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Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Let's say you lived

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Speaker 1: Bloomberg House and dovels right now were Bloomberg's Sharon Gafari

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Speaker 1: is in conversation with Sarah Fryar, the CFO of Opening.

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Speaker 2: Swearing, and first of all, thank you Bloomberg for having

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Speaker 2: us today. It's been amazing to spend my first twenty

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Speaker 2: four hours and Davus and to see that AI is

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Speaker 2: really top of the agenda and I think appropriately. So

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Speaker 2: you're right, there's been a lot of hype. Sam did

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Speaker 2: tell everyone to chill, so we'll message just.

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Speaker 3: Chill a little, and he's right.

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Speaker 2: We do internally feel that we have a path towards AGI.

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Speaker 2: We had a big breakthrough in twenty twenty four around

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Speaker 2: reasoning models in particular, so you saw us move from

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Speaker 2: a world of more chat models, chatbots, Chat schept fastest

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Speaker 2: app in the world to one hundred million users and

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Speaker 2: today well over three hundred million users. But last year

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Speaker 2: we really started to see the progression into reasoning and

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Speaker 2: what we call our OH series of models. So OH

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Speaker 2: one preview was launched and now we're all the way

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Speaker 2: to one. Hopefully you've all tried it on what of

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Speaker 2: those reasoning models do, they really start to help you

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Speaker 2: think about difficult problems.

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Speaker 3: So you see the model.

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Speaker 2: One of my favorite things to do is actually watch

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Speaker 2: what it's doing in real time. You see it go

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Speaker 2: down chains of thought trying to answer the prompt that

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Speaker 2: you've put in. You see it sometimes hit a cul

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Speaker 2: de sac, it doesn't quite get to the answer, and

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Speaker 2: so it almost has to reverse course and try a

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Speaker 2: different approach to solving that problem.

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Speaker 3: A lot like the way.

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Speaker 2: We all do to ultimately give you the best answer,

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Speaker 2: And frankly, I think often the answer is not a definitive,

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Speaker 2: singular answer. It's an exploration, it's a way to continue

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Speaker 2: a conversation.

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Speaker 3: We are upping the pace.

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Speaker 2: So we just went from OH one to we previewed

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Speaker 2: OH three, just the second model. We like to confuse

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Speaker 2: sometimes with our nomenclature, but OH three that just took

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Speaker 2: three months. And if you think about the length of

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Speaker 2: time from chat GPT three to chat GPT four, that

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Speaker 2: took about two years. So the pace of innovation is

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Speaker 2: really accelerating.

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Speaker 3: What's it like to work in a place like that.

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Speaker 2: It's exhilarating, it's inspiring, it's sometimes exhausting and it's definitely

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Speaker 2: done with a sense of privilege. I think all of

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Speaker 2: us at open AI think a lot about the magnitude

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Speaker 2: of what we're bringing into the world. We think about

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Speaker 2: it with regard to safety, with alignment, but also with

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Speaker 2: the optimism of what it can do. When you can

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Speaker 2: put that sort of human intelligence into the hands of

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Speaker 2: kids in schools, when you put it into the hands

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Speaker 2: of teachers, when you put it into the hands of

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Speaker 2: our colleagues at work. And we're just getting started.

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Speaker 3: Absolutely.

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Speaker 4: So you mentioned reasoning.

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Speaker 1: Can you tell us a little bit about how this

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Speaker 1: kind of really advanced reasoning that Open a Eye is

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Speaker 1: coming out with. How does that translate into real customer

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Speaker 1: value for you? How are you seeing Opening Eyes users

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Speaker 1: actually gain productivity, gain insights from that.

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Speaker 2: Yeah, so reasoning is on the path to agents, and

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Speaker 2: we really feel that we're now moving into this area

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Speaker 2: era of agentic technology and into a world of agents.

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Speaker 2: So what we see today with the reasoning models is

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Speaker 2: they are for some of the most difficult problems. We

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Speaker 2: see it used in areas like pharma, for example, deep

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Speaker 2: research into new molecules that help us think about drugs

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Speaker 2: and drug discovery. Those have been some of the areas

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Speaker 2: that I have been most excited about the progress so

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Speaker 2: far and how people are taking the O series and

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Speaker 2: using it. But as I talked about with agents, and

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Speaker 2: I'm sure you're probably going to ask you.

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Speaker 1: Yes, and let's find how do you because everyone has

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Speaker 1: a different sort of idea, But what's your take on

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Speaker 1: what an agent is?

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Speaker 4: And you know how Sam said agents are going to

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Speaker 4: going to be acting as employees.

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Speaker 1: Right in twenty twenty five, Y're actually, so, where do

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Speaker 1: you think we'll actually see agents start to do the

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Speaker 1: work of real people?

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Speaker 4: First? What sector is do you think it's going to hit?

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Speaker 2: So it's important with agents, first of all to think

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Speaker 2: about why is it even possible?

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Speaker 3: And so that's why I'm.

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Speaker 2: Made the point about with reasoning models that kind of

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Speaker 2: cul de sac, that a reasoning model goes down and

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Speaker 2: then has to back up and try a different way

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Speaker 2: to solve because in an old world of deterministic software

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Speaker 2: effactively the developer or the architect had to in some

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Speaker 2: ways envision all the possible routes that a question or

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Speaker 2: an output could take. Today, with a reasoning model acts

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Speaker 2: much more in the way that we humans think about problems,

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Speaker 2: and so that starts to bring forth this idea of

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Speaker 2: agents within our workplace, maybe even just as task workers

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Speaker 2: alongside us every day. So some of the earlier things

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Speaker 2: that we are looking at is how do we use

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Speaker 2: agents to just solve day to day problems, Like I'm

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Speaker 2: a working mom.

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Speaker 3: Often I'm in that all the women in.

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Speaker 2: The audience just smiled. You're in that moment. You're like,

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Speaker 2: oh no, it's that five o'clock. I have still got

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Speaker 2: multiple hours of work. I've got to get home. There's

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Speaker 2: supposed to be something on the table to feed my children.

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Speaker 2: And right now my husband is really useful, but maybe

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Speaker 2: not all the times.

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Speaker 3: The most useful. What am I going to do?

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Speaker 2: And this is a moment when you think about an

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Speaker 2: agent that can perhaps figure out what am I going

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Speaker 2: to get delivered to the home. I don't want to

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Speaker 2: have to be specific. It's like, please get me something healthy.

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Speaker 2: We had pizza last night, not Italian. Let's not do

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Speaker 2: too many carbs again. Might know my budget, might know

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Speaker 2: what local restaurants I typically have ordered from, might come

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Speaker 2: up with a new idea. So that would be an

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Speaker 2: agent more in my personal life. But you are also

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Speaker 2: starting to see this dawn of agents in our workforce,

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Speaker 2: and so it may be areas like software development, it

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Speaker 2: could be areas like research within labs deep science. We

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Speaker 2: see many folks here at Davos, you know, heralding that

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Speaker 2: age of agents in areas like customer relationship management for example.

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Speaker 2: So I think that you're going to see a lot

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Speaker 2: of companies talking about new agents coming to the forefront.

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Speaker 2: Customer success is another one. But you've got to remember

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Speaker 2: what that is built on top of. And it's really

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Speaker 2: built on top of these reasoning models that we open

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Speaker 2: AI have brought into the world as the frontier technology

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Speaker 2: and the frontier company.

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Speaker 1: Great, let's talk a little bit about you your bread

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Speaker 1: and butter, which is a world of finance, Right, how

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Speaker 1: do you top a record setting VC funding round?

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Speaker 4: What is next? What do you expect this year.

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Speaker 1: In terms of and obviously AI costs a lot, right,

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Speaker 1: the cost of computing increases year every year, So what

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Speaker 1: can we expect?

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Speaker 4: Will there be another mega around in twenty twenty five.

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Speaker 2: Our technology is built on three things. Great people who

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Speaker 2: are building the most frontier algorithms and models in the world.

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Speaker 2: It's built a top of tremendous amount of compute, and

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Speaker 2: I think we're just scratching the surface on that. And

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Speaker 2: then of course data and in the world of compute,

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Speaker 2: in order to buy that compute, to have access to it,

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Speaker 2: to control our own destiny, we've had to do an

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Speaker 2: extraordinary amount of fundraising. Luckily, we also have a business

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Speaker 2: model that supports it. I already talked about chat chept

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Speaker 2: for consumer over three hundred million users today.

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Speaker 3: It is and it's a workhourse.

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Speaker 2: When it comes to revenue, revenue growth and ultimately profitability.

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Speaker 2: But we are seeing now enterprises of every size embrace

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Speaker 2: this technology, and we really see ourselves also as the

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Speaker 2: enterprise company. In fact, there's a really good symbiosis between

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Speaker 2: those two areas because often when I meet customers, and

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Speaker 2: I'm meeting a lot of them here in Davas, it's

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Speaker 2: actually their personal experience they start with. When I say, hey,

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Speaker 2: how are you using chatchpt, they'll actually give me a

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Speaker 2: personal anecdote first. And for those of you who sell

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Speaker 2: into enterprises, you know that if you've won your customer

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Speaker 2: heart in just their day to day, being able to

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Speaker 2: go in and then sell them into their enterprise environment

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Speaker 2: gets an awful lot easier. And so that enterprise model

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Speaker 2: is really building across every sector of the economy, every

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Speaker 2: type of company, every scale of company, and I'd be

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Speaker 2: happy to talk through examples. Morgan's Stanley is a great example,

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Speaker 2: since we're talking financing, and they're also a good partner

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Speaker 2: in that front. But they're using our technology and areas

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Speaker 2: like wealth management, in areas like even their investment banking,

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Speaker 2: and they've been doing it now for multiple years. So

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Speaker 2: in terms of the future from a financing perspective, I

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Speaker 2: suspect we will continue to have to finance at pace,

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Speaker 2: but we will do that on the merits of our

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Speaker 2: business as well.

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Speaker 1: How about an IPO, what would that look like for

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Speaker 1: open Ai? You mentioned profitability? You know, how far do

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Speaker 1: you think we are from that? What are you know?

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Speaker 1: You're someone who's taken two companies public?

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Speaker 3: What would the.

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Speaker 4: Steps be for open ai to get there? And how

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Speaker 4: far are we from profitability? Yeah?

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Speaker 2: So, as I've told every company I've been associated with

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Speaker 2: on an IPO, it's a it's not a destination. An

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Speaker 2: IPO is just a marker on a journey. And if

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Speaker 2: you get wrapped around the idea that the IPO is

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Speaker 2: the destination is a very kind of dangerous world. Live

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Speaker 2: in because there's a feeling of finality and manland going

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Speaker 2: public is just the beginning of another very interesting part

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Speaker 2: of your journey. Like what are the positives in IPO

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Speaker 2: Number one? I think it's a very strong credentializing moment

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Speaker 2: for any company. I love that moment of sunlight, of

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Speaker 2: being able to show your financials externally, to show how

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Speaker 2: your business is building. It's the best disinfectant in any room.

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Speaker 2: It's a great way to fundraise because it opens the

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Speaker 2: door not just to equity and selling your equity in

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Speaker 2: that moment, but it also starts opening the door to

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Speaker 2: many more areas of financing, starting with mezzanine debt, structured debt,

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Speaker 2: and so.

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Speaker 3: On and again. In a world where we're buying a

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Speaker 3: lot of compute.

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Speaker 2: We need to get there fast because equity is an

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Speaker 2: expensive way to raise capital and to deploy capital. We

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Speaker 2: need to make sure we continue to bring down that

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Speaker 2: weighted average cost of capital. And then the third thing

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Speaker 2: that I like about an IPO I say that somewhat

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Speaker 2: tongue in cheek, is it does add an element of

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Speaker 2: rigor and discipline to the company's cadence. That is a

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Speaker 2: very good thing. The reason why I kind of caveat

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Speaker 2: at the statement.

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Speaker 3: Is you need to be very careful.

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Speaker 2: It doesn't make you short term in how you think

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Speaker 2: about your company. It's very easy to get wrapped around

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Speaker 2: ninety day cycles. But for folks who operate companies do

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Speaker 2: not work on ninety day cycles, particularly companies in our

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Speaker 2: sector right now that are having to take a very

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Speaker 2: long term perspective. Right the O series of models, the

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Speaker 2: work for that was really done probably two years previous,

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Speaker 2: when we kind of put shovels in the ground, built

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Speaker 2: data centers, built up infrastructure to do it. And so

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Speaker 2: this means that during an IPO process, it's incredibly important

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Speaker 2: to bring along the right type of investor, the investor

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Speaker 2: that understands the longevity that will be required to build

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Speaker 2: the business, probably a little bit more like biotech investors

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Speaker 2: in some way. So it's always a potential uh station

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Speaker 2: on the journey that we're on, but I don't want

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Speaker 2: to make it the destination, all right.

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Speaker 4: So we talked about funding, mentioned the recent round.

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Speaker 1: Which was really the first major accomplishment right under your

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Speaker 1: tenure a CFO and open AI. Now there's another big

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Speaker 1: task at hand, which is the restructure of open Ai.

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Speaker 4: From a nonprofit to a for profit.

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Speaker 1: Now Opening Eye plans to make it a public benefit

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Speaker 1: for profit corporation, but.

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Speaker 4: Nonetheless a for profit.

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Speaker 1: Why is this an important change for the company, Why

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Speaker 1: do you need this, and particularly why is it important

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Speaker 1: to investors?

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Speaker 3: Yeah?

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Speaker 2: So, first and foremost, as we think through the restructure,

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Speaker 2: the most important thing for those of us at open

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Speaker 2: AI focused on it is to also make sure that

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Speaker 2: the nonprofit is front and center. Our mission is everything

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Speaker 2: to create AGI that benefits humanity, that really benefits everyone

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Speaker 2: people in.

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Speaker 3: The world, and that is.

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Speaker 2: What the board. Remember today, the board is the board

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Speaker 2: of the nonprofit. That is what they are most focused on.

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Speaker 2: In regards to shifting over to being a PBC. The

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Speaker 2: reason that we are thinking through that is the right

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Speaker 2: corporate structure.

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Speaker 3: It's not a done deal.

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Speaker 2: But a PBC does allow us to balance both being

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Speaker 2: mission focused as well as being shareholder focused economically focused.

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Speaker 2: It's not the be all and end all, because we

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Speaker 2: are proven that we are able to access capital, that

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Speaker 2: we're able to continue to grow the business, and we're

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Speaker 2: able to attract great people. But those are the three

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Speaker 2: tenants that I look at and thinking about why we

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Speaker 2: might want to look more like a traditional company because

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Speaker 2: it is fun to innovate, but it's better to stay

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Speaker 2: innovating on the innovation edge that we want to be on,

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Speaker 2: which is less about our corporate structure and much more

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Speaker 2: about our models and the products that we're building.

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Speaker 1: Right, And so, as you said, it's not a done deal,

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Speaker 1: there are steps to go through. What are some of

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Speaker 1: the challenges of navigating this restructure, especially as some people,

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Speaker 1: including Elon Musk, have criticized the company for the move,

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Speaker 1: for the intended move.

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Speaker 2: So for Elon and Sam as being quite public about

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Speaker 2: this and others like Greg our co founder, that we

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Speaker 2: see Elon as a competitor. We think he's a strong competitor,

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Speaker 2: but we hope that he won't keep resorting to kind

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Speaker 2: of using law and lawfare to compete. I think even

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Speaker 2: in his own words, AI and building AI is a

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Speaker 2: very capital intensive business, and I think even he recognized

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Speaker 2: very early on that it would require us to be

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Speaker 2: much more than a nonprofit. So I actually think he's

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Speaker 2: even said that we don't we shouldn't be a nonprofit to.

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Speaker 3: Be able to raise the capital required.

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Speaker 2: So for right now, you kind of said, is it

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Speaker 2: complicated internally?

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Speaker 3: We try not to distract. We want to make sure.

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Speaker 2: Our researchers are able to do the job they want

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Speaker 2: to do, to be curious, to be serendipitous. We want

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Speaker 2: to make sure those that are taking that research and

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Speaker 2: turning it into product or customer focus first and foremost,

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Speaker 2: but also thinking about innovation things customers may never have

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Speaker 2: thought about. Make sure our go to market folks to

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Speaker 2: do what they do. And so we really try to

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Speaker 2: make sure our focus from a restructured perspective stays in

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Speaker 2: the right context and.

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Speaker 3: At the right scale.

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Speaker 1: So there's a legal challenge from write elend musk and

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Speaker 1: regulatory approvals, but there's also just this changes the you know,

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Speaker 1: the structure for existing investors and you know, kind of

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Speaker 1: reassessing the equity in the company.

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Speaker 4: So what can that what is that.

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Speaker 1: Going to look like with existing investors like Microsoft, and

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Speaker 1: you know what kind of equity what might we expect?

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Speaker 4: I know Altman has said that some of the numbers out.

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Speaker 1: There are maybe you know, not not quite right.

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Speaker 4: But what do you think may happen with equity at

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Speaker 4: the company for him and others?

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Speaker 2: Yeah, so if you look at the folks really involved

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Speaker 2: from a restructured perspective. It's not that many parties at

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Speaker 2: the table. As we said, the nonprofit is actually the

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Speaker 2: key focus for those of us at Open AI and

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Speaker 2: making sure that the nonprofit remains well capitalized, well funded, and.

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Speaker 3: Can really help us live up to this.

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Speaker 2: Very grand inspirational mission that we have for others like Microsoft.

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Speaker 3: A really early partner for.

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Speaker 2: Us, both from a capital perspective for helping us build

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Speaker 2: infrastructure initially to be a partner even in how we

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Speaker 2: think about some of our go to market that is

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Speaker 2: more of a negotiation. For the investors who came in

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Speaker 2: last year. We actually raised that round with them sitting

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Speaker 2: more atop so for them it will be much more simple.

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Speaker 2: It'll just be a conversion of the six point six

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Speaker 2: billion they put in over the one hundred and fifty

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Speaker 2: seven billion post money valuation. That's the percentage of the

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Speaker 2: company that they owned at that moment. It's easy math.

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Speaker 2: It feels much more like a growth equity around.

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Speaker 1: Well, we're probably not going to get to the bottom

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Speaker 1: of EQUI numbers today, so I'm.

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Speaker 3: Going to move on.

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Speaker 1: But I mean we put out there's seven percent was

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Speaker 1: a number that was out there. I think Altman it said, yeah,

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Speaker 1: that's that's not right, any kind of ballpark of what

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Speaker 1: we might expect.

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Speaker 2: Or nothing that we can discuss it in. I'm not

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Speaker 2: trying to shirk the question, but frankly, we don't have

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Speaker 2: a definitive answer, not really able to talk about it.

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Speaker 1: Let's talk about global policy and infrastructure. So just on

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Speaker 1: the policy front, last night we all watch tech leaders,

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Speaker 1: including Zambalman, atten Trump's inauguration. How does the company plan

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Speaker 1: on working with this new administration.

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Speaker 2: So we have released multiple blue papers now about Infrastructure

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Speaker 2: Being Destiny, and what we mean by that is we

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Speaker 2: recognize that this new era era of AI is going

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Speaker 2: to require a lot of both public and private sector

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Speaker 2: relationship building and coming.

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Speaker 3: To bringing it to the world.

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Speaker 2: That's because of the capital requirements, but also how we

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Speaker 2: think about the right regulatory environment. How do we make

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Speaker 2: sure we build this in a way where it benefits humanity.

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Speaker 2: And so with that, our blue paper on Infrastructure Is

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Speaker 2: Destiny really leans into things like building up economic zones,

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Speaker 2: recognizing a little bit with a US lens right now

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Speaker 2: that it is about productivity but also about national security.

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Speaker 2: But I sit in front of you as both an American,

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Speaker 2: a brit and European, so growing up in Northern Ireland

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Speaker 2: turns out had some really good advantages. And so from

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Speaker 2: a US perspective, we see the productivity build already beginning

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Speaker 2: to happen, frankly, and that's just good for people, right,

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Speaker 2: better standard of life, better quality of life, better way

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Speaker 2: to use their time. But we also want to make

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Speaker 2: sure that it's a Western alliance maybe one way to

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Speaker 2: put it, so that everyone is embracing it, because we

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Speaker 2: do have competitors that are going to come after this

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Speaker 2: also from a national level, and I think we need

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Speaker 2: to make sure also from a national security perspective, we're investing.

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Speaker 2: So we've thought about things like economic development zones, how

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Speaker 2: to governments really reach into their populace help them think

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Speaker 2: about reskilling and retooling. We're doing a lot of work

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Speaker 2: in the education sector. We've struck some kind of incredible deals.

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Speaker 2: ASU is a good example of university in the US

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Speaker 2: that has two hundred and eighty thousand seats deployed on

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Speaker 2: chat GPT. My alma mater, Oxford, has also been a

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Speaker 2: really good customer for US, for example, and we love

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Speaker 2: getting down into that level because we see students embracing

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Speaker 2: this technology. So that's been the second piece of the

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Speaker 2: infrastructure's destiny. And then beyond that, how do we think

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Speaker 2: about working in the right regulatory environment so that again

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Speaker 2: we deployed technology safely, but we also recognize that people

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Speaker 2: want to be able to use it and that governments,

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Speaker 2: for the benefit of their citizens, need to make sure

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Speaker 2: they're on the forefront as well.

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Speaker 1: So opener I recently put out an economic blueprint right

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Speaker 1: of their you know, the company's thoughts on what USAI

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Speaker 1: policy should be. In a big message there was that

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Speaker 1: the sort of technological arms race and AI between the

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Speaker 1: US and China, and that the US should invest.

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Speaker 4: And support the build out of infrastructure.

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Speaker 1: Any indication of if there is support for this in

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Speaker 1: the Trump administration and why are we hearing more rhetoric

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Speaker 1: about the US versus China sort of AI competition.

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Speaker 3: Now, I don't think it's rhetoric. I think it's fact.

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Speaker 2: There is absolutely competition going on right now between US

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Speaker 2: and China. China is absolutely investing in this area. They

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Speaker 2: absolutely know how critical it is to their economy, but

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Speaker 2: also from a security perspective, so we should not be

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Speaker 2: naive on that front. We absolutely see already with the

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Speaker 2: Trump administration a real willingness to lean in I think,

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Speaker 2: to be very kind of on the economic front foot

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Speaker 2: and whether that comes from perhaps being more open from

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Speaker 2: a regulatory standpoint, more open from just a business competition standpoint.

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Speaker 3: We're excited to actually get to work.

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Speaker 1: Let's talk about Europe maybe for a second, because you

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Speaker 1: mentioned your European heritage and we are here in Europe.

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Speaker 1: You know, it's no secret that's sort of policymakers in

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Speaker 1: the EU are considered tougher on tech. We have seen

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Speaker 1: AI regulation come out of E you right, whereas in

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Speaker 1: the US we don't have federal legislation really on the matter.

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Speaker 1: Do you think that regulation can threaten the pace of

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Speaker 1: AI innovation?

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Speaker 4: And are we seeing.

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Speaker 1: Maybe a changing appetite from some governments on that front?

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Speaker 1: You know, I know the UK is not is different,

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Speaker 1: but the PM recent announcements right there about fast tracking

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Speaker 1: AI infrastructure similar to some of the policy proposals that

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Speaker 1: you're putting forward in the US.

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Speaker 4: So what do you think about.

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Speaker 1: That balance right in the EU of regulation being tough

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Speaker 1: on tech versus innovation.

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Speaker 2: Yeah, I mean I would kind of start if I'm

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Speaker 2: sitting in the EU or the UK and think about

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Speaker 2: productivity and what that means for the growth of my economy. Right,

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Speaker 2: we've seen a real divergence between the United States and

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Speaker 2: other economies around the world, and it really kind of

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Speaker 2: hurts my soul in some ways to see the growth

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Speaker 2: rates the UK is as particularly being quite a low

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Speaker 2: growth rate relatively speaking. And I think technology is certainly

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Speaker 2: a major accelerant fact to hire GDP growth. Better quality

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Speaker 2: of life for citizens. We just did a survey that

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Speaker 2: said what do people want when they think about AI,

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Speaker 2: and it was really three things. Number One, they want

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Speaker 2: a better quality of life. Generally, they want to make

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Speaker 2: sure that they're getting jobs, that they're getting a lift

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Speaker 2: from the income that they take home every day. Second

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Speaker 2: thing they want is better healthcare. And the third thing

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Speaker 2: that they're looking for is just save me time, help

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Speaker 2: me be more efficient, help me just have that life

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Speaker 2: where I have time to spend with my kids or

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Speaker 2: have time to spend on the hobby that matters to me.

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Speaker 2: And so we think that from a government perspective, you

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Speaker 2: really want to be mindful of finding that balance between

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Speaker 2: overregulating but then perhaps not allowing technology to bear the

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Speaker 2: fruit that I think it's capable of. I think the

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Speaker 2: US has found a really nice path on that. The

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Speaker 2: blueprint that you're referencing has a whole conversation about the

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Speaker 2: advent of cars into society. Actually originally innovated in the UK,

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Speaker 2: but at the time we used people walking in front

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Speaker 2: of cars using red flags to remind estrians there was

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Speaker 2: this car coming. How could that be right? People don't

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Speaker 2: walk that fast. It's because we also regulated the speed

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00:22:05,760 --> 00:22:07,880
Speaker 2: of the car I think, down to something like three

444
00:22:07,880 --> 00:22:10,919
Speaker 2: miles an hour. And so hence US took up that

445
00:22:10,960 --> 00:22:15,600
Speaker 2: mental created an incredible automobile industry and really took the

446
00:22:15,600 --> 00:22:18,399
Speaker 2: baton at a moment when maybe the UK should have

447
00:22:18,440 --> 00:22:21,000
Speaker 2: thrived on that innovation in particular. So it is but

448
00:22:21,119 --> 00:22:24,320
Speaker 2: one example, and one example is not everything, but I

449
00:22:24,359 --> 00:22:27,240
Speaker 2: think it's a good metaphor for thinking about how do

450
00:22:27,359 --> 00:22:31,320
Speaker 2: you balance leaning into this new technology while doing it

451
00:22:31,359 --> 00:22:34,080
Speaker 2: in a way that feels safe and for the good

452
00:22:34,160 --> 00:22:35,040
Speaker 2: of your citizens.

453
00:22:35,720 --> 00:22:38,000
Speaker 1: I want to wrap up with a few more personal questions.

454
00:22:38,000 --> 00:22:40,800
Speaker 1: So you are, you know, one of the top ranking

455
00:22:41,000 --> 00:22:44,040
Speaker 1: female executives in tech right now?

456
00:22:44,119 --> 00:22:45,639
Speaker 4: There is a lot.

457
00:22:45,520 --> 00:22:49,800
Speaker 1: Of discussion about DEI in the tech field about how, you.

458
00:22:49,760 --> 00:22:52,800
Speaker 4: Know, how do women succeed in the tech industry. What's

459
00:22:52,800 --> 00:22:53,720
Speaker 4: the best way to do that?

460
00:22:53,960 --> 00:22:56,119
Speaker 1: How have you been able to navigate this and what

461
00:22:56,160 --> 00:22:58,040
Speaker 1: are your thoughts on the topic.

462
00:22:58,359 --> 00:23:02,040
Speaker 2: It's something very near and dear my heart. I started

463
00:23:02,040 --> 00:23:04,840
Speaker 2: my career as an engineer. In fact, my very first

464
00:23:04,960 --> 00:23:08,119
Speaker 2: job out of college or my first internship was working

465
00:23:08,160 --> 00:23:10,199
Speaker 2: on a gold mine in Ghana. Believe it or not,

466
00:23:11,080 --> 00:23:12,879
Speaker 2: I wrote my masters on how.

467
00:23:12,760 --> 00:23:14,480
Speaker 3: To extract gold out of sulfid doors.

468
00:23:14,480 --> 00:23:18,199
Speaker 2: If anyone wants to have a conversation later, and I

469
00:23:18,359 --> 00:23:21,480
Speaker 2: say that because I did that, and I came back

470
00:23:21,520 --> 00:23:24,160
Speaker 2: to the UK and thought that is not a job

471
00:23:24,240 --> 00:23:27,879
Speaker 2: I can be successful in because there were no other women.

472
00:23:28,320 --> 00:23:31,280
Speaker 2: And so I strongly grab hold of that statement about

473
00:23:31,320 --> 00:23:34,119
Speaker 2: it's very hard to be what you can't see. And

474
00:23:34,160 --> 00:23:36,840
Speaker 2: so I think in someone sitting and has the privilege

475
00:23:36,840 --> 00:23:39,280
Speaker 2: today of sitting in my seat, first of all, it's

476
00:23:39,440 --> 00:23:44,200
Speaker 2: showing that women can be and so hopefully that generation

477
00:23:44,320 --> 00:23:46,680
Speaker 2: that's coming up behind can see.

478
00:23:46,480 --> 00:23:48,000
Speaker 3: What they are capable of.

479
00:23:48,600 --> 00:23:50,800
Speaker 2: I think the second thing is how do we lean

480
00:23:50,840 --> 00:23:53,119
Speaker 2: into technology to go back to what we can do

481
00:23:53,160 --> 00:23:56,520
Speaker 2: with AI you really can start to create personal tutors.

482
00:23:56,560 --> 00:23:59,399
Speaker 2: I just saw an amazing piece out of World Bank

483
00:24:00,240 --> 00:24:01,879
Speaker 2: where they did a study in Nigeria.

484
00:24:02,119 --> 00:24:04,920
Speaker 3: For particularly, they looked at the gender dynamic.

485
00:24:05,320 --> 00:24:09,960
Speaker 2: So they created using chat SHEPT effectively a after school

486
00:24:10,000 --> 00:24:13,159
Speaker 2: tutor program, and I think for girls, I think they

487
00:24:13,240 --> 00:24:15,560
Speaker 2: zeroed in on the girls in particular. They saw almost

488
00:24:15,600 --> 00:24:18,960
Speaker 2: a two year leap in education outcomes in just a

489
00:24:19,000 --> 00:24:22,600
Speaker 2: few months of using the technology, because it really does

490
00:24:22,840 --> 00:24:25,840
Speaker 2: create a personalized moment. Like one of the things that

491
00:24:25,920 --> 00:24:29,080
Speaker 2: hurts my soul a lot is when I hear, particularly

492
00:24:29,119 --> 00:24:31,400
Speaker 2: young girls say I'm not good at math.

493
00:24:31,640 --> 00:24:33,280
Speaker 3: Which is just not true.

494
00:24:33,720 --> 00:24:37,280
Speaker 2: They would never say I'm not good at speaking German

495
00:24:37,840 --> 00:24:41,879
Speaker 2: because you don't know yet. You are just starting your life.

496
00:24:41,920 --> 00:24:44,600
Speaker 2: You do not know, but it may not be being

497
00:24:44,680 --> 00:24:47,960
Speaker 2: taught to you in a way that resonates. Computer science

498
00:24:48,119 --> 00:24:49,919
Speaker 2: is often taught first through gaming.

499
00:24:50,520 --> 00:24:51,480
Speaker 3: My daughter, when she.

500
00:24:51,480 --> 00:24:54,359
Speaker 2: First took her for CS classes, said Mom, I just

501
00:24:54,359 --> 00:24:56,200
Speaker 2: don't even like this. I don't want to build games.

502
00:24:56,560 --> 00:24:59,600
Speaker 2: Once she recognized that she could use it to create websites,

503
00:24:59,680 --> 00:25:03,680
Speaker 2: she could pull her artistic side, and she was fascinated

504
00:25:03,800 --> 00:25:06,200
Speaker 2: drawn to it. Today, you know, I'm really proud of

505
00:25:06,280 --> 00:25:09,240
Speaker 2: the fact that she is a hard scientist, taking chemistry

506
00:25:09,640 --> 00:25:13,359
Speaker 2: in university and doing computer science so I think a

507
00:25:13,400 --> 00:25:15,440
Speaker 2: lot of it comes down to how do we think

508
00:25:15,480 --> 00:25:19,000
Speaker 2: about approaching people where they are? And then in the workforce,

509
00:25:19,080 --> 00:25:22,800
Speaker 2: how do I turn around at the table, reach behind myself,

510
00:25:23,040 --> 00:25:26,680
Speaker 2: pull people up of all different types of diversity, not

511
00:25:26,760 --> 00:25:29,280
Speaker 2: just women, because I think in the end, the more

512
00:25:29,359 --> 00:25:32,400
Speaker 2: diversity we have around the table, the better the products

513
00:25:32,400 --> 00:25:35,000
Speaker 2: that we build, the better the outcomes we have for

514
00:25:35,119 --> 00:25:38,199
Speaker 2: society at large. And you cannot do that when you

515
00:25:38,280 --> 00:25:42,119
Speaker 2: have a homogeneous point of view sitting in the room.

516
00:25:44,359 --> 00:25:47,280
Speaker 1: This is a question that I got from others, so

517
00:25:47,320 --> 00:25:48,480
Speaker 1: I know it's on people's mind.

518
00:25:48,520 --> 00:25:50,480
Speaker 4: But what is Sam Altman like as a boss?

519
00:25:53,240 --> 00:25:57,920
Speaker 2: Let's see, where's the one when I need it? Sam

520
00:25:58,040 --> 00:26:00,720
Speaker 2: is a delight. He's super fun to work with because

521
00:26:00,720 --> 00:26:03,479
Speaker 2: his brain runs at one thousand miles an hour, and

522
00:26:03,520 --> 00:26:06,159
Speaker 2: so I kind of as someone who's really hyper curious.

523
00:26:06,240 --> 00:26:08,399
Speaker 2: I love that because it keeps me on my toes.

524
00:26:10,359 --> 00:26:14,320
Speaker 2: He has a good heart, and I think that's really important.

525
00:26:16,760 --> 00:26:19,760
Speaker 2: But he's always pushing and in fact, I had a

526
00:26:19,840 --> 00:26:22,120
Speaker 2: conversation with him recently you talk about you know when

527
00:26:22,119 --> 00:26:26,080
Speaker 2: you're having that personal moment where I said, Sam, hey,

528
00:26:26,240 --> 00:26:30,439
Speaker 2: just something to know about me that when you're giving

529
00:26:30,440 --> 00:26:33,120
Speaker 2: me feedback. If it's always on the thing that could

530
00:26:33,160 --> 00:26:35,439
Speaker 2: be a little bit better, that is good, bring it on,

531
00:26:36,200 --> 00:26:39,639
Speaker 2: but sometimes it's really helpful to kind of also remind

532
00:26:39,680 --> 00:26:42,640
Speaker 2: me what went really well, like I will respond better

533
00:26:42,680 --> 00:26:42,919
Speaker 2: to you.

534
00:26:43,560 --> 00:26:47,399
Speaker 3: And actually he was like, that is really helpful.

535
00:26:47,000 --> 00:26:49,160
Speaker 2: Because I'm someone who likes tough feedback all the time.

536
00:26:49,200 --> 00:26:50,959
Speaker 2: But I'm really going to use that and think about that.

537
00:26:51,000 --> 00:26:54,240
Speaker 2: And I've seen him shift how we interact and so

538
00:26:54,359 --> 00:26:58,040
Speaker 2: again it's like, this is why diversity is important, because

539
00:26:58,080 --> 00:26:59,440
Speaker 2: we need to be able to talk to each other

540
00:26:59,480 --> 00:27:01,640
Speaker 2: about differ from wise that we respond and I hope

541
00:27:01,640 --> 00:27:03,840
Speaker 2: actually that helps him thing about others that.

542
00:27:03,800 --> 00:27:04,800
Speaker 3: He's surrounding himself with.

543
00:27:05,000 --> 00:27:08,560
Speaker 2: So yeah, overall, it's been a wild and incredible and

544
00:27:08,600 --> 00:27:13,320
Speaker 2: inspirational first kind of run at the company. It's an

545
00:27:13,320 --> 00:27:14,600
Speaker 2: incredible group of people.

546
00:27:15,040 --> 00:27:16,360
Speaker 3: You are literally.

547
00:27:16,000 --> 00:27:18,760
Speaker 2: Working with the best in the world at what they do,

548
00:27:20,160 --> 00:27:23,320
Speaker 2: and you really have to work with very different personalities and.

549
00:27:23,400 --> 00:27:23,960
Speaker 3: I love it.

550
00:27:24,960 --> 00:27:26,600
Speaker 4: I would call that reinforcement learning.

551
00:27:26,680 --> 00:27:28,600
Speaker 3: It is reinforcement.

552
00:27:27,920 --> 00:27:33,760
Speaker 1: Learning talking IPOs and general intelligence that conversation OpenAI CFO.

553
00:27:33,800 --> 00:27:37,080
Speaker 4: Of course, Sarah Free are talking to Bloomberg at Bloomberg

554
00:27:37,160 --> 00:27:39,080
Speaker 4: House in DeVos