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Speaker 2: Welcome to in the City.
Speaker 1: Stand clear of the doors. Welcome to in the City.
Speaker 1: A podcast from Bloomberg about the story is important to
Speaker 1: the City of London. I'm Francin Laqua. This week a
Speaker 1: discussion of Europe's productivity problem and whether AI is perceived
Speaker 1: as a silver bullet or a threat. We're joined by
Speaker 1: Matt Britain, President of Google EMA. Matt, thank you so
Speaker 1: much for joining us. I mean, you've ever seen Google's
Speaker 1: business and opera in the region for ten years now,
Speaker 1: having joined Google in two thousand and seven. Before that,
Speaker 1: you worked in newspapers, technology and even road for TEAMGB
Speaker 1: at the nineteen ID eight Soul Olympics. So we need
Speaker 1: to talk about the Olympics. But you know, there's so
Speaker 1: much about AI and people understand it. People are excited,
Speaker 1: people are fearful. Where are we in this AI race?
Speaker 2: Thanks for having me, and I suppose it's been my
Speaker 2: joy and privilege to see how people harness technology over
Speaker 2: the last twenty to thirty years, and I think, you know,
Speaker 2: through that period, I think we're in the third big shift.
Speaker 2: At first, I think was the birth of the Internet.
Speaker 2: So I was at university and the Internet application you
Speaker 2: could use was email, and it wasn't mind blowing because
Speaker 2: there was somebody sitting next to me sending me the email.
Speaker 2: That was where it going to come from. And then
Speaker 2: when I started in the world of work, we saw
Speaker 2: the birth of the World Wide Web, which is actually
Speaker 2: invented as I was leaving university. And obviously I've worked
Speaker 2: for twenty years in a company in roles that have
Speaker 2: depended upon that innovation, but that wasn't earth shattering until
Speaker 2: it became accessible to everyone. And Google's played its part
Speaker 2: through Android and through Chrome and making it easy for
Speaker 2: people to use the web. And now we've got this
Speaker 2: AI revolution. And I suppose the way I think about
Speaker 2: AI is it's about spotting patterns, making predictions and learning.
Speaker 2: AI is not new. We've been working on it for
Speaker 2: about ten years. And Google Translate, my favorite product, is
Speaker 2: a great example of that, really understanding language well. But
Speaker 2: what is new, I think is the emergence of chatbots
Speaker 2: and people thinking, oh AI and chatbots are going to
Speaker 2: change everything, and I think it's a much broader based
Speaker 2: technology than that. So we don't yet know how it's
Speaker 2: going to be harnessed, but we can begin to see
Speaker 2: some of the opportunities and some of the risks of AI.
Speaker 1: So, Matt, one of the industries where actually, you know,
Speaker 1: AI has been applied for good is everything to do
Speaker 1: with health and protein folding, and that leads to many
Speaker 1: more discoveries like DeepMind.
Speaker 2: Yeah, so this is one of the things I think
Speaker 2: is most exciting is the scientific discovery impossibility. So there
Speaker 2: were one hundred and seventy five thousand proteins. They're the
Speaker 2: building blocks of life, they're essential for drug and disease research,
Speaker 2: one hundred and seventy five thousand of them, where researchers
Speaker 2: are painstakingly through observation come up with a three D structure.
Speaker 2: And deep Minds saw the opportunity to use AI that
Speaker 2: spots patterns, makes predictions and learns to uncover the three
Speaker 2: D structure of all the proteins, and in a matter
Speaker 2: of months they did just that. So we went from
Speaker 2: one hundred and seventy five thousand to two hundred and
Speaker 2: fourteen million and we chose to make that available for
Speaker 2: free in a database so that anyone researching a disease
Speaker 2: or looking at drug discovery can use it. And today
Speaker 2: two million people are using the database of proteins in
Speaker 2: their day to day work, and I think that means
Speaker 2: we'll see faster disease research, drug discovery, and breakthroughs that
Speaker 2: are for the general good. That's something we made available
Speaker 2: for free, and I feel proud of that because it's
Speaker 2: built on the back of all that painstaking research. We
Speaker 2: also have Isomorphic Labs, which is a commercial venture, and
Speaker 2: we're working in partnerships with Novarta and other drug companies
Speaker 2: to look at commercial opportunities to bring those discoveries to market.
Speaker 1: Mattter, how difficult is it to decide what it goes
Speaker 1: out in the open world so that everyone can use
Speaker 1: the underlying models, and how much you want to keep
Speaker 1: it at it for Google?
Speaker 2: Yeah, So what we did with Alpha fold and now
Speaker 2: I'm proud of this, you know, and Google's history is
Speaker 2: always about trying to be open about innovation. When you
Speaker 2: think about search, it's connecting to your website, not ours.
Speaker 2: What we did was we said, well, you know, we've
Speaker 2: built this on the hard work of all those people
Speaker 2: who got to the one hundred and seventy five thousand proteins,
Speaker 2: and it's too important a discovery to keep behind a paywall,
Speaker 2: so we made it available to everyone. Now. We also
Speaker 2: have a commercial operation called Isomorphic Labs that's looking, as
Speaker 2: others are to sort of exploit those those databases to
Speaker 2: try to find commercial opportunities, and we're working with Novartis
Speaker 2: and other farmer companies to do that because obviously you're
Speaker 2: going to need to commercialize these things too. So I
Speaker 2: think that's a smart but generous way of operating in
Speaker 2: this space, and that.
Speaker 1: Of course changes vaccines, that changes the way we do trials.
Speaker 1: Is there a part Is there an industry that's not
Speaker 1: been disrupted yet by AI that you think? Well?
Speaker 2: I mean from sine, I think we're at an incredibly
Speaker 2: early stage here, And you know, I talk a lot
Speaker 2: of business leaders as you do, and to governments, and
Speaker 2: I think last year was like the wow, you know,
Speaker 2: oh my god, this is amazing. Have you tried this yet?
Speaker 2: Et cetera. People could see for the first time in
Speaker 2: their practical ways, this is quite capable. But now we're
Speaker 2: into the how, and we're relatively short of concrete case
Speaker 2: studies yet of the genuine positive impact all the risks
Speaker 2: that can come right, but we're starting to see them
Speaker 2: so in an interesting area. So you know, one example
Speaker 2: that many of your listeners will sort of be aware of.
Speaker 2: If you think about fraud and money laundering in the
Speaker 2: banking sector, it's something like a two trillion dollar problem globally.
Speaker 2: And our team worked with HSBC spotting patterns, making predictions
Speaker 2: and learning to identify patents that could be fraudulent transactions,
Speaker 2: and they were able to massively reduce false positives but
Speaker 2: massively increase the number of potential transactions to look at.
Speaker 2: And so that's a good example of applying this technology
Speaker 2: in a very practical way that can create value.
Speaker 1: When you speak to a lot of the banking chief executives,
Speaker 1: they'll say units thanks to us that we spotted a pattern,
Speaker 1: for example, on the algorithm that led to a stopping
Speaker 1: a fraudulent case trying to get access money in a
Speaker 1: central bank, and the other that is great. It also
Speaker 1: means that you can replicate a voice pattern to access
Speaker 1: money when it's not needed.
Speaker 2: Technology is a tool and it can be used for
Speaker 2: good and it can be used for ill and I
Speaker 2: think we need to be really cognizant of that when
Speaker 2: we're designing and building things, to try to sort of
Speaker 2: factor in things that make it harder to use it
Speaker 2: for ill. But also we need rules and regulations, and
Speaker 2: that's why things like the AI Act in Europe are important.
Speaker 2: It's too important not to regulate AI and it needs
Speaker 2: to be regulated well, and that means sort of being dynamic,
Speaker 2: you know. The way to think about regulating AI is
Speaker 2: to think about the benefits that are possible and then
Speaker 2: how do we protect against the risks. And another example
Speaker 2: we're working on is breast cancer screening. Right, so one
Speaker 2: in seven women in their lives are going to be
Speaker 2: affected by breast cancer, and yet access to mammograms and
Speaker 2: simple diagnostic knostic technology is limited and so spotting patterns,
Speaker 2: making predictions learning. We work with the NHS here in
Speaker 2: the UK to look at thousands of mammograms, train and AI.
Speaker 2: Now the AI can diagnose the science of breast cancer
Speaker 2: as well as a clinician. It does not mean that
Speaker 2: clinicians are irrelevant. Actually, what it means is you come
Speaker 2: in and you get an answer quicker. It means that
Speaker 2: clinicians can help on the edge cases and the people
Speaker 2: need treatment, and so the outcomes for patients and clinicians
Speaker 2: are both better. And I think that's a good and
Speaker 2: we're in the process of looking at how to scale
Speaker 2: and roll that out. That's a good example of how
Speaker 2: I think AI will work. It's humans and technology working
Speaker 2: to get better outcomes as a result of the collaboration
Speaker 2: and partnerships. I think the brand AI isn't a very
Speaker 2: helpful thing. It's a set of tools that are smarter
Speaker 2: and different from some of the tools we've had.
Speaker 1: But is there a danger in Europe regulation at the
Speaker 1: stifles innovation because if you regulate too quickly then you
Speaker 1: don't know where it could go.
Speaker 2: Yeah, I think there's a real danger of that. We
Speaker 2: do need regulation, and the regulation needs to be clear
Speaker 2: on the sort of rules of the road. But if
Speaker 2: you look in Europe. In the EU, over the last
Speaker 2: five years, there have been over one hundred pieces of
Speaker 2: regulation around digital technology one hundred and those pieces of
Speaker 2: regulation typically are implemented in different ways at different speeds
Speaker 2: in all the twenty seven member states. Now think about
Speaker 2: that as a barrier to innovation for a moment, that's
Speaker 2: really hard to contend with and you might say, actually,
Speaker 2: it's something which makes it particularly hard for startups and
Speaker 2: scale ups or European single country companies, whereas a large
Speaker 2: organization like a Google or a Microsoft can absorb some
Speaker 2: of the costs of doing that. But even for us,
Speaker 2: you know, and our competitors, we've had to slow down
Speaker 2: the launchers of some of our AI based product products
Speaker 2: in Europe. So what's the right thing for the European consumer?
Speaker 2: You know, it's interesting Mario Draugi, who's I think really
Speaker 2: well respected, is working on a proposal for the EU
Speaker 2: around competitiveness. So one of the big opportunities of AI,
Speaker 2: I think is can it help us get more people
Speaker 2: into work, help people be more productive and drive growth
Speaker 2: which we all need.
Speaker 1: Is it difficult and too complex to understand what the
Speaker 1: roles of tomorrow will be with the jobs of tomorrow.
Speaker 2: We're in a moment where the headlines about you know,
Speaker 2: what could happen to work almost write themselves, and you know,
Speaker 2: jokingly you might say they can write themselves and you
Speaker 2: just put a prompt in, but nobody really knows. And actually,
Speaker 2: again it's an opportunity for us to seize the moment,
Speaker 2: and so how do we want the world of work
Speaker 2: to be? You know, I think there's lots of drudgery
Speaker 2: work that none of us would really like to do it,
Speaker 2: and our jobs that can go away, you know. And
Speaker 2: there are many technologies that we use today that people
Speaker 2: feared when they came in but actually just made us
Speaker 2: much more productive. So I think for most jobs, the
Speaker 2: most likely outcome is you'll be more productive. You'll be
Speaker 2: able to spend more time on things that only humans
Speaker 2: can do, like what So I think, you know, think
Speaker 2: about your work, you know, think about your work before
Speaker 2: the advent of the internet, and how easy it is
Speaker 2: now for you to research stories, to find leads, to
Speaker 2: reach out to contacts. How do you think about your
Speaker 2: work tomorrow? Well, you know, we have a product called
Speaker 2: Notebook LM where you can put in a whole bunch
Speaker 2: of files that you might be researching, videos, audio, transcripts,
Speaker 2: and then you can ask questions of the AI about
Speaker 2: specifically those those documents and artifacts that you feed in
Speaker 2: and that can really help you synthesize, get summaries and
Speaker 2: so on. So I think, you know, that's a small example,
Speaker 2: but an example of how new, more powerful tools will
Speaker 2: help you to do you do your work differently.
Speaker 1: Matt when you look at, you know, some of the
Speaker 1: public services. I keep on being told by heads of
Speaker 1: the departments that actually their computers don't even match, you know,
Speaker 1: and this is not necessarily NHS, this is other parts
Speaker 1: where they should match. So is there a danger that
Speaker 1: in an already divided society where some people have a
Speaker 1: lot and others don't, that this you know, social divide
Speaker 1: will grow even bigger with AI if we don't think
Speaker 1: about it holistically. I guess yeah.
Speaker 2: One of the things that most exercises is, you know,
Speaker 2: over the long term, do the divisions within countries and
Speaker 2: across countries widen? And I think technology historically has actually
Speaker 2: helped us if you look at the long view, has
Speaker 2: helped us to sort of see millions, billions of people
Speaker 2: lifted out of poverty. We're at an all time high,
Speaker 2: I think in terms of women's education and so on
Speaker 2: and so forth. So sometimes the big picture misses the small.
Speaker 2: But you're absolutely right, we need to avoid it haves
Speaker 2: and haves, not situation with technology. What's kept me working
Speaker 2: at Google for so long is, you know, we're all
Speaker 2: about making technology work for everyone. You know, whether you
Speaker 2: can type or spell, doesn't matter, whatever language you speak
Speaker 2: doesn't matter. We want it to work for you. And
Speaker 2: I think with AI it's the same. We need to
Speaker 2: really think about how do we harness technology for everyone.
Speaker 2: What the opportunity is. Well, you know, today somebody without
Speaker 2: a formal education can now write almost as well as you,
Speaker 2: and I could probably not quite as well as you,
Speaker 2: but as well as I could much better. That's a
Speaker 2: huge that's a huge opportunity. I can communicate with somebody,
Speaker 2: you know in Thailand and I don't speak type, but
Speaker 2: I can communicate in pretty good tie now thanks to
Speaker 2: these tools. So you think about that. I actually think
Speaker 2: we're going to see an opportunity for many people who
Speaker 2: don't have access to the formal education sector to participate
Speaker 2: much more fully in the world of work.
Speaker 1: Whose job is it to think about retraining? Is it
Speaker 1: private companies, is it actors like you and other AI proponents,
Speaker 1: or is it governments?
Speaker 2: I always think about three words when I think about this.
Speaker 2: It's bold, responsible, and together. So bold is about making
Speaker 2: sure we pursue the biggest benefits of innovations, and we've
Speaker 2: talked to some of them. Responsible is about from the
Speaker 2: start thinking about misuse mistakes and how we kind of
Speaker 2: design that out and create the rules of the road.
Speaker 2: But then together, and I think that's the most important bit.
Speaker 2: You know, when you look at these sort of scientific breakthroughs,
Speaker 2: it's not our job, and it can't be fully government's job,
Speaker 2: and it's not the individual's job. It's a collaboration. So
Speaker 2: you need government and companies and communities to work together
Speaker 2: to harness technology for good, to decide what the rules
Speaker 2: of the road are. You know, governments have to regulate.
Speaker 2: That tends to be a slower process, but principles and
Speaker 2: responsibilities can be and there companies come up with codes
Speaker 2: of practice and can be scrutinized, and that's an important
Speaker 2: thing there. But then communities actually are the places where
Speaker 2: you really want to define what is and isn't acceptable,
Speaker 2: and those things can vary across countries.
Speaker 1: As we know, we've seen some pretty extraordinary pictures on
Speaker 1: the riots in the UK. How far does the responsibility
Speaker 1: go for some of the tech platforms, some of the
Speaker 1: social media actors to keep that in check with disinformation.
Speaker 2: I mean, at first I want to say that Hart
Speaker 2: goes out to families in the community in Southport who
Speaker 2: are affected by that horrible attack at the start of this,
Speaker 2: and then we've seen this series of riots across the countries,
Speaker 2: sort of somewhat disconnected from the reality of that. Of course,
Speaker 2: you know, I think the real world and the internet,
Speaker 2: or the real world and the digital world are the
Speaker 2: same place and the same rules should apply, and the
Speaker 2: role of media, both social and traditional media in this
Speaker 2: is something we should be looking at. How do we
Speaker 2: deal with this? Google, I suppose you know two things. One,
Speaker 2: Search is often the place that people come to try
Speaker 2: and find out the truth, and that's why we work
Speaker 2: with news organizations around the world on search and on YouTube,
Speaker 2: so that when you ask about news, you're pointed to
Speaker 2: credible news sources. Now there's a difference of views across
Speaker 2: those news sources, but you can find a selection and
Speaker 2: that's a key role that we play, and that's why
Speaker 2: we work so closely with news organizations. Second thing for us, though,
Speaker 2: is to have policies that are clear about what we
Speaker 2: will and will not allow on our platforms. On something
Speaker 2: like hate speech. It's not easy, so people would say,
Speaker 2: you know, on YouTube, you know, you seem to be
Speaker 2: pretty good at keeping it clear of pornography, but actually
Speaker 2: if you ask people in the street, you have pretty
Speaker 2: clear agreement on what was and was not pornographic. But
Speaker 2: on something like hate speech or incitement of violence, there
Speaker 2: are lots and lots of shades of gray. And about
Speaker 2: ten years ago we had a real challenge on YouTube
Speaker 2: with videos and across the web, but YouTube as well,
Speaker 2: videos that were about sort of inciting extremist behavior, hate
Speaker 2: speech and so on. And we work with one hundred
Speaker 2: and fifty expert organizations to come up with policies which
Speaker 2: we then had people classify video. We then use the
Speaker 2: classified videos to train AI and then we published transparently
Speaker 2: how successful we are. So I can say that today
Speaker 2: over ninety percent of videos that violate our violent extremism
Speaker 2: policies never get seen by a single human, and then
Speaker 2: the rest of them we can take action very quickly.
Speaker 2: So in the case of the UK, our teams are
Speaker 2: able to move quickly to remove violative videos and channels
Speaker 2: than they've done.
Speaker 1: So how difficult is it to actually know where this
Speaker 1: piece of disinformation comes from? If it's from a state actor,
Speaker 1: is it more difficult for Google to channel than if
Speaker 1: it's from an individual?
Speaker 2: Yeah, So if a zoom out from the UK situation
Speaker 2: for a minute. What we see is, yes, there are
Speaker 2: an array of actors who try to intervene in the
Speaker 2: democratic process for example. So you know, we have a
Speaker 2: team that looks after election integrity across our platforms and
Speaker 2: in the recent EU elections, they're absolutely working closely with
Speaker 2: the election authorities training people on how to use the tools,
Speaker 2: et cetera. And we have a threat analysis group that
Speaker 2: shares with governments and key agencies. What we're seeing because
Speaker 2: we've got lots of popular products, so we see lots
Speaker 2: of attacks. Well, I can say is in the EU
Speaker 2: elections actually it was sort of more old style attacks.
Speaker 2: I think Microsoft suffered some attacks on accounts for example,
Speaker 2: rather than that was feared. You know a lot of
Speaker 2: fake AI generated imagery. But just as we have tools
Speaker 2: that people can use to generate with AI fakery, the
Speaker 2: technology we've got to be able to detect it is accelerating.
Speaker 2: And so we've been using AI on YouTube, as I
Speaker 2: just mentioned, extensively for years to classify and address videos.
Speaker 1: Are we over estimating how quickly some of the AI
Speaker 1: products will take over? We had this amazing interview with
Speaker 1: the Bumble founder and she was saying, you know, for dating,
Speaker 1: for example, you're going to have a personal assistant that
Speaker 1: kind of dates for your online and then reports back
Speaker 1: on who's a super suitable match. I mean that feels
Speaker 1: like science fiction.
Speaker 2: I don't know, bumble, I should just be clear, But
Speaker 2: I think it's up to us, Like you know, none
Speaker 2: of them, this is inevitable. And this is my point
Speaker 2: about like this is a moment where we have to
Speaker 2: sort of engage into this stuff and take control. So
Speaker 2: some people might want that for dating. I'm not sure
Speaker 2: I would, but you know, you'd have a choice. Or
Speaker 2: maybe you've got an advisor who said, here at some
Speaker 2: better suggestions or whatever. That's great. Same as you know,
Speaker 2: reading the news. Actually, I might want to have a
Speaker 2: little bit more dissonance in my consumption of news, and
Speaker 2: maybe an a I could suggest that. Or I might
Speaker 2: want to have an expert summary of a long running story,
Speaker 2: and if I could come to Bloomberg and your AI
Speaker 2: assistant would give me a brilliant summary of some of
Speaker 2: your best journalism around a complicated issue, that would be useful.
Speaker 2: But I think we have a choice, and it's up
Speaker 2: to us to make those choices.
Speaker 1: Who's it up to the consumer, or is it actually
Speaker 1: you know companies like Google to decide. I mean, if
Speaker 1: we were all to be given you an assistance that
Speaker 1: can help you in everyday life, is it too soon
Speaker 1: to say whether that's a good or a bad thing?
Speaker 2: Let me step back. I think where we are is
Speaker 2: we've got this broad based technology. We're beginning to see
Speaker 2: the potential, but most of the applications of that technology
Speaker 2: have yet to be invented. So we can get lots
Speaker 2: in generalizations. But you know, a specific example about how
Speaker 2: it might work with dating we could talk about, or
Speaker 2: how it might work with newsroom productivity we could talk about,
Speaker 2: or how it helps call center agents. Gives you better examples,
Speaker 2: So I think we need to look at the applications.
Speaker 1: How do you hire it at Google? I've heard from
Speaker 1: some tech companies that it's all about morals and values
Speaker 1: and it doesn't really matter if the candidate knows that
Speaker 1: much about technology to start with.
Speaker 2: Yes, absolutely, we try to hire people of good character,
Speaker 2: but also people who are collaborative, curious, creative, good communicators.
Speaker 2: I think the most important things when we're hiring are
Speaker 2: those skills of curiosity, creativity, collaboration, communication, Because we're asking
Speaker 2: people to invent technology that didn't exist before, and we're
Speaker 2: asking people to sort of explain that technology to the world,
Speaker 2: to listen and engage with governments and companies and partners
Speaker 2: on how we do that. What I'd say to people
Speaker 2: thinking about, you know, maybe choices for their children today
Speaker 2: with their children, I think it's really important we have
Speaker 2: a broad education. You know, innovation comes from interdisciplinary collisions, right,
Speaker 2: and I don't think we should be overly focused on STEM.
Speaker 2: I think we do need representation so that people building
Speaker 2: the technology have to be representative of everyone. And that's
Speaker 2: been a challenge and one of the reasons we've published
Speaker 2: our own data on diversity and trying to improve particularly
Speaker 2: representation representation of women and minority ethnic groups in the
Speaker 2: engineering side of Google. We've made progress, but there's more
Speaker 2: to do, so i'd encourage people. You need it broad
Speaker 2: based education, but those skills about curiosity, creativity, collaboration are
Speaker 2: going to be more.
Speaker 1: Important in the future early days. But how do you
Speaker 1: judge this new government in the UK and how they
Speaker 1: engage with technology.
Speaker 2: We don't make it want to make any political comment
Speaker 2: other than to say that I think the UK's had
Speaker 2: a long period of instability, and if you're an international
Speaker 2: investor and company like Google, having clarity and a sense
Speaker 2: of stability is important. I think also there's a recognition
Speaker 2: that the UK needs growth and that's an absolute priority,
Speaker 2: and I think harnessing technology across sectors can be one
Speaker 2: of the key ways in which we activate that growth. Growth. Secondly,
Speaker 2: harnessing young people and their talent and their desire. It's
Speaker 2: something that feels like it may have been a bit neglected,
Speaker 2: and you know, I really think that's an important thing
Speaker 2: for us to do as a country. So I think
Speaker 2: there's a big opportunity for the UK.
Speaker 1: Matt, you've rowed for a team GB. Congratulations. There's a lot.
Speaker 1: I mean is that, you know, we talk more and
Speaker 1: more about sports, and actually what you learn in sports,
Speaker 1: especially at that level for applications in business, what did
Speaker 1: you learn from it?
Speaker 2: I rowed when we were much less successful than we
Speaker 2: are today. But I have to say that, you know,
Speaker 2: I went to business school, I worked in a consulting company.
Speaker 2: The stuff I used most in my day to day
Speaker 2: work I probably learned from sport. And it's that being
Speaker 2: clear about defining a goal and then bringing people together
Speaker 2: around a vision of what's possible and collaborating. I always
Speaker 2: say to my teams, you know, get the best people
Speaker 2: in your boat, and then let's make sure we row together.
Speaker 2: And so there is something about that sort of skill
Speaker 2: of competing and collaborating together. The same applies to how
Speaker 2: we partner with companies. You know, we're going to be competing,
Speaker 2: we're going to be collaborating, we're going to be learning
Speaker 2: from each other, and we need to have a grown
Speaker 2: up approach to doing that. And I think I learned
Speaker 2: a lot from Sport about those things.
Speaker 1: Matt, thank you so much for joining us.
Speaker 2: Thank you.
Speaker 1: Thanks for listening to this week's In the City from Bloomberg.
Speaker 1: This episode was hosted by me franc I Laqua. It
Speaker 1: was produced by Summer Sami, production support from Isabella Ward,
Speaker 1: and sound designed by Moses and Dan Brendan Franz Newman
Speaker 1: is our executive producer. Sage Bauman is head of Podcasts
Speaker 1: Special thanks to Matt Britten. Please subscribe, rate, and review
Speaker 1: wherever you listen to podcasts