Twenty-Eighth Term Member Conference

Thursday and Friday, October 26–27, 2023
audience clapping
Stephen M. Kellen Term Member Program

The Stephen M. Kellen Term Member Program is supported by a generous gift from the Anna-Maria and Stephen Kellen Foundation.

U.S.-China Relations: Balancing Cooperation, Competition, and Interdependence

FROMAN: Well, good evening, everybody. I’m Mike Froman. I’m president of the Council. And it is truly great to see all of you here. This is by far the biggest meeting I think we’ve had. There are more people in the building today than I’ve seen in the last three-and-a-half months. It’s great to see people in person, and I know we’re joined by several more on Zoom. It’s really terrific to have you here for what is the Twenty-Eighth Term Member Conference.

First, I wanted to thank the folks who actually already got us started—Reena Ninan, Steven Cook, Mai El-Sadany, Farah Pandith, and Mara Rudman—for the earlier session on the Middle East and the conflict there.

I want to just note that this is the first full Term Member Conference in person in New York City since November of 2019, so I view this as—(applause)—I think we’re going to declare COVID officially over. (Laughter.) This—I don’t want to jinx it. (Laughs.)

I’m going to turn it over to Karen here in just a minute, but I wanted to say a few words about the Term Member Program itself. So the Term Member Program was actually started in 1970, fifty-three years ago. It’s named in honor of Stephen Kellen, who was a longtime member of the Council deeply committed to identifying/developing the next generation of foreign-policy experts. And he made it, really, a priority throughout his whole life to develop relationships with sort of up-and-coming talent and to help them in very substantive ways. He was really instrumental in deepening the conversations that they were having about international affairs here at the Council. And as I’ve talked about in some other contexts, I view a key function of the Council—one of our key objectives—is to identify/develop/promote the next generation of diverse foreign-policy talent. And the Term Member Program is absolutely key to that.

So I want to thank the Kellen family; the Anna-Maria and Stephen Kellen Foundation for supporting the Term Member Program; Andrew Gundlach, who is the grandson of Mr. Kellen, who’s been our primary partner in developing support for this program, instrumental in securing that support over many years.

You know, the program, you are joining or you are a part of a really quite illustrious group. I asked my team to pull together just a few of the prominent term member alumni. The list goes on for pages. I’ll just name a few of them here: Stacey Abrams, John Abizaid, Ash Carter, Tom Friedman, Tim Geithner, Jon Huntsman Jr., Walter Isaacson, Wes Moore, David Petraeus, Penny Pritzker, Susan Rice, Sheryl Sandberg, some guy named Charles Schumer—(laughter)—I think he goes by perhaps Chuck, Admiral James Stavridis, and of course Angelina Jolie. (Laughter.) So wherever you see your careers going, in any of those directions—(laughter)—would the next Angelina Jolie please stand up, please, though? (Laughter.) No, it’s terrific.

I have to admit that I was a term member myself. It was really a terrific experience. Back then Les Gelb, who was—who I never—when I had him at my house in Washington—my wife and I were both term members, and he asked us to host a reception at our house for term members, about fifty people there. And never in my wildest dreams would I be—was I dreaming that I’d be standing up here as one of his successors. But he really took a very personal interest in the Term Membership Program, and put his arms around a lot of us, mentored us. And I’m very much committed and my team is very much committed to continuing to do that today. So it’s really—when I say it’s a pleasure to see you, I really mean it from a very personal level. This program is absolutely critical, in my view, to the Council, but also to the generation of that next generation of foreign-policy experts. And you are very much among that group.

We’re going to have 350 people here in person over the next day or so, and again, dozens more in the virtual sessions. The agenda, I hope you’ve seen it. It’s really a terrific agenda: U.S.-China, artificial intelligence, Ukraine, climate change. I’m very much looking forward to the final keynote with Raj Shah, who’s the president of Rockefeller Foundation, also the author of a recent book on how to have—make a big difference in the world, which I know all of you are very much committed to do.

Needless to say, the world has served up an awful lot of issues for us to focus on. There’s no lack of things to be concerned about/interested in. And frankly, there’s been no more important time for the work of the Council. We’re being asked to help people understand what’s going on in the Middle East, what’s going on in Russia-Ukraine, what’s going on with China, going on with climate change. And in my view, there’s no better institution positioned to help everyone from policymakers on one hand to our members, local community leaders, religious leaders, journalists, the media, local and state officials understand these things than we are.

I’d be remiss if I didn’t also mention that January 10 is the next deadline for term membership applications. You are our best ambassadors. Please do be proactive in encouraging others to apply and helping us to expand and diversify our memberships. And if you have any questions, Nancy Bodurtha, who’s here, and Vera Ranola can certainly connect you with the necessary—there’s Nancy over there—and the necessary resources.

I’m really looking forward to the conversations over the next day. I hope you are as well.

And with that, let me turn it over to Karen Harris. Karen is a term member—or, excuse me, a Council member herself, and is managing director of the Macro Trends Group at Bain and Company. Over to you, Karen. Thank you.

HARRIS: Thank you so much. (Applause.)

Since COVID is over, that’s very convenient—(laughter)—as I welcome our guests here, our speakers here. Thank you all for being here in person and those who are streaming live. I am thrilled to welcome—(off mic)—into this conversation, which we’ll open up to you in a short while.

First, Ivan Kanapathy, who is, amongst many titles, an adjunct professor at Georgetown’s School of Public Service; a senior associate and the Freeman chair in China studies at CSIS; and former deputy director for Asia on the National Security—Asia on the National Security Council. (Off mic)—CFR member.

We have Zoe Liu, who is the Maurice Greenberg—(comes on mic)—thank you. My mic is not working. Luckily, I—my kids tell me I have no trouble being heard, but this may help. (Laughter.) Is the deputy—is the Greenberg fellow for China studies here at the Council.

And Lingling Wei, the chief China correspondent for the Wall Street Journal and author of Superpower Showdown.

So we have a superb panel. And I think one of the questions that if you are at all consumers of media today that you might have that I certainly do is we’re seeing a pretty broad spectrum of potential outcomes between the U.S., China. And I should say the title of this is “U.S.-China Relations: Balancing Cooperation, Competition, and Interdependence,” which sounds more like a clown juggling too many balls to me than a—than an aspiration. But maybe let’s start with you, Lingling. Help us understand: What do you think the full spectrum of potential scenarios is that people from various backgrounds in this room should be considering as where the situation could go?

WEI: Sure. Well, first of all, it’s really a(n) honor to be here. Thank you so much, Michael and the Council, for inviting me. It’s quite a privilege to share a stage with such great colleagues.

So, as I mentioned to Michael earlier, the last time we saw each other was 2016 in Beijing when I was based in Beijing for the Wall Street Journal and Michael was the USTR. How much things have changed.

Thank you. Thank you. (Comes on mic.) I think I also have no problem being heard. (Laughter.)

So, anyway, I was just saying last time I saw Michael was in 2016 in Beijing. At the time, I was still based in Beijing for the Wall Street Journal and Michael was the USTR. Oh, boy, you know—(laughter)—how much things have changed since then. You know, I’m glad COVID is over, but I kind of miss COVID in a way because, you know, we can do this just Zoom in your pajamas. (Laughter.)

But most importantly, U.S.-China relationship has, you know, gone from—just from, you know, this term, call it Chimerica, right, this term coined by some academics back—you will remember back in the ’90s. In the early twentieth century—twenty-first century, you know, the relationship got so close in 2008 then-President George Bush called then-President Hu Jintao twice in one month: Help me. Don’t sell Treasurys. Help me save the world. Right? And then Hu Jintao flew to Washington, sat right next to President Bush in the Oval Office, you know, the start of the G-20. Collectively, U.S. and China saved the world from the global financial crisis.

Fast-forward to today. Where are we in this relationship? Most recently, we have seen some signs of stabilization—a flurry of diplomacy from both sides, right? A series of Cabinet members visited Beijing. And we’re seeing China’s foreign minister, Wang Yi, going to Washington tomorrow; and then after him the—Xie Zhenhua, the climate envoy; and then perhaps the top economic aide to President Xi Jinping, He Lifeng; and then President Xi Jinping himself going to San Francisco to attend APEC and potentially have a separate meeting with President Biden. So things are stabilizing a little bit. And finally, the balloon incident is behind us. Two sides are talking again.

But nothing fundamental has changed. This competition is still very well and alive. And I think, you know, it falls upon both governments to try to make sure whatever competition we’re having here doesn’t devolve into an outright gunfight. So if they can keep things stable while continuing to compete in a range of matters—technology, economy, geopolitics—you know, that will be a win for both sides and for the whole world.

You know, the worst-case scenario will be just conflict, you know, triggered by some regional conflict. We are—kind of that kind of risk is there. To some extent, potentially, it’s rising as well, you know, given already in less than two years we’re having two wars going on right now, right, Russia-Ukraine and now Israel and Hamas. And China and the U.S. are taking the opposite sides, right, in both cases. So the competition is intensifying despite the recent signs of warming up in the relationship.

So, you know, in terms of laying out scenarios, really, you know, the best we can hope for is the kind of stabilization can be here to stay; both sides continue to talk, don’t just cut each other off on the first sign of, you know, provocation, right? Because both sides will continue to do things the other side doesn’t like. So don’t stop talking just because the U.S. intensifies export controls or China does something else that’s totally tone deaf or stupid.

So, anyway, so that’s the best-case scenario—you know, stabilization. But don’t—you know, neither side has any false hopes that things will improve significantly. Thank you.

HARRIS: Ivan, I want you to put on your national security hat for a minute because I think the—what is the definition of stabilization and what that actually looks like. And maybe you could weave in a little bit of your experience in Taiwan, which is not at all a hotspot now, thankfully, this week, so we can ignore that safely for about twelve more hours. (Laughter.) But in the meantime, if you can talk to us, what is the best-case scenario? What does stabilization look like from a U.S. foreign policy perspective and from China’s perspective?

KANAPATHY: Yeah. I think, you know, we have to have an idea in our head that is quite different than, you know, five-plus years ago about what a stable relationship looks like, and I think you’ve seen it for about the last five years. I think it is stable. I think we need to get used to it. There’s an elevated level of tension. I think that’s natural given the goals, given sort of the total power that China has accumulated, whether it’s military, whether it’s economic. And we’re fundamentally in some ways—not in every way, but in some ways we’re fundamentally at odds, like two—these two countries are. And so there is going to be a level of tension, I think, like Lingling says. You know, it’s a question of sort of managing it in a way that it doesn’t escalate to something, obviously, that nobody wants it to.

The one thing I’ll throw out there is, you know, I try to avoid saying, you know, the worst possible outcome. The thing—you know, the ultimate goal is to prevent conflict. I think in our heads we all sort of know that, but you’ll notice that China never says that. They never say that. And I don’t necessarily want them to believe, you know, that we’re not willing to, because that’s actually the worst thing, because then they might do something. Like, that’s inherently destabilizing for them to have that misperception. I think, you know, President Biden has said a few times now, depending on how you count, for example, that he would militarily intervene, you know, in a Taiwan Strait crisis, so—and I think he’s thinking about that, again, from a—from a deterrence perspective, and probably I’m guessing in the back of his mind thinking about what happened in Eastern Europe.

HARRIS: I have a thousand questions I’m dying to ask, but at the top of mind—and this one’s for you, Zoe, to start with—is we have a room full of people, a lot of whom are working in the finance industry and in business. And the question that we hear a lot, that I bet you do, is: What does all this mean with rising China, the BRICS currency, this cooperation? Is the U.S. dollar dead? Like, should we be buying renminbi? What is happening with trade? Can you shed a little bit of light with the political system? We have the economic system. What’s happening with the—with the financial system?

LIU: Yeah, sure. Happy to share my perspective on that. And, obviously, I could potentially be biased because I did write a book published by Cambridge University Press last year, published—I have no idea how they managed to do it, but I call it a pamphlet. So the pamphlet was released right on the day of Russia’s invasion of Ukraine. (Laughter.) And you know, I always remember that day. It was February 24, and that’s the day my pamphlet came out.

And the title of that book was Can the BRICS De-dollarize the Global Financial System? And I spent about two years working on that, and I remember during that time one of—during the review process, one of the reviewers literally said: What if this CIPS, what if this FPSs, why this matters? And he almost killed my project. And now, in retrospect, literally just over a month period everybody seems to be freaking out. Like, what if China is doing what is the—you know, the whole BRICS are doing. And then you also have my good friend Zoltan Pozsar throwing out this idea of Bretton Woods III. You know, Zoltan is great. I just love him, dear to my heart. But then, at the same time, he did—he and I did have a lunch and specifically talk about, you know, the BRICS system I explained to him.

But fundamentally, my conclusion still is the dollar is still very much strong. I strongly—yes, I do have a CFA, but I do not otherwise—I advise against trying to short the U.S. dollar—(laughter)—the same way that—the same way that the PBOC would advise you, hey, you know, for those—whoever want to short the renminbi, you aren’t going—you are betting on the wrong side. And you know, I’ll explain to you why the dollar is still dominant, and why the dollar is still probably going to dominate for the foreseeable future, until perhaps the real—the real threat comes down to we move beyond hydrocarbon. I’ll explain why that is the case.

To begin with, the dominance of the U.S. dollar actually can be expressed through whole wide, different spectrums, and I summarize it through, you know, three perspectives: in the real economy, in international finance, and investability in terms of whenever—even crisis we made here, you realize that the world still invests in U.S. Treasurys. And then when you think about it in terms of international reserve currency, the U.S. dollar, yes, we are no longer—we are no longer, you know, 80 percent of the global reserve currency, but, hey, we are still pretty good; 55 percent. And where is China? Starting from—China started from internationalized renminbi back in 2009, and they did in that initial report saying that we have the goal of making the renminbi a(n) international reserve currency together with the dollar and the euro. So from that perspective, they never even had the intention to de-dollarize or dethrone the U.S. dollar.

And if anything, you know, the dollar—I did write this for the international economic magazine, to say that in a de-dollarized world China would be the largest loser, specifically because on the one hand China has more than $3 trillion of official foreign exchange reserves that, you know, the U.S. Fed—really, the Federal Reserve of New York would know—the Federal Reserve of New York would know better to what extent they have it, they have invested in U.S. dollar asset. But a pretty safe bet is that about 80 percent of that is invested in U.S. dollar-denominated asset. And then you also have about 1 trillion (dollars)—somewhere between 1 trillion (dollars) to $2 trillion of sovereign funds investment again invested in dollar-denominated asset. So now, in a de-dollarized world, any instability in the dollar market, China would be the largest loser.

And why I think, you know, the biggest threat to the U.S. dollar perhaps come from climate change and perhaps the world—the fact that we are moving away from hydrocarbon, my real concern is here. The petrodollar system is pretty good and it has a pretty good run. And the fact that we have a petrodollar system and the dollar is so strong is particularly because the global economy—80 percent of the global economy is powered by hydrocarbon. And oil has a global market. It’s the global oil market. Doesn’t matter where you trade. Where is rare earth is trading? What kind of currency is rare earth trading? What about cobalt? And do we even have a rare-earth-denominated futures market? We don’t. But China does. So that’s where my concern comes from.

And, in fact, China is not only actively engaged in trying to figure out—trying to further develop a renminbi-denominated futures market, they are actually very much interested in the use of renminbi in developing the natural gas futures market, the spot market, as well as the rare earth mineral, and as well as other critical mineral very much needed for the energy transition. That’s where my fear comes from.

HARRIS: Thank you. Richard Fisher once described the U.S. dollar as the previous horse in the glue factory, which I think is—(laughter)—the quote that sticks with you.

I want to throw this out to all three of you, and I think it’s—Zoe, you’re talking about rare earths, energy transition, climate. What do we—the U.S. and China have to get right in terms of cooperation? It’s clear, I think, where competition comes in, but what do we—what should we all be working towards getting right?

LIU: I think I have a three-letter for this. The first—my three letter is C-F-R. (Laughter.) It’s about this platform.

And also, C start with climate change. That’s very much, though, that keeps the two—you know, two countries and all the officials at all these different level keep talking.

F is really about food security. China prioritizes starting from 2004. Every single one—number-one document that the Chinese government released every year has been focusing on food security. President Xi Jinping himself emphasizes this a lot.

And R, really—what I really mean by R really means retirement and aging, and this speaks to the broader problem of demographics. And I started to say this, that demographic, I really do—you can make all these argument to say that people today live relatively younger. You know, fifties and the new thirties and sixties are the new forties. But the problem with China is that negative demographic trend is really challenging in the—for two reasons.

The first reason is that, well, you know, by all estimate, by 2040—means within the next—within the next two decades—Chinese population—about 28 percent of the Chinese population is going to be above sixty years, which is the current legal retirement age in China. And that’s—in other words, China—in the next twenty years, China is going to have more than 400 million people who are aged above sixty years old. And that bode well for the United States in the sense that by 2040 our entire population is going to be something around 390 million. In other word, China is going to have more retirees than the entire U.S. population within our life future—within our lifespan. So from that perspective, I do think China need our help.

So C-F-R and this community, you guys are here. (Laughter.)

HARRIS: Lingling, Ivan, what else—where else do we need to cooperate?

KANAPATHY: So I’m not sure. You know, I think it really depends how you define the term “cooperation.” Like, I actually don’t see much hope of what I would describe as cooperation. I think I would see—whether it’s climate change, whether it’s, you know, global debt sustainability, or even global health, I see progress happening more in a framework of a positive-sum competition. I just don’t—you know, given the mutual distrust between the two sides. And I think, frankly, the last three years has borne that out. We’ve made very little progress on any of those. And that has been, literally, the mantra of this administration, that we’re going to cooperate in those areas.

There’s been one instance. About a year ago, Secretary Raimondo said that—and she was giving a tech speech up at MIT, and she said that we’re going to—the U.S. is going to maintain as large a lead as possible in foundational technologies. She said semiconductors, quantum, AI. She also said biotech. She also said clean energy. So she kind of at that point articulated that this is actually a competition, I think. That’s a frame that I think would lead the world toward a better place; like, progress would move faster. If we’re interested in results, that, I think, would be better for us than sort of talking a lot.

HARRIS: Lingling, in a lot of this conversation we’re kind of presupposing that there’s a peer conflict between the U.S. and China. But you’ve been writing a lot recently about some serious Achilles’ heels in the Chinese economy. How strong is China right now? And what do you think—and how would you assess their competitive ability right now, based on what you’ve seen both in and outside—

WEI: Sure. Before I get to that question, I just wanted to quickly address the—

HARRIS: Please.

WEI: —the areas they can cooperate. Zoe and Ivan just said 90 percent of what I wanted to say. (Laughter.)

But I also think—just one quick point. I also think China and the U.S. must cooperate on areas they compete as well, like AI, because AI is also used for military purposes. You know, you don’t want incidents to happen. So both militaries, in my view, should really resume dialogue as fast as it can, set certain standards. And the U.S. side needs to know how the Chinese is applying AI and what kind of standards they’re using; vice versa. And this is very key to prevent accidental conflicts from happening. So just that one quick point.

And to your question, I was told by a good hedge-fund friend of mine just today, he told me is that the most popular trade these days is shorting CNH, which is offshore, I mean, against U.S. dollar. So you long dollar, short CNH. And simultaneously, you short dollar and long Mexico—Mexican peso. So that means you’re shorting China’s economy and longing Mexico’s economy. And that pretty much answers your question. That’s the outlook for—the near-term outlook for China among market participants. Correct me if I’m wrong. You are all experts here.

But in my view, I think the market is a little bit too bearish about China near term but not bearish enough about China longer term. The reason I’m saying is that, you know, the housing crisis is brewing, is getting worse. Hardly a week goes by without another developer in China defaulting on offshore bonds. And the Chinese government to this day hasn’t come up with a comprehensive plan to address the problem, right? The Zhu Rongji era is far gone. Back then, China’s debt crisis was very huge, but he created a big—good bank, bad bank solution, right, allowed losses to be allocated among stakeholders, and the government provided guarantee for banks to restructure. But that kind of solution, we’re not seeing that thing repeated these days. And we are having questions about the quality of the economic management under President Xi Jinping.

And most importantly, back in the late ’90s when China was going through a very severe debt crisis, China was warming up to the United States and the Western world. The WTO entry provided this huge boost to China’s growth, so China essentially grew out of the problem. Fast-forward to today. That kind of external environment, it no longer exists. And the relationship between the U.S.-China we just talked about, we’re discussing nowadays not about what Michael—you know, back in your days it was how to try to get Chinese market more open for foreign companies. And nowadays, the conversation is mostly about, OK, how do we avoid a war, right? So the conversation dramatically changed. That shows how the relationship changed. So China no longer has a favorable external environment to help it grow out of the problem.

So that’s why I’m saying medium to longer term China faces bigger economic headwinds than a lot of people were expecting. Near term, because the—(inaudible)—as Zoe pointed out, the—why it’s all but impossible, at least near term, for renminbi to replace U.S. dollar is because, you know, China’s currency is not freely convertible, right? It’s not a normal currency. So it’s basically from your left pockets to your right pockets. It stays inside you, right? So won’t have the kind of Asian financial crisis of sort.

So near term, it’s manageable. It’s under control. But, you know, if this thing drags on, it would be a huge problem for China. The local—not just property market. It’s so connected to household wealth. Chinese households have 80 percent of their wealth tied in real estate. That’s why consumption’s so weak; people are having—feeling more poor these days. And local governments, a third of their revenues come from land sales. Local governments are running out of money, too. So it’s really hard to overstate the importance of the real estate market and the debt problems faced by China today.

So medium to longer term, it could be really, you know, pessimistic in terms of China’s outlook. And the only way for China to get out of it is to be back on the paths of reform and opening, back to the liberalization that Deng Xiaoping started. But, you know, we’re not seeing any signs of that happening. So, you know, that’s why I’m saying, you know, medium/longer term, China’s economic outlook is not that optimistic.

However, just one last point: China remains a very formidable economic power. It’s embedded in everything you do, right? It’s embedded in everything the Wall Street Journal does these days. So in that regard, you know, it’s still the biggest competitor to the United States. And that’s why managing this relationship in a responsible way is so important.

HARRIS: We’ve had some—I think some really interesting epiphanies around policy if I look back over the last eight years. I would say there was the 2015 moment of awakening of maybe they won’t open up if they get richer; like, the private-sector view that as China got more integrated they would somehow magically, like the force of gravity, become more like the West. I think we’ve put that to the side. If I haven’t hyperbolized it enough, I can murder it more. And then I think in—more recently, in 2022, we saw the death of the decline of the West narrative in China, or at least a reconsideration of just how unable to act the West was.

If we look back, I’d love your views on, first, what were the—what are the policy mistakes we made that led us to those mistaken beliefs on both sides? And how do we avoid the kind of catastrophic misunderstandings that could spin out of a simplistic narrative that leads to conflict unnecessarily? Ivan? (Laughter.)

KANAPATHY: I’ll stick more on the—on the policy side, right, on the national policy side, obviously, given my background. And I think—I don’t know that there was any one mistake. I think, for the United States, you know, we had good reason to pursue the policies we pursued for a long time, but those reasons started to look less rational over time. And I think we just sort of doubled down, and unfortunately at some point tripled down, even though we were getting sort of the blinking red lights. And that was probably, you know, the final push of that 2013 to ’15 of, you know, whether it’s Sunnylands or, you know, in really 2015 we had some pretty bad human rights crackdowns happening in China that year at the same time that we were negotiating this, you know, commercial cyberespionage agreement that turned out to be complete bunk, right, just like the South China Sea I’m not going to militarize the Spratly Islands. So we sort of invested a lot in those things. I think in some ways there were a lot of tremendous concessions made that same year in the Paris accords to China, which really has almost no restrictions. As we saw last year, China’s coal capacity, you know, starts, is tremendous; it’s, like, 85 percent of the world’s coal capacity that was added happened in China last year. So that was a period. But I think right following that period folks at the end of the Obama administration sort of recognized this, and obviously that fed into some of the assumptions that went into what happened in the Trump administration.


LIU: Yes. If I can just take a step back, answering the question about the economy, and then I’ll talk about the issue with regard to the policy mistakes.

You know, this is—this is how I describe the Chinese economy and why, ultimately, I do not think that the Chinese economy has peaked. So from this perspective, I’m still contrarian. Like, last October, last November I was contrarian when I said that the Chinese economy would not come back and—very quickly. And now I’m sort of, like, a contrarian again, like, you know, the Chinese economy, there is still hope there in the long run.

And the reason is math. You know, China—I was just explaining China’s, you know, population problem or demographic problem. But the reality is China has a four—you know, Chinese population is four times of that of the United States. Simple math simply means if the Chinese per capita GDP achieved that of the United States, you know—you know, one quarter of that of the United States in terms of per capita level, that means the economic size would be the same if measured by GDP. And the Chinese economy, as of last year, was about $17.9 trillion, and the United States is above $21 trillion. So at, you know—at, you know, a nominal level in terms—the Chinese GDP is about 70 percent of that of the United States. But at the per capita level, China was less than 17 percent of that of the United States.

Now, if we—if President Xi Jinping had get the policy right, if he can restore the mentality which Deng Xiaoping had after he visited United States on his plane back to China—he literally made the observation to say that, well, if you look around the world, every country that became the friends of the United States, they become rich. And in a way, yes, joining the WTO free trade agreement definitely helped China. Foreign direct investment, capital expertise all helped. But I do think that China’s growth also benefited tremendously from the tailwind of the confidence coming from abroad, the foreigners thinks that going to China is the gold rush. But I’m afraid now—I’m afraid that this mentality—sort of as Lingling pointed out correctly, the mentality seems to have been changed.

If President Xi Jinping can restore the politics right, there is still hope. It’s not that the Chinese economy will never reach that of the United States. If you ask Wall Street—if you ask Wall Street if you are going to bet against China, you are literally betting against a culture that have pride, hardworking, have a lot of talent. And that is scary. But President Xi Jinping, I’m afraid that he—you know, this is a phrase that I like to use to describe the situation: He does not or he has not planted the Chinese economic bomb, but he’s shortened it. In other word, ten years ago or for a long period of time the Chinese economy looks like an impressionist painting. It's like a Monet. It looks fantastic from afar. (Laughter.) But up close, it’s a jumbled mess. (Laughter.) And if you ask me, hey, Zoe, you know, what do you—how are you going to describe it, today I would be, like, perhaps a Jackson Pollock. It’s like—(laughter)—a jumbled mess from afar and a jumbled mess from up close. But, hey, it’s a big market. A Jackson Pollock sells a lot—(laughter)—as long as you get the market right. And now, in terms of—in terms of, you know, the policy mistakes, I feel like, you know, living in the United States, it seems to some level I feel like I’ve observed a lot that—in the sense that, you know, I still remember, like, my immigration officer, the day I become a naturalized citizen, I imagine this is so many times, how these things happen. I imagined this so many times, even more than my wedding and, you know, how my husband is going to—

HARRIS: This is on the record, I just want to—(laughter)—

LIU: Yes. Yes. Yes. You know, I literally thought about it so many times. And yet, I was literally not prepared for it. My immigration officer when—the moment she handed me my certificate, my naturalization certificate, that she told me: Hey, Zoe, you know what? Now you own the future of this country. Can you imagine anything more beautiful than that? I could—I was literally speechless. I could not find—you know, that’s one of my America is great moment. And in terms of policy mistake, I do think it matters how we measure mistakes.

And if we measure mistakes by saying that we actually implemented policies such that the unintended consequences cost more for us than, say, in this particular case for China. I think Trump-era tariff was bad. I think the bipartisan consensus with regard to bashing China rather than figuring out, you know what, we actually have to figure out how to deal with it. It is distracting. It has distracted us from a focus on finding the solutions. You know, what I think is the worst? I think the China initiative is the worst. And a lot of this actually always reminded me of, you know, like, how to how to deal with this. Like, we have this conversation in my household all the time. I debate it with my husband.

And it always reminded me—when we view history I’m always reminded—somehow, it just reminded that—especially in an era when we’re talking about supply chains. You know, Omar Bradley, he famously said that amateurs talks about strategy and professionals talk about logistics. And when we think about policy implementation and policymaking, I’m afraid that we are focusing too much about the lack of—we are lack—we are a country that are not good at planning. But you know who is good at planning? China. And is their economy doing well? Not so much. (Laughter.) Perhaps it’s really a time to focus on the logistics.

HARRIS: Yeah, that’s a very good point. When I had plans when I was little my mother used to say: Yeah, the Soviet Union had five-year plans too. (Laughter.)

Lingling, do you—(laughter)—do you want to have the last word before we turn it over to the members for questions? You can pass and wait to get a question from our—

WEI: Let’s get questions.

HARRIS: Wonderful. Thank you.

And so please—OK, wow. Just a reminder, this is on the record. I am going to invite term members to join our conversation. And I think there are microphones around the room. And I don’t even know where to start. And we also may have some questions coming through online. Why don’t we—is there someone in the middle aisle who can come? Great. So why don’t we start with this gentleman here, follow—and you’re on deck. And then we’ll regroup. Thank you. And please introduce yourself and your—

Q: Absolutely. First and foremost, thank you so much for an incredible event. My name is Lealand Lazarus. I’m from Florida International University in Miami.

And my question has to do with whether China’s domestic economic slowdown will impact its foreign policy in various ways. Of course, this year being the tenth anniversary of the Belt and Road Initiative. How will China’s slow down impact the kind of economic benefits it could provide to the developing world?

HARRIS: Want to jump on that, Ivan?

KANAPATHY: So I think that China’s sort of economic, I guess, slowdown is legitimate, but sort of crash is greatly exaggerated, which I think—I think these folks also agree. I think they’re going to hit, at least by their numbers, at least four and a half percent this year. Which by sort of our standards is pretty darn good, right? So but I will say that China’s economic—or China’s foreign policy has already been impacted by sort of the slowdown in that growth rate over the last decade. You can tell that Xi Jinping has shifted the basis of his legitimacy and the party’s legitimacy more and more away from providing sort of development, right, better standards of living, more and more towards nationalism. I mean, that’s blatantly obvious.

And if that is—again, they don’t have elections, right? That’s the basis of his legitimacy. And that’s how he’s responding to this. And so the question is, you know, no matter what happens with China’s economy, you know, my fear—and I think we’re watching it, realizing this fear—is that he still continues down this path because he’s insulated himself. It’s not about the economy over time. It’s less and less about providing that. And so what are the incentives for him, right? I mean, like, Kim Jong-un doesn’t need to provide for his people to stay in power. He doesn’t. So that’s the direction—that’s the clear direction that China is moving in. So we need to be worried about that. And, you know, it’s great to advise China to fix and change their structural economy. But I think, from a planning standpoint, it’s probably not really, you know, our number one sort of primary game plan right now.

HARRIS: We’ll go to that.

Q: Thank you very much, again, for being here. Joseph Gasparro, Royal Bank of Canada.

About a month ago, the CFR hosted a hearing for the U.S. House of Representatives Select Committee on the Chinese Communist Party. Former SEC Chairman Jay Clayton came up with this pretty innovative proposal, where large U.S. companies would have to disclose their exposure to China risk. The thesis being this would help investors, stakeholders, and, you know, broader policymakers, and potentially reduce systematic risk to China. If that proposal went through, what’s the second derivative effect in China and the world, given your worldview? Thank you.

HARRIS: Lingling. I’m going to make you take that for all your Wall Street Journal readers.

WEI: Well, so, yeah. I mean, China still has pretty good access to the U.S. market, capital market. And they really need to continue to keep that access. You know, there was some sort of audit deal, right, between China and the U.S. It was out of the pragmatic concern that if all those Chinese companies got delisted from U.S. markets then, you know, that will be disastrous for the state goal of, you know, advancing some key sectors, because there’s a very important funding source. So, you know, whatever the House committee was—has been proposing, you know, could have a huge impact on China’s ability to continue to access foreign investors, especially Western investors.

I would note that lately China has found, you know, alternative source of funding, which is Middle East. Especially Saudi money. You have seen, you know, the sovereign wealth fund in Saudi Arabia, has invested billions of dollars in Chinese tech ventures, tech firms. And also, Saudi has played a role in Belt and Road as well. So you know, it’s impossible to completely cut off China’s access to foreign capital. It’s just, you know, your ability—how much leverage the U.S. has over China, that’s the key, right? How much willing—how willing your partners and allies are willing to help you counter China?

I would just briefly answer the Florida—you know, the gentleman from—the question from—

HARRIS: You never want to say a man from Florida. We know this. (Laughter.)

WEI: Sorry. Yeah, I’m sorry. I love Florida. (Laughter.) And it’s my retirement plan. (Laughter.)

So, you know, Belt and Road is down but not out. I don’t think the economic slowdown—I mean, theoretically, the economic slowdown will limit China’s ability to, you know, spend money to buy influence overseas, right? But we’re not seeing any signs of that. Quite to the contrary. I think Xi Jinping is doubling down on countering the United States. The BRICS summit in South Africa is a perfect example, right? How much money is spent there? Again, billions of dollars. And you know, Ai Diago (ph) just came up with some new numbers about how much China has spent on Belt and Road. I think the outstanding data loan number is like $1.6 trillion, right? So it’s still dramatic.

I don’t think, you know, we should be complacent that, oh, the slowdown will impact China’s ability to assert itself on the world stage. I completely agree with Ivan. You know, Xi Jinping’s focus is on security, national security, you know, in laymen terms, is really just about how to counter the United States.

HARRIS: Thank you. Why don’t we take the next question? The woman—yes, you with your hands still up. And is there a microphone nearby? Thank you.

Q: Hi. I’m M.J. Crawford at the State Department.

And I wanted to insert AI a little bit into the conversation and hear your opinion on China’s capacity for dominance on—with AI in the economic domain. And we all already know in the United States, if you’re not already using AI in certain sectors you’re already behind. And it’s also projected over time to decrease the amount of white-collar jobs in the United States. And for a country like China that is extremely dependent on employing its entire population, does China have concerns about AI decreasing the amount of jobs available to employ its people? Thank you.

LIU: Yeah, I’m happy to chime in here. You know, one of my advisors in school once explained, you know, the spectrum of technology, artificial intelligence, in this way. Is that if the first industrial revolution made weak people stronger, why we will think that the smart revolution would make—would not make a dumber people smarter? (Laughter.) And actually, you know, he’s just talking about the tech. One aspect is the technological substitution effect, versus technological—the complementarity, the augmentation aspect of it, right? And I just—I happen to—for those of you who have not signed up for my roundtable series, please do—(laughter)—because this Monday I did host a state councilor from China talks virtually. And I did ask him about—he was the grandfather in terms of the mastermind about expanding China’s college entrance enrollment.

And, yet China has a huge unemployment problem. And he, without me even questioning him, he did ask about artificial intelligence and a small business and how great TikTok and all these smart platform economy, these can all be categorized as platform economy. They actually created a lot of new jobs. It’s just that they are not necessarily in the kind of the traditional jobs that, you know, we might potentially be anticipating. One, such example would have been the media—social media platform influencers. You might just see one person, whether it’s a real person or it’s just one artificial intelligence and image person, showing—being up there on the screen to sell stuff. But it actually is the whole team, like a human being team, supporting that platform.

And it’s not just about selling—you know, selling food or selling computers. Is actually even selling concrete stuff, like cars or furniture. So from that perspective, perhaps the hope for the Chinese economy could be driven by digital artificial intelligence. And this goes back to Lingling’s earlier point in terms of data security, digital security. And a lot of these would be in regard to cybersecurity as well.

HARRIS: Oh, my gosh. So I—Hall (ph) has been waiting very patiently, if you want to bring that mic there, and then we’ll try to get into right here.

Q: Hi, I’m Hall Waim (ph). I’m a naturalized citizen from Canada. (Laughter.)

And we talk a lot about the declining factors out of China. I like to point out there’s more youth unemployment, lying flat, and whatnot. And you point that rationally speaking it would incentivize China to be more cooperative. But I have a question indirectly about social media. And, as you know, the Chinese leadership, via things like wolf warrior diplomacy, have been rather hostile in its outward posture. The narrative has been a lot of that is feed the wolf, no pun intended, of the Chinese masses, via their separate social media ecosystem. So at what point would you say the genie is kind of out of the bottle, that the feeding frenzy has continued and keeps on continuing, and that although rationally there might be incentive for China to be more cooperative, the people, as we can see, want some level of aggressiveness towards the U.S.?

HARRIS: Ivan, do you want to start with that? And then we’ll—

KANAPATHY: Yeah. I mean, I agree with you. You know, it’s very self-fulfilling, I think. And I think there are at times—and we’ve seen historically with Senkakus, we’ve seen historically with Korea and THAAD—when the nationalism has gone overboard. But, you know, there is also a very high degree of control to kind of shut that down at various times, too. So I think that too often, in fact, we give credit to, sort of—well, let’s put it this way. The Chinese diplomats used to always tell me and others, they still do, I’m sure, that, you know, the 1.4 billion people can’t accept this or can’t accept that, and—or, the Chinese people don’t feel this way. And, you know, sort of, almost like they were, I don’t know, a democracy, right? (Laughter.)

And I think that—I think we need to consider the fact that, you know, not instantaneously, but over time, they have a lot of control. You know, like you said, they have their own internet. They have their own information system. And stuff seeps in and seeps out, so it’s not perfect. But I think overall, they are in control. And they can change narratives, again, over time, if they so choose. And kind of like we talked about before, what’s happening under Xi Jinping, clearly, that’s not the choice—you know, the choice that they’re making is quite evident, I think, to all of us. And I actually worry, because my impression is that younger generations are significantly more nationalistic than the ones that sort of lived through the ’80s and the ’90s, right? Like they sort of saw the potential, like, in the opening, and all that. And we’re losing that over time.

HARRIS: Michael, I think the takeaway here is we need a lot more programming for this group, because they have a lot of excellent questions. (Laughter.)

The gentleman—there we go. Yes.

Q: Thank you. Good evening. I’m Kashish Parpiani from Reliance Industries, India.

My question pertains to the attractiveness of the Chinese market. In context of recent developments that China has been retaliating against consultancies that are working on China+1 strategy, legacy auto brands are being squeezed out of the Chinese EV market, or last week’s development of China acting against Foxconn, do you think China is shooting itself in the foot? Thank you.

HARRIS: So why should we invest in India instead of China? Lingling. (Laughter.)

WEI: You know, I was already asked to leave by the Chinese government three years ago. And this question, if I tell you my honest answer, I will never go back again. (Laughter.) So, you know, first of all, I just wanted to quickly add to what Ivan said about public opinions in China. I think, you know, I agree with Ivan in terms of control by the party. But I have to say, opinions in China are just as diverse as they are in the United States. And please don’t lose touch with your Chinese friends, because, you know, you can tell. You can hear their frustration under the current regime. It’s one policy mistake after another, and people are not happy.

Just to your question, so, again, China’s still a huge market, as Zoe said. China constantly surprises. If China adopts the right policy, back to market reform, China will continue to prevail over India in a lot of areas, because they already built up infrastructure. But the big issue is they’re not doing that, right? They’re becoming increasingly isolated, as Ivan said, you know, from Western civilization. And, you know, these days I heard from my friends that asset prices are in India are soaring because people are leaving China, bidding up prices in India.

For sure, I think, you know, this is a strategic opportunity for India right now. But, you know, it—again, it’s all up—you know, it all comes down to how Beijing is going to respond, you know, what China’s strength is. For the past forty years, multinationals already spent so much money building up supply chains in China. It’s not that easy, and in some cases impossible, to completely leave China—either China’s markets or leave production—you know, take production out of China. So they still have quite a few, you know, advantages.

But the biggest advantage India has is, right now, the external environment. You know, United States and other, you know, advanced economies are leaning toward building ties with India. And increasingly, China’s getting isolated. So that is a strategic opportunity. You know, but I hate to say that, you know, everybody’s leaving China, because that’s not the case. They still have levers to pull. And the biggest advantage is that, you know, the mature market, and the 1.4 billion talented, hardworking Chinese people. And if only they go back to the path of reforming, opening, I think it’s still a hugely competitive, you know, neighbor of India’s.

HARRIS: So you had talked about logistics. And I’ll say, you know, Lingling, the companies that that I talk to, it took twenty years to develop those ecosystems in China. It’s not going to take twenty months to dismantle them. So talk to us about the logistics and the real time horizon that we should be looking at here.

WEI: Sure. I think, you know, a lot of the discussions about supply chain diversification, or China—it’s no longer China+1. It’s really about China+X, right? And so I’m afraid that a lot of this is going to take a long time to be played out, specifically because, you know, like, a good example would be—we have all moved to houses across our life at different stages. And you everybody knows how painful it is to pack up, to, you know, move, and then to unpack. (Laughter.) Right? So it takes—if individuals moving houses takes so long and so frustrating, you can imagine how difficult it is for a company that has spent more than twenty years getting familiar with a different market, develop your entire supply chain, especially navigating through a very different regulatory environment.

And this is why I still think if China can get the policy—the politics right, and the trying to sort of resist the temptation of heating up a lot of these tensions, China still can be a very much investable market. Part of this is specifically because, you know, what? The Chinese system—everybody knows. Everybody knows the Chinese market has never been the same as the West. And a lot of American CEOs they never even had the illusion thinking that the Chinese market would be as perfect or near perfect as the United States, right? They never had that kind of illusion.

However, the attractiveness of China’s—the Chinese premium really lies into policy predictability. Capital do not like unpredictability. And yet, I’m afraid of that as the part of that, President Xi Jinping shortened the fuse is specifically because of this major policies lens. So from that perspective, if he were able to correct that, restore stability, and more specifically policy predictability, I’m still thinking that the Chinese economy could—you know, the capital market is not necessarily saying that we are pulling out of China overnight. Obviously, you cannot because of capital control. But, you know, there is this policy moment. So we’ll have to see how—after APEC in particular—see how this is going to play out.

HARRIS: Did you want to jump in, or should I take another question? You don’t need to, you just looked contemplative.

KANAPATHY: No. Yeah. No, no, no. I’m happy to, I guess, again, sort of from the U.S. view, right? China’s economy recovering and drawing more investment, it clearly is not our policy direction. It’s not. It’s, you know, it’s diversification or de-risking, wherever you want to call it, right? And we’ve seen it happen. I mean, it has happened, I think. If you look purely at imports—and don’t quote me on the numbers, we can look them up—you know, import of goods in the United States back in 2017 was something like 21 percent, I think, came from China. It’s just steadily ticked down by about a percentage point every year, and it’s roughly fifteen, maybe?

That literally was the geostrategic policy goal of the folks that wrote the TPP. That’s what happened. And you know, I know Zoe doesn’t agree, but I think the 301 tariffs contributed to that goal. So from a, you know, foreign policy perspective, that 301 tariffs, and obviously the Biden administration seems to also think that there’s some value there to having those, they are accomplishing some of our foreign policy, national security goals. And, you know, we can disagree from a pure trade perspective, but that’s what’s happening. That’s what we want to happen, as a country. And that’s OK. So I’m not, you know, disagreeing at all that we should—we want the Chinese economy to recover. I think the whole world does. But I don’t know that it’s a top U.S. priority.

HARRIS: Oh, my goodness. Why don’t we take the woman in the green jacket, right there. (Laughter.)

Q: Hi, my name is Amy Eagleburger. I work for the State Department.

One question I have for you. We’re talking about, you know, challenging economic environment, a rise in nationalism that seems to be promoted by the PRC leadership. And then, of course, you have the West involved in Ukraine-Russia, now Israel-Gaza. So where does that leave Taiwan? Is Taiwan more at risk now than it was previously? Or, as you’re saying, from a stability argument, is the stability premium what protects Taiwan? I just would love your thoughts on that. Thanks.


KANAPATHY: Yeah, I very much agree. You know, from my past—and I actually worked in, you know, both the Trump and Biden administrations. And I agree that war in Taiwan Strait is neither imminent nor inevitable. Hey, you know, that’s what Assistant Secretary Ratner says, I think, quite often. I believe that Xi Jinping has a very low confidence in his military to actually accomplish that. Now, he’s working towards it. And we’ve all heard 2027 is his goal. I don’t know that they’ll make it there. We’re doing everything to sort of, you know, prevent him from achieving that, by sort of building our capabilities, right, and Taiwan’s, and where we are in the Philippines and Japan.

So, to me, these things don’t personally increase the likelihood. I don’t think Xi Jinping is licking his chops because things are going on. Now long-term strategically, I think he’s happy. China’s happy they went through this—you know, after 9/11—this period of strategic opportunity, as they called it, where they could do their hide and buy, right, and build up their own capabilities that were in China. And I think that’s still, in a long-term competition sense, a good thing, because it just diverts resources away from, you know, obviously, they view us as sort of enemy number one. And if we’re distracted, that’s good for China.

HARRIS: Thank you. We have time for one more question. Right here. Thank you all. We’re going to reward—rewarding people who sit in the front row. (Laughter.)

Q: Can I still take the dues reduction, though? (Laughter.) My name is Daniel Mandell. I’m a term member. And I also recently completed one of the international affairs fellowships in Japan.

And during my IAF, focused on the area that is literally between the U.S. and China, being the Pacific Island countries. And the takeaway that I got is that those countries, like other parts of the world, do not want to be in between the U.S. and China. And they do not want to be made to choose us or them. So if we do want to accomplish our free and open Indo-Pacific strategy, or whatever our strategy is, it seems like we need to find a way to cooperate and permit others to make choices and decisions. Within the Pacific region and elsewhere, the two top priorities are climate change and development assistance. And it seems like these are areas where the U.S. and China, and others, can constructively compete for the benefit of everyone. There does seem to be a positive-sum outcome here.

So with that framework in mind, if you could take any action—you know, you become the puppet master—what specific action would you take tomorrow in order to move us further towards whatever you think the appropriate goal should be to hopefully the agreed desire to avoid World War III?

HARRIS: So we’ll end on—

Q: Softball. (Laughter.)

HARRIS: Softball. Ending on a positive note. Let’s do a rapid-fire, one action that each of you might take to help to support a more cooperative Asia zone for those countries that don’t want to get swept up into one side or the other?

WEI: I would think that, you know, the U.S. should continue to build up the Indo-Pacific economic program, and really put some skin in the game. Not just talk the talk but walk the walk. You know, show the member countries real benefits of aligning with United States. And continue to, you know, be the positive force in the region. You know, the goal is not get in a conflict with China. The goal is to deter China from—either in terms of Taiwan or, you know, South China Sea, you know, you name it. Deterrence should be—remain the primary goal for the United States and aligning with other countries in the region. So you need to put more skin in the game.

HARRIS: OK, investment, deterrence, skin in the game, Zoe?

LIU: And I think I have two things to say. The first thing is we need more of people like you. We need more people who participate in CFR’s international affairs fellowship. (Laughter.) So that you actually realize that in order to increase America’s leverage against or over China, you actually need cooperation, you need not decoupling, but actually focusing on areas where you can, as you put it nicely, constructively compete.

And then the second point is, I will always—I will also go back to my three letters, C and F and R. C-F-R. C is its climate. You usually talk about it. F s is food. And R is retirement and aging. So these all need money. And this is actually where—we do have DFC folks here in the room, right? You know, Development Finance Corporation. You guys—actually, I do have a lot of hope in you guys, in a sense, and also State Department and the Department of Defense, absolutely. But why I highlighted the DFC is specifically because DFC is perhaps some very much underutilized economic tool of the United States. DFC partnered with Japan Bank for International Cooperation, with the Import and Export Bank of Korea. Why not China?

HARRIS: And, Ivan, the last word. You stand between these people in cocktails. No pressure. (Laughter.)

KANAPATHY: I mean, I’ll just repeat, I think the way you asked that question is really interesting, because you said cooperation and then you said constructive competition, which is sort of that’s, I think, where you get things done. We see that in the Pacific Islands. I mean, the United States has given more attention in the islands in the last three or four years than in the thirty before that, probably, or whatever it was. But, you know, the COFAs are done, the compacts are done, and waiting for Congress now. That’s going to be a bunch of money for those three Pacific Island states. The Solomons has chosen sort of a different path. But that’s literally the result of competition. But they are getting things out of those, I mean, Pacific Island nations, that previously they weren’t getting, from a development standpoint. And it’s not because we’re cooperating with China. It’s just not. That is not how I define cooperation.

HARRIS: Oh, well, fantastic, constructive competition. I just want to say thank you, Ivan, Zoe, Lingling. (Applause.) And thank you all for your questions.


Series on Emerging Technology, U.S. Foreign Policy, and World Order: How Artificial Intelligence is Reshaping Our World

BODURTHA: Good morning. I’m Nancy Bodurtha. I’m the vice president of meetings and membership here at the Council on Foreign Relations. And I’d like to welcome you. For those of you who were here last evening, I’d like to welcome you back to the 28th Conference of the Stephen M. Kellen Term Member Program. The conference got off to a very energetic start last night with plenary discussions on the war in the Middle East as well as U.S.-China relations. And then we had a reception that did not want to end. (Laughter.) I am told that some of you may have taken the party offsite. So for any of you who are in need of extra hydration this morning, please know that we have lots of beverage stations that are located throughout the house and they’ll be available all day today.

We have got an extraordinarily full agenda that’s designed to offer you good content, good conversation, and good community. The plenaries today will focus on artificial intelligence, the war in Ukraine, climate change, and we will also have a special keynote conversation with Rockefeller Foundation President Raj Shah, in conversation with Council on Foreign Relations President Mike Froman. They’re going to share some thoughts on how to make a difference in the world. Bit of a spoiler alert, I think Dr. Shah’s compelling thesis basically boils down to go big or go home. And I think that this will be a very provocative note and an inspiring call to action on which to end the conference late this afternoon, before we adjourn for happy hour this evening.

In addition to happy hour, there are going to be lots of opportunities to engage with one another throughout the day. There are breakout sessions both this morning and this afternoon on a really rich range of regional and functional issues. We’ll take you on a little field trip for lunch, a couple of blocks—just a couple blocks south on Park Avenue, and I think half a block to the east, to the Cosmopolitan Club, where you will have an opportunity to have tabletop conversations, again, on a really fascinating range of issues, that will be led by your fellow term members. We’ve also built in plenty of networking breaks, and I really hope that you’ll take full advantage of these opportunities to meet one another. You are an impressive group. This is a uniquely special community. And my hope is that you will all leave this gathering with new connections, lots of LinkedIn invitations, and maybe even some new friendships.

Also, I’d like to just ask that you be on the lookout next week on Monday in your email inbox. We’ll send you a brief survey. We are eager to hear your thoughts about the conference as we start to plan for the 2024 edition. I think it comes as no surprise to you all that it really takes a Council on Foreign Relations village to produce this conference. So I want to just give a round of thanks. I want to double down on our thanks to Andrew Gundlach and the Kellen family for the generosity of the Anna-Maria and Stephen Kellen Foundation in supporting the Term Member Program and making this conference possible. I also want to thank my Council colleagues, Meaghan Fulco and Sam Dunderdale for their extraordinary leadership of the Term Member Program—(applause)—thank you—and their vision—their vision for this conference. Together with Meaghan and Sam, I also want to thank the indefatigable teams from meetings membership, events management, AV, and facilities without whom we could not produce a conference of this scope. So a little more applause for that gang. Thank you. (Applause.)

Mike mentioned this next point last night, and I want to mention it again. January 10 is the application deadline for anyone who would like to apply to the Term Member Program. You all are our best recruiters. So if you have colleagues, friends, family who you think would contribute to and benefit from the program, please encourage them to consider it. The director of membership, Vera Ranola, and I are always happy to speak with prospective candidates and offer guidance on the process. We’re both here at the conference throughout the day. So please feel free to find us, say hello, and hit us up with any questions that you might have. I’m easy to spot, since I’m wearing my orange blazer today. Vera Ranola is standing in the back of the room. Give another wave, Vera. Anyway, we look forward to speaking with you all.

All right. Let’s pivot to this morning’s conversation on artificial intelligence. First, I want to welcome the members who are joining us via Zoom. This is such a vitally important conversation that we’ve opened it up to life members to participate virtually. And I understand that we have over 200 in the Zoom room with us this morning. I’m going to turn the proceedings over to our moderator and my CFR colleague, Kat Duffy. This is Kat’s first Term Member Conference, as she recently joined the Council’s think tank, which we also refer to as our Studies Program. She is our senior fellow for digital and cyberspace policy. Kat, welcome. The floor is yours.

DUFFY: Thank you. Thank you. Good morning, everybody. Oh, man. Yeah, y’all have a late night, huh? (Laughter.) Yeah, you did. All right. OK.

I have been given by this unbelievable Meetings team beautiful—a couple of beautifully prepared sentences that I am on my honor to read. And so I’m going to start with welcoming you to the third plenary of the Council on Foreign Relations’ 28th annual Term Member Conference. This session is titled “How Artificial Intelligence is Reshaping our World.” As Nancy said, I’m Kat Duffy. I’ll be presiding over our session today. I’m joined by three amazing panelists, including Michael joining virtually. And Michael I—are you—you’re dialing in from California, is that right?

CHUI: Yes.

DUFFY: At an ungodly hour. (Laughter.) And Jared flew in from San Francisco, like, overnight last night, y’all. So let’s just—can we just start by giving a round of applause to our early birds? Like, first—(applause)—they’re doing it. I’m so appreciative. OK, team, I want to start with just a collective agreement here, OK? It’s early. Everyone had a late night. If you need coffee, just get up and go get coffee. (Laughter.) And come back. I won’t be offended. No one will be offended. Like, I just—I really want you to feel free if you need to get up and caffeinate, go caffeinate, and come back. So I want to just give you that blessing out of the gate. Are we in agreement that you guys are comfortable and you can do that?


DUFFY: Fantastic. All right. So we’re going to start there. Now, one of the things that I wanted to get a sense of—and unfortunately I can’t do it in the Zoom room, but for this amazing room—I wanted to get a sense first of, like, where people are on this topic. So can I get a show of hands from, like—let’s say, like, one to ten, one being when artificial intelligence comes up at a party I panic because I have like nothing to say. I have nothing to contribute. I’m like, ahh, right? Who are my, like, one to threes, one to fours? Who feels like they’re sort of in that range? Don’t be shy. This is a safe space. (Laughter.) Yeah, OK. Now, where are my, like, four to sixes? Like, I have thoughts and feelings. I’ve read some stuff. I’ve played around. But, like, it’s not—I’m not going to call myself an AI expert? Yeah, OK. That’s solid, about half the room. Who are my people who are, like, I know this. I know this. Like, I work on this. I work in this. I care about this. Yeah? I want it—hands high, y’all. Hands high. Own your expertise. (Laughter.) OK, so that is—that’s a pretty solid number.

Now, the thing that I love about our panel today is that I think we have a tendency sometimes to talk about artificial intelligence as a vertical. And I think of it very much as a lateral, right? It’s going to be impacting across the foreign affairs space. It’s going to be impacting across the economic space. It’s going to be impacting across sort of all of our different journeys. And so what we’re going to do today is offer you a bit of a—like, a speed run through major sectors and how they are thinking about artificial intelligence.

And so we’re going to be starting with Michael, who’s really coming in with deep expertise from McKinsey, and how McKinsey has been working with the private sector, with the corporate sector, and doing surveying to think about what the impacts will be there. From then we’re going to go on to Jared, who is coming in with a lot of deep background and work with the Defense Innovation Unit and has been thinking a lot about how the defense sort of system and the military is thinking about these things, and what the commensurate commercial applications are there. And from there, we’re going to go on to Camille François, who is going to give us a readout on how things are looking in the land of democracy rights and governance. Spoiler alert, it’s tricky. And how other governments are thinking about this as well. And where we are in sort of global governance conversations.

So when you come out of this room today, my hope is that if you’re an AI expert, you will have a little more expertise or a little more insight into an area that hasn’t been your specific focus. And that if you are a proud newbie, you feel better about walking into a happy hour or walking into a meeting and feeling like you have a little more in your toolkit in order to think through these things. Does that sounds good for everybody in terms of a run? OK, fantastic. And so with that, I want to, again, welcome our members who are joining online. Welcome all of you. And I’m going to start now by asking Michael, Jared, and Camille to give us just one to two minutes on where their area of focus is and sort of how they came to these questions. So, Michael, can I start with you?

CHUI: Sure. So my area of focus—I’m part of McKinsey, which is sort of the think tank-y part of McKinsey. I’m at the McKinsey Global Institute. All of us—or, the few of us who are at MGI were all McKinsey consultants first. And so my research program has been around the impact of long-term technology trends. And obviously AI is one of the most important ones recently. So I’ve been doing a bunch of research there. I jokingly describe myself as akin to a private sector professor, but I don’t get tenure. But instead of grad students, I have much more motivated McKinsey consultants join us for our research. (Laughter.) I actually—much better than I was when I was in grad school. (Laughter.)

I actually started as an AI practitioner, a couple AI winters ago. So, you know, I’m no longer on the keyboard as much but, you know, back in the day, you know, I once studied with the late, great David Rumelhart. And at the time, you know, a reasonably sized neural network was a few dozen neurons. And as people know now, you know, 175 billion is kind of not that big a deal nowadays. So things have changed drastically in the time that I’ve been around.

DUFFY: Fantastic.

Jared, how about you?

DUNNMON: So my journey here was a bit of a broken road. I started as an engineer building energy systems. And I was putting sensors on those energy systems in grad school. And I got really annoyed with the fact that I couldn’t interpret the data coming out of it. This was around 2015-2016. And there were whisperings in the air that this thing called machine learning was kind of starting to work. And so I started using it. And in fact, it did work sufficiently well that I went and spent some time in grad school as a postdoc in the AI Lab over at Stanford doing that, working on building systems for a number of different applications. And then I got a chance to work standing up a company coming out of that. And then about two years in there, I got a chance to go and do public service in the way that I would have wanted it to do it, kind of at the intersection of commercial technology and national security. So I was at the Defense Innovation Unit for three years running programs specifically focused on applications of AI throughout the DOD.

Those obviously have major kind of implications for interactions with policy, not just within DOD but across the interagency. And so I went from, you know, asking—for those of you that know the Pentagon—walking in and asking, please tell me exactly what OSD policy does, I don’t know, to having thought about these things in the context of not just our individual programs but the National Defense Strategy, how we interact with allies and partners, you know, et cetera and so forth. And now I’m actually back in the energy industry, working on—working with a company that’s focused on building advanced battery cells, LFP cells. And to do that, you have to optimize pretty much every section of the—of the operation. You’ve got to find new electrolytes, you’ve got to optimize your manufacturing lines, you’ve got to get data off these batteries. And so that’s what I spend my time doing now.

DUFFY: And Camille.

FRANҪOIS: Yeah. So if the pink pants don’t communicate, I’ll preface by saying I’m a real optimist. (Laughter.) And for the past ten years, or so, I’ve been working in Silicon Valley in a job that we call trust and safety, which is essentially like a doctor disaster. When something goes really wrong with technology and its impact on society, you call your friendly trust and safety person. So I’ve been working on how terrorist groups leverage technologies, including social media, to recruit. I’ve been working on foreign interference, troll farms. I’ve been working on the disastrous impacts of social media on kids’ health, child sexual abuse and exploitation online and all those very nice fun things.

And I think over the years, you know, the few of us who specialize in this have been working quite closely on AI and how does that change those sociotechnical impacts? In which ways is it sort of accelerated some of those bad impacts of tech on society, in which ways is there a paradigm shift both in how that impact manifests and the tools that we have to mitigate for these impacts? These days, I still do this in Silicon Valley with a trust and safety team, this time in the augmented reality and gaming industry. And I have the great honor to teach those issues too, over at Columbia University. And a few months ago, President Macron of France asked me to lead one of his big initiatives on AI and democracy. So we said, that sounds like a large topic. And so we tried to sort of go at it through small programs, which we’re now doing through an innovation lab based a few blocks up over at Columbia University, SIPA.

DUFFY: Having known Camille for many years, I would say it is wildly on brand, that her side hustle is co-chairing an AI initiative with a Nobel Prize winner, Maria Ressa, for the president of France. That’s just, like, her side hustle. (Laughter.)

OK, fantastic. And so having everyone sort of understanding the lenses from what you all are coming, Michael, can I turn back to you, and can I ask what are the biggest trends that you’re seeing? Where are you seeing the greatest moments of opportunity? And also, where are you seeing the risks that will have to be addressed or mitigated in order to achieve those opportunities?

CHUI: Well, I’ll let Camille take care of the risks, you know, trust and safety. We’ll just call them up. No, but seriously, let me tell you a little bit about some of our research in terms of what we have observed. And I say we don’t do projections, but, you know, what potential might be going forward in some of our research. We’ve taken a number of different lenses to it. One is looking at, you know, use cases that businesses largely can apply. We sometimes describe our methodology as micro to macro. And, you know, the advantage I have as a researcher is thousands of consultant colleagues who are in the world. They don’t share any, you know, client-proprietary information with me but, you know, we have experts in every geography, in every function, every sector.

And so what we’ve done is identify sixty different potential use cases, particularly of generative AI. Again, we’ve actually studied AI for the better half of a decade. And so I’ll leave off, you know, sort of analytical AI to the side, but we can come back to that as well. We looked at over sixty different use cases, or potential use cases, of generative AI around the world. And basically, found the potential to add $2.6 to $4.4 trillion of value, call it potential profit, by corporations by applying these technologies in every function and every industry. They largely fall into four different categories. So folks who have used generative AI, you know, you they you enter something unstructured, like natural language, and you get something unstructured out the other side.

Those categories, you know, correspond with those capabilities pretty well. You know, one of them is around marketing because, again, if you want to market to a million people or you want to just ask something to create a thirty-second video spot, these systems are moving in that direction. The second category is customer service, because, you know, if it’s a chatbot, it makes sense you can actually use it to be a virtual expert. The other two categories are a little bit, again, for some people more surprising. Computer language is just a language. And you might have heard the term “large language models.” You can now ask these systems to write computer code for you. It doesn’t come out perfectly, but it can accelerate the productivity of software engineers by 10 to 70 percent. You know, call it 50 percent maybe in the median. That’s huge.

By the way, that doesn’t mean we’re going to fire a third of our software engineers. As people have heard, software is eating the world. We need more software. But if we can make, you know, the software engineers, you know, more productive, that’s all to the good. And then, though, I think the under-recognized potential for generative AI is in research and development. Because, again, you know, if you can ask a system like this, you know, please generate some drug candidates that don’t have these side effects—we’re not quite there yet, but there’s a lot of work being done in this area. And so, you know, Jared might be talking about, you know, what this might mean for defense. But, you know, that’s a place where I don’t think everyone recognizes it’s not just being able to write something and, you know, Shakespeare and iambic pentameter. It is, please develop me a design for this circuit.

So, you know, that’s a lot of the potential in the business space. We also looked at it in terms of its potential impact on labor. Again, we’ve been studying the potential automation impacts of technology in general. You know, previous generations of AI, robotics, et cetera. The interesting thing about generative AI—a few interesting things about it, I guess. But one of them is, it has greatly accelerated the potential for automation. And why is that? Because previously our assessment was that, you know, reaching a median level of capability in understanding human language wouldn’t happen until, call it, the 2040s or so. Or maybe around 2040, late 2030s. Because of generative AI, and many of us have experienced this, now that’s moved forward about, you know, arguably more than a decade. And a large percentage of the activities we ask people to do in business require human levels of understanding of natural language.

And so we’ve looked at every occupation in the economy. We’ve looked at not only every occupation, but all of the constituent detailed work activities because, again, all of our jobs are heterogeneous. You’re rarely going to have a robot show up and it will do everything that anybody does in their job. As we analyzed it at that level. We scored it against eighteen different capabilities which could be automated, including natural language. And then tried to understand, look, if this is the cost of automation and this is the, you know, how much you pay for, you know, this occupation in all of these forty-seven countries that we have data for, you know, how fast might this all happen?

So again, we’ve seen—we’re seeing a potential acceleration and the potential to automate work in the economy, to the extent to which in the U.S. economy it might be 30 percent of today’s activities could be automated by 2030, in the midpoint of a wide range of scenarios that we modelled. That said, do we think there’s still enough important work to be done? Yeah, we don’t see a lack of work to be done. But what that will require is a large amount of transitions in the skills that people have in the workforce. And we need to make sure if this is going to work out well that people are paid in a way that actually is sustaining and hopefully increases economic growth.

If all that happens, though, then we could see an increase in productivity. And again, half of the sources of our economic growth in the largest economies in the world over the past half century have come about because of increases in population or increases in workforce. People are living longer. We have more women in the workforce. That’s all to the good. Half of it because of increases in productivity. In the next half century, right, you know, if you look at demographics there are large swaths of the world where the size of the workforce is actually declining.

And so we don’t have enough people to have the historical rates of economic growth that we need to make sure the next generation has better lives than we do. So we need to accelerate productivity growth. And for the economics geeks in the room, you know that productivity growth has been plateauing and in some cases is lower than it has been over time. And so this technology has the potential to accelerate productivity growth. But we do need the reskilling and we do need to make sure people actually get incomes in order for that all to happen.

Kat, I don’t know if I exceeded my time. But that’s what I got for now.

DUFFY: No, no, no. That’s great. And so I think just to sort of do some highlights with like, 2.6 (trillion dollars) to $4 trillion value that can be applied. Marketing, customer service, coding software, and R&D are going to be really key areas. We’re looking at extreme impacts on labor, but those don’t necessarily have to be disastrous impacts on labor, right? They’re going to be shifts in labor and we’re going to have to be getting ready and getting prepared for those shifts.

So I’m going to move to Jared now, because, Jared, in the Defense Innovation Unit you’ve been thinking a lot, and folks have been thinking for a long time about what shifts need to look like, what different capabilities and capacities are. And so I want to turn it over to you. If you can give us five to seven, eight minutes on what you’ve seen and what you’ve been learning.

DUNNMON: Absolutely. And so just to—you know, I’m here in my private sector capacity. So I will be giving kind of my views on this, my experience on this, kind of in that capacity.

In the security community, there are a couple of application sets of AI that we, I would say, focus on a lot. But I would actually abstract up from that for this conversation and look, at dovetailing off of what Michael said, at where are the impacts going to be and how does that affect the security community horizontally? I tend to bucket this into three areas that I—that I like to focus on. They’re not the only three areas, but they’re areas to emphasize. One is accelerating scientific progress. Another is the implications of the productivity increases that Michael was talking about across the economy on the security community. And then lastly, is the effect of these of these technologies on the state of geopolitical competition. And I’ll dive into that a bit.

So on the science part to start with, I can’t emphasize this enough, as someone who’s been a scientist and engineer, the things that we spend time doing in science—running experiments, testing hypotheses, asking questions of the literature, doing literature reviews, trying to figure out which of the thousand experiments I should run when I only have a budget for ten—all of these things, if I can—I don’t have to be perfect. If I can rank experiments and say, look, I probably got a good one in the top twenty versus, you know, me throwing a dart at a dartboard, that’s massive improvement. And I expect to see that across applications. And we’re already starting to see it across applications—biology, chemistry, even pure mathematics, physics, you name it. And so that’s one aspect.

The example that I’ll give there is the one that I think a lot of folks in this room might know, which is AlphaFold, where, you know, protein structure prediction where you’re trying to take a sequence of amino acids and predict how a protein is going to fold in three dimensions. That is a—that was decades old, almost a century-old problem. And, you know, in the last couple of years, we’ve gotten substantial increases in performance to the point that we’ve opened up an entirely new area of what you would call AI-driven molecule design, not just for drugs but also for things like biologically based production, et cetera. So that’s the science angle.

There’s also the economics angle. And if you think about, you know, the security community, and particularly the Department of Defense, it’s very much a microcosm of society in a lot of ways. You know, we have to do things like medicine, predictive maintenance. We have to optimize business processes. We have to do all the things that society, broadly speaking, has to do. And so in—so in the same way that we expect there’s trillions of dollars of headroom in in society, we also expect that we can be, you know, more efficient with taxpayer dollars, and we expect that, you know, we can do things—and I would say along three axes that are important—unprecedented speed, unprecedented scale, and unprecedented performance.

So I can do things faster than I could ever do them before. So I could look across the entire world every day, like looking for something, like I couldn’t—I can’t—physically couldn’t do that before at scale. I can I can ask questions of the millions of documents and get an intelligent answer. We’re just—there’s no—that was physically inaccessible beforehand. And then performance. They are just things that I can—that machines can perceive that humans have a hard time perceiving.

And so given those implications, I’ll focus on—I would make a comment here that a reason that you’re seeing this—the transformation happen now, is, I would argue, with an infrastructural one. So when I say AI in the context that I’m talking about right now, I’m really talking about post-2014 neural network based systems. If you want to go back to, you know, 1900, running linear regression on a computer, over a million points would have seemed like AI. You know, today, we take this for granted. So I’m talking about this in that context.

The infrastructure, from a software perspective in particular—and, yes, hardware underlie this—but if you believe that cloud computing has democratized hardware, go back even five or six years. Forget generative AI. Even just normal, you know, I want to take an image and tell if it’s a cat or a dog, the infrastructure to train that model, to test it, to deploy it, to monitor it after deployment, to figure out when it breaks, to redo that cycle for tens, hundreds, even thousands of models a day, which is common in your biggest—your biggest companies. You know, so your Microsoft, your Google, your Apple—most companies in the Fortune 500 were not data-first companies. They were not built to do that. So they couldn’t do that.

You now have a world where, because of that, you know, kind of percolation in open-source software and in—and in software offerings in the private sector that are aimed at small and medium enterprise, the rest of the Fortune 500 and the rest of the economy is now able to do that in a way that they couldn’t even a couple years ago. And so you’re starting to see that diffusion now and across the economy, I would argue. So that’s an important concept as we go into, you know, the aspects of this that are, you know, kind of specific to geopolitical competition.

So in the security community, I tend to bucket the application space that we—that we think about, in addition to kind of being a microcosm of civil society, into five core buckets. So one is mission forecasting and planning. I need to go do something, have a bunch of historical data, how do I go do it best? Number two, real-time decision making. I have a bunch of things that are streaming at me in real time, what do I do with them? And I may not have all of them all the time.

Number three, control of complex systems. I’ve got 9,000 things that I need to orchestrate to accomplish something. How do I do that? Number four, anomaly detection. It goes without saying. Anyone who’s been in the intelligence community you’re sitting there, you know, rolling your eyes because, like, yes, this is what I do for a living. And then last, there’s an entire area that’s related to what I talked about before, which is, I would say, the information and infrastructure protection and verification. Because none of these systems work unless the information that’s going into that makes sense, and unless you have the infrastructure to run them.

And so across—and so in the security community, those are five buckets of things that we tend to care a lot about. I’m not going to go into specifics on, you know, individual applications there, because I’m actually going to get to talk a bit more broadly about how, that the dynamic that these—where these systems are diffusing throughout the economy, throughout the commercial sector, and how—and how the commercial sector is driving the security community in this world. This is not something that was invented in in DOD and then percolated out. Yes, fundamental research drove it, but ultimately a lot of these technologies are coming from the commercial and open-source communities. And that means that competition in the security community in this sector has some really interesting dynamics.

So AI, I would argue, is, is shaping, but is also emblematic of what we’re seeing in the twenty-first century more broadly. So if we think about the AI stack, the technology stack, there is at the bottom, hardware. So the chips that you run on. There’s data that you need to run, you know, modern machine learning systems. There’s software that you need to actually run algorithms. There’s the algorithms themselves. And there’s kind of a user-interface layer, that kind of humans interact with. I would say, and folks can argue with me on any of this, to be clear. This is my biased view of the world. I would argue that the user interface layer is not wildly competitive. There are folks who are good at it, but it’s something that it’s doable.

There’s algorithms. Shocking the core algorithms in this area, most of them are open source. Most of them are built either in academia, or, interestingly, by big companies who then release them. Most of the research that went into, you know, the generative AI platforms that you would think of—your, you know, GPT-style models—a lot of that stuff was published, you know, years ago. And then it took a while to scale it. And so that’s kind of an interesting dynamic. And there is competition in that space, but you kind of assume that it’s table stakes this stuff is released in the open source that’s kind of accessible. OK, so those two things don’t seem that interesting.

So now we get into software. And this starts to get a bit interesting, because most of these algorithms right now, if you go and find an implementation of them, they’re built in—so raise your hand. Who knows what TensorFlow is? PyTorch? Oh, there’s more PyTorch than TensorFlow. That’s an opinion. How about PaddlePaddle? Not one. So PaddlePaddle is a Chinese equivalent of TensorFlow and PyTorch. And the statistics on this don’t actually surprise me because they’re actually reflected worldwide. There aren’t that many people that use it. They’re mostly in—you know, obviously, in mainland China.

And that has implications for talent competition, because what it means is that if everybody around the world is coding in TensorFlow, and PyTorch, because they have the libraries that support building machine-learning systems, you’re not competing—you’re not having a workforce that’s being built to use the thing that, you know, the Chinese state-owned enterprises want folks to use. And that has major implications for talent. And there’s—so there’s this interesting dynamic where you have software companies paying millions, billions of dollars, arguably, to support these open-source software packages that they’re releasing out into the world so that people will build on top of them and create the talent base that they need.

Then there’s the data. The data is really interesting, because there was—there was a prevailing wisdom for a while that, you know, China has a huge amount of data and, you know, therefore its AI systems were going to, you know, quote/unquote, “eat the world.” That being said, and I say this only half tongue in cheek, that data has Chinese characteristics. And so what that means is that, you know, there’s a homogeneity to some of it. There is a context to some of it that is both—that is cultural, that involves censorship. That has implications for function. That has implications for how well do these systems work when you take them outside of, say, a Chinese context?

That has implications not just economically, you know, am I going to build on something that doesn’t—you know, doesn’t give me the answers I want or am I going to build on something that’s restricted in some way? But it also has implications in the security community, because the collective—I would argue—the collective West has decades of operational data from real-world operations that other folks in the world do not. And so if you’re going to build AI systems on top of that data, there is a good question—there’s a legitimate question as to whether the value of that data and how it compares to a large amount of data that was that was recorded outside of conflict.

The last piece I’ll end with it’s hardware, which I think folks are probably pretty aware of. So the graphics processing units that—you know, and there are other chips as well—but graphics processing units mostly that modern machine learning neural networks are built on, you know, in a cosmic irony, built using American designs, Dutch and Japanese equipment, raw materials from the Chinese mainland, in Taiwan. And so that is the state of the world that we find ourselves in.

And you start to see as a result, you know, export controls saying, hey, you know, we don’t want—you know, we may not want, you know, chips of a certain type being used, you know, outside the U.S. So we’re going to put export controls on them. You know, and then there’s a question of how do you disambiguate that and make the argument, and pass the red face test saying, well, these are—this is for security reasons not economic competition reasons? So there’s a very interesting dynamic there, where there’s an interplay. And then you started to see the Chinese export controls on things like gallium and kind of other core ingredients in in semiconductors.

So there’s this whole—this across the stack competition happening that affects what we can do in the security community and how we think about how we build our systems in a secure way. assume that they’ll work, assume that they’ll not work, et cetera. And I’ll end with just saying that there’s, again, kind of an interesting fact here, which is the graphics processing unit came to be not because we were driving towards better neural network performance, but because people wanted to play better video games. (Laughter.) So I just leave you with the fact that most of what we’re talking about here is fundamentally resulting from a class of algorithms that was developed in the ’80s and ’90s, but never worked until people really want to play video games in the 2000s. And so in just terms of being able to predict where things are going, it’s not necessarily the easiest.

DUFFY: So when we think about that, we think about the chips, we think about the data, we think about the algorithms, we think about the software, we think about the user face, and we’re also thinking about within that, right, like, what is the “knowledge,” quote/unquote, underneath that data? Where is it coming from, right? What does it actually increase in terms of speed? What is it increased in terms of scale? And then what does it increase in terms of stakes? And how do governance—and particularly, like, governments that care about a deliberative process and about building consensus, how are they going to deal with that speed and that scale? Because it does fundamentally outpace what is needed for a deliberative process? And so with that, I’m going to turn it over to Camille to give us a read on what you see.

FRANҪOIS: Sure. So I’m taking it from video games are awesome to the doom of democracy, is that right? (Laughter.)

DUFFY: I mean, this is your brand.

FRANҪOIS: Yeah. (Laughter.)

DUFFY: You do make Pokémon safe.

FRANҪOIS: That’s true. (Laughter.) All right. So I think Michael and Jared give a really clear picture of how we are at a really interesting moment for that technology, right? It’s a really interesting moment in AI, because while some of these technologies—notably, the transformers technology underpinning the generative AI era—are not new, we are in a moment of radical acceleration where new developments are really coming much faster than what they used to. And, more importantly, these technologies, who used to be a little bit in the lab, a little bit to the researchers, are now becoming mainstream. We have a lot more people having actually played with generative AI.

And so as we look at this from a governance perspective, we’re saying, all right, that’s a really interesting, crucial moment in AI. And I think a few people are saying, well, that’s also a really pivotal moment for global democracy, for a number of reasons. Recent survey approximate that about 72 percent of the world right now lives under authoritarian rule. That’s up from 60 percent last year. If I look at 2024, we have sixty-five elections around the world in more than fifty-four countries. That includes huge democracies in the world, right? We’re talking India. We’re talking Indonesia. The U.S. That includes pivotal elections, Taiwan, European Union parliamentary elections. I know, it sounds really boring but, you know, trust me, this actually matters, shapes the worlds too.

And this is also a moment where I think everybody recognizes that the impact of technology on democracy, on society, on elections can be really complicated to tackle, to mitigate, and to govern. And so as I think about how to share, you know, Cliff’s Notes on that governance debate on AI, I would think about some of the fault lines that might be worth highlighting. And I think about three fault lines. I’m just going to go through them. The first one is, which type of harms are we talking about? The second one is, how do these harms come to be? And the third one is, what the hell do we do about them?

And so for the first one, you generally see sort of two camps from people who are saying the harms of this new sort of generation of AI technology and AI systems are either immediate harms or far-reaching existential harms. The far-reaching existential harms camp is an interesting one. If you want to sort of fast-forward your reading through it, you can pick up a book that was published now ten years ago, Nick Bostrom’s book on superintelligence.

And ten years ago, scientists who were working on AI systems were starting to wonder, hey, what if we create systems that actually surpass the level of intelligence of humans? And what does that mean for existential risk? Are we going to create machines that are going to be smarter than humans, and are we going to create machines who are going to want to get rid of humans and replace the human race? Now if that sounds crazy, it kind of does, I will highlight that a lot of serious AI scientists feel strongly that this should be the conversation of global governance. They are saying very seriously we should invest global governance muscles on figuring out what are we going to do for this existential risk of AI.

On the other side of that camp, you’ll have a series of people saying, wait a minute, that’s cute. We also have 99 risks that are manifesting right now. There’s so many harms that we already know about, that are already documented, and that we need to tackle with much more seriousness. That are harms like discrimination, right? We know that those AI systems are biased. They have a lot of issues with making racist prediction, sexist predictions. We know that we when we deploy those AI systems, we can actually accelerate and sharpen some of the discriminatory mechanics that come into the way they’ve been built and the data they’ve been trained on.

Again, I don’t want to sound too academic about it, but if you want to pick up one book on this—and a fun one—there’s one that’s going to be published in two days. It’s called Unmasking AI. It’s a great book by Joy Buolamwini, who’s talking about her own journey realizing that there were some racist dynamics embedded in a lot of these AI systems and going about having these conversations with major players in the industry. So a lot to do there.

There are also a lot of immediate harms with what does it mean when a lot of the people who’ve been doing disinformation and foreign interference in the context of elections, for instance, suddenly have access to generative AI? There are also, of course, a lot of harms related to privacy, and even to classified materials, right? A lot of scientists are saying, hey, it seems a lot of these models that are now publicly accessible to a lot of people have absorbed a lot of the internet. That also means that I may retrieve information about people that they didn’t want me to retrieve. I may actually be able to figure out where somebody lives, and that’s not what they wanted. Or I may have these models hallucinate fake things about people that are harmful.

Everybody know what we call a hallucination? So some of these models will very confidently make up a very fake answer. And either you’re very used to dealing with such things because, for instance, I don’t know, you work in foreign affairs and you’ve been, you know, in rooms where that happens. (Laughter.) But, you know, the nickname that we that we call it—it’s a debate nickname—but we call that a hallucination. It’s essentially models producing wrong content and yet, you know, feeling very confident about it.

DUFFY: Lawyers, do not do your briefs with this! (Laughter.)

FRANҪOIS: No. Don’t do that. And so that’s sort of the first fault line, which is we understand that there are harms. They go all the way from a lot of very immediate harms—privacy, discrimination, disinformation—to a lot of very far-fetched harms, are robots going to take over the world. And it’s kind of complicated to have a structured conversation on how to go about it if people don’t agree which harms we should tackle first.

The second fault line related to that is the one between open models, closed models. Now, I suppose most of you have used ChatGPT. Yeah, OK. I suppose that most of you have tried to make it do things it’s not supposed to do, right? (Laughter.) That’s pretty fun, right? Like, I don’t know, give me a recipe for a bomb. And then if everything goes according to the plan it’s supposed to say, no, that’s not what I want to do today. I have terms of services. And really, I just don’t want to answer this question. And then, you know, my students at Columbia, I have them break those safety safeguards. So essentially, you say, OK, I’m giving you thirty minutes and you do need to come up with a recipe for a bomb. I know, this raises questions in faculty. But, you know, the point is—(laughter)—

DUFFY: Camille and I throw really fun parties. (Laughter.)

FRANҪOIS: It turns out that, you know, if you really double down, those safety safeguards, which are important, are also easy to go around. And so similarly, if you say, hey, nice model that I’m talking to through a very, you know, clever UI, I really do want you to give me a plan to overthrow my government. Again, its first answer should be, no, we’re not doing that today. But if you really want to, eventually you will get your plan. And so that leads to a few people saying, well, those safety safeguards while they’re imperfect, right now they’re all we have. And so it’s a really bad idea for people to get open models. The only models that really we should be investing in are those models that large corporations control, what we call the closed models, right?

So ChatGPT here is a good example. It’s produced by Open AI. It has a trust and safety team. Although somebody just—you know, the person who was leading it, just quit from it. But that’s a story for another day. It shows you that it’s not super straightforward to keep those models in line. And so that’s sort of the first camp that says, OK, it’s too dangerous and we don’t really know how to deal with that. So let’s make sure that any AI is produced by people who are vetted and who have invested safety safeguards, and who have safety teams, and who have terms of services.

Other side of the pond, people who are saying, no, that’s a terrible idea. That’s the opposite of what you want to do if you actually care about safety and security, because if you only have Google and Open AI building those models, then how’s academic research going to find out what those real harms are? And how are we going to build alternatives? And how our regulators going to be able to build the models that they need? We’re going to need a lot of specific models that are not necessarily in line with business interests, right?

I’ll give you a very—not particularly fun, but very real example, on the matter of producing child sexual abuse material. A lot of the NGOs who are working on this are saying, well, unfortunately, there’s a lot of this material that’s now producing synthetically. And what you need as a society in order to best deal with that is to be able to create generators that can tell you what is synthetic material and what’s a real picture of the child being harmed that is in need of law enforcement investigation and rescue.

DUFFY: Can I just pick up for some people, synthetic is a very term—synthetic material is material that’s been produced by an AI system that is not actually a picture of a human being, but is an AI-generated picture. For those who are newer in this space.

FRANҪOIS: Yeah. Essentially, you’re facing a picture of harm, and you’re trying to figure out, is there a real person being harmed? And should an investigation be opened? Is there, you know, need for rescue, for instance? Or is this a fake person, a fake situation, in which case we might still have questions but it’s sort of a different ways to tackle that harm. And so, you know, we often think about those very big models, but there’s going to be a need for a lot of very specific models, including for a lot of various central functions of democracy. And those models might not be produced by Open AI, or by Google, by Microsoft, by all these giants. We might want people to have access to these open models in order to tackle those harms. So that’s sort of the second fault line, open versus closed.

Third, faut line, about governance. And essentially, this one is saying a lot of these issues, why they seem novel, are issues that we’ve tackled with other issues—I’m saying issues a lot—with other technologies. And so, for instance, we have the FCC, and we want to empower our existing organizations to tackle those new harms that are produced by new systems. So essentially, folks saying, well, let’s use the institutions we have, both on the domestic side and on the international side, versus folks saying, no, this is an entirely new beast. We want entirely new frameworks and new institutions. And this is creating an interesting competition on the global scale for who is going to show leadership in regulating this new object.

So in a week I am heading to the U.K. AI Safety Summit, where the U.K. is going to announce probably a bunch of new things on how they see the regulation of AI. On Monday, you will see coming probably from the White House a new executive order on regulating AI. It is expected that France will announce a few things too at the Paris Peace Forum in a few weeks. This is really a moment where you see a lot of governments saying, all right, let’s come up with something new, a new object, a new set of ideas, to regulate AI. And I’m also missing that the G-7 has put out in Hiroshima Declaration on how to govern AI. Yesterday, the U.N. announced a new advisory body. So you can kind of see, like, a little bit of a race.

DUFFY: China.

FRANҪOIS: China, of course.

DUFFY: Through BRI, 155 countries.

FRANҪOIS: Absolutely. I quite like Anu Bradford’s model that she put through in this book called Digital Empires, where she says generally when it comes to regulating new technology you can see China as a state-based system, the U.S. as a market-based system, and the EU is a rule-based system. And we kind of see those different approaches manifesting in AI too.

So that sort of, you know, leaves us with a set of questions which are also interesting from a foreign affairs perspective because it also becomes what is the object that we can compare it to? So folks are saying, for instance, AI, if I believe in existential risk, if I am coming from this idea that we really need to close access, let’s say we’re going to treat it as nuclear nonproliferation with a set of rules that are inspired from this. That doesn’t fully work for a bunch of very specific reasons. But this is sort of like that pivotal moment right now, where all global powers are trying to figure out what can it be compared to, what are the harms that we need to focus on, and what are the strategy that we’re going to deploy both domestically and internationally to go out and tackle those harms?

DUFFY: I think Global Partners Digital, which is a great NGO in the digital space, did a recent tally and I think they calculated forty-seven different multilateral initiatives occurring right now on AI governance.

And so I know one of the titles of this session was U.S. foreign policy but given how much is going to happen in the next week to week and a half, truly, we didn’t want to try to put a focus today on what the existing foreign policy is because literally in three and four days it’s going to look different and we’re going to have different things to be grappling with.

So I would say just hold on to your hats. (Laughter.) It’s coming. And so—all right. So, Cam, I think from you so much of what we’re hearing I really can’t emphasize enough this question of thinking about existential risk versus thinking about existent risk.

You hear people in this space talk about ex risk and they’re generally talking about existential risk. It’s worth pushing on, like, why aren’t we looking at existent risk as opposed to existential risk, right?

This question of open versus closed is going to be a really important and fascinating one as well in particular because it gets deeply technical and this is an area where policymakers are going to really struggle and have historically always struggled with understanding how something that is open can also be more secure.

So it’s going to be in the weeds on what is already a weedy topic and so this one in particular, I think, is going—it’s going to be tricky and fundamentally impact how this space evolves.

FRANÇOIS: Kat, can I—

DUFFY: Yeah.

FRANÇOIS: —add the fun side of it? Which is—

DUFFY: For sure.

FRANÇOIS: —a lot of people do, as I’m sure you heard, you know, in—(laughs)—in my tone of voice do believe that openness here is going to be key and critical. And while that position is shared by a lot of players, it is also fair to say that nobody agrees what openness means in the context of AI.

This is this kind of fun moment where we kind of all agree that openness is key, but really is this acknowledgement that openness is a spectrum from things that are fully open—they’re trained on open data, they’re running on an open infrastructure, their license is open—to things that are, like, maybe a little bit less open but still on that spectrum. So it’s a fault line and, yet, you know, the contour of that debate is still very much up in the air.

DUFFY: And so with that we’re going to turn it over. I am certain that there are a number of questions in this room but everyone has been sitting and listening for a long time at this moment. So I want to do one quick thing. As we think about the complexities also of governing and of talking about this across different countries and across different cultures who in this room speaks French?

Who in this room has thought about how you pronounce ChatGPT in French? (Laughter.) Cam, would you do us the honor, being French, of giving us the pronunciation?

FRANÇOIS: ChatGPT. (Laughter.)

DUFFY: Now let’s translate that into English for the non-French speakers. And why don’t you direct it at me because it feels apropos?

FRANÇOIS: Why do I have to do that?

DUFFY: Because, you know.


DUFFY: And in English, please?

FRANÇOIS: Cat, I farted. (Laughter.)

DUFFY: I support you. I support you.

All right. And so with that, we’re going to take literally thirty seconds. Thirty seconds. I want everyone to stand up, get your wiggles out. I want you to look to your neighbor. Find a neighbor and I want you each to say as solemnly as you can to each other ChatGPT. (Laughter.)

Excellent. Well done. Well done. All right. Everyone take your seats. Take your seats. We’re going to go into a Q&A now. All right. Oh, my lovely term members, shhh.

FRANÇOIS: I swear France has more to bring to the global governance of AI debate than that.

DUFFY: This is—this is true, but it’s good to let—I’m a mom and it’s good to let people get their wiggles out. OK. So we’re going to start with questions. I’m going to take maybe three questions at a time, turn them over to our amazing panelists to answer, and then try to do another round.

So can we start—you have a very enthusiastic hand up. Do you need a microphone?

Q: Good morning. Tao Tan, Perception Capital Partners. Michael Chui, so good to see you.

The question is this. You’ve raised the point that AI can significantly accelerate the pace of R&D productivity and R&D has been on a downward trend in our country for several decades now. So the question for you is: How are economic actors thinking of this? Are they thinking of this as an opportunity to backfill decades of foregone R&D or are they thinking of this as I can now get more for less and resume the downward—potentially accelerate the downward pace of R&D spending?


Yes, Joseph?

Q: Thank you very much. Joseph Gasparro, RBC Capital Markets.

So this room is filled with the most ambitious, smartest, curious people probably in the world. Only some of us knew about PaddlePaddle. So how do we find the next Kat, the next Jared, the next Camille, or the next Michael when it comes to colleges and universities and talent development and competition?

Thank you.

DUFFY: OK. Great. And let me do one more question.

Q: Good morning. Carrie Lee from the U.S. Army War College.

My question is sparked by something that Michael said but mostly for Camille. AI will accelerate productivity in some areas. But, Michael, something that you said struck me that as it accelerates efficiency in some, you know, historically we have supplanted the labor force using immigration rather than kind of just declining workforces and it occurs to me that this may—that the introduction of AI particularly into the economy may accelerate inequality, particularly in some aspects and some kind of—depending on your industry.

We’ve already seen kind of the results of significant inequality domestically, which has posed problems for global governance, the emergence of populism, et cetera. How are we thinking about that kind of almost not immediate harm but, like, mid-level harm of AI when it comes to thinking about kind of global governance and what that means for the rest of the world?

DUFFY: Fantastic. So the through line that I hear at least between these conversations is as ever we’re looking at speed and scale, right. So how are we speeding up and scaling R&D versus thinking of speeding and scaling up essentially, like, market efficiencies that allow for R&D to be cheaper, right?

How are we speeding and scaling up talent identification and talent development and how are we potentially speeding and scaling up bias, right, marginalization, and then I think that by extension what would we do about that.

And so with that I will—I’ll just turn it over to our panelists. But, Michael, I wanted to start with you because I know you’re—you know, you’re coming in virtually and we love you when we see you. And so do you want to—do you want to weigh in first?

CHUI: I didn’t get to go to the reception.

No, first of all, Tao, great to see you. (Laughter.) Great to see you.

Look, companies in terms of their R&D they’re not necessarily thinking about should I, you know, backfill the R&D I didn’t do years ago. They are looking from a competitive standpoint what do I need to do in order to succeed.

Some companies will say, look, I need to do as little as I can just to—others are, you know, you can see it. So there’s a huge amount of individual variation. But to the extent to which this increases competitiveness that’s all for the good because that drives into productivity.

If you don’t mind, I’ll just offer a couple thoughts on the others. On the talent side we need to look for a lot more talent particularly for these technologies and, hopefully, we can look beyond, you know, like, all the usual suspects. You know, I went to one of those usual suspects. But we don’t necessarily need people who got Ph.D.s from D-1 schools or what have you. We need people from all over the place.

And so understanding, you know, there’s a—some of our work on future of work is looking at having a more skills-based view. And then just to segue into the inequality question, I mean, one of the interesting things that I think people don’t recognize that inequality has actually declined over the past few years in the United States. Now, a lot of that is a result of public policy but, nevertheless, it’s interesting as we look at it.

That said, historically, you know, returns to capital are—you know, can increase versus returns to labor and so we need to think really hard about how we actually, you know, distribute the gains of these brilliant machines.

That said, I mean, the one interesting thing about generative AI as opposed to previous AI and previous types of automation for—again, for economics geeks, you know, there’s this idea of skill bias technological change. Most of these technologies historically have affected low and middle wage workers or occupations, which had lower levels of occupational attainment as their requirements.

Generative AI is exactly the opposite. You know, losers like me who have Ph.D.s it actually affects us more as it turns out and so exactly the opposite. You could argue that’s one of the reasons why generative AI has become so interesting because now the people in power realize, oh, gosh, like, this affects me, too.

With that said, let me just—a quick heads up. I have a low grade amount of anxiety right now because despite having my machine plugged in—by the way, power is one of the big issues or energy is one of the big issues with generative AI. (Laughter.) My battery is slowly declining.

So, hopefully, Jared, some of your, you know, battery chemistry people can fix that. But if I disappear I will return on a backup device. (Laughter.)

DUFFY: Thank you for letting us know.

FRANÇOIS: I think it’s existential risk at play is really what’s going on right now. (Laughter.)

DUFFY: Cam, Jared, what are thoughts?

DUNNMON: So I could give these in order. So on the R&D front the way that I would think about this is that there’s been a change in the kind of activation (of energy service ?). And what I mean by that is there are—R&D would take directions where you would say, OK, I assume that this process—I’m at a certain point. To get to the next point in my R&D process, I need to—I need to invest a certain amount, right? And there are certain ways I can do that. And the most inexpensive way I can do it, the most cost-effective way to do that, is this one, you know, option A over here; and option A is going to give me a good result 10 percent of the time.

Well, now there’s another path where I can maybe get a good result 30 percent of the time for the same cost and it changes what I’m willing to do because I say, well, if I can do—run these experiments for that cost, well, then I’m actually going to decide to do it versus not do it.

So I think it changes the cost benefit calculation for R&D and deciding which path and technological tree you’re going to explore. So that’s thing one and that’s where, for instance, the computational design pieces are major parts of that. So if you’re able to replace, you know, physical experiments with computational experiments your cost structure decreases massively. So that’s a big area.

On the talent piece, I have a lot to say about this but I’ll frame it around something that is concrete, which is on the DOD side one of the things that—one of the programs that we ran during my time in government was called xView3.

It was a—in fact, the xView sort of prize challenges. It was a set of programs that was focused on actually putting out applications that would—for which there’s a DOD use case as well as a civil society use case and putting those things out in a way that was well curated where you actually had good data, you actually had good labels on outcomes, and you wanted someone to predict one from the other. Like, that’s what you needed to do.

And when you went through the work of actually defining, like, what’s my task, what’s my baseline, what’s my metric, be very clear about those things—having the data to support it and the provenance on those—on that, having thought about beforehand, like, harms analysis—like, what are the things that can go wrong here, like, how should we build these models and communicating those things, that we’ve thought about those things and those risks and putting those out in the world—one of them was, for instance, for doing post-disaster damage detection from satellite imagery. One of them was for—you know, one of them was for detecting activity that looked like illegal fishing from satellite imagery. So there was a lot of satellite imagery involved. We put those out in the world and just said, like, could people please work on these? Because they’re important.

If you look at the winners from those competitions, they were a mixture of not giant companies. It was often individuals or small—it was mostly individuals actually, and if you look at the countries that they came from from all over the world.

And so, you know, what I would say is that we—you know, moving away from—yes, and I say this, you know, it’s a funny world where yes, like, I spent a long time, you know, getting—you know, getting a Ph.D. But in reality that’s what people look at. That’s a proxy for, like, do you know what you’re doing and so—and it is often not, by the way. (Laughter.)

You know, but in this case, right, I can very clearly define what do you know what you’re doing means because if you can build me something that gets me this input from that output, that output from this input that’s what I need. And so you can move to that from the standpoint of talent without saying, I need this, I need that, I need the other, I need go to these universities, which you should. You should go out there and do that. But you can also just state here’s what I need you to do and then let people work on it. So there’s that.

DUFFY: And then let’s actually move on to Camille.

FRANÇOIS: Yeah. I love this example. There are great uses of these technologies for progress. I think about the satellite imagery example. The other one that comes to mind is folks using AI to look for illegal deforestation patches in the Amazon.

Generally, there’s also such fantastic investigative journalism around this. And it goes back to the talent question and the importance of openness, right? Because I think what we’re saying here is, sure, all those fancy American schools are great, but we also want the hackers, we want the pirates, we want the makers, and whoever are these young women in pink pants somewhere, you know, from their basement playing with those models that are going to create new alternatives, new pathways. And I think that is really important both in how we think about the harms, how we think about access, and how we think about this governing debate on open versus closed, right?

Like, right now I don’t think we can say in the U.S. that the education system produces equality at scale and it’s really important that we make sure that the way we teach and give access to these technologies don’t reproduce those harms. That goes to the question on what do we do with the sort of massive amounts of inequality, and here I don’t want to sound like the most clichéd French person you’ve ever had on stage but regulation, right, I think is a good tool for how we tackle sort of the mass production of inequalities at scale and the other one—I feel like this is a joke but strikes, right—labor movements. (Laughter.) It’s been really interesting to see how generative AI has shaped the labor movement in Hollywood.

I think a lot of people didn’t expect it to come from Hollywood first but writers have said, like, hey, we don’t want our work to be absorbed, reproduced, and replaced by machines and we are going to organize. We’re going to rely on our unions and we’re going to have a strike and we’re going to do social movement in order to bring about the change that we want to see.

And so yes, apologist with this sort of, again, very French perspective but regulation can help, I think. There’s also a good place in that debate for social movements.

DUFFY: And I’m going to take moderator’s privilege here to just add on this I think when we consider talent we also have to think a lot more extensively than we have about what talent is and what expertise is.

If we know that systems are built, right, based on information that is ingested across fundamentally inequitable societies that means that we’re building on some fundamentally inequitable models and understandings of what knowledge is, right.

And so I think there’s really interesting and creative work that can be done. As governments are thinking about this there’s a real push right now to think about the sticks, to think about how to constrain this. I would really love to be seeing more creative outputs from governments in terms of the carrots, right, in terms of how government gets ahead of this and specifically how government gets ahead of looking at the scaling of marginalization.

So how do we—if we know that that is coming potentially with some of these models how do we use government programs, how do we use government outreach, how do we use our existing systems to actually get ahead of that, start working with communities, and that is a different type of expertise and a different type of talent—that lived expertise, that knowledge of how bias is going to hit you.

And that is not specific to the United States. It’s going to be the same thing if you’re a Dalit in India instead of Brahmin, right? So that’s another area where I would really push for more creative solutions in thinking about how we address inequity in particular and ways that we can be proactive about it as opposed to simply reactive.

Sorry, moderator’s privilege. OK. We have time for, like, maybe three more questions. I’m going to go to the back because we did a lot of questions from the front. So start there.

Q: Thank you so much. I’m Amy Larsen. I’m director of strategy of Microsoft’s Democracy Forward team working on AI elections, information environment in Ukraine, among other issues.

DUFFY: Are we moving forward, Amy?

Q: Yes. Yes, we are trying. It’s a team effort. Team support always.

I really appreciated the framework that you laid out, Camille, and I’m just curious about each person’s sort of perspective and their thoughts about how you would sort of move through that framework and whether you’d add anything as well.


DUFFY: Yeah?

Q: Thanks. I’m Heather Hwalek from the Bill and Melinda Gates Foundation.

Just a quick plug. Through our Grand Challenges initiative earlier this month in Dakar, Senegal, we awarded nearly fifty grants to locally-led projects seeking to innovate on community-driven AI applications towards global health and development.

But my question is not about that. My question is for these three practitioners of AI who spend a lot of time thinking about this. On a very personal level, talk about harms for you be it existential or immediate. What is the one thing that keeps you up at night about AI?

DUFFY: And there in the back.

Q: Hi. My name is Imani Franklin. I serve as counsel in the office of Elizabeth Warren.

Concerned about monopolistic trends in the AI space and I’m curious what you think the implications of developing public options for AI—public cloud infrastructure, public data resources—could look like and what implications that would have for some of the trends that you discussed, Camille.

DUFFY: Fantastic. OK. So we’re looking at—to come back, we’re looking at, sorry, starting with sort of, like, Camille’s framework, right, and then what is keeping folks up at night and then also how do we think about sort of the monopolistic options. Michael, I think you heard that question as well, right?

So why don’t we—why don’t we reverse? Camille, can we start with you?


DUFFY: And then we’ll flip back. Michael, how’s your battery? Are you good, man? Should we start with you? Should we start—let’s actually go to Michael.

FRANÇOIS: Go to Michael.

DUFFY: I’m really—I’m really big into contingency planning. Let’s start with Michael.

CHUI: Like, a resiliency problem here. I love Camille’s framework, too. I don’t—I don’t think I could go through all of them. Let me just share two things.

One is we’ve been surveying thousands of executives on their views just on risks in general. In general, while in forums like this we talk about those risks a lot, actual companies—except for cybersecurity, most companies don’t recognize most of the risks that we’ve talked about with regard to AI as being relevant to them and, furthermore, of course, even fewer have tried to mitigate them. So I think that’s a real challenge.

I think openness is really interesting, right, because to a certain extent openness and if you believe in existential or other risks, if you have these open systems where anybody can use them and see them that transparency is terrific. But that also means all kinds of either intentional or mal actors can use these technologies as well when you’re extremely open. So it gets really complicated.

Let me really quickly touch on this question about consolidation in the industry. I think one thing to think hard about is that the old idea that it takes billions of dollars to train a foundation model turns out not to be as true at least if you’re away from the frontier.

So while it might have taken, you know, a billion dollars to reach, you know, GPT 3.5 level performance that’s down into the millions, which, yeah, I don’t have that in my back pocket. But around here where I live in Silicon Valley it’s not that hard to find a few million bucks and so I think—but there are other reasons why you might see consolidation, whether it’s consumer preference and, you know, all kinds of other commercial things.

So I think it’s a little bit complicated about how you want to think about industry structure there. So anyway.

DUFFY: Fantastic. Camille, over to you and then Jared. But we’re almost at time, so let’s try to do quick answers.

FRANÇOIS: Yes. I will disclose where I live in this—my own framework and before I do that I will say thank you for working on electoral integrity inside of the company. I know it’s not fun every day and so grateful that those big Silicon Valley companies continue to invest in those topics. It’s not a given and it matters a lot. So thank you for the work that you do.

When it comes to the framework I do—I will admit that I sit on the side of immediate harms in the immediate versus existential. Yann LeCun, who’s one of the architects of those new generative AI systems and who leads AI Meta—I keep on calling it Facebook, Meta—recently wrote a piece saying that he thinks that this existential risk completely overblown. He says that current AI is as dumb as a cat, which is not very nice. I have a very smart cat. I wouldn’t say that just yet. (Laughter.)

DUFFY: I would like to say that I am too.

FRANÇOIS: Yeah. You know, I think that a lot of people who’ve played with those systems can see that, yes, it does something that’s a little bit magic. But I think we can also say we’re not yet at the existential risk and a lot of these harms that are already manifesting are really impacting societies, are impacting freedom, are impacting human rights, are impacting equality. I think they’re extraordinarily important and tactical now. So this is where I live on immediate versus existential.

On open versus closed, I live strongly in the open camp. I think it’s going to be extraordinarily important for innovation, for competition, for equality, for hackers and tinkerers to get access to these models to create smaller models focused on immediate harms, too.

And, finally, on the, you know, new governance mechanisms versus old governance mechanisms, this I really don’t know because as you said, Kat, it’s head-spinning to see all those new bodies, governance systems, new mechanisms that are coming out of the hat at a very, you know, head-spinning pace.

So I don’t know. I think at the end of the month I will look at all the objects that all of those different bodies have put on the table and try to figure out which ones is best positioned to tackle those immediate harms while ensuring that those models remain open and accessible to a broad number of people.

DUFFY: Jared?

DUNNMON: Yeah. So on the framework I would say I tend a little bit more towards the immediate than the existential and this has to do with what keeps me up at night. What keeps me up at night is not, you know, a model taking over the world.

What keeps me up at night is someone building a model, not documented it well. Someone else going and using it for something it wasn’t supposed to be used for and it breaking. Like, that’s what keeps me up at night and that almost certainly will happen all the time unless we are careful and that’s the collective we. Everyone who builds and deploys these systems, particularly if you’re building it for someone else.

On the open versus closed piece I actually think about this more from a practical perspective. I think the release of the Llama weights from Meta was instructive, which is they tried to do it in a controlled way and it did not happen, right. And then eventually they said, like, OK, well, we’re just going to kind of make them open.

DUFFY: And we meant to do that the whole time.

DUNNMON: Right. So—well, right. So but the point being is I think there’s a practicality question both because of, like, are these things—is it practical to keep these things closed and the open source if it’s following so fast. There’s a really kind of, I would argue, pretty good blog post. It’s written by my post doc advisor called kind of—you know, is AI having its Linux moment, you know, from an open source perspective.

Everybody can see this stuff. You know, we can all look at it. You know, we have these same exact conversations about Linux in the OS space. Now, there are differences in terms of interpretability, in terms of inspectability, in terms of can we see all the data, et cetera.

But I think those are—that’s kind of where I fall is it’s almost certainly—you’re going to have highly capable models out in the open and we need to design for a world for which that’s true regardless of whether we have closed models that continue to be a little more capable.

And then the last piece on the public option I think we absolutely need to make sure that we have resources for folks who are, you know, not at these very highly resourced places to do AI research for reasons that include some of the equity and quality reasons that were mentioned earlier but also for reasons that just go to the fact that we—you know, most of the innovations that have driven these large scalable things came from a world where we didn’t have these large scalable things and then we eventually did.

So if we start doing research and keep doing research in this world where everybody only has—you assume you only can do it when you have these large scalable things. You don’t actually do anything interesting because you’re just focused on, like, kind of moving to the next thing versus thinking, OK, why am I doing what I’m doing, and some of the—I think some of these public options and getting the folks that would use those on those systems is critically important to making that progress.

DUFFY: And I will just—I’ll take moderator’s privilege on this one as well and just say what’s keeping me up at night is that we have entered what I call a post-market pre-norms world and we’re going to be there for a hefty chunk of time, right. And when I say norms I’m not just talking about governance. I’m talking about societal norms, right?

We have tools increasingly like generative AI tools for image production, for video production, for audio production, right. For video production former sort of nation state capabilities to produce completely fake and/or altered information is now going to be available literally for $1.99 in an app store, right, and the thing is that that’s going to be available for all sorts of reasons that we fundamentally want to protect as well.

We want to protect satire. We want to protect creativity. We want to protect play. We want to protect experimentation, right. And so we don’t—I think we’re going to live in the land of voluntary principles, right?

But voluntary principles without teeth, without accountability, aren’t particularly meaningful and we don’t actually have strong frameworks for voluntary principles that have accountability mechanisms associated with them that we can peg off of.

And so what’s keeping me up at night is the fact that all of the decisions that we’re talking about, all of the governance that we’re talking about, is potentially going to be taking place over what I think of as, like, an emerging post-truth world. And there is a phrase that many of you might have heard that was created in 2018 by Danielle Citron and her colleague, the liar’s dividend, and I think this is an area that’s going to be of deep concern in the foreign affairs space.

The liar’s dividend is the idea that if everything can be fake nothing can absolutely be true, right, and so if something is true and it’s awful it’s easy to discount it and if something is awful and not true it’s easy to argue that it is in fact true, and we will see that the more that LLMs get more advanced in different languages the more that we will see that capacity migrate across the world, right, in countries where there’s significantly less rule of law and significantly less capacity and significantly less, like, media, for example, to vet and validate it. And so that’s what’s keeping me up at night.

What does not keep me up at night, however, is the incredible capacity of emerging leadership that we have especially here at the Council to grapple with these issues, right? And so for those of you who raised your hands at the beginning and were, like, I’m an AI person, I hope you’re coming out of this feeling, like, galvanized and like you heard something new.

For those of you who did not raise your hands and who were, like—or, like, I’m sort of this is not my area I hope that what you take from this conversation is that this is in fact absolutely your area and you are going to bring critical expertise and capacity to these questions, and I hope that you will engage in it and put yourself into it because it is going to be one of the leading questions of our time.

And so with that, I would like to thank profoundly first our colleagues in the meetings team for arranging this fantastic session and for doing all of the work that they’ve done. (Applause.)

I would like to thank—(applause)—I’d like to thank the wonderful Council members who joined online. I apologize that we really taking questions from the crowd, but it’s the Term Member Conference so we prioritize the term members who are in the room, and we have a term member on the stage so let’s give it up for having a term member on the panel. (Applause.)

And finally, certainly, not last—last but not least I want to thank Michael and his battery. I want to thank Jared and I want to thank Camille for joining us today. I’m so appreciative of you all taking the time.

I think there’s going to be coffee now. Is that right? If anybody can hang for a few minutes. Thank all of you. I look forward to seeing you throughout the rest of the day. (Applause.)


A Conversation With Rajiv Shah

FROMAN: All right. Well, welcome, everybody. This is the last event of what’s been a very busy twenty-four, twenty-eight hours. I think some of you actually used all of those twenty-eight hours and—(laughter)—went to the afterparty last night after the reception. And we’ve got also people online here as well. So I don’t know, how many people do we have online at this point? About forty, OK. There we go.

SHAH: Wow.

FROMAN: It’s great to have you.

SHAH: Thank you.

FROMAN: And it’s great to have—you know, Raj and I are old friends. We’re neighbors. Our children grew up together. So this is not exactly an unbiased conversation, OK? (Laughter.) Just like interest of full—interest of full disclosure. And we’ve had the pleasure of working together in government and outside of government. And so it’s a real—a real pleasure and honor.

So let me start. You have taken over an organization that is more than a hundred years old after a longtime leader—(laughter)—filled with tradition—(laughter)—and you’re trying to make sure it is as relevant as possible going forward to have an impact on all sorts of new issues. I can’t imagine what that must be like. (Laughter.) So how do you think about change, change management? How do you maintain—I mean, the Rockefeller Foundation has such a wonderful legacy. How do you maintain the best of that legacy and position it best for the future?

SHAH: Well, first let me just say thank you, Mike, for having me here. And you’ve already shared that we—we’re friends, our families are friends, and so it’s really special to be here. And congratulations on leading an institution that is more than a hundred years old—(laughter)—and has an extraordinary legacy, and is needed perhaps now more than ever to bring some sanity and understanding of the world to our domestic affairs and our politics and our leadership across society and across this country.

And congratulations to all of you that are part of the Term Member Program. That’s a(n) incredibly selective and special group of leaders. You must all be extraordinary, and we are counting on you to do amazing things for the world over the course of time. And many of you already are doing that now, which is why you’re in the room.

You know, I write—part of why I’m here is to talk about the book, Big Bets, which you’ll all hopefully get your hands on today. And I write in the book about many mistakes I’ve made and some successes, and—because I feel like you can learn from both. And when I started at Rockefeller I think I made both good decisions about trying to update the institution to be more relevant to the moment we’re in and also, frankly, some things I would have done over in terms of the pace and the speed with which—with which we did them.

So, to me—the institution was founded by John D. Rockefeller Sr. 115 years ago, 113 years ago, and the idea was to bring science and innovation to those areas where they could uplift humanity at greatest scale. And the early investments helped create modern science-based medicine and public health, both in the domestic context in the United States and globally. Many decades later, that same mission was applied to agriculture and agricultural sciences. That helped more than a billion people move off the brink of hunger through an effort called the Green Revolution and earned the foundation a Nobel Peace Prize for its—for its contributions to that effort.

And so when I started, I was really focused on going back to the roots of our core mission and saying, OK, if you took that same core principle and applied it today, what are those areas of science and innovation in society that could create more equity, more justice, more inclusion on a global basis, and trying to find those areas that were a little bit different from what the private sector might do through its own commercial endeavors. And so our big bet as an institution is around the climate transition and climate change, and we believe the renewable energy technology frontier brought to people who often live in the dark can actually help a billion people move out of poverty while averting many—well, while averting the reality that 75 percent of all carbon emissions in 2050 will come from emerging and developing economies that today are really not the focus of the big climate transitions that are happening in the world.

But getting to have the flexibility to make that pivot was where I would have done things differently. And probably instead—I waited a couple of years before making real changes to our programs to free up the resources to—you know, to go big on what we thought was the right thing to do. I wish in retrospect I had done that faster.

FROMAN: You know, one of the things you talk about in the book—but I’ve also seen you do it when you were head of USAID, when you were at the Gates Foundation, and when you were in the private sector—is to think creatively about partnerships and unusual partnerships. And the Global Energy Alliance for People and Planet, one of your big initiatives, governments, foundations, private sector, how do you—how do you bring together those sectors? All of them are represented in this room. How do you bring together those sectors, each of which has quite different priorities and principles and values? How do you bring them together around something like a common purpose on climate change and make that effective?

SHAH: Well, I—you know, the truth is Rockefeller today is too small to go it alone and expect to make transformational change happen at scale. And frankly, even at USAID, which was much, much bigger than Rockefeller financially, we didn’t have the flexibility or the scale to really change systems—food systems, health systems, in this case energy systems—without genuine public-private collaboration.

So the energy program started ten, eleven years ago. My predecessor had invested in an effort to really see if small-scale, distributed renewable electrification could help villages in India and Africa that effectively lived in the dark, had less energy consumed per person than it takes to power one light bulb and one home appliance for the course of a year. And you know, if you live in that environment, you’re absolutely trapped in poverty or extreme poverty, and we could discuss that much more extensively.

But over time, they toyed with these solar panels, batteries, AI-based remote management of energy management and battery management systems. They brought a company called SparkMeter, that actually is D.C.-based, that was creating a way to do individual metering and mobile-based payment into the system. And they got the cost down over seven or eight years from $1.20 a kilowatt-hour to provide energy and power to these communities to about $0.20 a kilowatt-hour. And at that price point, it became clear that this was going to be if not super profitable if you’re a dollar-based investor, certainly sustainable if you were a rupee-based investor. These were—most of these programs were in India.

So we engaged Tata Power, one of the largest power companies in the world and certainly the largest in India, to scale it up, and we created a joint venture that’s on its way to building out ten thousand of these solar minigrids for 25 million people. We could never do that just funding projects directly. And frankly, we didn’t have the scale to bring the different component costs down or the operational management system to scale that up at that scale.

And then, based on that collaboration and other potential ones, we built a global alliance to help mobilize public and private resources to do this in twenty, thirty countries around the world. So just a few weeks ago we announced an effort to roll out these minigrids in eastern Congo, where over time we think 7.1 million people can benefit from energy services provided by these systems. We just announced 1,400 grids to be built out in Zambia, reaching roughly a quarter of the rural population, and bringing electricity and productivity improvements to agriculture and food production. We’re doing the same in Ethiopia, Nigeria, South Africa.

So it's pretty exciting now that that partnership’s together. But we never could have done it on our own. It’s not done yet. (Laughs.)

Alliances are tough to build. And the chapter in the book that describes this is called “Give Up Control: The Lesson Learned,” because you actually have—the hardest part of all of this was, you know, we—Estelle’s on our team, who’s such a great leader at the Rockefeller Foundation. We got to run this program ourselves for years, right, so you have a lot of control over what you’re doing, who you’re doing it with. Now the IKEA Foundation, the Bezos Earth Fund, nineteen other partners sit around the table and we have far less control, but it's a much bigger, much more ambitious project for humanity.

FROMAN: That’s one of the things I like about the book, Raj, is that, I mean, it’s not intended to be a memoir, but not coincidentally you happen to be not only present but a pretty central figure in some of the biggest, most important evolutions and innovations in development. So global health, food security, energy, climate, you were very much driving many of these big changes over the last couple decades. And you end each chapter with some quite practical advice for anybody. You don’t have to be head of a foundation. You don’t have to be head of USAID. It’s sort of how you live your life and how you approach problems to, you know, make big bets.

And so, looking back, talking to the Raj Shah of twenty, twenty-five years ago, OK, what advice would you give to your younger self? What—because we’ve got a lot of people right at that sweet spot right about here. What are the two or three things you wish you knew and did then to make you even more effective as a young professional heading towards the career that you did?

SHAH: Well, that’s a great question, Mike. And Mike’s being modest in the sense that especially in our time in government and beyond we got to do a lot of this together—you know, Feed the Future, President Obama’s big bet around responding to the food crisis after the 2008 financial crisis that was really a major component of how our administration presented our work around the world. So it was great to get to do that together.

You know, I do—I did—doing the book was a—was a unique exercise because—(laughs)—yeah, there’s like, well, what’s the lesson learned, what’s the lesson learned, and it forced a lot of introspection. And so I have two answers to your question.

One is—I wrote the book because I think all of us can think bigger about solving the problems we confront.

AUDIENCE MEMBER: (Off mic.) (Laughter.)

SHAH: Exactly.

FROMAN: And Siri, too. (Laughter.)

SHAH: Exactly. And we’re all going to have tools in our fingertips—(laughter)—that will make us more capable of doing so over time.

And yet, to actually do that, it ultimately comes down to a series of really practical actions and behaviors. And so—but the one I’d share for you, two things.

One is I’d ask that you—especially when I was younger in my career, spend a little more time thinking about the people you are surrounded by, and getting to know them, and recognizing especially early in your career—and you might feel this way from your extraordinary experiences. A lot of times it’s those people you’re surrounded by earlier in your career that end up shaping how you think even much later in your career, so many years later. I write about chapters working with Bill Gates and then—but also women like Molly Melching, who’s this hero in Western Africa who went tribal village by tribal village convincing mostly male leaders to protect young girls, get them into school, get them immunizations, and prevent the practice of female genital cutting. And what I learned in retrospect is a lot of what I learned from both Bill Gates and Molly Melching ended up shaping a lot of my thinking, like, ten years later or fifteen years later, but at the time I wasn’t thinking that way. I was just—I was just running to do the work and, you know, succeed at getting the next promotion or the next job, and I was less focused on, OK, what am I learning from Bill in this moment or what am I really learning from Molly in this moment. So I’d ask you to just give a lot more thought to who’s around you. Are you giving yourself the time and space to learn from them? And really consider how they’re shaping your thinking.

And the second one is actually around, you know, staying optimistic. I think all of you are in this room because you’re extraordinary optimists. Mike and I got to work with so many people who were extraordinarily optimistic, even in tough times and tough moments. And as part of this book tour it’s been fascinating because I’ve gone to a bunch of college campuses and even some high schools, and I’ll say some young people are so optimistic about the future and it’s so uplifting, but many are also quite affected by what they see on social media, what they see in the news, what they believe is an inability to trust big institutions and leaders of big institutions. And so I would just ask you to do anything you can to stay an optimist because your optimism is what we’re counting on to confront the challenges we face.

FROMAN: Your brilliance and effectiveness wasn’t always immediately recognized.

SHAH: (Laughs.)

FROMAN: You tell the story in the book about an interaction—sort of a near-interaction with then-Vice President Biden—

SHAH: Yeah. (Laughs.)

FROMAN: —on the outside of the Oval Office. Do you mind telling this story? And then how you—how you dealt—because I’ve stood exactly where you stood and been in those difficult meetings. Sort of wondered how you dealt with it. What happened in the next half-hour that allowed you to keep going forward?

SHAH: (Laughs.) OK. So—and Mike was very much a part of this because he and his team, I think, made it possible for me to be in the role I was in.

But I was about a week into leading the USAID—the U.S. Agency for International Development, and one evening there was an extraordinarily tragic earthquake in Haiti. And you know, in a moment in Port-au-Prince—ultimately, we lost 220,000 people in that earthquake. But in a moment, the whole place had collapsed, twenty-one of twenty-two ministries. The U.N. team that we would normally have relied on for intelligence and information in managing a response had perished to a large extent, including many people we knew and worked with. And so in the midst of that tragedy, I was asked by the White House and President Obama—it was the only time the president actually called me on my BlackBerry—(laughs)—to lead—to lead a civilian/military whole-of-government response to the crisis. And the next—and so we went the next—actually, that phone call was kind of funny because the White House called on my BlackBerry and then, like—

FROMAN: BlackBerry. Do you know—do you guys know what BlackBerry—

SHAH: Do you guys know what BlackBerrys are? (Laughter.)

FROMAN: You know, these are the term members, right? They may not know what a BlackBerry is.

SHAH: Yeah. And Nishan (sp) right here in the front row was working with me and was the one who was—actually had my BlackBerry in his hand. And he said, the White House just called and the president’s going to call you. And we look at my phone and it has, like, one bar. (Laughter.) And you know, if you’ve been—if any of you have been in the Ronald Reagan Building, it’s got these cinder walls—

FROMAN: Huge, right.

SHAH: —that are huge. So we found a window, and we pulled a little desk up to the window and put it in, and then we just waited. And a minute goes by, a minute goes by, a minute goes by, and then the phone rings. And sure enough, the White House operator’s, like, the president would like to speak to you. The president gets on and he’s, like, Raj, this tragedy is a terrible thing. I’m putting you in charge of a—of a large-scale, whole-of-government response. I want you to make us proud. And I’m, like, yes, sir. And I, like, get my notepad out thinking there’s more to it than that—(laughter)—and the—and the phone line goes dead. (Laughter.) And so I was, like, Nishan (sp), did we just hang up on the president? And then literally, like, ten seconds later we’re turned around and watching CNN in my office and the president’s behind the podium. And he’s, like, I just spoke to Administrator Shah—(laughter)—and I instructed him to X, Y, Z, deploy the Coast Guard, do all this stuff. And I was, like, oh, OK. (Laughter.) Then I started taking notes.

So the next morning—so then we have whole series of meetings. Actually, for those of you that have been in government, it was my first set of it was called a DC, a PC, and an NSC. And when I first went, I didn’t even know what those words meant, you know, and I didn’t know the difference, and we were in the same room the whole time, just different people kept coming in and out. And then the president comes in and it’s an NSC and all this stuff.

So the next morning there is a briefing in the Oval Office. And I get there a few minutes early because you don’t want to be late, I don’t think, to that—(laughter)—and the person lets me in. They take the phone and let me in, and the president and vice president are speaking over by the window. And I overheard Biden telling Obama, you know, are you sure about putting this Raj Shah guy in charge of this? (Laughter.) He’s, like, thirtysomething, he just got to town. And he said Craig Fugate, who was—is an outstanding leader and led FEMA—the Federal Emergency Management Agency—he said, you know, Craig’s got a lot more experience, would be great—would be great at this. And then Obama saw me, so he didn’t respond. He just came over to me and he’s, like, Raj, come in, sit down. (Laughter.) So we have our meeting.

FROMAN: Awkward. (Laughter.)

SHAH: Exactly. (Laughter.)

So we have our meeting. And I—honestly, I was—the whole thing was such a daze. I hadn’t slept. Like, I didn’t know what to think. And we sat down, had the meeting. Craig was in the meeting. And I write about the meeting because during the meeting Craig was incredibly helpful, and on the way out I basically, like, put my arm around Craig and I’m, like, Craig, I really need your help. (Laughter.) And he was, like, look, anything you need. And so he, a young person named Brent Colburn who was his communications person and chief of staff, but a small team from FEMA basically came to USAID and Craig worked out of our emergency response center with Nishan (sp), with a woman named Susan Rikeley (ph), and with others for the next several weeks.

And the lesson I draw from that experience was called open the turnstiles, because when we got back to the building that morning there was a long line of people from mostly DOD that couldn’t get into our gates because they were waiting to get badged in while the USAID folks were kind of able to swipe in and go upstairs. And we talked to security about saying can you just—for this extraordinary moment of crisis and response, can we keep the gates open so people can go in and out without badges. They did that, and I think it made a—it just created a culture of we’re all in this together.

But I write in the book about responding to emergencies. One of the things I love about the opportunity to respond to those emergencies is a lot of the usual bureaucratic infighting goes away and people do understand that this is a moment of real moral clarity. Like, we were trying to save lives with a clock running out, trying to do the best we could to live up to the president’s charge to make this a real moral moment for American foreign policy. And I’m proud of the way the whole team came together to do it, including Craig.

FROMAN: I want to go back to that image of you by the window with the pad of paper waiting—

SHAH: (Laughs.) Yeah.

FROMAN: —waiting for instructions, because—

SHAH: Oh yeah. Yeah.

FROMAN: —there is that moment in one’s career that you’ve had where you go from being staffer to principal, right? You know, you always have bosses. You had the president of the United States as your boss, and 535 members of Congress were your boss, and you know—

SHAH: And Mike—and by extension, Mike Froman was my boss. (Laughs.)

FROMAN: No, no, no. Never, never. But it’s that moment where you realize you’re the one who knows more about this than anybody else in the room and people are looking to you to, to paraphrase George Bush, to be the decider, right?

SHAH: Yeah.

FROMAN: And you know, people go—that’s a hard transition for some people. You have doubts. There is imposter syndrome. How do you—how do you think about that? Do you remember that moment when you realized, OK, I no longer work for Bill Gates; I am a colleague of Bill Gates in dealing with some of these big issues facing the world? And how did you deal with that as a leader?

SHAH: Yeah. That’s a great—I wish I could ask the same of you on that one.

You know, I just—I would just say that imposter syndrome is real. Like, I don’t think there was ever a moment where I sort of said, OK, I now feel like I really absolutely belong here. It’s more you just get absorbed in the work, and you trust your instincts, and you trust the way you think about solving problems.

And one of the things I loved about serving in the Obama administration was, for whatever reason, it just felt like the goals were fairly clear, you know? And so, like, even the Haiti earthquake, I remember the president saying—and you remember this—he said, look, both Haiti is two hours from our shores and this is a moment of real humanitarian need, but also we’re in a complicated world out there. And if we can show the world that America’s power can be used for moral good, that is beneficial to our nation and the idea of our country all around the world. And so that—those were the instructions. It was just sort of clear and you could—you could plow ahead.

I guess the two things I learned about imposter syndrome in lots of different settings, one is just trust your instincts and trust yourself. Like, no one else has this figured out any better than you do, even though they might seem like they do. I mean, we would sit in rooms. These guys would have, like, four stars on shoulders, but it seemed like there were thirty, you know? (Laughs) And—you know? And they were still trying to figure it out alongside you in a very open, inquisitive way.

I remember when Mike Mullen would first reach out and be, like, yeah, I’m really trying to figure out, what happens after the humanitarian piece in Haiti? How do we really establish investment and governance in a way that’s going to be better? And that was, obviously, very difficult to do.

And the second, I would say, is really be a good listener to others because—the Haiti earthquake is a great example. I mean, people from around the world at USAID called that first twenty-four hours and they said: Raj, you don’t know me, but I’m really one of the best you have on this topic, so I’m going to—I’m right now in Kabul, but I’ll be in D.C. in eighteen hours, and I’m going to come to help you. And so take all the help you can get in those moments because I think people genuinely want to be helpful, and you’re going to be better served if you can learn from and surround yourself with folks who can make you better.

FROMAN: Last question before we open it up, because I know there’s going to be a huge number of questions here. Lots of hundred-year-old organizations think about impact, and how do you have impact, how do you measure impact, how do you know that you’re really getting anything done. How do you think about—you’ve been at Gates, USAID, been in the private sector, now Rockefeller. How do you think about impact? And how do you hold yourself accountable for the work that you do?

SHAH: Yeah. This is—I feel very strongly about the answer to this question because I think you just have to measure in a very disciplined way the impact you’re having in a simple, clear manner. For earthquakes and fights against pandemics, we created scorecards and tracked every day performance on a handful of metrics. When data systems didn’t exist to generate that data, we put bioterror labs throughout, you know, rural Liberia to more quickly validate data, and how many Ebola cases existed, and exactly where were they. And investing in data that is maybe not perfect but is fast is something we tend not to do as much in our work in philanthropy or in government efforts, and I think it’s a—it’s a big missing piece of what it takes to understand impact. And so I’d encourage you to overinvest in data and information that can give you a sense of outcomes and impact.

And too often—and this is another pet peeve of mine—we do it in a—in an effort to be, like, really thorough and totally validated. We put in—we want to do these exhaustive surveys and counterfactuals and things that three years from now will tell you how you did, when you really have to know how you did three months from now. And so I actually have a whole chapter in the book—(laughs)—about the Ebola response in 2014.

FROMAN: It’s a terrifying chapter, by the way.

SHAH: (Laughs.)

FROMAN: I mean, it really is a terrifying chapter. I mean—

SHAH: Yeah. I mean, the CDC thought we could have 1.6 million cases. We thought we could have hundreds of thousands in the United States. The president deployed, I think for the first time in history, U.S. troops into a pandemic hot zone to fight a pandemic far away when we actually didn’t know what would work. We thought we did. (Laughs.) Some of the team did. But we actually didn’t. And we built a data architecture, actually led by an extraordinary Swedish epidemiologist named Hans Rosling, that helped us put it together.

But the point is, invest in data. Even if it’s not perfect, you need feedback loops fast in order to make adjustments and to optimize work. I think it’s the single biggest gap we have in the work we do at the Rockefeller Foundation. I think it’s a huge, huge opportunity for all of you to do this kind of work better because you’re just much more data-oriented.

FROMAN: Terrific.

Let’s open it up. Please, right here in the front.

Q: Hi. I’m Artemis Seaford from OpenAI.

I really appreciate the sort of personal, intimate perhaps, candor of this conversation and what sounds like it also is a very personal book. So I wanted to ask you, with your permission, also what is perhaps a slightly personal question. So, particularly given the focus of your career, which is around helping people that are most downtrodden and in the most difficult circumstances in the world, it seems like it requires a certain amount of self-sacrifice and looking outside the self and, of course, caring about the others and being self-effacing. At the same time, I suspect for many of us term members, we also appreciate that getting positions of power in order to be able to do all these good things required being quite self-centered, right—as you mentioned, caring about promotions, getting a CFR term membership. (Laughter.)

FROMAN: Maybe just that.

SHAH: Whaaat? (Laughter.)

Q: All this good stuff. So how in your career have you balanced and do you think about balancing sort of this focus on others and forgetting the self versus the focus on the self and getting in positions of power to help others?

SHAH: You know, that’s a—that’s a really good and honest question, and thank you for asking it.

I write in the book about when I was a kid—I grew up in suburban Detroit and I’m Indian American, if you didn’t already know that. (Laughter.) And so in my community and in my family, I had two kind of career options. One was to be an engineer. The other was to be a doctor. I chose doctor, and we can talk about why. But I knew early on that I wanted to be involved in social service and public policy; I just didn’t know all of you. I didn’t know people who were kind of doing it, and I didn’t know there was a place called CFR where you could get a term membership and all that. I just didn’t know. And so I kind of meandered until I found my way.

But one of the meanderings was I had decided I was going to be one of those people who, like, really sacrificed to be out in the field working to serve those who are most downtrodden, as you put it. And I spent a summer with someone I met who I still consider a hero named Dr. Sudarshan who worked in a place called the BR Hills in southern India with a tribal community called the Soliga. And we would go door to door, hut to hut, and we would screen for leprosy, and we would find kids who are malnourished and take them back. And Dr. Sudarshan has basically sacrificed his life. Like, he lived in that community. And I realized within about three weeks that I couldn’t do that; you know, that I was bit by mosquitoes, it was super hot, I wasn’t eating well. I was, like, OK. And it was just—there wasn’t enough scale to it for me for that to be my calling in life. And then—and then one thing led to another, and I landed with Bill and Melinda Gates when they started a big immunization program and learned how to work on the same problems but from a very different perspective.

So what I take from that, to answer your question, is I do think you have to be—I think—I think you want to be as genuine as you can about serving others or whatever it is that is your core purpose. Like, as yourself—one of my heroes is a woman named Patty Stonesifer, who’s on my board, and she always writes for herself a personal mission statement. Every few years, she’ll update it. And I think it’s useful to think about: What is your mission? What is going to make you fulfilled deep down inside, whether or not you amass power and titles and resources and, you know, the admiration of others? And then understand that for yourself, even if it’s just totally private.

And then, second, I’m a big fan and the book is really about, you know, zigzagging; finding different ways to have influence and impact and scale of impact. And that is—you know, that is a path. And all of us work hard to sort of rise to positions where we think we have more authority, but hopefully we are doing that with a clean understanding—even if it’s totally personal—as to why that matters to you.

And the reason both of those are important is because, you know, Mike’s been in these incredibly important roles, but when you’re there, that’s when it really matters the most that you understand why you’re there to begin with. And I—and I’ll be honest with you, I think we’ve both served with people who are really smart, are really lauded in society, but maybe are not quite as grounded about why they’re there. And then—you know, then it becomes about themself as opposed to the work they want to do for others.

FROMAN: Great.

Right here. Second row. Charles.

Q: Hi. Thank you, again, for being here. Joe Gasparro, RBC Capital Markets.

It feels like a lot of folks in this room want to take big bets. And we have big ideas, and when we go to our board of directors or our clients with these big ideas they counter with there’s big risks and big failures and small profits. So, over your career, how do you square that circle? Is it better measurement? Is it a government backstop? Help us help you. How do we get there?

SHAH: Well, there are two ways to think about it. One is as an individual in your career and in your work. I think it’s great you—I advocate for and I wrote the book so people think bigger about what we can accomplish together, but each chapter is designed to give you really practical tools you can use, because ultimately, you know, big bets start with super-practical actions. Our effort right now to reach a billion people with renewable energy to move them out of poverty, and energy poverty specifically, started with some of my now-colleagues, like, taking across the Ganges River a set of solar panels—(laughs)—and some wiring and some other equipment to really get started ten or eleven years ago. So super-practical, super-small example, but it enabled a big bet a decade later. So that’s one way to think about it.

The second way to think about it is I believe that to tackle some of the big challenges in the world we have to be good at public-private. There’s some things that only governments can do and do alone, but most things, in my view, require a public-private mindset. So if you’re in the private sector or in banking and investment, you know, I would encourage you, spend—figure out how to get some exposure to public service. And vice versa; if you’re in public service or a think tank or something like that, try to get some exposure—meaning actual experience—in the private sector, because having respect for both sides and having the ability to be a bridge builder I think is a part of making those big bets work.

FROMAN: Yes. Gentleman here, third row.

Q: Thank you very much. Joe McReynolds, Peraton Labs.

I was wondering, in talking about data and impact as the head of Rockefeller, do you have an opinion or a thought about the—sort of the rise of the effective altruist movement and that way of looking at data and impact, for better or for worse? I would just really be curious to hear your thoughts on that.

SHAH: Yeah. I’m so glad you asked that. (Laughter.) I’ve been—I’ve been wanting to answer that question for, like, two months—(laughter)—and nobody’s asked me that question.

Early on when I got started, I—the exercise that Bill and Melinda Gates put us through in 2000 and 2001 is what I would have called the effective altruist movement. Like, OK, we—you know, if you looked at—this is a little nerdy, but if you—if you go to the 1993 World Development Report, in the appendices are all these disability-adjusted life year tables. And I got my graduate school training doing cost effectiveness regression analysis on the datasets that were used to generate those tables. So I love nothing more than sort of going off with spreadsheets and with software and figuring out, OK, distributing a bed net gets you, you know, so many lives saved—life years saved relative to investing in a health-care worker or some other solution.

And what I learned is that that kind of analysis is useful. It’s part of the solution. But it also misses the reality of how things work, you know, in villages and in communities. And one of the reasons I to this day have unbridled admiration for Bill and Melinda Gates and how they pursued their work is, in those first five, six years, we spent so much time sitting in huts with women asking them: OK, if you had more income, how would you use it? If you had better farming output here, what would happen to your children? And you learn so much by actually sitting with folks in a humble, mindful way and just listening. And no one ever told that story about Bill and Melinda because the impression was always, like, data analytics.

So then this movement got a name and it got these wonderful leaders, and I found that they picked up on the quantitative analytics part but they didn’t really, in all honesty, do the I’m going go sit in—if I’m trying to improve education, I’m going to sit with kids at Harlem Village Academies and ask them how they learn, and ask them what their life is like, and explain and understand that, oh, this kid is late every day because they’re taking their, you know, toddler sister to school and trying to make breakfast and do all this before they even show up. And I find that if you’re going to be in service of humanity and these types of social causes, like, actually listening to people and living in their shoes in some way is an important balancing act to that.

And then, frankly, this whole Sam Bankman-Fried thing has just—(laughter)—taken those words and made it absurd, because it never occurred to me that the purpose of that was, like, all this bad behavior to just amass a lot of money so you could somehow give it away. That’s kind of goofy. I don’t know that I even think of that as effective altruism at all. I think that’s a warped construct. So, anyway.

FROMAN: Thanks.

SHAH: But thank you so much for asking that—(laughter)—because I have wanted to talk about it.

FROMAN: Let me get—let me see if I can get to the back. There’s right over there by the pillar. Here comes a microphone.

Q: Hi. I’m Sabeen Dhanani. I’m the deputy director for digital technology at USAID. And I actually joined—

SHAH: Wonderful! (Applause.)

Q: Well, thanks. I think—yeah, I don’t know if there’s others. But I actually joined USAID to work in the Global Development Lab, which I would agree with one of your big bets. But as you know, the lab no longer exists. And so for those of us who work in government and want to make big bets, or want to invest or convince our leaders to make big investments, how do you reconcile that with, you know, a change in administration, a change in leadership can really set back a lot of the good work that we’re trying to do or the progress that we’re trying to make?

SHAH: Well, first, thank you for your service. Thanks for doing what you do. I learned over time that 11,000 colleagues at USAID when I was there, that the spirit of service and the commitment to really reflect America’s best values really came through our team in a really powerful way. So thank you.

I’d say, you know, the lab you mentioned was something we were able to create during the Obama administration, and we had bipartisan support. It got funded. It was—it was written into statute. So that was very exciting. And it enabled us, by the way, to build that data system in Liberia during the Ebola crisis. We wouldn’t have been able to do it had we not had the lab already in place. So it was very exciting. And I totally was saddened when, you know, it basically went away during the Trump administration.

I think that’s a challenge of working in government, right, is that things get built and then things get taken down, and other things get built and get taken down, and don’t often persist. But some things do persist. And I look back even on my tenure. Mike and I worked very hard to pass something called the Global Food Security Act, which really did reestablish America as the world’s leader in fighting hunger by investing in agriculture but also optimizing the way we delivered food assistance. That act has been reauthorized twice on a bipartisan basis, and was most recently leveraged and used to help support the implementation of the Black Sea Grain Initiative, which, of course, was an important part of addressing hunger caused by Russia’s invasion of Ukraine.

And so—and I—you know, there are so many other examples. Under President Bush, the PEPFAR program was created. This is the president’s emergency management program for AIDS relief. And I think 26 million people are alive and getting antiretrovirals today because of that effort. Now, shockingly, you know, it’s—(laughs)—I think this current Republican Congress might very well not fund PEPFAR for the first time in decades, which would be, you know, an insane outcome if it happens. But you got to just fight for these things. Sometimes they stick because the bipartisan politics is strong enough, and sometimes they don’t because people have different ideas.

FROMAN: And even Power Africa. They maintained it during the Trump administration and it continued beyond, right?

SHAH: Yeah. Yeah, exactly. The bill was called, I think, Electrify Africa, and it’s been reauthorized as well.

And I will say, though, there’s a chapter in the book called “Make It Personal,” and it’s about the work on food and hunger, and specifically an effort—you know, when I—when I arrived in Congress—or, when I arrived in D.C., I was very attached to my spreadsheets and the quantitative analysis. And there was a moment when the Tea Party won the Congress and there was a bill that would have zeroed out a lot of USAID programs, so I went up to Capitol Hill and testified that the Republican budget would kill 70,000 kids, and I detailed the programmatic consequences and had it broken down. It was all very accurate. But I got back to my desk and Tom Vilsack, who was the secretary of agriculture, called me and said: Raj, I was just with Speaker Boehner, and he’s offended by what you said. Like, he spent decades building a bipartisan coalition around the humanitarian work, and your comments were offensive. And it led to me going to see him, apologizing, getting a list of members that he asked me to get to know. And it, frankly, led to some profound and unique friendships, even with people like Senator Jim Inhofe and very conservative, faith-based Christian senators who, ultimately, are the reason the Global Food Security Act and Electrify Africa and some of the other efforts we pursued persisted long after Mike and I were kicked out. (Laughter.)

FROMAN: I think one of the most sort of poignant moments is when those friendships and alliances that you built led to them asking you to give the keynote address at the National Prayer Breakfast, which is a major event in Washington of—for the Christian prayer groups up on Capitol Hill, as somebody who was raised—

SHAH: Hindu.

FROMAN: —Hindu. And they had you up there giving the Prayer Breakfast keynote, and it was because you developed these incredible relationships with those prayer groups.

SHAH: Yeah. I mean, I was actually part of the Senate Prayer Breakfast for a number of years, and I—that one I was very nervous about, as you remember.

FROMAN: I remember. I remember.

SHAH: (Laughs.) As you remember. And—but you know, ultimately, those settings were less about a particular theology and more about people getting to know the values that brought you to Washington.

And so Jim Inhofe’s a great example. I mean, he’s the reason I was asked to give that speech. And you know, he had once taken a snowball onto the floor of the Congress, Senate floor, to explain that global warming wasn’t a huge problem because it was very cold outside. (Laughter.) But when he and I traveled through Africa together, and farmers would say in Ethiopia, you know, it’s hotter and it’s drier every year, and there’s less food for my family and for my community, he led the charge to get the Global Food Security Act passed and to expand those programs in resilient, climate-smart agriculture. So I think—and he’s adopted a lovely young woman from Ethiopia. And you know, you’ve got to get to know people personally before you make too many judgments about what’s possible and what isn’t, and what—why people are there.

FROMAN: Exactly.

Naima and then the gentleman across the way. All the way—all the way in the back. I’m sorry.

Q: Thanks a lot. I am Naima Green-Riley. I am an assistant professor at Princeton.

I am so, you know, in awe and I really respect your commitment to serving not only, you know, our country, but really people all over the world. And what I’m curious about is whether or not you think that the USAID budget should be increased and if that’s a possibility. When you ask people in the U.S. how much is the aid budget, they usually think it’s a lot bigger than 1 percent, right? And unfortunately, I think that many people in our country aren’t necessarily interested in doing more. And so I’m just curious about your thoughts.

SHAH: Yeah. America has, in my view, not done enough to really make sure that our foreign policy is characterized by our understanding that expanding dignity to everybody is a core part of our national security over the long term. And so as a result, the USAID budget, when I ran it, was maybe 23 (billion dollars), 25 billion (dollars), around there. Today, it’s a little big higher. Core programs like Feed the Future, which we authorized in—I think at 1.1 billion (dollars), today are authorized at 1.2 billion (dollars) but funded at 1 (billion dollars), you know? (Laughs.) And you’re, like, it’s been ten years, and you take these super results-oriented programs that are so relevant right now, and you see they’ve been stuck.

The net effect of all that is on a—on a global basis, official development assistance to Africa has gone down each of the last two years by about 9 percent at a time when, for really the first time since World War II, we have developing countries growing at a rate that’s not significantly faster than wealthy economies, and so the natural convergence that happens around human development is going the other way. And what going the other way means is, you know, more kids dying of malaria, not less; fewer people getting electricity, not more; more poverty and more unwinding of the Sustainable Development Goals.

So, yeah, it’s a pretty—it’s a pretty difficult time right now, and it’s a time a little bit like 1998, 1999 that calls for a genuine political reassessment of, are we all in this together? Or was it like COVID, where rich countries put 30 percent of their GDP into creating a floor under their economies, developing countries put 2 percent of their GDP into doing the same? And today, if you go to—I was in Kenya a few weeks ago. Sixty-two cents on the public dollar is used for debt repayment. And with what’s left, they’re supposed to expand school meals for kids, and keep health programs running, and pay school fees for girls, and then invest in greening their economy and get off of heavy fuel oil—which is a primary source—and not build a coal plant that China has financed. Like—(laughs)—it’s just not going to happen unless we rethink the deal.

And so, sorry, I don’t mean to be too pessimistic, but, yes, I think America should do a lot more. I think America should mobilize Western partners to do a lot more.

Just to put it in stark relief, President Xi just had a tremendous number of leaders from around the world at his Belt (and) Road Initiative Summit and announced a hundred-billion-dollar recapitalization of a series of Chinese lending institutions to provide finance in developing economies. America’s done nothing of that sort over the last ten years. And so, you know, we’re going to lose the future on this issue in a pretty profound way if we don’t rethink what’s possible and get creative about making it happen.

FROMAN: The gentleman all the way in the back there. Thank you.

Q: Thank you. My name is Aaron Mertz. I’m the founding director of the Aspen Institute’s Science and Society Program.

SHAH: Oh, good.

Q: Could you either—and this stems from the previous question—could you either correct or offer insights into a trend that my colleagues and I have observed, which is around the time of pandemic onset we saw a lot of interest and support from American philanthropy abroad, and your foundation remains—and the Gates Foundation as well—good examples of that. But what we’re finding now is that American philanthropists Are not as interested in causes abroad, but rather in focusing on things very local, very American, very American community-based. Could you offer any insights into that trend and possible ways that we could excite American philanthropists to consider and support—

SHAH: Abroad?

Q: Abroad.

SHAH: Yeah. Well, just a few numbers, just to put it in perspective. I think philanthropy writ large is about $810 billion a year.

FROMAN: American philanthropy?

SHAH: American philanthropy is about $810 billion a year. Of that, the vast majority is—you know, is what you’d expect. It’s sort of giving to your alma mater, giving to your local communities, giving to museums, anything that qualifies for certain tax benefits and gets characterized as that. The amount of sort of structured giving that is focused on social inequity is much, much, much smaller. Some argue it’s somewhere between 50 (billion dollars) and a hundred billion (dollars) of all that. And then, within that, the amount that goes abroad is probably 10 (billion dollars) or 15 billion (dollars) at the most, and half of that comes from one institution, the Gates Foundation.

So I think—I think it’s—just to put that in—and by the way, I’m not against giving to your alma mater because, you know, go blue, for those that are—(laughter)—from Michigan. But I just want to put it in perspective. It’s much smaller than people think.

I’ve always felt philanthropists, when they see how efficient it can be to help people by doing some of this giving abroad, they get more excited to do it. The thing that has held people back is the belief that it’s so far away, and can it work, is it going to be corruption and stolen and inefficient. So you have to go the extra mile to demonstrate the impacts.

An institution—just one example—that has gone that extra mile is the Vaccine Alliance. And I write two chapters in the book about establishing that effort with Bill and Melinda Gates and with the U.S. government, all these partners. And you know, they can say definitively over twenty years that effort has immunized 980 million kids and saved 16 million child lives. And you know, I’ve sat in families’ homes where they’ve lost a two-year-old or a three-year-old to a very simple, easily-preventable disease, and knowing that you’ve saved 16 million child lives is, you know, so powerful, and people should now want to give to that.

But I find for these big international things you have to be much more disciplined about connecting to the results because people are so skeptical that it’s possible to be successful.

FROMAN: Right here. Chloe.

Q: Chloe Demrovsky, DRI International. Thank you so much for being with us tonight.

So, to cap off, you know, what has been such a personal conversation, you’ve been in leadership positions and in a spotlight for a very long time. So what do you do to make sure that your thinking is still being challenged and that your blind spots are being checked by the people around you? Thank you.

SHAH: Gosh, that’s a good question.

FROMAN: I know his children, so that’s what I’m—(laughter).

SHAH: Yeah, it helps if you live in my home. (Laughter.) There’s a natural humility that is instilled in one in that setting.

FROMAN: Indeed. (Laughs.)

SHAH: But in reality, you know, I think it’s actually a great question. And I’d say very practically I put on my board at the Rockefeller Foundation people I, like, really, really respect. So, as a result, you know, even if I could kind of cut corners and sweet talk my way through things, the people that I’ve put on that board are literally the folks I most admire, and I want—I want them to hold me to a very, very high standard. So that’s a structural answer in the current role.

In other settings, you know, during the Ebola crisis in our effort to design this social-impact bond for vaccines and immunization, I benefited from having a few people in my life who I got to know early in my career and who always seemed to kind of jump in and help when things are tough. And I’ve called them—in different settings we call them out-of-the-box groups, but it’s actually just a handful of people that do it for me. So it’s almost—one is a person named Rick Klausner, who I learned a lot from when I was at Gates Foundation. But you know, during COVID, for example, we were trying to figure out what to do in the United States. We ended up focusing on building out the antigen-testing infrastructure in America. But Rick helped put a little group together and said, look, let’s get together every week and just make sure you’re getting data and information from other sources and not just the people you’re interacting with day to day. That proved essential on Ebola, it proved essential on COVID, and it actually proves essential on all the things we do because it makes you more aware of things you’re not thinking about.

FROMAN: All right.

SHAH: And, I would just add—and those are people who have been challenging my thinking, you know, when I worked for them twenty-five years ago, so there’s no, like, sense of, oh, you’re in a position of authority, you know? (Laughs.) They just are, like, you either know what you’re talking about or you don’t. Right now, you sound like you don’t know what you’re talking about. (Laughter.)

FROMAN: The gentleman there. Yes.

Q: Hi. Jesse Burdick. I work for the U.S. Marine Corps. Thanks for your comments tonight. Really appreciate it.

So there’s also another side of the coin with providing aid. There’s a challenge in enabling corrupt powerbrokers and creating dependencies. I think Haiti’s an example. Afghanistan’s another example. And we have a tendency as a country to focus on the those crisis moments, and we inject a lot of funding during the crisis, and then it falls off over time. How has your thinking changed over the course of your career in how to create sustainable, lasting impact where we just don’t, you know, create issues in that crisis moment?

SHAH: Yeah. Well, first, thank you for your service. And you know, we’ve—Mike and I have worked a lot, and presumably you have as well, on Afghanistan and other crisis settings like that.

I think what I have come to learn is unless you have local partners wo are really committed to the outcome, you know, which will often require them to be quite selfless and vigilant along the way, you’re not going to be able to be successful. And so spending more time finding, nurturing, having relationships with those types of local leaders is actually quite critical.

You know, Afghanistan’s a great example. I think for the total cost of the war, about 2 percent of the total cost was developmental expenditure to build schools, roads, energy systems. You know, 8 million girls were in school because of American commitment. But all of that was within that 2 percent. You know, what really drove extraordinarily corrupt behavior were large-scale contracting, right—and the people who had large scale in a war is DOD—(laughs)—and a number of other country partners, and you know, I think it’s probably well known, but you know, the way intelligence has worked on behalf of our country and many others for many decades. And so I struggle with the—like, you can only be so vigilant on building schools and making sure there isn’t what people would call leakage in that—(laughs)—in that exercise, when with another side of U.S. presence you’re using money and cash in a much more short-term, strategic manner to acquire certain outcomes.

And so I personally think, especially in those conflict settings, we need a much better sort of whole-of-government approach to managing how we do this work. And again, it goes back to the reality that America wildly underinvests in development and humanitarian affairs as part of those exercises.

FROMAN: Last question. Heather.

Q: Thanks. I’m Heather Hwalek from the Bill and Melinda Gates Foundation.

FROMAN: Ah! (Applause.)

Q: One thing that, Raj, you might have heard Bill and Melinda talk about a while ago is being impatient optimists. And we don’t talk about that as much anymore under the recognition that sometimes change takes a lot of time and maybe patience is key, even though the problems feel very urgent. My question to you is, what helps you make a decision on when to wait it out and when to change course?

SHAH: Well, I try to articulate in the book a sort of methodology for approaching big bets that start with setting big, bold aspirations, but then doing a lot of listening, learning, and analysis, and finding those innovations or solutions that can make that goal viable. Sometimes you find solutions that make that goal viable very quickly, but many times you’re searching for solutions that’ll make it viable over decades. And so that’s—you know, that’s how I’d urge you to think about it.

The other two components of making big bets work, in my mind, is really investing in the unlikely partnerships that are required to sustain these things. In government, it was—it was left-right, but often it’s public-private. And I’ve been struck—I’m still stuck today—in my career by how much mistrust there is across different sectors of the economy in doing these kinds of efforts. And I think you all, because you are a mixed group—(laughs)—have the ability to be the kind of venue that overcomes that and starts to build more trust.

And then the final piece is really—is really measuring results, as we talked about, and being true to them over the long term, because you can’t stay focused on something if you can’t understand how you’re performing and stick with it.

A lot of those characteristics I call in the book big bets, but they’re actually delineated nicely in some letters between John D. Rockefeller Sr. and his philanthropic advisor at the turn of the last century, a gentleman named Frederick Gates. And they called it scientific philanthropy back then. And when Bill and Melinda started their work, they actually studied the history of that exercise and I think have updated it in a pretty dramatic and super-effective way.

FROMAN: So I want to conclude with two things.

One, couple decades ago Joe Nye—many of you know, professor at Harvard—wrote an article about tri-sector athletes; people who had succeeded in government, in business, and in philanthropy. And Raj is one of those rare tri-sector athletes, and we’re privileged to have him here.

And secondly, I think you got a sense from this somebody who combines the intellectual rigor, the values, the passion all together, it’s a whole package.

And we’re so pleased to have you. Thank you for spending time. He’s agreed—

SHAH: Thank you. Thank you very much. (Applause, laughs.)

FROMAN: There are—there are books outside for each of you and Raj has agreed to stick around for a little while to sign them. Thanks very much for joining the Term Member Conference and we’ll see you all soon.

SHAH: Thank you. (Applause.)


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