --> 0:18:08.840
<v Speaker 9>shift that. I'm very excited. For example, with what we're

0:18:08.840 --> 0:18:13.360
<v Speaker 9>seeing with AI and healthcare. There's a startup called bloomer

0:18:13.440 --> 0:18:17.320
<v Speaker 9>Tech where they've identified a major health gap which is

0:18:17.600 --> 0:18:20.760
<v Speaker 9>about one in three women die of cardiovascular disease, but

0:18:20.840 --> 0:18:24.800
<v Speaker 9>less than a quarter of research participants are women when

0:18:24.840 --> 0:18:27.960
<v Speaker 9>it comes to actually studying the disease, so we have

0:18:28.040 --> 0:18:31.840
<v Speaker 9>a very male centric model of heart disease, and this

0:18:31.880 --> 0:18:35.440
<v Speaker 9>does mean that women have worse outcomes. So they've developed

0:18:35.440 --> 0:18:39.800
<v Speaker 9>this innovation of smart fabrics that can give you digital biomarkers,

0:18:40.119 --> 0:18:43.480
<v Speaker 9>and they are also addressing this data gap because there's

0:18:43.520 --> 0:18:46.760
<v Speaker 9>such a lack of data when it comes to women's

0:18:47.000 --> 0:18:50.840
<v Speaker 9>heart health. So those sorts of areas, I'm very excited

0:18:50.960 --> 0:18:55.199
<v Speaker 9>about the possibilities where AI isn't taking away livelihoods but

0:18:55.320 --> 0:18:57.360
<v Speaker 9>improving a life outcomes.

0:18:58.400 --> 0:19:02.240
<v Speaker 1>Doctor Joy BOLLAMWENI reflect on that meeting with President Biden

0:19:02.240 --> 0:19:04.000
<v Speaker 1>here in San Francisco last week. Thank you so much

0:19:04.000 --> 0:19:14.920
<v Speaker 1>for your time that in New York. All right, time

0:19:14.960 --> 0:19:18.040
<v Speaker 1>for talking tech. First up, India's buy dju is talking

0:19:18.080 --> 0:19:22.200
<v Speaker 1>with prospective new shareholders for a one billion dollar fundraising round,

0:19:22.200 --> 0:19:26.080
<v Speaker 1>seeking delay investors who want to clip its founder's control.

0:19:26.160 --> 0:19:30.280
<v Speaker 1>The education tech firm is offering sweeteners to win over newbackers,

0:19:30.520 --> 0:19:35.959
<v Speaker 1>including preferential treatment in the case of liquidation. Plus. Baidu's

0:19:36.040 --> 0:19:40.120
<v Speaker 1>chat GBT style service called Ernie has outperformed open ai

0:19:40.280 --> 0:19:43.879
<v Speaker 1>seminal product on several measures in China. That's according to

0:19:43.920 --> 0:19:47.040
<v Speaker 1>a statement from China's search leader, who implies the latest

0:19:47.040 --> 0:19:50.240
<v Speaker 1>dis seration of Baidu's foundation model won't have to compete

0:19:50.240 --> 0:19:54.720
<v Speaker 1>with open Ai directly in China. A Meta has quadrupled

0:19:54.720 --> 0:19:58.000
<v Speaker 1>the number of companies using it's WhatsApp business tool in

0:19:58.040 --> 0:20:00.359
<v Speaker 1>the past three years. The app now has more than

0:20:00.359 --> 0:20:03.919
<v Speaker 1>two hundred million customers, up from fifty million in the

0:20:03.960 --> 0:20:06.560
<v Speaker 1>middle of twenty twenty, making headway in a push to

0:20:06.600 --> 0:20:11.439
<v Speaker 1>generate more money from the popular messaging service. Right coming up,

0:20:11.440 --> 0:20:13.880
<v Speaker 1>we're going to continue our conversation and all things artificial

0:20:13.880 --> 0:20:17.760
<v Speaker 1>intelligence and speak to one entrepreneur in the space, Joseph Miller.

0:20:17.800 --> 0:20:20.760
<v Speaker 1>He's a former Bridgewater hedge Fund employee. He joins us

0:20:20.800 --> 0:20:23.600
<v Speaker 1>next to discuss the launch of his new app, Quiver,

0:20:23.880 --> 0:20:28.919
<v Speaker 1>which uses AI and the blockchain for digital identity loads.

0:20:28.960 --> 0:20:31.520
<v Speaker 1>More to come here on Bloomberg Technology. From here in

0:20:31.560 --> 0:20:35.880
<v Speaker 1>San Francisco, focus NAI and don't forget Gary Tan's coming up.

0:20:36.200 --> 0:20:38.520
<v Speaker 1>Why Combinator That is one you don't want to miss.

0:20:38.760 --> 0:20:41.520
<v Speaker 1>A gloomy day in San Francisco, but a beautiful show.

0:20:41.600 --> 0:20:54.239
<v Speaker 1>This is Bloomberg Technology. Welcome back to Bloomberg Technology. I'm

0:20:54.320 --> 0:20:56.760
<v Speaker 1>Ed Ludlow in San Francisco. Let's check in on the markets,

0:20:56.800 --> 0:21:00.520
<v Speaker 1>and it is the technology sector that is powering this rebound.

0:21:00.520 --> 0:21:02.560
<v Speaker 1>We're seeing inequities and as that one hundred up by

0:21:02.560 --> 0:21:05.679
<v Speaker 1>more than a percentage point follows Friday and Monday session

0:21:05.680 --> 0:21:07.879
<v Speaker 1>that gave us the biggest two day drop on the

0:21:07.960 --> 0:21:10.600
<v Speaker 1>NAS that one hundred going back to March, even as

0:21:10.600 --> 0:21:12.840
<v Speaker 1>Bonyield's kind of pushing higher up three basis points on

0:21:12.880 --> 0:21:16.800
<v Speaker 1>the US tenure yield three point seventy five percent. In crypto,

0:21:16.960 --> 0:21:19.920
<v Speaker 1>the story has been about bitcoin cash, this spin off

0:21:20.080 --> 0:21:23.439
<v Speaker 1>from twenty seventeen. It was at a time where you know,

0:21:23.520 --> 0:21:26.200
<v Speaker 1>software engineers were banking on anything with bitcoin in the

0:21:26.280 --> 0:21:28.880
<v Speaker 1>name being a success. But it has become an old

0:21:28.920 --> 0:21:32.560
<v Speaker 1>coin that's become popular. A one hundred percent gain in

0:21:32.640 --> 0:21:35.080
<v Speaker 1>a sort of seven to nine day span, and we

0:21:35.119 --> 0:21:37.560
<v Speaker 1>continue to push higher one point eight percent, trading at

0:21:37.600 --> 0:21:40.640
<v Speaker 1>the highest level since May of twenty twenty two. More

0:21:40.640 --> 0:21:43.840
<v Speaker 1>headlines this morning giving energy to the crypto space as

0:21:43.880 --> 0:21:47.080
<v Speaker 1>well in terms of single name movers, that there's no

0:21:47.280 --> 0:21:50.479
<v Speaker 1>huge focus on any one theme. We are looking at

0:21:50.480 --> 0:21:54.080
<v Speaker 1>palabalto Networks hit a fresh record high three point five percent,

0:21:54.160 --> 0:21:56.480
<v Speaker 1>gain two hundred and fifty two dollars a share in

0:21:56.520 --> 0:21:58.720
<v Speaker 1>the AI space and now to the downside, but we've

0:21:58.800 --> 0:22:01.760
<v Speaker 1>kind of clawed black some of the declines as Alphabet

0:22:01.840 --> 0:22:05.080
<v Speaker 1>parent of Google Burnstein out with a note saying this

0:22:05.280 --> 0:22:07.800
<v Speaker 1>was a stock that was like a warm hug, but

0:22:07.880 --> 0:22:10.000
<v Speaker 1>it's time to sit on the sidelines because there are

0:22:10.080 --> 0:22:13.240
<v Speaker 1>some risks about the shift from generator to generative AI

0:22:13.359 --> 0:22:16.520
<v Speaker 1>that could impact its ad streams in the near term.

0:22:17.240 --> 0:22:19.560
<v Speaker 1>Sit on the sidelines, but hoping to come back to it.

0:22:19.600 --> 0:22:21.160
<v Speaker 1>I thought that was an interesting note, but it has

0:22:21.200 --> 0:22:23.840
<v Speaker 1>impacted the shares had been much lower, now off by

0:22:23.880 --> 0:22:27.320
<v Speaker 1>two tenths of one percent. Now sticking with generative AI.

0:22:27.600 --> 0:22:31.160
<v Speaker 1>Zoo is rolling out access to Microsoft's Azure Open AI

0:22:31.280 --> 0:22:36.040
<v Speaker 1>service this week, allowing forty five thousand employees to test

0:22:36.080 --> 0:22:38.720
<v Speaker 1>out the product in its core lending unis. The firm

0:22:38.760 --> 0:22:42.080
<v Speaker 1>is seeking ideas from workers in Japan on how to

0:22:42.200 --> 0:22:46.080
<v Speaker 1>best use the technology. All right, let's stick with the

0:22:46.119 --> 0:22:49.399
<v Speaker 1>conversation in AI. Joining us now is Joseph Miller, the

0:22:49.440 --> 0:22:53.200
<v Speaker 1>co founder and chief data science scientist for Quiver, venture

0:22:53.240 --> 0:22:58.080
<v Speaker 1>backed consumer app for validating and monetizing digital identity prior

0:22:58.119 --> 0:23:01.320
<v Speaker 1>to Quiver. Joseph, you're a manager. You're at Bridgewater Associates,

0:23:01.359 --> 0:23:05.360
<v Speaker 1>who focused on building AI decision making systems for senior management,

0:23:05.520 --> 0:23:08.520
<v Speaker 1>and you also co founded viven, the world's first aipower

0:23:08.600 --> 0:23:12.439
<v Speaker 1>platform for aligning sales and products in tech companies. You know,

0:23:12.560 --> 0:23:16.439
<v Speaker 1>Bridgewater just a giant hedge fund, right, Ray Dalio is

0:23:16.440 --> 0:23:18.959
<v Speaker 1>where my mind goes. But when you hear that story

0:23:19.000 --> 0:23:23.320
<v Speaker 1>about Mazoo, another sort of financial institution on a global scale,

0:23:23.920 --> 0:23:27.359
<v Speaker 1>rolling out a generative AI tool to its employees, what

0:23:27.440 --> 0:23:29.639
<v Speaker 1>do you think you've been in this domain? You know

0:23:29.720 --> 0:23:30.639
<v Speaker 1>how difficult it is?

0:23:31.200 --> 0:23:31.440
<v Speaker 2>Yeah?

0:23:31.480 --> 0:23:33.480
<v Speaker 10>I think that it's a good question. So I think

0:23:33.480 --> 0:23:35.240
<v Speaker 10>that generative AI does a lot of.

0:23:35.200 --> 0:23:36.879
<v Speaker 2>Things for startups.

0:23:36.880 --> 0:23:39.639
<v Speaker 10>Like you know, I'm a serial entrepreneur doing these things

0:23:39.640 --> 0:23:43.119
<v Speaker 10>building products. I think that it does has enabled us

0:23:43.200 --> 0:23:46.000
<v Speaker 10>to do things that we otherwise weren't able to do

0:23:46.080 --> 0:23:49.120
<v Speaker 10>at scale, but also brand new technologies. So like we're

0:23:49.160 --> 0:23:51.760
<v Speaker 10>you know, Quivers, a series seed company, Like we're a

0:23:51.760 --> 0:23:54.199
<v Speaker 10>small group who are just starting up, and you know,

0:23:54.280 --> 0:23:56.360
<v Speaker 10>to be able to do the sort of generative AI,

0:23:56.400 --> 0:23:59.480
<v Speaker 10>the natural language processing at scale that you're able to

0:23:59.480 --> 0:24:03.639
<v Speaker 10>do now with a simple API key is pretty unbelievable,

0:24:03.680 --> 0:24:06.359
<v Speaker 10>and it's allowing us to do things that are was

0:24:06.440 --> 0:24:07.680
<v Speaker 10>otherwise prohibited before.

0:24:09.119 --> 0:24:11.119
<v Speaker 1>We will get into Quiver in just a moment and

0:24:11.160 --> 0:24:13.919
<v Speaker 1>talk about the specific use case that you're trying to crack.

0:24:14.520 --> 0:24:20.639
<v Speaker 1>But when you left Bridgewater, did you consider developing an

0:24:20.720 --> 0:24:23.520
<v Speaker 1>AI product or platform that could be used in the

0:24:23.560 --> 0:24:27.920
<v Speaker 1>world of financial markets and financial institutions.

0:24:28.400 --> 0:24:32.119
<v Speaker 10>Yes, actually so when I left, I also started working

0:24:32.160 --> 0:24:36.760
<v Speaker 10>on trading algorithms of my own, mostly based in causal inference.

0:24:36.800 --> 0:24:39.680
<v Speaker 2>And the biggest use case.

0:24:39.560 --> 0:24:43.359
<v Speaker 10>For generative AI or really LLLM models is actually the

0:24:43.400 --> 0:24:45.399
<v Speaker 10>synthesis of the amount of data that you have to

0:24:45.840 --> 0:24:48.200
<v Speaker 10>crunch in order to try to understand the market right

0:24:48.600 --> 0:24:50.600
<v Speaker 10>and being able to do that again, like I said,

0:24:50.600 --> 0:24:52.040
<v Speaker 10>being able to do that at scale is a thing

0:24:52.080 --> 0:24:55.440
<v Speaker 10>that has really only become available and possible and say

0:24:55.480 --> 0:24:56.840
<v Speaker 10>the last few years.

0:24:57.800 --> 0:25:02.600
<v Speaker 1>So Quiver, let's get into it. The primary artificial intelligence

0:25:03.200 --> 0:25:07.959
<v Speaker 1>use case is to protect the badging process. Well, what

0:25:08.080 --> 0:25:09.160
<v Speaker 1>is the badging process?

0:25:09.359 --> 0:25:11.680
<v Speaker 10>That's right, so Quivers, we're trying to do something a

0:25:11.720 --> 0:25:14.320
<v Speaker 10>little different with digital identity. I think that people tend

0:25:14.320 --> 0:25:16.720
<v Speaker 10>to think about digital identity as like a KYC know

0:25:16.760 --> 0:25:18.960
<v Speaker 10>your customer, you know, what's social security number, what's your

0:25:19.000 --> 0:25:21.479
<v Speaker 10>driver's license, where do you live? This kind of stuff,

0:25:22.160 --> 0:25:24.159
<v Speaker 10>And instead, what we wanted to do is focus on

0:25:24.200 --> 0:25:27.120
<v Speaker 10>the more human aspects of our identity and create a

0:25:27.119 --> 0:25:30.240
<v Speaker 10>platform where people can aggregate all of these different facets

0:25:30.280 --> 0:25:32.399
<v Speaker 10>they have on different platforms. Right now, you know, your

0:25:32.480 --> 0:25:36.440
<v Speaker 10>music preferences might sit on Spotify, maybe your lifestyle preferences

0:25:36.440 --> 0:25:39.719
<v Speaker 10>are sitting in somewhere in some collection of Instagram photos

0:25:39.720 --> 0:25:42.000
<v Speaker 10>and Twitter tweets and things like this, and we wanted

0:25:42.040 --> 0:25:43.600
<v Speaker 10>to create a space where you could bring all of

0:25:43.640 --> 0:25:47.560
<v Speaker 10>that together and represent yourself more holistically. And of course

0:25:48.119 --> 0:25:50.080
<v Speaker 10>not you know, the things that make us most interesting

0:25:50.240 --> 0:25:52.800
<v Speaker 10>are often not online at all, and so the question

0:25:52.880 --> 0:25:55.320
<v Speaker 10>is how do you bring that online and how do you.

0:25:55.240 --> 0:25:56.119
<v Speaker 2>Get that validated?

0:25:56.359 --> 0:25:59.200
<v Speaker 10>And so we have this social protocol that allows people

0:25:59.200 --> 0:26:01.840
<v Speaker 10>to create video and sumbod evidence that gets voted on

0:26:01.880 --> 0:26:05.360
<v Speaker 10>by the community and you get validated for that way too.

0:26:05.400 --> 0:26:07.120
<v Speaker 10>So for example, if you're a dog lover, like there's

0:26:07.160 --> 0:26:09.360
<v Speaker 10>probably not you know, that's a silly thing, but it's

0:26:09.600 --> 0:26:11.719
<v Speaker 10>a big aspect of our humanity, right, So when you

0:26:11.720 --> 0:26:14.159
<v Speaker 10>put that next to say your accolades, and you know

0:26:14.160 --> 0:26:15.960
<v Speaker 10>you worked at Bridgewater, but you're also a dog lover,

0:26:16.040 --> 0:26:16.880
<v Speaker 10>it makes you more human.

0:26:17.000 --> 0:26:18.320
<v Speaker 2>And I think a big part that.

0:26:18.359 --> 0:26:21.119
<v Speaker 10>Is missing in the Internet is that aspect of our humanity.

0:26:21.480 --> 0:26:23.160
<v Speaker 10>And so that's what we're trying to do. And then

0:26:23.280 --> 0:26:26.080
<v Speaker 10>of course our AI use case there is this is

0:26:26.280 --> 0:26:29.359
<v Speaker 10>obviously very delicate, right, we need we need a lot

0:26:29.359 --> 0:26:32.160
<v Speaker 10>of trust in this space. And so again going back

0:26:32.160 --> 0:26:35.240
<v Speaker 10>to like things like agent models in AI, it allows

0:26:35.320 --> 0:26:38.480
<v Speaker 10>us to do to protect that authenticity and that validation

0:26:38.600 --> 0:26:41.520
<v Speaker 10>process at scale in a way that was completely impossible

0:26:41.560 --> 0:26:42.600
<v Speaker 10>even six months ago.

0:26:43.280 --> 0:26:45.320
<v Speaker 1>Jersey, if I'm almost certain that a big portion of

0:26:45.320 --> 0:26:50.439
<v Speaker 1>the Bunomberg technology audience dog glovers, It's okay, it's not silly.

0:26:50.520 --> 0:26:55.119
<v Speaker 1>It's built on graph AI algorithms explain the underlying technology

0:26:55.160 --> 0:26:57.080
<v Speaker 1>to me, what is the point of difference there on

0:26:57.119 --> 0:26:58.560
<v Speaker 1>a graph AI algorithm.

0:26:58.760 --> 0:27:01.840
<v Speaker 10>Yeah, So what we have is a sort of graph

0:27:01.880 --> 0:27:03.720
<v Speaker 10>that connects all of the users and their interests and

0:27:03.760 --> 0:27:06.119
<v Speaker 10>their likes, and then what we can do in that

0:27:06.240 --> 0:27:07.280
<v Speaker 10>is do community detection.

0:27:07.400 --> 0:27:08.960
<v Speaker 2>We can look at fraud detection.

0:27:09.119 --> 0:27:10.919
<v Speaker 10>We can look at if there are bad actors in

0:27:10.960 --> 0:27:12.840
<v Speaker 10>the community, if there's a group of people that are

0:27:12.880 --> 0:27:16.840
<v Speaker 10>sort of taking over some sort of some some you

0:27:16.880 --> 0:27:18.480
<v Speaker 10>know niche of the of.

0:27:18.440 --> 0:27:21.080
<v Speaker 2>The of the badge like library, if you will.

0:27:21.840 --> 0:27:25.320
<v Speaker 10>If there's things like prejudice, if there's things like you know,

0:27:25.760 --> 0:27:30.359
<v Speaker 10>people trying to you know, discriminate in systematic ways. The

0:27:30.440 --> 0:27:33.119
<v Speaker 10>graph allows us to suss that out and see and

0:27:33.160 --> 0:27:34.280
<v Speaker 10>detect these bad actors.

0:27:35.680 --> 0:27:38.800
<v Speaker 1>Joseph Miller of Quiver formerly Bridgewater, thank you so much

0:27:38.840 --> 0:27:39.280
<v Speaker 1>for your time.

0:27:39.320 --> 0:27:40.000
<v Speaker 2>Thank you very much.

0:27:48.640 --> 0:27:50.720
<v Speaker 1>It's time for VC Spotlight, and today we're going to

0:27:50.760 --> 0:27:54.400
<v Speaker 1>talk about why Combinator YC hosts two three month programs

0:27:54.440 --> 0:27:57.159
<v Speaker 1>a year to help a select group of startups and

0:27:57.200 --> 0:28:01.080
<v Speaker 1>founders in an accelerated program. One program runs from January

0:28:01.119 --> 0:28:04.719
<v Speaker 1>through March and the other from June through August. The

0:28:04.760 --> 0:28:07.440
<v Speaker 1>summer twenty twenty three batch has officially started, and for

0:28:07.480 --> 0:28:11.159
<v Speaker 1>the first time since the pandemic YC is hosting the

0:28:11.160 --> 0:28:14.800
<v Speaker 1>group in person and all in the Bay Area. Of

0:28:14.880 --> 0:28:18.480
<v Speaker 1>the four thousand startups yc's funded since two thousand and five,

0:28:18.560 --> 0:28:23.040
<v Speaker 1>many have become household names, including Airbnb and Stripe. Joining

0:28:23.080 --> 0:28:26.440
<v Speaker 1>us to discuss Gary Tan, the CEO of y Combinator,

0:28:26.440 --> 0:28:29.399
<v Speaker 1>Welcome to Bloomberg Technology. It's great to have you here

0:28:29.720 --> 0:28:32.159
<v Speaker 1>in person. Thank you so much for having me in

0:28:32.280 --> 0:28:36.199
<v Speaker 1>San Francisco, indeed in the epicenter of tech. And we

0:28:36.240 --> 0:28:38.360
<v Speaker 1>will get to that. And that is your message to

0:28:38.440 --> 0:28:42.640
<v Speaker 1>this class of Summer twenty three. Why so insistent that

0:28:42.800 --> 0:28:44.800
<v Speaker 1>this group be here in person?

0:28:44.920 --> 0:28:45.560
<v Speaker 6>Absolutely so.

0:28:45.600 --> 0:28:48.240
<v Speaker 11>As you know, hy Combinator funds people at the earliest

0:28:48.280 --> 0:28:51.560
<v Speaker 11>possible stage, sometimes when it's literally just an idea and

0:28:51.600 --> 0:28:55.000
<v Speaker 11>a couple co founders, and so really in order to

0:28:55.040 --> 0:28:57.280
<v Speaker 11>go from zero to one, to create something that has

0:28:57.360 --> 0:29:01.040
<v Speaker 11>never existed before. There's nothing like having the energy of

0:29:01.120 --> 0:29:04.520
<v Speaker 11>people in person, not just right next to each other,

0:29:04.560 --> 0:29:07.120
<v Speaker 11>writing the code and shipping it to users. You know

0:29:07.200 --> 0:29:09.160
<v Speaker 11>that you could do from any living room or any

0:29:09.160 --> 0:29:13.360
<v Speaker 11>bedroom in the world. There's something special about hundreds of

0:29:13.680 --> 0:29:18.080
<v Speaker 11>founders coming together and actually helping each other. And that's

0:29:18.160 --> 0:29:21.000
<v Speaker 11>really why YC has been so successful over the number

0:29:21.000 --> 0:29:21.360
<v Speaker 11>of years.

0:29:21.440 --> 0:29:23.560
<v Speaker 1>Really we're the same Bay area, but really the message

0:29:23.560 --> 0:29:24.800
<v Speaker 1>from you is San Francisco.

0:29:25.240 --> 0:29:27.520
<v Speaker 11>Yeah, I think San Francisco in particular, what the AI

0:29:27.600 --> 0:29:31.800
<v Speaker 11>boom is really about Cerebral Valley, which we call Hayes Valley,

0:29:31.880 --> 0:29:35.000
<v Speaker 11>right down the street. There's something very special happening here

0:29:35.040 --> 0:29:39.040
<v Speaker 11>where literally the foundational models, the open source, the smartest

0:29:39.040 --> 0:29:42.360
<v Speaker 11>people in the world are sitting in those cafes. You're

0:29:42.400 --> 0:29:46.200
<v Speaker 11>having discussions not just about starting their companies, but also

0:29:46.280 --> 0:29:49.080
<v Speaker 11>what is the cutting edge of what these AI models

0:29:49.120 --> 0:29:49.440
<v Speaker 11>can do.

0:29:49.880 --> 0:29:51.880
<v Speaker 1>Let's go through the mechanics of it. You know, the

0:29:51.960 --> 0:29:55.840
<v Speaker 1>initial investment pledge is five hundred thousand US dollars to

0:29:55.920 --> 0:29:59.360
<v Speaker 1>each of the startups and then a three month program,

0:29:59.440 --> 0:30:01.760
<v Speaker 1>you know, going through the operational side of things. But

0:30:01.760 --> 0:30:03.440
<v Speaker 1>what happens in that time.

0:30:03.400 --> 0:30:06.200
<v Speaker 11>Absolutely, I think the most important thing that's crazy to

0:30:06.280 --> 0:30:09.959
<v Speaker 11>me is the people who start these companies. They are

0:30:10.000 --> 0:30:12.680
<v Speaker 11>the most eminent of all of the people who could

0:30:12.720 --> 0:30:14.959
<v Speaker 11>be starting companies at that moment. So we had more

0:30:15.000 --> 0:30:18.560
<v Speaker 11>than twenty four thousand applications for about two hundred and

0:30:18.600 --> 0:30:19.240
<v Speaker 11>forty spots.

0:30:19.240 --> 0:30:21.080
<v Speaker 1>Twenty four thousand was a record, that's right.

0:30:21.440 --> 0:30:25.040
<v Speaker 11>So our acceptance rate was just under one percent, which

0:30:25.120 --> 0:30:27.840
<v Speaker 11>it's never been as selective as today.

0:30:28.280 --> 0:30:29.960
<v Speaker 6>But that means that those.

0:30:29.840 --> 0:30:32.480
<v Speaker 11>People in that room, they actually go on to become

0:30:32.800 --> 0:30:35.200
<v Speaker 11>the most eminent people in startup them.

0:30:35.400 --> 0:30:38.000
<v Speaker 1>Sorry to interrupt, but what does that day to represent

0:30:38.040 --> 0:30:41.200
<v Speaker 1>If you had the most applications on record and you're

0:30:41.240 --> 0:30:44.040
<v Speaker 1>accepting the fewest on record, what does that tell you

0:30:44.080 --> 0:30:45.960
<v Speaker 1>about the big picture of this economy?

0:30:46.480 --> 0:30:48.440
<v Speaker 11>Well, I think at the end of the day, we're

0:30:48.480 --> 0:30:51.640
<v Speaker 11>trying to set these founders up for success, and so

0:30:52.200 --> 0:30:56.080
<v Speaker 11>you know, if there is ongoing high interest rates, one

0:30:56.080 --> 0:30:57.920
<v Speaker 11>of the things that we're really trying to focus on

0:30:58.040 --> 0:31:00.360
<v Speaker 11>is how do we make sure the founders we do

0:31:00.400 --> 0:31:02.840
<v Speaker 11>fund are the people who are most likely to go

0:31:02.960 --> 0:31:07.520
<v Speaker 11>on and succeed. And so in a time of cheap capital,

0:31:07.840 --> 0:31:11.160
<v Speaker 11>that means that maybe we can fund more people. In

0:31:11.200 --> 0:31:15.280
<v Speaker 11>a time of more distress like right now, we have

0:31:15.360 --> 0:31:18.600
<v Speaker 11>to be much more mindful of what are those businesses

0:31:18.760 --> 0:31:22.760
<v Speaker 11>and can we see really great revenue, great gross margin.

0:31:22.840 --> 0:31:24.600
<v Speaker 11>And that's one of the big reasons why today we

0:31:24.680 --> 0:31:27.560
<v Speaker 11>launched our Top Companies list that's on our website.

0:31:27.600 --> 0:31:30.000
<v Speaker 1>Today we can discuss the Top Companies list. I think

0:31:30.000 --> 0:31:31.719
<v Speaker 1>I want to go back to the mechanics, because what

0:31:31.840 --> 0:31:35.440
<v Speaker 1>surprises so many people, whether you cool why combinator, an accelerator,

0:31:35.840 --> 0:31:38.840
<v Speaker 1>or an incubator, it's just a three month program. How

0:31:38.840 --> 0:31:40.440
<v Speaker 1>can you get anything done in that time?

0:31:40.800 --> 0:31:42.760
<v Speaker 11>Well, I think the cool thing that I get to

0:31:42.760 --> 0:31:45.320
<v Speaker 11>do is I get to work with twelve of some

0:31:45.400 --> 0:31:48.760
<v Speaker 11>of my closest friends who are known as group partners.

0:31:48.760 --> 0:31:52.280
<v Speaker 11>And the big thing that's different now is actually as CEO,

0:31:53.160 --> 0:31:55.480
<v Speaker 11>I am actually also a group partner, So we actually

0:31:55.520 --> 0:31:57.600
<v Speaker 11>have thirteen people working with the companies.

0:31:58.000 --> 0:31:58.920
<v Speaker 6>And so these are some.

0:31:58.880 --> 0:32:01.560
<v Speaker 11>Of the people who literally work with these top revenue

0:32:01.560 --> 0:32:05.840
<v Speaker 11>and top valuation companies from the zero to one stage,

0:32:05.960 --> 0:32:10.000
<v Speaker 11>literally a few people just starting out. For me, I

0:32:10.040 --> 0:32:13.240
<v Speaker 11>got the privilege of working with Apor Vimetta from Instacart,

0:32:13.400 --> 0:32:17.840
<v Speaker 11>or Brian Armstrong from Coinbase, or Kyle Vote from Cruz Automation.

0:32:17.960 --> 0:32:20.280
<v Speaker 11>These are all companies that have gone on to sort

0:32:20.280 --> 0:32:24.239
<v Speaker 11>of change both their industry but also tech broadly. But

0:32:24.520 --> 0:32:26.400
<v Speaker 11>I can't tell you how crazy it is to meet

0:32:26.400 --> 0:32:30.520
<v Speaker 11>people when it's literally a YC application on the web.

0:32:30.560 --> 0:32:34.160
<v Speaker 11>And that's really I think one of the great innovations

0:32:34.520 --> 0:32:36.880
<v Speaker 11>of our time that Paul Graham, the founder of YC

0:32:37.360 --> 0:32:40.320
<v Speaker 11>came up with here's this website where you go to

0:32:40.400 --> 0:32:43.200
<v Speaker 11>y combinator, dot com slash apply and anyone with an

0:32:43.240 --> 0:32:46.640
<v Speaker 11>idea anywhere in the world on the internet can apply

0:32:46.760 --> 0:32:49.560
<v Speaker 11>and someone will go and read it, and our community

0:32:49.600 --> 0:32:52.320
<v Speaker 11>will figure out, hey, should this person be a part

0:32:52.320 --> 0:32:53.160
<v Speaker 11>of our community.

0:32:53.440 --> 0:32:55.640
<v Speaker 1>Of the twenty four thousand that applied and then the

0:32:55.680 --> 0:32:57.960
<v Speaker 1>two hundred and forty that were accepted, I guess the

0:32:58.000 --> 0:32:59.600
<v Speaker 1>question goes to, like, what do they have in common?

0:33:00.400 --> 0:33:03.720
<v Speaker 1>Or paradoxically, are any of them not AI companies?

0:33:03.880 --> 0:33:06.240
<v Speaker 11>Oh yeah, so actually only thirty five percent of the

0:33:06.280 --> 0:33:09.920
<v Speaker 11>companies in this batch are specifically AI focused. I'd say

0:33:09.960 --> 0:33:13.320
<v Speaker 11>maybe half of them have some AI component. YC has

0:33:13.320 --> 0:33:16.040
<v Speaker 11>always been generalists, you know. That's how YC was able

0:33:16.040 --> 0:33:20.040
<v Speaker 11>to fund coinbase, really even years before anyone he'd even

0:33:20.120 --> 0:33:23.400
<v Speaker 11>heard of bitcoin. And I think the key message here

0:33:23.440 --> 0:33:25.800
<v Speaker 11>is that the best people in the world to sort

0:33:25.800 --> 0:33:29.080
<v Speaker 11>of create these startups are actually the technologists, the builders,

0:33:29.080 --> 0:33:31.240
<v Speaker 11>who are just a few people writing lines of code.

0:33:31.280 --> 0:33:34.400
<v Speaker 6>It's a very fringe thing, and what YC does is

0:33:34.440 --> 0:33:36.240
<v Speaker 6>take people who otherwise would.

0:33:36.080 --> 0:33:38.560
<v Speaker 11>Be on the fringe, but they're some of the most

0:33:38.560 --> 0:33:41.959
<v Speaker 11>technically and engineering wise brilliant people in the world, and

0:33:42.280 --> 0:33:44.960
<v Speaker 11>you know, we sort of teach them how the pieces move.

0:33:45.040 --> 0:33:48.120
<v Speaker 11>You know, we teach them chess the part of, you know,

0:33:48.240 --> 0:33:53.240
<v Speaker 11>building a business that frankly is the easier part to

0:33:53.360 --> 0:33:56.000
<v Speaker 11>being highly technical and being on the cutting edge of

0:33:56.000 --> 0:33:57.600
<v Speaker 11>what's happening in technology.

0:33:57.880 --> 0:34:00.360
<v Speaker 1>Gary, how do you ensure you've got the funds to

0:34:00.480 --> 0:34:04.920
<v Speaker 1>deploy every year for two programs? Is there's still LP

0:34:05.080 --> 0:34:07.440
<v Speaker 1>interest in backing you? Absolutely?

0:34:07.520 --> 0:34:09.719
<v Speaker 11>I Mean one of the amazing things about YC is

0:34:09.719 --> 0:34:12.640
<v Speaker 11>if you look at every single top venture capital firm

0:34:12.719 --> 0:34:15.520
<v Speaker 11>in the world, pretty much all of them have a

0:34:15.640 --> 0:34:19.120
<v Speaker 11>YC company that is either on their homepage or often

0:34:19.880 --> 0:34:22.440
<v Speaker 11>multiple of them that have become fund returners for those

0:34:22.560 --> 0:34:23.240
<v Speaker 11>VC funds.

0:34:23.440 --> 0:34:27.360
<v Speaker 1>So to me, states becoming LPs in themselves. I mean,

0:34:27.400 --> 0:34:29.360
<v Speaker 1>where are you getting the funds from?

0:34:29.520 --> 0:34:29.719
<v Speaker 2>Oh?

0:34:29.880 --> 0:34:32.040
<v Speaker 11>And really speaking, all the same kind of limited partners

0:34:32.080 --> 0:34:34.480
<v Speaker 11>that you would expect in venture capital.

0:34:34.480 --> 0:34:38.000
<v Speaker 6>And that makes sense because YC to me is actually.

0:34:37.640 --> 0:34:40.840
<v Speaker 11>The fountain of prosperity for this type of activity in

0:34:40.880 --> 0:34:41.760
<v Speaker 11>the world.

0:34:42.800 --> 0:34:45.240
<v Speaker 1>Two pieces of news. So we've got through the class

0:34:45.239 --> 0:34:47.760
<v Speaker 1>of summer twenty twenty three. You're also publishing this list

0:34:48.440 --> 0:34:52.320
<v Speaker 1>of the top net revenue generating companies you backed in

0:34:52.360 --> 0:34:56.000
<v Speaker 1>twenty twenty two, private or otherwise. Why, I mean, those

0:34:56.040 --> 0:34:58.720
<v Speaker 1>are two different scales, ends of the scale.

0:34:58.840 --> 0:34:58.920
<v Speaker 10>Ye.

0:35:00.200 --> 0:35:03.080
<v Speaker 11>The reality is, and this is no news to anyone.

0:35:03.560 --> 0:35:06.080
<v Speaker 11>Twenty twenty one was such an extreme sort of time

0:35:06.440 --> 0:35:08.800
<v Speaker 11>and one of the things that we became very concerned

0:35:08.840 --> 0:35:11.759
<v Speaker 11>about because we actually want to be a bellweather of

0:35:11.800 --> 0:35:14.719
<v Speaker 11>trying to teach the next generation what they should focus on.

0:35:15.040 --> 0:35:18.120
<v Speaker 11>There was this over reliance on thinking about what's my

0:35:18.239 --> 0:35:21.359
<v Speaker 11>next round. The fast money meant that you were going

0:35:21.400 --> 0:35:24.440
<v Speaker 11>to chase top line growth at all costs, and of

0:35:24.480 --> 0:35:26.359
<v Speaker 11>course now we're on the other side of a very

0:35:26.440 --> 0:35:30.400
<v Speaker 11>deep hangover about that, and so we thought, how do

0:35:30.440 --> 0:35:34.360
<v Speaker 11>we as a community put forward the things that founders

0:35:34.400 --> 0:35:36.719
<v Speaker 11>and people who are sort of creating this technology, what

0:35:36.760 --> 0:35:37.400
<v Speaker 11>should they.

0:35:37.239 --> 0:35:40.080
<v Speaker 6>Be thinking about? And right now you know, now more

0:35:40.080 --> 0:35:42.240
<v Speaker 6>than ever, it's about gross margin.

0:35:42.400 --> 0:35:45.080
<v Speaker 11>It's actually about net revenue sort of one of the

0:35:45.080 --> 0:35:47.759
<v Speaker 11>things I'm calling it is literally edible revenue. And one

0:35:47.760 --> 0:35:50.440
<v Speaker 11>of the wild things now just can you pay people?

0:35:50.560 --> 0:35:53.960
<v Speaker 11>Can you pay salaries? Can you actually run your operations

0:35:53.960 --> 0:35:57.400
<v Speaker 11>on this revenue? And what's funny is looking at the

0:35:57.480 --> 0:36:02.040
<v Speaker 11>different financial and accounting sort of shenanigans that people pull

0:36:02.280 --> 0:36:04.960
<v Speaker 11>often it's about sort of hiding this number. So I

0:36:04.960 --> 0:36:07.840
<v Speaker 11>think maybe we should make this an industry standard, like

0:36:07.880 --> 0:36:10.480
<v Speaker 11>should you know whether or not it's a marketplace or

0:36:10.840 --> 0:36:13.760
<v Speaker 11>you know, there are lots of different specific accounting rules

0:36:13.800 --> 0:36:15.120
<v Speaker 11>for specific comps.

0:36:15.160 --> 0:36:16.120
<v Speaker 6>But why is that.

0:36:16.280 --> 0:36:19.720
<v Speaker 11>Shouldn't we just use one metric that actually will allow

0:36:19.840 --> 0:36:22.400
<v Speaker 11>founders to make the right decision, which is to build

0:36:22.600 --> 0:36:26.759
<v Speaker 11>great sustainable companies and know that they can grow over

0:36:26.800 --> 0:36:27.560
<v Speaker 11>the long term.

0:36:27.760 --> 0:36:30.640
<v Speaker 1>We should talk about San Francisco and where to build

0:36:30.640 --> 0:36:32.880
<v Speaker 1>for the long term. I mean, your position on this

0:36:32.960 --> 0:36:38.239
<v Speaker 1>city is well known, but as of today, where is

0:36:38.239 --> 0:36:38.759
<v Speaker 1>your head at?

0:36:38.960 --> 0:36:39.120
<v Speaker 6>Yeah?

0:36:39.160 --> 0:36:40.000
<v Speaker 1>Absolutely, you know.

0:36:40.440 --> 0:36:43.160
<v Speaker 11>The great thing about San Francisco to me is that

0:36:44.040 --> 0:36:46.280
<v Speaker 11>it gave me everything I have. I learned a code

0:36:46.280 --> 0:36:49.439
<v Speaker 11>in the city, taking bart into Petrero Hill in Web

0:36:49.480 --> 0:36:52.080
<v Speaker 11>one point zero, I learned how to make database backed websites.

0:36:52.320 --> 0:36:55.879
<v Speaker 11>And what I realize is San Francisco and the Bay

0:36:55.920 --> 0:36:58.920
<v Speaker 11>Area is the place that is a magnet for all

0:36:58.960 --> 0:37:00.839
<v Speaker 11>of the most technical people in the world. And when

0:37:00.840 --> 0:37:03.120
<v Speaker 11>you have those type of agglomeration effects, when you have

0:37:03.440 --> 0:37:06.360
<v Speaker 11>all the smartest, most brilliant, most driven people in the

0:37:06.360 --> 0:37:09.160
<v Speaker 11>world coming here, of course they're going to create wealth.

0:37:08.960 --> 0:37:11.440
<v Speaker 1>But we have problems as well, and you've been very

0:37:11.520 --> 0:37:14.759
<v Speaker 1>vocal acknowledging them absolutely. And I guess where my question

0:37:14.840 --> 0:37:17.600
<v Speaker 1>goes is how much more deeply you would be involved

0:37:18.040 --> 0:37:22.839
<v Speaker 1>in political cycles, putting money into initiatives and candidates, yourself

0:37:23.040 --> 0:37:24.319
<v Speaker 1>being involved in politics.

0:37:24.600 --> 0:37:27.360
<v Speaker 11>Well, some of my friends started an organization called growsf

0:37:27.400 --> 0:37:29.799
<v Speaker 11>which is one of the most important things. How do

0:37:29.880 --> 0:37:32.480
<v Speaker 11>we take really some of the smartest people who have

0:37:32.520 --> 0:37:35.120
<v Speaker 11>created this wealth, and how do we have an impact

0:37:35.160 --> 0:37:39.320
<v Speaker 11>that's positive on our local economy. We want that wealth

0:37:39.400 --> 0:37:41.279
<v Speaker 11>to actually be shared with everyone. I think a lot

0:37:41.360 --> 0:37:44.440
<v Speaker 11>of people criticize tech and tech people broadly as sort

0:37:44.480 --> 0:37:49.240
<v Speaker 11>of ann Randian libertarians, and speaking for myself and my friends,

0:37:49.480 --> 0:37:53.960
<v Speaker 11>you know we're not Randians where Gene Roddenberry type of

0:37:54.800 --> 0:37:55.960
<v Speaker 11>Gene Roddenberry is sort of.

0:37:55.960 --> 0:37:58.080
<v Speaker 6>Our spiritual future.

0:37:58.239 --> 0:38:00.600
<v Speaker 11>You know, what we believe is Starfly The Academy was

0:38:00.640 --> 0:38:03.520
<v Speaker 11>in San Francisco for a reason, and we can actually

0:38:03.560 --> 0:38:06.880
<v Speaker 11>build San Francisco into San Francoccio if we actually have

0:38:07.000 --> 0:38:09.799
<v Speaker 11>the right policies and the right politicians in place. And

0:38:09.840 --> 0:38:12.200
<v Speaker 11>in November of twenty twenty four, I think.

0:38:12.080 --> 0:38:14.480
<v Speaker 6>If you follow grows up. You'll see our plan to

0:38:14.560 --> 0:38:15.200
<v Speaker 6>make that happen.

0:38:15.239 --> 0:38:17.120
<v Speaker 1>This is the first opportunity I've had speech to you

0:38:17.160 --> 0:38:20.080
<v Speaker 1>since the collapse of Silicon Valley Bank, and the commonality

0:38:20.120 --> 0:38:23.040
<v Speaker 1>with y Combinator is that SVB was often the first

0:38:23.320 --> 0:38:27.359
<v Speaker 1>institution to write a check. Bloomberg reported last week that

0:38:27.440 --> 0:38:31.359
<v Speaker 1>the FDIC had mistakenly released this document to our news

0:38:31.400 --> 0:38:36.280
<v Speaker 1>organization that revealed the backstop on all depositors with balances

0:38:36.320 --> 0:38:39.360
<v Speaker 1>exceeding two hundred and fifty thousand was also inclusive of

0:38:39.440 --> 0:38:45.440
<v Speaker 1>Sequoia and many large tech startups that didn't need it. Frankly,

0:38:45.560 --> 0:38:47.640
<v Speaker 1>and you were an advocate for that initiative in the

0:38:47.640 --> 0:38:50.000
<v Speaker 1>first place, on the smallert side of things. What's your

0:38:50.080 --> 0:38:51.640
<v Speaker 1>kind of thoughts on that and reaction?

0:38:51.920 --> 0:38:53.040
<v Speaker 6>Absolutely, you know, I.

0:38:53.000 --> 0:38:56.120
<v Speaker 11>Think the hard part about SVB is that it really

0:38:56.160 --> 0:38:58.400
<v Speaker 11>did hit the little guy. You know what I realized

0:38:58.600 --> 0:39:01.680
<v Speaker 11>for us, you know, YCS fun over four thousand companies,

0:39:01.960 --> 0:39:04.560
<v Speaker 11>and of course the top one hundred, top two hundred

0:39:04.600 --> 0:39:06.920
<v Speaker 11>are some of the biggest names that you could think of.

0:39:07.760 --> 0:39:12.960
<v Speaker 11>But what the backstop was really about was saving the

0:39:13.000 --> 0:39:16.520
<v Speaker 11>tens of thousands of small and medium sized businesses that

0:39:16.640 --> 0:39:18.840
<v Speaker 11>literally would not be able to make payroll. And I

0:39:18.880 --> 0:39:22.040
<v Speaker 11>think this fact coming out doesn't change any of that.

0:39:22.120 --> 0:39:26.240
<v Speaker 11>We would have set back technology perhaps five years, perhaps

0:39:26.280 --> 0:39:29.319
<v Speaker 11>a decade if you just suddenly killed at a very

0:39:29.360 --> 0:39:32.719
<v Speaker 11>early stage of company. And so you know, I hear

0:39:32.760 --> 0:39:36.600
<v Speaker 11>that argument, but the truth remains that there were hundreds

0:39:36.640 --> 0:39:38.680
<v Speaker 11>of thousands of jobs that were saved.

0:39:38.440 --> 0:39:39.120
<v Speaker 6>In that moment.

0:39:39.239 --> 0:39:42.719
<v Speaker 11>And I think the FED and the FDIC and the

0:39:42.760 --> 0:39:44.440
<v Speaker 11>people in charge they did the right thing.

0:39:44.680 --> 0:39:46.839
<v Speaker 1>Gary. When I think of you, I think have also

0:39:46.880 --> 0:39:51.279
<v Speaker 1>about initialized and you returned to YC in January. You

0:39:51.320 --> 0:39:55.600
<v Speaker 1>were the key man alongside Alexisanian and you've kind of

0:39:56.120 --> 0:39:58.960
<v Speaker 1>come back to y see quickly. How did that conversation

0:39:59.080 --> 0:40:01.840
<v Speaker 1>go with LPs of analized? And is it now just

0:40:01.880 --> 0:40:03.399
<v Speaker 1>all alexis initialized?

0:40:03.520 --> 0:40:05.719
<v Speaker 11>Oh so alexis left to start his own fund called

0:40:05.760 --> 0:40:07.560
<v Speaker 11>seven Semi six a number of years ago.

0:40:08.000 --> 0:40:11.440
<v Speaker 6>The new managing partners are Brett Gibson and Jen Wolf.

0:40:11.520 --> 0:40:13.359
<v Speaker 6>They've done an incredible job.

0:40:13.440 --> 0:40:16.239
<v Speaker 11>I mean, the number one thing I love is that

0:40:16.560 --> 0:40:19.600
<v Speaker 11>in venture, the best and highest to me is not

0:40:19.680 --> 0:40:22.480
<v Speaker 11>to elevate a single person, but to really create an

0:40:22.560 --> 0:40:26.440
<v Speaker 11>institution that lasts well beyond anyone who sort of started

0:40:26.440 --> 0:40:28.520
<v Speaker 11>the business. And you know, I looked at Paul Graham

0:40:28.520 --> 0:40:32.440
<v Speaker 11>and Jessica Livingston who created YC for us. They're still there,

0:40:32.800 --> 0:40:36.200
<v Speaker 11>but they made space for what is now an institution,

0:40:36.360 --> 0:40:40.360
<v Speaker 11>and I am merely the steward of that institution going forward.

0:40:40.440 --> 0:40:42.160
<v Speaker 1>Why you comminated to see you, Gary Town was so

0:40:42.160 --> 0:40:47.000
<v Speaker 1>grateful for your time here in San Francisco. This is

0:40:47.000 --> 0:40:49.799
<v Speaker 1>going viral. Taylor Swift's Eras Tool is on track to

0:40:49.840 --> 0:40:53.040
<v Speaker 1>become the biggest in concert history. It could potentially gross

0:40:53.080 --> 0:40:56.040
<v Speaker 1>over a billion dollars that milestone or break the record

0:40:56.120 --> 0:40:59.760
<v Speaker 1>for global concert tours currently held by Elton John followed

0:41:00.040 --> 0:41:02.319
<v Speaker 1>by Ed Sheeran. Swift will play one hundred and six

0:41:02.360 --> 0:41:07.200
<v Speaker 1>contexts by next summer, including fifty four shows over seats.

0:41:07.440 --> 0:41:10.080
<v Speaker 1>There does it, guys, for this edition of Bloomberg Technology,

0:41:10.239 --> 0:41:13.480
<v Speaker 1>so much to recap. Don't forget the podcast. Wherever you

0:41:13.560 --> 0:41:17.480
<v Speaker 1>get yours, Apple, Spotify, iHeart, and of course Bloomberg from

0:41:17.560 --> 0:41:22.360
<v Speaker 1>San Francisco. This is Bloomberg Technology.