Speaker 1: We're going to now head over to the AI Action
Speaker 1: Summit in Paris, where Bloomberg's Tom mackenzie is sitting down
Speaker 1: with open AI's chief Global affairs officer, Chris Lahane.
Speaker 2: Guys, thank you very much doing yes at Le Grand
Speaker 2: Ballet in Paris. I'll start with that question about deep
Speaker 2: sea because it is a key question for people attending
Speaker 2: this event. Chris Lhane, head of Global policy of course
Speaker 2: at open AI deep Sea, how do you characterise the
Speaker 2: impact of deep sea. We're just hearing from Demisisarvice, who
Speaker 2: I sat down with earlier. He said their claims around
Speaker 2: the way they built and trained this model, the cost
Speaker 2: and the amount of chips they were using. He says,
Speaker 2: that's exaggerated.
Speaker 1: So I think there can be two things that they
Speaker 1: are true here. One that they have built a really
Speaker 1: impressive model. It basically competes with what open AI had
Speaker 1: put out back in September. Now we've since put out
Speaker 1: other more advanced models, but clearly a very capable model.
Speaker 1: I think the second thing with Demis said can also
Speaker 1: be true, which is perhaps and we've seen news reports
Speaker 1: on this, that the costs, that how the technology was derived,
Speaker 1: that you know, whether they access certain types of chips,
Speaker 1: you know, whether what was initially said was not actually
Speaker 1: the case. I do think the big takeaway though, even
Speaker 1: if everything I just said turns out to be accurate
Speaker 1: and crewe, it is still a very impressive and competitive model.
Speaker 1: And so to me, the big, big, big takeaway in
Speaker 1: all of this is that this really reaffirmed something that
Speaker 1: open ai has been saying since the summer of twenty
Speaker 1: twenty four that there are two countries in the world
Speaker 1: that can build AI at scale, the US, the CCP
Speaker 1: led China. And what that really means is that there
Speaker 1: is a global competition right now between whether the world's
Speaker 1: going to be built on small, d democratic AI rails
Speaker 1: or authoritarian, autocratic AI rails. And that's the big takeaway
Speaker 1: for this.
Speaker 2: I want to get to that, but I want to
Speaker 2: push you on what you'll see with discovery because I
Speaker 2: know this is investigation that has been a problem within
Speaker 2: open ai as to whether or not deepseek inappropriately use
Speaker 2: some of the inferencing data distilling from your own models.
Speaker 2: Have you come to a conclusional We're.
Speaker 1: Still looking at it. Obviously, there's we and we've already
Speaker 1: made public that we had seen some evidence of that
Speaker 1: taking place, And just so folks understand, because distillation is
Speaker 1: not a normal word, at least certainly that I don't
Speaker 1: use at the breakfast table all the time. There is
Speaker 1: different kinds of distillation. And I'll use an analogy. If
Speaker 1: you go to the library and you take out a
Speaker 1: book and you learn from that book, and that ultimately
Speaker 1: informs some of your work. That's fine. That takes place
Speaker 1: a lot of that. That's part of what happens in
Speaker 1: the AI space. There's another version of it where you
Speaker 1: go in to the library, take the book, keep the book,
Speaker 1: put your name on the book, slap a cover on
Speaker 1: the book, and hand it out as if it's your book.
Speaker 1: And that's the replication. And I think that's what we're
Speaker 1: concerned about, and again what we've seen some evidence of
Speaker 1: and are continuing to review to have a better understand
Speaker 1: We've talked with government officials about it, and we'll share
Speaker 1: more as we learn more.
Speaker 2: Well open AIS critics have said, then trend your models
Speaker 2: on data, and you haven't been fully transparent in terms
Speaker 2: of the use of that data. I don't know you
Speaker 2: would push back.
Speaker 1: That's when there's good calories and bad calories there's a
Speaker 1: good distallation and problematic distillation.
Speaker 2: Have any of your customers pushed back and said, look,
Speaker 2: we're uncomfortable with the pricing of your models at this
Speaker 2: at this junct shot.
Speaker 1: Well, let me put it this way. You know, open
Speaker 1: ai came out in November of two years ago, twenty
Speaker 1: twenty two, right, and within two months was it a
Speaker 1: one hundred million users? Right? Were well over three hundred
Speaker 1: million today continuing to see you know, that really strong growth.
Speaker 1: And what we try to do is have different models
Speaker 1: priced at different levels depending on how you ultimately want
Speaker 1: to use it. And that's something just given the nature
Speaker 1: of how fast the technology is moving and the pace
Speaker 1: this moving at that you're always constantly trying to evaluate.
Speaker 2: Prices go down from it.
Speaker 1: Oh yeah, So I think amongst the most interesting things
Speaker 1: that we have seen this is a an interesting aspect,
Speaker 1: which is the efficiencies of systems, particularly using the reasoning
Speaker 1: technology is coming down. Sam, When our CEO put a
Speaker 1: blog out last night, maybe technically this morning, I don't know,
Speaker 1: but at some point in the last twenty four hours
Speaker 1: that sort of looked at three observations of AI. The
Speaker 1: first was that the more you spend on compute to
Speaker 1: build frontier models. It's pretty logrhithmic. The more powerful of
Speaker 1: those gets so you're going to require more and more compute,
Speaker 1: more and more investment in infrastructure. Secondly, over the last
Speaker 1: year or so, we've seen a big increase in efficiencies
Speaker 1: which are bringing down the cost of a token. Of
Speaker 1: that I think of a token as a price of
Speaker 1: computer as a unit of computing about one hundred and
Speaker 1: fifty percent. But even as the costs come down, the
Speaker 1: amount of people using it go up, so that then
Speaker 1: puts pressures back on computing. Like an analogy here is
Speaker 1: car prices come down, more people drive cars, but you
Speaker 1: then need more energy, more roads. Third, piecet that has
Speaker 1: come out and his observation, sorry, just bear with me
Speaker 1: for a second, is that is that the economic productivity
Speaker 1: that you're getting in is super exponential.
Speaker 2: Where are you getting the money to spend one hundred
Speaker 2: billion initially on star gates, elon must says, you probably
Speaker 2: don't have the money, even something Adela says, we're good
Speaker 2: for our eight two billion. That's Onion.
Speaker 1: So first of all, we have incredible partners. We have
Speaker 1: soft Bank, which is a proven track record of raising
Speaker 1: enormous syndicated money from sovereigns and pensions.
Speaker 2: Thought is coming from nails.
Speaker 1: And then and then we have Oracle, right, which actually
Speaker 1: builds these. Then you have open aies. What are piece
Speaker 1: of this? First of all, in terms of the media question,
Speaker 1: there's one hundred billion that's going to be going out
Speaker 1: the door in the immediate future. We already have a
Speaker 1: facility in Abilene. You guys need to come down. You
Speaker 1: can abline Texas. Sorry, we'd love to give you, guys.
Speaker 1: You can hang out the Oracle. Guys have done an
Speaker 1: awesome job down there. No, no, no, no, why don't
Speaker 1: you come see it? Show? We like to show, not tell.
Speaker 1: And then and then the open AI piece comes in
Speaker 1: two different pieces here, and I think this is to
Speaker 1: understand the economic model here. First of all, what we
Speaker 1: bring to this is the IP and you can think
Speaker 1: of compute the same way. Maybe you can think of
Speaker 1: gas regular gas, medium gas, and then premium gas. The
Speaker 1: premium gas is what people are gonna pay for. The
Speaker 1: premium compute is going to be the most expensive compute
Speaker 1: in the world because it's gonna be the highest level compute.
Speaker 1: You only get that premium compute with our IP. IP
Speaker 1: going too, the chip design IP going into the data centers,
Speaker 1: IP going how the clusters are sort of structured. And
Speaker 1: then we also are a buyer of the compute, right,
Speaker 1: so we commit to buying a certain amount of the
Speaker 1: compute that's coming out, which helps the whole economic model work.
Speaker 2: Chris, you worked in the Clinton White House. Is the
Speaker 2: Trump administration there go go to great headline is to
Speaker 2: the Trump administration right to slash AI regulation.
Speaker 1: I think what the Trump administration is focused on is
Speaker 1: one thing, and one thing very clearly, who is going
Speaker 1: to prevail in the competition between democratic That's what they
Speaker 1: get up and think about every day, at least from
Speaker 1: what I have seen and from what I have heard,
Speaker 1: And so I think they understand that you do have
Speaker 1: to really be leading in leaning into the innovation. If
Speaker 1: you think about about where the sort of comparative advantages are.
Speaker 1: Right at the end of the day, this is actually
Speaker 1: pretty simple. Whoever has access to compute is going to
Speaker 1: be in a strong position. What makes up compute, it's data,
Speaker 1: it's energy, it's chips, and it's talent. Right, and if
Speaker 1: you think of what the PRC has, they have an
Speaker 1: enormous amount of data. Authoritarian State Energy ten nuclear facilities
Speaker 1: last year, another ten coming on this year. Our chips
Speaker 1: are better. They're throwing a ton of money at it talent,
Speaker 1: and I think the talent piece is really interesting because
Speaker 1: they do have great talent in China. But in capitalist systems,
Speaker 1: it is capitalism that unleashes the developer, the builder, the entrepreneur,
Speaker 1: the people who are actually making this stuff in the
Speaker 1: tools that starve hauled problems. And that's where our advantage is.
Speaker 2: Is that why you've joined up and partnered with Aurae'm
Speaker 2: to thinking of the AI principles a Google. They've adjusted
Speaker 2: them and Google is no longer ruling out building tech
Speaker 2: for defense for the military. Yeah, you're signing up, you
Speaker 2: partner with Angurill. How far does that relationship go? How
Speaker 2: much are you prepared to embed tech into weapons system?
Speaker 1: Yeah? And we also just announced a partnership with the
Speaker 1: National Labs, which are the you know, which play an
Speaker 1: incredibly important role in how the US government thinks about
Speaker 1: national security. Is on the FILS, Yeah, yes, and the
Speaker 1: labs you know obviously like Los Alamos and others, are
Speaker 1: very big players in the broader national security ecosystem. So
Speaker 1: you know, for US, right, we do want to be
Speaker 1: a partner on innovation with the government. We do believe
Speaker 1: it's incredibly important that democratic AI prevails, and that means
Speaker 1: making sure that the government is getting access to the
Speaker 1: highest capabilities. You know, we'll certainly do it consistent with
Speaker 1: our values and our principles, but at the end of
Speaker 1: the day, like we do believe it is very consistent
Speaker 1: with their mission that is to make sure AI benefits everyone.
Speaker 1: That you're ensuring that AI is going to be built
Speaker 1: in a democratic way with democratic values.
Speaker 2: Chris la Haye, thank you very much. Indeed, Global head
Speaker 2: of Policy at Open AI.