Mariana Mazzucato Thinks We Need More Moonshots

22:45That itself is what we're talking about, right? So whether it's going to the moon, whether it's trash collection, whether it's school meals, whether it's getting prepared for the next pandemic, which unfortunately the science tells us will happen. How are we even thinking within government in a problem-oriented way, a solutions-oriented way. And this, by the way, is why the CityLab conference is so wonderful and Bloomberg's government innovation team is so important for so many cities is because they then share their experiences of solving problems. And then they ask, and we're trying to help them do this with this public sector capability index, what did we learn along the way that we were missing?

23:21Where were the bottlenecks? What can we do better? But especially in terms of that flexibility, adaptability, willingness to experiment, right? Remember what Kennedy said, We're doing it because it's hard, not because it's easy. Yet all the words and policy papers are about making things easier, facilitating. I'm Italian. Facile. We're in Spain. Facile. Right. So if you're facilitating someone, it's not going to be a good contract. If you're de-risking someone, it's not going to be a good contract. If you're simply enabling, facilitating, fixing, it's going to be a very bad public-private relationship.

23:52Just going back to consultants for a second, and I think this is actually relevant to the discussion of having cooperation among, like, different parts of the government on big projects. But how much of the consultancy fetish just has to do with diffusing responsibilities? So I always think back to the old saying about the purchasing manager thing, you'll never get fired for buying IBM, right? Like how much of it is just like, well, you know, I did my best. I hired McKinsey. What more can I do if it goes wrong? It's McKinsey's fault. Accountability. Yeah, absolutely. So that's a really important point because, you know, one problem is when when government doesn't have those capabilities for the reasons we said before.

24:29Another is even when they have it, why are they not using it? Australia, again, is an interesting example because they had really interesting capability within government with their innovation agency, CSIRO, and yet they gave this massive contract to McKinsey to do their climate strategy. And it's absolutely about diffusing that responsibility, but also because of the culture we have, again, within government where if they do make mistakes, unlike in the entrepreneurial ecosystems and VC and so on. We don't accept that. But it's also in the private sector, right? So we also talk in the book about the consultification of management, basically.

25:03And there as well, if you're going to be doing a merger or downsizing or a massive share buyback scheme, isn't it great if you have, you know, McKinsey told us to do it. So also just not taking on, I mean, it's kind of cowardly, right? Like you're not kind of owning your decisions. I do think it's different in government. I think that, you know, changing that culture, or having more gov labs like they have in Chile, el laboratorio de gobierno, everything sounds better in Spanish. You can say anything in Spanish. Gov lambs. It sounds like an awful lot. I always say that in Italian, every time we have a reforma de la publica administración, it's public sector reform, it's just cuts, but it sounds nice.

25:36It's just somewhere. La reforma de la publica administración. It's literally just cuts. And so that idea that what we need is kind of a laboratory within government, but also between governments, I think is really important. By the way, the head of procurement in NASA, Ernest Brackett, in the 60s with the Apollo program, not only did he help change the procurement policy of NASA, but he also said, we got to watch out. There's too many consultants in these corridors. And his exact quote was, if this continues, we will get captured by brochuremanship, which is kind of endearing because they didn't have PowerPoints at the time.

26:10Right. So now the idea that you're ruling by PowerPoint and that's basically all they know how to do. At the time, it was kind of shiny brochures. But but he didn't say we don't want to work with the private sector. Right. He said we won't know how to work with the private sector. We won't know how to write the terms of reference if our own brains are becoming weak. So investing within in order to work also outside with others. So it's not working with others. It's working smartly with them.

26:36Tracy Alloway:So this gets to the sort of one of the big questions of the day, obviously, and that is AI, right? And so you mentioned, okay, school lunches. You said a mission. It doesn't mean like the private sector isn't going to play a role in providing food or whatever, but first you establish what the mission is. Then you talk about the moon. It doesn't mean that the private sector isn't going to play a role. Private sector played a significant role through various technologies and procurement, but there was an overall mission. How are you thinking about AI? Should governments first decide, like, what is our mission for this?

27:09Tracy Alloway:What missions could it theoretically enable? Like, does this feel different than other endeavors? We obviously know a lot of public sector money is going to wind its way up in AI and already has a lot of innovation. It already has. But how are you? Does this fit? Is this different? Talk to us about how you think about this particular moment of extreme sort of technological ambition. Right. I mean, there's so much to say. So first, the investments that went into what we call today artificial intelligence, including, you know, LLM models, language models, speech recognition, dates, you know, decades and like so many other areas that was led by government.

27:46So if you look at even DARPA, which, as you know, was the lead investor in the Internet, it came from problems mainly in the military industrial complex that then required ultimately what we're calling artificial intelligence today. But again, in the entrepreneurial state, I talked about how everything in our smartphones that make them smart are not stupid, Internet, GPS, touch, Siri, and so on, were government finance. What's very scary today, what makes today different with AI, is that that's not really necessarily going to be true. much longer. Why? Because these massive economic rents, and I call them rents, not profits, so excess profits and excess of what these companies actually did, because we privatized all the rewards from this massive social and collectively created value in this area.

28:32They have so much money, right? Trillions, not billions, trillions. The salaries they are paying to the top researchers in universities, both public and private universities, and to people who used to work in the NASA's, the DARPA's, the COFOS in Chile, they are going now to work in these companies. And that hemorrhaging of talent, of top research expertise, I don't think people are talking about this enough. It literally is the biggest change, right? Because otherwise, just, you know, the fact that we've had, you know, big technological changes, general purpose technologies completely affecting, you know, how we produce, how we distribute and, you know, from the rise of electrification and so on.

29:11Tracy Alloway:The thing like at one point, I mean, I don't think it actually panned out that great for them, but didn't Uber hire the entire Carnegie Mellon robotics team in one fell swoop? I think they did. I think when they were doing self-driving car technology, they just wrote a check for the entire faculty of the CMU robotics team. And it was just like that. But not enough people are talking about this. So it's very hard to govern a process for good ethically and so on if you don't understand it. So if this talent is in in fact, leaving these publicly financed institutions, even private universities, most of the research, as you know, has been funded by NSF and so on.

29:49Once they leave, and the knowledge is so concentrated in these few AI companies, I don't think we've thought about that enough. But sorry, just the other thing, just coming back to your question, which I don't think I answered properly. Yes, of course, government should be thinking because of its, at least a democratically elected government, right? This is different for dictatorships, what we would expect from them. But we would expect governments to make sure that any big change, large kind of opportunities around technological change are done in ways that are, again, good for people and planet, right?

30:19But that requires not only that capacity that, as I mentioned, is being decimated, but also a certain type of regulation, which is making sure we even understand the process of, for example, how algorithms are currently being designed and whether we have a situation like Shoshana Zuboff in her great book, Surveillance Capitalism, she says, you think you're searching Google for free, they're searching you for free. You know, that could have been avoided, right, through the design of the algorithms. And given that the algorithms initially, at least, were publicly financed, there is this issue of how do we, in a pre-distributive way, instead of kind of exposed with redistribution or regulation or market fixing, think about innovation collaborations that have some of these really important ethical concerns thought about upfront.

31:04And an example of this would be during COVID, where the vaccines were not the mission. The mission was to allow these vaccines to be produced and available globally, given it was a global health pandemic. We were all better off if the world was vaccinated. And yet only one of the vaccines, the one that was the collaboration between Oxford University and AstraZeneca, had that kind of conditionality about what good looks like at the start in terms of how they collaborated. So it was the publicly financed researchers, Oxford is a state school, that put that as a condition with their relationship with AstraZeneca, that they would share the knowledge, that they would join the patent pool, keep costs and prices low.

31:42So more of that.

31:44Tracy Alloway:Every time I hear, you know, Tracy, like someone talk about studying AI in college and like their professors, I'm like, that's great, but why are those professors not in the private sector making 100 times? I mean, many of them are, but I'm always like amazed that given how much money is in AI. People leave money on the table. Yeah. What are you doing at a university? You're probably making 10 times more. But where did that money come from? This is the issue, right? So this money that's being used to pay these very large salaries that you're talking about, it's very important to recognize that that didn't come about because of, you know, early on this amazing kind of entrepreneurship and all the knowledge was in these companies.

32:18They gathered that knowledge. Now they're using it. Of course, they're pushing the frontier. Of course, they're doing research. But that idea of also making sure that companies are not earning in excess, right? So why did the public purse not benefit in those early days? Why are they evading so much tax? I mean, literally in so many countries paying almost no tax. You know, also labor exploitation in the case of Amazon. You know, you don't hear it from me. You hear it from all sorts of different investigations, even during COVID, by the way, that they wouldn't even put the ambulances apparently outside the warehouse.

32:49I don't know if I've read that. I'm not sure if it's true. But anyway, the point is that they're, you know, what does a good company look like? And how does that then affect the returns that they're earning? And what is a just return versus these excess returns that now are being used to, you know, higher end? You know, you mentioned the black box of the algorithms, which I think is a really interesting point. And we did an episode with the CTO of Goldman Sachs recently, and we asked him, like, Goldman Sachs is a highly regulated bank. When you have bank supervisors who go in and want to understand your AI system, what are they actually understanding?

33:25Do they understand the underlying code that's driving the algorithm? And the answer was, well, no, not really. You can't expect them to understand all that stuff. It's more about having the right controls in place that prevent the algod from running amok. And so my question is with AI, where we have a lot of experts, highly paid experts and engineers in the private sector developing all this new stuff versus a shrinking body of government officials, like what should our expectation be about how much they understand this technology? Right. Well, it actually comes back also to that previous point.

33:58Do you need all that expertise inside? Or also how do you regulate this in such a way that also stimulates more innovation? Because you don't want to put a cap on the innovation just by over-regulating it. This is where, by the way, I disagree with the abundance theorem, which makes it sound like, you know, there's all these opportunities out there and it was regulation and too much planning and too many conditions that kind of hurt that, which I think actually corporate governance and shareholder value maximization has really actually stifled the opportunities that we have today. So they don't have to, of course, understand the algorithms, but they do need to do exactly as you said, like we do with climate, right, where there's climate disclosures.

34:33We need to know what it is that we want to be disclosed. In fact, we have a project that we just did, that we just finished with, do you know Tim O 'Reilly? Yeah. So it was Tim and myself with a grant from the Amityar Foundation, a project we called Algorithmic Rents, and how to reduce these rents and how we're designing algorithms also through disclosures. So we thought about what would be the equivalent of AI-related disclosures that could do exactly, as you said, be almost the equivalent of what banks now have to do, but also companies have to do around kind of ESG kind of metrics. Interesting.

35:08And so, I mean, the project is still underway. And what we want is actually to find kind of a coalition of AI companies that would be willing to think about this with us. But that assumes, right, that there's also enough of the science as there has been with climate change, which said this is an urgent problem. And unless we fix it now, we're going to reach a tipping point where there's no coming back. That's really when things started to change around the climate disclosures. We haven't, for some reason, reached that yet, even though there's such a link also, by the way, between AI and climate, like in terms of these data centers guzzling, you know, like for every, you know, chat GPT search you do, apparently it's like using a bottle of water, like the small plastic bottles of water, like that's literally how much we're using.

35:53And yet, you know, we're not making that kind of systemic understanding of, you know, AI problems and how they are connected to climate and water problems and so on. And that requires, again, government not just having an indicator like GDP to think about, but a dashboard, right? When you're driving your car, if you had one number, you know, how much gas you have or how fast you're going, you would crash it. So what does a dashboard look like for government that would allow it to make sure it's on track to making sure that also with the evolution of technology, it's thinking about these kind of more systemic features and not just thinking about it as innovation policy.

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38:12Tracy Alloway:Just from a pure sort of history of technology standpoint, does AI feel different? I think it definitely fits with the kind of characteristics of a general purpose technology in the sense that it really does kind of change everything. However, there's also a lot of kind of myths around that, kind of a bubble in terms of how we're thinking, not only in terms of the financial market bubble around it, but, you know, just like with electricity, it took about 30 years for it to actually really affect how governments were operating. Similarly, with the Internet, you'll remember when Robert Solow was saying, you know, there's computers everywhere except in the productivity statistics.

38:46I think we're still very early in the phase with AI in terms of it having a really meaningful impact on improving. Again, coming back to the point about problems, right? This ultimately should be helping us solve problems. Which problems is AI really helping us to solve in a systemic way that can scale and that there's learning between governments? We're definitely using AI. Is it actually helping us solve some of the biggest problems of our time? Again, health problems, climate problems. Surely there's some of that. But until we manage this process, until we govern it both ethically, but also in a way that is with government instead of kind of, you know, sidetracking government in order to extract these mega rents, then we're going to have a huge problem.

39:29Why do you think that hasn't happened yet or governments haven't stepped in faster? Because I do think AI is kind of unusual in the sense that, you know, you have people like Sam Altman who will say very publicly in interviews like, yeah, this creates a lot of negative externalities and society is going to have to figure out how to deal with this and governments are going to have to figure out how to actually best deploy this technology. OpenAI published a big industrial policy document, which again, I think is kind of unusual to previous technological developments. And yet governments seem kind of slow.

40:04I don't want to generalize too much, but many governments seem kind of slow to get in and really start shaping what they want to do with AI. Yeah. I mean, I think, again, it seems like I'm saying the same thing, but I really, it's because I believe in it. This technology on its own will not solve anything. If it's accompanied by a strong, for example, health system that with AI thinking about health problems, then I can see that being absolutely revolutionary. And in fact, I once heard a really interesting discussion between Nandan Nilekhani, you know, it's an incredible entrepreneur of computer systems.

40:37He was one of the co-founders of Infosys in India. And Eric Schmidt at one of these dialogues we have in Bellagio, this beautiful villa in Lake Como run by the Rockefeller Foundation. And we had kind of Eric Schmidt on the one hand saying, you know, in the future, all we're going to really need is an app, a health app, and that's going to be fine. And Nan Daniel Kani, who really is one of the biggest innovators around computing, was like, what? Like, let me tell you, in India, without a proper health system, no matter how many apps you have, we will continue to have misery. And so it's not one or the other, but what you need is more people like Nandan who think about what is the relationship between, you know, the power of AI and the structure of a health system and who's thinking about that.

41:19And if we do have at the same time, not just austerity, but this, you know, kind of dumbing down of what we think government is for. So even the health systems we have are not kind of, you know, fit for purpose, then I don't see any sort of future of AI kind of helping us with health problems. And by the way, just look at water. I mean, like 55 % of the global food system right now is at risk because of how we're treating the global hydrological cycle. You know, biodiversity loss also in the Amazon is affecting droughts and floods in other parts of the world. That's a huge problem. To what degree are we really using AI to fix that problem?

41:55Not much. So it's also about where we're putting kind of the emphasis. And for that, I think we do need these moonshots, government led, working with the private sector. And again, using the power of AI, well regulated to solve very concrete problems. Yeah, Joe, I was thinking about this specifically related to healthcare recently, because I've seen a bunch of startups that are saying they're going to simplify the billing process for hospitals. And then you also know that there are a bunch of insurance companies that are also using AI. And it's like, well, if we're just going to have more...

42:26Tracy Alloway:And computers debating with computers. Yeah, exactly. And like if the system itself doesn't change, nothing's going to improve. Right. No, I've totally thought about the same thing where it just like feels like we're going to have this arms race where it's like my bot will argue with your bot. And then the only entities that make any money are the bot makers, the bot makers. Do you know that Ada Colau, who was the mayor of Barcelona, another city here in Spain that you know well, she when she was mayor, she came from a housing movement. So she was really, really concerned with housing issues, but also, you know, public transport, public schools and so on.

43:02And her thing was, why is it that when the citizens of Barcelona, you know, like click on Uber or CityMapper, this data that's created from that, right? Because every time we click on something, data is created. She said, why aren't we in the city using that data to improve our decisions and understanding of our public transport and public housing challenges? And so she ended up hiring computer hackers into the city government and made it a really cool place to work. And I think that, again, kind of insourcing back in those kind of, you know, cool hackers that currently are working in these companies, but to come and work with the city administration that says, we want you to come in, fail, but help, you know, like, don't worry about failing.

43:42Sorry, not fail, but, you know, take risks to help us, though, kind of really target our big challenges around housing, transport and so on. inequality in terms of access.

43:51Tracy Alloway:So obviously, we're talking about the public sector. We're talking about the government's role in facilitating or guiding various technologies. But there's also just like politics, right? Winning elections. And the fact that, you know, as you mentioned, failures become a scandal and they're on the paper and maybe like politicians lose their jobs, then someone comes in. When you just in your own personal work, when you like think about this stuff. Like, how much do you have to calculate the reality that one big job of politicians is to win reelection and that a loss of election has the potential to just take the government in 90 or 180 degree turn from whatever the previous administration at any level in any country does?

44:35Tracy Alloway:And how much do you think about that reality when you're thinking about strengthening government? So the first time I started to work on missions wasn't on mission oriented policy. It was on mission oriented organizations, right? To better understand the DARPA kind of organization, right? Corfo in Chile is similar. Citra in Finland, MindLab in Denmark, Vinova in Sweden. These are innovation agencies that are in fact making these big bets that are mission oriented. But my question was, how are they organized? Are they also unstable due to the electoral cycle? And they have thought about this. I mean, it's not a coincidence that DARPA, for example, people come in for five years, but it's not the four year electoral cycle.

45:13So they're actually told, come in and do take risks. That's how you'll be evaluated, you know, not by just if you're succeeding all the time. That means you're not taking those risks, but also the impact that your successes have. And so that kind of cultural shift, but also the fact that people are coming in kind of on secondment. You know, they're not there to be a civil service, civil servant their whole life. So we started studying. Well, for some for some areas, I think that's fine, especially around innovation. You want to, coming back to the idea that we want to bring in and crowd in the top talent into government, having like a five-year period that you're going to help as a civil servant paid by the government, not as a consultant, working with, not at the civil service.

45:54I do think there's lots of kind of room for that. So that idea that because we value the private sector, there has been, for example, think of Harvard Business School where they have this case study methodology of businesses. We've never really done that with government entities because we don't value basically government as a value creator. It's just seen as a redistributor, a fixer, a facilitator, an enabler of the private sector. And that's, of course, where then we expect creativity and value to be created, which is not right. And so one of the things that we do in the Institute, which is actually a department.

46:25So we train up civil servants around the world, also through our own MPA, Masters in Public Administration, but also through applied learning programs, was to start developing these cases. You know, what do we know about the BBC? You know, how is it different from other public broadcasters? How do they measure what they call public value? What is public value? So even having, you know, comparison learning between, say, a public bank, the BBC, a government digital agency on what it means to crowd in or crowd out the private sector, what it means to shape markets, not fix them. What does it mean to have a culture of experimentation versus this huge risk averseness that, as we said before, is a cause for the consultification.

47:02So I think a lot about that. But it's not, you know, there's so much instability, obviously, also in the private sector. So there is a bit of a myth that it's all unstable in the public sector because of electoral turnover. We can't shape these bureaucracies to be creative bureaucracies, resilient bureaucracies, they don't have to be vertical, and so inertial. But the other point, I think that's sort of stemming, I think, in your question, tell me if this is not related, is literally winning the election. What are we learning globally? You know, why is it that Biden, whose economic policies were actually quite successful?

47:33in the red states, at least starting to be quite successful. Why in those states did he not win? And I think there's something going on in a lot of countries, definitely also in Italy and the UK, where people who have been, let's just use the concept, left behind on terms of the economic benefits, at least in the past, even when new economic policies work, that's not enough if people don't feel valued, if they don't have their dignity back, if they continue to feel condescended upon. So one of the really cool things I've been working on with city governments, but even councils. So my neighborhood in London is called Camden.

48:06It's about 250 ,000 people. I worked with the council on mission-oriented procurement for adult social care across 10 housing estates, what you call projects in the US. And we brought the carers and the caries to the table to design that policy. So working with people, really valuing their lived experience to help design policies that are meaningful and will improve their lives. I think it's just so important, firstly, to get those policies to be designed right, but also to give people, again, dignity and self-worth. And I've seen it also, you know, because we have so much inequality in the UK.

48:44Unfortunately, we have food banks, which is barbaric, if you think about it, in the 21st century, food banks, like, you know, we should not have that. People should have food, you know, on the table, enough and healthy food. We don't have that. So transforming food banks into food cooperatives, green food cooperatives, where the people benefiting from what was a food bank are now also in the place of governing, of having real deliberation, of thinking together. I can tell you the people I saw working in the food banks who are also receiving the food, the facial expression, the dignity, just even how people are standing is completely different from a system where, you know, you...

49:20Here's someone's expired, like, pumpkin from last Thanksgiving. No, but even if it's good food, And, you know, having, again, you know, bringing back dignity and value. It's so important, I think, to fight populism. I'm going to ask what is potentially an unfair and loaded question, but I think it might be quite illustrative of everything that we've been discussing. When you look across the world, are there particular countries or cities that you think are doing industrial policy right in the sense that they're taking maybe a holistic approach with a defined strategy slash mission? So I tend to also look at like very specific things that a government did instead of just saying the whole government's perfect, right?

49:59So for example, in Brazil, something they've done that I think has been very positive is that they've put what I call missions at the center of government. So the ecological transition is at the center. And that then required the Department of Finance, for example, to rethink its own tools. For example, a public bank, right? So BNDS, which is one of the largest public banks in the world, it can either just, again, give money out to save the agrobusiness industry or save the steel industry when it's going bust. Or because there's an ecological transition, it can think about how these sectors themselves need to change in order also to access the loans that the bank is giving.

50:35Germany, by the way, did that when they had the Energiewende policy, their public bank, the KFW, the way they provided support to the steel sector, which in the US and the UK and so many countries, steel is under pressure, the loans to the steel sector were conditional, that the sector lower the material content of production, which they did in their own way. Had government told them how to do it, you kill innovation. But strong direction, conditional loans, and they ended up now having the greenest steel in the world. It might not be competitive yet, and that's a scale issue that has also to do with regulation.

51:08But repurpose, reuse, recycle technology in steel only happened because the public bank that was mission aligned. So I'm very interested in procurement, public loans, state-owned enterprises, digital public infrastructure, examples where they're not just things that are there, but they're used to really transform and help development. Sweden is also really interesting because they had a high-level challenge, right? Missions are somewhere between a challenge, like the space race, and a sector, like aerospace, right? So the moon mission required lots of different sectors, but it was very concrete with the challenge of, say, climate change or all the sustainable development goals, those are kind of very broad.

51:44So transforming them into missions. And their challenge was, I think they said they wanted a fossil-free welfare state. And that's why then they said, well, what are the missions? We were working with them on this. So we were kind of stimulating some of this thought through Vinnova, their innovation agency. What are the missions that will help us achieve that? And that's where then the healthy, tasty, sustainable school meals policy came from. We worked with them on that also in Brazil. We just actually put out a report about this with the World Food program. But what was interesting, again, was that then that required government to work in a different way.

52:15Again, interministerially, you know, catalyzing bottom-up experimentation, local manufacturing, but through also the redesign of the tools themselves. The UK, which I don't think is a very good example right now. I mean, there's lots of instability. There's also been 14 years of austerity. Some things that were really interesting, and this is why I look more the organizational kind of examples was government digital services, GDS, which basically began by government back in the early 2000s saying, why does everyone have to go to say Google to download a white paper? Why don't we have our own kind of digital platform?

52:49They did what most governments do, outsourced it to a company called Circo, which is not a very innovative company at all, gets lots of government contracts. They failed miserably. And then people from the iPlayer team and the BBC said, we'll do it. So they went over to the cabinet office, set up government digital services, came up with this incredible digital platform called gov.uk, which won an international design award. But what was interesting to me from that example was that the first thing they did was look out the window and said, with arrows pointing out the window, those are not clients and customers.

53:23Let's stop talking about people as clients and customers. They're users with human rights and how they will access their driver's license, their passport, their voter registration has to enhance their like their souls and not make them want to die. If you see the Ken Loach movies where, you know, people literally want to die when they're accessing their welfare payments because of just the complications around it. And so making, you know, having kind of like a user friendly government digital platform that changes the experience of a citizen with those rights that they have through the technology just requires a very different kind of mind shift.

53:57And it became the coolest place to work. So if you were a top software engineer or even, you know, whatever, these hackers that Adekolao wanted to hire, that's where they wanted to work to the point that lots of private companies were having a hard time finding the top talent because they all wanted to work in GDS. So again, those are the examples I think we need to look for, not like which is the government that's doing everything perfectly.

54:18Tracy Alloway:All right, Professor Matsukato, thank you so much. great to finally catch up with you. It took us all like randomly being in the same location. But thank you so much. Thank you so much for coming on AdLog. Thank you so much for having me.

54:42Tracy Alloway:Tracy, I'm glad we finally caught up with Professor Matsukato. But this was the place to do it. This is definitely the place to do it. I think it really did. Her framing makes a lot of sense. This idea that like outsourcing is not per se bad, that obviously any major mission is going to have to have significant private sector involvement and innovation, even if it's somehow publicly led. But that there's no chance of going anywhere if there's, you know, if the public sector doesn't have the internal muscle of who to talk to or who to talk to at the right time. Or how to judge performance. How to judge performance seems absolutely a key question.

55:21And that kind of gets back to the mission idea, which I do. I really I like the idea of focusing on what you're trying to achieve rather than just how you're actually going about achieving it. Like that makes a lot of sense to me if you're dealing with a vast bureaucracy with lots of different silos. And also, I think, like, getting back to that expertise point does mean that you do develop that muscle internally within the organization rather than just like, OK, we're going to hire McKinsey to figure out how we're going to do something. Instead, you say, well, we want to do X. Let's all get together and figure out how to do it.

55:59Tracy Alloway:You know, AI seems like a weird thing as a technology because, all right, on the one hand, you could say, like, all right, we want to massively improve our health care system. We want to massively improve health care outcomes. And I think you could, like, very easily say, well, AI is going to be a really big part of that, right? And maybe make things a lot more efficient, maybe give information access to a lot of people, identify experts, et cetera. Develop new medicines. Yeah, totally. It's so great. So maybe the mission has something to do with health and AI plays an important component of it.

56:31Tracy Alloway:I guess what's strange, though, is that AI itself creates its own potential pitfalls. I mean, the industry, as you mentioned, is obsessed with the pitfalls of its own making. Right. Yeah. And so there is almost no way that AI can just be a tool in service of some other mission, Because almost everyone who knows something about AI sees potential for extreme exacerbation of inequality, potentially AI robots that will be misaligned and want to kill us all when they have sufficient capability. Just little things. Yeah, right. And so like on the one hand, like, yes, as a technology, it might fit into some of these other big missions.

57:12Tracy Alloway:But on the other hand, it sort of feels like it has there has to be some like AI specific goal of like, where do we want this technology to go? Or how do we how do we curb it or whatever it is that seems very distinct? No, totally. And I do think the unusual part of this moment in time and a lot of people will argue that maybe it's marketing or whatever, but you do see the big tech CEOs like basically going on TV and and saying like we as a whole society need to figure out what we want to do here. And I don't think it is marketing or just marketing because, A, there's already this very big tech backlash.

57:47Tracy Alloway:Right. So like it's not working. You know, if the idea is, oh, we want to plump our valuations. And so we do that by saying the TAM is all human labor and human life. Yeah. Well, you're really upsetting a lot of people by saying this. It's not that's not obviously good. And B, you know, as we've written about or talked about, you know, some of these big labs, like they were founded from day one with the premise that this is not normal technology, which is why it's like housed in a nonprofit or something like this. So I tend to think that when the CEOs of these companies talk about this stuff, they kind of mean what they say.

58:24Tracy Alloway:They, too, are they, too, are concerned. I mean, I also worry without some sort of government intervention or government strategy here, we are going to get to that situation where we deploy AI. And because we're not fixing the underlying system, we're just sort of nippling at the edges and making it worse per that idea of like, OK, the insurance bot is going to talk to the hospital bot. And both of them are going to say that they're streamlining the billing process for medical services. But because we're all doing the same thing without actually fixing how medical bills work and who pays for what in the U.S., like it's just going to be bots fighting bots.

59:02No one's going to benefit from that.

59:03Tracy Alloway:No. And most likely there will be sort of, you know, just ongoing increased complexity. It's bots all the way. Bots all the way down. All right. Shall we leave it there? Let's leave it there. OK, this has been another episode of the All Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Allaway. And I'm Joe Weisenthal. You can follow me at The Stalwart. Follow our guest, Mariana Mazzucato at Mazzucato M. Follow our producers, Carmen Rodriguez at Carmen Armand, Dashiell Bennett at Dashpot, Kale Brooks at Kale Brooks, and Kevin Lozano at Kevin Lloyd Lozano. And for more OddLots content, go to Bloomberg.com slash OddLots where we have a daily newsletter and all of our episodes.

59:39Tracy Alloway:And you can chat about all these topics 24-7 in our Discord, discord.gg slash OddLots. And if you enjoy Odd Lots, if you like it when we define state capacity, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

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