Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lawns Podcast.
Speaker 3: I'm Jill Wisenthal and I'm Tracy Alloway.
Speaker 2: We're still here in Madrid. Tracy, how are still here?
Speaker 2: You have a good time?
Speaker 3: I am. I've eaten a lot of ham and cheese
Speaker 3: and that's pretty much all I can say.
Speaker 2: Yeah, I'm going to turn into a hormone by the
Speaker 2: time I leave. I'm certain of that. So we are
Speaker 2: at the Bloomberg City Lab conference. You know, it's funny,
Speaker 2: like the mayoral level of politics, not something we spend
Speaker 2: a ton of time typically on, but I would say,
Speaker 2: like it definitely. You know when I think about it,
Speaker 2: when I'm like here and like listening to a lot
Speaker 2: of the conversations, it's just so obviously like connected to
Speaker 2: a lot of the themes we talk about, because so
Speaker 2: much of our discussions have to do with something related
Speaker 2: to you know, innovation or technology or implementation of policy
Speaker 2: and how it spans both the public and private sectors. Yeah.
Speaker 3: I mean also when I think about a lot of
Speaker 3: a thloughts topics like AI or housing affordability or inequality
Speaker 3: like cities, I think City lab actually use this phrase
Speaker 3: at one point, but cities are really at the front
Speaker 3: line of all of those challenges, right, and trying to
Speaker 3: implement policy in that local level in a way that's
Speaker 3: like very easy to see and observe and also to
Speaker 3: judge totally.
Speaker 2: Yeah, no, it's exactly right. And I think maybe there's
Speaker 2: something like, you know, I don't it feels like to
Speaker 2: some extent, the mayoral level of any governance to sort
Speaker 2: of maybe the least ideological and the most like, you know,
Speaker 2: we talked to the Baltimore mayor, for example, and so
Speaker 2: much of his theme was just like talking to other mayors,
Speaker 2: like what's working in your town, that's working in your city,
Speaker 2: and what's not working and so forth. They all have
Speaker 2: such similar challenges, so many things have been like repeated,
Speaker 2: you know, one time after another that like they all
Speaker 2: can sort of speak the same language and all have
Speaker 2: the same issues.
Speaker 3: Yeah, it's funny. You kind of got that local idea
Speaker 3: swapping at a mayor a level that I cannot imagine
Speaker 3: necessarily happening in national politics. Like can you imagine Trump
Speaker 3: and Chi Himpin getting together and be like, oh, we
Speaker 3: implemented this really cool, like national transportation program. Have you
Speaker 3: tried it?
Speaker 2: Yeah, you know, know you hear like these stories right
Speaker 2: of a governor sometimes or a mayor will go to
Speaker 2: like another country like oh we can like learn from
Speaker 2: what the city did on transit or whatever. But no,
Speaker 2: you don't really hear that the same way at the
Speaker 2: real national level, where a lot of our discussions tend
Speaker 2: to sit.
Speaker 4: Absolutely Anyway, I'm really.
Speaker 2: Excited about today's episode. We're going to be speaking with
Speaker 2: a guest who I would say, like, since we've been
Speaker 2: doing Odd Lives, is actually one of the more frequently requested,
Speaker 2: long requested, and so it's sort of a failure on
Speaker 2: our part that like we just like never made it
Speaker 2: happen before. But yeah, someone who like really like those
Speaker 2: whole career is like dedicated to a lot of odd,
Speaker 2: lozzy things.
Speaker 3: Well, we waited until we could do it at Madri,
Speaker 3: but I.
Speaker 2: Didn't want to just do it at any other random venue. Yes,
Speaker 2: it was all very strategically designed. So we really do
Speaker 2: have the perfect guest. Some of that A lot of
Speaker 2: guests have wanted to listen to here from for a
Speaker 2: long time. We're going to be speaking with Mariana Matzicato,
Speaker 2: professor a University College London, founder of the Institute for
Speaker 2: Public Purpose, author of several books sort of touching on
Speaker 2: these themes of technology, public sector, private sector, the roots
Speaker 2: of innovation, how these things actually get deployed. So, Professor Matzakato,
Speaker 2: thank you so much for coming on ODL. Finally, finally,
Speaker 2: are completely our fault for not having made it happen sooner.
Speaker 2: But what do you just started? Like, what are you
Speaker 2: doing here? What brings you to this particular conference a
Speaker 2: what attracts you here?
Speaker 4: Well, first of all, I'm here also because the government
Speaker 4: of Spain is really kind of leading the way in
Speaker 4: many areas that I'm very interested in, especially the kind
Speaker 4: of new economic thinking that needs to underpin how we
Speaker 4: rethink government. And so I was meeting with both the
Speaker 4: Prime Minister and the Minister for the Economy, Carol Squerpo,
Speaker 4: who I'm on a panel today with about a council
Speaker 4: that we've just set up. It's called the Global Council
Speaker 4: for a Common Good Economy anyway, And besides that, I'm
Speaker 4: also here to speak at the Bloomberg City Labs event
Speaker 4: about this new public sector Capability index that we've been
Speaker 4: developing with them, as a partner, which is really about
Speaker 4: reinvesting inside the civil service so that they can really
Speaker 4: tackle those wicked complex problems instead of you know, hiring
Speaker 4: Deloitte during COVID and giving them one point five million
Speaker 4: a day to do test and trace, which they fail
Speaker 4: at in the UK.
Speaker 3: I definitely want to get you to go off on consultants.
Speaker 3: But before we do, like when you meet with someone
Speaker 3: like the Prime Minister of Spain, what is it that
Speaker 3: they want to know from you? Like, what information or
Speaker 3: expertise are they seeking?
Speaker 4: Right? Well, I'm actually quite lucky that my books actually
Speaker 4: get read by the Prime minister. So usually what happens
Speaker 4: is that they've especially the Entrepreneurial State, which I know
Speaker 4: rud in twenty thirteen that had quite a bit of
Speaker 4: an effect here. They even wrote a report called el
Speaker 4: Espae and they we really want to ask me, you know,
Speaker 4: what does that mean? What does it mean for actually
Speaker 4: even being able to fail? For example? Right, so a
Speaker 4: venture capitalists are was bragging about all the failures that
Speaker 4: they had in order to get a success, you know,
Speaker 4: whereas as soon as a civil servant or a minister
Speaker 4: fails or prime minister fails. Front page of the papers,
Speaker 4: So they're very interested in the kind of narrative change,
Speaker 4: but also well the cultural change, but especially the theoretical underpinning.
Speaker 4: Would then say, a finance ministry or an economics ministry
Speaker 4: that needs to then a company rethinking government, because otherwise,
Speaker 4: if you continue to have the old economic models where
Speaker 4: we judge things by cost benefit analysis and at present value,
Speaker 4: all these really static metrics, we would have never even
Speaker 4: bothered going to the moon. Okay, first of all, in
Speaker 4: the nineteen sixties, had we thought of it as a
Speaker 4: cost benefit calculation and so what should replace that? Right?
Speaker 3: So you mentioned the Entrepreneurial State being published in twenty thirteen,
Speaker 3: and I feel like I need to emphasize this because
Speaker 3: there has been this mind shift since the twenty twenty
Speaker 3: pandemic on industrial policy, and we kind of take it
Speaker 3: almost as a given, especially on this show, that there
Speaker 3: is a role for governments to play when it comes
Speaker 3: to innovation and entrepreneurialism. But you were there very early.
Speaker 3: Did you feel vindicated by the twenty twenty shift.
Speaker 4: Well, I think there's a bit of a misunderstanding that
Speaker 4: just because government's spending a lot of money, investing a
Speaker 4: lot of money, whether it's the IRA or the next
Speaker 4: GENUU to trillion recovery plan in Europe. That that means
Speaker 4: that industrial strategy is back, or that you know, government
Speaker 4: is being really strategic. It depends, And I mean that's
Speaker 4: the whole point, right, you know, how are we actually
Speaker 4: framing it? Is it just a lot of tax incentives?
Speaker 4: Are we focused too much on sectors? To what degree
Speaker 4: is industrial strategy actually helping to create a more inclusive, sustainable,
Speaker 4: innovation driven, you know, economy, or is it just another
Speaker 4: wave of handouts and subsidies to particular sectors and then
Speaker 4: we end up socializing risks and privatizing rewards. And also,
Speaker 4: I mean I used to joke that the US government
Speaker 4: has always had an industrial strategy, but you know, pretended
Speaker 4: not to. And they've always talked to the Jefferson talk
Speaker 4: but acted the Hamilton talk or the Hamiltonian kind of
Speaker 4: more proactive government strategy. But by pretending that wasn't there,
Speaker 4: they kept a lot of things under the radar. And
Speaker 4: the joke bit is just that finally people understood what
Speaker 4: the hell was talking about because of the musical before
Speaker 4: the musical came out Hamilton anyway, So I think that's
Speaker 4: really important to recognize that industrial strategy has always been there,
Speaker 4: except that in certain phases, including now in some countries,
Speaker 4: it's not really strategy in terms of being driven by
Speaker 4: public purpose, which as you said, is the title in
Speaker 4: this Institute for Renovation of Public Purpose that I direct.
Speaker 4: It's been just kind of vertical strategies focused on sectors, technologies,
Speaker 4: types of firms, right, the whole focus on small, medium enterprises.
Speaker 4: So what I've been trying to do is to say,
Speaker 4: stop focusing on sectors, technologies and firms. Focus on big problems, right,
Speaker 4: as bold as going to the moon and back at
Speaker 4: a short amount of time, the mission idea, the missions
Speaker 4: that then require sexual support. But you're not getting support
Speaker 4: because you are a particular sector. You're getting support if
Speaker 4: you're willing. So moving away from picking winners to picking
Speaker 4: the willing, if you're willing to work with government around
Speaker 4: these very difficult challenges, which could be as I don't
Speaker 4: want to say simple, but as concrete as making sure
Speaker 4: that every child in a country has healthy, tasty, sustainable
Speaker 4: school lunch, right, not just school lunch. That was the
Speaker 4: Reagan thing where he said ketchup is a vegetable, So
Speaker 4: we can reduce the cost. We all had T shirts
Speaker 4: of Reagan and ketchup which was the vegetable is what
Speaker 4: the T shirt said. So having moonshots, even on something
Speaker 4: as simple as the cafeteria, that then requires innovation across
Speaker 4: many different areas. It requires government to wake up and
Speaker 4: not just have that be done. For example, from the
Speaker 4: Department of Education, you know, Department of Agriculture, Health, Education,
Speaker 4: Finance would work together on a moonshot of healthy, tasty,
Speaker 4: sustainable school lunch. So that interministerial coordination which we saw
Speaker 4: during COVID, right, so we had the war room, the
Speaker 4: situation room, you know, the military, education, health, and so
Speaker 4: on together because we had these really difficult challenges. But
Speaker 4: as soon as COVID stops, we go back to very
Speaker 4: siloed ways of thinking.
Speaker 2: As someone with two kids in the New York City
Speaker 2: of public schools, I can absolutely confirm that the degree
Speaker 2: to which they associate the mayor with school lunch policy
Speaker 2: is extremely real. And they like talk about like, oh,
Speaker 2: we used to have these like really nice waffles or something,
Speaker 2: and then Eric Adams got rid of home, or like
Speaker 2: they're really like key to single issue, the single issue.
Speaker 2: They're like, really pay attention to the degree to where
Speaker 2: school lunch policy shifts with administration, Like this is like
Speaker 2: the really they really think? What is state capacity? We've
Speaker 2: been using this term for years on the podcast and
Speaker 2: we're like during COVID, we're like, no, it took a took,
Speaker 2: you know, a long time to stand up testing facilities
Speaker 2: for since and people's like, oh, well, lot lack state capacity,
Speaker 2: But what is it? What's the definition?
Speaker 4: We actually distinguish between the word capacity routines kind of
Speaker 4: administrative routines and then capabilities. Okay, so what states are
Speaker 4: often what governments are often lacking or those kind of
Speaker 4: dynamic capability. So capacity partly is like literally do you
Speaker 4: even have fiscal space? Do you have a budget? But
Speaker 4: also how are you thinking about that budget in terms
Speaker 4: of do you well put it this way? Capacity is
Speaker 4: you know a number of people working in your administration,
Speaker 4: the budget that's been allocated, perhaps also the training you
Speaker 4: know that the civil service actually has. But capabilities are
Speaker 4: what you actually then do with it. Are you agile?
Speaker 4: Are you flexible? Are you able to pivot during COVID
Speaker 4: and actually start working in this more again inter ministerial way.
Speaker 4: Do you know how to work with others, right, you know,
Speaker 4: do you set up good partnerships or they problematic partnerships?
Speaker 4: And also you were talking before about what you're learning here,
Speaker 4: which is fantastic about how mayors can learn from each other. Right,
Speaker 4: have you invested in your ability to learn to adapt?
Speaker 4: And so I think that Ladder, you know this, this
Speaker 4: concept of dynamic capabilities is a much more complex area
Speaker 4: to be investing in. And the only reason you would
Speaker 4: do it is if you actually have a theory about
Speaker 4: government that is more than what traditional economists think about government,
Speaker 4: which is at best, well, at worst, get out of
Speaker 4: the way, at best, fix the market failure. So as
Speaker 4: soon as you say, actually, it's about shaping and co
Speaker 4: creating a different type of economy and society that works
Speaker 4: for people in planet, then the question is what does
Speaker 4: that mean for the capabilities that you need. If you're
Speaker 4: just fixing, then you just need a lot of bandages.
Speaker 4: And that's in fact what we get. We get very
Speaker 4: reactive kind of filling the gap kind of.
Speaker 3: Policies, pendulum constantly swinging exactly exactly.
Speaker 4: But I mean capacity, of course is essential. Without a
Speaker 4: budget and fiscal space, you can do nothing. That third
Speaker 4: category that I mentioned quickly administrative routines, that's you know,
Speaker 4: are you also you know, do you have a stable
Speaker 4: environment where you can learn by doing? Because if you're
Speaker 4: constantly changing what you're doing, it's going to be hard
Speaker 4: to have a learning by doing dynamics. So those kind
Speaker 4: of administrative routines I even see this on my university,
Speaker 4: where as soon as you get a lot of turnover,
Speaker 4: even those kind of basic routines aren't there. But capability.
Speaker 4: So these three areas, capacity, routines and capabilities are equally important,
Speaker 4: but the capabilities are really what I find are lacking.
Speaker 4: And it goes back, as I was mentioning before, to
Speaker 4: the underlying economic dogma that has underpinned the way that
Speaker 4: we think about policy government at different levels that by design,
Speaker 4: not by coincidence, is reactive.
Speaker 3: Have consultants become a substitute for state capacity slash routine
Speaker 3: slash administrative ability?
Speaker 4: Right?
Speaker 3: I always wonder how we got to the point where
Speaker 3: consultants are so big anyway, because it feels like every
Speaker 3: time you hire a consultant, it's almost an admission of
Speaker 3: failure on your part to be able to do something right.
Speaker 3: And nevertheless, it's a widely accepted practice across governments.
Speaker 4: So my last book actually was called The Big con
Speaker 4: So Confort Consulting, and the subtitle was how We've weakened
Speaker 4: businesses and fantalized governments and warped our economies basically due
Speaker 4: to this consultification. I wouldn't blame the consultants. I mean
Speaker 4: I actually blame governments, right, like, why are you opening
Speaker 4: the door so wide to consultants? And it's fine to
Speaker 4: have advisors and some consultants. The problem is when they
Speaker 4: are actually doing the core tasks that government should do. Again,
Speaker 4: test and trace during COVID was a core task. And
Speaker 4: so I think it really stems from i'd say the
Speaker 4: eighties when we you know, the kind of Reagan Thatcher years.
Speaker 4: If you want, it always goes back to yeah, but
Speaker 4: actually in the UK, for example, it even increased more
Speaker 4: during the Labor government. So it's not it's not you know,
Speaker 4: one party, but it did begin i'd say in the
Speaker 4: eighties with this kind of downsizing of governments, which then
Speaker 4: ironically cost them more. Because as soon as you start
Speaker 4: downsizing without really strategically thinking what you need and what
Speaker 4: you don't need, of course you should trim the fat.
Speaker 4: There's no reason to have, you know, a bloated government structure.
Speaker 4: But when it's done for ideological reasons and not strategic reasons,
Speaker 4: then you know, ironically, then you end up not having
Speaker 4: those capabilities that you need as soon as you have
Speaker 4: a flood or you know, Brexit or COVID, And so
Speaker 4: I think then what happens with the consultants is it's
Speaker 4: not their fault that they're invited in. I do think
Speaker 4: it's very problematic what they end up doing once they're in.
Speaker 4: So there's huge conflicts of interest. Well, the biggest conflict
Speaker 4: of interest being that they have no incentive really to
Speaker 4: make government better later because they wouldn't have no contracts.
Speaker 4: It'd be like having a therapist your whole life, that therapist,
Speaker 4: which they're very.
Speaker 2: Good, which is which is how the therapy models are.
Speaker 3: Well, do you remember this came up in the episode
Speaker 3: we did about construction in New York and this idea
Speaker 3: that like one of the reasons it's so expensive and
Speaker 3: takes so long for like public funded projects is because
Speaker 3: consultants have no incentive to actually get the project done.
Speaker 4: Yes, but also they're often working on both sides of
Speaker 4: the street, right, so they'll be for example, consulting for
Speaker 4: I don't know, a state own enterprise like ESCOM in
Speaker 4: South Africa as well as the treasury. We should be
Speaker 4: regulating ESCOM or in Australia. The it's a famous case
Speaker 4: with PwC where they were consulting for a medical device
Speaker 4: company as well as the regulators of the medical device
Speaker 4: companies that come on. So that should just be illegal,
Speaker 4: right and again you know, getting the right kind of
Speaker 4: regulation that makes sure that we don't have these kind
Speaker 4: of scams. So what we also argued was, you know,
Speaker 4: the first thing is start investing back inside government so
Speaker 4: you don't need so much consulting. But also when you
Speaker 4: do bring in the consultants, make sure the contract's actually
Speaker 4: in bed learning within them and that you are also
Speaker 4: bringing in the right people. You know, if you have
Speaker 4: an oncology strategy, of course you should get the top
Speaker 4: doctors and consultants and cancer to advise. So the other
Speaker 4: huge problem is that these consultants when they're coming in,
Speaker 4: they often actually don't know very much and they end
Speaker 4: up really bothering the poor public servants that end up
Speaker 4: emailing oh would you mind telling me what you think
Speaker 4: about or sending me your you know, plan that we
Speaker 4: can study. It's like, why are you even working with
Speaker 4: government if you don't have within the consulting companies that
Speaker 4: deep expertise, which you know, I'm not saying this just
Speaker 4: because I'm an academic, but I don't think academics are
Speaker 4: used enough. You know, if you have a research center
Speaker 4: that's been thinking about climate change for the last forty years,
Speaker 4: use them. Don't ask McKenzie as Australia did to design
Speaker 4: your climate strategy, which ended up, by the way, being terrible.
Speaker 4: So there is a bit of why is it that
Speaker 4: governments A don't invest in their own capacity capabilities, and
Speaker 4: B when they do go out there and look for
Speaker 4: the advisors and consultants are kind of getting the ones
Speaker 4: that simply kind of provide a rubber stack right that
Speaker 4: makes them feel more secure. They haven't even done the
Speaker 4: homework to make sure they are getting the top people
Speaker 4: in the world to help advise them on doing.
Speaker 2: The process, just to take the other side of the client.
Speaker 2: So recently in New York City it was I don't know,
Speaker 2: I think scandal is too strong a word, but there
Speaker 2: are a lot of headlines about how much the city
Speaker 2: had paid consultants for working on redesigning the trash collection
Speaker 2: system in the city. And on the one hand, it's
Speaker 2: a trash collection core city function, why do you have
Speaker 2: to bring in a consultant. On the other hand, reimagining
Speaker 2: the trash collection system is like, that's a one time
Speaker 2: so maintaining it, implementing it, servicing it, okay, that's like
Speaker 2: a permanent government function. But the actual like, okay, we
Speaker 2: have to do a redesign. That is that necessarily something
Speaker 2: that like we need to have in house in government
Speaker 2: that capacity, because do you want to permanently have that
Speaker 2: muscle because that's a one shot thing, doesn't it? Like
Speaker 2: to me, that makes sense? Is the time to bring in.
Speaker 3: At argue for something like that. There's a value to
Speaker 3: having an external viewpoint, right, that makes sense.
Speaker 2: There's like the redesign that's a one time job. Like
Speaker 2: to me, I was like, all right, that doesn't seem
Speaker 2: crazy to bring in a third party to like help
Speaker 2: figure out what that plan is exactly.
Speaker 4: So that's the myth, right that somehow we're talking about
Speaker 4: either government does everything, yeah, right, or even nationalize everything,
Speaker 4: or it does nothing and it privatizes and brings them
Speaker 4: the consultants. So the truth is obviously somewhere in the middle. So,
Speaker 4: of course you're absolutely right. Government doesn't have to have
Speaker 4: all those skills. It definitely needs the skills to know
Speaker 4: who to work with outside of government, but it also
Speaker 4: needs to even under stand that kind of outside landscape
Speaker 4: to even think about what might we need, how might
Speaker 4: we start developing a strategy that reimagines, say the trash
Speaker 4: collection process. So that's why you talk about missions. So
Speaker 4: when you know NASA wanted to go to the moon
Speaker 4: and back at a short amount of time, they didn't
Speaker 4: say we're going to do it all by ourselves. And
Speaker 4: they also didn't say we're just going to do it
Speaker 4: with the aerospace sector, right, They said, we're going to
Speaker 4: have to work with so many different private sector people.
Speaker 4: They ended up working with something like four hundred thousand
Speaker 4: people in the private sector. They said, we have a
Speaker 4: lot of problems, but we don't know the solutions, but
Speaker 4: we're going to set very clearly a direction for working
Speaker 4: with the private sector in a problem oriented way. So
Speaker 4: the first thing they did was change procurement. Procurement, you know,
Speaker 4: government purchasing is often like thirty percent of a government's budget.
Speaker 4: It's a very important part of their budget, whether it's Barbados,
Speaker 4: a small island state, or the US a very large government,
Speaker 4: procurement is there. How were we using it? So they
Speaker 4: realized they had the wrong type of procurement. It was
Speaker 4: just again minimizing costs. It was cost plus procurement. They
Speaker 4: changed it to outcomes into procurement and they started to
Speaker 4: ask themselves, what are the outcomes that we need. We
Speaker 4: need to figure out how are the astronauts going to
Speaker 4: go to the bathroom? Right, which, by the way, was
Speaker 4: just a problem again with Artemis the toilet, Oh is
Speaker 4: a problem up in space? What are they going to eat?
Speaker 4: What are they going to wear? How will we communicate
Speaker 4: with them? And it was the solutions to those problems
Speaker 4: that happened within mainly not only private sector institutions, with
Speaker 4: NASA's also kind of leading investment, but especially leading kind
Speaker 4: of thought process of what the problems were that ended
Speaker 4: up getting US camera phones, foil blankets, home insulation software,
Speaker 4: so many different innovations across many different sectors, aerospace, nutrition, materials, electronics.
Speaker 4: That itself is what we're talking about, right, So, whether
Speaker 4: it's going to the moon, whether it's trash collection, whether
Speaker 4: it's school meals, whether it's getting prepared for the next pandemic,
Speaker 4: which unfortunately the science tells us will happen. How were
Speaker 4: we even thinking within government in a problem oriented way,
Speaker 4: a solutions oriented way. And this, by the way, is
Speaker 4: why the City Lab conference is so wonderful and bloom
Speaker 4: Berg's Government Innovation Team is so important for so many cities,
Speaker 4: is because they then share their experiences of solving problems
Speaker 4: and then they ask and we're trying to help them
Speaker 4: do this with this public sector Capability index. What did
Speaker 4: we learn along the way that we were missing? Where
Speaker 4: were the bottlenecks? What can we do better? But especially
Speaker 4: in terms of that flexibility, adaptability, willingness to experiment. Right,
Speaker 4: remember what Kennedy said, We're doing it because it's hard,
Speaker 4: not because it's easy. Yet all the words and policy
Speaker 4: papers are about making things easier. Facilitating. I'm Italian fatchia
Speaker 4: or in Spain fascia. Right, So if you're facilitating someone,
Speaker 4: it's not going to be a good contract. If you're
Speaker 4: de risking someone, it's not going to be a good contract.
Speaker 4: If you're simply enabling, facilitating fixing, it's going to be
Speaker 4: a very bad public private relationship.
Speaker 3: Just going back to consultants for a second, and I
Speaker 3: think this is actually relevant to the discussion of having
Speaker 3: cooperation among different parts of the government on big projects.
Speaker 3: But how much of the consultants you fetish just has
Speaker 3: to do with diffusing responsibilities. So I always think back
Speaker 3: to the point the Olds saying about the purchasing manager thing,
Speaker 3: you'll never get fired for buying IBM, right, Like, how
Speaker 3: much of it is just like, well, you know, I
Speaker 3: did my best, I hired McKenzie. What more can I
Speaker 3: do if it goes wrong?
Speaker 2: It's mckenney's accountability set.
Speaker 1: Yeah.
Speaker 4: Absolutely, So that's a really important point because you know,
Speaker 4: one problem is when when government doesn't have those capabilities
Speaker 4: for the reasons we said before. Another is, even when
Speaker 4: they have it, why are they not using it. Australia
Speaker 4: again is an interesting example because they had really interesting
Speaker 4: capability within government with their Innovation Agency CSIRO, and yet
Speaker 4: they gave this massive contract to McKinsey to do their
Speaker 4: climate strategy. And it's absolutely about diffusing that responsibility. But
Speaker 4: also because of the culture we have again within government,
Speaker 4: where if they do make mistakes, unlike in the entrepreneurial
Speaker 4: ecosystems and you know, VC and so on. We don't
Speaker 4: accept that. But it's also in the private sector, right,
Speaker 4: So we also talk in the book about the consultification
Speaker 4: of management basically and there as well. You know, if
Speaker 4: you're going to be doing amergere or downsize and we're
Speaker 4: a massive share buyback scheme, isn't it great if you
Speaker 4: have you know, McKenzie told us to do it. So
Speaker 4: also just not taking on I mean it's kind of cowardly, right,
Speaker 4: like you're not kind of owning your decisions. I do
Speaker 4: think it's different in government. I think that you know,
Speaker 4: changing that culture, having more gov labs like they have
Speaker 4: in Chile. Everything sounds better than Spanish and Spanish I
Speaker 4: always say that in Italian every time we have it's
Speaker 4: public sector reform, it's just cuts, but it sounds nice.
Speaker 4: It's literally just cuts. And so that idea that what
Speaker 4: we need is kind of a laboratory within government but
Speaker 4: also between governments, I think is really important. By the way,
Speaker 4: the head of procurement in NASA, Ernest Brackett, in the
Speaker 4: sixties with the Apollo program, not only did he help
Speaker 4: change the procurement policy of NASA, but he also said,
Speaker 4: we got to watch out. There's too many consultants in
Speaker 4: these corridors. And his exact quote was, if this continues,
Speaker 4: we will get captured by brochuremanship, which is kind of
Speaker 4: endearing because they don't have powerpoints at the time, right,
Speaker 4: So now the idea that you're ruling by a PowerPoint
Speaker 4: and that's basically all they know how to do at
Speaker 4: the time. It was kind of shiny bushures. But he
Speaker 4: didn't say we don't want to work with the private sector, right,
Speaker 4: He said, we won't know how to work with the
Speaker 4: private sector. We won't know how to write the terms
Speaker 4: of reference if our own brains are becoming weak. So
Speaker 4: investing within in order to work also outside with others.
Speaker 4: So it's not working with others, it's working smartly with them.
Speaker 2: So this gets to the sort of one of the
Speaker 2: big questions of the day obviously, and that is AI. Right,
Speaker 2: And so you mentioned okay school lunches. You said a mission.
Speaker 2: It doesn't mean like the private sector isn't going to
Speaker 2: play a role in providing food or whatever. But first
Speaker 2: you establish what the mission is, then you talk about
Speaker 2: the moon. It doesn't mean that the private sector isn't
Speaker 2: going to play a role. Private sector played a significant
Speaker 2: role through various technologies of procurement, but there is an
Speaker 2: overall mission. Well, how are you thinking about AI? Should
Speaker 2: governments first decide like what is our mission for this?
Speaker 2: What missions could it theoretically enable? Like does this feel
Speaker 2: different than other endeavors? We obviously know a lot of
Speaker 2: public sector money is going to wind its way up
Speaker 2: in AI and already has a lot of it already has,
Speaker 2: But how are you does this fit? Is this different?
Speaker 2: Talk to us about how you think about this particular
Speaker 2: moment of extreme sort of technological ambition.
Speaker 4: Right, I mean, there's so much to say. So first,
Speaker 4: the investments that went into what we call today artificial intelligence,
Speaker 4: including you know LLM models, language models, speech recognition dates
Speaker 4: you know, decades, and like so many other areas, that
Speaker 4: was led by government. So if you look at even DARPA,
Speaker 4: which as you know, was the lead investor in the Internet,
Speaker 4: it came from problems mainly in the military industrial complex
Speaker 4: that then required ultimately what we're calling artificial intelligence today.
Speaker 4: But again in the entrepreneurial state. I talked about how
Speaker 4: everything in our smartphones that make them smart, not stupid
Speaker 4: Internet GPS, touch being series and so on, we're a
Speaker 4: government finance. What's very scary today, what makes today different
Speaker 4: with AI is that that's not really necessarily going to
Speaker 4: be true much longer. Why because these massive economic rents
Speaker 4: and I call them rents not profits, so excess profits
Speaker 4: and excess of what these companies actually did, because we
Speaker 4: privatized all the rewards from this massive social and collectively
Speaker 4: created value in this area. They have so much money, right, trillions,
Speaker 4: not billions, trillions, The salaries they are paying to the
Speaker 4: top researchers in universities, both public and private universities, and
Speaker 4: to people who used to work in the NASAs, the darpas,
Speaker 4: the Kaffos in Chile, they are going now to work
Speaker 4: in these companies, and that hemorrhaging of talent of top
Speaker 4: research expertise. I don't think people are talking about this enough.
Speaker 4: It literally is the biggest change, right because otherwise just
Speaker 4: you know the fact that we've had, you know, big
Speaker 4: technological changes, general purpose technology is completely affecting you know,
Speaker 4: how we reduce, how we distribute, and you know, from
Speaker 4: the rise of electrification so on.
Speaker 2: The thing like at one point, I mean, I don't
Speaker 2: think it actually panned out. They had great for them,
Speaker 2: but didn't Uber hire the entire Carnegie Mellon robotics.
Speaker 4: Team, and I think they did.
Speaker 2: I think when when they were doing self driving car technology,
Speaker 2: they just wrote a check for the entire faculty of
Speaker 2: the CMU robotics team and it.
Speaker 4: Was just but not enough people are talking about this,
Speaker 4: so it's very hard to govern a process for good,
Speaker 4: ethically and so on if you don't understand it. So
Speaker 4: if this talent is in fact leaving these publicly financed institutions,
Speaker 4: even private universities, most of the research, as you know,
Speaker 4: has been funded by NSF and so on once they leave,
Speaker 4: and the knowledge is so concentrated in these few AI companies,
Speaker 4: I don't think we've thought about that enough. But sorry.
Speaker 4: Just the other thing is just coming back to your question,
Speaker 4: which I don't think I answered properly. Yes, of course
Speaker 4: government should be thinking because of its at least a
Speaker 4: democratically elected govern This is different for dictatorships what we
Speaker 4: would expect from them, but we would expect governments to
Speaker 4: make sure that any big change, large kind of opportunities
Speaker 4: around technological change are done in ways that are again
Speaker 4: good for people in planet right, But that requires not
Speaker 4: only that capacity that, as I mentioned, is being decimated,
Speaker 4: but also a certain type of regulation which is making
Speaker 4: sure we even understand the process of, for example, how
Speaker 4: algorithms are currently being designed, and whether we have a
Speaker 4: situation like Shshana Zoobuff in her great book Surveillance Capitalism,
Speaker 4: she says, you think you're searching Google for free, they're
Speaker 4: searching you for free. That could have been avoided right
Speaker 4: through the design of the algorithms. And given that the algorithms,
Speaker 4: initially at least were publicly financed, there is this issue
Speaker 4: of how do we in a predistributive way instead of
Speaker 4: kind of exposts with redistribution or regulation or market fixing.
Speaker 4: Think about innovation collaborations that have some of these really
Speaker 4: important ethical concerns thought about upfront. And an example of
Speaker 4: this would be during COVID, where you know, the vaccines
Speaker 4: were not the mission. The mission was to allow these
Speaker 4: vaccines to be produced and available globally. Given it was
Speaker 4: a global health pandemic, we were all better off if
Speaker 4: the world was vaccinated. And yet only one of the vaccines,
Speaker 4: the one that was the collaboration between Oxford University and Astrosenica,
Speaker 4: had that kind of conditionality about what good looks like
Speaker 4: at the start in terms of how they collaborated. So
Speaker 4: it was the publicly financed researchers. Oxford is a state
Speaker 4: school that put that as a condition with their relationship
Speaker 4: with astra Zenica, that they would share the knowledge, that
Speaker 4: they would join the patent pool keep costing price as well.
Speaker 4: So more of that.
Speaker 2: Every time I hear you know, Tracy like someone talk
Speaker 2: about studying AI and college and like their professors, I'm like,
Speaker 2: that's great, but why are those professors not in the
Speaker 2: private sector making one hundred times? I mean, many of
Speaker 2: them are, but I'm always like amazed that given how
Speaker 2: much money you go money on the table, Yeah, what
Speaker 2: are you doing at a university you'd brib making ten times?
Speaker 4: Where did that money come from? As the issue? Right,
Speaker 4: So this money that's being used to pay these very
Speaker 4: large salaries that you're talking about, it's very important to
Speaker 4: recognize that that didn't come about because of you know,
Speaker 4: early on this amazing kind of entrepreneurship and all the
Speaker 4: knowledge was in these companies. They gathered that knowledge. Now
Speaker 4: they're using it. Of course they're pushing the frontier. Of
Speaker 4: course they're doing research, but that idea of also making
Speaker 4: sure that companies are not earning in excess, right, So
Speaker 4: why did the public person not benefit in those early days?
Speaker 4: Why are they evading so much tax? I mean literally
Speaker 4: in so many countries paying almost no tax. You know,
Speaker 4: also labor exploitation in the case of Amazon. You know,
Speaker 4: you don't hear it from me. You hear it from
Speaker 4: all sorts of different investigations, even during COVID by the
Speaker 4: way that they wouldn't even put the ambulances apparently outside
Speaker 4: the warehouse. I don't I've read that. I'm not sure
Speaker 4: if it's true, But anyway, the point is that they're
Speaker 4: you know, what does a good company look like? And
Speaker 4: how does that then affect the returns that they're earning?
Speaker 4: And what is a just return versus these excess returns
Speaker 4: that now are being used to you know, hire in You.
Speaker 3: Know, you mentioned the black box of the algorithms, which
Speaker 3: I think is a really interesting point. And we did
Speaker 3: an episode with the CTO of Goldman Sachs recently and
Speaker 3: we asked him, like, Goldman Sachs is a highly regulated bank.
Speaker 3: When you have bank supervisors who go in and want
Speaker 3: to understand your AI system, what are they actually understanding
Speaker 3: Do they like understand the underlying code that's driving the
Speaker 3: algorithm And the answer was, well, no, not really. You
Speaker 3: can't expect them to understand all that stuff. It's more
Speaker 3: about having the right controls in place that prevent the
Speaker 3: altgoth from running amok. And so my question is with AI,
Speaker 3: where we have a lot of experts, highly paid experts
Speaker 3: and engineers in the private sector developing all this new stuff,
Speaker 3: versus a shrinking body of government officials, like, what should
Speaker 3: our expectation be about how much they understand this technology?
Speaker 4: Right? Well, it actually comes back also to that previous point.
Speaker 4: Do you need all that expertise inside or also how
Speaker 4: do you regulate this in such a way that also
Speaker 4: stimulates more innovation, because you don't want to put a
Speaker 4: cap on the innovation just by overregulating it. This is where,
Speaker 4: by the way, I disagree with the abundance theorem, which
Speaker 4: makes it sound like, you know, there's all these opportunities
Speaker 4: out there, and it was regulation and too much planning
Speaker 4: and too many conditions that kind of hurt that, which
Speaker 4: I think actually corporate governance and shareholder value maximization as
Speaker 4: really actually stifled the opportunities that we have today, so
Speaker 4: they don't have to of course understand the algorithms, but
Speaker 4: they do need to do exactly as you said, like
Speaker 4: we do with climate right where there's climate disclosures, we
Speaker 4: need to know what it is that we want to
Speaker 4: be disclosed. In fact, we have a project that we
Speaker 4: just did that we just finished with, do you know
Speaker 4: Tim Moriley. Yeah, So it was Tim and myself with
Speaker 4: a grant from the Amitior Foundation, a project we called
Speaker 4: Algorithmic Rents and how to reduce these rents and how
Speaker 4: we're designing algorithms also through disclosures. So we thought about
Speaker 4: what would be the equivalent of AI related disclosures that
Speaker 4: could do exactly as you said, be almost the equivalent
Speaker 4: of what banks now have to do, but also companies
Speaker 4: have to do around g kind of metrics. And so
Speaker 4: I mean the project is still you know, underway, and
Speaker 4: what we want is actually to find kind of a
Speaker 4: coalition of AI companies that would be willing to think
Speaker 4: about this with us. But that assumes, right that there's
Speaker 4: also enough of the science as there has been with
Speaker 4: climate change, which said this is an urgent problem and
Speaker 4: unless we fix it now, we're going to reach a
Speaker 4: tipping point where there's no coming back. That's really when
Speaker 4: things started to change around the climate disclosures. We haven't
Speaker 4: for some reason, reached that yet, even though there's such
Speaker 4: a link also by the way, by the way, between
Speaker 4: AI and climate, like in terms of these data centers
Speaker 4: guzzling you know, like for every you know, chat, GPT
Speaker 4: search you do, apparently it's it's like using a bottle
Speaker 4: of water, like the small plastic bottles of water, Like
Speaker 4: that's literally how much we're using. And yet you know,
Speaker 4: we're not making that kind of systemic understanding of you know,
Speaker 4: AI problems and how they are connected to climate and
Speaker 4: water problems and so on. And that requires again government
Speaker 4: not just having an indicator like GDP to think about,
Speaker 4: but a dashboard. Right when you're driving your car, if
Speaker 4: you had one number, you know, how much gas you
Speaker 4: have or how fast you're going, you would crash it.
Speaker 4: So what does a dashboard look like? You know? For government,
Speaker 4: that would allow it to make sure it's on track
Speaker 4: to making sure that also with the evolution of technology,
Speaker 4: it's thinking about these kind of more systemic features and
Speaker 4: not just thinking about it as innovation policy.
Speaker 2: Just from a pure sort of history of technology standpoint,
Speaker 2: does AI feel different?
Speaker 4: I think it definitely fits with the kind of characteristics
Speaker 4: of a general purpose technology in the sense that it
Speaker 4: really does kind of change everything. However, there's also a
Speaker 4: lot of kind of myths around that kind of a
Speaker 4: bubble in terms of how we're thinking, not only in
Speaker 4: terms of the financial market bubble around it. But you know,
Speaker 4: just like with electricity, it took about thirty years to
Speaker 4: actually really affect how governments were operating. Similarly with the Internet,
Speaker 4: you'll remember when Robert Solo was saying, you know, there's
Speaker 4: computers everywhere except in the productivity statistics. I think we're
Speaker 4: still very early in the phase with AI in terms
Speaker 4: of it having a really meaningful impact on improving Again,
Speaker 4: coming back to the point about problems, right, because ultimately
Speaker 4: should be helping us solve problems? Which problems is AI
Speaker 4: really helping us to solve in a systemic way that
Speaker 4: can scale that there's learning between governments. We're definitely using AI.
Speaker 4: Is it actually helping us solve some of the biggest
Speaker 4: problems of our time again? Health problems, climate problems? Surely
Speaker 4: there's some of that, But until we manage this process,
Speaker 4: until we govern it both ethically but also in a
Speaker 4: way that is with government instead of kind of you know,
Speaker 4: sidetracking government in order to extract these mega rents. Then
Speaker 4: we're going to have a huge problem.
Speaker 3: Why do you think that hasn't happened yet or governments
Speaker 3: haven't stepped in faster, because I do think AI is
Speaker 3: kind of unusual in the sense that, you know, you
Speaker 3: have people like Sam Altman who will say very publicly
Speaker 3: in interviews like, yeah, this creates a lot of negative externalities,
Speaker 3: and society is going to have to figure out how
Speaker 3: to deal with this, and governments are going to have
Speaker 3: to figure out how to actually best deploy this technology.
Speaker 3: Open AI publish a big industrial policy document, which again
Speaker 3: I think is kind of unusual to previous technological developments.
Speaker 3: And yet governments seem kind of slow. I don't want
Speaker 3: to generalize too much, but many governments seem kind of
Speaker 3: slow to get in and really start shaping what they
Speaker 3: want to do with AI.
Speaker 4: Yeah. I mean, I think again, it seems like I'm
Speaker 4: saying the same thing, but I really it's it's because
Speaker 4: I believe in it. This technology on its own will
Speaker 4: not solve anything if it's accompanied by a strong For example,
Speaker 4: health system that with AI thinking about health problems, then
Speaker 4: I can see that being absolutely revolutionary. And in fact,
Speaker 4: I once heard a really interesting discussion between Nandan Nilekani,
Speaker 4: you know, it's an incredible entrepreneur of computer systems. He
Speaker 4: was one of the co founders of Inphasis in India,
Speaker 4: and Eric Schmidt at one of these dialogues we have
Speaker 4: in Bilajo, this beautiful villa in Lake Como run by
Speaker 4: the Brockefeller Foundation, and we had kind of Eric Schmidt
Speaker 4: on the one hand saying, you know, in the future,
Speaker 4: all we're going to really need is an app, a
Speaker 4: health app, and that's going to be fine. And Nandan Nilkani,
Speaker 4: who really is one of the biggest innovators around computing,
Speaker 4: was like, what, like, let me tell you in India,
Speaker 4: without a proper health system, no matter how many apps
Speaker 4: you have, we will continue to have misery. And so
Speaker 4: it's not one or the other. But what you need
Speaker 4: is more people like Nandan who think about what is
Speaker 4: the relationship between you know, the power of AI and
Speaker 4: the structure of a health system and who's thinking about
Speaker 4: that and if we do have at the same time,
Speaker 4: not just austerity, but this you know kind of dumbing
Speaker 4: down of what we think government is for. So even
Speaker 4: the health systems we have are not kind of you know,
Speaker 4: fit for purpose. Then I don't see any sort of
Speaker 4: future of AI kind of helping us with health problems.
Speaker 4: And by the way, just look at I mean, like
Speaker 4: fifty five percent of the global food system right now
Speaker 4: is at risk because of how we're treating the global
Speaker 4: hydrological cycle. You know, biodiversity loss also in the Amazon
Speaker 4: is affecting droughts and floods in other parts of the world.
Speaker 4: That's a huge problem. To what degree are we really
Speaker 4: using AI to you know, fix that problem. Not much.
Speaker 4: So it's also about where we're putting kind of the emphasis,
Speaker 4: and for that, I think we do need these moonshots
Speaker 4: government led working with the private sector and again using
Speaker 4: the power of AI well regulated to solve very concrete problems.
Speaker 3: Yeah, Joe, I was thinking about this specifically related to
Speaker 3: healthcare recently, because I've seen a bunch of startups that
Speaker 3: are saying they're going to simplify the billing process for hospitals.
Speaker 3: And then you also know that there are a bunch
Speaker 3: of insurance companies that are also using AI, and it's like, well,
Speaker 3: if We're just going to have.
Speaker 2: More suters debating with computers.
Speaker 3: Yeah, exactly, And like if the system itself doesn't change,
Speaker 3: nothing's going to improve, right.
Speaker 2: No, I've totally thought about the same thing where it
Speaker 2: just like feels like we're going to have this arms
Speaker 2: race where it's like my bot will argue with your bot,
Speaker 2: and then the only entities that make any money are
Speaker 2: the volt makers, the voughtmakers.
Speaker 4: You know that who was the mayor of Barcelona, another
Speaker 4: city here in Spain that you know, well, she when
Speaker 4: she was mayor, she came from a housing movement, so
Speaker 4: she was really really concerned with housing issues, but also
Speaker 4: you know, public transport, public schools, and so on. And
Speaker 4: her thing was, why is it that when the citizens
Speaker 4: of Barcelona, you know, like click on Uber or city Mapper,
Speaker 4: this data that's created from that, Right, every time we
Speaker 4: click on something, data's created. She said, why aren't we
Speaker 4: in the city using that data to improve our decisions
Speaker 4: and understanding of our public transport and public housing challenges?
Speaker 4: And so she ended up hiring computer hackers into the
Speaker 4: city government. It made it a really cool place to work.
Speaker 4: And I think that again kind of insourcing back in
Speaker 4: those kind of you know, cool hackers that currently are
Speaker 4: working in these companies, but to come and work with
Speaker 4: the city administration that says, we want you to come
Speaker 4: in fail but help you know, like don't worry about failings.
Speaker 4: Are you not fail, but you know, take risks to
Speaker 4: help us though, kind of really target our big challenges
Speaker 4: around housing, transport and so on, inequality in terms of access.
Speaker 2: So obviously we're talking about the public sector. We're talking
Speaker 2: about the government's role in facilitating or guiding various technologies.
Speaker 2: But there's also just like politics, right, winning elections and
Speaker 2: the fact that you know, as you mentioned, failures become
Speaker 2: a scandal and they're on the paper, and maybe like
Speaker 2: politicians lose their jobs, then someone comes in when you
Speaker 2: just in your own personal work, when you like think
Speaker 2: about this stuff, like how much do you have to
Speaker 2: calculate the reality that one big job of politicians is
Speaker 2: to win reelection and that a loss of election has
Speaker 2: the potential to just take the government in ninety or
Speaker 2: one hundred and eighty degree turn from whatever the previous
Speaker 2: administration at any level in any country does, And how
Speaker 2: much do you like think about that reality when you're
Speaker 2: thinking about strengthening government.
Speaker 4: So The first time it started to work on missions
Speaker 4: wasn't on mission oriented policy, it was on mission oriented
Speaker 4: organizations right to better understand the DARPA kind of organization. Right,
Speaker 4: CORTFO in Chile is similar, CITRA in Finland, mind Lab
Speaker 4: in Denmark, Lenova and Sweden. These are innovation agencies that
Speaker 4: are in fact making these big bets that are mission oriented.
Speaker 4: But my question was how are they organized? You know,
Speaker 4: are they also unstable due to the electoral cycle? And
Speaker 4: they had thought about this. I mean, it's not a
Speaker 4: coincidence that DARPA, for example, people come in for five years,
Speaker 4: so it's not the four year electoral cycle. They're actually
Speaker 4: told come in and do take risks. That's how you'll
Speaker 4: be evaluated, you know, not by just if you're succeeding
Speaker 4: all the time that means you're not taking those risks,
Speaker 4: but also the impact that your successes have, and so
Speaker 4: that kind of cultural shift, but also the fact that
Speaker 4: people are coming in kind of on se conment. You know,
Speaker 4: they're not there to be a civil service civil servant
Speaker 4: their whole life. So we started studying Well, for some areas,
Speaker 4: I think that's fine, especially around innovation. Right, you want
Speaker 4: to coming back to the idea that we want to
Speaker 4: bring in and crowd in the top talent into government,
Speaker 4: you know, having like a five year period that you're
Speaker 4: going to help as a civil servant paid by the government,
Speaker 4: not as a consultant, you know, working with not at
Speaker 4: the civil service. I do think there's lots of kind
Speaker 4: of room for that. So that idea that because we
Speaker 4: value the private sector, there has been for example, think
Speaker 4: of Harvard Business School where they have this case study
Speaker 4: methodology of businesses. We've never really done that with government
Speaker 4: entities because we don't value basically government as a value creators,
Speaker 4: just seen as a redistributor, a fixer, a facilitator and
Speaker 4: enabler of the private sector. And that's of course where
Speaker 4: then we expect creativity and value to be created, which
Speaker 4: is not right. And so one of the things that
Speaker 4: we do in the Institute, which is actually a department,
Speaker 4: so we train up civil servants around the world also
Speaker 4: you know through our own MPa Masters and public administration,
Speaker 4: but also through applied learning programs, was to start developing
Speaker 4: these cases. You know, what do we know about the BBC,
Speaker 4: You know, how is it different from other public broadcasters.
Speaker 4: How do they measure what they call public value. What
Speaker 4: is public value? So even having you know, comparison learning
Speaker 4: between say a public bank, the BBC, a government digital agency,
Speaker 4: on what it means to crowd in or crowd out
Speaker 4: their private sector, what it means to shape markets not
Speaker 4: fix them. What does it mean to have a culture
Speaker 4: of experimentation versus this huge risk averseness that, as we
Speaker 4: said before, is a cause for the consultification. So I
Speaker 4: think a lot about that, but it's not you know,
Speaker 4: there's so much instability obviously also in the private sector,
Speaker 4: So there is a bit of a myth that it's
Speaker 4: all unstable in the public sector because of electoral turnover.
Speaker 4: We can't shape these bureaucracies to be creative bureaucracies, resilient bureaucracies.
Speaker 4: They don't have to be vertical and so inertial. But
Speaker 4: the other point, I think that's sort of stemming. I
Speaker 4: think in your question, tell me if this is not
Speaker 4: related is literally winning the election? What are we learning globally?
Speaker 4: You know, why is it that Biden, whose economic policies
Speaker 4: were actually quite successful in the red states at least
Speaker 4: starting to be quite successful, Well, why in those states
Speaker 4: did he not win? And I think there's something going
Speaker 4: on in a lot of countries, definitely also in Italy
Speaker 4: in the UK where people who have been let's just
Speaker 4: use the concept left behind on terms of the economic
Speaker 4: benefits at least in the past. Even when new economic
Speaker 4: policies work, that's not enough if people don't feel valued,
Speaker 4: if they don't have their dignity back, if they continue
Speaker 4: to feel condescended upon. So one of the really cool
Speaker 4: things I've been working on with city governments but even councils.
Speaker 4: So my neighborhood in London is called Camden, It's about
Speaker 4: two hundred and fifty thousand people. I worked with the
Speaker 4: council on mission oriented procurement for adult social care across
Speaker 4: ten housing estates what you call projects in the US,
Speaker 4: and we brought the careers and the careys to the
Speaker 4: table to design that policy. So working with people really
Speaker 4: valuing their lived experience to help its design policies that
Speaker 4: are meaningful and will improve their lives, I think it's
Speaker 4: just so important firstly to get those policies to be
Speaker 4: designed right, but also to give people again dignity and
Speaker 4: self worth. And I've seen it also, you know, because
Speaker 4: we have so much inequality in the UK, unfortunately we
Speaker 4: have food banks, which is barbaric if you think about it.
Speaker 4: In the twenty first century, food banks, like you know,
Speaker 4: we should not have that. People should have food, you know,
Speaker 4: on the table, enough and healthy food. We don't have that.
Speaker 4: So transforming food banks into food cooperatives, green food cooperatives,
Speaker 4: where the people benefiting from the what was a food
Speaker 4: bank are now also in the place of governing, of
Speaker 4: having real deliberation, of thinking together. I can tell you
Speaker 4: the people I saw working in the food banks who
Speaker 4: are also receiving the food. The facial expression, the dignity,
Speaker 4: just even how people are standing is completely different from
Speaker 4: a system where.
Speaker 3: You know, you here, someone's expired, like yeah, exactly from
Speaker 3: last Thanksgiving.
Speaker 4: No, but even if it's good food, you know, having again,
Speaker 4: you know, bringing back dignity and value it. It's so important,
Speaker 4: I think to fight populism.
Speaker 3: I'm going to ask what is potentially an unfair and
Speaker 3: loaded question, but I think it might be quite illustrative
Speaker 3: of everything that we've been discussing. When you look across
Speaker 3: the world, are there particular countries or cities that you
Speaker 3: think are doing industrial policy right in the sense that
Speaker 3: they're taking maybe a holistic approach with a defined strategy
Speaker 3: slash mission.
Speaker 4: So I tend to also look at very specific things
Speaker 4: that a government did instead of just saying the whole
Speaker 4: government's perfect right. So, for example, in Brazil, something they've
Speaker 4: done that I think has been very positive is that
Speaker 4: they've put what I call missions at the center of government,
Speaker 4: so the ecological transition is at the center, and that
Speaker 4: then required the Department of Finance, for example, to rethink
Speaker 4: its own tools. For example, public bank right so BNDS,
Speaker 4: which is one of the largest public banks in the world.
Speaker 4: It can either just again give money out to say
Speaker 4: the agrobusiness industry or save the steel industry when it's
Speaker 4: going bust, or because there's an ecological transition, it can
Speaker 4: think about how these sectors themselves need to change in
Speaker 4: order also to access the loans that the bank is giving. Germany,
Speaker 4: by the way, did that when they had the end
Speaker 4: Givende policy. They're public bank, the KfW. The way, they
Speaker 4: provided support to the steel sector, which in the US
Speaker 4: and the UK and so many countries steel is under pressure.
Speaker 4: The loans to the steel sector were conditional that the
Speaker 4: sector lower the material content of production, which they did
Speaker 4: in their own way. Had government told them how to
Speaker 4: do it, You kill innovation, but strong direction, conditional loans,
Speaker 4: and they ended up now having the greenest steel in
Speaker 4: the world. It might not be competitive yet, and that's
Speaker 4: a scale issue that has also to do with regulation.
Speaker 4: But you know, repurpose, reuse, recycle technology in steel only
Speaker 4: happened because the public bank that was mission aligned. So
Speaker 4: I'm very interested in procurement. Public loans stayed on enterprises, digital,
Speaker 4: public infrastructure examples where they're not just things that are there,
Speaker 4: but they're used to really transform and help development. Sweden
Speaker 4: is also really interesting because they had a high level challenge.
Speaker 4: Missions are somewhere between the challenge like the space race
Speaker 4: and the sector like aerospace, right, so the Moon mission
Speaker 4: required lots of different sectors, but it was very concrete.
Speaker 4: Was the challenge of say, climate change or all the
Speaker 4: sustainable development goals, those are kind of very broad, so
Speaker 4: transforming them into missions and their challenge was I think
Speaker 4: they said they wanted a fossil free welfare state, and
Speaker 4: that's why then they said, well what are the missions.
Speaker 4: We were working with them on this and so we
Speaker 4: were kind of stimulating some of this thought through Venova.
Speaker 4: Their Innovation Agency, what are the missions that will help
Speaker 4: us achieve that? And that's where then the Healthy, Tasty,
Speaker 4: Sustainable school Meals policy came from. We worked with them
Speaker 4: on that also in Brazil. We just actually put out
Speaker 4: a report about this with the World Food Program. But
Speaker 4: what was interesting again was that then that required government
Speaker 4: to work in a different way again interminousterely catalyzing bottom
Speaker 4: up experimentation, local manufacturing, but through also the redesign of
Speaker 4: the tools themselves. The UK, which I don't think is
Speaker 4: a very good example right now. I mean there's lots
Speaker 4: of instability. There's also been fourteen years of austerity. Some
Speaker 4: things that were really interesting, and this is why I
Speaker 4: look more at the organizational kind of examples is Government
Speaker 4: Digital Services GDS, which basically began by government back in
Speaker 4: the early two thousand saying why does everyone have to
Speaker 4: go to say Google to download a white paper, Why
Speaker 4: don't we have our own kind of digital platform. They
Speaker 4: did what most governments do, outsourced it to a company
Speaker 4: called Circo, which is not a very innovative company at all,
Speaker 4: gets lots of government contracts. They failed miserably, and then
Speaker 4: people from the iPlayer team and the BBC said we'll
Speaker 4: do it, So they went over to the Cabinet Office
Speaker 4: set up Government Digital Services, came up with this incredible
Speaker 4: digital platform called gov dot UK, which won an International
Speaker 4: Design Award. But what was interesting to me from that
Speaker 4: example was that the first thing they did was look
Speaker 4: out the window and said, with arrows pointing out the window,
Speaker 4: those are not clients and customers. Let's stop talking about
Speaker 4: people as clients and customers. Their users with human rights
Speaker 4: and how they will access their driver's license, their paths
Speaker 4: for their voter registration has to enhance their like their souls,
Speaker 4: and I make them want to die. You see the
Speaker 4: kind Looach movies where you know, people literally want to
Speaker 4: die when they're accessing their welfare payments because of just
Speaker 4: the complications around it. And so making you know, having
Speaker 4: kind of like a user friendly government digital platform that
Speaker 4: changes the experience of a citizen with those rights that
Speaker 4: they have through the technology just requires a very different
Speaker 4: kind of mind shift. And it became the coolest place
Speaker 4: to work. So if you were taught you know, software
Speaker 4: engineer or even you know whatever, these hackers that add
Speaker 4: the cola wanted to hire. That's where they wanted to work.
Speaker 4: To the point that lots of private companies were having
Speaker 4: a hard time finding the top talent because they all
Speaker 4: wanted to work in and gidah. So again, those are
Speaker 4: the examples I think we need to look for, not
Speaker 4: like which is the government. This thing everything perfectly.
Speaker 2: All right, Professor Matchakato, thank you so much. Great to
Speaker 2: finally catch up with you. It took us all like
Speaker 2: randomly being in the same location. But thank you so pleasure.
Speaker 2: Thank you so much for coming on.
Speaker 4: Oudlocks, thank you so much for having the.
Speaker 2: Tracy. I'm glad we finally, professor the place to do it.
Speaker 2: This is definitely the place to do it. I think
Speaker 2: it really did. Her framing makes a lot of sense.
Speaker 2: This idea that like outsourcing is not per se bad.
Speaker 2: That obviously any major mission is going to have to
Speaker 2: have significant private sector involvement and innovation, even if it's
Speaker 2: somehow publicly led, but that there's no chance of going
Speaker 2: anywhere if there's you know, the public sector doesn't have
Speaker 2: the internal muscle of who to talk to or to
Speaker 2: talk to at.
Speaker 4: The right time.
Speaker 3: How to judge performance.
Speaker 2: How to judge performance seems absolutely a key question.
Speaker 3: And that kind of gets back to the mission idea,
Speaker 3: which I did. I really I like the idea of
Speaker 3: focusing on what you're trying to achieve rather than just
Speaker 3: how you're actually going about achieving it. Like that makes
Speaker 3: a lot of sense to me if you're dealing with
Speaker 3: a vast bureaucracy with lots of different silos. And also,
Speaker 3: I think like getting back to that expertise point does
Speaker 3: mean that you do develop that muscle internally within the
Speaker 3: organization rather than just like, Okay, we're going to hire
Speaker 3: McKenzie to figure out how we're going to do something.
Speaker 3: Instead you say, well, we want to do X, let's
Speaker 3: all get together and figure out how to do it.
Speaker 2: You know, AI seems like a weird thing as a
Speaker 2: technology because all right, on the one hand, you could say, like,
Speaker 2: all right, we want to massively improve our healthcare, so
Speaker 2: we want to massively improve healthcare outcomes. And I think
Speaker 2: you could like very easily say well, AI is going
Speaker 2: to be really big part of that, right, and maybe
Speaker 2: make things a lot more efficient, maybe give information access
Speaker 2: to a lot of people, identify experts, et.
Speaker 3: Cetera, the results to develop new medicines.
Speaker 2: Yeah, totally, it's so great. So maybe the mission has
Speaker 2: something to do with health, and AI plays an important
Speaker 2: component of it. I guess what's strange though, is that
Speaker 2: AI itself creates its own potential pitfalls. I mean, the industry,
Speaker 2: as you mentioned, is obsessed with the pitfalls of its
Speaker 2: own making, right, Yeah, and so there is almost no
Speaker 2: way that AI can just be a tool in the
Speaker 2: service of some other mission because almost everyone who knows
Speaker 2: something about AI sees potential for extreme exacerbation of inequality,
Speaker 2: potentially AI robots that will be misaligned and want to
Speaker 2: kill us all when they have sufficient capabilities. Yeah, right,
Speaker 2: and so like on the one hand, like, yes, as
Speaker 2: the technology, it might fit into some of these other
Speaker 2: big missions, But on the other hand, it sort of
Speaker 2: feels like it has there has to be some like
Speaker 2: AI specific goal of like where do we want this
Speaker 2: technology to go or how do we how do we
Speaker 2: curb it or whatever it is. It seems very distinct.
Speaker 3: No, totally, and I do think the unusual part of
Speaker 3: this moment in time, and a lot of people will
Speaker 3: argue that maybe it's marketing or whatever, but you do
Speaker 3: see the big Tech CEO is like basically going on
Speaker 3: TV and saying like, we as a whole society need
Speaker 3: to figure out what we want to do here.
Speaker 2: And I don't think it is marketing or just marketing
Speaker 2: because A there's already this very big tech backlash, right,
Speaker 2: so like if we're like, it's not working, you know,
Speaker 2: if the idea is, oh, we want to plump our valuations,
Speaker 2: so we do that by saying that TAM is all
Speaker 2: human labor.
Speaker 3: And human life.
Speaker 2: Yeah, well, you're really upsetting a lot of people by
Speaker 2: saying this. It's not that's not obviously good, and b
Speaker 2: you know, it's we've written about or talked about. You know,
Speaker 2: some of these big labs, like they were founded from
Speaker 2: day one with the premise that this is not normal technology,
Speaker 2: which is why it's like housed in a nonprofit or
Speaker 2: something like this. So I tend to think that when
Speaker 2: the CEOs of these companies talk about this stuff, they
Speaker 2: kind of mean what they say. They two are they
Speaker 2: two are concerned.
Speaker 3: I mean I also worry without some sort of government
Speaker 3: intervention or government strategy here, we are going to get
Speaker 3: to that situation where we deploy AI and because we're
Speaker 3: not fixing the underlying system, we're just sort of nippling
Speaker 3: at the edges and making it worse. Per that idea
Speaker 3: of like, Okay, the insurance bot is going to talk
Speaker 3: to the hospital bot and both of them are going
Speaker 3: to say that they're streamlining the billing process for medical services.
Speaker 3: But because we're all doing the same thing without actually
Speaker 3: fixing how medical bills work and who pays for what
Speaker 3: in the US, like, it's just going to be bots
Speaker 3: fighting bots. No one's going to benefit from that.
Speaker 2: No, and most likely there will be sort of you know,
Speaker 2: just ongoing increase complexity. It's bots all the way bot's
Speaker 2: all the way down.
Speaker 3: All right, shall we leave it there?
Speaker 2: Let's leave it there.
Speaker 4: Okay.
Speaker 3: This has been another episode of the Authoughts podcast. I'm
Speaker 3: Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm Jill Wisenthal. You can follow me at the Stalwart.
Speaker 2: Follow our guest Mariana Matsukato at Matsukato m follow our
Speaker 2: producers Carmen Rodriguez at Kerman armand dash Ol Bennett at Dashbot,
Speaker 2: Cale Brooks at Kale Brooks and Kevin Lizano at Kevin
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