Speaker 1: Bloomberg Audio Studios, podcasts, radio news.
Speaker 2: These companies have consolidated a historic amount of economic and
Speaker 2: political power, just like the empires of old. You do
Speaker 2: not need to unlock the benefits of AI through a
Speaker 2: corrosive approach. So why are we not holding these companies
Speaker 2: to those higher standards?
Speaker 1: Karen Howe, who says the AI industry is a reckless
Speaker 1: race for global domination, I think you do have a
Speaker 1: problem with the people at the top. Right.
Speaker 2: We often obsess over the cast of characters at the top,
Speaker 2: who's the good guy, who's the bad guy, who's the
Speaker 2: less bad guy? And what I'm saying is that's not
Speaker 2: the only option. We shouldn't have to live with companies
Speaker 2: that are so vastly powerful, where one person at the
Speaker 2: top is able to decide all these things.
Speaker 1: From Bloomberg Weekend. This is the Michelle Hussein Show. I'm
Speaker 1: Michelle Hussein. I don't know if you at the point
Speaker 1: that you tap a prompt into Chat, GPT or CLAWD, Gemini,
Speaker 1: Microsoft co Pilot, whatever you use, if you ever stopped
Speaker 1: for a moment and wonder how the data got there
Speaker 1: in the first place. I suspect you don't. Few of
Speaker 1: us do. These AI tools have been revolutionary in the
Speaker 1: last few years in the way that so many of
Speaker 1: us have changed how we work or how we learn.
Speaker 1: But Karen Howe, who's talked to me for this episode,
Speaker 1: wants us all to step back and to really think
Speaker 1: about what the process involves. That is, not only the
Speaker 1: land used for the data centers, the water, the energy involved,
Speaker 1: but the actual process of gathering and preparing the data,
Speaker 1: where it comes from. Who does the work in putting
Speaker 1: it together. Karen trained as a mechanical engineer and then
Speaker 1: did work in Silicon Valley before turning to journalism. She's
Speaker 1: written a book called Empire of Ai, and it's that
Speaker 1: use of the word empire to describe the top AI
Speaker 1: companies that I ask her to explain right at the
Speaker 1: start of this It started, I think, when she spent
Speaker 1: a few days inside Open Ai observing them. They were
Speaker 1: not a well known company at the time she wrote
Speaker 1: about them in a less than flattering way. Today, it
Speaker 1: is safe to say that there is no love lost
Speaker 1: between Karen Howe and the top execs of the AI world,
Speaker 1: and you'll get a sense of that in my written
Speaker 1: notes that accompany this conversation. As ever, you'll find them
Speaker 1: at Bloomberg dot com. Forward slash Michelle, you'll get the
Speaker 1: backstory to this conversation and what I followed up on afterwards.
Speaker 1: Karen and I in London, where there was something on
Speaker 1: the table that caught her eyes she came into the studio.
Speaker 1: It's a different way that I make notes ahead of
Speaker 1: a conversation, with a spider's web style diagram that reminds
Speaker 1: me what I really need to cover. So that moment
Speaker 1: between us is where this begins.
Speaker 2: I love this mind map. Do you do this? Oh?
Speaker 1: Yes, I do do this.
Speaker 2: This is awesome.
Speaker 1: I do this, but I wish my writing was a
Speaker 1: little bit Nita.
Speaker 2: Oh it's so me. I can just about.
Speaker 1: I can just about, but no, it helps me. But
Speaker 1: I never did it before this show began.
Speaker 2: I'm always so fascinated by the format in which people
Speaker 2: take notes, because it really is such a window into
Speaker 2: how they they think. Yeah, and I'm such a linear
Speaker 2: thinker that diagram confuses me.
Speaker 1: Interesting, So what does this reveal about me that I
Speaker 1: think in a I don't think in a linear way,
Speaker 1: in a non linear way.
Speaker 2: Way I think, which I think is probably a lot
Speaker 2: more information done.
Speaker 1: Essentially it's what enables recall because you know you don't
Speaker 1: want to be referring to it too much as whatever
Speaker 1: helps imprint it in your in your brain. So at
Speaker 1: least I feel it's hope question very good. You're reading
Speaker 1: upside down now? Yeah? Okay, Well, Karen, I would love
Speaker 1: to start with your own words, because you write in
Speaker 1: Empire of Ai that over the years you found only
Speaker 1: one metaphor that encapsulates the nature of what these AI
Speaker 1: power players are, and that is empires not engaged in
Speaker 1: overt violence and brutality, as we know from history, but
Speaker 1: they too seize and extract precious resources to feed their
Speaker 1: vision of artificial intelligence. Now, that is a real attack
Speaker 1: on the companies behind some of the defining tools of
Speaker 1: our age. What is it that's made you feel so strong?
Speaker 2: These companies have consolidated historic amount of economic and political power,
Speaker 2: and they are becoming fast the supreme powers in the world,
Speaker 2: just like the empires of old. And they do this
Speaker 2: through the dispossession of resources, labor value from the majority.
Speaker 2: They claim to resources that are not their own, the
Speaker 2: data of individuals, the intellectual property of artists, writers and creators.
Speaker 2: They exploit an extraordinary amount of labor through both the
Speaker 2: workers that they use and exhaust to produce their technologies
Speaker 2: as well as the workers whose jobs get automated away
Speaker 2: by the deployment of these technologies. So there is this
Speaker 2: category of work that is involved in the production of
Speaker 2: these technologies, including content moderation, including the labeling and cleaning
Speaker 2: of training data. That's called data work. That kind of
Speaker 2: work is now the number four fastest growing work in
Speaker 2: the US. And the types of people who are doing
Speaker 2: this work are scientists, lawyers, college graduates, PhD graduates, and
Speaker 2: they are essentially providing their knowledge and their expertise to
Speaker 2: the training of these models, but under deeply exploitative, poorly
Speaker 2: paid conditions where they are not working nine to fives.
Speaker 2: They are in their homes waiting for work to arrive.
Speaker 2: They never know when it's going to arrive. They never
Speaker 2: know when it's going to go away. They're being pitted
Speaker 2: against each other. You know, I've been covering this kind
Speaker 2: of work for almost eight years now, and it's the
Speaker 2: same story again and again with every single community that
Speaker 2: it hits. You see the same exact patterns of exploitation.
Speaker 1: Are you saying that we should be thinking about the
Speaker 1: exploitation of labor at the point that we're using clawed
Speaker 1: or chatt GPT.
Speaker 2: Yeah. Absolutely, So other industries have had similar problems. The
Speaker 2: fashion industry also engaged in a lot of labor exploitation
Speaker 2: and a lot of environmental degradation. The answer was never
Speaker 2: let's just get rid of clothes altogether. The answer was
Speaker 2: always let's hold ourselves to higher standards in producing clothes
Speaker 2: without those problems.
Speaker 1: It's still work, though, isn't it. And there are so
Speaker 1: many communities where in different parts of the world there
Speaker 1: is an absence of work altogether. I just want to
Speaker 1: ask you whether you think that there are definitely challenges
Speaker 1: in this industry and your work really details them and
Speaker 1: deconstructs them. But is there an aspect of this that
Speaker 1: is about the challenges of our age that it's not
Speaker 1: necessarily more exploitative than what's gone before. It's the problems
Speaker 1: of our time.
Speaker 2: I want to go back to what you were saying
Speaker 2: that it is work though, because what is happening with
Speaker 2: this US case now is that the people are actually
Speaker 2: people that are highly educated and could have gotten full
Speaker 2: time employment, but the reason why they do not have
Speaker 2: access to better work now is also because of the
Speaker 2: AI industry.
Speaker 1: Taking away jobs.
Speaker 2: It's because of the way that the AI industry designs
Speaker 2: its technology and sells it as a tool for automating
Speaker 2: knowledge work. And so there's a shrinking job market for
Speaker 2: the unemployed population in the US twenty five percent of
Speaker 2: them our four year college degree holders. That's a historic record.
Speaker 2: And so it's not actually just giving work to people
Speaker 2: who don't have work. It's actually degrading the work opportunities
Speaker 2: that exist right now, taking knowledge workers out of a
Speaker 2: full time employment kind of situation into a gig work
Speaker 2: kind of situation.
Speaker 1: What do you say to a country like Malta, which
Speaker 1: has just signed a major agreement with OpenAI where everyone
Speaker 1: who lives in Walta is going to get free access
Speaker 1: to chat GPT plus for a year. They're going to
Speaker 1: go on a course to learn how to use it.
Speaker 1: And the government of Malta is very proud of this
Speaker 1: agreement because it says it puts Malta at the full
Speaker 1: front of the digital age.
Speaker 2: I've met a lot of policymakers from developing countries, from
Speaker 2: smaller countries that do see themselves as they can only
Speaker 2: participate in this revolution if they offer themselves up for
Speaker 2: sale just because you know, the government is proud of
Speaker 2: this partnership and they think that this is what gives
Speaker 2: them the seat at the table, that it's actually leading
Speaker 2: to their citizens having a more dignified life.
Speaker 1: But their government would see it as equipping them for work,
Speaker 1: or access to knowledge or everything else that they will
Speaker 1: need to use large language models for.
Speaker 2: I would argue they are being exploited, and I would
Speaker 2: be curious what the multi people think, not what the
Speaker 2: multi government thinks. And the reason why I think they
Speaker 2: are being exploited is because they are having their data
Speaker 2: harvested to train the next generations of models. And that's
Speaker 2: you know, that's why these companies are going out and
Speaker 2: striking these partnerships. They need more and more and more data.
Speaker 2: They need more and more and more data centers to
Speaker 2: produce their technologies. And one of the challenges when it
Speaker 2: comes to searching for different sources of data is that
Speaker 2: they have run out of Internet data. They've run out
Speaker 2: of high quality Internet data, and so now one of
Speaker 2: the best sources of high quality data is straight from
Speaker 2: the user. And so if they can strike these whole
Speaker 2: of country partnerships, I mean you get a whole of
Speaker 2: country database to upload freely all.
Speaker 1: Kinds of thing like everything that's if I was part
Speaker 1: of it, it would be everything that's on my phone, which
Speaker 1: is which is.
Speaker 2: Every Yeah, everything you used. You know. Some people think
Speaker 2: of this as just oh, well I was using Google.
Speaker 2: I was, you know, saying a lot of things to
Speaker 2: Google and personal data as well. But I mean, I've
Speaker 2: never seen people upload their medical files to Google to
Speaker 2: ask for an interpretation of it. But people do that
Speaker 2: with CHATGBT, with Gemini, with Claude, and that's intimate medical information.
Speaker 1: Is there no positive side of it for you, Karen?
Speaker 1: Like that uploading of medical data, if it's done with consent,
Speaker 1: isn't it part of the progress of science. You might
Speaker 1: get something that troubles you diagnosed, or you might be
Speaker 1: part of helping wid diagnosis.
Speaker 2: What I have problems with is the fact that it
Speaker 2: is in the hands of deeply powerful companies that have
Speaker 2: absolutely no accountability to the people that they're actually affecting
Speaker 2: around the world. And this is why I call them empires.
Speaker 2: That's the governance structure of an empire. You at the
Speaker 2: top are able to make hundreds of decisions in a
Speaker 2: day that then have cascading effects on billions of people
Speaker 2: around the world, and there are no formal mechanisms by
Speaker 2: which those billions of people can actually challenge, contest, provide feedback.
Speaker 2: That's why we've historically moved from empires to democracies, because
Speaker 2: democracies are these institutionalized ways for people to say when
Speaker 2: they like something, when they don't like something, and how
Speaker 2: they want to collectively govern and self determine their future.
Speaker 1: Are you actually saying that the power of these companies
Speaker 1: is greater than the power of governments.
Speaker 2: They are definitely increasingly becoming that way, which.
Speaker 1: Means even the US government, the most powerful country in the.
Speaker 2: World, I would say so. I mean, when there was
Speaker 2: the recent spat between Andthropic and the Department of War,
Speaker 2: the Department of War used the nuclear option to try
Speaker 2: and get a company to fall in line, which is
Speaker 2: to threaten to declare this company as a supply chain risk,
Speaker 2: which would.
Speaker 1: Affect its work right across the US government.
Speaker 2: The company Anthropic still did not fall in line.
Speaker 1: But isn't that because it did well in another way.
Speaker 1: It took the moral high ground, and maybe we can
Speaker 1: talk about that a bit later, but it got lots
Speaker 1: more users from the pr of that momentolutely.
Speaker 2: One in the situation, both pr and in the SPAT.
Speaker 2: What I'm saying is this was a moment that demonstrates
Speaker 2: that these companies can, in fact have more power than
Speaker 2: the US government. They are will to actually go against
Speaker 2: the US government because they know that whatever penalty comes
Speaker 2: at them from the government might actually be counterbalanced by
Speaker 2: the other benefits that come directly to the company.
Speaker 1: By profit elsewhere. So tell me, then, Karen, what is
Speaker 1: the answer, Because I know there's a call to action
Speaker 1: in your book. You clearly think that things can be
Speaker 1: and should be done another way. What are you hoping for?
Speaker 1: And do you think a figure like the Pope, like
Speaker 1: a unique figure way outside the sort of government policy realm,
Speaker 1: is the kind of person who might provide some kind
Speaker 1: of roadmap here.
Speaker 2: I think it's important for all leaders in society, whether
Speaker 2: religious or otherwise, to be thinking about these questions and
Speaker 2: thinking about how to ultimately protect human agency at a
Speaker 2: time when the way that Silicon Valley conceives is this
Speaker 2: technology is in fact threatening it. And we've seen Popelio
Speaker 2: be quite strong on these issues and identify early on
Speaker 2: in his papacy that this is a particular issue where
Speaker 2: he is deeply concerned about the pathway of technology development
Speaker 2: undermining human dignity. And to go to your question of
Speaker 2: what is actually the answer here, I mean, one of
Speaker 2: the things that I always say is AI is like
Speaker 2: the word transportation, transportation refers to everything from bicycles to rockets,
Speaker 2: and in the same way, AI refers to a very
Speaker 2: vast collection of different technologies, some of which are designed
Speaker 2: kind of like rockets. They're very resource intensive and they
Speaker 2: exact a lot of cost to produce, and so in general,
Speaker 2: we only deploy rockets for a very narrow subset of
Speaker 2: transportation needs. We never go around and say every single
Speaker 2: person should have a rocket and use that rocket for
Speaker 2: every single one of their transportation needs. That would destroy
Speaker 2: the environment, and it would also just be really inefficient
Speaker 2: and probably not actually get people to places faster because
Speaker 2: of the amount of time it would take to boot
Speaker 2: up the rocket. And so what I suggest is in
Speaker 2: my book and throughout my other work, is that we
Speaker 2: need to shift our portfolio of AI technologies to better
Speaker 2: reflect the kinds of specialized AI that we in fact
Speaker 2: need to tackle real societal challenges, things like improving healthcare
Speaker 2: and accelerating drug discovery. Deep Minds alpha fold it is
Speaker 2: an example of a system that I call a bicycle
Speaker 2: of AI. It is a system that predicts with high
Speaker 2: accuracy how an amino acid sequence will fold into a
Speaker 2: protein structure. This is really important for accelerating the understanding
Speaker 2: of human disease and then designing drugs to target those diseases,
Speaker 2: and it won the Nobel Prize for chemistry. This is
Speaker 2: a technology. Even though it's called AI and Chatchibt's also
Speaker 2: called AI. Deep Mind's alpha fold is one that is
Speaker 2: fundamentally different from how chatchebt is produced and how it operates.
Speaker 2: It uses very little data, it uses very little computing resources,
Speaker 2: and there's so hewpful and there's no need for labor
Speaker 2: exploitation either, and it's still able to provide a lot
Speaker 2: of benefits. So my question is, when you look at
Speaker 2: the portfolio of AI technologies, wouldn't you want to pick
Speaker 2: the ones that have great benefit very little cost. Why
Speaker 2: would you want to pick the ones that have very
Speaker 2: great costs? Right?
Speaker 1: But these are value judgments, right, And something that you
Speaker 1: think has no value, someone else might like. Let's take
Speaker 1: AI in defense. We know already that many armies use
Speaker 1: it for targeting and the use of weapons. You and
Speaker 1: I might see that as wrong. Others might see that
Speaker 1: this is necessary defense. It's part of the defense of
Speaker 1: your realm, and if you're not going to do it
Speaker 1: through the tools of our age, then you're going to
Speaker 1: fall behind and the bad actors, terrorists, bad governments, whatever
Speaker 1: are going to get you.
Speaker 2: Yeah. I think that's a particularly controversial issue where people
Speaker 2: fall in different places. But what I'm arguing is there's
Speaker 2: many different ways to produce AI. You do not need
Speaker 2: to unlock the benefits of AI through a corrosive approach.
Speaker 2: So why are we not holding these companies to those
Speaker 2: higher standards.
Speaker 1: I'm conscious that both anthropic and open AI are heading
Speaker 1: towards public offerings of their shares. Is that a moment
Speaker 1: that you think could bring greater accountability, that once a
Speaker 1: company is public and it's got all kinds of investors
Speaker 1: and it's got annual general meetings, that a level of
Speaker 1: scrutiny that is lacking comes in a useful one.
Speaker 2: I do think it could be. I am hopeful that
Speaker 2: it could be, because when companies become public, they do
Speaker 2: institute more government structors. But I Laigh this critique on
Speaker 2: existing public companies as well. I call Google, Amazon, Microsoft
Speaker 2: empires of AI as well. I do think that ultimately
Speaker 2: governance needs to come from the public and from publicly
Speaker 2: elected officials as well, not just the market forces and
Speaker 2: not the founders.
Speaker 1: It sounds like you don't have faith in the actual
Speaker 1: people running these companies. A lot of your book is
Speaker 1: about open Ai, and Sam Mortman in particular. Tell me
Speaker 1: the story of how you got to know him and
Speaker 1: the company back in the beginning of this work.
Speaker 2: I started covering AI in twenty eighteen for MIT Technology Review,
Speaker 2: and that is a publication that focuses a lot on
Speaker 2: fundamental AI research. So I was reading scientific papers every week,
Speaker 2: talking with researchers at universities and corporate labs and open Ai.
Speaker 2: You know, it became part of my radar because at
Speaker 2: the time it was a nonprofit fundamental AI research lab,
Speaker 2: and it was one of the only ones. There was
Speaker 2: open Ai, then there was Google, then there was deep
Speaker 2: Mind and just a few others, but that was kind
Speaker 2: of it. And so in twenty nineteen I proposed to
Speaker 2: open Ai that there were a lot of changes happening
Speaker 2: at the organization. They had just created a for profit
Speaker 2: arm they'd just gotten a big investment from Microsoft, that
Speaker 2: maybe they wanted to reintroduce themselves to the public, and
Speaker 2: I suggested that if they were interested in that, I
Speaker 2: could profile them. So I went to the corporate offices
Speaker 2: and embedded within the organization for three days, interviewed a
Speaker 2: lot of employees and executives, including Sam Wltman. Sam Wtman
Speaker 2: was actually not very important at the time, so no,
Speaker 2: because he had just officially become CEO. He was previously
Speaker 2: just co chairman and didn't really have much to do
Speaker 2: with the organization at all. And so they set up
Speaker 2: interviews for me with Greg Brockman, the chief technology officer,
Speaker 2: in Ilios Setskaver, the chief scientist, who were the main
Speaker 2: runners of the organization's day to.
Speaker 1: Day and they didn't like the profile you wrote.
Speaker 2: They did it, they they did not.
Speaker 1: You said at that time that there was a misalign
Speaker 1: between what they were actually doing and their promises. What
Speaker 1: was it that made you say that? What did you
Speaker 1: see that led you to that.
Speaker 2: At that time? The misalignment that I saw was they're
Speaker 2: called open a eye for what they said was a
Speaker 2: commitment to transparency, and this organization was already deeply secretive,
Speaker 2: and I noticed this culture in the way that employees
Speaker 2: were very very nervous about what they said to me
Speaker 2: and where I was allowed to go within the offices.
Speaker 2: So that seemed already quite bizarre. And they also said
Speaker 2: as a nonprofit that they would never commercialize their technologies, and.
Speaker 1: Now, of course they're fully for profit, which is the
Speaker 1: source of the case that collapsed between Elon Musk and
Speaker 1: Sam Ortman. Is Elon Musk then the good guy in
Speaker 1: this for you?
Speaker 2: No? No? I mean, here's the thing. I think we
Speaker 2: often obsess over the cast of characters at the top,
Speaker 2: and we're always trying to think, like, who's the good guy,
Speaker 2: who's the bad guy, who's the less bad guy that
Speaker 2: I'm able to settle with? Because that's all the option
Speaker 2: set there is. And what I'm saying is that's not
Speaker 2: the only option set. We shouldn't have to live with
Speaker 2: companies that are so vastly powerful where one person at
Speaker 2: the top is able to decide all these things. It's
Speaker 2: a governance structure that's the problem. It's not that I
Speaker 2: think the problem will be solved if we just swap
Speaker 2: out one of these characters for one another. Or for
Speaker 2: a new person.
Speaker 1: But I think you do have a problem with the
Speaker 1: people at the top, right or certainly the small number
Speaker 1: of people at the top, And you kind of know
Speaker 1: the world that they came from because you were part
Speaker 1: of that same world. You went to MIT work for
Speaker 1: a startup. This could actually have been the industry of
Speaker 1: your life. It could have been reporting from the outside.
Speaker 1: So I get the impression that you do have a
Speaker 1: problem with these characters.
Speaker 2: Yes, I do have a particular problem with the culture
Speaker 2: that they're steeped in and the ideologies that they have,
Speaker 2: because it's an imperialist ideology. I mean, you know Sam
Speaker 2: Altman said before he stepped into his position as CEO
Speaker 2: of Opening Eye, who's the president of y Combinator, and
Speaker 2: he said, I'm going to invest in ten x more
Speaker 2: companies year after year until I invest in every company
Speaker 2: under the sun. And there's this kind of sense of
Speaker 2: all the people that are steeped in that Silicon Valley
Speaker 2: culture which I was a part.
Speaker 1: Of, which was pretty young at the time, though maybe
Speaker 1: it was hubris foolishness.
Speaker 2: Except that it's echoed through the way that he approaches
Speaker 2: Opening Eye strategy. Lets scale the models tenx more every
Speaker 2: year until we have reached the scale of the human brain.
Speaker 2: You know, like He's always thinking in these kinds of terms,
Speaker 2: and all of them are thinking in these kinds of
Speaker 2: terms of let's just keep scaling and scaling and scaling
Speaker 2: and its growth at all costs. And that is very
Speaker 2: particular to the culture of Silicon Valley innovation right now.
Speaker 2: So I do have a particular problem with the culture
Speaker 2: and with the people that have grown up in that culture.
Speaker 2: And also I don't think that the fundamental issue that
Speaker 2: I'm articulating in this book will be solved by simply
Speaker 2: swapping in another person.
Speaker 1: Are there any less bad guys? Like do you think
Speaker 1: Anthropic is a more principled company than Open AI? No,
Speaker 1: even though it stood up to the Pentagon.
Speaker 2: So interestingly, Dario amade Ceoanthropic said that he actually did
Speaker 2: not have issues with full autonomous weapons. In fact, he
Speaker 2: believed that this was a technology where the US probably
Speaker 2: should keep up. The problem was simply that he didn't
Speaker 2: want this version of Claude to be used for that.
Speaker 1: And claud has been used in AI assisted targeting, It's
Speaker 1: been used in Iran. I think it's reportedly been used
Speaker 1: on the raid on Madua and Venezuela as well.
Speaker 2: Yeah, that's right.
Speaker 1: Are you also following the Chinese AI players Deep Seek
Speaker 1: and other companies like that very very loosely? Do you
Speaker 1: believe that they behaved for le or do you accuse
Speaker 1: them of imperialism?
Speaker 2: Also, it's really interesting they do behave differently because of
Speaker 2: a very particular reason. So when chat Gibt first came out,
Speaker 2: all of the Chinese tech giants did the same thing
Speaker 2: as the the other US tech giants, which was try
Speaker 2: to make their own version of Chatchibt. I was at
Speaker 2: the Wall Street Journal at the time, so it was
Speaker 2: actually right before Chatgibt came out. The US government used
Speaker 2: this mechanism called export controls to block Chinese companies from
Speaker 2: getting access to the most cutting edge AI computer chips,
Speaker 2: and those are the ones that are designed by Nvidia,
Speaker 2: And so all of a sudden, the entire country of
Speaker 2: China and these Chinese companies faced an enormous constraint on
Speaker 2: one of the core resources that US companies were using
Speaker 2: to supercharge their AI development. So Chinese companies had to
Speaker 2: take a different route. They were not able to scale
Speaker 2: the same way that the US s companies were, and
Speaker 2: that I think is what led to deep Seek. Deep
Speaker 2: Seek was an example of a Chinese company figuring out
Speaker 2: how to develop the same capabilities of AI these AI models,
Speaker 2: but for a significantly less fraction of computational resources. Deep
Speaker 2: Sek illustrates the exact point that I'm making that it
Speaker 2: is in fact possible to produce AI different types of
Speaker 2: AI and also the same exact kind of AI with
Speaker 2: significantly less resources and therefore have a much less corrosive
Speaker 2: impact on community and planetary health. So why are we
Speaker 2: not doing that?
Speaker 1: But ultimately, are you more comfortable by the leaders in
Speaker 1: this industry being from the United States rather than from China,
Speaker 1: even though you think there's a lack of governance, do
Speaker 1: you still think the fact that you and I can
Speaker 1: talk about the US players these companies in the way
Speaker 1: that we are, that there is again a degree of
Speaker 1: scrutiny and accountability which is entirely absent in China. On
Speaker 1: that field, I think there's.
Speaker 2: A tendency a lot in the media to assume that
Speaker 2: the race is between US and China, between nation states
Speaker 2: rather than open versus closed models, And for me, I
Speaker 2: feel much more comfortable developing an open source ecosystem around
Speaker 2: AI innovation, and I feel comfortable if that open source
Speaker 2: model is coming from China or if it's coming from
Speaker 2: the US, because it's open, it's scrutinizable, it's something that
Speaker 2: can be audited, it can be improved on and built
Speaker 2: upon by many researchers and scientists around the world. Closed
Speaker 2: models simply don't do that. And there are plenty of
Speaker 2: open source models coming out of China as out of
Speaker 2: the US, but also the major dominant players that I
Speaker 2: critique are all.
Speaker 1: Closed and they've ended up being the most successful ones.
Speaker 2: I mean, it depends on how you define success.
Speaker 1: Yeah, okay, fine, fair enough. I want to ask about
Speaker 1: you as well, Karen, because you've clearly devoted a huge
Speaker 1: amount of time and energy to your work in this area.
Speaker 1: And I know that you said like you were looking
Speaker 1: for some kind of service in your life, like what
Speaker 1: would you do with your life? Do you think this
Speaker 1: is your mission that pointing out the flaws in these companies,
Speaker 1: digging into this critical industry of our time. Have you
Speaker 1: found your mission in this?
Speaker 2: I would say my mission has always been to ensure
Speaker 2: that everyone is able to live a dignified life in
Speaker 2: this particular moment. I see the AI industry and its
Speaker 2: approach to I development deeply challenging the ability of people
Speaker 2: around the world to live dignified lives, and so I'm
Speaker 2: focusing on holding them accountable. But that doesn't necessarily mean that,
Speaker 2: you know, for the rest of my life, I'm just
Speaker 2: going to be fixated on this thing because hopefully it
Speaker 2: won't be a problem.
Speaker 1: Would you ever work for one of these companies? No,
Speaker 1: categorically no. I mean it's a big thing to say no.
Speaker 1: Never too, not least because of the massive amount of
Speaker 1: money that are involved these days in the way that
Speaker 1: they recruit.
Speaker 2: If I orchestrated my life around money, I wouldn't be
Speaker 2: a journalist.
Speaker 1: So are there other players than perhaps the smaller ones
Speaker 1: where you do see principle where you can imagine perhaps
Speaker 1: being tempted to work for them. We had Fay Failure
Speaker 1: on the podcast a few months ago, and she clearly
Speaker 1: feels very strongly about values and the most important thing
Speaker 1: is that you're a person with values and you bring
Speaker 1: that to your work, and that that's the answer.
Speaker 2: When I say that I'm not willing to work for
Speaker 2: any of these companies, I'm talking about them persevera. I'm
Speaker 2: not talking about literally all AI companies that could ever
Speaker 2: come to pass. I think if there were companies out
Speaker 2: there that started focusing on the kinds of problems that
Speaker 2: I think are important to solve, and also are developing
Speaker 2: AI with an approach towards protecting human agency, sustainability and
Speaker 2: so forth, then yeah, I would happily consider I mean,
Speaker 2: not that I think I would be a very good
Speaker 2: asked that to them. I think I'm better off being
Speaker 2: a journalist. But that's what I meant. I mean. In fact,
Speaker 2: in my epilogue of my book, I talk about a
Speaker 2: nonprofit organization called Tahiku Media that built this AI speech
Speaker 2: recognition tool for the Todeo Maori language language of the
Speaker 2: indigenous people in New Zealand, and they have a really
Speaker 2: beautiful story around how they went about developing their AI model.
Speaker 2: They went to their community and first asked, do you
Speaker 2: even want this tool? We think that it would help
Speaker 2: with revitalizing the Maori language. We can open up our
Speaker 2: archives of all of this Todayo Maudi that they had
Speaker 2: recorded as a radio station for over thirty years, and
Speaker 2: you'll be able to listen to that audio. You'll see
Speaker 2: the transcriptions. You can click on the words and get
Speaker 2: translations automatically. But they were like, also, we recognize that
Speaker 2: speech recognition tools can be used to surveil our community
Speaker 2: as well, And it was only through participation with the
Speaker 2: community and the consent of the community that they then
Speaker 2: start developing this technology. They only used two computer chips
Speaker 2: to train their model, and this is just a totally
Speaker 2: different approach. And so you know, if the Eucomedia we're hiring,
Speaker 2: I'd be like, sure, why not.
Speaker 1: And the fact that you respond as passionately as you
Speaker 1: do to an example like that, do you think that
Speaker 1: is to do with your own heritage and the fact
Speaker 1: that being Chinese American you have been able to see
Speaker 1: the world from at least two angles through what you've
Speaker 1: seen from your parents and growing up.
Speaker 2: In the US. Yeah, I do think so. I think
Speaker 2: that growing up Chinese American made me realize that there's
Speaker 2: always many, many signs to a story. There are always
Speaker 2: many worldviews and many value systems by which you can
Speaker 2: approach the world. And when I see one particular narrative,
Speaker 2: one view of the world, really monopolizing the conversation, saying
Speaker 2: this is how it's going to be, This is the
Speaker 2: way that the world works. It just makes me question, well,
Speaker 2: what are the other stories that are not being told?
Speaker 2: Who are the other people that are not at the table,
Speaker 2: that don't get a voice to contest and challenge this
Speaker 2: dominant narrative.
Speaker 1: Finally, Claude or chat GPT, what do you use neither? Really?
Speaker 1: What is the alternative in your life?
Speaker 2: None? My mind?
Speaker 1: You don't, okay, but you do. You do Google searches obviously,
Speaker 1: of course, but you don't at all, And you don't
Speaker 1: feel it holds you back, You don't feel.
Speaker 2: I feel it actually liberates me.
Speaker 1: That is quite inspiring to hear, Karen. How I'm going
Speaker 1: to think more about that and much else that you've shared.
Speaker 1: Thank you so much, Thank you so much for having me.
Speaker 1: And that's where we left things as we liberated Karen
Speaker 1: from the studio, and I hope you found her as
Speaker 1: thought provoking as I did. I want to add one
Speaker 1: note to the discussion on Malta, where all residents over
Speaker 1: fourteen can do an AI literacy course and get free
Speaker 1: access for a year to either Chat, GPT plus or
Speaker 1: Microsoft three six y five Personal Copilot. Karen said people
Speaker 1: in Malta were having their data harvested to train the
Speaker 1: next generation of models, and we put this to OpenAI
Speaker 1: and to Microsoft. OpenAI directed us to users being able
Speaker 1: to opt out of having their data used for training.
Speaker 1: Microsoft said we do not train on data from users
Speaker 1: of Copilot within Microsoft three sixty five apps with personal subscriptions.
Speaker 1: Karen also said she'd liked to know what the people
Speaker 1: of Malta think rather than the government. A recent survey
Speaker 1: suggested more than fifty percent of Maltese expect AI to
Speaker 1: have a positive impact on their lives over the next decade,
Speaker 1: and that was above the EU average. It is also
Speaker 1: worth noting Sam Altman's own perspective in something that he
Speaker 1: said in a blog post a few months ago. He wrote,
Speaker 1: the fear and anxiety about AI is justified. We are
Speaker 1: in the process of witnessing the largest change to society
Speaker 1: in a long time and perhaps ever. He added, AI
Speaker 1: has to be democratized. Power cannot be too concentrated. This,
Speaker 1: by the way, is just one of several conversations that
Speaker 1: we've had on the show that relate to AI. So
Speaker 1: in the show notes you'll find links to episodes with
Speaker 1: Mustapha Suliman of Microsoft AI. He gets pretty evangelical about
Speaker 1: what AI personal assistance can do. And you can also
Speaker 1: contrast that with signals Meredith Whittaker, who is having none
Speaker 1: of Mustapha's AI can buy your Christmas presents. And there's
Speaker 1: the conversation with fay Fei Lee, the so called godmother
Speaker 1: of AI, where she describes the light bulb moment when
Speaker 1: she understood how images could be gathered into a data
Speaker 1: set to train AI. And with that my final note
Speaker 1: which is on the team. The producers are Jessica Beck
Speaker 1: and Chris Martlouth. The video producer is Andy Haywood. Our
Speaker 1: social media is by Alex Morgan and the music is
Speaker 1: composed by Bart Walshaw. The audio mixing was by Richard Ward.
Speaker 1: Our executive producer is Louisa Lewis and at Bloomberg Weekend,
Speaker 1: our thanks to Brendan Francis Newnham and our executive editor
Speaker 1: Catherine Bell. Until next time, goodbye,