Reid riffs on Trump’s $100K visa fee, 3-day work weeks, and AI trust issues

0:00Some people in the tech community are saying this is going to destroy innovation. This is we are getting the best talent from all over the world to come here. If you haven't found something where the tool, where AI is useful to you in a serious way, then you haven't tried hard enough because it exists for everybody right now. Reid, great to be here today. Yes, in person in New York. Exactly. In this beautiful setting. So the tech world is abuzz because last week, President Trump announced that they were going to institute$100 ,000, perhaps annual. We don't know. some people thought annual, some people thought not, on H-1B visas.

0:36And as we all know, Microsoft, Amazon, Google, these big tech companies really rely on H-1B visas for a lot of their workers. And so some people in the tech community are saying this is going to destroy innovation. This is we are getting the best talent from all over the world to come here. Other people are saying these big tech companies, they can afford it. This is fantastic. This is a way to raise revenue but still have these employees here. What do you think about this new potential? We'll see it's an evolving topic. This potential idea of$100 ,000 fees on H-1B visas annually. Well, so this is a funny, and you know, here we are.

1:13So I'll share a first awkward moment, which is actually Trump's idea resembles an idea that I've been pitching for eight plus years. So it's like, okay right now i think you have to do the whole idea otherwise it's a disaster and it's the problem is only a part which is roughly the idea that i've been pitching is you should have unlimited h1bs you should impose an additional tax on them whether 100k once a yearly whatever is the right thing and you should make some um you know kind of provisions for for startup companies because the startup companies obviously can't afford the 100K or that sort of thing.

1:53So for startups, would it be cheaper than for the Amazons of the world? Yes. Because you absolutely want the talent. That is one of our great superpowers from the very founding of our country is immigration. You want that talent. And by the way, does it take an American job? It's like, no. If this person comes over here and does this high-paid job, because it should be there's a bunch of regulation about the fact that it has to be you know comparable salaries and all the rest does this high pay job then they're also um you know uh patronizing restaurants hiring accountants using dry cleaners like like you know hiring electricians renting you know apartments and all the rest and all of this stuff is actually adding to our economy that's why you want it here and frankly one of the challenges that you have to be careful about in this is that and you have to set the pricing right is you say well for the large multinationals, they can hire in other countries too.

2:47So you say, don't hire them here, hire them in a different country, then America loses all of that derivative revenue. So you actually want that immigration, you want the people here. But adding an extra tax is good for a couple reasons. One, is this seriously talent you can't get here? Because then you've actually got an economic incentive, and that's why unlimited, because it's like, well, hey, if I'm paying extra for it, I'm only going to pay for it if I can't hire it locally. But then we're bringing that talent in. We're having all that economics on our side. So unlimited H-1Bs, yes, a tax, 100K, whatever, figure it out.

3:24Is it 100K? Is it something else? It could simply be like an additional X percent payroll tax, in which case, by the way, you might even then be able to not make a special provision for startups because that may be within the economic envelope. If it was based on revenue, if it was based on something so that smaller companies, people who are starting out were paying less. Yes. Bigger companies were paying their fair share. And so what do you say to the people who are like, yeah, that's great innovation, but like this absolutely takes an American job. This is an American tech worker coder who would be working in that job.

3:53And you say, no, that job would be overseas anyway. What's the why doesn't that explanation work? Well, so the the for most of these companies, they're they're hiring. There's a quality bar they want to get to. But at that quality bar, they're more or less hire as many people as they can find. Right. So and there's all kinds of natural incentives why they would hire an American. Yeah. And the usual complaint is, oh, you're lowering the salaries because the immigrant will take the lower salary. Now, there's a bunch of different regulation about how to make that not happen, what you have to prove and all the rest of that within H1Bs.

4:29But that's part of the reason why, like, you know, I think it may even be 10 years ago when I started peddling an idea like this. It was like, look, if you just make it structurally more expensive for the companies, that will naturally play it out. And so any answer of, well, could you hire an American? It's like, well, if hiring an American is cheaper for me, I'll hire an American. So then you don't have to get the, well, does the regulation work? Am I actually really not lowering wages across it by bringing in people, et cetera, et cetera? And that's part of the reason why this portion of the idea of making it more expensive for companies is, I think, a good system.

5:07Gotcha. So ultimately agree with making it more expensive, but there has to be provisions for startups because we cannot disallow that ecosystem. And, by the way, you should make it unlimited. Right? And that's part of the dumb thing that exists right now is this lottery system and all the rest of it. Absolutely. Okay, so when we talk about AI, we often talk about programmers, developers, like those are the people who especially now can get some of the very early benefits of AI. And a new survey from Stack Overflow said that 84 % of software developers use AI or are going to use AI in creating their code, which is super positive.

5:42But 46 % have real concerns and don't trust the code that they're deploying. So half of them say they waste time debugging, they have to go check the code, they go get a second opinion. And so the question is, how do we bridge this gap from software developers using AI but not trusting it? Or do we not need to bridge it? Is it good to get a second opinion, to debug, to make sure to check the AI output? Or is this a problem that needs to be solved? Well, it's definitely not a problem that we need to focus on. So the baseline is it's good, not because there's necessarily saying AI code is buggy, but it's good to be diligent about what the outputs are and what you're doing and and how to do that and of course we're in the early stages of both this this you know in the in the classic the worst ai you're going to use is the ai you're using today and so it's and it's accelerating but also like look what is our new pattern of doing software development and how is the software development work relative to producing all different kinds of code.

6:50And this is one of the things is like some kinds of code you'd say, like, for example, if it's like, hey, I'm producing kind of the equivalent of a bunch of different scripts, I wouldn't be that wonkish about it unless there was mission critical in something. I'm producing something that is kind of infrastructure code about how the whole service is working. I'd be more wonkish about it, as you should even before AI in terms of how this works. So I don't think it's a big challenge. And I do think that one of the things that is probably the most important thing for developers to keep in mind is to say, because the natural thing for every skilled professional, a developer, a lawyer, a doctor, every skilled professional is to say, oh, I discovered one bad output, tool not ready.

7:39And you're like, nope, that's a bad way of putting it because the tool is constantly improving. And one of the things that I tell people pretty constantly is if you haven't found something where AI is useful to you in a serious way, not just, hey, what can I make from dinner from the ingredients in my fridge? Or can you craft me a sonnet for my friend's birthday or things, all of which are great. but like something that actually is in fact part of the way you're working and actually adds to your skill set and your capability then you haven't tried hard enough because it exists for everybody right now now and that's true for developers so the don't find an error wave it off but be constantly experimenting with okay how do i use this which are the things that work really well right now which are the ones that don't work well right well now but by the way even if they don't work right very well right now, then keep trying it in various ways and keep open in mind because it's going to be improving.

8:36I feel like so often we hold AI to the standard of 100 % when humans aren't 100%. And if we can use AI just to get better than where we are with humans, we're going to be seeing big improvement. And one place where you and I talk about this all the time, where AI can be enormously helpful is healthcare. care. So a recent study from John Hopkins found that when they were doing pre-surgery echocardiograms, this is what they do. They get a score so that they can see in the next 30 days, how likely is this patient to have complications, a stroke, problems from surgery? And their model right now spits out a number.

9:13They're able to understand, and they have 60 % accuracy as to what patients are going to have complications. They created a new model. They fused existing echocardiogram data with also the age, type of surgery, all this info about the patient. And they found that AI, and this was a study of 37 ,000 patients, AI can get it right 85 % of the time. So you're going from 60 % to 85%. And still some people are saying, you know, that's not good enough because we're only at 85%, even though there's this big delta. What do you think are sort of the ethical considerations that we're going to have to navigate, especially with something as critical as life and death?

9:53And how can we convince people that this step change improvement is worth it? Well, there's a couple of things where most people misunderstand medicine and a bunch of other things. So the first is a lot of this serious, when you're kind of like taking a drug about a serious condition or a surgery, it's a percentage game already. It's not zero, a hundred. You're already playing a percentage game. And even though like a doctor might say, well, I'm actually prescribing this medicine to you because this is the way we understand the biology to work. And this is the kind of the question around, you know, what we're trying to do.

10:30It's actually never a hundred percent. It's actually, we think this on a high percentage actually works in your condition and sometimes doesn't work for people. And maybe once we get to really deep precision medicine and genetics and a whole bunch of other things my guess is it won't get to 100 just improve the percentage so it's a percentages game so the fact that people misunderstand that because they do have exactly as you say it's like well humans are infallible it's like no that's not the way it works and by the way they're very very good and they're playing a percentage game on your behalf so the short answer is in this case you you need to say no no we need to deploy the thing that the numbers you know dictate now this gets to the second part of it, which is part of the progress of how medicine in specific works is people start trying things and they see if it works.

11:20And then if it works a percentage of the time, they go, oh, we've got something here. This could possibly be like, and then they start frequently, not always, sometimes they figure it out because of causal stuff in advance, but sometimes they figure out the causal stuff afterwards. That after they started going, oh, this works. And by the way, this is how the earliest medicine started working. It's like, oh, aspirin works. Right. Tylenol works too? Yes. Tylenol works too. Good. I think, you know, we all should be carrying around some Tylenol as part of our, you know, we're pro science. We understand.

11:53And so that percentages game is the thing that makes it work. And so people need to understand it's like, oh my God, you increased the percentages. Give me that. Right. And so give me the AI, even though we go, we don't understand, like, okay, it's making a prediction. We don't understand why it's making that prediction. But if that prediction is all the more accurate, then we'll do it. Now, we don't stop there. We go, of course, you should always try to increase the prediction, of course, but also go, okay, why is that? And then you go, great, we've got this thing. Why is it making, like, why is it, because then, by the way, we can begin to see disorder cases.

12:28Why is it identifying these people who we weren't identifying before as very important to keep in the hospital, treat again, bring in again more soon, etc. Why is that? What is the thing it's seeing that we're not seeing? And then that improves our science. So to make changes and improvements in any field, but especially in the medical field, you obviously need better technology. You need humans to come on board. You need the clinicians and practitioners to say like, oh, this is working. And then third is you have the legal and regulatory framework. and that's going to be an impediment here too.

13:02What do you think are the ways that we can get, you know, sort of the government, legal, all of those things on board so that we can integrate AI better? Well, it's one of the, I think there's two parts to how government regulation affects in this area. One part is a set of, you know, very good thoughts around, kind of like, okay, so does it work appropriately? Are you avoiding downsides? Are you being kind of appropriately inclusive and broad-minded across many different conditions or many different like young people, old people, multiple kind of racial characteristics, a bunch of other things, men, women, like let's cancel a whole bunch of science studies because they have the word women in them.

13:53It's like, that's a great idea. That's sarcastic in case anyone misunderstands that. And so that's good. The problem also is that we tend to be very influenced in our legal system by trial lawyers. And so the trial lawyers go, no, no, we should have this to a much higher standard and we should do it because that way we can try to impose essentially the trial lawyer tax on the whole system. And that one should be much more contained and regulated. And so, you know, like not allowed to infect what the regulations are, how it operates as much, not zero even in that case, but much less. And so you need to say, well, we should be able to deploy, we should experiment, as long as we're not, like we can demonstrate good evidence that But where we, like, for example, the classic problem with this is where we're going to save 10 new lives, but we're going to lose this other life that we didn't lose.

14:53It's like, yeah, but that's, that's, by the way, how medicine works too. So it's like, okay. And that should not be like, if you're saving the 10 and doing this in the right way, even if you lost a different life than you would have lost before, that's, that's how we progress on medicine. Absolutely. I mean, medicine is trial and error, and we're just getting better and better and building upon previous evidence to see how we can make it even better. This podcast is supported by Google. Hey, everyone. David here, one of the product leads for Google Gemini. If you dream it and describe it, VO3 and Gemini can help you bring it to life as a video, now with incredible sound effects, background noise, and even dialogue.

15:33Try it with the Google AI Pro Plan or get the highest access with the Ultra Plan. Sign up at Gemini.Google to get started and show us what you create.

16:07people think that this doesn't sound good. So I think there's two parts of it. It's one is, is this going to happen? And then also sort of what are the technical and cultural barriers? And I think the alternative, some people are saying, is that instead of a reduced work week for everyone, we actually might just have a bifurcated system where there's a lot of unemployment, especially entry-level roles, and then other folks are doing just fine with AI. What do you think about the three-day work week predictions and what are the barriers to getting there? So the first is, if you had to choose from a society perspective between a three-day work week and a bunch of people out of work, you choose the three-day work week.

16:46It's a really important kind of part of our social objective to actually make sure that a bunch of people actually, in fact, have employment, have things to do, have a sense of at least some purpose in the work. I mean, obviously, people say there's a deep purpose, great, even better to have deep purpose. But I feel like I have a role. I have a role in the organization side. I contribute, et cetera. I earn my money. And I think that's good to have. And I think there's various ways that people are worried about the cognitive industrial revolution that AI is bringing. They go, oh, shit, we're going to have a bunch of unemployment.

17:21And by the way, we'll have a lot of job transitions, which will involve unemployment at minimum in the job transitions and so forth. And we need to do things to solve that. and one of the benefits as you know is ai is a good tool for that whether you know the ai can you know help you figure out other work you can do ai can help you upskill reskill for that ai can help you do the work we just want to make sure we're deploying ai to help these job transitions too even as like what the work looks like what the job looks like now your average person says i don't want the job change i'm perfectly like i've been doing this for x years i'm perfectly happy it's like i don't want the work and it's like jobs change i understand that you know you got to think about it as like what you were was a horse and buggy driver and the cars are here now and like you you can say well but we should all run with horses horses and buggies like no actually in fact society's much better with cars that's what we're gonna do um and so like let's help you adjust you know as part of it now um that being said the question i actually don't think that um One is I don't think we're heading, I think we have a lot of job transition, but actually, in fact, I think that in a lot of cases, even though you have massive productivity increase with AI, just like you had massive productivity increases in the industrial age, I don't think that ends up with a systemic, well, people don't need to work anymore.

18:47I think that's way further out than most of the critics who are on that side think. And I'm not sure exactly when we might get there. I mean, it might be many lifetimes, right? Not just my lifetime, your lifetime, our children's lifetime, et cetera. So I'm uncertain about that prediction. Now, not 100 % uncertain. It's one of the things to think about and prepare for and whatnot. Now you get to the, okay, well, does that mean on the productivity we go to three to four day work weeks? Which, by the way, some societies have done in various ways. The Germans are an obvious example of that. Now, the problem that's brittle with that is that human beings divide into groups and we compete.

19:33And part of how we competition is this group is going to work a lot harder. so one of the things i think we see coming for european auto industry is the chinese auto industry and it's also coming of course for the american auto industry too because if they achieve much higher productivity and some of that's robotics and all the rest but they go well we're willing to work six days a week right we're willing to do 996 that's what we're doing then their industry can very well wipe out the other industries and so there's a kind of competitive landscape to this that's the underlying kind of point of view in this and we it's part of the progress of capitalism everything else that's part of like that competitive landscape is part of what happens now part of the reason why the germans could do that is they had a very well-tuned system of technology high quality like not just technology of the end product of the cars but also how they make it the the apprenticeship system but how they trained like deeply skilled you know people in doing it i think there's a bunch of organizational techniques that i think are good to uh learn from in the rest of the world which is you know how do you have you know kind of capital and management working alongside labor and train and transition and but what you have to understand and this is one of the things going to be very difficult for germany in the next 10 years difficult for the u.s is more for germany i think in this case is the competitive, like your competitive situation has suddenly increased.

21:05And all of a sudden then three-day work weeks, four-day work weeks don't work in that competitive situation. And I think this prediction of, well, we have this much work to do and now we have this much productivity. It's like, well, actually work always goes up. There's always competition. We're always generating productivity. So I don't think we're anywhere close to a three or four-day work week either. Okay, okay. So, I mean, economists have got this wrong forever. You know, 100 years ago, they predicted it in the U.S. As people's incomes went up, they would work less. And now in the U.S., we're in this sort of strange situation where actually the more you make, the more likely you are to work more.

21:42And folks at the lower end are actually fighting to work more. Some of them only are working 24 hours in either a gig economy or in an entry-level job. And they want to work more, but their employees actually won't give them that more time. And so they're sort of asking for more hours. And so I know you don't like predicting, especially more than two years because you think it's a fool's errand. But so if I had to say 10 years from now, would you predict that the economy that we're in now looks more like a three or four day work week or you don't think that's going to happen? I don't think it's going to happen.

22:13All right. So we are now going to shift from a more focus on tech to a focus on government and institutions. For our listeners who don't know, you launched the Trust in American Institutions Challenge with Lever for Change, which is all about rebuilding trust in important American institutions. Our government, hospitals, libraries, the media, like how can we ensure that we have a cohesive American electorate, consumers, etc. And the good news is that yesterday we announced the five finalists for the challenge. I'm going to get to them in a second, but I would love for you to talk about Lever for Change and why in particular you want to partner with them and their model for doing this.

22:56So unsurprising to many people who know me, we live in a networked age is my point of view. There's a lot of different things that come from that. Now, that also is how should we do philanthropy? Because when you think about like a serious problem in society and climate and anything else, you should be thinking, how do I have a potential network solution? Right. Because we have network challenges. We should have network solutions. And Lever4Change realizes this because it says, well, actually, in fact, let's layer in multiple layers of networks to help solve problems that otherwise might look at like, ah, is that solvable?

23:34You know, really, we need some new ideas on this, a bunch of other things. So it's like, okay, we use a RFP prize process to kind of go out to many, many different individuals and nonprofits and all say, here is a problem we're trying to solve. Then, because the money is serial, people then go, okay, I'm going to submit a proposal, request for a proposal. That's an RFP. And then you deploy two other networks, one network of experts that Lever for Change has through its excellent origins in MacArthur that are experts on all kinds of things. And it's not just the MacArthur genius people. It's many others in terms of how this operates to kind of look at these proposals, you know, kind of comment on them, rank them, score them on different things, say which things, you know, have high probability, which things have low probability, which might be challenges that are not there.

24:30Then you have another network of judges that kind of works through it and kind of says, OK, these are the ones that we get to. And you run through this process by which you get to, you know, kind of semifinalists and finalists and then allocation of money for doing this. And it's a call from a network of ideas, by the way, just like networks of entrepreneurs and Silicon Valley and all else, networks of experts and kind of decision makers, which investors and venture capital kind of plays into. Now they tie it to the capital, then kind of the judging of it and making it happen. But it doesn't actually even just end there in terms of having networks.

25:06Because then what you've done is you've just built massive network databases, including of interesting kind of individuals and nonprofits with projects. And Leverever Change has a whole bunch of funders who are specifically interested. And you go, well, actually, in fact, this one won this particular challenge. But what this nonprofit is doing over here is of exact interest to this funder. So part of the benefit to participating in this network to the nonprofit is Lever for Change puts actually as much energy into routing every good project to any funder or any other kind of thing. They go, oh, this one is actually a good match for this.

25:45And that's, again, a network property. And so that's why Lever for Change, which has just been doing amazing work. You believe in Lever for Change and the model. It's so based on networks, which is what you believe in. And we were talking about what issue should we focus on. We love this model, but what is an important issue? And we chose trust in American institutions. Why was that so critical to you? Obviously, we live in this very tumultuous time. There's a whole bunch of erosion in trust of all kinds of institutions. And in the last couple of months, a sustained assault on the trust of the institution of science, among many other things, whether it's universities, corporations, government, et cetera.

26:26And what people don't realize is it's fundamentally, even as institutions have limitations, this is how our society works. Like the mistake, if you look at any history, is tearing all the institutions down is the French Revolution, is the Cultural Revolution, is year zero in Cambodia. It's a disaster to tear institutions down. You want to renovate them, right? And trust is an important part of it. And people say, well, how do I trust that one has this? Well, one, you should have a trust in constant renovation anyway, but we should all be working on how do we restore trust? Because that's how we live in a healthy society.

27:07This isn't even just talking about democracy. This is talking about a healthy society. Like, why does money work? Because we trust it. Why does banking work? Because we trust it. If you don't do that, we have a very, very serious problem in society. So it was partially like, okay, what is this problem that I think many, many people understand? What is countervailing to the general, many people saying, burn it all down, including people who are in government in terms of how it operates. And to say, no, no, what we should be focusing on is what are the steps we do to renovate and build trust. And so focusing on that building of trust is something is like is, again, in the level of change, it's something I want everyone to to think about how important that is and start acting in that direction.

27:52And I think importantly, this is a nonpartisan initiative. You decided on this challenge in this area long before the presidential election. So now no matter who's in charge, what party, trust in American institutions is critical. And so as I said just yesterday, we announced the five finalists. And again, the great thing about Lever for Change is that each of these five finalists will get$200 ,000 to create their project, plan for the future, make a better plan. And then one of them will receive$9 million, which we're very excited about. But to your point, all five of them are going in the Lever for Change Network.

28:26And so regardless of who wins, we're excited for all five of them to get the spotlight. They're all very worthy projects. So I'm going to go through each of the projects. I would love for you super briefly to tell us why that is important. And just for those who are watching and listening, we are not putting thumbs on the scale. All five of these are amazing. And we just want everyone to know about them. And there's a judging panel. This is not, Ari and I are not the judging panel. All right. Right. So the first finalist is the American Journalism Project. So local journalism. Part of when you say, well, there's obviously a whole bunch of aspersions around journalism on a national level.