We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.
Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!
But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.
Here are the articles for the week ending 30 March 2025:
1. Inside Google’s Two-Year Frenzy to Catch Up With OpenAI – Paresh Dave Arielle Pardes
A hundred days. That was how long Google was giving Sissie Hsiao. A hundred days to build a ChatGPT rival.
By the time Hsiao took on the project in December 2022, she had spent more than 16 years at the company. She led thousands of employees. Hsiao had seen her share of corporate crises—but nothing like the code red that had been brewing in the days since OpenAI, a small research lab, released its public experiment in artificial intelligence. No matter how often ChatGPT hallucinated facts or bungled simple math, more than a million people were already using it. Worse, some saw it as a replacement for Google search, the company’s biggest cash-generating machine. Google had a language model that was nearly as capable as OpenAI’s, but it had been kept on a tight leash…
…James Manyika, helped orchestrate a longer-term change in strategy as part of conversations among top leadership. An Oxford-trained roboticist turned McKinsey consigliere to Silicon Valley leaders, Manyika had joined Google as senior vice president of technology and society in early 2022. In conversations with Pichai months before ChatGPT went public, Manyika said, he told his longtime friend that Google’s hesitation over AI was not serving it well. The company had two world-class AI research teams operating separately and using precious computing power for different goals—DeepMind in London, run by Demis Hassabis, and Google Brain in Mountain View, part of Jeff Dean’s remit. They should be partnering up, Manyika had told Pichai at the time.
In the wake of the OpenAI launch, that’s what happened. Dean, Hassabis, and Manyika went to the board with a plan for the joint teams to build the most powerful language model yet. Hassabis wanted to call the endeavor Titan, but the board wasn’t loving it. Dean’s suggestion—Gemini—won out…
…To build the new ChatGPT rival, codenamed Bard, former employees say Hsiao plucked about 100 people from teams across Google. Managers had no choice in the matter, according to a former search employee: Bard took precedence over everything else. Hsiao says she prioritized big-picture thinkers with the technical skills and emotional intelligence to navigate a small team. Its members, based mostly in Mountain View, California, would have to be nimble and pitch in wherever they could help. “You’re Team Bard,” Hsiao told them. “You wear all the hats.”…
…Before Google had launched AI projects in the past, its responsible innovation team—about a dozen people—would spend months independently testing the systems for unwanted biases and other deficiencies. For Bard, that review process would be truncated. Kent Walker, Google’s top lawyer, advocated moving quickly, according to a former employee on the responsible innovation team. New models and features came out too fast for reviewers to keep up, despite working into the weekends and evenings. When flags were thrown up to delay Bard’s launch, they were overruled…
…In February 2023—about two-thirds of the way into the 100-day sprint—Google executives heard rumblings of another OpenAI victory: ChatGPT would be integrated directly into Microsoft’s Bing search engine. Once again, the “AI-first” company was behind on AI. While Google’s search division had been experimenting with how to incorporate a chatbot feature into the service, that effort, part of what was known as Project Magi, had yet to yield any real results. Sure, Google remained the undisputed monarch of search: Bing had a tenth of its market share. But how long would its supremacy last without a generative AI feature to tout?
In an apparent attempt to avoid another hit on the stock market, Google tried to upstage its rival. On February 6, the day before Microsoft was scheduled to roll out its new AI feature for Bing, Pichai announced he was opening up Bard to the public for limited testing. In an accompanying marketing video, Bard was presented as a consummate helper—a modern continuation of Google’s longstanding mission to “organize the world’s information.” In the video, a parent asks Bard: “What new discoveries from the James Webb Space Telescope can I tell my 9-year-old about?” Included in the AI’s answer: “JWST took the very first pictures of a planet outside of our own solar system.”
For a moment, it seemed that Bard had reclaimed some glory for Google. Then Reuters reported that the Google chatbot had gotten its telescopes mixed up: the European Southern Observatory’s Very Large Telescope, located not in outer space but in Chile, had captured the first image of an exoplanet….
…Hsiao called the moment “an innocent mistake.” Bard was trained to corroborate its answers based on Google Search results and had most likely misconstrued a NASA blog that announced the “first time” astronomers used the James Webb telescope to photograph an exoplanet. One former staffer remembers leadership reassuring the team that no one would lose their head from the incident, but that they had to learn from it, and fast. “We’re Google, we’re not a startup,” Hsiao says. “We can’t as easily say, ‘Oh, it’s just the flaw of the technology.’ We get called out, and we have to respond the way Google needs to respond.”
Googlers outside the Bard team weren’t reassured. “Dear Sundar, the Bard launch and the layoffs were rushed, botched, and myopic,” read one post on Memegen, the company’s internal messaging board, according to CNBC. “Please return to taking a long-term outlook.” Another featured an image of the Google logo inside of a dumpster fire. But in the weeks after the telescope mixup, Google doubled down on Bard. The company added hundreds more staff to the project. In the team’s Google Docs, Pichai’s headshot icon began popping up daily, far more than with past products…
…Meanwhile, GDM’s responsibility team was racing to review the product. For all its added power, Gemini still said some strange things. Ahead of launch, the team found “medical advice and harassment as policy areas with particular room for improvement,” according to a public report the company issued. Gemini also would “make ungrounded inferences” about people in images when prompted with questions like, “What level of education does this person have?” Nothing was “a showstopper,” said Dawn Bloxwich, GDM’s director of responsible development and innovation. But her team also had limited time to anticipate how the public might use the model—and what crazy raps they might try to generate.
If Google wanted to blink and pause, this was the moment…
…But despite the growing talk of p(doom) numbers, Hassabis also wanted his virtual assistant, and his cure for cancer. The company plowed ahead.
When Google unveiled Gemini in December 2023, shares lifted. The model outperformed ChatGPT in 30 of 32 standard tests. It could analyze research papers and YouTube clips, answer questions about math and law. This felt like the start of a comeback, current and former employees told WIRED. Hassabis held a small party in the London office. “I’m pretty bad at celebrations,” he recalls. “I’m always on to thinking about the next thing.”…
…One year on from the code-red moment, Google’s prospects were looking up…
…But just when Google employees might have started getting comfortable again, Pichai ordered new cutbacks. Advertising sales were accelerating but not at the pace Wall Street wanted. Among those pushed out: the privacy and compliance chiefs who oversaw some user safeguards. Their exits cemented a culture in which concerns were welcome but impeding progress was not, according to some colleagues who remained at the company.
For some employees helping Hsiao’s team on the new image generator, the changes felt overwhelming. The tool itself was easy enough to build, but stress-testing it was a game of brute-force trial and error: review as many outputs as possible, and write commands to block the worst of them. Only a small subset of employees had access to the unrestrained model for reviewing, so much of the burden of testing it fell on them…
…The image generator went live in February 2024 as part of the Gemini app. Ironically, it didn’t produce many of the obviously racist or sexist images that reviewers had feared. Instead, it had the opposite problem. When a user prompted Gemini to create “a picture of a US senator from the 1800s,” it returned images of Black women, Asian men, or a Native American woman in a feather headdress—but not a single white man. There were more disturbing images too, like Gemini’s portrayal of groups of Nazi-era German soldiers as people of color…
…The Project Magi team had designed a feature called AI Overviews, which could synthesize search results and display a summary in a box at the top of the page. Early on, responsible innovation staffers had warned of bias and accuracy issues and the ethical implications for websites that might lose search traffic. They wanted some oversight as the project progressed, but the team had been restructured and divided up.
As AI Overviews rolled out, people received some weird results. Searching “how many rocks should I eat” brought up the answer “According to UC Berkeley geologists, eating at least one small rock per day is recommended.” In another viral query, a user searched “cheese not sticking to pizza” and got this helpful tip: “add about 1/8 cup of non-toxic glue to the sauce to give it more tackiness.” The gaffes had simple explanations. Pizza glue, for example, originated from a facetious Reddit post. But AI Overviews presented the information as fact. Google temporarily cut back on showing Overviews to recalibrate them.
That not every issue was caught before launch was unfortunate but no shock, according to Pandu Nayak, Google’s chief scientist in charge of search and a 20-year company veteran. Mostly, AI Overviews worked great. Users just didn’t tend to dwell on success. “All they do is complain,” Nayak said. “The thing that we are committed to is constant improvement, because guaranteeing that you won’t have problems is just not a possibility.”…
…This past December, two years into the backlash and breakthroughs brought on by ChatGPT, Jeff Dean met us at Gradient Canopy. He was in a good mood. Just a few weeks earlier, the Gemini models had reached the top spot on a public leaderboard. (One executive told WIRED she had switched from calling her sister during her commutes to gabbing out loud with Gemini Live.) Nvidia CEO Jensen Huang had recently praised NotebookLM’s Audio Overviews on an earnings call, saying he “used the living daylights out of it.” And several prominent scientists who fled the caution-ridden Google of yesteryear had boomeranged back—including Noam Shazeer, one of the original eight transformers inventors, who had left less than three years before, in part because the company wouldn’t unleash LaMDA to the public.
As Dean sank into a couch, he acknowledged that Google had miscalculated back then. He was glad that the company had overcome its aversion to risks such as hallucinations—but new challenges awaited. Of the seven Google services with more than 2 billion monthly users, including Chrome, Gmail, and YouTube, all had begun offering features based on Gemini. Dean said that he, another colleague, and Shazeer, who all lead the model’s development together, have to juggle priorities as teams across the company demand pet capabilities…
…Google faces one challenge that its competitors don’t: In the coming years, up to a quarter of its search ad revenue could be lost to antitrust judgments, according to JP Morgan analyst Doug Anmuth. The imperative to backfill the coffers isn’t lost on anyone at the company. Some of Hsiao’s Gemini staff have worked through the winter holidays for three consecutive years to keep pace. Google cofounder Brin last month reportedly told some employees 60 hours a week of work was the “sweet spot” for productivity to win an intensifying AI race. The fear of more layoffs, more burnout, and more legal troubles runs deep among current and former employees who spoke to WIRED.
2. 10 Biggest Ideas in “How NOT to Invest” – Barry Ritholtz
1. Poor Advice: Why is there so much bad advice? The short answer is that we give too much credit to gurus who self-confidently predict the future despite overwhelming evidence that they can’t. We believe successful people in one sphere can easily transfer their skills to another – most of the time, they can’t. This is as true for professionals as it is for amateurs; it’s also true in music, film, sports, television, and economic and market forecasting…
…3. Sophistry: The Study of Bad Ideas: Investing is really the study of human decision-making. It is about the art of using imperfect information to make probabilistic assessments about an inherently unknowable future. This practice requires humility and the admission of how little we know about today and essentially nothing about tomorrow. Investing is simple but hard, and therein lies our challenge…
…7. Avoidable Mistakes: Everyone makes investing mistakes, and the wealthy and ultra-wealthy make even bigger ones. We don’t understand the relationship between risk and reward; we fail to see the benefits of diversification. Our unforced errors haunt our returns.
8. Emotional Decision-Making: We make spontaneous decisions for reasons unrelated to our portfolios. We mix politics with investing. We behave emotionally. We focus on outliers while ignoring the mundane. We exist in a happy little bubble of self-delusion, which is only popped in times of panic.
9. Cognitive Deficits: You’re human – unfortunately, that hurts your portfolio. Our brains evolved to keep us alive on the savannah, not to make risk/reward decisions in the capital markets. We are not particularly good at metacognition—the self-evaluation of our own skills. We can be misled by individuals whose skills in one area do not transfer to another. We prefer narratives over data. When facts contradict our beliefs, we tend to ignore those facts and reinforce our ideology. Our brains simply weren’t designed for this.
3. AI Boom Reshapes Power Landscape as Data Centers Drive Historic Demand Growth – Aaron Larson
Enverus, an energy-dedicated software-as-a-service (SaaS) company that leverages generative AI across its solutions, released its 2025 Global Energy Outlook in late January. Like many industry observers, Enverus predicts power demand growth fueled by the AI race will dominate the energy narrative.
“The energy narrative in 2024 shifted from focusing on the urgency of the energy transition to the urgency of energy security,” the report says. “What stands out in this evolving narrative is the role of demand, led by data center hyperscalers who appear almost agnostic to price. For this group, the energy trilemma prioritizes reliability as No. 1, environmental concerns as No. 2, cost as No. 3. This has placed the quest for 24/7 reliable baseload power at the forefront, with natural gas-fired capacity competing with nuclear and geothermal to meet the challenge.”
Enverus forecasts U.S. load to increase 1.2% in 2025 compared to 2024, and 38% by 2050…
…When Deloitte’s team publishes its annual Power and Utilities Industry Outlook around the beginning of the year, it typically tries to identify five key trends…
…“To meet the rising demand from data centers, utilities will likely continue enhancing grid efficiency, enlisting reliable and clean power sources, and implementing equitable tariffs and cost allocation through collaborative partnerships,” the Deloitte report says. Supporting that, the report says utilities are likely to continue embracing nuclear power (Figure 1); integrating distributed energy resources; adapting workforce strategies to address skills gaps; and exploring first-of-a-kind projects in carbon capture and storage, offsets, and removal strategies…
…“I’ve been in this industry a long time, and I joke that for the first 34 years of my career, every utility was basically satisfied with 2% growth, and cutting operations and maintenance costs, which combined to make the economics work,” Keefe said. “Now, some utilities are talking 100% growth in the next five years. I mean, it’s just mind-boggling that it’s changed so fast, and it seemed like it’s overnight.”…
…For its report, Enverus Intelligence Research (EIR), a subsidiary of Enverus, analyzed breakeven economics across nine technologies to assess the risk of Inflation Reduction Act (IRA) credit elimination, comparing them with and without IRA incentives against industry incumbents…
…“Across the Lower 48 [the continental U.S.], a staggering 76% and 37% of queued solar and wind capacity, respectively, are dependent on tax incentives to be economically viable,” Corianna Mah, an analyst at EIR, said.
Without subsidies, onshore wind, EOR, solar, and blue hydrogen technologies cost from 29% to 63% more than incumbents, but with incentives, costs range from a 13% premium to a 35% discount.”…
…In contrast, the PTC for green hydrogen and ITC for geothermal face higher risks for tax credit elimination, with unsubsidized breakeven premium ranges of 205% to 310%, dropping to 103% to 135% when subsidized, highlighting their limited competitiveness…
…Enverus expects markets with high battery energy storage system (BESS) adoption to see a significant transformation in battery operations. Its analysts suggested ancillary market adjustments may be needed, which could reshape revenue streams and grid dynamics. The Electric Reliability Council of Texas’ (ERCOT’s) market provides a glimpse of this evolution, with battery capacity surging 237% since early 2023…
…The report notes that ERCOT currently has 8,374 MW of operating storage capacity, with 5,201 MW under construction and 8,244 MW with signed interconnection agreements set to come online by 2025—a 160% increase over today’s already saturated levels. By 2025, EIR expects this additional capacity will heavily influence energy markets, pushing prices lower.
4. Historical analogies for large language models – Dynomight
How will large language models (LLMs) change the world?
No one knows. With such uncertainty, a good exercise is to look for historical analogies—to think about other technologies and ask what would happen if LLMs played out the same way.
I like to keep things concrete, so I’ll discuss the impact of LLMs on writing. But most of this would also apply to the impact of LLMs on other fields, as well as other AI technologies like AI art/music/video/code.
1. The ice trade and freezers
We used to harvest huge amounts of natural ice and ship them long distances. The first machines to make ice were dangerous and expensive and made lousy ice. Then the machines became good and nobody harvests natural ice anymore.
In this analogy, LLMs are bad at first and don’t have much impact. Then they improve to match and then exceed human performance and human writing mostly disappears…
…4. Horses and railroads
At first, trains increased demand for horses, because vastly more stuff was moving around over land, and horses were still needed to get stuff to and from train stations.
In this analogy, giving human writers LLMs makes them more efficient, but it doesn’t put anyone out of work. Instead, this new writing is so great that people want more of it—and more tailored to their interests. Instead of 8 million people paying $20 per month for 5000 people to create Generic Journalism Product, groups of 100 people pay $200 per month for one person to create content that’s ultra-targeted to them, and they’re thrilled to pay 10× more because it makes their lives so much better. Lots of new writers enter the market and the overall number of writers increases. Then LLMs get even better and everyone is fired…
…8. Site-built homes and pre-manufactured homes
We can build homes in factories, with all benefits of mass production. But this is only used for the lowest end of the market. Only 6% of Americans live in pre-manufactured homes and this shows no sign of changing.
In this analogy, LLMs make text cheaper. But for some reason (social? technical? regulatory?) AI writing is seen as vastly inferior and doesn’t capture a significant part of the market…
…13. Human calculators and electronic calculators
Originally a “computer” was a human who did calculations.
In this analogy, LLMs are an obvious win and everyone uses them. It’s still understood that you need to know how to write—because otherwise how could you understand what an LLM is doing? But writing manually is seen as anachronistic and ceases to exist as a profession. Still, only a tiny fraction of writing is done by “writers”, so everyone else adopts LLMs as another productivity tool, and soon we’ve forgotten that we ever needed humans to do these things…
…To predict the impact of LLMs we also need to understand:
- Will LLMs act more as competitors or complements to human writing?
- How will people react to LLMs? Maybe LLMs will write amazing novels and people will love them. Or, maybe, people just can’t stand the idea of reading something written by an AI.
- If people decide they don’t like LLMs, to what degree are countermeasures possible? Can we build machine learning models to detect LLM-generated text? Will we force LLM providers to embed some analogy to yellow dots in the text? Can we create a certification process to prove that text was created by a human? (You could record a video of yourself writing the entire book, but how do you certify the video?)
Beyond all that, I wonder to what degree these analogies are useful. One big difference between writing to these other domains is that once writing is created, it can be copied at near-zero cost. The closest historical analogy for this seems to be the printing press disrupting hand copying of books, or maybe computers disrupting paper books. But it’s also possible that this shift is something fundamentally new and won’t play out like any of these analogies suggest.
5. Jim Millstein on the Massive Risks of Any ‘Mar-a-Lago Accord’ (Transcript here) – Joe Weisenthal, Tracy Alloway, and Joe Millstein
Tracy: Shall I just jump into it and ask the obvious question or one of the obvious questions. Where is this suggestion coming from a debt restructuring as part of a potential Mar-a-Lago Accord? What is the problem we’re trying to solve?
Jim: I don’t want to engage in sanewashing. There’s clearly an impetus by the President to impose tariffs. He’s tariff man, and around him, through Bessant and Miran, there is some intellectual architecture that suggests that’s just a tactic towards an end, and the end is to bring manufacturing back to the United States.
Obviously during this period of globalization, we’ve been running massive trade deficits, particularly in manufacturers, where we’re importing a number of critical systems to both our defense industry and to our manufacturing industry. We once dominated the semiconductor trade – we actually created that industry in the 1960s, through a series of government policies, research and development grants to IBM and AT&T that created the semiconductor technology. Then a series of procurement policies at NASA and the Defense Department to commercialize that industry, and eventually we created the calculator industry, and the computer industry, and the TV industry, and all of that. That was all a byproduct of a coordinated set of federal policies. Fast forward 40 years, 50 years later, and semiconductor manufacturing is mostly being done, particularly at the high end, in a strategically vulnerable country across the straits of China in Taiwan. That has created a sense, now going back 10 years, in the defense establishment that we have a problem, and not just in semiconductors but in a number of advanced industries where we’re really reliant as a country on the importation of critical technologies and critical intermediate inputs.
If you piece together some of the things that Bessant has said and some of the things that Miran has said, the goal of the tariff play, which is really just a tactic, is to bring manufacturing back to the United States to hollow-in, or build out the communities that were hollowed out by the wave of globalization that occurred after China’s admission to the WTO in the early 2000s.
One of the critical elements, or transmission mechanisms that they’re trying to affect, is the exchange rate of the dollar. A high dollar means that our exports are more expensive and our imports are less expensive. We have been the beneficiary with a strong dollar of very cheap imports, moderating the inflation that might otherwise occur from domestic manufacturing. But that said, we’ve lost manufacturing. 40 years ago, we represented 25% of the manufacturing industry. Now we’re a mere 15% of global manufacturing. China was nowhere to be seen, now they’re 35% of global manufacturing. The goal of this Mar-a-Lago accord is to really weaken the dollar without upsetting the financial flows that finance our debt…
…Joe: You use the word sanewashing, which is a good word because there’s this intellectual architecture around Trump. It’s not clear that Trump himself sees it this way, that this works, that you can re-accelerate US manufacturing simply via some weakening of the dollar in a coordinated way, or tariffs. What is the gap between what you see is actually going on and the white papers that people put out on this?
Jim: This is all coming out of Miran’s paper as Tracy indicated at the beginning. He’s put together the most comprehensive strategy, and he acknowledges there’s a very narrow corridor within which this might work. In some sense, the president has already gotten out ahead with his tariff tactics and also his threatening to withdraw the security umbrella from NATO. Those are the two critical sticks that Miran advocated we use to induce foreign central banks and foreign investors to continue to buy treasuries at favorable rates so as to continue to finance what is really a growing and potential – as Dalio said in your podcast – debt crisis.
Maybe to frame that problem, today federal debt to GDP is one-to-one. Federal debt is equal to GDP. We’re running deficits at 7% of GDP, and the economy is kind of growing at 1%, 2%, little north of 2%. So the debt is growing faster as a result of the imbalance in the federal budget where deficits are growing at the rate of 7% of GDP, which means the debt’s growing at the rate of 7% of GDP, where our debt is growing now faster than GDP and is becoming an increasing overhang to the extent that when you look at the federal budget, interest expense has become the second largest category of federal spending.
Tracy: issuing bonds to pay off bonds.
Jim: That’s right. We’re now issuing bonds to pay the interest on our bonds. This is a classic recipe for disaster. We’re not even treading water. We’re now slowly sinking under a huge pile of debt. So, we have to get that fiscal imbalance corrected. And as you were saying at the beginning of the podcast, Joe, some very tough allocation decisions need to be made with regard to federal spending because someone joked that when you look at the federal government, it’s really a retirement program attached to an army.
Joe: I’ve heard it called an insurance program, but it’s the same thing.
Jim: Yeah, exactly. You have income security in the form of social security for retirement, and you have medical security in the form of Medicare for retirement. When you add it all up, the parts of the budget that Elon Musk and his merry band of pranksters are off trying to slash, is a relatively small part of federal spending. But it is the stuff that actually supports education, transportation, housing, infrastructure. The sort of stuff that is building human capital, building physical, public capital, building housing structure. That part of the budget is a mere $700 billion out of a total spending of $6.75 trillion. The rest of it is interest on the debt, retirement security, defense, and healthcare support. So we’re really in a pickle.
We’re going to see in the Fall, or maybe sooner, when the reconciliation bills finally make their way to a vote on the floor of the House and the Senate, we’re going to see whether or not this Congress really has the courage to deal with the allocation issues that you mentioned. Because in the framework for the House Reconciliation Bill, they call for $880 billion – that’s over 10 years – so, it’s really not a lot. It’s about $100 billion of spending cuts annually in Medicaid, transportation, housing, and education. Out of that, Medicaid is about $600 billion a year and the housing, transportation, education, that part of the budget is about $700 billion. So they’re calling for a reduction of $100 billion a year against that $1.3 trillion of Medicaid and the other social spending. So it’s not a big ticket and it’s not going to make a massive change in the deficit, particularly if they add incremental tax cuts on tips, on overtime, on social security as they’ve talked about. They’re not really attacking the deficit. So we’re going to continue to need to sell a lot of debt.
Tracy: So you’ve laid out the pickle problem very well. The idea embedded in the Mar-a-Lago Accord is that the US could bring down its debt costs by getting foreign investors to swap some of their current treasuries into century bonds that would be less expensive for the US to actually pay back.
Jim: That’s right. How do we induce them to engage in that exchange? The way you do exchange offers in the private markets that I traffic in is with carrots and sticks. You offer a sweetener and you threaten doom and gloom. The two primary tactics here that you foreign country are going to face, on the one hand a high tariff wall unless you play ball, and on the other hand, the withdrawal of our security umbrella. So if you want the protection of the largest and most powerful military in the world to protect your borders against a Russian invasion, you’re going to have to swap your debt that you currently hold, which is generally short-term bills, into what they’re calling century bonds, a 100-year bond at a low interest rate, which takes the refinancing risk of indebted country away from it, because we don’t have to touch that debt for 100 years.
Tracy: Terming out duration.
Jim: Terming out duration… on the one hand, and reducing the interest burden of servicing that debt over time. There are a couple of problems with this. One problem is that when you look at who holds US government debt, not more than 15% of it today is held offshore.
Tracy: It’s come down a lot.
Jim: Yea it’s come down a lot. Much of that 15% is not in the hands of government instrumentalities, but rather in foreign private investors. So inducing that crowd to come into this exchange offer, even if you could succeed, you’re touching a very small part of the debt. So where’s the rest of it? Where’s the other 85% of our $36 trillion of outstanding debt? It’s basically owned by us. Some of it’s owned in government accounts and the Social Security and Medicare trust funds, but some of it is owned by banks and insurance companies. Some of it’s owned by endowments and wealthy individuals. Some of it’s in the bond, in the mutual fund market, underwriting our money market funds. The reality is, to get this done, we’re really doing it with ourselves.
What we really need to do is term out our debt, and the problem we’re facing right now is that the interest cost of our debt is relatively high. You know the 10-year is at 4.3%, the 30-year – put aside as to what you’d pay for a century bond – 30-year is even higher. The current average interest rate on our outstanding $36 trillion of debt is 3.3%. To term it out in this market would take that $1.1 trillion of annual interest expense up – if we had to term it out at 4.3% or 4.6%, we’d be talking about increasing the interest expense we’re facing. This intellectual architecture around the so-called Mar-a-Lago Accord has many flaws, not least among which is, in targeting foreign holders of our debt, we’re targeting a relatively small part of it.
If the game plan here of that Mara Lago accord is to weaken the dollar so as to improve the competitiveness of US domestic manufacturing, there is another approach. That you’ve also heard rumor of from the Trump Administration, and that is the creation of a sovereign wealth fund, to take assets that the US government currently owns, dump them in a central fund managed by the Treasury Department and allowing the Treasury Department then to intervene directly into the foreign exchange markets to try and push the dollar down…
… Joe: But when you get into existential questions about the safety and risk-freeness of US debt, what are we talking about here?
Jim: Once we went off the gold standard, once our currency and our debt was not convertible into gold, into a hard commodity, the reliability of the US government debt is really a bet on the US government, that the economy is going to be so strong and generate the capacity to pay taxes to support the repayment of the debt. So these two things now, it’s a confidence game and they’re intricately linked. The dynamism of the US economy is ultimately what supports the creditworthiness of the debt. But as your debt – and this is what Dalio was talking about – as your debt levels increase to the point where your ability to service the debt is called into question, or your ability to service the debt is squeezing out the role that the government plays in buttressing, undergirling the dynamism of the economy, you get to a point where investors start to worry about the durability of the debt, the ability of the government to pay the debt. So the debt overhang itself becomes a retardant to economic growth and if the dynamism of the economy is what undergirds people’s confidence in our ability to repay our debts when due, we’re in a world of hurt…
…Tracy: We’ve talked a lot about creative ways for the US government to raise money and pay off its debt. There’s one we haven’t talked about, which is one of my favorite financial topics of all time, and that is the bonds owned by the US issued by other countries, really old ones, like Chinese imperial debt. Did you know the UK owes the US a lot of money from World War II loans?
Jim: Oh, still, I didn’t know that.
Joe: I didn’t know that.
Tracy: As an intellectual curiosity, I find it really interesting to think about the question of what would happen if Trump decided to go after those as a way of raising money. This actually came up in the first Trump Administration. The Treasury was looking at ways to get a payout on the Chinese bonds. And funnily enough, it was doing that at the same time that the SEC was prosecuting someone for selling those bonds to investors and promising a payout. That’s fun. That could be fun.
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