Bloomberg Audio Studios, podcasts, radio news. The fact is China has cutting edge AI labs. There are only six months behind the frontier in the US. And if they get a really powerful model that can do cyber hacking, for example, and they put it in their systems and their systems are open source, it's game over. This is just going to proliferate over all around the world. So that is a very bad outcome. So let's try and be creative about at least trying with China to talk to them.
You're going to need both sides to slow down. But my point about the discussion in China, which I've came into touch with when I was there, is that there is at least a discussion and openness to think about safety. The door is opened, just a crack, and so it's up to American negotiators to go there and try and give it a nudge and open it more.
I'm Stephanie Lander's head of Government and Economics at Bloomberg, and this is Trumpnomics, the podcast that looks at the economic world of Donald Trump, how he's already shaking up
the global economy. Modern Earth is going to happen next last week we looked ahead to this week's summit between the US and Chinese presidents and recording this on Tuesday, May twelfth, and by the time you listen to this, President Trump should already have touched down in Beijing ready for what feels like a high stakes encounter on Thursday with President Chi Jinping. Even though the expectations for concrete policy outcomes, as we heard last week, are firmly under
control now. Along with trade, one of the big issues hanging over that conversation will be US China competition in advanced technologies and the race to dominate AI. Tesla's Elon Musk and Apples. Tim Cook are in the group of top executives Trump decided to invite with him to China, and there is some mild speculation that President Trump might further loosen the export controls on US cutting edge semiconductors
for Chinese companies. But the safety of this race, protecting the world from the misuse of these increasingly powerful tools both countries are now capable of producing, well, that doesn't seem to be high on the agenda. This week's guest on Trumpnomics thinks that is a big and potentially dangerous missed opportunity. Sebastian Mallaby is a senior fellow at the Council and Farm Relations, author of multiple award winning books, and most recently of The Infinity Machine. Demisseivis Deep Mind
and the Quest for Superintelligence. I sat down with him in our New York studio a few weeks ago now to talk about his new book, The Future of AI and the Role of China, which he has recently visited. I think you'll find what he had to say fascinating, whether hopeful or pretty alarming. Well, i'lli that to you. Sebastian. Welcome, Thank you very much for being here in the flesh in the New York studio, which is always fun. There's so many themes coming out of your book, The Infinity Machine.
I wanted to start with something that we talk about a fair bit on Trumpanomics, which is the impact of AI on the economy, society, I guess especially jobs. But I think given the themes of the book, and I know things that you've been thinking about since it came out, will move on fairly quickly to the question of safety. How worried would we be about the pace of the improvement in AI's capacity, but also how likely is it that governments or anyone else will be able to bend
it to their will? Humanities will So first thing first, we had a conversation with darren Os and Moglu about it recently. I mean economists. I've noticed they tend to divide into those who say, AI is going to have a very radical impact on business models, on the economy, on society, but that's going to take a long time and we will have time to prepare. There's another group who say, no, no, this is all going to happen
much sooner, but it will probably be less seismic. You had all these years researching this book really up close, looking at the evolution of this technology. What's your best guess on that?
Yeah. I think the second view, which is that there'll be an explosion in the power of the models and it will immediately affect the economy, is put about by the technologists who are just focused on the algorithms and on the models and what the base model can do,
and it completely underestimates the rollout challenges. If you think about this idea of recursive self improvement, which preoccupies a lot of people in Silicon Valley, once you've got a machine that is good enough to write the code for the next iteration of the machine, and then it recursively self improves. It'll just explode upwards. And that is true
so far as the computer science goes. It's not remotely true as far as actually applying it to the real economy goes, because you need to build the computing capacity to serve those models. So you need all the chips, and there's a shortage of chips. You then need all the power to fuel all those chips. The people who are in the center of this a talking about having to launch data centers in space. That just gives you a sense of how difficult this is going to be.
And that's not to say anything about the institutional frictions of integrating AI into say a law firm or a bank or whatever. I mean, that's a non trivial challenge. You've got to get all your clients to sign off on the confidentiality of their information and so forth. So I do think that rolling it out such that it makes a difference in terms of job displacement or productivity is more kind of a decade plus than it is a kind of three to five year thing.
And on your point about the exponential improvement, I guess the other thing and the colleagues who are much more focused on this than the economics team have also said this to me that when you think about sort of pie chart of all the kinds of time asks that humans do that are part of the workplace, that exponential improvement is really only in a certain subset of those tasks, and there are other areas where they're really quite far behind that are quite important for jobs. Yeah.
Another way of saying the same thing is that there are sectors which are probably going to be quite difficult to disrupt, and the public sector is pretty slow at adopting tech, and that's a pretty big chunk of some economies, right construction, healthcare. There's a whole slew of economic sectors which don't seem to be very right for disruption at all. And then the ones that are right for disruption the archetypical people working on a screen doing knowledge work and analysis,
which is very similar to what AI does. As I say, you've got to get the customers okay with it, and that's quite a challenge.
Railways is the example that's often used, and it takes a long time for business models to adapt to that. If I think of something like the smartphone, which is extraordinarily new by historical standards, less than a decade old, day to day, all of us have been transformed by that. So is it comparable to that that it's not necessarily having a big change on the economy or on productivity, but day to day we're all being affected really quite quickly.
Yeah. I think there's a lot of truth of that. That people will be using it. They already are using it. It'll occupy quite a lot of their headspace, but it's not necessarily making them more productive. One of my favorite stories about the anthropic agent agentic system, which was out in January of this year. This is before Mythos, but the earlier one. People got very excited about, Oh, this agent can organize my email, it can create a bespoke
new summary for me in the morning. And so a friend of mine was implementing all these productivity enhancing hacks, and finally, after doing about four of these, she said to the agent, would you please now sync up my calendar such that you check that the train timetables on average are not too late, or some sort of extra productivity thing to which Anthropic answered or Claude answered, Are you sure you're not just giving me this taskause you're
trying to avoid your own work, you know? So, I mean, I do think there's a difference between the excitement around it and the adoption of it to actually make you more productive. And then even in science, Alpha fold, which was the deep mind system published in twenty twenty end of twenty twenty for which de mister Sabis got the Nobel Prize, which unraveled protein folding. This was said at the time to be the breakthrough for faster drug discovery,
and it probably will be over the longer term. But now we're six years later, there isn't a single drug that's been discovered thanks to alpha fold. Not to say that there hasn't been progressed, because now there's a much more sophisticated build out of alpha fold, with other aspects of the drug discovery process being automated by AI. So I think on a kind of ten to fifteen year horizon, yes it will massively accelerate drug discovery, but on a six year horizon hasn't.
That's a really interesting example. There is a wonderful one of the good documentaries about that whole period. It's almost a sort of companion to your book or a part of your book. But there's a wonderful bit of footage where they have decided to just identify all of the proteins and then and just put them onto the Internet
and have them allow researchers anywhere to access them. And they're watching in real time the uploads from all over the world on a map of the world, and it's very moving because you're sort of seeing in India and Africa or all these researchers a lot more than they thought in the space of a few hours uploading all this information. But it's fascinating to hear that they have not actually produced any new drugs on the back of it.
I mean that goes to something and we move into the broader sort of policy and safety territory a little bit.
But Darrenus and Moglu was on the show recently and I was checking in with him because we talked to him over time on this very issue, and his focus and his advice to governments had been to think about out you can't alter the nature of the technology, but you can change the way it's adopted, and that it was adoption was going to be the main thing that determined how jobs were affected, whether you're enhancing productivity of
humans or merely just allowing businesses to replace humans. I later have a conversation with Jason Furman about that, the former head of the Council of Economic Advisors, and he thought that was just completely wishful thinking that you can't tilt the technology even to that extent that you can encourage it to be more complementary to labor as opposed to just substituting for labor. So what do you think about that?
You know, I think in a race dynamic where you have multiple labs in multiple countries producing this stuff, the idea that you're going to control all of them in some direction that makes the technology complementary to labor, not replacing, does strike me. I'm kind of on Jason's side on that.
The other thing to say is that the labs are going to create what the consumers want, and if the consumer's meaning in some cases the business consumers, the enterprises have an opportunity to replace labor, they want to take that, and if they don't, their competitors and some other country will, And so that the laws of competition and capitalism, I think are very much on Jason's side.
I think part of that is this dynamic which is also very relevant to the book of the race with other countries, particularly the race with China. But if you're a country, if you're the UK for example, or could be any European country that's not necessarily going to be at the forefront of the technology, but clearly does want to be at the forefront or close to the front in adoption in a way that also helps the economy,
helps their workers. Is there any advice from your book or from the research that you've done that you would have for government who are trying to do that?
Yeah, very much. I mean, I think there is a lot of commentators, especially in Britain, draw exactly the wrong lesson about the story of deep Mind. So the classic complaint is deep Mind is a British company founded in London, and main founder is British. She's very patriotic about it. He refuses to move to Silicon Valley, and it turns out to be the most consequential sort of pioneer AI
lab in the world. That's amazing, But these idiots sell it to Google in twenty fourteen and it becomes a subsidiary of an American company, and that's terrible, and I think that's wrongheaded in the sense that really the sale to Google by deep Mind in twenty fourteen was a trick, one could say, to get the American parent to pour nearly a billion dollars a year of research and development money into London, with enormous spill of effects for the tech ecosystem in London. So I think that's a win
for Britain. The company stayed in London, the founder stayed in London. That's great for Britain. What was terrible for Britain, and about which Britain should beat itself up mightily, is that My book also tells the story of deep Mind trying to help the National Health Service, and both Demis's mother trained as as a nurse, his co founder, me Stuffer Sulliman, his mother trained as a nurse. For this reason,
they really wanted to help the NHS. They were willing to do it pro bono for the first five years, and they started helping and rolling out AI algorithms that recognized early signs of blindness, early signs of cancer. This was just super positive, helpful stuff that was going to save the NHS time and help h based patients, and it got blown up by a stupid backlash over privacy or privacy depending on which side of the Atlantic youuron, which I mean, I really kicked the tires on this.
The complaint about leaks of patient data were fictitious and so Britain because of this political climate of suspicion of tech, particularly suspicion of the subsidiary of the American tech company, created such a backlash against this collaboration between the NHS and deep Mind that both the doctors and the computer scientists at deep Mind got cold feet and the collaboration collapsed, and Britain could have been really the cutting edge country in the world on medical AI and they blew it.
Putting together the two pieces of what you said, they are related because one of the reasons that there was a lack of trust was the foreign ownership, the US ownership, and particularly the association with major tech companies in Silicon Valley who have not necessarily earned our trust. Many people would say, over the last twenty years, they've made promises about privacy, about sharing of information that have been broken.
Every single tech company has broken them, including Google. They have changed their terms on which even they were developing AI. In different cases there were promises even that had been built originally into the deep Mind Charter around safety and other things, which then got overridden as they needed just
more and more money from the parents. So it seems to me, yes, in principle it would have been great to have the UK on the cutting edge, but also completely understandable that having given tech companies a benefit of the doubt of various other times and rather regressed that they wouldn't want to do it in the case of this very sensitive subject.
Yeah, that's all true. At the same time, it would have been nice if the critics looked at the specifics or what was going on in this collaboration between deep Mind and the National Health Service, and if they'd look more carefully the specifics, they would have seen that this was positive for Britain. And if you just have a blanket view that anything to do with American tech is ipso facto a matter of suspicion because there's been bad behavior in other instances, I'm not in favor of that.
I think that Britain did miss an opportunity there, but I think the issue that arises out of this is that it's in the interest of the tech companies, especially now with this super powerful technology AI, to get governance right, to be proactive in inviting governments with democratic legitimacy to
hold sway over the technology. Because if you just take it unto yourself as a youngish startup and you say this is how healthcare is going to evolve, I'm doing it, it's not legitimate and people won't accept it.
I mean, some of this gets to that sort of broader question of our degree of control and also the degree to which any of the people involved in this can shape the outcome in line with what they themselves want when they're in the competitive context. You have a very striking phrase in your book towards the end, as inventors dream of shaping the technology that they create, often the technology shapes them. The technology plus the business, political,
and geopolitical currents that it unleashes. There's a bit in your book where it starts to feel I felt like it's some of the historical analysis that you see of the start of World War One. You have everyone individually in your story had potentially the right incentives. Was very focused on being careful, identifying risks ahead of time, managing them ahead of time, not inflicting things on society before we were ready for them. The end result has not been that at all.
That's right. The central character in my book, demister Sabez, is a good person, has good values, but whether he can actually do good even if he is good, is the central conundrum. And he really wanted to make things safer. That's why he fought a secret three year war with Google trying to force them to put more safety guardrails around AI. That's why he suggested to Rishie Sunak the idea of a Bletchley Park AI safety conference, which happened
in late twenty twenty three. So he does care about safety, but when other labs are willing to go fast, there isn't much a single AI leader can do. And what, of course that points you to is that the collective action problem needs to be solved by government.
But what we've seen in a lot of these cases is the researchers involve the companies involved say, yes, we think it's very important to have a collective agreement. But if a government comes along with concrete rules, whether it's the I'm sure you probably feel strong about but the EU Internet Safety Rules or AI Safety rules. President Biden had a rather different set of rules. There tends to be the response, how could governments possibly understand this technology?
We want some agreement, but not this one. So how do you solve that?
Yeah, look, you're right that you know at the moment there is lots of state level initiatives to regulate AI in the US, and for example, open AI is actually funding the opposition to those regulatory efforts. So there is hypocrisy sometimes.
And after the fact they often say yes, actually it's quite good and we've adopted it, but they never want it in advance.
Yeah. On the other hand, there is a counterexample, which is that in the Biden administration when they set up the AI Safety Institute, and it included a mandatory requirement for all the frontier labs to submit their models to this institute for review. The institute didn't have a veto, which it should have done in my view, but still it was mandatory to submit. And I've spoken to the Biden people who created that executive order and they say
that there was zero resistance from the labs. All the labs work more in the mode of saying, look, you need to be aware something's coming down the pike. It's very powerful. You need to get ahead of it. And when they did get ahead of it, or they started to at least move on it, there wasn't resistance. That's what the Biden people say, So I think it depends. It's a bit like with Google and deep Mind and the National Health Service in Britain. There can be good
instances of technology company behavior. And again, if we paint this is true of anything, like if you paint the Chinese as uniformly bad, you can never collaborate with them on anything, and that's actually a bad outcome in itself. Same with the tech companies.
Mentioned China in a US context and the group of companies that you're writing about in your book. I could totally see how you have a kind of circle of trust among those companies and shared incentives. But the thing that I think God in the way then and gets in the way often in practice, is this fear of China the outsider, which is not necessarily part of this system.
And if you want to not be applying the breaks, if you want to not have these kind of control, all you need to say is what China is going to get ahead of us.
Yeah. So what I think about that is that the West really needs to remember what happened in the Cold War. What happened in the Cold War in the nineteen fifties was you had simultaneously very moments of extreme tension, the Hungarian uprising, Soviet tanks going to Hungary. You have the Sewis Canal crisis at the same time, which prompts the Soviets to actually threaten the West with nuclear attack because of the Sewis intervention was viewed as imperiodists by the Russians.
And simultaneously with those events you had the creation of the IAEA, the Institute for Atomic Energy and forgetting them but basically counting all the nuclear material and trying to prevent the nuclear material from going into loose nukes. Then in the sixties you get the Cuban missile crisis, enormously tense moment, and six years later you get the Nuclear Non Proliferation Treaty. So you can do what Reagan said, which is sort of trustpor verified, do arms control, even
if there is competition with China. And when I went to China in March, because my book came out there first and spent eight days talking to AI leaders, both in academia and in the private sector. It was stunning to me how often they raised the subject of safety spontaneously. They would say, these agents are becoming powerful. We have a window of opportunity before they are too powerful to make them aligned with human incentives. The government should be
warning people about dangers with these models. And in fact, while I was there, open Claw, this open source agent, which is not a good thing to put in your computer because it can do whatever it wants with all of the data that was being downloaded by Chinese mums and pups onto their laptops, and the government warned them not to. So the idea that China doesn't care about safety is wrong. There is a debate there, and they love regulating the Internet. That's what they're very good at
in China. So why wouldn't we talk to them and say, look to probably compete on a sort of grand geostrategic military level, but we don't want this technology leaking out all over the world and being used by terrorists, criminals, nonstate actors to crash the web and do cyber hacking and build a bio weapon. Nobody wants that.
You talked about the importance of trust earlier. I guess the question is have we got to a point with China where there is enough trust? I mean, the comparison with Russia is an interesting one, and that you could say in a sense that was a semi stable equilibrium. The trust to verify was one of the things that made it stable, but it was an established competition of superpowers.
China is a much more in flux. We've had administrations have quite different approaches to controlling or competing or working with China. A lot of people will even listen to what you've said about your trip and say yes, they discourage their own people from using these things, just as they have much tougher controls on social media with their own population, and they don't tend to be so worried when they export all these products to the rest of the world. So do you think there's trust.
That I think at a high level, what we should first get on the table is that the alternative to not even trying with China is that we just resign ourselves to having AI go viral all over the world with zero controls. The fact is China has cutting edge AI labs. There are only six months behind the frontier in the US. And if they get a really powerful model that can do cyber hacking, for example, and they put it in their systems, and their systems are open source,
it's game over. This is just going to proliferate over all around the world. So that is a very bad outcome. So let's try and be creative about at least trying with China to talk to them. And I think the notion that it was easier in the Cold War not really. I mean, look, Krischev was this sort of mercurial, unpredictable, semi stable l leader, and the Russians would go to the UN and take their shoes off and bang them
on the table. It was not an easy relationship in the sixties, right, and yet there was the nuclear non Proliferation Treatise. I really don't think we should give up.
Okay. So the latest example we've had, and we've discussed it while back on Trumponomics, is mythos And there's obviously been a care in the way that it's been introduced, controls on who can have it. I know you feel strongly about the difference between the sort of safety differences between open source and closed source technologies, talk us through how some of the stuff we've just been talking about, how that's been illuminated or not by the example of Mythos. Yeah.
So I think what's happened here is that whilst there was progress towards some AI safety regulation by governments in twenty twenty three and twenty twenty four, with national AI safety institutes being set up in multiple countries, a meeting of all those institutes in San Francisco in the fall of twenty twenty four, Once President Trump came into office, the whole system shifted towards acceleration, competition, and the momentum
for regulation diet even though the technology itself was speeding ahead. So now we find ourselves in twenty twenty six and what we all knew was going to happen has happened. There is a model that, if in the wrong hands, could be used to basically steal everybody's money from their online bank accounts, crash internet systems, control dams, and so forth.
Is a pretty scary prospect, and there is no government mechanism to stop that from leaking out, and there's no international mechanism because you need to do it internationally because this will be copied by the Chinese at some point, so it's a big problem. So Anthropics response to this is to say, there's no governmental governance, so we're going
to do private governance. We Anthropic, a five year old startup in San Francisco, are going to arrogate unto ourselves, the power to say these are the forty entities that will get access to this technology, and the rest of you, cyber peons, you're outside the castle, outside our protection, and you can just stuff it. I don't think that's a sustainable position for a private company to be in, to be the arbiter of who is safe on the Internet
and who isn't. But it's fort demurur. It's like there is no governance from the government, We're left with this private solution, which is not sustainable. So I think what this is a wake up call in terms of our earlier conversation is we don't have an alternative but to try and do something with China because they have these open source labs which are putting this AI into everybody's hands in a way that is totally not controlled. So it's not theoretical now that we might have an AI
that's dangerous. We have one that's dangerous. So I think mythos is a wake up call, and looking back at the history of AI in my book, there are other moments when private labs thought that they could do governance themselves. Open AI thought that it had a kind of for profit, nonprofit capitalist hybrid that would make it all safe. It didn't work. Deep Mind tried the same thing with Google. It didn't work. Basically, individual labs just can't deliver governance. It needs to be government.
If you think that the open source putting and putting open source technology into people's hands is probably the single most dangerous thing around at this level of technology, and China is still doing that despite, as you say, being so concerned about safety. Doesn't that suggest that there's the same dynamic operating in China that despite much talk and concern about safety, they are still hardwired to be moving ahead. Yeah.
I think there's a couple of things there. I mean, one is that any lab that is not right at the frontier can't compete by telling customers we've got the best technology. So they compete by saying my technology is more available to you. You can download it into your server and do whatever you want with it. And that's what Meta does in the US their open source, that's
what Mistra does in France they are open source. And that's what all the Chinese labs do because all of these players have in common the fact that they are not one of the big three anthropic open AI Google Deep Mind, and so this is just what followers do, and to stop them from doing that again you need some government action. And yes, the Chinese do have a
sort of race mentality. They do want to build AI as fast as possible because they're frightened the falling behind the West, and that's just the normal, natural, obvious thing for them to do. In the face of an American leadership which is also racing, you're going to need both sides to slow down. But my point about the discussion in China, which I've i came in to touch with when I was there, is that there is at least
a discussion and openness to think about safe. The door is opened, just a crack, and so it's up to American negotiators to go there and try and give it a nurge and open it more.
It's very striking to me the conversations about AI safety, the Bletchley summit that Chally Soon I had, there was a lot of focus on killer robots, existential threats, the sort of much darker potential scenarios, the elimination of humanity, all of those things, And there was a general feeling in the industry and even among some policymakers that the focus on that was to some extent counterproductive because it was distracting from the sort of short term things that
actually might cause us more harm and the impact of chatbots on children, all of those kind of things. It feels like we've now swung completely in favor of thinking about those things, those kind of social harms. Even today we had a devastating story about AI generated child porn and how that's making extremely difficult for investigators to to identify the true abuse of children and the footage and
just well, you we get very depressed reading it. But sort of ironic to me that just as we've been focused on those social harms, actually those slightly more existential harms have become all the risks have become much greater.
Yeah, I mean, I think that the existential risks are still there. And when I talked to Demissabis, as I did again this week, he still worries about that stuff both and by existential, what I really mean is two things. First is that bad guys get the technology and do something really catastrophic with it, build a bioweapon, do an enormous cyber attack which bloods a city by hacking a dam or something. Second, the machine itself goes rogue and
starts attacking humans. And I always thought while I was writing this book that that second thing was science fiction nonsense. The machines will be more intelligent than humans, but they don't have a motive to attack humans because not evolved to survive. Unlike humans, we're always watching out for the lion on the savannah because we want to pass on our DNA. Machines don't have DNA. But then I had this rather disturbing conversation with the computer scientist Jeffrey Hinton.
I went up to Toronto and sat in his kitchen for two hours, and he said to me, look, supposing you have an AI which is very powerful, but you're worried that the enemy AI will attack it. What are you going to do? You're going to tell your AI to defend itself if you see the attack coming, counterattack. Make sure you don't get killed. You've just given it a survival instinct. So now are you so comforted, Sebastian, do you think that AI won't have a survival instinct.
I think this existential stuff is real. It can be addressed by engineering what they call alignment into the models, where you're basically giving instructions to the system not to turn against humans. And that's an engineering challenge which I think properly is solvable, but you need time to solve it. And the thing about alignment is that what we really care about is aligning not necessarily today's model, but the the tougher, more dangerous, more powerful one which we might
have in six months or a year. And you can't invent the alignment of the future model until you've got it in the lab and now you're trying to align it. So the faster that progress goes, the less likely we are to do alignment. And that's another reason why we need government controls.
The government we have currently, as you pointed out, the US administration, and obviously it's the US government that's going to have the biggest influence by definition on this at the moment, is one that is not minded towards safety, is very focused on the arms race aspect with China.
Given what you've been saying about the pace of change, the fact that we already have potentially enormously damaging instruments that could be tomorrow in the wrong people's hands, is it going to really matter a lot to all of us that this administration, with those instincts was in power for at least the next two Yeah.
I think there's a real danger that will turn out to be decisive on the upside. It's interesting that with Mythos, the Treasury Secretory, Scott Vesant, immediately worried that this was going to be catastrophic for the stability of the banking system because bank accounts would be hacked, and he called in the heads of the big banks and told them to take it seriously. And he apparently inside the administration favored a nationalization of Mythos because he thought it was
so scary. So I think it's possible that as these models become more threatening, the Trump administration, which is flexible, one could say, might do the kind of u turn. It's done on the notion that you shouldn't intervene in other countries and start wars. It clearly forgot that principle, and so perhaps it will change on AI as well.
Even the instincts that we're seeing on that seem to be much more about protecting American banks and maybe UK banks. But to your point about Anthropic really controlling who has this, is it really realistic that you can be just picking and choosing countries that get this defense.
I didn't think it's realistic for Anthropic to continue to do this. Therefore, I think we have to hope that the sheer evidence of the disruptive power of the models eventually gets the US government to change his position, and then we could see a sort of norm cascade where other countries also change.
Well, you say in your book that you start off as an optimist on these things, and I hope that you've still managed to sustain a little bit of optimism. But Sebastian, thank you so much.
So nice to be with you.
Stephanie, thanks for listening to Trumponomics from Bloomberg. It was hosted by me Stephanie Flanders. I was joined by Sebastian Mallaby, Senior Fellow at the Council and Foreign Relations and author of The Infinity Machine. DeNisi Sibi's Deep Mind and the Quest for super Intelligence. Trumponomics was produced by Samasai and Moses and with help from Amy Keane, and sound design
was by Blake Maples and Kelly Garrett. To help others find this show and enjoy it, please rate and review it highly wherever you listen.