Episodes

Dan Ferris:                 Hello and welcome to the Stansberry Investor Hour. I'm Dan Ferris. I'm the editor of Extreme Value and The Ferris Report, both published by Stansberry Research.

Corey McLaughlin:    And I'm Corey McLaughlin, editor of the Stansberry Daily Digest. Today, we talk with Rob Spivey of our corporate affiliate, Altimetry.

Dan Ferris:                 It's been a little while since we've spoken with Rob, our friend and colleague, and he is a very smart guy. He'll teach us about accounting and businesses that he likes, and we're also going to talk about AI and DOGE, and he's going to make all of this make sense and put it all together for us, and I can't wait for him to do it. So, let's do it right now.

Corey McLaughlin:    For the last 25 years, Dan Ferris has predicted nearly every financial and political crisis in America – including the collapse of Lehman Brothers in 2008 and the peak of the Nasdaq in 2021. Now, he has a new major announcement about a crisis that could soon threaten the U.S. economy and could soon bankrupt millions of citizens. As he puts it, "There's something happening in this country. Something much bigger than you may yet realize. And millions are about to be blindsided unless they take the right steps now."

                                    Find out what's coming and how to protect your portfolio by going to www.americandarkday.com and sign up for his free report. The last time the U.S. economy looked like this, stocks didn't move for 16 years and many investors lost 80% of their wealth. Learn the steps you can take right away to protect and potentially grow your holdings many times over at www.americandarkday.com.

Dan Ferris:                 Rob, welcome back to the show. Always a pleasure to speak with you.

Rob Spivey:                 Dan, same – same to you and to Corey. Thanks guys, for the time.

Dan Ferris:                 It's been a little while. I just looked you up here and I think you were last on the show in December of 2023. So, we – I think we need to catch our listeners up with you – and you up with them, I should say. So, why not just tell our listeners a little bit about who you are and what you do and maybe what Altimetry is about?

Rob Spivey:                 Yeah. Of course. For those of you who don't know us, Altimetry, we're focused on the idea really that the problem with a lot of investing out there and a lot of the investment research you get out there in the world is a lot of it starts with fundamentals. It's starting with bad numbers, just first and foremost. Bad numbers that are coming from Wall Street; bad numbers that are coming from filings and everything else, because the fact that the underlying accounting behind all of it – accountants, structurally, are – they're all about, basically just balancing the numbers, not about giving investors an understanding on what real corporate profitability is, what real corporate growth is, what real corporate valuations are.

                                    So, what we do at Altimetry is we do what we call "Uniform Accounting". We clean up the accounting. We restate it to get to what real economic profitability is, growth, valuations. Then, all of a sudden, Dan, we can uncover things like, which companies really have a margin of safety in their business? Which companies have strong profitability?

                                    Then, once you get all those numbers accurate, all of a sudden, then, we can start getting [a] better understanding on what's going for the U.S. as a whole and the economy and the market as a whole, because we got better company data from companies that then gives us better data from everywhere else. And that's what drives our research process and everything else that lets us pick the stocks that we recommend, lets us pick the bonds that we recommend, and have high conviction on them. It starts with better, cleaner data and then, better fundamental research after it.

Dan Ferris:                 So, it sounds like you're doing better bottom-up analysis with Uniform accounting, but you also imply there – and correct me if I'm wrong – that you think it also gives you better top-down insights as well.

Rob Spivey:                 Yeah. I mean, when you think about making decisions – like, for instance, right now, a topical thing – right after all the news earlier this week on the change in tune from tariffs – that tariffs are no longer – we're leveling massive ones on everyone. We're really just kind of targeting China and everything else. Then, you step back, and you think, "Well, what's going on there?" So, what we can do – when we clean up accounting data – is we can actually side by side Chinese and U.S. data and Chinese and U.S. profitability and say, "Wow. U.S. corporates are actually way more profitable than Chinese corporates."

                                    Chinese corporates are playing a volume game, and we can only do that in aggregate because we've cleaned up this data. And when we get – in the bigger question about macro – and this is something that we always come back to/harp on – is when you want to focus on the world of macro and understanding where the market's going, credit signals are really, really important. Where the credit markets are going, where credit risk is going is essential to know. And when you try to do credit research – like a Moody's or an S&P does with actual reported metrics, you just get bad data. But what we can do it when we aggregate − we can get better data on the U.S. as a whole on "How high is credit risk and where is credit creation/destruction happening?" when we get that bigger aggregate data off of that cleaned up data, to your point.

                                    So, we can get better macro data, too, along with better bottoms-up data.

Dan Ferris:                 Gotcha. It's all of a piece. It makes sense, right? You get better data from the bottom up and it kind of filters up, doesn't it? So –

Rob Spivey:                 Exactly.

Dan Ferris:                 But you guys really though, at Altimetry, you're specializing in bottom-up fundamentals, stock picking and bond picking, right? Your strategy isn't top-down; you just get some better top-down insights.

Rob Spivey:                 Yeah. Exactly. We are not Stanley Druckenmiller. We're not –

Dan Ferris:                 Well said.

Rob Spivey:                 Yeah. Exactly. Our focus, first and foremost, is finding great companies and then, finding great, fundamental, thematic stories that can help us identify those companies. Like, right now, big themes that we're talking about are areas like, for instance, the huge AI investment that's happening around the U.S. and around the globe, and we're saying, "Hey. We can then use our better, cleaned-up, fundamental, bottoms-up accounting data to identify which of the companies that we can pick off that are riding that." Exactly, Dan.

Dan Ferris:                 OK. Well, I'm glad –

Corey McLaughlin:    AI's still happening? I thought everything was about tariffs now. AI's still going on? That's a good point, right? Did we forget about that?

Rob Spivey:                 Shockingly, right? Shockingly. It's funny. We talk about this idea that when you look from '94 to 2000 – look, history never repeats. History always rhymes.

                                    So, it's not like ever the exact same, but if you want to look at the most recent graph that we can do for something like this, in '94 to 2000, we had a huge tech-adoption cycle. In that huge tech-adoption cycle, Nasdaq up 600%. Who knows where the Nasdaq or anything is going to go at the end of the AI adoption cycle, but the important thing is everybody remembers that, and we draw this magical line that goes up and to the right. Everybody forgets – there were 10 10% pullbacks in the Nasdaq from late 1994 until the 2000 bubble. Ten.

                                    Ten 10% pullbacks. There was one in 1998 that was 35% because we had a combination of – wait. Ready for this?

Dan Ferris:                 Yeah.

Rob Spivey:                 We had geopolitical uncertainty because we had the Asian financial crisis, we had Russia defaulting, and we had LTMC. So, you look at all that, and that sent the market crashing. But anybody who stopped and said, "Well, wait a second" – exactly to your point, Corey – does any of this disrupt the fact that everybody wants to get on the Internet and every single thing that's happening from a technology perspective is happening there? No. And, quite frankly, whatever tariffs do to try to redistribute – however unproductive they are – to redistribute global growth, the fact that we are in a global arms race for AI is not changing, and every company wants to.

                                    So, to your point, Corey, you really have to make sure to not take your eye off the ball for that. In our opinion, anyways.

Corey McLaughlin:          Well said. Yeah. It is. And a lot of people have taken their eye off the ball, I think, right now. So, it's interesting where we are in the AI cycle because – my mind goes to these last two years of the stock market – the 20% gains [in the] S&P 500 and the last time we saw that was the lead up to the dot-com bubble.

                                    I would presume that you're going to have pullbacks until that point becomes obvious. The top of it, maybe we're seeing it now, I can't say for sure. It's interesting the points you brought up there. For sure.

Dan Ferris:                 All right. Let's go there. The topic is broached. You guys have an upcoming presentation, and you've got some serious views about AI relative to DOGE – the Department of Government Efficiency – created by Donald Trump and Elon Musk. And Vivek Ramaswamy, who apparently went off to become the governor of Ohio or something.

                                    But like, all I see is somebody trying to cut out waste, but you see something entirely different happening here. And when you told it to me a little while ago, I was like, "Oh. I never put two and two together and made four out of it," as you did in this situation. So, how do you get from DOGE trying to get rid of government excess and fraud and waste and so forth to AI? What's the connection?

Rob Spivey:                 Yeah. Which is a reasonable question, right? But if you actually step back and you look at what Elon has been doing and what DOGE's philosophy has been… When you look underneath in the background in terms of what David Sacks is basically tasked to do as the "AI czar," a lot of what Elon has been doing is about actually uncovering data. So, yes, there's been headline stories that have – obviously, the amount that DOGE has saved has changed, right? The numbers have, regularly, up and down.

                                    But the broader thing that's been happening is he has been going after – and the people working underneath him who are really acting like junior consultants; this is what junior consultants do. They go out and they get a bunch of data, and they try to pull it back and they try to create insights from it – is they're trying to gather a ton of data to be able to figure out a couple of things. One: What are the things that the U.S. government does that AI can automate? Two: What are things that the U.S. government does that AI can help actually identify how to do it more smoothly, even if it can't automate? So, all comes this idea of – you know, Elon has this five-step plan that he does every single time that he goes out and does anything.

                                    He did it at Tesla. He did it at X/Twitter. He did it at Space X. And this idea is he tears in to say, "First, let's question a requirement. Let's question why we're doing anything that we're doing."

                                    You saw this with USAID. You saw this with the U.S. Department of Education. Then, it's "Delete any part or process you can." Where he's basically saying, "Hey, let's cut deep. Let's cut, sometimes, even into muscle to make sure we are getting to the root of what we're trying to do." Then, simplify; then, accelerate – which we've seen – and then, last, automate.

                                    And so, what we're seeing him do is this idea of "Let's first get all the data together." And now, already, they're starting to roll out AI tools, right? They rolled out this thing, GSAI. With GSAI, what that was all about was to basically say, "Hey, we analyze tons of data at the government. Let's actually build a tool that can let [ the government dump data in] and then, to be able to come out with conclusions."

                                    So, for instance, the U.S. Treasury, "Hey, let's be able to dump data in to identify where we're seeing weird things going on with the IRS. Where are we seeing weird things going on with Social Security or anything else?" And all this is happening – and what we've seen so far is really only phase one. Because phase one, like any good consulting engagement and having experienced a few myself – what you do in a consulting engagement is: Phase one is always "Gather as much data as you possibly can. One: Data you get the lay of the land, and also, the data that you're going to lose." And then, phase two is when you actually do the implementation.

                                    And this is what we think that we're about to see right now. We're about to go to phase two. They've gone through all the data gathering and some of the cost-cutting is going to stick around. Some of the cost-cutting may go up or down. But the real story is going to be about how they unleash AI in the government and that's going to be the real way that they can save money and make the government more efficient – is once we get to the point where AI is actually doing a lot of the stuff that – you know, I think it was the Turing Institute basically said that more than 80% of U.S. government global government tasks are mundane, process-driven data entry.

                                    All that stuff is what AI can do. And so, our point – which we think is really interesting, Dan – is this is a kind of environment where what Elon and what DOGE is kicking off is going to be transformational for years to come in terms of the U.S. government, but also, in terms of how it's moving the Overton window for the whole entire U.S. economy on what it's OK for us to use AI for and not. So, that's the big story that we think is coming now – that when you get past the noise of tariffs, what the real big story if we look a year from now is going to be.

Dan Ferris:                 All right. And why don't you tell our listeners what "Overton window" means?

Rob Spivey:                 Yeah. Sure. "Overton window" is this idea of basically changing what, in common discussion, is an OK and appropriate thing for you to discuss. So, in terms of – I mean, literally, all the conversations that have been happening for the last few years in terms of "What is it OK for us to discuss and for us to debate and for us to do and everything?" The Overton window is how do you change that perspective in terms of what is OK in common conversation?

                                    What is OK in a business meeting? What's OK in a C-suite? What is OK in a government conversation to say, "Yeah, we can actually let that happen. Yes, we can actually unleash AI to potentially mean a lot of government workers get redistributed. That's an OK conversation to have." That kind of thing.

Dan Ferris:                 It's purely political, but you're right – it's become a more general term now. It was like the politically accepted boundaries, right? And then, other people started using it. OK. So, it will change that.

                                    It'll be – you're right. Elon comes in and he's the first principles. Everything is torn down and rebuilt from first principles, and that certainly looks – if it sticks, that will certainly change the Overton window. That would change that boundary quite a bit. It's going to be like Overton knocked down the side of the house kind of thing.

                                    And, let's face it, it's desperately needed. Something has to change with $36 trillion in debt and growing, right? And massive deficits, etc., etc. I find it interesting that while they're talking about greater efficiency, Trump is also talking about a potential trillion-dollar defense budget. It's not like we're really going to reduce spending a whole lot here.

                                    I don't see that happening because you can't touch most of it, right?

Rob Spivey:                 Yeah. Right?

Dan Ferris:                 I think it really came up with like, 86% of federal spending – I don't know if that's the real number. I think I came up with a smaller number than that. I forget what number I came up. But we all agree that you can't touch a whole bunch of it. My question for you, Rob, is: Is there any reasonable expectation of the government using AI for all that untouchable stuff – like, Social Security?

                                    What could it do there? Medicare – what could it do there? Defense, etc. Is the question even worth asking?

Rob Spivey:                 No. I love the question because it's a great point and a great question to have. And, if you think – remind me, Dan [and Corey], if I go off in a tangent for one of these to come back to another one. So, I'm going to start with health care, but I think that there's three different parts of where this is actually a good topic. So, one – in terms of health care.

                                    So, when you get in the weeds on how Medicare and Medicaid operate – so, a lot of what they do is effectively approving services that somebody else is going to manage, right? One of the big HMOs might actually manage the actual – the Medicare execution, but Medicare/Medicaid is just giving a stamped blessing. There's a whole ton of work that goes in CMMS and that Medicare does that if you could basically hone in on getting to saying, "OK. Where does this make sense and where does it not make sense?" There's all these things like – in terms of how we approve and deny treatments, how we think about what is the right process for us to give a treatment for somebody who say, has an appendectomy.

                                    What are the things that we should be doing? And making sure that those steps are being followed. A lot of that stuff, in terms of that – making sure that we have the right health care steps being followed, AI being on top of that and making sure that those things are happening – all of sudden, one – can speed up your cycle times from a health care perspective and also can reduce waste. And so, it's not about actually cutting services or even saying, "We're going to refuse stuff" – which is the – basically deny, deny, deny, right? What people talk about for HMOs.

                                    It's about making sure that the right care is happening at the right place by the right person at the right time. And being able to do that, a lot of that gets slowed down. You talk about this idea – you probably heard the idea of pre-authorization for things. What happens is you basically have to call before you get treatment to say "Yes. You're allowed to basically have that surgery that might be life-changing surgery for somebody."

                                    Then, if you can speed up some of that stuff, what actually happens is health outcomes improve. So, you can have some stuff there, and there's some really interesting thoughts, also, of – and this gets not into so much DOGE, but also, the broader things that are happening to reduce health care costs – if you want to talk about reducing the inefficient parts of the budget. When you talk about – it's not happening right now, but when RFK talks about the idea of saying, "Hey, we're not going to have ads for pharma anymore in the U.S." – that that's a goal and aspirations. Whether or not pharma lobbyists will ever let that happen is a separate question. But when you think about it from that perspective, the biggest reason why we spend so much more on, say, drugs in the U.S. than we do anywhere else, is because of the fact that it's a consumer relationship. If we can break that relationship – we spend three times more on an average prescription that we have here in the United States than anywhere else in the OECD spends.

                                    If we cut that by basically saying, "Hey, we're going to allow negotiations," that can drive down cost. So, there's a lot of ways that, in health care, we can actually save costs without reducing treatment, and then, you get into everything else in terms of defense, and you get into the broader things. But actually, one last thing before I really, really rant, because I know I've gotten on a soap box… We at Altimetry, the way that we think about it – and I know that you've heard Joel – our Chief Investment Strategist, Joel Litman, talk about the U.S. deficit and everything else. We actually like to make a distinction when we think about the U.S. deficit and U.S. government spending that there is – and it all comes to what basically the fiscal multiplier effect is. And what this is, is the idea when the government puts $1 in, how many dollars come out for U.S. GDP?

                                    And there are things that are spending – which is the U.S. government spends money, and the fiscal multiplier effect is below one. So, this is something like, say, Medicare or Medicaid or Social Security. You give somebody Social Security, there's plenty of people – my parents are a great example of this – who get Social Security [and] don't need it. They put it into savings. That dollar that's sent to them does not go back in the economy to create GDP growth.

                                    But stuff like defense spending has a fiscal multiplier effect of 1.1, 1.2, 1.3. You can argue some of that is investment. It's not actually just spend. Because when the U.S. government spends that, that means greater GDP growth. And for every trillion dollars the U.S. government takes on debt only costs $50 billion in actual interest expense.

                                    And when that leads to a lot more tax growth – which it has the last few years – you kind of look at it and you go, "Not all spending is bad for the U.S. government." But the whole entire idea of unlocking AI is about getting more efficient for that less fiscal-multiplier spend that we can get there, is our thinking on what could happen.

Dan Ferris:                 Yeah. I mean, you're never going to convince me that the federal government doesn't need drastic amounts of shrinkage, but I understand what you're saying. And it's a very businesslike approach to thinking about it and I appreciate that for sure.

Corey McLaughlin:    I think we could all agree on the government-efficiency point. I'm wondering how – so, AI, it all sounds great. How does that translate into some profitability for companies and margins? And how does – say – take health care as an example. How do you look at these major themes and what AI can do and try to find a company or companies or sector that actually gets the most out of the opportunity?

Rob Spivey:                 Yeah. And when you look at it, the first thing you have to do is you have to chart out the whole entire ecosystem to understand where the players are and everything else. And we spent a lot of time in 2023 and early 2024 doing this – what we call the AI Blast Zone or ecosystem. With the center, it's right, obviously, the hyperscalers − the Nvidia, but also, you have the Meta, the Amazons, the Microsofts and everything in the world. And then, one step from that, it's a spline.

                                    You've got the people who are basically helping build out AI – which is the bottom of what we think of the ecosystem in the blast zone, which is you've got your data centers, data-center suppliers, and all that, but what's really interesting – to your point, Corey – is when you get to the top half of that blast zone when we're visualizing it – which is we think, "Have you got the users?" So, these are the people who are taking AI and using AI companies – they're taking AI and using AI to either turn it into a product to sell to somebody or also using it to improve their own business – or, ideally, doing all three – doing both, right? All three. Then, you've got the enablers. These are the people who if you want to use AI, you're going to have to lean on – not from a hardware perspective, but from – they're the ones who are going to help you figure out how to do this.

                                    This is consultants. This is cybersecurity solutions. This is some level of cloud computing outside of the hyperscalers. And then, you've got – outside of that, you've got, basically, the people who are going to make the equipment for those people, and then, you've got commodities that are using everything else. And for us, when we look, in terms of – that user side of it is going to be – when you think about the Amazons of the world nowadays, when you think about the Alphabets – the Googles of the world nowadays – those were the ones from the last generation who basically became that.

                                    This generation, we're seeing really interesting companies. Like, a company that we think of is Twilio. So, Twilio is this company that – they basically make the software and the solutions for you to run your call center, for you to do anything that you need to do with your customers. Well, what Twilio sits on, because of that, is a mine of data and a mine of process understanding on that, because they've been doing this for ten-plus years. The guy who actually built Twilio left from AWS after he helped stand up AWS, one of its creators, and then, he stood up this business.

                                    And what they're doing now is they're leveraging AI for huge things. They're first thing they're leveraging AI is if you're in a call center, literally, the AI side by side with you will listen to the conversation that you're having with a customer and go, "Oh, the last time a customer who asked this question, really, they were asking this. Point them towards this and also, offer them this sell-up, because this is a person who looks like they want to be sold." The other thing that they're training the AIs to do and that people are leveraging their technology to do is to literally be able to be the first inbound when somebody calls – that the AI answers and it actually sounds like a person as opposed to a "Please, hold and press one or two or three, whichever you want" or "Please tell me, in one or two words, what you need." It's not that anymore.

                                    Those kind of things are when – and think about how much of the U.S. government – health care, specifically, but so much of the U.S. government – is effectively customer service. And so, companies like Twilio are a great example of how we're seeing AI enablement. Companies use AI to all of a sudden productize and improve their own businesses that we think are going to be that long-lasting value creation that's going to have high ROA [inaudible] businesses – the kind of companies that we, in Altimetry, love to own.

Dan Ferris:                 Yeah. I'm glad to hear you say that, because when I look at the – just the mega tech companies, the data-center builders, I swear to you, Rob, I know they're ten times better businesses and they're ten times better financed and they're profitable than like, WorldCom and Global Crossing, but I swear to you, I can't get that image out of my head of all that capital. It's just like, tens of billions of dollars building out this stuff, right? It's hard for me to not think of those two together. So, I'm glad you got me to Twilio.

                                    I'm glad you got me out to the user, right? Because that's where the value sort of rubber hits the road, right?

Rob Spivey:                 And I think that it's – the two big places that we really think the rubber hits the road are one – the people who are building AI solutions and tools, because right to your point, Dan, those are the ones who – they're not the capital committers. When you look at Amazon, and Amazon is someone who's literally going from – I mean, they already were a capital-intensive business, but holy cow, just think about it. They're talking about $100 billion in spend, in capex, in data centers. And, to your point, Dan, if you think about the late 1990s, the last three [inaudible] of the late 1990s – the Internet boom – it was all these people spending massive amounts of money – WorldCom; you said it – on fiber optics – on putting all these wires in the ground and saying, "If we make it, they will come." And what happened?

                                    I mean, by the way, those fiber-optic cables, we're still using – they still have excess bandwidth for those that we're using right now, right? But, to your point, where you want to go is you want to go to the people who are going to unlock the value – and there's two big parts to that. One is, as I said, the users – the Twilios of the world. The second part of it is if you want to have great AI, it's not just about what open AI builds as a tool that you can build on top of – what Nvidia builds as a chip in the warehouse. It's about actually having good data.

                                    And not just good data – good, organized, data. We use the analogy of "Data's the new oil" which is an analogy that people have thrown around for like, 20 years – that in the 21st century, data's the new oil. But everybody has been missing the most important part of oil. I know you two know this. There are multiple different prices for crude oil.

                                    If you want to buy Louisiana Light Sweet versus Brent crude versus crude from Venezuela or Mexico, they all have different prices because they all have different qualities, right? Some of them are easy to refine. Some of them require crackers and all this other crazy stuff. And because of that, prices are different. When you talk about data's the new oil, that's the important part of the puzzle.

                                    It's data needs to be refined and organized and be correctly characterized for AI to be able to chew on it to be able to unlock all the value AI can unlock. This is, by the way, a huge part of what Elon and everybody at DOGE has been doing with gathering stuff for the U.S. government – is to organize it. And there's other companies – which are not the people who are building tools off of AI, but the companies that are going to help these others – be it the government, be it companies – organize the data so that AI can use it. And get the correct authorizations for that data because if everybody understands that data's the new oil, it becomes even more valuable than people realized before. And so, it's that second kind of company – and this is the idea of enablers, right, Dan and Corey, when you asked – that these are the companies that make it so you can actually unlock AI without being the ones who have to commit a bunch of capital investment.

                                    They're just really smart software companies. And so, we think that those – if you think about – when we talked about the idea and you said, "Hey, you know, our philosophy on what's happening with DOGE" – when we talk about the idea that we're about to see a bunch of contracts unlocked with DOGE that are going to be deployed for the U.S. government to unlock AI. But we talk about corporates because of the Overton window move on what is OK to use AI for. Corporate's rushing to use AI. These data management, data access, data warehouse, data organizing companies are going to be incredibly important and nobody's paying attention to them now – especially because everybody's focused on tariffs.

Dan Ferris:                 I see. And I'm sorry, you gave me a lot of information there. Who are they?

Rob Spivey:                 Sorry.

Dan Ferris:                 Specifically.

Rob Spivey:                 I'll give you some examples. So, if I think about the people who are going to start helping you organize AI, the first part is the consultants, right? So, the consultants – these are guys like [inaudible], who are going to figure out how to organize the data. But then, you've got software – this isn't a recommendation because this company is not profitable − but an example of a company that's the kind of company that's going to help you warehouse and keep your data in an organized way so you can leverage AI is a Snowflake. What Snowflake does is effectively, basically, lets you create a world where your data is organized − which everybody is going to need.

                                    And there's a bunch of other companies that we've identified that we're really excited about, but I'm not going to tell you all of them, Dan, because of the fact that some of them we're holding back for our subscribers.

Dan Ferris:                 Absolutely.

Rob Spivey:                 But that are like Snowflake but profitable – that are profitable Snowflakes. And actually –

Dan Ferris:                 Good description.

Rob Spivey:                 – really good high ROA businesses – return on assets – the things that, Dan, you know, because you and I talk about this all the time in terms of economic moats and return on assets and everything else – that actually really are making money. Because that's what's matters to us − are they really making money is what matters. Not just the story. The story can only get you so far.

Dan Ferris:                 All right. So, that –

Corey McLaughlin:    Yeah. I like that. Profitable Snowflakes. I feel like there's some analogy there as well.

                                    [Laughter]

Rob Spivey:                 Works on multiple levels, right?

Dan Ferris:                 Yeah.

Corey McLaughlin:    Yeah. Get the Snowflakes out. Yeah. Move on, Snowflakes. We want the profitable ones.

                                    Yeah. The point about data's the new oil – yeah, you have heard that for a long time. But you also know that – I think we all know, just from personal interactions with whatever company or whoever – like, why doesn't this just – why can't – why do I have to two-factor authenticate this and whatever else? I feel like there's tons of opportunity there. And with Elon, one of the things you hear about is like, the government's still using the mainframe computers and every agency having a different system.

                                    And that just goes for every industry. And you see things over history, right? The private sector gets ahead of – or innovates, right? And then, the government catches on. And then, what you're talking about now, I think, is it going the other way now to then the government kind of adopts it and now, what's OK to speak about and implement in the private world?

Rob Spivey:                 Yeah. And we would never expect that to be – that order happens every once in a while, right? If you think about – the Internet was DARPANET – was ARPANET before it was the Internet. There's times when the government actually leads. It doesn't happen a lot lately, but this is an opportunity because of the change in the conversation that we've had because of DOGE and Elon where the government has the opportunity to actually literally paint the lines on the road to help people understand, "Here's the direction you need to go in."

Corey McLaughlin:    Yeah.

Dan Ferris:                 Right. Yeah. That's interesting. That's an interesting point about DARPA. We don't think of the government as a technology leader at all.

                                    In fact, politically speaking, if you're running for office, like, promises around entitlements and even defense or regulatory issues are much more likely to get you elected than saying, "I'm going to upgrade the IT."

                                    [Laughter]

Rob Spivey:                 Yeah. This is the thing.

Dan Ferris:                 Nobody runs on "I'm going to upgrade the IT," right?

Rob Spivey:                 Exactly, right? But when you really think about it, this is the whole entire idea. When we interact with the government, when we actually do – outside of when you're enjoying those entitlements – if you're enjoying Social Security – outside of that, when you're interacting with the government, you're interacting with stuff that you probably wish the DMV ran better. You probably wish that when you apply for your passport, you didn't have to wait for your passport for – or if you applied for TSA pre or global entry, that you didn't have to wait three to six months to get it back and get an approval or something else. You probably wish that those things ran faster and that all comes to that boring, mundane, phrase of IT. You're exactly right.

Dan Ferris:                 You know something, Rob? You're exactly right. I recently – I had to go down to the DMV, and I was shocked at how efficient and organized it was. I think licensing is a scam. I hate the government.

                                    I want it to be 80% smaller. I'm a total – I'm almost an anarchist, you might say – libertarian, whatever. So, don't misunderstand me, but I came out of there thinking, "Oh, you know, that was such a pleasant experience. I'm less angry about it, the existence of the DMV." So, if we are at a divisive difficult moment in our country, they could help themselves by being more efficient, being better at their job and worrying less about all the things they worry about – not firing people who've been there for 50 years or whatever.

                                    So, yeah. We'd all like to see that. Now, I do worry, I have to admit – like, I hate what the government does. Do I want them doing it more efficiently? And it's kind of a real question.

Rob Spivey:                 Yeah. Right. So, this is – Joel presents in front of the Pentagon in terms of where U.S. economic power is relative to the rest of the world and everything else. One of the points that he's actually made before – which isn't exactly to your point, Dan, but leans into it – is the point that I was making earlier – that idea of if you run a $1 trillion deficit at the U.S. government but that $1 trillion deficit has any fiscal multiplier effect – meaning it has some level of economic growth that it contributes to the world – and you get to then tax that – which you do, right? The U.S. government gets to tax that. Well, then you can easily afford as much as you want in terms of government spending.

                                    But here's the big reason why – and this gets to it – the big reason why is because of the fact that the U.S. is the most profitable country in the world from our corporations. The reason why the U.S. has the ability to run the deficits that we run – anybody who wants to talk about trade deficits and U.S. government deficits being correlated – which I've heard people throw around a little bit – no. They have no connection. The reason why we're able to do what we do is because of the fact that we have a massive profit surplus here in the United States. When you look at our profits, the economic profits that U.S. corporates make – when we look at in Uniform Accounting basis − $1.2 trillion.

                                    You combine China and the rest of the world's economic profits, it's less than half of that. And so, the whole entire idea is, when you look at it, we have such an ability to do what we do because when the U.S. government does some spending, U.S. companies have this ability to turn that into profits because we sit on the best corporate profitability in the world and the best corporate business models in the world. And so, you look at China – and this is what's really interesting about us going after – really, basically, kind of zeroing in on China after April 9 – when you look at what's going on here. China does not have that leg to stand on. China doesn't have the corporate profitability to allow its government to be able to tax, to be able to then drive growth by actually being able to spend on a deficit basis. They don't have that, and we do in the U.S.

                                    And this is this huge dichotomy, which is why a lot of the times, the point that Joel is making when we're meeting with people at the Pentagon is, one – look, don't tell – to your point, Dan – don't tell people on Capitol Hill this, but we don't need to worry about $1 trillion or $2 trillion deficit. An $8 trillion deficit all day we need to worry about, but a $1 trillion or $2 trillion deficit – but the bigger thing is, maybe don't kick China so hard because China actually doesn't really have another leg to stand on. And if we kick them too hard, we might end up in a hot war, a kinetic war, as opposed to a trade war, which is the observation we have. But to your point, we don't want anybody to be telling Congress that either, Dan, but it's the truth.

Dan Ferris:                 I think you and I probably part ways at the fiscal deficit because it does have to be funded, and we do add lots of debt every single day. And I don't think there's any way that an incremental dollar of debt from this point is going to be anything but unproductive. I think we're long past that. The other points you make, we're right – this idea of other countries ripping us off, basically, and the trade balance – that's absurd. Like, we get all their stuff and all they get are dollars and you're explaining, basically, the mechanism.

                                    We actually – we don't just print them. We create them. They're profit. They're created wealth. So, we have that behind us.

Corey McLaughlin:    And they are smaller – they're smaller countries – population and everything.

Dan Ferris:                 Absolutely.

Rob Spivey:                 And this is the misunderstanding with tariffs, right? Tariffs are about revenue. Tariffs are not about profits. And this gets to your point, Dan and Corey – so, yeah, maybe other people sell a bunch of stuff for us and a bunch of revenue comes to the United States, but if that revenue is very low-margin revenue, low-profit revenue – which most of it is – there's a reason we got rid of it, right? The reason we got rid of it – because we used to do a lot of it – is we said, "Wow. Let's focus on the high-return/high-profit parts of what we can do, and we'll dominate those."

                                    And, by the way, we do. Hands down. But maybe the stuff that we can't make money on – because nobody can – yeah, guess what? We'll have a revenue deficit, but we'll have a massive profit surplus on the other side because all them – they have to keep buying Microsoft, and the margins we make on Microsoft Outlook and Microsoft Office are pretty fat.

Dan Ferris:                 Right. Yeah.

Corey McLaughlin:    To your other point about the deficit, can we have both? Can we have a smaller deficit and the American companies still doing what they can do compared to China?

Rob Spivey:                 Oh, yeah. I mean – so, first off, right – and the whole entire idea of what DOGE is focused on – in the short term, [any] conversation about the deficit is going to go like this. But if we get AI unlocked, DOGE is going to benefit that, but more importantly, for U.S. corporates. U.S. corporates – the reason why U.S. corporates are so dominant really doesn't have anything to do – it has to do with the U.S. government, but not with the U.S. government and deficit. The reason why U.S. corporates are so dominant is because of the first 10 amendments to the U.S. Constitution.

                                    The Bill of Rights is what makes U.S. corporates so dominant. Because the fact that the Bill of Rights is what allows us to guarantee if I invent something, you can't take it. If I build something, you have to justify before you can basically say, "That's mine" and you can tax or anything else. It gives us this wall of protection for U.S. innovation, and it's why we are the greatest innovators in the world. If you look at U.S. corporate market cap – so, U.S. corporate R&D – I think it's something like 50% to 60% of all U.S. corporate R&D for public U.S. companies is funded by companies that were venture-backed in the last 20 years.

                                    If you look at companies that – if you look at U.S. corporate market cap – U.S. corporate market cap – the leaders – the top 20 companies in the U.S. corporate market cap have changed completely in the last 20 to 25 years. Buffett actually has a great presentation on this that Buffett takes you through and he goes, "Let's look at who were the leaders in the world in terms of aggregate size and market cap in the world over the last – like, 25 years ago." And you've got ExxonMobil in there. You've got GE in there. You've got a bunch of Japanese companies in there.

                                    Now, he goes, "Let's throw it up right now." All those Japanese companies have been replaced by Chinese companies, but guess what? There's also still a ton of U.S. companies up there and they're all different because the venture capital cycle of innovation that we have in the U.S. – which is all backed by the Bill of Rights – is here and is so powerful and gives us this massive leg up and advantage to anyone else in the world on innovation, which is why Joel and I often say this and we go, "It's not MAGA. It's not 'Make America Great Again.' It's America is the Greatest and it's set up to be the greatest for forever, full stop." And that's the truth.

Dan Ferris:                 Forever.

Rob Spivey:                 I'm off my soap box now.

Dan Ferris:                 Forever is a long time.

Rob Spivey:                 Forever probably is too generous, but at least for the next 40 to 60 years, assuming that somebody doesn't do something completely stupid to destroy our rights that are enshrined in the Bill of Rights. We're set up for – as we say, "Pax America 2100" for the next 80 years.

Dan Ferris:                 Yeah. I sincerely hope that you are correct. I feel like the – I mean, we're getting into a weird area here. People probably don't care about our political views, but I personally am afraid that we have – the assault on the rule of law has been substantial for decades, and there's too much – just very quickly, I promise, people, we'll get off of this – Dan just has an infirmity of character where if he doesn't get rid of this stuff, his head will explode. And you don't want to see that, do you?

Corey McLaughlin:    I want to hear this. Yeah.

Dan Ferris:                 So, all I'm going to say is that – also, I'm into The Road to Serfdom by [Friedrich Hayek]. So, there's no way – like, my head is filled with this right now. It's absolutely filled with it. And the point is just that you get to totalitarianism by assaulting the rule of law and the rule of law simply doesn't respect persons in Hayek's phrase – meaning the place for influence is dramatically reduced and everyone is held to the same objective "rule of law" standard. Whereas in totalitarianism – Lenin had this famous phrase "Who whom?" right? Who is going to decide what for whom?

                                    So, it's all about influence. It's all about who you know and how much power you have with your contacts and stuff. And we all know that – like, I think I just saw a story about Zuckerberg buying a mansion in DC – they think it was him, anyway – and I'm like, "Well, he didn't do it because he wants to vacation there." I'm just saying, right? But I hope you're right.

                                    And we all know that the U.S. model has created this massive advantage and massive standard of living for a long time now, and nobody wants to lose that. Nobody wants to lose their standard of living. And a lot of people – even in this country – want a better one. And I hope we all realize that we get there by respecting those rights and having a rule of law, etc. And shrinking the $36 trillion debt burden, etc.

                                    So, what I wonder now is, we talk about AI a lot in the generic, right? But we know there's such a thing as large language models, the ChatGPT and so forth. There has to be other super important categories of AI applied, and it sounds like – am I – is all the stuff that we expect government to use based on large language models or is it something entirely different is what I wonder?

Rob Spivey:                 No. I mean, there's going to be – yeah, I think that there's going to be a medley of stuff that we're going to see go on. You're going to have everything from robotic process automation and traditional machine learning stuff that we can unlock, and that's going to kick off. Those things are going to happen. We're going to see large language models and other generative AI that's going to be important for this.

                                    There's stuff that is going on from – when you talk about NSAs of the world and DARPAs of the world, in terms of the next level of how you apply this – not large language models, but other generative AI and truly neuro network type things that are going to continue to accelerate from what, the Palantirs of the world do to unlock that to anything else. And so, I think, when you look at that, there's going to me a medley of different levers of AI and ways of doing data analysis that are all going to be important for – so, to not get hooked on, to your point, the idea of it's ChatGPT and OpenAI or using the large language model. It's really about the idea – to your point, Dan – of unlocking a lot of that stuff. That's all going to happen.

Dan Ferris:                 I wonder – the reason I bring this up is there was a paper that was forwarded to me by our colleague, Greg Diamond. It was forwarded to a bunch of us internally, and it was called ChatGPT is Bullshit. That was the title of the paper. I'm not making it up. And the idea was it's sort of like your highly intelligent, but not so well-informed relative or something who is full of it at family events, but a lot of what he's saying is correct and his facts are great, but something is off about all of it.

                                    He doesn't really know what he's talking about.

Rob Spivey:                 Stitching.

Dan Ferris:                 And you get that with these ChatGPT and Bard and whatever else there is out there that people are using. So, I guess I wonder – is the problem that there's too much data and we're asking too much of those models, and used within the government by intelligent people, they'll have just a better pool of data? Or is there something inherent about the large language model that just doesn't quite always get it right?

Rob Spivey:                 All right. Now, everybody – whoever – when their eyes rolled back in their head when Dan was talking about The Road to Serfdom – and, Dan, by the way, once you're done with Road to Serfdom, I recommend you re-read The Republic by Plato if you have it. If you want to talk about the evolution of governments, great refresher for that. But now, for everybody who rolled their eyes in the back of their head for talk about Hayek stuff – so, when you look, the important thing that happened for large language models – you talk about hallucination and all that other stuff, but also, you talk about it is a general AI, right? Eventually, in terms of it covers everything.

                                    When you do this correctly – when you look at, for instance, in the defense department, they have – the Navy has their own – call it "generative" AI model. The Air Force does. The Army does. The Marines do and Special Ops does, etc. And each of them, for those models, they lean on things like rags, which is basically effectively a finite, smaller, shrinking set of data that you say, "Hey, focus on this set of data only when you're giving me answers, and focus on this framework of doing things when you give me answers."

                                    And a lot of the – when you talk about the GPTs or anything else that people engage with, when you're talking about what the government is going to be working with, we're talking about what companies work with. We talk about a Twilio, to come back with Twilio. Twilio isn't basically saying, "Hey, ChatGPT, sit next to this guy who's having a conversation and listen to him and help him." They're actually saying, "All right. Let's take the bare bones of how a large language model works and how these frameworks work.

                                    Now, what we're going to do is we're going to train you on a specific subset of data and say, 'Hey. Don't venture outside of this subset of data. This subset of data is where you live. And, by the way, these rules about how we talk about that data? That's how you live.'"

                                    It's in the exact same way as when you try to teach a – Dan, I don't think this is a hot topic to bring up; I know that you are a gun enthusiast. If you ever take somebody who's maybe an eighth grader out and you're like, "Hey, I'm going to teach you how to shoot a gun" you don't basically go, "Hey, go do whatever you want." You go, "Hey, we're going to give you a very finite universe of what you can do to shoot this gun and we're going to draw those lines around it. And, by the way, the moment that you do something that's not that, we're taking the gun away from you." It's the same thing when you properly train these large language models to be specialists using a lot of different tools – like rags – with specific data that you can do, specific prompting and frameworks that you do.

                                    It's the same thing. It's like basically teaching a person how to first shoot a gun until maybe after 10 years of them shooting a gun and you go, "You know what? I trust you enough to say that you can do what you're going to do. Obviously, at the shooting range, we still have rules, but when we're out and about and we're hunting or whatever, I'll 'All right. I trust you.' I'm not going to basically watch you like a hawk."

                                    I don't know if that's a good analogy, by the way.

Dan Ferris:                 No. That makes a lot of sense. Trained on a specific set of data – I guess the problem I'm seeing in ChatGPT is you can't train it to know everything there is available on the Internet, right? It's just – it's asking too much of it. That's why you get that BS effect, maybe.

                                    But it's not asking too much to learn how the Navy builds submarines or whatever it is.

Rob Spivey:                 And, Dan, if you ever want to play with this – and anybody else who wants to play with this – there's a great tool that Google actually has called NotebookLM, and what NotebookLM does is it's kind of large language model behind it. But what you can do is – there's other tools that can do this, but this is the most easily accessible one because it's made by Google. What you do is you can drop in, let's say, 20 different pieces documents. Let's say that you really want to learn all about how Hayek and how all of the Austrian thinkers think.

                                    And you want to basically say, "I want to have a conversation with Hayek and everybody else." You can drop in all of their books and say, "Hey, look. I want you to basically just read their books and, based on synthesizing of everything that's just coming from them, I want to have a conversation with you, and I want to debate. I want to debate Hayek. I want to debate any of the other greats" and basically say, "Hey, let's have this conversation." You can actually do that, because all of a sudden, it's not pulling from, "Oh, let's look at what Milton Friedman says."

                                    It's not pulling from what you're going to get if you talk to Keans. You're just talking to, effectively, Hayek and the Austrian greats if you want to. That's a tool that NotebookLM can do and that's what's called – it's basically "building a rag" is what it's called.

                                    [Crosstalk]

Dan Ferris:                 Well, you know how to sell it to me, I'll give you that.

                                    [Laughter]

Rob Spivey:                 I do what I can.

Corey McLaughlin:    Yeah. I think Perplexity has a similar one or similar feature in it – you know, another one of those models that – I've gotten as far as – the challenge with keeping up with this stuff for a normal person is – I've got as far as knowing that it exists and not having it as part of my daily life yet, you know?

Dan Ferris:                 You and me both.

                                    [Crosstalk]

Corey McLaughlin:    And I just – I mean, these are all like, actual points. This will happen, I think, over time, as people get more comfortable with the tools.

Rob Spivey:                 But that right there – because I know that Dan would love this and I'm sure you would, too, Corey – literally, think about one of the truly – the greats that you respect, and then, if you can find a PDF of their work, as long as – look, I mean, ideally, you've already bought the book so that when you find the PDF that you find for free online you're not stealing the data because you've already done it, but you load up the three PDFs of the three books that you love and you basically say, "I want to –" and this is a great way for somebody who's never really experienced the idea of using a bigger-picture AI other than just going to ChatGPT and saying, "Hey, I want to go on a trip here. What are the five things that I should do?" Or "Why did the market fall today?" If you want to say, "Hey, I want to actually understand the bigger ways to do it." This is a super easy – everyone.

                                    You want to experience what are the kind of things you can do, do that. Find three books that you love from one author – I might do it for Kurt Vonnegut now that I think about it so I can have a conversation with Kurt Vonnegut. Great author, in my opinion. Load him in and then, you go and you say, "OK. Now, I'm going to ask you questions. I'm going to have a conversation with you because I know that the model is just focusing on that data I've given it."

                                    And that's the kind of power that you can unlock. So, imagine that writ large with the U.S. government, I guess, what I'm saying, but that's a useable thing that you all can do to actually experience this in a fun way that's useful for you.

Dan Ferris:                 If I were really smart, I would create a program that would just go to Project Guttenberg, read everything, and then, let's talk, you know?

Rob Spivey:                 Right.

Dan Ferris:                 That would be awesome.

Rob Spivey:                 Today you are so and so, right? Today, you are Minsky, and I want to learn all about when markets are overly steady and calm, and we had leverage. What's a good example, right now, of a part of the market where that might be happening? "Minsky, go find me." And he's going to talk to you like Minsky. So, you're going to hear all about Minsky moments and stuff like that. That's genuinely the kind of thing that you can do.

Dan Ferris:                 Awesome. Well, I'm sold.

Corey McLaughlin:    Sounds like a Friday Digest to me, Dan.

Dan Ferris:                 I'll take one of those. In about – in a year or two it will be – but I'll definitely taken one of those. And I just pulled it up here – Google's NotebookLM app – so, I'm ready to go and maybe I'll –

Corey McLaughlin:    Yeah. You've been with me to retry it as well – to dip my foot in again.

Dan Ferris:                 All right. So, this has been great. We're at the point when we ask our final question, and I'm dying to hear what you've got on your mind with everything we've spoken about. And it's just the same final question for every guest. It's identical, even if it's a non-financial topic. Same identical question. If you've already said the answer, feel free to repeat it. So, it's just – for our listener, if you could leave them with a single thought today, a single takeaway, what might it be?

Rob Spivey:                 I think that the single thought would genuinely be this idea that when you look at what's happening in the U.S. government right now, that's going to happen in the next three to six months around AI – that no one's really talking about right now – and the lattice work that we've laid and the structure – it's going to unlock a revolution around what's happening in AI that's not just about the U.S. government. And U.S. government is actually going to end up not being the most important part of this. It's that this is going to be somebody's opportunity inside the public sphere to show people the exact conversation that we just had around NotebookLM and the power of that – the power of actually unlocking AI to do powerful tasks and create significant value for the U.S. economy – that's the biggest thing that's going to happen for the next three to six months and so, just be watching that and be watching it not in the mode of, "Oh, what's happening from a political perspective? What's happening from a tariff perspective?" Keep your eye on the ball on what's going to happen with AI because that's going to set up a potentially decade-long investment opportunity to those people who are paying attention to it the right way.

Dan Ferris:                 And before we sign off here, you do have a presentation about this coming out soon. So –

Rob Spivey:                 We do.

Dan Ferris:                 Is there a link that listeners can go to to sign up to see it?

Rob Spivey:                 Yep. Absolutely. Yeah. Our presentation goes live on April 24 is when it does, which is all about what we're calling "DOGE Phase II" and I think that we're going to have some info, Dan and Corey, that we'll probably give you in terms to link. I don't have a link yet, but keep an eye on that, because I think that we're going to be talking a lot about it, and for everybody to be able to get aware of it. Absolutely.

Dan Ferris:                 OK. All right. Thanks a lot, Rob. It's always a pleasure to talk with you. Thanks for joining us.

Rob Spivey:                 Dan, Corey – thanks so much. Always a pleasure. Always enjoy the conversation, no matter where it goes.

Dan Ferris:                 Great.

                                    If you want to hear more from Rob Spivey about his ideas on DOGE and AI, you can go to dogesummit.com and sign up to reserve your spot for his upcoming presentation on Thursday, April 24 at 8:00 p.m. Eastern time. Again, that's dogesummit.com.

                                    Well, I learned a lot in that discussion with Rob Spivey. I'm sure you did, too. AI is a big topic for me. I don't understand a lot of it, but I was really interested to hear the connection between what DOGE is doing, and AI and I thought he made it pretty well.

Corey McLaughlin:    I think so. And you're not alone in the grappling with AI, still. It's a lot to think about, but I think he broke it down there in a couple of different ways where you can think about how to actually – I think a lot of people – if you're paying attention to AI at all – understand the potential there. It's just a matter of forgiving out how it applies and how it gets reflected. You said it during the conversation – this generic like – we refer to it as "it".

                                    Or I do. What is this AI thing? It's like the Internet but there's a lot that goes beyond that. And if you think of it as oil – you know, if you go through that energy sector, you can go all kinds of different directions that way. And if you think of data – think of it as data, really, maybe, and go in all the different pathways you can go from there.

Dan Ferris:                 As usual, whenever you learn anything, you find out all that you don't know is the main thing you learn when you learn anything. And I think Rob did a good job of making me feel like I really don't know anything, which is good, because I need to learn more.

Corey McLaughlin:    I did like the point, too, about – or the big idea, I think, about – and we've talked about this. The Amazons, the Googles, Microsoft that are becoming capital intensive when it come to AI with these data centers and everything. But now, what are the adopters, the enablers – as Rob called them – those companies that are, essentially, I guess, software companies that are – maybe it's the same as like, the SAAS companies that became popular. It's just that phase now. The companies that are able to do that at scale and become the leaders of that group are the ones to pay attention to now.

                                    It could be. So, it's not over yet. It's what I'm also taking notes. This trend is not over yet.

Dan Ferris:                 No, no, no, no. There are other things happening in the world besides tariffs, and AI's one of them. All right. Well, that was a lot of fun. That's another interview and that's another episode of The Stansberry Investor Hour.

                                    I hope you enjoyed it as much as we really truly did. We do provide a transcript for every episode. Just go to www.investorhour.com – click on the episode you want, scroll all the way down, click on the word "Transcript" and enjoy. If you liked this episode and know anybody else who might like it, tell them to check it out on their podcast app or at investorhour.com, please. And, also, do me a favor -- subscribe to the show on iTunes, Goole Play, or wherever you listen to podcasts.

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                                    Tell us what's on your mind and hear your voice on the show. For my co-hose, Corey McLaughlin, until next week, I'm Dan Ferris. Thanks for listening.

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