WEBVTT - OpenAI CFO Sara Friar Talks AI Development

0:00:02.360 --> 0:00:07.360
<v Speaker 1>Bloomberg Audio Studios, podcasts, radio news. Let's say you lived

0:00:07.400 --> 0:00:10.360
<v Speaker 1>Bloomberg House and dovels right now were Bloomberg's Sharon Gafari

0:00:10.680 --> 0:00:14.720
<v Speaker 1>is in conversation with Sarah Fryar, the CFO of Opening.

0:00:14.520 --> 0:00:16.840
<v Speaker 2>Swearing, and first of all, thank you Bloomberg for having

0:00:16.880 --> 0:00:19.840
<v Speaker 2>us today. It's been amazing to spend my first twenty

0:00:19.840 --> 0:00:22.560
<v Speaker 2>four hours and Davus and to see that AI is

0:00:22.600 --> 0:00:25.560
<v Speaker 2>really top of the agenda and I think appropriately. So

0:00:26.560 --> 0:00:29.120
<v Speaker 2>you're right, there's been a lot of hype. Sam did

0:00:29.120 --> 0:00:31.960
<v Speaker 2>tell everyone to chill, so we'll message just.

0:00:32.040 --> 0:00:34.159
<v Speaker 3>Chill a little, and he's right.

0:00:34.680 --> 0:00:38.240
<v Speaker 2>We do internally feel that we have a path towards AGI.

0:00:39.040 --> 0:00:42.720
<v Speaker 2>We had a big breakthrough in twenty twenty four around

0:00:42.760 --> 0:00:45.839
<v Speaker 2>reasoning models in particular, so you saw us move from

0:00:45.880 --> 0:00:50.680
<v Speaker 2>a world of more chat models, chatbots, Chat schept fastest

0:00:50.720 --> 0:00:52.720
<v Speaker 2>app in the world to one hundred million users and

0:00:52.760 --> 0:00:56.400
<v Speaker 2>today well over three hundred million users. But last year

0:00:56.440 --> 0:00:59.360
<v Speaker 2>we really started to see the progression into reasoning and

0:00:59.400 --> 0:01:02.080
<v Speaker 2>what we call our OH series of models. So OH

0:01:02.120 --> 0:01:04.120
<v Speaker 2>one preview was launched and now we're all the way

0:01:04.160 --> 0:01:06.800
<v Speaker 2>to one. Hopefully you've all tried it on what of

0:01:06.800 --> 0:01:10.040
<v Speaker 2>those reasoning models do, they really start to help you

0:01:10.120 --> 0:01:12.360
<v Speaker 2>think about difficult problems.

0:01:12.600 --> 0:01:13.959
<v Speaker 3>So you see the model.

0:01:14.040 --> 0:01:15.959
<v Speaker 2>One of my favorite things to do is actually watch

0:01:16.040 --> 0:01:18.399
<v Speaker 2>what it's doing in real time. You see it go

0:01:18.520 --> 0:01:21.560
<v Speaker 2>down chains of thought trying to answer the prompt that

0:01:21.600 --> 0:01:23.759
<v Speaker 2>you've put in. You see it sometimes hit a cul

0:01:23.800 --> 0:01:26.000
<v Speaker 2>de sac, it doesn't quite get to the answer, and

0:01:26.040 --> 0:01:28.399
<v Speaker 2>so it almost has to reverse course and try a

0:01:28.440 --> 0:01:30.480
<v Speaker 2>different approach to solving that problem.

0:01:30.680 --> 0:01:31.760
<v Speaker 3>A lot like the way.

0:01:31.760 --> 0:01:35.320
<v Speaker 2>We all do to ultimately give you the best answer,

0:01:35.400 --> 0:01:39.000
<v Speaker 2>And frankly, I think often the answer is not a definitive,

0:01:39.160 --> 0:01:43.360
<v Speaker 2>singular answer. It's an exploration, it's a way to continue

0:01:43.360 --> 0:01:44.119
<v Speaker 2>a conversation.

0:01:44.720 --> 0:01:45.959
<v Speaker 3>We are upping the pace.

0:01:46.040 --> 0:01:48.120
<v Speaker 2>So we just went from OH one to we previewed

0:01:48.160 --> 0:01:51.200
<v Speaker 2>OH three, just the second model. We like to confuse

0:01:51.240 --> 0:01:54.520
<v Speaker 2>sometimes with our nomenclature, but OH three that just took

0:01:54.560 --> 0:01:57.160
<v Speaker 2>three months. And if you think about the length of

0:01:57.200 --> 0:02:00.880
<v Speaker 2>time from chat GPT three to chat GPT four, that

0:02:00.960 --> 0:02:04.560
<v Speaker 2>took about two years. So the pace of innovation is

0:02:04.640 --> 0:02:05.680
<v Speaker 2>really accelerating.

0:02:06.000 --> 0:02:07.640
<v Speaker 3>What's it like to work in a place like that.

0:02:08.280 --> 0:02:15.120
<v Speaker 2>It's exhilarating, it's inspiring, it's sometimes exhausting and it's definitely

0:02:15.160 --> 0:02:17.280
<v Speaker 2>done with a sense of privilege. I think all of

0:02:17.360 --> 0:02:20.480
<v Speaker 2>us at open AI think a lot about the magnitude

0:02:20.520 --> 0:02:22.640
<v Speaker 2>of what we're bringing into the world. We think about

0:02:22.680 --> 0:02:26.560
<v Speaker 2>it with regard to safety, with alignment, but also with

0:02:26.720 --> 0:02:29.919
<v Speaker 2>the optimism of what it can do. When you can

0:02:29.960 --> 0:02:32.840
<v Speaker 2>put that sort of human intelligence into the hands of

0:02:33.280 --> 0:02:35.160
<v Speaker 2>kids in schools, when you put it into the hands

0:02:35.160 --> 0:02:37.360
<v Speaker 2>of teachers, when you put it into the hands of

0:02:37.360 --> 0:02:40.120
<v Speaker 2>our colleagues at work. And we're just getting started.

0:02:41.080 --> 0:02:41.520
<v Speaker 3>Absolutely.

0:02:41.600 --> 0:02:43.080
<v Speaker 4>So you mentioned reasoning.

0:02:44.040 --> 0:02:45.640
<v Speaker 1>Can you tell us a little bit about how this

0:02:45.800 --> 0:02:48.120
<v Speaker 1>kind of really advanced reasoning that Open a Eye is

0:02:48.120 --> 0:02:51.080
<v Speaker 1>coming out with. How does that translate into real customer

0:02:51.160 --> 0:02:53.240
<v Speaker 1>value for you? How are you seeing Opening Eyes users

0:02:54.160 --> 0:02:57.560
<v Speaker 1>actually gain productivity, gain insights from that.

0:02:58.000 --> 0:03:02.400
<v Speaker 2>Yeah, so reasoning is on the path to agents, and

0:03:02.440 --> 0:03:05.560
<v Speaker 2>we really feel that we're now moving into this area

0:03:06.280 --> 0:03:10.160
<v Speaker 2>era of agentic technology and into a world of agents.

0:03:10.520 --> 0:03:13.640
<v Speaker 2>So what we see today with the reasoning models is

0:03:13.680 --> 0:03:16.760
<v Speaker 2>they are for some of the most difficult problems. We

0:03:16.840 --> 0:03:20.080
<v Speaker 2>see it used in areas like pharma, for example, deep

0:03:20.160 --> 0:03:24.320
<v Speaker 2>research into new molecules that help us think about drugs

0:03:24.360 --> 0:03:27.760
<v Speaker 2>and drug discovery. Those have been some of the areas

0:03:27.800 --> 0:03:30.120
<v Speaker 2>that I have been most excited about the progress so

0:03:30.160 --> 0:03:33.000
<v Speaker 2>far and how people are taking the O series and

0:03:33.080 --> 0:03:35.880
<v Speaker 2>using it. But as I talked about with agents, and

0:03:35.880 --> 0:03:37.080
<v Speaker 2>I'm sure you're probably going to ask you.

0:03:37.120 --> 0:03:39.280
<v Speaker 1>Yes, and let's find how do you because everyone has

0:03:39.280 --> 0:03:41.240
<v Speaker 1>a different sort of idea, But what's your take on

0:03:41.280 --> 0:03:42.120
<v Speaker 1>what an agent is?

0:03:42.360 --> 0:03:45.520
<v Speaker 4>And you know how Sam said agents are going to

0:03:46.480 --> 0:03:48.280
<v Speaker 4>going to be acting as employees.

0:03:47.880 --> 0:03:50.280
<v Speaker 1>Right in twenty twenty five, Y're actually, so, where do

0:03:50.320 --> 0:03:52.280
<v Speaker 1>you think we'll actually see agents start to do the

0:03:52.320 --> 0:03:53.240
<v Speaker 1>work of real people?

0:03:53.320 --> 0:03:54.960
<v Speaker 4>First? What sector is do you think it's going to hit?

0:03:55.280 --> 0:03:57.600
<v Speaker 2>So it's important with agents, first of all to think

0:03:57.640 --> 0:03:59.160
<v Speaker 2>about why is it even possible?

0:03:59.280 --> 0:04:00.160
<v Speaker 3>And so that's why I'm.

0:04:00.120 --> 0:04:03.200
<v Speaker 2>Made the point about with reasoning models that kind of

0:04:03.400 --> 0:04:05.800
<v Speaker 2>cul de sac, that a reasoning model goes down and

0:04:05.840 --> 0:04:08.040
<v Speaker 2>then has to back up and try a different way

0:04:08.080 --> 0:04:12.200
<v Speaker 2>to solve because in an old world of deterministic software

0:04:12.560 --> 0:04:15.960
<v Speaker 2>effactively the developer or the architect had to in some

0:04:16.000 --> 0:04:21.680
<v Speaker 2>ways envision all the possible routes that a question or

0:04:21.960 --> 0:04:25.600
<v Speaker 2>an output could take. Today, with a reasoning model acts

0:04:25.720 --> 0:04:28.400
<v Speaker 2>much more in the way that we humans think about problems,

0:04:28.720 --> 0:04:31.240
<v Speaker 2>and so that starts to bring forth this idea of

0:04:31.320 --> 0:04:35.719
<v Speaker 2>agents within our workplace, maybe even just as task workers

0:04:35.760 --> 0:04:39.520
<v Speaker 2>alongside us every day. So some of the earlier things

0:04:39.520 --> 0:04:41.800
<v Speaker 2>that we are looking at is how do we use

0:04:41.839 --> 0:04:44.480
<v Speaker 2>agents to just solve day to day problems, Like I'm

0:04:44.480 --> 0:04:45.200
<v Speaker 2>a working mom.

0:04:45.839 --> 0:04:48.080
<v Speaker 3>Often I'm in that all the women in.

0:04:48.080 --> 0:04:51.039
<v Speaker 2>The audience just smiled. You're in that moment. You're like,

0:04:51.240 --> 0:04:54.680
<v Speaker 2>oh no, it's that five o'clock. I have still got

0:04:54.760 --> 0:04:57.479
<v Speaker 2>multiple hours of work. I've got to get home. There's

0:04:57.520 --> 0:05:00.000
<v Speaker 2>supposed to be something on the table to feed my children.

0:05:00.000 --> 0:05:03.120
<v Speaker 2>And right now my husband is really useful, but maybe

0:05:03.160 --> 0:05:04.000
<v Speaker 2>not all the times.

0:05:04.120 --> 0:05:06.599
<v Speaker 3>The most useful. What am I going to do?

0:05:07.400 --> 0:05:09.080
<v Speaker 2>And this is a moment when you think about an

0:05:09.120 --> 0:05:12.960
<v Speaker 2>agent that can perhaps figure out what am I going

0:05:13.000 --> 0:05:15.599
<v Speaker 2>to get delivered to the home. I don't want to

0:05:15.600 --> 0:05:18.720
<v Speaker 2>have to be specific. It's like, please get me something healthy.

0:05:18.960 --> 0:05:22.240
<v Speaker 2>We had pizza last night, not Italian. Let's not do

0:05:22.320 --> 0:05:25.880
<v Speaker 2>too many carbs again. Might know my budget, might know

0:05:25.920 --> 0:05:28.720
<v Speaker 2>what local restaurants I typically have ordered from, might come

0:05:28.800 --> 0:05:31.080
<v Speaker 2>up with a new idea. So that would be an

0:05:31.120 --> 0:05:33.640
<v Speaker 2>agent more in my personal life. But you are also

0:05:33.680 --> 0:05:36.080
<v Speaker 2>starting to see this dawn of agents in our workforce,

0:05:36.400 --> 0:05:39.640
<v Speaker 2>and so it may be areas like software development, it

0:05:39.680 --> 0:05:43.880
<v Speaker 2>could be areas like research within labs deep science. We

0:05:43.920 --> 0:05:47.279
<v Speaker 2>see many folks here at Davos, you know, heralding that

0:05:47.360 --> 0:05:51.360
<v Speaker 2>age of agents in areas like customer relationship management for example.

0:05:51.760 --> 0:05:53.839
<v Speaker 2>So I think that you're going to see a lot

0:05:53.880 --> 0:05:57.760
<v Speaker 2>of companies talking about new agents coming to the forefront.

0:05:57.880 --> 0:06:01.120
<v Speaker 2>Customer success is another one. But you've got to remember

0:06:01.160 --> 0:06:03.600
<v Speaker 2>what that is built on top of. And it's really

0:06:03.640 --> 0:06:08.440
<v Speaker 2>built on top of these reasoning models that we open

0:06:08.480 --> 0:06:12.120
<v Speaker 2>AI have brought into the world as the frontier technology

0:06:12.160 --> 0:06:13.279
<v Speaker 2>and the frontier company.

0:06:13.800 --> 0:06:17.320
<v Speaker 1>Great, let's talk a little bit about you your bread

0:06:17.320 --> 0:06:20.200
<v Speaker 1>and butter, which is a world of finance, Right, how

0:06:20.240 --> 0:06:23.479
<v Speaker 1>do you top a record setting VC funding round?

0:06:24.279 --> 0:06:26.880
<v Speaker 4>What is next? What do you expect this year.

0:06:26.720 --> 0:06:30.240
<v Speaker 1>In terms of and obviously AI costs a lot, right,

0:06:30.279 --> 0:06:33.599
<v Speaker 1>the cost of computing increases year every year, So what

0:06:33.680 --> 0:06:34.200
<v Speaker 1>can we expect?

0:06:34.240 --> 0:06:36.400
<v Speaker 4>Will there be another mega around in twenty twenty five.

0:06:36.800 --> 0:06:41.160
<v Speaker 2>Our technology is built on three things. Great people who

0:06:41.200 --> 0:06:45.640
<v Speaker 2>are building the most frontier algorithms and models in the world.

0:06:46.200 --> 0:06:49.760
<v Speaker 2>It's built a top of tremendous amount of compute, and

0:06:49.800 --> 0:06:52.400
<v Speaker 2>I think we're just scratching the surface on that. And

0:06:52.440 --> 0:06:55.240
<v Speaker 2>then of course data and in the world of compute,

0:06:55.240 --> 0:06:57.720
<v Speaker 2>in order to buy that compute, to have access to it,

0:06:57.760 --> 0:07:01.000
<v Speaker 2>to control our own destiny, we've had to do an

0:07:01.040 --> 0:07:04.760
<v Speaker 2>extraordinary amount of fundraising. Luckily, we also have a business

0:07:04.800 --> 0:07:07.960
<v Speaker 2>model that supports it. I already talked about chat chept

0:07:08.120 --> 0:07:11.040
<v Speaker 2>for consumer over three hundred million users today.

0:07:11.440 --> 0:07:14.200
<v Speaker 3>It is and it's a workhourse.

0:07:13.640 --> 0:07:17.200
<v Speaker 2>When it comes to revenue, revenue growth and ultimately profitability.

0:07:17.640 --> 0:07:22.480
<v Speaker 2>But we are seeing now enterprises of every size embrace

0:07:22.520 --> 0:07:25.720
<v Speaker 2>this technology, and we really see ourselves also as the

0:07:25.840 --> 0:07:29.640
<v Speaker 2>enterprise company. In fact, there's a really good symbiosis between

0:07:29.680 --> 0:07:33.200
<v Speaker 2>those two areas because often when I meet customers, and

0:07:33.240 --> 0:07:35.440
<v Speaker 2>I'm meeting a lot of them here in Davas, it's

0:07:35.480 --> 0:07:38.040
<v Speaker 2>actually their personal experience they start with. When I say, hey,

0:07:38.120 --> 0:07:40.880
<v Speaker 2>how are you using chatchpt, they'll actually give me a

0:07:40.920 --> 0:07:44.640
<v Speaker 2>personal anecdote first. And for those of you who sell

0:07:44.680 --> 0:07:47.160
<v Speaker 2>into enterprises, you know that if you've won your customer

0:07:47.240 --> 0:07:50.040
<v Speaker 2>heart in just their day to day, being able to

0:07:50.040 --> 0:07:52.920
<v Speaker 2>go in and then sell them into their enterprise environment

0:07:53.000 --> 0:07:56.200
<v Speaker 2>gets an awful lot easier. And so that enterprise model

0:07:56.280 --> 0:07:59.600
<v Speaker 2>is really building across every sector of the economy, every

0:07:59.640 --> 0:08:02.120
<v Speaker 2>type of company, every scale of company, and I'd be

0:08:02.160 --> 0:08:06.560
<v Speaker 2>happy to talk through examples. Morgan's Stanley is a great example,

0:08:06.600 --> 0:08:08.880
<v Speaker 2>since we're talking financing, and they're also a good partner

0:08:08.880 --> 0:08:11.520
<v Speaker 2>in that front. But they're using our technology and areas

0:08:11.520 --> 0:08:14.920
<v Speaker 2>like wealth management, in areas like even their investment banking,

0:08:14.920 --> 0:08:17.520
<v Speaker 2>and they've been doing it now for multiple years. So

0:08:18.440 --> 0:08:21.400
<v Speaker 2>in terms of the future from a financing perspective, I

0:08:21.400 --> 0:08:24.160
<v Speaker 2>suspect we will continue to have to finance at pace,

0:08:24.560 --> 0:08:26.960
<v Speaker 2>but we will do that on the merits of our

0:08:27.000 --> 0:08:27.800
<v Speaker 2>business as well.

0:08:29.160 --> 0:08:32.240
<v Speaker 1>How about an IPO, what would that look like for

0:08:32.320 --> 0:08:36.280
<v Speaker 1>open Ai? You mentioned profitability? You know, how far do

0:08:36.280 --> 0:08:37.760
<v Speaker 1>you think we are from that? What are you know?

0:08:37.760 --> 0:08:39.440
<v Speaker 1>You're someone who's taken two companies public?

0:08:39.480 --> 0:08:40.679
<v Speaker 3>What would the.

0:08:40.640 --> 0:08:42.719
<v Speaker 4>Steps be for open ai to get there? And how

0:08:42.760 --> 0:08:44.720
<v Speaker 4>far are we from profitability? Yeah?

0:08:44.880 --> 0:08:47.880
<v Speaker 2>So, as I've told every company I've been associated with

0:08:48.120 --> 0:08:51.920
<v Speaker 2>on an IPO, it's a it's not a destination. An

0:08:51.960 --> 0:08:54.600
<v Speaker 2>IPO is just a marker on a journey. And if

0:08:54.600 --> 0:08:57.199
<v Speaker 2>you get wrapped around the idea that the IPO is

0:08:57.240 --> 0:09:00.280
<v Speaker 2>the destination is a very kind of dangerous world. Live

0:09:00.320 --> 0:09:04.160
<v Speaker 2>in because there's a feeling of finality and manland going

0:09:04.200 --> 0:09:08.920
<v Speaker 2>public is just the beginning of another very interesting part

0:09:08.920 --> 0:09:11.400
<v Speaker 2>of your journey. Like what are the positives in IPO

0:09:11.640 --> 0:09:14.240
<v Speaker 2>Number one? I think it's a very strong credentializing moment

0:09:14.320 --> 0:09:17.320
<v Speaker 2>for any company. I love that moment of sunlight, of

0:09:17.400 --> 0:09:20.440
<v Speaker 2>being able to show your financials externally, to show how

0:09:20.440 --> 0:09:24.960
<v Speaker 2>your business is building. It's the best disinfectant in any room.

0:09:25.800 --> 0:09:28.440
<v Speaker 2>It's a great way to fundraise because it opens the

0:09:28.480 --> 0:09:32.520
<v Speaker 2>door not just to equity and selling your equity in

0:09:32.559 --> 0:09:35.240
<v Speaker 2>that moment, but it also starts opening the door to

0:09:35.360 --> 0:09:40.120
<v Speaker 2>many more areas of financing, starting with mezzanine debt, structured debt,

0:09:40.160 --> 0:09:40.600
<v Speaker 2>and so.

0:09:40.480 --> 0:09:42.480
<v Speaker 3>On and again. In a world where we're buying a

0:09:42.480 --> 0:09:43.840
<v Speaker 3>lot of compute.

0:09:43.400 --> 0:09:45.960
<v Speaker 2>We need to get there fast because equity is an

0:09:45.960 --> 0:09:49.600
<v Speaker 2>expensive way to raise capital and to deploy capital. We

0:09:49.679 --> 0:09:51.520
<v Speaker 2>need to make sure we continue to bring down that

0:09:51.640 --> 0:09:54.959
<v Speaker 2>weighted average cost of capital. And then the third thing

0:09:55.280 --> 0:09:59.199
<v Speaker 2>that I like about an IPO I say that somewhat

0:09:59.200 --> 0:10:02.679
<v Speaker 2>tongue in cheek, is it does add an element of

0:10:02.760 --> 0:10:05.560
<v Speaker 2>rigor and discipline to the company's cadence. That is a

0:10:05.640 --> 0:10:09.080
<v Speaker 2>very good thing. The reason why I kind of caveat

0:10:09.120 --> 0:10:10.200
<v Speaker 2>at the statement.

0:10:10.280 --> 0:10:12.080
<v Speaker 3>Is you need to be very careful.

0:10:12.200 --> 0:10:14.400
<v Speaker 2>It doesn't make you short term in how you think

0:10:14.440 --> 0:10:17.080
<v Speaker 2>about your company. It's very easy to get wrapped around

0:10:17.160 --> 0:10:21.360
<v Speaker 2>ninety day cycles. But for folks who operate companies do

0:10:21.400 --> 0:10:24.240
<v Speaker 2>not work on ninety day cycles, particularly companies in our

0:10:24.280 --> 0:10:26.640
<v Speaker 2>sector right now that are having to take a very

0:10:26.720 --> 0:10:31.199
<v Speaker 2>long term perspective. Right the O series of models, the

0:10:31.800 --> 0:10:35.480
<v Speaker 2>work for that was really done probably two years previous,

0:10:35.559 --> 0:10:38.080
<v Speaker 2>when we kind of put shovels in the ground, built

0:10:38.160 --> 0:10:41.320
<v Speaker 2>data centers, built up infrastructure to do it. And so

0:10:41.920 --> 0:10:45.439
<v Speaker 2>this means that during an IPO process, it's incredibly important

0:10:45.440 --> 0:10:48.280
<v Speaker 2>to bring along the right type of investor, the investor

0:10:48.320 --> 0:10:51.200
<v Speaker 2>that understands the longevity that will be required to build

0:10:51.240 --> 0:10:54.920
<v Speaker 2>the business, probably a little bit more like biotech investors

0:10:54.960 --> 0:11:01.520
<v Speaker 2>in some way. So it's always a potential uh station

0:11:01.760 --> 0:11:04.079
<v Speaker 2>on the journey that we're on, but I don't want

0:11:04.080 --> 0:11:06.280
<v Speaker 2>to make it the destination, all right.

0:11:06.320 --> 0:11:09.040
<v Speaker 4>So we talked about funding, mentioned the recent round.

0:11:09.520 --> 0:11:12.280
<v Speaker 1>Which was really the first major accomplishment right under your

0:11:12.280 --> 0:11:15.040
<v Speaker 1>tenure a CFO and open AI. Now there's another big

0:11:15.080 --> 0:11:18.000
<v Speaker 1>task at hand, which is the restructure of open Ai.

0:11:18.040 --> 0:11:20.480
<v Speaker 4>From a nonprofit to a for profit.

0:11:20.520 --> 0:11:22.800
<v Speaker 1>Now Opening Eye plans to make it a public benefit

0:11:22.880 --> 0:11:24.079
<v Speaker 1>for profit corporation, but.

0:11:24.040 --> 0:11:25.200
<v Speaker 4>Nonetheless a for profit.

0:11:25.720 --> 0:11:29.199
<v Speaker 1>Why is this an important change for the company, Why

0:11:29.200 --> 0:11:32.000
<v Speaker 1>do you need this, and particularly why is it important

0:11:32.000 --> 0:11:32.640
<v Speaker 1>to investors?

0:11:33.240 --> 0:11:33.520
<v Speaker 3>Yeah?

0:11:33.600 --> 0:11:36.679
<v Speaker 2>So, first and foremost, as we think through the restructure,

0:11:37.080 --> 0:11:39.680
<v Speaker 2>the most important thing for those of us at open

0:11:39.720 --> 0:11:42.400
<v Speaker 2>AI focused on it is to also make sure that

0:11:42.480 --> 0:11:47.160
<v Speaker 2>the nonprofit is front and center. Our mission is everything

0:11:47.360 --> 0:11:50.959
<v Speaker 2>to create AGI that benefits humanity, that really benefits everyone

0:11:51.080 --> 0:11:51.760
<v Speaker 2>people in.

0:11:51.679 --> 0:11:54.000
<v Speaker 3>The world, and that is.

0:11:53.960 --> 0:11:56.200
<v Speaker 2>What the board. Remember today, the board is the board

0:11:56.240 --> 0:11:58.679
<v Speaker 2>of the nonprofit. That is what they are most focused on.

0:11:59.600 --> 0:12:02.760
<v Speaker 2>In regards to shifting over to being a PBC. The

0:12:02.840 --> 0:12:05.080
<v Speaker 2>reason that we are thinking through that is the right

0:12:05.280 --> 0:12:06.000
<v Speaker 2>corporate structure.

0:12:06.040 --> 0:12:07.160
<v Speaker 3>It's not a done deal.

0:12:07.320 --> 0:12:10.680
<v Speaker 2>But a PBC does allow us to balance both being

0:12:11.000 --> 0:12:15.000
<v Speaker 2>mission focused as well as being shareholder focused economically focused.

0:12:16.280 --> 0:12:18.560
<v Speaker 2>It's not the be all and end all, because we

0:12:18.600 --> 0:12:22.240
<v Speaker 2>are proven that we are able to access capital, that

0:12:22.240 --> 0:12:24.439
<v Speaker 2>we're able to continue to grow the business, and we're

0:12:24.480 --> 0:12:27.320
<v Speaker 2>able to attract great people. But those are the three

0:12:27.520 --> 0:12:30.240
<v Speaker 2>tenants that I look at and thinking about why we

0:12:30.360 --> 0:12:33.520
<v Speaker 2>might want to look more like a traditional company because

0:12:33.800 --> 0:12:37.240
<v Speaker 2>it is fun to innovate, but it's better to stay

0:12:37.240 --> 0:12:41.240
<v Speaker 2>innovating on the innovation edge that we want to be on,

0:12:41.280 --> 0:12:43.679
<v Speaker 2>which is less about our corporate structure and much more

0:12:43.800 --> 0:12:46.440
<v Speaker 2>about our models and the products that we're building.

0:12:47.240 --> 0:12:49.000
<v Speaker 1>Right, And so, as you said, it's not a done deal,

0:12:49.280 --> 0:12:53.120
<v Speaker 1>there are steps to go through. What are some of

0:12:53.160 --> 0:12:56.640
<v Speaker 1>the challenges of navigating this restructure, especially as some people,

0:12:56.720 --> 0:12:59.760
<v Speaker 1>including Elon Musk, have criticized the company for the move,

0:13:00.040 --> 0:13:01.000
<v Speaker 1>for the intended move.

0:13:01.200 --> 0:13:04.280
<v Speaker 2>So for Elon and Sam as being quite public about

0:13:04.280 --> 0:13:07.679
<v Speaker 2>this and others like Greg our co founder, that we

0:13:07.720 --> 0:13:11.480
<v Speaker 2>see Elon as a competitor. We think he's a strong competitor,

0:13:11.840 --> 0:13:14.440
<v Speaker 2>but we hope that he won't keep resorting to kind

0:13:14.440 --> 0:13:18.280
<v Speaker 2>of using law and lawfare to compete. I think even

0:13:18.320 --> 0:13:21.560
<v Speaker 2>in his own words, AI and building AI is a

0:13:21.679 --> 0:13:25.079
<v Speaker 2>very capital intensive business, and I think even he recognized

0:13:25.200 --> 0:13:28.560
<v Speaker 2>very early on that it would require us to be

0:13:28.760 --> 0:13:31.480
<v Speaker 2>much more than a nonprofit. So I actually think he's

0:13:31.520 --> 0:13:34.520
<v Speaker 2>even said that we don't we shouldn't be a nonprofit to.

0:13:34.520 --> 0:13:36.240
<v Speaker 3>Be able to raise the capital required.

0:13:37.080 --> 0:13:38.800
<v Speaker 2>So for right now, you kind of said, is it

0:13:38.840 --> 0:13:39.920
<v Speaker 2>complicated internally?

0:13:39.960 --> 0:13:42.439
<v Speaker 3>We try not to distract. We want to make sure.

0:13:42.400 --> 0:13:44.800
<v Speaker 2>Our researchers are able to do the job they want

0:13:44.800 --> 0:13:48.079
<v Speaker 2>to do, to be curious, to be serendipitous. We want

0:13:48.120 --> 0:13:50.440
<v Speaker 2>to make sure those that are taking that research and

0:13:50.480 --> 0:13:53.480
<v Speaker 2>turning it into product or customer focus first and foremost,

0:13:53.720 --> 0:13:57.720
<v Speaker 2>but also thinking about innovation things customers may never have

0:13:57.760 --> 0:14:00.240
<v Speaker 2>thought about. Make sure our go to market folks to

0:14:00.280 --> 0:14:02.240
<v Speaker 2>do what they do. And so we really try to

0:14:02.240 --> 0:14:05.360
<v Speaker 2>make sure our focus from a restructured perspective stays in

0:14:05.480 --> 0:14:06.679
<v Speaker 2>the right context and.

0:14:06.679 --> 0:14:07.600
<v Speaker 3>At the right scale.

0:14:08.120 --> 0:14:11.480
<v Speaker 1>So there's a legal challenge from write elend musk and

0:14:11.600 --> 0:14:16.360
<v Speaker 1>regulatory approvals, but there's also just this changes the you know,

0:14:16.480 --> 0:14:19.600
<v Speaker 1>the structure for existing investors and you know, kind of

0:14:19.600 --> 0:14:21.560
<v Speaker 1>reassessing the equity in the company.

0:14:22.000 --> 0:14:24.080
<v Speaker 4>So what can that what is that.

0:14:24.040 --> 0:14:26.880
<v Speaker 1>Going to look like with existing investors like Microsoft, and

0:14:27.240 --> 0:14:29.560
<v Speaker 1>you know what kind of equity what might we expect?

0:14:29.560 --> 0:14:32.200
<v Speaker 4>I know Altman has said that some of the numbers out.

0:14:32.000 --> 0:14:35.240
<v Speaker 1>There are maybe you know, not not quite right.

0:14:35.320 --> 0:14:38.000
<v Speaker 4>But what do you think may happen with equity at

0:14:38.000 --> 0:14:39.120
<v Speaker 4>the company for him and others?

0:14:39.360 --> 0:14:42.360
<v Speaker 2>Yeah, so if you look at the folks really involved

0:14:42.360 --> 0:14:45.040
<v Speaker 2>from a restructured perspective. It's not that many parties at

0:14:45.040 --> 0:14:48.080
<v Speaker 2>the table. As we said, the nonprofit is actually the

0:14:48.160 --> 0:14:50.400
<v Speaker 2>key focus for those of us at Open AI and

0:14:50.440 --> 0:14:55.080
<v Speaker 2>making sure that the nonprofit remains well capitalized, well funded, and.

0:14:55.040 --> 0:14:57.680
<v Speaker 3>Can really help us live up to this.

0:14:58.120 --> 0:15:03.400
<v Speaker 2>Very grand inspirational mission that we have for others like Microsoft.

0:15:03.080 --> 0:15:05.440
<v Speaker 3>A really early partner for.

0:15:05.360 --> 0:15:08.440
<v Speaker 2>Us, both from a capital perspective for helping us build

0:15:08.440 --> 0:15:11.680
<v Speaker 2>infrastructure initially to be a partner even in how we

0:15:11.720 --> 0:15:14.920
<v Speaker 2>think about some of our go to market that is

0:15:14.920 --> 0:15:17.640
<v Speaker 2>more of a negotiation. For the investors who came in

0:15:17.720 --> 0:15:20.680
<v Speaker 2>last year. We actually raised that round with them sitting

0:15:20.760 --> 0:15:23.560
<v Speaker 2>more atop so for them it will be much more simple.

0:15:23.640 --> 0:15:26.280
<v Speaker 2>It'll just be a conversion of the six point six

0:15:26.360 --> 0:15:29.040
<v Speaker 2>billion they put in over the one hundred and fifty

0:15:29.040 --> 0:15:32.560
<v Speaker 2>seven billion post money valuation. That's the percentage of the

0:15:32.560 --> 0:15:35.080
<v Speaker 2>company that they owned at that moment. It's easy math.

0:15:35.120 --> 0:15:38.520
<v Speaker 2>It feels much more like a growth equity around.

0:15:39.320 --> 0:15:41.360
<v Speaker 1>Well, we're probably not going to get to the bottom

0:15:41.360 --> 0:15:42.600
<v Speaker 1>of EQUI numbers today, so I'm.

0:15:42.520 --> 0:15:43.120
<v Speaker 3>Going to move on.

0:15:43.240 --> 0:15:46.040
<v Speaker 1>But I mean we put out there's seven percent was

0:15:46.040 --> 0:15:48.640
<v Speaker 1>a number that was out there. I think Altman it said, yeah,

0:15:49.360 --> 0:15:53.040
<v Speaker 1>that's that's not right, any kind of ballpark of what

0:15:53.040 --> 0:15:54.280
<v Speaker 1>we might expect.

0:15:53.960 --> 0:15:56.080
<v Speaker 2>Or nothing that we can discuss it in. I'm not

0:15:56.120 --> 0:15:58.720
<v Speaker 2>trying to shirk the question, but frankly, we don't have

0:15:58.760 --> 0:16:01.760
<v Speaker 2>a definitive answer, not really able to talk about it.

0:16:02.160 --> 0:16:07.360
<v Speaker 1>Let's talk about global policy and infrastructure. So just on

0:16:07.480 --> 0:16:10.160
<v Speaker 1>the policy front, last night we all watch tech leaders,

0:16:10.240 --> 0:16:13.920
<v Speaker 1>including Zambalman, atten Trump's inauguration. How does the company plan

0:16:14.000 --> 0:16:15.760
<v Speaker 1>on working with this new administration.

0:16:16.480 --> 0:16:21.000
<v Speaker 2>So we have released multiple blue papers now about Infrastructure

0:16:21.040 --> 0:16:23.560
<v Speaker 2>Being Destiny, and what we mean by that is we

0:16:23.600 --> 0:16:27.520
<v Speaker 2>recognize that this new era era of AI is going

0:16:27.560 --> 0:16:30.480
<v Speaker 2>to require a lot of both public and private sector

0:16:31.320 --> 0:16:33.280
<v Speaker 2>relationship building and coming.

0:16:33.040 --> 0:16:35.240
<v Speaker 3>To bringing it to the world.

0:16:35.760 --> 0:16:40.320
<v Speaker 2>That's because of the capital requirements, but also how we

0:16:40.360 --> 0:16:43.520
<v Speaker 2>think about the right regulatory environment. How do we make

0:16:43.560 --> 0:16:46.680
<v Speaker 2>sure we build this in a way where it benefits humanity.

0:16:47.160 --> 0:16:50.880
<v Speaker 2>And so with that, our blue paper on Infrastructure Is

0:16:50.960 --> 0:16:54.920
<v Speaker 2>Destiny really leans into things like building up economic zones,

0:16:55.040 --> 0:16:57.800
<v Speaker 2>recognizing a little bit with a US lens right now

0:16:58.120 --> 0:17:01.520
<v Speaker 2>that it is about productivity but also about national security.

0:17:02.160 --> 0:17:05.680
<v Speaker 2>But I sit in front of you as both an American,

0:17:06.200 --> 0:17:09.919
<v Speaker 2>a brit and European, so growing up in Northern Ireland

0:17:09.960 --> 0:17:14.400
<v Speaker 2>turns out had some really good advantages. And so from

0:17:14.480 --> 0:17:19.440
<v Speaker 2>a US perspective, we see the productivity build already beginning

0:17:19.440 --> 0:17:22.959
<v Speaker 2>to happen, frankly, and that's just good for people, right,

0:17:23.160 --> 0:17:26.080
<v Speaker 2>better standard of life, better quality of life, better way

0:17:26.080 --> 0:17:29.280
<v Speaker 2>to use their time. But we also want to make

0:17:29.280 --> 0:17:32.919
<v Speaker 2>sure that it's a Western alliance maybe one way to

0:17:32.920 --> 0:17:36.000
<v Speaker 2>put it, so that everyone is embracing it, because we

0:17:36.119 --> 0:17:39.639
<v Speaker 2>do have competitors that are going to come after this

0:17:39.920 --> 0:17:43.159
<v Speaker 2>also from a national level, and I think we need

0:17:43.200 --> 0:17:46.160
<v Speaker 2>to make sure also from a national security perspective, we're investing.

0:17:46.400 --> 0:17:48.960
<v Speaker 2>So we've thought about things like economic development zones, how

0:17:48.960 --> 0:17:51.800
<v Speaker 2>to governments really reach into their populace help them think

0:17:51.800 --> 0:17:54.760
<v Speaker 2>about reskilling and retooling. We're doing a lot of work

0:17:54.840 --> 0:17:58.879
<v Speaker 2>in the education sector. We've struck some kind of incredible deals.

0:17:58.920 --> 0:18:01.200
<v Speaker 2>ASU is a good example of university in the US

0:18:01.200 --> 0:18:04.600
<v Speaker 2>that has two hundred and eighty thousand seats deployed on

0:18:04.680 --> 0:18:08.359
<v Speaker 2>chat GPT. My alma mater, Oxford, has also been a

0:18:08.400 --> 0:18:12.160
<v Speaker 2>really good customer for US, for example, and we love

0:18:12.320 --> 0:18:15.320
<v Speaker 2>getting down into that level because we see students embracing

0:18:15.359 --> 0:18:18.520
<v Speaker 2>this technology. So that's been the second piece of the

0:18:18.520 --> 0:18:21.360
<v Speaker 2>infrastructure's destiny. And then beyond that, how do we think

0:18:21.400 --> 0:18:24.919
<v Speaker 2>about working in the right regulatory environment so that again

0:18:24.960 --> 0:18:29.600
<v Speaker 2>we deployed technology safely, but we also recognize that people

0:18:29.640 --> 0:18:31.920
<v Speaker 2>want to be able to use it and that governments,

0:18:32.000 --> 0:18:34.640
<v Speaker 2>for the benefit of their citizens, need to make sure

0:18:34.680 --> 0:18:35.920
<v Speaker 2>they're on the forefront as well.

0:18:37.680 --> 0:18:40.800
<v Speaker 1>So opener I recently put out an economic blueprint right

0:18:40.840 --> 0:18:43.840
<v Speaker 1>of their you know, the company's thoughts on what USAI

0:18:43.880 --> 0:18:45.960
<v Speaker 1>policy should be. In a big message there was that

0:18:46.040 --> 0:18:49.240
<v Speaker 1>the sort of technological arms race and AI between the

0:18:49.359 --> 0:18:52.480
<v Speaker 1>US and China, and that the US should invest.

0:18:52.080 --> 0:18:54.960
<v Speaker 4>And support the build out of infrastructure.

0:18:57.200 --> 0:19:00.399
<v Speaker 1>Any indication of if there is support for this in

0:19:00.440 --> 0:19:04.080
<v Speaker 1>the Trump administration and why are we hearing more rhetoric

0:19:04.080 --> 0:19:07.520
<v Speaker 1>about the US versus China sort of AI competition.

0:19:07.680 --> 0:19:10.359
<v Speaker 3>Now, I don't think it's rhetoric. I think it's fact.

0:19:10.520 --> 0:19:14.040
<v Speaker 2>There is absolutely competition going on right now between US

0:19:14.119 --> 0:19:17.240
<v Speaker 2>and China. China is absolutely investing in this area. They

0:19:17.320 --> 0:19:21.000
<v Speaker 2>absolutely know how critical it is to their economy, but

0:19:21.080 --> 0:19:23.840
<v Speaker 2>also from a security perspective, so we should not be

0:19:23.960 --> 0:19:27.600
<v Speaker 2>naive on that front. We absolutely see already with the

0:19:27.600 --> 0:19:30.600
<v Speaker 2>Trump administration a real willingness to lean in I think,

0:19:30.640 --> 0:19:33.280
<v Speaker 2>to be very kind of on the economic front foot

0:19:34.280 --> 0:19:37.120
<v Speaker 2>and whether that comes from perhaps being more open from

0:19:37.160 --> 0:19:41.000
<v Speaker 2>a regulatory standpoint, more open from just a business competition standpoint.

0:19:41.880 --> 0:19:43.439
<v Speaker 3>We're excited to actually get to work.

0:19:46.200 --> 0:19:48.280
<v Speaker 1>Let's talk about Europe maybe for a second, because you

0:19:48.359 --> 0:19:52.480
<v Speaker 1>mentioned your European heritage and we are here in Europe.

0:19:53.359 --> 0:19:56.359
<v Speaker 1>You know, it's no secret that's sort of policymakers in

0:19:56.400 --> 0:19:58.640
<v Speaker 1>the EU are considered tougher on tech. We have seen

0:19:58.680 --> 0:20:00.960
<v Speaker 1>AI regulation come out of E you right, whereas in

0:20:01.000 --> 0:20:03.480
<v Speaker 1>the US we don't have federal legislation really on the matter.

0:20:04.560 --> 0:20:06.840
<v Speaker 1>Do you think that regulation can threaten the pace of

0:20:06.880 --> 0:20:07.760
<v Speaker 1>AI innovation?

0:20:08.080 --> 0:20:09.080
<v Speaker 4>And are we seeing.

0:20:08.920 --> 0:20:13.760
<v Speaker 1>Maybe a changing appetite from some governments on that front?

0:20:14.080 --> 0:20:17.439
<v Speaker 1>You know, I know the UK is not is different,

0:20:17.440 --> 0:20:21.440
<v Speaker 1>but the PM recent announcements right there about fast tracking

0:20:21.800 --> 0:20:24.719
<v Speaker 1>AI infrastructure similar to some of the policy proposals that

0:20:24.920 --> 0:20:26.040
<v Speaker 1>you're putting forward in the US.

0:20:26.080 --> 0:20:27.240
<v Speaker 4>So what do you think about.

0:20:27.080 --> 0:20:29.760
<v Speaker 1>That balance right in the EU of regulation being tough

0:20:29.760 --> 0:20:30.960
<v Speaker 1>on tech versus innovation.

0:20:31.400 --> 0:20:33.160
<v Speaker 2>Yeah, I mean I would kind of start if I'm

0:20:33.200 --> 0:20:36.119
<v Speaker 2>sitting in the EU or the UK and think about

0:20:36.320 --> 0:20:39.640
<v Speaker 2>productivity and what that means for the growth of my economy. Right,

0:20:39.640 --> 0:20:43.399
<v Speaker 2>we've seen a real divergence between the United States and

0:20:43.520 --> 0:20:46.479
<v Speaker 2>other economies around the world, and it really kind of

0:20:46.680 --> 0:20:49.280
<v Speaker 2>hurts my soul in some ways to see the growth

0:20:49.359 --> 0:20:52.720
<v Speaker 2>rates the UK is as particularly being quite a low

0:20:52.760 --> 0:20:57.400
<v Speaker 2>growth rate relatively speaking. And I think technology is certainly

0:20:57.880 --> 0:21:02.520
<v Speaker 2>a major accelerant fact to hire GDP growth. Better quality

0:21:02.560 --> 0:21:05.320
<v Speaker 2>of life for citizens. We just did a survey that

0:21:05.400 --> 0:21:07.920
<v Speaker 2>said what do people want when they think about AI,

0:21:08.240 --> 0:21:10.200
<v Speaker 2>and it was really three things. Number One, they want

0:21:10.200 --> 0:21:13.080
<v Speaker 2>a better quality of life. Generally, they want to make

0:21:13.119 --> 0:21:16.760
<v Speaker 2>sure that they're getting jobs, that they're getting a lift

0:21:16.840 --> 0:21:19.520
<v Speaker 2>from the income that they take home every day. Second

0:21:19.520 --> 0:21:22.160
<v Speaker 2>thing they want is better healthcare. And the third thing

0:21:22.280 --> 0:21:24.440
<v Speaker 2>that they're looking for is just save me time, help

0:21:24.480 --> 0:21:26.640
<v Speaker 2>me be more efficient, help me just have that life

0:21:26.640 --> 0:21:28.199
<v Speaker 2>where I have time to spend with my kids or

0:21:28.240 --> 0:21:30.080
<v Speaker 2>have time to spend on the hobby that matters to me.

0:21:30.560 --> 0:21:33.760
<v Speaker 2>And so we think that from a government perspective, you

0:21:33.880 --> 0:21:36.600
<v Speaker 2>really want to be mindful of finding that balance between

0:21:36.840 --> 0:21:40.760
<v Speaker 2>overregulating but then perhaps not allowing technology to bear the

0:21:40.760 --> 0:21:42.960
<v Speaker 2>fruit that I think it's capable of. I think the

0:21:43.040 --> 0:21:45.159
<v Speaker 2>US has found a really nice path on that. The

0:21:45.200 --> 0:21:49.119
<v Speaker 2>blueprint that you're referencing has a whole conversation about the

0:21:49.160 --> 0:21:53.440
<v Speaker 2>advent of cars into society. Actually originally innovated in the UK,

0:21:54.480 --> 0:21:57.320
<v Speaker 2>but at the time we used people walking in front

0:21:57.320 --> 0:22:00.800
<v Speaker 2>of cars using red flags to remind estrians there was

0:22:00.840 --> 0:22:03.320
<v Speaker 2>this car coming. How could that be right? People don't

0:22:03.320 --> 0:22:05.760
<v Speaker 2>walk that fast. It's because we also regulated the speed

0:22:05.760 --> 0:22:07.880
<v Speaker 2>of the car I think, down to something like three

0:22:07.880 --> 0:22:10.919
<v Speaker 2>miles an hour. And so hence US took up that

0:22:10.960 --> 0:22:15.600
<v Speaker 2>mental created an incredible automobile industry and really took the

0:22:15.600 --> 0:22:18.399
<v Speaker 2>baton at a moment when maybe the UK should have

0:22:18.440 --> 0:22:21.000
<v Speaker 2>thrived on that innovation in particular. So it is but

0:22:21.119 --> 0:22:24.320
<v Speaker 2>one example, and one example is not everything, but I

0:22:24.359 --> 0:22:27.240
<v Speaker 2>think it's a good metaphor for thinking about how do

0:22:27.359 --> 0:22:31.320
<v Speaker 2>you balance leaning into this new technology while doing it

0:22:31.359 --> 0:22:34.080
<v Speaker 2>in a way that feels safe and for the good

0:22:34.160 --> 0:22:35.040
<v Speaker 2>of your citizens.

0:22:35.720 --> 0:22:38.000
<v Speaker 1>I want to wrap up with a few more personal questions.

0:22:38.000 --> 0:22:40.800
<v Speaker 1>So you are, you know, one of the top ranking

0:22:41.000 --> 0:22:44.040
<v Speaker 1>female executives in tech right now?

0:22:44.119 --> 0:22:45.639
<v Speaker 4>There is a lot.

0:22:45.520 --> 0:22:49.800
<v Speaker 1>Of discussion about DEI in the tech field about how, you.

0:22:49.760 --> 0:22:52.800
<v Speaker 4>Know, how do women succeed in the tech industry. What's

0:22:52.800 --> 0:22:53.720
<v Speaker 4>the best way to do that?

0:22:53.960 --> 0:22:56.119
<v Speaker 1>How have you been able to navigate this and what

0:22:56.160 --> 0:22:58.040
<v Speaker 1>are your thoughts on the topic.

0:22:58.359 --> 0:23:02.040
<v Speaker 2>It's something very near and dear my heart. I started

0:23:02.040 --> 0:23:04.840
<v Speaker 2>my career as an engineer. In fact, my very first

0:23:04.960 --> 0:23:08.119
<v Speaker 2>job out of college or my first internship was working

0:23:08.160 --> 0:23:10.199
<v Speaker 2>on a gold mine in Ghana. Believe it or not,

0:23:11.080 --> 0:23:12.879
<v Speaker 2>I wrote my masters on how.

0:23:12.760 --> 0:23:14.480
<v Speaker 3>To extract gold out of sulfid doors.

0:23:14.480 --> 0:23:18.199
<v Speaker 2>If anyone wants to have a conversation later, and I

0:23:18.359 --> 0:23:21.480
<v Speaker 2>say that because I did that, and I came back

0:23:21.520 --> 0:23:24.160
<v Speaker 2>to the UK and thought that is not a job

0:23:24.240 --> 0:23:27.879
<v Speaker 2>I can be successful in because there were no other women.

0:23:28.320 --> 0:23:31.280
<v Speaker 2>And so I strongly grab hold of that statement about

0:23:31.320 --> 0:23:34.119
<v Speaker 2>it's very hard to be what you can't see. And

0:23:34.160 --> 0:23:36.840
<v Speaker 2>so I think in someone sitting and has the privilege

0:23:36.840 --> 0:23:39.280
<v Speaker 2>today of sitting in my seat, first of all, it's

0:23:39.440 --> 0:23:44.200
<v Speaker 2>showing that women can be and so hopefully that generation

0:23:44.320 --> 0:23:46.680
<v Speaker 2>that's coming up behind can see.

0:23:46.480 --> 0:23:48.000
<v Speaker 3>What they are capable of.

0:23:48.600 --> 0:23:50.800
<v Speaker 2>I think the second thing is how do we lean

0:23:50.840 --> 0:23:53.119
<v Speaker 2>into technology to go back to what we can do

0:23:53.160 --> 0:23:56.520
<v Speaker 2>with AI you really can start to create personal tutors.

0:23:56.560 --> 0:23:59.399
<v Speaker 2>I just saw an amazing piece out of World Bank

0:24:00.240 --> 0:24:01.879
<v Speaker 2>where they did a study in Nigeria.

0:24:02.119 --> 0:24:04.920
<v Speaker 3>For particularly, they looked at the gender dynamic.

0:24:05.320 --> 0:24:09.960
<v Speaker 2>So they created using chat SHEPT effectively a after school

0:24:10.000 --> 0:24:13.159
<v Speaker 2>tutor program, and I think for girls, I think they

0:24:13.240 --> 0:24:15.560
<v Speaker 2>zeroed in on the girls in particular. They saw almost

0:24:15.600 --> 0:24:18.960
<v Speaker 2>a two year leap in education outcomes in just a

0:24:19.000 --> 0:24:22.600
<v Speaker 2>few months of using the technology, because it really does

0:24:22.840 --> 0:24:25.840
<v Speaker 2>create a personalized moment. Like one of the things that

0:24:25.920 --> 0:24:29.080
<v Speaker 2>hurts my soul a lot is when I hear, particularly

0:24:29.119 --> 0:24:31.400
<v Speaker 2>young girls say I'm not good at math.

0:24:31.640 --> 0:24:33.280
<v Speaker 3>Which is just not true.

0:24:33.720 --> 0:24:37.280
<v Speaker 2>They would never say I'm not good at speaking German

0:24:37.840 --> 0:24:41.879
<v Speaker 2>because you don't know yet. You are just starting your life.

0:24:41.920 --> 0:24:44.600
<v Speaker 2>You do not know, but it may not be being

0:24:44.680 --> 0:24:47.960
<v Speaker 2>taught to you in a way that resonates. Computer science

0:24:48.119 --> 0:24:49.919
<v Speaker 2>is often taught first through gaming.

0:24:50.520 --> 0:24:51.480
<v Speaker 3>My daughter, when she.

0:24:51.480 --> 0:24:54.359
<v Speaker 2>First took her for CS classes, said Mom, I just

0:24:54.359 --> 0:24:56.200
<v Speaker 2>don't even like this. I don't want to build games.

0:24:56.560 --> 0:24:59.600
<v Speaker 2>Once she recognized that she could use it to create websites,

0:24:59.680 --> 0:25:03.680
<v Speaker 2>she could pull her artistic side, and she was fascinated

0:25:03.800 --> 0:25:06.200
<v Speaker 2>drawn to it. Today, you know, I'm really proud of

0:25:06.280 --> 0:25:09.240
<v Speaker 2>the fact that she is a hard scientist, taking chemistry

0:25:09.640 --> 0:25:13.359
<v Speaker 2>in university and doing computer science so I think a

0:25:13.400 --> 0:25:15.440
<v Speaker 2>lot of it comes down to how do we think

0:25:15.480 --> 0:25:19.000
<v Speaker 2>about approaching people where they are? And then in the workforce,

0:25:19.080 --> 0:25:22.800
<v Speaker 2>how do I turn around at the table, reach behind myself,

0:25:23.040 --> 0:25:26.680
<v Speaker 2>pull people up of all different types of diversity, not

0:25:26.760 --> 0:25:29.280
<v Speaker 2>just women, because I think in the end, the more

0:25:29.359 --> 0:25:32.400
<v Speaker 2>diversity we have around the table, the better the products

0:25:32.400 --> 0:25:35.000
<v Speaker 2>that we build, the better the outcomes we have for

0:25:35.119 --> 0:25:38.199
<v Speaker 2>society at large. And you cannot do that when you

0:25:38.280 --> 0:25:42.119
<v Speaker 2>have a homogeneous point of view sitting in the room.

0:25:44.359 --> 0:25:47.280
<v Speaker 1>This is a question that I got from others, so

0:25:47.320 --> 0:25:48.480
<v Speaker 1>I know it's on people's mind.

0:25:48.520 --> 0:25:50.480
<v Speaker 4>But what is Sam Altman like as a boss?

0:25:53.240 --> 0:25:57.920
<v Speaker 2>Let's see, where's the one when I need it? Sam

0:25:58.040 --> 0:26:00.720
<v Speaker 2>is a delight. He's super fun to work with because

0:26:00.720 --> 0:26:03.479
<v Speaker 2>his brain runs at one thousand miles an hour, and

0:26:03.520 --> 0:26:06.159
<v Speaker 2>so I kind of as someone who's really hyper curious.

0:26:06.240 --> 0:26:08.399
<v Speaker 2>I love that because it keeps me on my toes.

0:26:10.359 --> 0:26:14.320
<v Speaker 2>He has a good heart, and I think that's really important.

0:26:16.760 --> 0:26:19.760
<v Speaker 2>But he's always pushing and in fact, I had a

0:26:19.840 --> 0:26:22.120
<v Speaker 2>conversation with him recently you talk about you know when

0:26:22.119 --> 0:26:26.080
<v Speaker 2>you're having that personal moment where I said, Sam, hey,

0:26:26.240 --> 0:26:30.439
<v Speaker 2>just something to know about me that when you're giving

0:26:30.440 --> 0:26:33.120
<v Speaker 2>me feedback. If it's always on the thing that could

0:26:33.160 --> 0:26:35.439
<v Speaker 2>be a little bit better, that is good, bring it on,

0:26:36.200 --> 0:26:39.639
<v Speaker 2>but sometimes it's really helpful to kind of also remind

0:26:39.680 --> 0:26:42.640
<v Speaker 2>me what went really well, like I will respond better

0:26:42.680 --> 0:26:42.919
<v Speaker 2>to you.

0:26:43.560 --> 0:26:47.399
<v Speaker 3>And actually he was like, that is really helpful.

0:26:47.000 --> 0:26:49.160
<v Speaker 2>Because I'm someone who likes tough feedback all the time.

0:26:49.200 --> 0:26:50.959
<v Speaker 2>But I'm really going to use that and think about that.

0:26:51.000 --> 0:26:54.240
<v Speaker 2>And I've seen him shift how we interact and so

0:26:54.359 --> 0:26:58.040
<v Speaker 2>again it's like, this is why diversity is important, because

0:26:58.080 --> 0:26:59.440
<v Speaker 2>we need to be able to talk to each other

0:26:59.480 --> 0:27:01.640
<v Speaker 2>about differ from wise that we respond and I hope

0:27:01.640 --> 0:27:03.840
<v Speaker 2>actually that helps him thing about others that.

0:27:03.800 --> 0:27:04.800
<v Speaker 3>He's surrounding himself with.

0:27:05.000 --> 0:27:08.560
<v Speaker 2>So yeah, overall, it's been a wild and incredible and

0:27:08.600 --> 0:27:13.320
<v Speaker 2>inspirational first kind of run at the company. It's an

0:27:13.320 --> 0:27:14.600
<v Speaker 2>incredible group of people.

0:27:15.040 --> 0:27:16.360
<v Speaker 3>You are literally.

0:27:16.000 --> 0:27:18.760
<v Speaker 2>Working with the best in the world at what they do,

0:27:20.160 --> 0:27:23.320
<v Speaker 2>and you really have to work with very different personalities and.

0:27:23.400 --> 0:27:23.960
<v Speaker 3>I love it.

0:27:24.960 --> 0:27:26.600
<v Speaker 4>I would call that reinforcement learning.

0:27:26.680 --> 0:27:28.600
<v Speaker 3>It is reinforcement.

0:27:27.920 --> 0:27:33.760
<v Speaker 1>Learning talking IPOs and general intelligence that conversation OpenAI CFO.

0:27:33.800 --> 0:27:37.080
<v Speaker 4>Of course, Sarah Free are talking to Bloomberg at Bloomberg

0:27:37.160 --> 0:27:39.080
<v Speaker 4>House in DeVos