Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Let's say you lived
Speaker 1: Bloomberg House and dovels right now were Bloomberg's Sharon Gafari
Speaker 1: is in conversation with Sarah Fryar, the CFO of Opening.
Speaker 2: Swearing, and first of all, thank you Bloomberg for having
Speaker 2: us today. It's been amazing to spend my first twenty
Speaker 2: four hours and Davus and to see that AI is
Speaker 2: really top of the agenda and I think appropriately. So
Speaker 2: you're right, there's been a lot of hype. Sam did
Speaker 2: tell everyone to chill, so we'll message just.
Speaker 3: Chill a little, and he's right.
Speaker 2: We do internally feel that we have a path towards AGI.
Speaker 2: We had a big breakthrough in twenty twenty four around
Speaker 2: reasoning models in particular, so you saw us move from
Speaker 2: a world of more chat models, chatbots, Chat schept fastest
Speaker 2: app in the world to one hundred million users and
Speaker 2: today well over three hundred million users. But last year
Speaker 2: we really started to see the progression into reasoning and
Speaker 2: what we call our OH series of models. So OH
Speaker 2: one preview was launched and now we're all the way
Speaker 2: to one. Hopefully you've all tried it on what of
Speaker 2: those reasoning models do, they really start to help you
Speaker 2: think about difficult problems.
Speaker 3: So you see the model.
Speaker 2: One of my favorite things to do is actually watch
Speaker 2: what it's doing in real time. You see it go
Speaker 2: down chains of thought trying to answer the prompt that
Speaker 2: you've put in. You see it sometimes hit a cul
Speaker 2: de sac, it doesn't quite get to the answer, and
Speaker 2: so it almost has to reverse course and try a
Speaker 2: different approach to solving that problem.
Speaker 3: A lot like the way.
Speaker 2: We all do to ultimately give you the best answer,
Speaker 2: And frankly, I think often the answer is not a definitive,
Speaker 2: singular answer. It's an exploration, it's a way to continue
Speaker 2: a conversation.
Speaker 3: We are upping the pace.
Speaker 2: So we just went from OH one to we previewed
Speaker 2: OH three, just the second model. We like to confuse
Speaker 2: sometimes with our nomenclature, but OH three that just took
Speaker 2: three months. And if you think about the length of
Speaker 2: time from chat GPT three to chat GPT four, that
Speaker 2: took about two years. So the pace of innovation is
Speaker 2: really accelerating.
Speaker 3: What's it like to work in a place like that.
Speaker 2: It's exhilarating, it's inspiring, it's sometimes exhausting and it's definitely
Speaker 2: done with a sense of privilege. I think all of
Speaker 2: us at open AI think a lot about the magnitude
Speaker 2: of what we're bringing into the world. We think about
Speaker 2: it with regard to safety, with alignment, but also with
Speaker 2: the optimism of what it can do. When you can
Speaker 2: put that sort of human intelligence into the hands of
Speaker 2: kids in schools, when you put it into the hands
Speaker 2: of teachers, when you put it into the hands of
Speaker 2: our colleagues at work. And we're just getting started.
Speaker 3: Absolutely.
Speaker 4: So you mentioned reasoning.
Speaker 1: Can you tell us a little bit about how this
Speaker 1: kind of really advanced reasoning that Open a Eye is
Speaker 1: coming out with. How does that translate into real customer
Speaker 1: value for you? How are you seeing Opening Eyes users
Speaker 1: actually gain productivity, gain insights from that.
Speaker 2: Yeah, so reasoning is on the path to agents, and
Speaker 2: we really feel that we're now moving into this area
Speaker 2: era of agentic technology and into a world of agents.
Speaker 2: So what we see today with the reasoning models is
Speaker 2: they are for some of the most difficult problems. We
Speaker 2: see it used in areas like pharma, for example, deep
Speaker 2: research into new molecules that help us think about drugs
Speaker 2: and drug discovery. Those have been some of the areas
Speaker 2: that I have been most excited about the progress so
Speaker 2: far and how people are taking the O series and
Speaker 2: using it. But as I talked about with agents, and
Speaker 2: I'm sure you're probably going to ask you.
Speaker 1: Yes, and let's find how do you because everyone has
Speaker 1: a different sort of idea, But what's your take on
Speaker 1: what an agent is?
Speaker 4: And you know how Sam said agents are going to
Speaker 4: going to be acting as employees.
Speaker 1: Right in twenty twenty five, Y're actually, so, where do
Speaker 1: you think we'll actually see agents start to do the
Speaker 1: work of real people?
Speaker 4: First? What sector is do you think it's going to hit?
Speaker 2: So it's important with agents, first of all to think
Speaker 2: about why is it even possible?
Speaker 3: And so that's why I'm.
Speaker 2: Made the point about with reasoning models that kind of
Speaker 2: cul de sac, that a reasoning model goes down and
Speaker 2: then has to back up and try a different way
Speaker 2: to solve because in an old world of deterministic software
Speaker 2: effactively the developer or the architect had to in some
Speaker 2: ways envision all the possible routes that a question or
Speaker 2: an output could take. Today, with a reasoning model acts
Speaker 2: much more in the way that we humans think about problems,
Speaker 2: and so that starts to bring forth this idea of
Speaker 2: agents within our workplace, maybe even just as task workers
Speaker 2: alongside us every day. So some of the earlier things
Speaker 2: that we are looking at is how do we use
Speaker 2: agents to just solve day to day problems, Like I'm
Speaker 2: a working mom.
Speaker 3: Often I'm in that all the women in.
Speaker 2: The audience just smiled. You're in that moment. You're like,
Speaker 2: oh no, it's that five o'clock. I have still got
Speaker 2: multiple hours of work. I've got to get home. There's
Speaker 2: supposed to be something on the table to feed my children.
Speaker 2: And right now my husband is really useful, but maybe
Speaker 2: not all the times.
Speaker 3: The most useful. What am I going to do?
Speaker 2: And this is a moment when you think about an
Speaker 2: agent that can perhaps figure out what am I going
Speaker 2: to get delivered to the home. I don't want to
Speaker 2: have to be specific. It's like, please get me something healthy.
Speaker 2: We had pizza last night, not Italian. Let's not do
Speaker 2: too many carbs again. Might know my budget, might know
Speaker 2: what local restaurants I typically have ordered from, might come
Speaker 2: up with a new idea. So that would be an
Speaker 2: agent more in my personal life. But you are also
Speaker 2: starting to see this dawn of agents in our workforce,
Speaker 2: and so it may be areas like software development, it
Speaker 2: could be areas like research within labs deep science. We
Speaker 2: see many folks here at Davos, you know, heralding that
Speaker 2: age of agents in areas like customer relationship management for example.
Speaker 2: So I think that you're going to see a lot
Speaker 2: of companies talking about new agents coming to the forefront.
Speaker 2: Customer success is another one. But you've got to remember
Speaker 2: what that is built on top of. And it's really
Speaker 2: built on top of these reasoning models that we open
Speaker 2: AI have brought into the world as the frontier technology
Speaker 2: and the frontier company.
Speaker 1: Great, let's talk a little bit about you your bread
Speaker 1: and butter, which is a world of finance, Right, how
Speaker 1: do you top a record setting VC funding round?
Speaker 4: What is next? What do you expect this year.
Speaker 1: In terms of and obviously AI costs a lot, right,
Speaker 1: the cost of computing increases year every year, So what
Speaker 1: can we expect?
Speaker 4: Will there be another mega around in twenty twenty five.
Speaker 2: Our technology is built on three things. Great people who
Speaker 2: are building the most frontier algorithms and models in the world.
Speaker 2: It's built a top of tremendous amount of compute, and
Speaker 2: I think we're just scratching the surface on that. And
Speaker 2: then of course data and in the world of compute,
Speaker 2: in order to buy that compute, to have access to it,
Speaker 2: to control our own destiny, we've had to do an
Speaker 2: extraordinary amount of fundraising. Luckily, we also have a business
Speaker 2: model that supports it. I already talked about chat chept
Speaker 2: for consumer over three hundred million users today.
Speaker 3: It is and it's a workhourse.
Speaker 2: When it comes to revenue, revenue growth and ultimately profitability.
Speaker 2: But we are seeing now enterprises of every size embrace
Speaker 2: this technology, and we really see ourselves also as the
Speaker 2: enterprise company. In fact, there's a really good symbiosis between
Speaker 2: those two areas because often when I meet customers, and
Speaker 2: I'm meeting a lot of them here in Davas, it's
Speaker 2: actually their personal experience they start with. When I say, hey,
Speaker 2: how are you using chatchpt, they'll actually give me a
Speaker 2: personal anecdote first. And for those of you who sell
Speaker 2: into enterprises, you know that if you've won your customer
Speaker 2: heart in just their day to day, being able to
Speaker 2: go in and then sell them into their enterprise environment
Speaker 2: gets an awful lot easier. And so that enterprise model
Speaker 2: is really building across every sector of the economy, every
Speaker 2: type of company, every scale of company, and I'd be
Speaker 2: happy to talk through examples. Morgan's Stanley is a great example,
Speaker 2: since we're talking financing, and they're also a good partner
Speaker 2: in that front. But they're using our technology and areas
Speaker 2: like wealth management, in areas like even their investment banking,
Speaker 2: and they've been doing it now for multiple years. So
Speaker 2: in terms of the future from a financing perspective, I
Speaker 2: suspect we will continue to have to finance at pace,
Speaker 2: but we will do that on the merits of our
Speaker 2: business as well.
Speaker 1: How about an IPO, what would that look like for
Speaker 1: open Ai? You mentioned profitability? You know, how far do
Speaker 1: you think we are from that? What are you know?
Speaker 1: You're someone who's taken two companies public?
Speaker 3: What would the.
Speaker 4: Steps be for open ai to get there? And how
Speaker 4: far are we from profitability? Yeah?
Speaker 2: So, as I've told every company I've been associated with
Speaker 2: on an IPO, it's a it's not a destination. An
Speaker 2: IPO is just a marker on a journey. And if
Speaker 2: you get wrapped around the idea that the IPO is
Speaker 2: the destination is a very kind of dangerous world. Live
Speaker 2: in because there's a feeling of finality and manland going
Speaker 2: public is just the beginning of another very interesting part
Speaker 2: of your journey. Like what are the positives in IPO
Speaker 2: Number one? I think it's a very strong credentializing moment
Speaker 2: for any company. I love that moment of sunlight, of
Speaker 2: being able to show your financials externally, to show how
Speaker 2: your business is building. It's the best disinfectant in any room.
Speaker 2: It's a great way to fundraise because it opens the
Speaker 2: door not just to equity and selling your equity in
Speaker 2: that moment, but it also starts opening the door to
Speaker 2: many more areas of financing, starting with mezzanine debt, structured debt,
Speaker 2: and so.
Speaker 3: On and again. In a world where we're buying a
Speaker 3: lot of compute.
Speaker 2: We need to get there fast because equity is an
Speaker 2: expensive way to raise capital and to deploy capital. We
Speaker 2: need to make sure we continue to bring down that
Speaker 2: weighted average cost of capital. And then the third thing
Speaker 2: that I like about an IPO I say that somewhat
Speaker 2: tongue in cheek, is it does add an element of
Speaker 2: rigor and discipline to the company's cadence. That is a
Speaker 2: very good thing. The reason why I kind of caveat
Speaker 2: at the statement.
Speaker 3: Is you need to be very careful.
Speaker 2: It doesn't make you short term in how you think
Speaker 2: about your company. It's very easy to get wrapped around
Speaker 2: ninety day cycles. But for folks who operate companies do
Speaker 2: not work on ninety day cycles, particularly companies in our
Speaker 2: sector right now that are having to take a very
Speaker 2: long term perspective. Right the O series of models, the
Speaker 2: work for that was really done probably two years previous,
Speaker 2: when we kind of put shovels in the ground, built
Speaker 2: data centers, built up infrastructure to do it. And so
Speaker 2: this means that during an IPO process, it's incredibly important
Speaker 2: to bring along the right type of investor, the investor
Speaker 2: that understands the longevity that will be required to build
Speaker 2: the business, probably a little bit more like biotech investors
Speaker 2: in some way. So it's always a potential uh station
Speaker 2: on the journey that we're on, but I don't want
Speaker 2: to make it the destination, all right.
Speaker 4: So we talked about funding, mentioned the recent round.
Speaker 1: Which was really the first major accomplishment right under your
Speaker 1: tenure a CFO and open AI. Now there's another big
Speaker 1: task at hand, which is the restructure of open Ai.
Speaker 4: From a nonprofit to a for profit.
Speaker 1: Now Opening Eye plans to make it a public benefit
Speaker 1: for profit corporation, but.
Speaker 4: Nonetheless a for profit.
Speaker 1: Why is this an important change for the company, Why
Speaker 1: do you need this, and particularly why is it important
Speaker 1: to investors?
Speaker 3: Yeah?
Speaker 2: So, first and foremost, as we think through the restructure,
Speaker 2: the most important thing for those of us at open
Speaker 2: AI focused on it is to also make sure that
Speaker 2: the nonprofit is front and center. Our mission is everything
Speaker 2: to create AGI that benefits humanity, that really benefits everyone
Speaker 2: people in.
Speaker 3: The world, and that is.
Speaker 2: What the board. Remember today, the board is the board
Speaker 2: of the nonprofit. That is what they are most focused on.
Speaker 2: In regards to shifting over to being a PBC. The
Speaker 2: reason that we are thinking through that is the right
Speaker 2: corporate structure.
Speaker 3: It's not a done deal.
Speaker 2: But a PBC does allow us to balance both being
Speaker 2: mission focused as well as being shareholder focused economically focused.
Speaker 2: It's not the be all and end all, because we
Speaker 2: are proven that we are able to access capital, that
Speaker 2: we're able to continue to grow the business, and we're
Speaker 2: able to attract great people. But those are the three
Speaker 2: tenants that I look at and thinking about why we
Speaker 2: might want to look more like a traditional company because
Speaker 2: it is fun to innovate, but it's better to stay
Speaker 2: innovating on the innovation edge that we want to be on,
Speaker 2: which is less about our corporate structure and much more
Speaker 2: about our models and the products that we're building.
Speaker 1: Right, And so, as you said, it's not a done deal,
Speaker 1: there are steps to go through. What are some of
Speaker 1: the challenges of navigating this restructure, especially as some people,
Speaker 1: including Elon Musk, have criticized the company for the move,
Speaker 1: for the intended move.
Speaker 2: So for Elon and Sam as being quite public about
Speaker 2: this and others like Greg our co founder, that we
Speaker 2: see Elon as a competitor. We think he's a strong competitor,
Speaker 2: but we hope that he won't keep resorting to kind
Speaker 2: of using law and lawfare to compete. I think even
Speaker 2: in his own words, AI and building AI is a
Speaker 2: very capital intensive business, and I think even he recognized
Speaker 2: very early on that it would require us to be
Speaker 2: much more than a nonprofit. So I actually think he's
Speaker 2: even said that we don't we shouldn't be a nonprofit to.
Speaker 3: Be able to raise the capital required.
Speaker 2: So for right now, you kind of said, is it
Speaker 2: complicated internally?
Speaker 3: We try not to distract. We want to make sure.
Speaker 2: Our researchers are able to do the job they want
Speaker 2: to do, to be curious, to be serendipitous. We want
Speaker 2: to make sure those that are taking that research and
Speaker 2: turning it into product or customer focus first and foremost,
Speaker 2: but also thinking about innovation things customers may never have
Speaker 2: thought about. Make sure our go to market folks to
Speaker 2: do what they do. And so we really try to
Speaker 2: make sure our focus from a restructured perspective stays in
Speaker 2: the right context and.
Speaker 3: At the right scale.
Speaker 1: So there's a legal challenge from write elend musk and
Speaker 1: regulatory approvals, but there's also just this changes the you know,
Speaker 1: the structure for existing investors and you know, kind of
Speaker 1: reassessing the equity in the company.
Speaker 4: So what can that what is that.
Speaker 1: Going to look like with existing investors like Microsoft, and
Speaker 1: you know what kind of equity what might we expect?
Speaker 4: I know Altman has said that some of the numbers out.
Speaker 1: There are maybe you know, not not quite right.
Speaker 4: But what do you think may happen with equity at
Speaker 4: the company for him and others?
Speaker 2: Yeah, so if you look at the folks really involved
Speaker 2: from a restructured perspective. It's not that many parties at
Speaker 2: the table. As we said, the nonprofit is actually the
Speaker 2: key focus for those of us at Open AI and
Speaker 2: making sure that the nonprofit remains well capitalized, well funded, and.
Speaker 3: Can really help us live up to this.
Speaker 2: Very grand inspirational mission that we have for others like Microsoft.
Speaker 3: A really early partner for.
Speaker 2: Us, both from a capital perspective for helping us build
Speaker 2: infrastructure initially to be a partner even in how we
Speaker 2: think about some of our go to market that is
Speaker 2: more of a negotiation. For the investors who came in
Speaker 2: last year. We actually raised that round with them sitting
Speaker 2: more atop so for them it will be much more simple.
Speaker 2: It'll just be a conversion of the six point six
Speaker 2: billion they put in over the one hundred and fifty
Speaker 2: seven billion post money valuation. That's the percentage of the
Speaker 2: company that they owned at that moment. It's easy math.
Speaker 2: It feels much more like a growth equity around.
Speaker 1: Well, we're probably not going to get to the bottom
Speaker 1: of EQUI numbers today, so I'm.
Speaker 3: Going to move on.
Speaker 1: But I mean we put out there's seven percent was
Speaker 1: a number that was out there. I think Altman it said, yeah,
Speaker 1: that's that's not right, any kind of ballpark of what
Speaker 1: we might expect.
Speaker 2: Or nothing that we can discuss it in. I'm not
Speaker 2: trying to shirk the question, but frankly, we don't have
Speaker 2: a definitive answer, not really able to talk about it.
Speaker 1: Let's talk about global policy and infrastructure. So just on
Speaker 1: the policy front, last night we all watch tech leaders,
Speaker 1: including Zambalman, atten Trump's inauguration. How does the company plan
Speaker 1: on working with this new administration.
Speaker 2: So we have released multiple blue papers now about Infrastructure
Speaker 2: Being Destiny, and what we mean by that is we
Speaker 2: recognize that this new era era of AI is going
Speaker 2: to require a lot of both public and private sector
Speaker 2: relationship building and coming.
Speaker 3: To bringing it to the world.
Speaker 2: That's because of the capital requirements, but also how we
Speaker 2: think about the right regulatory environment. How do we make
Speaker 2: sure we build this in a way where it benefits humanity.
Speaker 2: And so with that, our blue paper on Infrastructure Is
Speaker 2: Destiny really leans into things like building up economic zones,
Speaker 2: recognizing a little bit with a US lens right now
Speaker 2: that it is about productivity but also about national security.
Speaker 2: But I sit in front of you as both an American,
Speaker 2: a brit and European, so growing up in Northern Ireland
Speaker 2: turns out had some really good advantages. And so from
Speaker 2: a US perspective, we see the productivity build already beginning
Speaker 2: to happen, frankly, and that's just good for people, right,
Speaker 2: better standard of life, better quality of life, better way
Speaker 2: to use their time. But we also want to make
Speaker 2: sure that it's a Western alliance maybe one way to
Speaker 2: put it, so that everyone is embracing it, because we
Speaker 2: do have competitors that are going to come after this
Speaker 2: also from a national level, and I think we need
Speaker 2: to make sure also from a national security perspective, we're investing.
Speaker 2: So we've thought about things like economic development zones, how
Speaker 2: to governments really reach into their populace help them think
Speaker 2: about reskilling and retooling. We're doing a lot of work
Speaker 2: in the education sector. We've struck some kind of incredible deals.
Speaker 2: ASU is a good example of university in the US
Speaker 2: that has two hundred and eighty thousand seats deployed on
Speaker 2: chat GPT. My alma mater, Oxford, has also been a
Speaker 2: really good customer for US, for example, and we love
Speaker 2: getting down into that level because we see students embracing
Speaker 2: this technology. So that's been the second piece of the
Speaker 2: infrastructure's destiny. And then beyond that, how do we think
Speaker 2: about working in the right regulatory environment so that again
Speaker 2: we deployed technology safely, but we also recognize that people
Speaker 2: want to be able to use it and that governments,
Speaker 2: for the benefit of their citizens, need to make sure
Speaker 2: they're on the forefront as well.
Speaker 1: So opener I recently put out an economic blueprint right
Speaker 1: of their you know, the company's thoughts on what USAI
Speaker 1: policy should be. In a big message there was that
Speaker 1: the sort of technological arms race and AI between the
Speaker 1: US and China, and that the US should invest.
Speaker 4: And support the build out of infrastructure.
Speaker 1: Any indication of if there is support for this in
Speaker 1: the Trump administration and why are we hearing more rhetoric
Speaker 1: about the US versus China sort of AI competition.
Speaker 3: Now, I don't think it's rhetoric. I think it's fact.
Speaker 2: There is absolutely competition going on right now between US
Speaker 2: and China. China is absolutely investing in this area. They
Speaker 2: absolutely know how critical it is to their economy, but
Speaker 2: also from a security perspective, so we should not be
Speaker 2: naive on that front. We absolutely see already with the
Speaker 2: Trump administration a real willingness to lean in I think,
Speaker 2: to be very kind of on the economic front foot
Speaker 2: and whether that comes from perhaps being more open from
Speaker 2: a regulatory standpoint, more open from just a business competition standpoint.
Speaker 3: We're excited to actually get to work.
Speaker 1: Let's talk about Europe maybe for a second, because you
Speaker 1: mentioned your European heritage and we are here in Europe.
Speaker 1: You know, it's no secret that's sort of policymakers in
Speaker 1: the EU are considered tougher on tech. We have seen
Speaker 1: AI regulation come out of E you right, whereas in
Speaker 1: the US we don't have federal legislation really on the matter.
Speaker 1: Do you think that regulation can threaten the pace of
Speaker 1: AI innovation?
Speaker 4: And are we seeing.
Speaker 1: Maybe a changing appetite from some governments on that front?
Speaker 1: You know, I know the UK is not is different,
Speaker 1: but the PM recent announcements right there about fast tracking
Speaker 1: AI infrastructure similar to some of the policy proposals that
Speaker 1: you're putting forward in the US.
Speaker 4: So what do you think about.
Speaker 1: That balance right in the EU of regulation being tough
Speaker 1: on tech versus innovation.
Speaker 2: Yeah, I mean I would kind of start if I'm
Speaker 2: sitting in the EU or the UK and think about
Speaker 2: productivity and what that means for the growth of my economy. Right,
Speaker 2: we've seen a real divergence between the United States and
Speaker 2: other economies around the world, and it really kind of
Speaker 2: hurts my soul in some ways to see the growth
Speaker 2: rates the UK is as particularly being quite a low
Speaker 2: growth rate relatively speaking. And I think technology is certainly
Speaker 2: a major accelerant fact to hire GDP growth. Better quality
Speaker 2: of life for citizens. We just did a survey that
Speaker 2: said what do people want when they think about AI,
Speaker 2: and it was really three things. Number One, they want
Speaker 2: a better quality of life. Generally, they want to make
Speaker 2: sure that they're getting jobs, that they're getting a lift
Speaker 2: from the income that they take home every day. Second
Speaker 2: thing they want is better healthcare. And the third thing
Speaker 2: that they're looking for is just save me time, help
Speaker 2: me be more efficient, help me just have that life
Speaker 2: where I have time to spend with my kids or
Speaker 2: have time to spend on the hobby that matters to me.
Speaker 2: And so we think that from a government perspective, you
Speaker 2: really want to be mindful of finding that balance between
Speaker 2: overregulating but then perhaps not allowing technology to bear the
Speaker 2: fruit that I think it's capable of. I think the
Speaker 2: US has found a really nice path on that. The
Speaker 2: blueprint that you're referencing has a whole conversation about the
Speaker 2: advent of cars into society. Actually originally innovated in the UK,
Speaker 2: but at the time we used people walking in front
Speaker 2: of cars using red flags to remind estrians there was
Speaker 2: this car coming. How could that be right? People don't
Speaker 2: walk that fast. It's because we also regulated the speed
Speaker 2: of the car I think, down to something like three
Speaker 2: miles an hour. And so hence US took up that
Speaker 2: mental created an incredible automobile industry and really took the
Speaker 2: baton at a moment when maybe the UK should have
Speaker 2: thrived on that innovation in particular. So it is but
Speaker 2: one example, and one example is not everything, but I
Speaker 2: think it's a good metaphor for thinking about how do
Speaker 2: you balance leaning into this new technology while doing it
Speaker 2: in a way that feels safe and for the good
Speaker 2: of your citizens.
Speaker 1: I want to wrap up with a few more personal questions.
Speaker 1: So you are, you know, one of the top ranking
Speaker 1: female executives in tech right now?
Speaker 4: There is a lot.
Speaker 1: Of discussion about DEI in the tech field about how, you.
Speaker 4: Know, how do women succeed in the tech industry. What's
Speaker 4: the best way to do that?
Speaker 1: How have you been able to navigate this and what
Speaker 1: are your thoughts on the topic.
Speaker 2: It's something very near and dear my heart. I started
Speaker 2: my career as an engineer. In fact, my very first
Speaker 2: job out of college or my first internship was working
Speaker 2: on a gold mine in Ghana. Believe it or not,
Speaker 2: I wrote my masters on how.
Speaker 3: To extract gold out of sulfid doors.
Speaker 2: If anyone wants to have a conversation later, and I
Speaker 2: say that because I did that, and I came back
Speaker 2: to the UK and thought that is not a job
Speaker 2: I can be successful in because there were no other women.
Speaker 2: And so I strongly grab hold of that statement about
Speaker 2: it's very hard to be what you can't see. And
Speaker 2: so I think in someone sitting and has the privilege
Speaker 2: today of sitting in my seat, first of all, it's
Speaker 2: showing that women can be and so hopefully that generation
Speaker 2: that's coming up behind can see.
Speaker 3: What they are capable of.
Speaker 2: I think the second thing is how do we lean
Speaker 2: into technology to go back to what we can do
Speaker 2: with AI you really can start to create personal tutors.
Speaker 2: I just saw an amazing piece out of World Bank
Speaker 2: where they did a study in Nigeria.
Speaker 3: For particularly, they looked at the gender dynamic.
Speaker 2: So they created using chat SHEPT effectively a after school
Speaker 2: tutor program, and I think for girls, I think they
Speaker 2: zeroed in on the girls in particular. They saw almost
Speaker 2: a two year leap in education outcomes in just a
Speaker 2: few months of using the technology, because it really does
Speaker 2: create a personalized moment. Like one of the things that
Speaker 2: hurts my soul a lot is when I hear, particularly
Speaker 2: young girls say I'm not good at math.
Speaker 3: Which is just not true.
Speaker 2: They would never say I'm not good at speaking German
Speaker 2: because you don't know yet. You are just starting your life.
Speaker 2: You do not know, but it may not be being
Speaker 2: taught to you in a way that resonates. Computer science
Speaker 2: is often taught first through gaming.
Speaker 3: My daughter, when she.
Speaker 2: First took her for CS classes, said Mom, I just
Speaker 2: don't even like this. I don't want to build games.
Speaker 2: Once she recognized that she could use it to create websites,
Speaker 2: she could pull her artistic side, and she was fascinated
Speaker 2: drawn to it. Today, you know, I'm really proud of
Speaker 2: the fact that she is a hard scientist, taking chemistry
Speaker 2: in university and doing computer science so I think a
Speaker 2: lot of it comes down to how do we think
Speaker 2: about approaching people where they are? And then in the workforce,
Speaker 2: how do I turn around at the table, reach behind myself,
Speaker 2: pull people up of all different types of diversity, not
Speaker 2: just women, because I think in the end, the more
Speaker 2: diversity we have around the table, the better the products
Speaker 2: that we build, the better the outcomes we have for
Speaker 2: society at large. And you cannot do that when you
Speaker 2: have a homogeneous point of view sitting in the room.
Speaker 1: This is a question that I got from others, so
Speaker 1: I know it's on people's mind.
Speaker 4: But what is Sam Altman like as a boss?
Speaker 2: Let's see, where's the one when I need it? Sam
Speaker 2: is a delight. He's super fun to work with because
Speaker 2: his brain runs at one thousand miles an hour, and
Speaker 2: so I kind of as someone who's really hyper curious.
Speaker 2: I love that because it keeps me on my toes.
Speaker 2: He has a good heart, and I think that's really important.
Speaker 2: But he's always pushing and in fact, I had a
Speaker 2: conversation with him recently you talk about you know when
Speaker 2: you're having that personal moment where I said, Sam, hey,
Speaker 2: just something to know about me that when you're giving
Speaker 2: me feedback. If it's always on the thing that could
Speaker 2: be a little bit better, that is good, bring it on,
Speaker 2: but sometimes it's really helpful to kind of also remind
Speaker 2: me what went really well, like I will respond better
Speaker 2: to you.
Speaker 3: And actually he was like, that is really helpful.
Speaker 2: Because I'm someone who likes tough feedback all the time.
Speaker 2: But I'm really going to use that and think about that.
Speaker 2: And I've seen him shift how we interact and so
Speaker 2: again it's like, this is why diversity is important, because
Speaker 2: we need to be able to talk to each other
Speaker 2: about differ from wise that we respond and I hope
Speaker 2: actually that helps him thing about others that.
Speaker 3: He's surrounding himself with.
Speaker 2: So yeah, overall, it's been a wild and incredible and
Speaker 2: inspirational first kind of run at the company. It's an
Speaker 2: incredible group of people.
Speaker 3: You are literally.
Speaker 2: Working with the best in the world at what they do,
Speaker 2: and you really have to work with very different personalities and.
Speaker 3: I love it.
Speaker 4: I would call that reinforcement learning.
Speaker 3: It is reinforcement.
Speaker 1: Learning talking IPOs and general intelligence that conversation OpenAI CFO.
Speaker 4: Of course, Sarah Free are talking to Bloomberg at Bloomberg
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