Transcripts
Erik: Joining me now is Dr Anas Alhajji, former chief economist for NGP energy management and founder of Energy Outlook Advisors. Anas, it's great to get you back on the show. I want to start by crediting you. When we first spoke just after October 7 last year, just after the Gaza attacks, I was convinced that we were about to see a huge blowout to the upside in oil prices. And you very correctly and presciently corrected me and said, look, it's not 1973, it's a different situation, in that situation, which is what everybody was drawing analogs to at the time. You had all of the other Arab states wanting to get behind Iran. This is a situation which is opposite. You nailed that call. I didn't listen to your advice. Lost money on the trade by not heeding the master. So, I won't repeat that mistake. It's a year later. Give us the update. How should we be thinking about the Middle East, where it could be headed from here and what it will mean for energy markets.
Anas: We had a situation in recent weeks, basically, where we had a rumor that Israel was going to retaliate for the Iranian attack by attacking its oil facilities, and prices started going up. And then at the end of last week, someone asked President Biden about the situation and whether Israel will attack the oil facilities of Iran, and President Biden basically made the statement, kind of a strange statement, he said, we are discussing it. The market, and many analysts understood it as it's a green light from the United States, and probably the United States will even participate in an attack on Iran. Our view from that, when I say our I'm talking about me and my colleagues at Energy Outlook Advisors, we thought that the market misunderstood what President Biden said. He meant we are discussing it. It means that we are trying to tell the Israelis not to do it. So, we've seen prices going up to $80, then we have the anniversary of the October 7 on Monday. And everyone was expecting something to happen, but prices went up, and then they went down by 5% on Tuesday. Went down a little yesterday, and now they are going up by about 3%. What happened is, why we have this rise on Monday and this rise again, etc., that someone asked Kamala Harris about Netanyahu, whether he is an ally or not, and Harris basically tried to avoid the question. That gives the impression to the market that Netanyahu is a leech and the United States has little influence over him, and therefore he can do whatever he wants, and therefore the Iranian oil facilities are still under threat, no matter what. The other issue we have, basically, the hurricane, just the news of the hurricane, basically kind of make traders on the edge. We have some companies that withdrew workers from certain platforms in the Gulf of Mexico. And then we have, of course, no gasoline in many counties in Florida right now. We have major chaos, etc. So, all of these combined led to the increase in oil prices today.
Erik: Joining me now is Harley Bassman, Convexity Maven, as he is known, and also with Simplify Asset Management, where he runs several ETFs. Harley prepared a slide deck to accompany today's interview. Registered users will find the download link in your Research Roundup email. If you don't have a Research Roundup email, just go to our homepage, macrovoices.com, look for the red button above Harley's picture that says, looking for the downloads. Harley, it's great to get you back on the show. Let's start with the big picture on mortgage-backed securities, because there's something I've never understood about them. We all know about these things, everybody does. We're not talking about the esoteric securities that only professionals can trade, because they're not available on listed markets. We're talking about bonds here. Yet, for some reason, it seems to me like mortgage-backed securities are kind of a mystery world that are, all these bonds get sucked up into CDOs for some reason, and nobody trades the bonds directly. Am I missing something? What's going on here?
Harley: Well, there's a number of things. First off, thank you for having me back. It's a great compliment. You guys have the best financial show on the market, so it's great to be here. I think what's important to note is that when we say the word mortgages, people generally think, great financial crisis, subprime, default risk. That's fine. That is true. But that's not what I'm talking about here. When we say mortgage-backed securities, MBS, we're talking about basically the second largest asset class in the bond market, only behind US Treasuries, and certainly more than corporate bonds and junk bonds. And these securities, they would drive the whole mortgage process, because most homes are purchased with loans that get put into mortgage-backed securities that are wrapped guaranteed by Fannie, Freddie or Ginnie. Civilians, what I call retail non-professionals, have no contact whatsoever with these bonds. Almost no one buys these securities. They're not available because they're just dirty little animals. You will buy them on ETFs or mutual funds or others, lots of ways to do it. But most people do not trade mortgage-backed securities for a variety of reasons we could talk about.
Erik: Joining me now is Forest For The Trees Founder, Luke Gromen. Luke, it's great to have you back on the program, it's been a long, long time. I want to start with what the heck is going on with China, because I've been trying to get my head around this all year. I've been asking lots of our feature interview guests to explain the China situation. And the explanation has been, everybody thought China was going to recover and the stock market was going to recover, and it just didn't happen. Well, that was only two weeks ago. I swear, I’m not making it up. Now, it seems like China is outperforming everything. The Chinese equities are overbought, outperforming the S&P on the year. What just happened, what's driving it, what's the bigger picture, and how does this fit?
Luke: I don’t know if I'm the right person to talk to on that…
Erik: I think you’re the perfect person to ask for that question.
Luke: So for me, I've said this in a couple like the video appearances that I do and what have you is, I couldn't figure out what China's plan was. To me, we know China tends to not do anything without a plan behind it, and we knew overwhelming Western consensus was China's making a mistake by not stimulating, etc., and sort of, China is uninvestable, and China is collapsing. And I, knowing that China tends to have a plan, knowing that clearly the economy was weakening meaningfully, and they weren't stimulating, etc., I was sort of grasping for possible reasons, one of which I came to was maybe they're engaged in basically a pain contest, which was to say, you know what, we're not going to stimulate. And the problem with that is, if we don't grow, that's going to force you to cry uncle first, USA, you're going to have to cut rates. You want to weaponize the dollar against us? Great. We just won't stimulate. We'll take pain, we'll take pain, we'll take pain, and we'll see who ends up going first. And that was kind of my working theory, loosely held. So fast forward two weeks ago, we have Powell come out and surprise most of Wall Street by doing 50 basis points and promising 50 more by year end. Dollar weakens, US asset crisis rip. Then, we have China follow up four days later, doing what they did. And so, now it starts to look like one of two things, which is either Powell cried uncle first, which gave China the ability to sort of then finally, do the stimulus without having to do stimulus with the yuan at a key weak threshold, the yuan has obviously rallied meaningfully versus the dollar from July through today, and that gives China the breathing room. Or, the US and China are coordinating on some level. We've been writing since November of last year, when Treasury went over to Beijing and talked about, China needs to basically raise their costs, right? Well, they raised their costs by raising the currency, by weakening the dollar. So, we've been saying for 10 months, starting last November, that it looks like the that Yellen and the Chinese are working on some sort of deal to strengthen the yuan, weaken the dollar. And maybe this is part and parcel to that, that basically, sort of a point has been reached where, okay, they agreed to something, and we're moving in that direction. So, now the Americans can cut rates and weaken the dollar, and the Chinese can do what they're doing, which is stimulating. And I think, maybe the most important takeaway that I took so far from the stimulus is the yuan rose against the dollar on this, that is, if you would have asked 100 people, including me, hey, China comes out last weekend, before they did this, China is going to do a big stimulus this week. What does the yuan do? I would have said, and all other 100 probably would say, yuan falls sharply, and instead, the yuan rose pretty notably. And so that points to sort of a capital repatriation, tight dynamic that I don't think a lot of people are talking about yet. I don't even feel that strong about it yet, just been too short, but that's how I'm thinking about as a framework. I could go either way. I could be pushed either way on it. I don't have a strong feeling on it, but that's kind of the framework I'm looking at about it.
Erik: Joining me now is Eric Peters, CIO of One River Asset Management and also of Coinbase Asset Management. Eric, it's great to get you back on the show. Let's start by talking about the US dollar. What's going on here in terms of Fed policy, big picture? It seems like everybody thought the Fed should not wait until this close to the election to start easing, because that would create the appearance of them becoming political. Seems like they have hit across that Rubicon. They sure do appear political. What is happening? What should we expect? And where do you see things headed for the dollar from here?
Eric Peters: Well, for starters, great to be back. It's been a while, actually, as we were chatting pregame here, it's been a little while, really nice to be back. Boy, you know the Fed being political is, I think the Fed is naturally political, given that the chairman is appointed. Question is, how removed are their decisions from the political calendar and process? And I think they've got a very difficult job here. They were well behind the curve for a long time. They had thought inflation would be transitory. They actually changed that tune and raised rates at a pretty unprecedented pace relative to history, and now we're sitting here with quite high real rates. Prime rates are extraordinarily high on a real basis, when you look back historically, which is where many businesses borrow, and I think that they've really been praying that we get lower inflation here, and a bit of softness in inflation, just so they can try to bring that rate back down. And, hopefully create a “soft landing.” I don't think it's extremely political. I think there are all sorts of other political manipulations that lead up to the election. The budget deficit this year has gone up 25% roughly, relative to last year coming to the election. So, I think that's probably a more powerful force than what the Fed can do, just briefly before the election.
Erik: Joining me now is freelancer.com founder and AI expert, Matt Barrie. Matt, it's been eight months since we spoke about AI last, it seems like there's been a disturbance in the force, some kind of a blow off. I don't know if it was a top or just an intermediate step along the way, but it seems like the enthusiasm is starting to abate a little bit on AI. Is this the beginning of the end? The end of the beginning, or something different? Let's recap what's happened since we spoke last.
Matt: Well, it's certainly interesting times. I think perhaps the best way to understand where we are in the space is probably to just do a bit of a recap first on the fundamental breakthrough that's happened in AI and how that translates to the underlying economics. Because, while the breakthroughs have been astonishing and unpredictable, even for the inventors of many other systems themselves, once one understands the economics, I think your listeners will get a good feeling for what's actually going on in the space and how it may play out. Now, fundamentally, what's going on, and the big breakthrough that's happened in the last few years, is the ability for effectively, machine learning or artificial intelligence, to consume very, very large data sets, to train and do so in a way where the more data you feed it, the better the AI gets. If you look at one of the very common forms of AI that are out there, which are these large language models like ChatGPT, which I think it is probably the AI that most of your listeners will be familiar with, essentially what these LLMs are, at the very core, a next word predictor. So, you take a lot of training data, you train the model, you then give it some new input, which is what you type into the ChatGPT interface. And all it's really doing, fundamentally, is predicting the next likely bit from your question, which was effectively the answer. So it's a bit like a next word predictor. So you give it a sentence, and ChatGPT will predict the next likely word, next word after that, and so on. Now, the fundamental breakthrough that happened in this space was really what's called a Transformer, which was invented by Google, which allows neural networks or machine learning to consume large amounts of training data without getting lost, and to do so in a very highly paralyzable way. So that reduces the time to train it and partly the cost. So you take a large amount of text, say, a large amount of English text that you scrape off the internet or get from books or other places, and you train it, and in the first instance, the AI will complete the sentence in a way that the output looks okay, but you could tell it was obviously written by a computer. But then what the Transformers allowed these models to do is step up the amount of data you feed in by an order of magnitude. Step up by an order of magnitude to compute and the parameters in the model. And then all of a sudden, the AI starts to get really good at completing that sentence. You know, the English is perfect, and so forth and it can carry a conversation. It becomes a little bit mesmerizing. But you step up again and again and again, and all of a sudden, that's where you get this sort of voodoo magic coming out the end of the model, where it can suddenly speak to you in Persian. It doesn't know math, it doesn't know math, it doesn't know math, suddenly, it can do University calculus. It can pass the bar exam. It can write the next Harry Potter book. It can do pattern recognition. It can bounce a pencil on a robot arm. It can control the HVAC system in the building. Because it's consumed so much data into this model that these abilities start emerging now. These abilities are emerging in a way where the inventors, they didn't predict them, they don't know how it's really happening. It's just kind of coming out with a certain amount of magnitude. And I think this is the important thing to remember, in terms of the fundamental economics, is that, the breakthroughs that we're seeing as we go from ChatGPT to GPT 4.0. And, we're waiting for GPT 5.0 to come out sometime, it's been at least, I think, since was it March 2023? We've been waiting for GPT 5.0 to come around. These models need to consume ever large amounts of data. They need to consume and do so with ever increasing orders of magnitude in terms of compute that has real constraints in terms of access to chips, access to data centers, access to energy, and fundamentally, you also need access to the actual raw data itself. I mean GPT 2.0, which was really only interesting to computer scientists, and came out many, many years ago that had about 1.4, 1.5 billion parameters in the model, and scraped out about 8 million web pages. And to give people probably a bit of an idea, there's probably about a billion websites on the internet, and about 200 million of them are basically active. GPT 3.0, when that came along, stepped it up by an order of magnitude. So, you went from 1.5 billion parameters, you can think of a parameters like a synapse in a brain, and it went from 1.5 billion to about 175 billion parameters. And the data it scraped down was about 400 billion tokens, which is about 3% of Wikipedia, about 8% of books, 22% of the web, and 60% of what we call a common crawl data set, which is what all these models are using the fundamentals to train on.
When GPT 4.0 came along, which is that big leap, where all of a sudden it could pass any exam that any human could take in the top decile, or sometimes in the top percent, it stepped up from 175 billion parameters, or synapses, to 1.8 trillion parameters, and it went from 400 billion tokens to 13 trillion tokens. And pretty much consumed very, very large percentages of the web. It added in Twitter, it's believed to add in Reddit, YouTube, etc., and so forth. But it consumed a huge, huge amount of data. Now we've been sitting around waiting for some time before GPT 5.0 comes along. And you think about, well, where does it go from here? Where does it get the data? Where does it get the compute? Where do the chips come from? And where does the money come from? I think the last estimate of the training model, the last training instance for GPT 4.0 was something like $80 million, just to do a training run. And I think Gemini is estimated to be something around $200 billion. So yeah, as we kind of step up going through the scale, you can see here, we're starting to reach some fundamental constraints in terms of economics and reality, in terms of where's the data coming from. If you scrape down the entire internet, I mean, there are some really good contemporary data sets, for example, in your phone, and there are many other data sets that we will consume. They're trying to get into video, which is obviously, there's a lot of data there we haven't really got into, in a very, very big way. But where's the money coming from? We've seen Nvidia's stock price go tilt, and I think they're punching about $300 billion a quarter of revenue, but 46% of that revenue that's coming in is coming from four customers, because you probably guessed the likely suspects of who those four are, because there's not many companies out there that can afford to buy the chips, set up the data centers and run these training runs. And so that's kind of why I think we're in this little bit of a lull right now, is because we're starting to see that we've kind of caught up to the available easy data, the available easy compute within the constraints of what companies have to spend on training runs. And now we're starting to jump up that next order of magnitude. And that's why, despite OpenAI promising all these things, I mean, what have they talked about since GPT 4.0 has come out? We've had talked about this advanced voice mode search GPT, which is their attempt to go after perplexity in providing a better version of Google. There's all this cryptic meme activity on Twitter, which I don't know if it's Sam Altman kind of just running a soft puppet account, or what have you, talking about strawberry, whatever that may end up being, Orion and obviously Sora, the video modality of the GPT model, where some pretty impressive videos were shown.
But then there's been nothing for months as OpenAI is just trying to figure out how to make it all work. And it's also very clear, I think, to anyone, the layman even out there, that there's a complete lack of sustainable competitive advantage, open source is catching up very, very quickly. Facebook is really leading the way there by open sourcing a lot of the Llama tooling that they're developing, in order to neuter the competition and make it really a war of attrition in terms of resources. And there's no business model right at the moment charging a few cents for an API call. The end reality is, there's no actual business model here with these foundational models. Now, there's going to be some incredible applications, which we'll talk about later with the AI and the incredibly lucrative opportunities. But in terms of these foundational models that are taking $100 billion to do a training run, and those numbers going up by laws of magnitude, you've got to think about it, if your cost of goods is $100 million to do a trading run, you've got to generate probably at least $500 million of revenue in order for that to be an economically viable activity as a business. So, we're kind of in this lull at the moment. There's certainly been some interesting things happening at Open Source. There's been some interesting bits and pieces coming out in the space around the edges, certainly in the image space, and the ability to generate high fidelity images of any particular type, and the reverse, in terms of being able to analyze your images and then kind of extract what's going on in the scene, some pretty amazing applications that are going to be quite possible. It's very clear that the text modality is pretty much solved, and now they're trying to chip away on video. So, some pretty amazing things have come out. But fundamentally, we're reaching these limits in terms of just the world. Compute, chips, access to data, and ultimately the underlying physics of all of that, which will get into energy and so forth, which I know we'll talk about later in the episode.
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