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 05 November 2023:
1. Lessons from Charlie Munger’s podcast interview – Thomas Chua
2. Why algo driven trading firms like Renaissance technology are taking excessive risk
“The easiest trade is to front run what you know, what the average is, what the index funds have to buy, and you know what it is. Exactly. They all know that. And the way they get their returns year after year is taking the leverage, the midday leverage, up higher and higher and higher and higher.
So they’re making smaller and smaller profits on more and more volume, which gives them this big peak leverage risk, which I would not run myself. And that’s the only way they make these big returns, is to have this huge leverage that would make you crazy if you were already rich.”
3. How Warren and Charlie changed their mind quickly with Diversified Retailing after they realized it was too competitive
(and how they made a ton of money after changing their mind)
Some context: On January 30, 1966, Buffett, Munger, and Gottesman formed a holding company, Diversified Retailing Company, Inc., to “acquire diversified businesses, especially in the retail field.”
Buffett and Munger then went to the Maryland National Bank and asked for a loan to make the purchase. The lending officer looked at them goggle-eyed and exclaimed, “Six million dollars for little old Hochschild-Kohn?” Even after hearing this, Buffett and Munger—characteristically—did not question their own judgment and run screaming out the door.
“We thought we were buying a second-class department store at a third-class price” is how Buffett describes little old Hochschild-Kohn.
“We made nothing but money at Diversified. We didn’t exactly make it in retailing, but we made a lot of money.
What happened was very simple. We bought this little department store chain in Baltimore. Big mistake. Too competitive.
As the ink dried on the closing papers we realized we’d made a terrible mistake. So we decided just to reverse it and take the hits to look foolish rather than go broke. You just told us how to get us out of this. By that time we’d already financed half of it on covenant free debt and so forth. And they had all this extra cash and our own stocks got down to selling an enormous (discounts).
In the middle of one of those recessions, we just bought, bought and bought and bought and all that money went right into those stocks and of course we tripled it.”…
...11. Why Warren’s investment in Japan was a no-brainer
“If you’re as smart as Warren Buffett, maybe two, three times a century, you get an idea like that. The interest rates in Japan were half a percent per year for ten years. And these trading companies were really entrenched old companies, and they had all these cheap copper mines and rubber foundations, and so you could borrow for ten years ahead, all the money, and you could buy the stocks, and the stocks paid 5% dividends.
So there’s a huge flow of cash with no investment, no thought, no anything. How often do you do that? You’ll be lucky if you get one or two a century. We could do that [because of Berkshire credit]. Nobody else could.”…
…14. His view on China
“Well, my position in China has been that the Chinese economy has better future prospects over the next 20 years than almost any other big economy.
That’s number one. Number two, the leading companies of China are stronger and better than practically any other leading companies anywhere, and they’re available at a much cheaper price. So naturally I’m willing to have some China risk in the Munger portfolio.
How much China risk? Well, that’s not a scientific subject. But I don’t mind. Whatever it is, 18% or something.”…
…15. What about BYD that captivated Munger?
“Guy (Wang Chuanfu) was a genius. He was at a PhD in engineering and he could look at somebody part, he could make that part, look at the morning and look at it in the afternoon. He could make it. I’d never seen anybody like that. He could do anything. He is a natural engineer and get it done type production executive.
And that’s a big thing. It’s a big lot of talent to have in one place. It’s very useful. They’ve solved all these problems on these electric cars and the motors and the acceleration, braking, and so on.”
Comparing Elon with Wang Chuanfu
“Well, he’s a fanatic that knows how to actually make things with his hands, so he has to he’s closer to ground zero. In other words, the guy at BYD is better at actually making things than Elon is.”
2. The Crash Callers Won’t Save You – Ben Carlson
Here’s something Henry Blodget wrote about notorious stock market bear John Hussman:
Every historical indicator Hussman is looking at is suggesting that the stock market is wildly overvalued and headed for a period of lousy returns. How lousy? John Hussman thinks there’s a good chance the stock market will soon crash 40-50 percent.
And even if the market doesn’t crash, Hussman thinks stocks are priced to produce returns of only a couple of percentage points per year over the next decade–far below the 7 percent inflation-adjusted long-term return that everyone is used to and the double-digit returns of the last few years. If you want to feel comfortable and happy, go ahead and ridicule John Hussman with everyone else. If you want to prepare yourself for what seems like a likely possible stock-market future, however, read on.
Sounds scary, right?…
…Here’s the problem with Blodget and Hussman’s predictions — this piece was written in the summer of 2013!… In the 10 years following Hussman’s prediction of a 40-50% crash or lousy returns for a decade, the S&P 500 was up more than 230% in total or 12.8% on an annual basis:…
…Did Hussman relent from his crash-calling ways? No. He’s still out there calling for a crash, only this time it’s going to be even bigger!…
…When Hussman called for a 40-50% crash in August 2013, he said the Dow could fall somewhere in the 7,500-8,500 range. From current levels at around 32,800 the Dow would need to fall 55% just to get back to the point where Hussman made his initial prediction in 2013 and then another 50% from there to hit that target range…
…I looked at the daily returns on the S&P 500 going back to 1950 to see how often the market was in a state of drawdown at different levels of losses… We’ve had 40% and 50% crashes but it’s pretty rare. You don’t spend all that much time there as an investor. Sometimes you’re going to get your face ripped off in the markets and learn to live with it but you can’t shouldn’t expect it to happen all the time…
…I looked at the rolling 10 year returns for the S&P 500 going back to 1950 to find the distribution of annual returns at various levels… More than 3% of the time returns have been negative over 10 year time frames. Annual returns have been 5% or worse 14.1% of the time. That’s not great. However, annual returns have been 10% or higher 55% of the time. Annual returns of 8% or more have occurred in nearly 70% of all rolling 10 year windows since 1950…
…The people who predict a crash every single year will be “right” eventually. The same is true for those who are constantly forecasting a recession. But they will be wrong the majority of the time. The stock market has been up roughly 75% of the time over one year periods and nearly 97% of the time over 10 year time frames over the past 70+ years.
3. The Long, Long View of Interest Rates – Byrne Hobart
The single most important variable in economics is the risk-free interest rate, i.e. the price of money. Over time, the available data indicate that money has gotten much, much cheaper…
…First things first: a loan to the Duke of Burgundy in the 15th century and a loan to the US treasury in 2023 are completely different things. In the latter case, it’s a loan to a global hegemon that issues the world’s most-accepted reserve currency, a currency in which that loan will be repaid. The Duke, by contrast, is a person, not a country. He collects taxes, but perhaps intermittently, and some fraction of Burgundy consists of his personal property. His big source of uncertain expenses is military campaigns, which are also one of the few legible investments that can produce some theoretical return. But they’re usually a bad deal…
…So for the tiny set of people (like the Duke) who regularly borrowed large sums, their big lumpy expenses were probably ransoms, which means ransoms were potentially a big lumpy source of income as well. In other words, they took part in a physical contest which would lead to an unpredictable positive or negative payoff. 12% is expensive for a sovereign credit, but pretty cheap for a loan to an athlete whose primary source of income is wagering on the outcome of their own matches.
The process of replacing personal relationships with institutional ones has been gradual. Over time, though, it’s created more low-risk or even essentially risk-free lending opportunities. It’s hard to draw a dividing line here, especially because some countries move in and out of risk-free status depending on the market’s general fears and the specifics of their political situation (the spread between German and Italian rates, for example, is a good proxy for the market’s view of how stable Europe is). In the case of the US, we really do have a stable, low-risk borrower—but even the US likes to periodically stress-test the market with debt ceiling fights.
There were lower-risk bonds in the early period of the chart; the paper mentions people earning 5% lending to the governments of Florence and Venice, for example. But in practice, the only risk-free investment at the time was taking hard currency and literally burying it; a risk-free asset with a positive yield is a 19th-century innovation.
Adjust for that, and you’d end up with a new chart: the rate of return on a risk-free investment was on average slightly negative (the inconvenience of hiding it, the risk of losing it, and the possibility, for some currencies, of devaluation). Then it jumped some time in the 19th century despite the fact that governments still needed to borrow, they were now larger and better at tax collection than their predecessors, but their bonds now competed with high-return private sector opportunities). And then we see a decline, or a reversion to the mean, starting in the 1980s: growth declined, rates declined, and the real rate of return on risk-free assets dropped. That’s recently been disrupted again, with the resurgence in inflation since 2021.
But the other big driver of that secular-decline chart is still in place, and it pushes the equilibrium interest rate relentlessly lower. The biggest factor is the existence of retirement…
…The key difference in modernity is that we took a luxury previously available only to the elite, i.e. the ability to live well entirely off the labor of others, and made it an option for anyone who chose to sock away enough money in their 401(k). (The “labor of others” is now some miniscule share of the profits from every company in whatever index the retiree in question has invested in, and their capital comes from forgone consumption when they themselves had a job, but the fundamentals are unchanged.) Longer lifespans that mostly lead to longer retirement rather than more working years will necessarily increase global savings; whether this old-age saving is mediated through private sector investments or through public pensions like social security, it creates an implicit asset on the retiree’s (or future retiree’s) economic balance sheet, a corresponding liability on the part of whoever is offering that income, and thus a demand for income-producing assets that match that liability…
…Technology is also a driver of rates. But the direction is noisy. The more sophisticated the financial system, the more likely it is that deploying new technology will be inflationary. There are two forces at work: in the long run, new technology is deflationary over time, since we’re getting more from less—the number of labor-hours required for illuminating a room for an hour, traveling across the country, or getting a nutritious meal has continuously declined. But when technology is being deployed, it’s inflationary, because there’s more demand for investment and labor. So asking whether the impact of a given technological development is net inflationary or deflationary over, say, the next decade, amounts to asking: how quickly is it getting deployed? If we developed some radically transformative new technology, like a way to generate low-cost, low-emissions energy from trivial amounts of a fairly abundant natural resource, taking advantage of this would require spending money on construction labor, equipment, and raw materials, but would lead to energy abundance over time…
…Last big feature in the real rates model is the existence of a reserve currency. Early in the time series, there were reserve-like currencies; some kinds of money were good for transacting or paying taxes in a specific place, but a ducat or florin was useful just about anywhere, because Venetian and Florentine merchants were almost everywhere, and people who did business with them were everywhere else. But these were small, open economies, of the sort that can’t absorb significant inflows. They’re closer to the Swiss franc than to the dollar: everyone knew they were safe, but it wasn’t possible for everyone in the world to denominate savings in the same currency.
What the dollar as a reserve currency does is to create demand for dollar-denominated savings from exporters, who a) want to keep their currency from appreciating too quickly, and b) want to have local dollar liquidity to ensure that they don’t have a ruinous financial crisis if their exports slow down. The relevant exporters, and the policy consequences, have varied over time; sometimes the petrodollar is the dominant form, and sometimes it’s manufacturing economies. But the direction persists, and as long as the dollar has such strong network effects, there will be foreign demand for dollar-denominated savings with minimal interest rate sensitivity.
Extremely long-term trends are important, because they’re the closest thing we have to true economic fundamentals. If something was true under feudalism and democracy, in wartime and peacetime, in an agrarian economy, a manufacturing economy, and a services-based one, it’s probably just a fact of economic life. The decline in real rates is noisy in the chart and noisier still in reality, but it’s something we should accustom ourselves to: if people live longer than they work, and provide for their old age by saving money; if technological advances are deflationary over time and haven’t been happening as often as they did at the peak; and if countries still grudgingly rely on the dollar; then the long-term set point for rates will decline over time.
4. How Does the World’s Largest Hedge Fund Really Make Its Money? – Rob Copeland
Since founding Bridgewater in his Manhattan apartment in 1975, Mr. Dalio has been said to have developed prodigious skill at spotting, and making money from, big-picture global economic or political changes, such as when a country raises its interest rates or cuts taxes. That made both a lot of sense and none at all; what was it about Bridgewater that made it so much better at predictions than any other investor in the world trying to do the exact same thing?
Bridgewater earned worldwide fame for navigating the 2008 financial crisis, when the firm’s main fund rose 9 percent while stocks dropped 37 percent, making Mr. Dalio a sought-after adviser for the White House and Federal Reserve and attracting new deep-pocketed clients to his fund. Yet the hedge fund’s overall descriptions of its investment approach could be maddeningly vague. Mr. Dalio often said he relied on Bridgewater’s “investment engine,” a collection of hundreds of “signals,” or quantitative indicators that a market was due to rise or fall. Bridgewater rarely revealed any details of these signals, citing competitive pressure, but if they pointed to trouble ahead or even to uncertainty, Bridgewater said it would buy or sell assets accordingly — even if Mr. Dalio’s own gut might have told him otherwise…
…What confused rivals, investors and onlookers alike was that the world’s biggest hedge fund didn’t seem to be much of a Wall Street player at all. Much smaller hedge funds could move the markets just by rumors of one trade or another. Bridgewater’s heft should have made it the ultimate whale, sending waves rolling every time it adjusted a position. Instead, the firm’s footprint was more like that of a minnow.
What if the secret was that there was no secret?…
…In early 2015, Bill Ackman, the endlessly opinionated hedge fund manager, took the first whack. The billionaire founder of Pershing Square Capital had long found Mr. Dalio’s public pronouncements about his quantitative investment style to be generic and even nonsensical. At a charity event in February that year, Mr. Ackman grilled Mr. Dalio during an onstage interview about how Bridgewater handled the assets it managed.
Mr. Dalio responded: “Well, first of all, I think it’s because I could be long and short anything in the world. I’m basically long in liquid stuff. And I can be short or long anything in the world, and I’m short or long practically everything.” He also noted that some 99 percent of Bridgewater trading was automated, based on longtime, unspecified rules. “They’re my criteria, so I’m very comfortable,” Mr. Dalio said.
Mr. Ackman tried another tack. He gave Mr. Dalio a layup, the sort of question asked six times an hour on business television. “Let’s say you were to buy one asset, or one stock, or one market, or one currency. Where would you put your money?” There was a pause, then Mr. Dalio said, “I don’t do that.” He went on to lay out how Bridgewater’s hundreds of investment staff members spent their days, describing a data-driven approach.
Onstage, Mr. Ackman would remark that it was “one of the most interesting conversations I’ve ever had.” But he walked away shaking his head.
“What was he even talking about?” he vented afterward…
…This all piqued the interest of a Boston financial investigator, Harry Markopolos, who had been a no-name analyst in the late 1990s when his boss asked him to reproduce a rival’s trading strategy that seemed to pay off handsomely. Mr. Markopolos couldn’t, but he figured out enough that he began chatting with the Securities and Exchange Commission. Six years later, when his warnings about Bernie Madoff proved right, Mr. Markopolos earned national fame.
To Mr. Markopolos, what was happening in Westport, Conn., where Bridgewater has its headquarters, raised serious questions, according to people who worked with him. Here lay another giant hedge fund famed for an investment approach that no competitors seemed to understand. He got his hands on Bridgewater’s marketing documents, including a summary of the firm’s investment strategy and a detailed chart of fund performance. Bridgewater described itself as a global asset manager, yet these documents didn’t name a single specific asset that had made or lost the firm money. An investment-performance chart indicated the firm seldom had a down year — even when Mr. Dalio’s public predictions proved off, Bridgewater’s main fund, Pure Alpha, consistently seemed to end the year around flat.
As he looked over the documents, Mr. Markopolos felt a familiar flutter in his heart…
…Mr. Markopolos also went to see David Einhorn of Greenlight Capital, the hedge fund billionaire famed for spotting frauds. Mr. Einhorn welcomed Mr. Markopolos into his Manhattan office, and they sat down with a team of Greenlight analysts who Mr. Einhorn said were interested in investigating Bridgewater themselves, two people present recalled.
After hearing Mr. Markopolos’s talk, Mr. Einhorn said it tracked with his suspicions, too. That was all the encouragement Mr. Markopolos needed. Bridgewater, he wrote to the S.E.C., was a Ponzi scheme.
Bridgewater was not a Ponzi scheme. Which is not to say that all was as Mr. Dalio so often described it.
The S.E.C. and other regulators dutifully took meetings with Mr. Markopolos and his team. The whistle-blowers’ report was passed through the organization, and a team at the agency looked into it. (The S.E.C. declined to comment.)
According to a person briefed on the investigation, what they concluded, in part, was that the world’s biggest hedge fund used a complicated sequence of financial machinations — including relatively hard-to-track trading instruments — to make otherwise straightforward-seeming investments. It made sense to the S.E.C. that rivals couldn’t track them…
…As it turned out, by the time the S.E.C. received Mr. Markopolos’s submission, the regulators had already looked into Bridgewater. In the wake of the Madoff fraud, and never having really dug into the world’s biggest hedge fund, S.E.C. staff spent a stretch in Westport, deeply studying the firm’s operations. The S.E.C. did not much bother with how Bridgewater made money, just that it did indeed invest its clients’ accounts…
…Of Bridgewater’s roughly 2,000 employees at its peak — and hundreds more temporary contractors — fewer than 20 percent were assigned to investments or related research. (The rest worked on operations tasks, including the expansion of Mr. Dalio’s “Principles.”) And of those investment staff members, many held responsibilities no more complicated than those of the average college student. They worked on economic history research projects and produced papers that Mr. Dalio would review and edit. As for whether those insights made it into Bridgewater’s trading, most research employees knew not to ask, current and former investment employees said.
Only a tiny group at Bridgewater, no more than about 10 people, enjoyed a different view. Mr. Dalio and his longtime deputy, Greg Jensen, plucked the members from the crew of Bridgewater investment associates and offered them entry to the inner sanctum. In exchange for signing a lifetime contract — and swearing never to work at another trading firm — they would see Bridgewater’s inner secrets…
…There were two versions of how Bridgewater invested hundreds of billions of dollars in the markets. One version, Mr. Dalio told the public and clients about. The other version, current and former investment employees said, happened behind closed doors.
In the first version, Bridgewater’s hedge funds were a meritocracy of ideas. Every investment staff member or researcher could suggest an investment notion, and the Bridgewater team would debate the merits of the thesis dispassionately, incorporating a broad study of history. Ideas from investment employees with a record of accurate predictions would over time carry more weight and earn backing with more client money. Investors flocked to the approach, assured that Bridgewater — unlike other hedge funds — would not rise or fall off a single trade or prediction from the firm founder. It was the Wall Street equivalent of Darwinism, with a thick wallet…
…The bottom line: Mr. Dalio was Bridgewater and Mr. Dalio decided Bridgewater’s investments. True, there was the so-called Circle of Trust. But though more than one person may have weighed in, functionally only one investment opinion mattered at the firm’s flagship fund, employees said. There was no grand system, no artificial intelligence of any substance, no holy grail. There was just Mr. Dalio, in person, over the phone, from his yacht, or for a few weeks many summers from his villa in Spain, calling the shots.
Lawyers for Mr. Dalio and Bridgewater said the hedge fund “is not a place where one man rules because the system makes the decision 98 percent of the time.” They said that “the notion that Mr. Dalio ‘call[ed] the shots’ on Bridgewater’s investments is false.”…
…On Wall Street, the phrase “information advantage” often carries an unseemly implication, suggesting that one is engaged in insider trading. Mr. Dalio’s information advantage, however, was as legal as it was vast.
Bridgewater’s target was information about entire nations. According to employees involved with the effort, Mr. Dalio heavily courted well-connected government officials from whom he might divine how they planned to intervene in their economies — and Bridgewater used these insights to make money in its funds.
Anywhere seemed fair game, even Kazakhstan. The Central Asian nation was not on the first page in any Wall Street manual. Ruled by an authoritarian government, it is the globe’s largest landlocked country yet sparsely populated. In 2013, Kazakhstan began developing what was then the most expensive oil project — a giant field in the Caspian Sea — helping it grow a $77 billion sovereign wealth fund. That money would have to be invested somewhere, and Bridgewater’s client services squad put a meeting on Mr. Dalio’s calendar with Berik Otemurat, the fund’s chief, a bureaucrat who had begun his career barely 10 years earlier…
…Inside Bridgewater, a relationship meant access. The country’s new oil field had taken more than a decade to develop, with near-constant delays. Anyone who knew how the project was proceeding could adjust bets on oil accordingly. Bridgewater’s representatives told the delegation that their firm would be happy to offer free investing advice, and Bridgewater’s team would likewise appreciate the opportunity to ask questions about industries of local expertise…
…The longest-term project for Mr. Dalio was in China, where he made frequent trips. Mr. Dalio hired China Investment Corporation’s former chairman to a cushy job as head of a Dalio charity in China, and he became close with Wang Qishan, who would later become China’s vice premier and widely considered the second most powerful person in the country. Mr. Dalio would occasionally tell Chinese government representatives that when they invested with Bridgewater, their fees were not merely being sent back to America. “Whatever fees you pay, I will donate back to China personally,” he said in one meeting, according to a person present.
In media interviews, Mr. Dalio stuck to a fixed, laudatory line about the country’s leadership. It was “very capable,” he said, over and again, sometimes repeating the phrase more than once in an interview. Those same leaders, he would also say inside Bridgewater, were quick to ask him for advice.
To any reasonable observer — and even to the Chinese themselves — Mr. Dalio was the paradigm of a China booster. But there was also an advantage that could be played. He asked the Circle of Trust to help create a way for Bridgewater’s funds to place bets against Chinese assets, in an offshore way that China’s government couldn’t track. That way, when Bridgewater took the wrong side of China, no one would know…
…With the hope of turning around the firm’s investment performance, members of the Circle of Trust put together a study of Mr. Dalio’s trades. They trawled deep into the Bridgewater archives for a history of Mr. Dalio’s individual investment ideas. The team ran the numbers once, then again, and again. The data had to be perfect. Then they sat down with Mr. Dalio, according to current and former employees who were present. (Lawyers for Mr. Dalio and Bridgewater said that no study was commissioned of Mr. Dalio’s trades and that no meeting took place to discuss them.)
One young employee, hands shaking, handed over the results: The study showed that Mr. Dalio had been wrong as much as he had been right. Trading on his ideas lately was often akin to a coin flip.
5. Palmer Luckey – Inventing The Future Of Defense – Patrick O’Shaughnessy and Palmer Luckey
Patrick: [00:01:42] Palmer, I always like starting somewhere of recent passion, you started to give me some amazing materials, so we stopped and we restarted the recording here. And maybe we’ll just begin with this idea that you were telling me about I’m always interested by major changes that might happen in the world that nobody is really talking about.
And until you said the word synthetic long chain hydrocarbon fuel to me 5 minutes ago, I’d never heard that combination of words before. So maybe you can start there and explain why that topic is of interest to you today.
Palmer: [00:02:16] Well, it’s of interest because there’s a lot of money being bet by companies, but also governments on a handful of specific technological pads for electrifying vehicles, battery electric vehicles, hydrogen electric vehicles.
If you can make synthetic long chain hydrocarbon fuels, in other words, synthetic gasoline, synthetic diesel synthetic jet fuel using carbon from the atmosphere in particular, there’s a lot of ways to do it. Boiling it down, one of the ways to do it. You take water, you crack it into hydrogen and oxygen using some kind of energy source like a nuclear power plant and then you bond it with carbon to make hydrocarbons and then you’ve got artificial gasoline coming out the other end.
If someone can figure out how to do that, cheaply enough. First of all, it’s an incredible carbon capture mechanism. Two, if you can do it cheaply enough, let’s say, $1 per gallon, then all of these trillions of dollars in investment into battery electric vehicles and hydrogen electric vehicles become really a waste of money and a waste of time. There are, of course, some advantages to battery electrical vehicles, hydrogen electric vehicles that wouldn’t apply.
But for the most part, especially on the aviation side, the ability to make fuels to just plug into existing fully known, fully optimized, fully understood and even fully certified systems that are better than the ones that cost hundreds of billions of dollars to develop. That also aren’t as good electric planes spend most of their energy hauling around their energy storage, not people or payload, which, of course, means you need to put more energy into them in the first place than even synthetic fuels with a pretty low conversion efficiency.
The reason it’s so interesting to me is that the bet seems so mis-apportioned, you have so much money going into battery electric vehicles and electrification of electrical infrastructure that’s not moving. And almost nobody betting that you can build systems that make dollar per gallon hydrocarbon fuels using either biological processes like algae farms or mechanical processes, however you’re making the synthetic fuels.
And of course, if someone figures it out, they’re going to really knock a whole bunch of stuff sideways. And we talked about this before, but it’s especially interesting because lots of companies make poor technical decisions and they decide to go down a product path, it doesn’t make sense.
I personally feel like this is a case where you have dozens of governments around the world have decided to commit to a particular product path that isn’t optimal. It’s not the optimal end state or the optimal near term, and they’re dumping hundreds of billions, maybe trillions of dollars into that bet. It’s something that I’m worried about.
Patrick: [00:04:38] When you find an idea like this. First of all, I’m curious how you found this one in particular, but the world has just gone through the superconductor craze, which for a week there it was like, well, if this is real, it changes everything. And then very quickly realize, oh, it’s not real, and it seems like, again, a remote possibility and not terribly likely. So maybe there won’t be loads of dollars trying to create superconductors. With something like this, how do you weigh the potential against the odds of us being able to do it with your own time and investigation?
Palmer: [00:05:05] Well, in this case, it’s 1950s era Department of Energy documents regarding potential energy futures for the United States, accounting for what they assumed would be a nuclear future. I always find it interesting when I go into an area that I don’t understand to try and understand it better.
Even — if I want to understand what’s going on in the modern day, you want to go back to the future and say, what were people saying, back then, what are the ideas that people aren’t even discussing right now? Because I don’t want to be too pessimistic on present, but if you look through a lot of the academic literature and government literature today on energy solutions for the United States, they’re really, really narrow minded.
They are really, really politically driven, it’s all about what is aligned with the current debates going on between political parties. The people in these agencies are largely tied to the things that have already been deemed important. And if you go back on the other hand, to let’s say, post-World War II America, where we were really thinking from first principles, what do we want the world to look like? What do we want the United States to look like?
And what are all of the ways we could get there? They were thinking very expensively. And so this idea of extremely cheap synthetically manufactured biofuels that would get rid of strategic dependence on limited oil supply or allow us to sell off our oil supply to make money in the near term while still having a robust renewable base of energy to power our industrial machine, our war machine, you name it
That was an idea that was of interest to people in the ’40s, the ’50s, the ’60s. I think mostly all of this fell apart when it became clear that we were not going to be a nuclear economy mostly in for political reasons, not practical or technological reasons. So this was the case, right?
I didn’t actually have to be a big thinker. I just had to go say, what were people thinking when they were allowed to think whatever they wanted and when they could think really, really big things? And it’s not even just on fuel. There’s other interesting things they were thinking about back then, like today, if you say, what’s the best way to help the environment in the United States? It’s actually very calcified.
There’s very little consideration for things that are better than what currently exists. You kind of preserving the status quo as the ultimate good. There’s very little consideration for what is better for people, what would be better for more animals. And if you look back again the earlier parts of the United States history, there were serious proposals by the Department of Interior to say, what should the United States ecosystem look like if we could make it whatever we wanted?
What animals would we have? Would we have hippos? Would we have rhinos? Why not? Why don’t we put hippos in Louisiana? There was just this endless possibility, big thinking. But what’s crazy is it’s not even big thinking in the way that we would think of today. When people think a big thinking, they immediately jump to really hard ideas, fusion power and what if we could bio-engineer ourselves? The ideas they were having, they would have a big impact, but they’re actually easy ideas.
Just what if we brought some hippos, put them over there on that swamp? The big idea is what could we do economically? Would that be a good meat source? Could we use that as a better protein source that is less damaging to environments that we’re trying to preserve than what we’re currently doing with cows? And those types of ideas, they’re not taken seriously today. People treat you like a crank if you step outside the orthodoxy…
…Patrick: [00:22:27] When you approach those problems, I have heard you talk about those other two, which is totally fascinating how you approach things. Maybe even before I ask this question, I’m just curious what your method of invention is. So you find an interesting problem either that you want to work in this case, don’t want to work on necessarily. Is your method iterative? Is it more theoretical? Describe the way that you start to invent in a field when you approach something for the first time.
Palmer: [00:22:50] Well, it depends on if it’s a field that I know a lot about or don’t know a lot about. If you know a lot about something, it’s easier to get right into the iterative side of things and know that you’re probably on a pretty reasonable path. In that case, iteration is a valuable tool to move very quickly, find out what works, find out what doesn’t and then continuously make it better.
The risk with going with a strongly iterative approach in areas that you maybe don’t understand, and you might even think you do, but let’s say you truly don’t is that there might be much better approaches that you should have started iterating on. Or that you should have examined before you committed to one particular path. I talked about this earlier, but it’s really about going back to the future. I love to go and see what everyone else who solved this problem thinks about it, not in the recent times.
I don’t want to know what my competition looks like because when I started Oculus, I wasn’t looking at what existing companies were doing in VR because clearly, they were all doing it wrong. Whatever they were doing was not working. I was not going to look around at the handful of VR companies that existed in that time and learn anything except how to fail. So I wanted to look into the past, what were people thinking when they were thinking bigger, when they were willing to look at wackier paths.
When they were willing to consider things that have been eliminated often because technologies just weren’t ready. There’s a lot of technologies that have been discarded because they weren’t practical at the time, and nobody ever revisited them and said, “Hey, I actually think the time has come.” A good example is with the Rift. Doing real-time distortion correction is not a new idea. It existed in the 1980s and 1990s in the virtual reality community. They have been discarded even by NASA.
There’s a fascinating NASA paper where they talk about doing real-time geometry distortion correction on a virtual reality headset that made the optics lower distortion and allow them to, therefore, use wider field of view. Lighter weight optics than would have otherwise been required to have an optically perfect image. And the conclusion was, yes, this is a really good way to save money and to save weight, but it’s too computationally expensive. We’re using most of our processing power to warp the image in real time rather than render this wire frame image.
And so this is not a good approach. You should just do it optically, and then you don’t have to have a more expensive computer. But back then, compute was the expensive part and the optic transform used up a lot of your compute. Nobody reexamined this idea even as computers got better. Nobody went back until me and said, “Wait a second, you can do real-time transform on a modern graphics card for like 1% or 2% of your render horsepower.” And also your graphics card doesn’t cost $100,000 anymore. It costs a few hundred dollars.
And so if you’re worried about that 1% or 2% impact, just buy a graphics card that costs a few dollars more, so you can save hundreds or thousands of dollars on the VR headset itself by using optics that have geometric distortion or in chromatic aberration, for example. That was an idea that had been discarded and nobody ever came back to it.
And most of the things that made the Rift successful were ideas like that, there’s a few others where I was just going back to the future and realizing, “Wait a sec, these ideas, they were actually pretty good. They were just a little too early.”…
…Patrick: [00:35:55] Just trying to think about a framework to discuss the state of weapons technology or the history of weapons technology with you. And the cleanest I could come up with a stupid consultant 2×2, where on one axis, you have offensive versus defensive technologies dominating. And on another, you have democratic versus very non-democratic. Musket’s on one end, everyone has the same amount of power. And on nuclear weapons on the other, one person has a gazillion times as much as the Musket guy or something. Is that a good way to think about where we might pop through history and weapons technology? Is there some other way that you would approach thinking about an era and weapons technology?
Palmer: [00:36:29] Offensive and defensive is definitely the right scale. Distributed or not distributed, I’m not sure. That I actually think matters less by weapon system and more by the power dynamic of the nation. There’s a question here, are the people aligned with the government or are they opposed to the government and to what degree? If you have them where there are neutral parties that accept each other’s existence, that’s one thing.
Let’s say you have a country like Ukraine where implausibly to the Russians, they had formed a strong national identity, and there were people who were willing to die in large numbers for their country. That’s a case where there’s people who are very much aligned with the broader goals of their nation. On the other hand, if you look at a lot of African nations, even a lot of Middle Eastern nations, you have a huge mismatch between what the political class wants and what the every man wants.
And so I think a better access is actually more like democratic versus autocratic technologies. In that, there are a lot of technologies that are much more useful for controlling your own population than for preserving their rights against hostile actors. There’s a lot of countries whose military effectively is an internal peacekeeping force to crush dissent. That’s actually what it’s for.
And there’s a lot of tools made by companies like SenseTime in China that are fundamentally — they are not useful for going to other countries and preserving our rights. They’re not useful for defending yourself from an invader. They are only useful for controlling people in your nation. This is one of the reasons that China is exporting these technologies in the same way that the Soviet Union exported AK-47s, which you could say are on this distributed and offensive side, if you were to look at it that way.
But the reason they were actually doing that is they wanted to arm nations with the tools that they needed to keep their civilian population in check and keep them in power and they wanted to threaten them and say, “Hey, if you ever get out of line, we’re going to stop providing you with these arms and systems you need, and then you’re going to immediately get a violent revolution and you’re probably going to get killed.” It was a great motivation for people to stay stuck to the Soviet Union. This idea that, “We are the thing that allows you to keep your people in check, and without us, that is over.”
China is pursuing a similar strategy. They’re going to African nations and saying, “Hey, we’re going to help build out this infrastructure in your ports, on your roads, in your police force, in your military. We’re going to build AI camera systems that track dissidents for you. They track where they’re shopping, where they’re going, where they’re riding trains. We’re going to allow you to monitor all their communications on the telecommunications side. So let us sell you telecommunications gear, and you get all these back doors that allow you to control people who’re trying to come after you.”
But a bargain with the devil that they’re making is, “Oh, and by the way, if you ever do anything that’s counter to Chinese interests, we’re going to pull all of this and you’re going to lose all your tools for controlling your population and you’re going to be dead inside of a week.” And say SenseTime is on the autocratic, authoritarian side of that scale because it has almost no application in preserving freedom or in deterring an invasion. It’s only for controlling your own people…
…Patrick: [00:48:53] So you built Lattice. Now the world is catching up. Everyone wants to build an AI platform system. What have you learned about building one? What are the components of it? What do you think about AI at large, the cost associated with compute and AI? All of these big things you’ve been working in for a while, and now it matters to everybody. So what would you contribute as the lessons that you’ve learned there so far?
Palmer: [00:49:11] It’s been a double-edged sword, I think. We’ve been working on AI for defense since literally day one. That was the whole pitch. The second page of our pitch deck was a quote from Vladimir Putin. He was talking about artificial intelligence, and he said, “The country that wins in this sphere will become the ruler of the entire world,” which I love. It’s a very James Bond villain quote.
On the one hand, it sounded crazier at that time because AI wasn’t hot seven years ago. There were people who were interested in it, but it’s obviously not even a 0.01 of the attention that’s being dedicated to it today. The flip side of that is now that everyone is saying they’re AI, all of a sudden our message is getting diluted or, “Hey, we’ve actually been doing AI for defense for almost seven years now.” And now everyone is changing to say, “Yes, our systems are all powered by AI. It’s all AI-driven.” Some of that’s true, some of it’s not.
Now it actually is less differentiated than we were. You have to now get very clear about what the difference between a real usable, fieldable AI system looks like versus strapping together ChatGPT with whatever your quadcopter thing is and saying that it’s going to change the world. I do think what’s been helpful to us, though, with members of Congress, people in The Pentagon, even investors has been the explosion of firsthand understanding of how powerful AI can be that’s been driven by these large language models.
Obviously, the things that we are building that fuse data from thermal vision and radar and signals intelligence processors, that then calculate optimal weapons pairing against that target, very different use of AI than a thing that you tell to write a poem about your car.
But the fact that every member of Congress has been able to use ChatGPT, the fact that all these people in The Pentagon have seen and used systems, doing things that they never believed a computer could do has, I think, expanded people’s minds in general towards the possibility that maybe people can be replaced by AI in certain use cases, maybe there are areas where computers really can do a job as well or better than a person. A lot of skeptics, I think, have changed their minds because they type something into ChatGPT, it did something for them, and they said, “Wow, computers sure are amazing these days.
Patrick: [00:51:20] What has AI most unlocked?
Palmer: [00:51:22] The most important thing that it has unlocked for us is ability to scale. People focus on use cases where AI can do better than a person or even better than a team of 100 or 1,000 people at some one specific task. I think a lot of the more interesting use cases are where you can do as good of a person but without a person having to do it.
Let’s use an example. Let’s say that I’m going to deploy 1,000 autonomous cruise missiles. Those are going to be a lot more impactful than they would be if I had to have 1,000 people trying to remotely pilot 1,000 systems and tell them what to do, how to do it, have all those data links active. I guess it’s using AI to do things that would be impossible to do otherwise, either for real technical reasons like bandwidth or just for practical reasons. We don’t have thousands of pilots that we could dedicate to such a task. For me, I think that’s the biggest thing AI enables.
I’m less focused on the superhuman, super intelligent side of things and more, “Hey, this AI that’s running my autonomous helicopter, is it about as good as a pretty good helicopter pilot? Okay, that means that I can have one soldier managing a fleet of 25 airframes himself and just telling them, “Hey, I need you to clear this area. I need you to find this target I’m looking for. I need you to fly ahead of my convoy and watch for anything.” Now he doesn’t need 25 pilots and 25 helicopters to do that. That’s what I’m most excited about.
And it’s really important in a world where I think quantity is going to have a quality all of its own in these types of weapon systems. The best way to defeat a lot of our adversaries’ defenses is not through building a small number of exquisite systems. But quantities that are so large, they can’t possibly stop them.
And it’s especially important in a world where militaries are struggling to recruit. They’re trying to be more cost effective. They’re trying to put less money into salaries and disability payments and more into systems that are going to be fighting the adversary directly, robotic systems.
For example, the United Kingdom has said that they want to, over the next few years, reduce the size of their Navy by 30%, 30% personnel reduction. And because of that, they are doing things like dedicating one of their two aircraft carriers to being an autonomous aircraft launch system.
In other words, they want one of their carriers to only launch autonomous systems. You don’t have to have huge numbers of people to run and maintain these traditional manned systems. If that’s the world we’re going to live in where we need to ramp up the number of systems but also ramp down the number of people, the only thing that can fill the gap is automation.
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