Author: Scott Buchanan

How Many People Think the Earth is Flat, and Doesn’t Move?

Scott Buchanan2 Comments

Most of us have read or heard references to flat-earthers. I have always assumed they are some tiny tongue-in-cheek group which is just playing out an elaborate joke on the rest of us. The Greeks figured out by 300 A.D. that the earth was round, and this tidbit was incorporated into medieval scholarship, so there has never been much religious or traditional impetus for a flat earth. I was therefore a bit surprised to learn that flat earth beliefs are very serious to many folks, and that such beliefs seem to be on the rise.

From Wikipedia:

In 2020, it was reported that based on polling by Datafolha, 7% of Brazilians believed in a flat Earth. A 2018 YouGov poll found that around 4% of the population of the United States believed in flat Earth while the POLES 2021 Survey found around 10% of the United States population believed that the Earth is flat. A 2019 YouGov survey found that around 3% of British people supported flat Earth.

Digging into that 2018 YouGov poll finds that 2% of Americans resolutely say the earth is flat, but many others who lean towards a round earth are not quite sure. Flat-earthism is more prevalent in Millennials than in older folks, only 66% of Millennials firmly believe the earth is round :

While an overwhelming majority of Americans (84%) believe that the Earth is round, at least 5% of the public say they used to believe that but now have their doubts.
Flat earthers find traction in their beliefs among a younger generation of Americans. Young millennials, ages 18 to 24, are likelier than any other age group to say they believe the Earth is flat (4%).

Apparently, a YouTube channel launched in 2015 by real-life pinball wizard Mark Sargent (“…a competitive video game player, winning one virtual pinball tournament”), which has amassed over two million views, has played a role in popularizing flat earth beliefs. In his brand of geography, the center of the earth-disk is roughly the North Pole, and the edge of the earth-disk lies in what we normally think of the extreme south, and is surrounded by an ice-wall. Several basketball players (Kyrie Irving, Wilson Chandler, Draymond Green) and a rapper (B.o.B) have come out in favor of flatness. The NASA conspiracy of a round earth is crumbling…

 I think some of this flat-earth polling is just ignorance, especially those who are not sure. But there are those who “have their reasons”, often citing various (pseudo) scientific arguments to support their beliefs:

Research by Carlos Diaz Ruiz and Tomas Nilsson on the arguments that flat Earthers wield, shows three factions, each one subscribing to its own set of beliefs.

The first faction subscribes to a faith-based conflict in which atheists use science to suppress the Christian faith. … their arguments use the Scripture – word-by-word – to support an argument that enables God to really exist.

The second faction believes in an overarching conspiracy for knowledge suppression. Building upon the premise that knowledge is power, the flat Earth conspiracy argues that a shadowy group of “elites” control knowledge to remain in power. In their view, lying about the fundamental nature of the Earth primes the population to believe a host of other conspiracies. …

The third faction believes that knowledge is personal and experiential. They are dismissive of knowledge that comes from authoritative sources, especially book knowledge. 

Belief in geocentricity (i.e., that the earth is stationary and the sun goes around the earth) is even more widespread than belief in a flat earth. From Wikipedia:

According to a report released in 2014 by the National Science Foundation, 26% of Americans surveyed believe that the Sun revolves around the Earth.  Morris Berman quotes a 2006 survey that show currently some 20% of the U.S. population believe that the Sun goes around the Earth (geocentricism) rather than the Earth goes around the Sun (heliocentricism), while a further 9% claimed not to know. Polls conducted by Gallup in the 1990s found that 16% of Germans, 18% of Americans and 19% of Britons hold that the Sun revolves around the Earth.  A study conducted in 2005 by Jon D. Miller of Northwestern University, an expert in the public understanding of science and technology,  found that about 20%, or one in five, of American adults believe that the Sun orbits the Earth.  According to 2011 VTSIOM poll, 32% of Russians believe that the Sun orbits the Earth.

Geocentrism seems particularly driven by religious concerns, although I think the polls also heavily reflect plain ignorance. There are passages in the Bible which, if taken literally, seem to mandate a stationary earth and a moving sun. The Roman Catholic church has tiptoed away from its condemnation of Galileo four hundred years ago, and essentially accepted his contention that such passages were never intended to be taken literally. Nevertheless, Catholic layman Robert Sungenis has vigorous argued for geocentricity and Bible literalism, publishing books such as Galileo Was Wrong. On the fundamentalist Protestant side, there is the Association for Biblical Astronomy, with its web site www.geocentricity.com. They make arguments to dismiss the usual scientific conclusions on this matter.

Geocentricity is somewhat poignant for me, because a good friend of mine from college later became deeply attached to it, to the point that he rejected my thinking as apostate when I disagreed. He was a bright guy and an Ivy League graduate. Which just goes to show that fringe beliefs can have unexpected appeal.

Charles Hugh Smith: Six Reasons the Global Economy Is Toast

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If you are feeling OK about the world after a nice Labor Day weekend, I can fix that. How about six reasons why global economic growth will slow to a crawl, courtesy of perma-bear Charles Hugh Smith?

Smith is recognized as an earnest, good-willed alternative economic thinker. His OfTwoMinds blog and other publications bring out many valid facts and factors. He has been extrapolating from those factors to global financial collapse for well over fifteen years now, growing out of the imminent peak oil movement of circa 2007 vintage and the scary 2008-2009 financial crisis. Obviously, he has continually underestimated the resilience of the national and global systems, especially the ability of our finance and banking folks at keeping the debt plates spinning, and our ability to harness practical technology (e.g. fracking for oil production). Smith recommends preparing to become more self-reliant: we should learn more practical skills, and prepare to barter with local folks if the money system freezes up.

For now, I will let him speak for himself, and leave it to the readers here to ponder countervailing factors. From August 11, 2024, we have his article titled, These Six Drivers Are Gone, and That’s Why the Global Economy Is Toast:

The six one-offs that drove growth and pulled the global economy out of bubble-bust recessions for the past 30 years have all reversed or dissipated. Absent these one-off drivers, the global economy is stumbling off the cliff into a deep recession without any replacement drivers. Colloquially speaking, the global economy is toast.

Here are the six one-offs that won’t be coming back:

1) China’s industrialization.

2) Growth-positive demographics.

3) Low interest rates.

4) Low debt levels.

5) Low inflation.

6) Tech productivity boom.

( 1 ) Cutting to the chase, China bailed the world out of the last three recessions triggered by credit-asset bubbles popping: the Asian Contagion of 1997-98, the dot-com bubble and pop of 2000-02, and the Global Financial Crisis of 2008-09. In each case, China’s high growth and massive issuance of stimulus and credit (a.k.a. China’s Credit Impulse) acted as catalysts to restart global expansion.

The boost phase of picking low-hanging fruit via rapid industrialization boosting mercantilist exports and building tens of millions of housing units is over. Even in 2000 when I first visited China, there were signs of overproduction / demand saturation: TV production in China in 2000 had overwhelmed global and domestic demand: everyone in China already had a TV, so what to do with the millions of TVs still being churned out?

China’s model of economic development that worked so brilliantly in the boost phase, when all the low-hanging fruit could be so easily picked, no longer works at the top of the S-Curve. Having reached the saturation-decline phase of the S-Curve, these policies have led to an extreme concentration of household wealth in real estate. Those who favored investing in China’s stock market have suffered major losses.

( 2 ) Demographics

Where China’s workforce was growing during the boost phase, now the demographic picture has darkened: China’s workforce is shrinking, the population of elderly retirees is soaring, and so the cost burdens of supporting a burgeoning cohort of retirees will have to be funded by a shrinking workforce who will have less to spend / invest as a result.

This is a global phenomenon, and there are no quick and easy solutions. Skilled labor will become increasingly scarce and able to demand higher wages regardless of any other factors, and that will be a long-term source of inflation. Governments will have to borrow more–and probably raise taxes as well–to fund soaring pension and healthcare costs for retirees. This will bleed off other social spending and investment.

( 3 ) The era of zero-interest rates and unlimited government borrowing has ended. As Japan has shown, even at ludicrously low rates of 1%, interest payments on skyrocketing government debt eventually consume virtually all tax revenues. Higher rates will accelerate this dynamic, pushing government finances to the wall as interest on sovereign debt crowds out all other spending. As taxes rise, households are left with less disposable income to spend on consumption, leading to stagnation.

( 4 ) At the start of the cycle, global debt levels (government and private-sector) were low. Now they are high. The boost phase of debt expansion and debt-funded spending is over, and we’re in the stagnation-decline phase where adding debt generates diminishing returns.

( 5 ) The era of low inflation has also ended for multiple reasons. Exporting nations’ wages have risen sharply, pushing their costs higher, and as noted, skilled labor in developed economies can demand higher wages as this labor cannot be automated or offshored. Offshoring is reversing to onshoring, raising production costs and diverting investment from asset bubbles to the real world.

Higher costs of resource extraction, transport and refining will push inflation higher. So will rampant money-printing to “boost consumption.”

( 6 ) The tech productivity boom was also a one-off. Economists were puzzled in the early 1990s by the stagnation of productivity despite the tremendous investments made in personal and corporate computers, a boom launched in the mid-1980s with Apple’s Macintosh and desktop publishing, and Microsoft’s Mac-clone Windows operating system.

By the mid-1990s, productivity was finally rising and the emergence of the Internet as “the vital 4%” triggered the adoption of the 20% which then led to 80% getting online combined with distributed computing to generate a true revolution in sharing, connectivity and economic potential.

The buzz around AI holds that an equivalent boom is now starting that will generate a glorious “Roaring 20s” of trillions booked in new profits and skyrocketing productivity as white-collar work and jobs are automated into oblivion.

There are two problems with this story:

1) The projections are based more on wishful thinking than real-world dynamics.

2) If the projections come true and tens of millions of white-collar jobs disappear forever, there is no replacement sector to employ the tens of millions of unemployed workers.

In the previous cycles of industrialization and post-industrialization, agricultural workers shifted to factory work, and then factory workers shifted to services and office work. There is no equivalent place to shift tens of millions of unemployed office workers,as AI is a dragon that eats its own tail: AI can perform many programming tasks so it won’t need millions of human coders.

… As for profits, as I explained in There’s Just One Problem: AI Isn’t Intelligent, and That’s a Systemic Risk, everyone will have the same AI tools and so whatever those tools generate will be overproduced and therefore of little value: there is no pricing power when the world is awash in AI-generated content, bots, etc., other than the pricing power offered by monopoly, addiction and fraud–all extreme negatives for humanity and the global economy.

Either way it goes–AI is a money-pit of grandiose expectations that will generate marginal returns, or it wipes out much of the middle class while generating little profit–AI will not be the miraculous source of millions of new high-paying jobs and astounding profits.

(End of Smith excerpt; emphases mainly his)

~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~

Have a nice day…

Funds Paying “Return of Capital” Give You (Sort of) Tax-Free Income

Scott Buchanan2 Comments

The stock of an individual company like AT&T, or a stock fund, often pays a dividend or distribution. Typically, these dividends are taxed as income. If you buy shares of a fund like MUNI that hold municipal bonds from U.S. states and cities, the dividends from that are not taxed by the feds (they are taxed on state income taxes). That’s nice, but the yield from a muni fund MUNI is only 3.3%, and the share price of MUNI drifts around with bond prices; it does not grow like the S&P500 stocks do.

What if there was a way to get highish dividends that are not taxed, at least not in the short term? There is. Funds classify their distributions or dividends in various categories. Net investment income or short-term capital gains are taxed like interest or ordinary income (highest rates). Qualified dividends or long-term capital gain returns are taxed at a lower rate. But “Return of Capital” (ROC) distributions are not taxed at all, when you receive them. (The accounting fiction is that ROC is simply your own investment money being handed back to you, rather than you getting interest or profit, which is why it is not taxed).

ROC only catches up with you when you sell your shares. Every dollar you pocket in ROC goes to lower the formal cost basis of your shares, so that increases the capital gains tax you pay when you sell.  Still, it can mean you defer paying taxes for many years, and when you do sell after many years, you will pay mainly long-term capital gains. Long-term capital gains have relatively low tax rates, and sometimes can be offset with capital losses elsewhere. So, this is a pretty good deal overall. All this only benefits you if you are holding these stocks in a taxable account, not in an IRA.

And, there are ways to not sell your shares, and hence never pay an inflated capital gains tax from all that ROC. One way not to sell your shares is to die (!). Your heirs inherit the funds at the current market value i (stepped-up basis”), without having to pay capital gains. So older folks do deliberately lard up their portfolios with ROC-paying funds or stocks, to leave to their heirs.

Another tactic is to donate the shares to charity. As I understand it, the donation gets valued at current market price, regardless of your cost basis. So, for instance, you might buy shares of XYZ fund at $100/share, collect say $50 in untaxed ROC over the next five years, and then donate the shares for a tax deduction at say $100/share (if their market price had not changed in five years). Obviously, this is only attractive if you wanted to make a charitable donation anyway.

OK, what are some funds or stocks that pay out ROC? There are number of funds which hold stocks, and write (sell) call options on them to generate income. (See here on selling options). Some (not all) of these funds pay out as mainly ROC, and are discussed here. SPYI and ETV are plain vanilla funds holding a basket of S&P500 type stocks, usually with a skew towards tech, and selling call options on them. (Or usually, selling options on an index like SPX or QQQ).  SPYI is currently paying about 11.5% yield, and ETV about 9%, both mainly ROC. ETV happens to be a closed-end fund, which can be good or bad, depending on whether you buy in when the share price is at a discount or premium to the asset value. Right now, ETV is at about a 5% discount, so it is a relatively good time to buy.

It is essential to note with these high yielding funds, the raw yield is practically meaningless. You have to look at total return, which factors in stock price over time as well as cash payout. The reason is that some funds “cheat” by paying huge yields, which sucks in investors, but those yields are not really earned by the fund, so those big payouts gradually deplete the fund’s assets.

FEPI holds an equally-weighted basket of fifteen tech stocks, and sell options on them. By selling options on individual stocks, the options income is huge; FEPI pays about 20% yield. The share price bounces around heavily, being so narrowly concentrated. If tech has a bad/good day, FEPI goes way down/up. QDTE also pays about 20%. It has a more novel strategy, selling “zero-day” options, which I won’t try to explain here. It has only been running about 6 months, but is doing OK.

A problem with all these option-selling funds is that their asset value goes down 10% if the underlying stocks go down 10%, but if stocks recover fast, the value of the funds typically do not recover as much. So, the share price of these funds keeps slipping below the price of a plain stock fund like SPY or QQQ. Now, if stocks go up (which they do most years), the price of an options fund can also go up, just not as much. The lag of these options fund is significant enough that on a total return basis (i.e. with dividends and stock price included), they usually lag behind just holding the stocks. Thus, the only reason to hold these funds is to harvest the tax-free ROC, or if you have a reason to want to generate steady income without selling off stocks.

Some 1-year total returns:

SPY        26.7%   Plain S&P 500  stock fund

SPYI       8.5%      Option fund

ETV        8.8%      Option fund

FEPI       20.2%   Option fund

QDPL     25.9%   Quadruple stock divi fund          

(Note, it is a little random that FEPI looked so good and SPYI and ETV looked poor in the past 12 months; that is not always the case. In the past 6 months, FEPI fared much worse than SPYI and ETV, which only lagged SPY by 1-2%). Some other newish option funds that pay mainly ROC are ISPY (8% yield, sells daily options, very little return lag) and three more with fairly low return drag: XDTE and QDTE (~20% yields, daily options on S&P500 and on NASDAQ 100); QYLG (6% yield; monthly options on half of NASDAQ 100).

Another fund I became aware of recently that pays mainly ROC is QDPL. It does not sell options, so it does not suffer the return lag the other funds do. It uses a futures strategy to take about 15% of the fund assets to garner roughly 4X the normal stock dividends of the S&P500 stocks. It only yields about 5.5%, but its total return keeps up pretty well with SPY. I like this one, and am including it in my portfolio with some of the options funds discussed above.

A whole other class of stocks that pay out mainly ROC is limited partnerships. These are common, e.g., among oil and gas pipeline companies like ET and EPD. These pay 7-8% and also are having strong share price appreciation. But they issue K-1 tax forms, which most mortals don’t want to deal with (I don’t).

As usual, this discussion does not constitute advice to buy or sell any security.

Many Impressive AI Demos Were Fakes

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I recently ran across an article on the Seeking Alpha investing site with the provocative title “ AI: Fakes, False Promises And Frauds “, published by LRT Capital Management. Obviously, they think the new generative AI is being oversold. They cite a number of examples where demos of artificial general intelligence were apparently staged or faked.  I followed up on a few of these examples, and it does seem like this article is accurate. I will quote some excerpts here to give the flavor of their remarks.

In 2023, Google found itself facing significant pressure to develop an impressive innovation in the AI race. In response, they released Google Gemini, their answer to OpenAI’s ChatGPT. The unveiling of Gemini in December 2023 was met with a video showcasing its capabilities, particularly impressive in its ability to handle interactions across multiple modalities. This included listening to people talk, responding to queries, and analyzing and describing images, demonstrating what is known as multimodal AI. This breakthrough was widely celebrated. However, it has since been revealed that the video was, in fact, staged and that it does not represent the real capabilities of Google’s Gemini.

… OpenAI, the company behind the groundbreaking ChatGPT, has a history marked by dubious demos and overhyped promises. Its latest release, Chat GPT-4-o, boasted claims that it could score in the 90th percentile on the Unified Bar Exam. However, when researchers delved into this assertion, they discovered that ChatGPT did not perform as well as advertised.[10] In fact, OpenAI had manipulated the study, and when the results were independently replicated, ChatGPT scored on the 15th percentile of the Unified Bar Exam.

… Amazon has also joined the fray. Some of you might recall Amazon Go, its AI-powered shopping initiative that promised to let you grab items from a store and simply walk out, with cameras, machine learning algorithms, and AI capable of detecting what items you placed in your bag and then charging your Amazon account. Unfortunately, we recently learned that Amazon Go was also a fraud. The so-called AI turned out to be nothing more than thousands of workers in India working remotely, observing what users were doing because the computer AI models were failing.

… Facebook introduced an assistant, M, which was touted as AI-powered. It was later discovered that 70% of the requests were actually fulfilled by remote human workers. The cost of maintaining this program was so high that the company had to discontinue its assistant.

… If the question asked doesn’t conform to a previously known example ChatGPT will still produce and confidently explain its answer – even a wrong one.

For instance, the answer to “how many rocks should I eat” was:

…Proponents of AI and large language models contend that while some of these demos may be fake, the overall quality of AI systems is continually improving. Unfortunately, I must share some disheartening news: the performance of large language models seems to be reaching a plateau. This is in stark contrast to the significant advancements made by OpenAI’s ChatGPT, between its second iteration (GPT-2), and the newer GPT-3 – that was a meaningful improvement. Today, larger, more complex, and more expensive models are being developed, yet the improvements they offer are minimal. Moreover, we are facing a significant challenge: the amount of data available for training these models is diminishing. The most advanced models are already being trained on all available internet data, necessitating an insatiable demand for even more data. There has been a proposal to generate synthetic data with AI models and use this data for training more robust models indefinitely. However, a recent study in Nature has revealed that such models trained on synthetic data often produce inaccurate and nonsensical responses, a phenomenon known as “Model Collapse.”

OK, enough of that. These authors have an interesting point of view, and the truth probably lies somewhere between their extreme skepticism and the breathless hype we have been hearing for the last two years. I would guess that the most practical near-term uses of AI may involve some more specific, behind the scenes data-mining for a business application, rather than exactly imitating the way a human would think.

Behind Last Week’s Stock Minicrash: Unwind of the Yen Carry Trade

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Last Monday, August 5, the S&P 500 crashed by 3.5% from the previous close. That is a huge daily move, which seems to have been a surprise to most market watchers. The VIX index, a measure of the cost of options and widely seen as a measure of fear in the markets, went off the charts that day. What happened?

The previous week, there was an employment report that showed higher than expected jobless claims. Although that led to angst over a recession, a genuine serious dent in employment would bring the Fed roaring in with interest rate cuts, and the stock market loves rate cuts. In addition, as we have highlighted in recent posts (here and here), there is increasing skepticism that the monster spends on AI will produce the profits that Big Tech hopes. However, the AI skepticism and the employment worries seemed already baked into stock prices by the Friday close.

What apparently happened over the weekend was the unwinding of a big part of the yen carry trade.

What is that, you ask? To frame this, imagine you have $100 to invest in something very safe, like short term Treasury securities. In the simplest case, you go buy a 1-year T-bill which yields 4.5%. You will make $ 4.50 in a year, from this transaction. If you had $100 million to invest, you would make $ 4.5 million.

Now suppose that you could use that $100 as collateral to borrow $1000 at 0.05%. You then take that $1000 and buy $1000 worth of 4.5% T-bills. Voila, instead of making a measly $ 4.50, you can now make  1000*(4.5% – 0.05%) = $44.5. This is nearly ten times as much, a 44.5% return on your $100. Financial alchemy at its finest!

Now, if instead of investing in boring 4.5% T-bills, you had been buying Microsoft and Apple shares (up 25% and 21%, respectively, in the past twelve months), just imagine the profits from this 10X leveraged trade. Especially if you started with a $100 million hedge fund instead of $100.

Where, you may ask, could you borrow money at 0.05%? The answer is Japan. The central bank there has kept rates essentially zero for many years, for reasons we will not canvass here. This scheme of borrowing in yen, and investing (mainly in the US) in dollars is termed the yen carry trade. Besides this borrowing/investing, simply betting that the Japanese yen would decline against the dollar has been profitable for the past 18 months.

What could possibly go wrong with such a scheme? Well, you have to do this borrowing in Japanese yen. So, if you borrow in yen and then convert it to dollars and invest in the dollar world, you can be in a world of hurt if the value of yen in dollars goes up by the time you need to close out this whole trade (i.e. cash in your T-Bills into dollars, convert back to yen, and pay off your yen borrowings.

What happened on Wednesday, July 31 was the Bank of Japan unexpectedly raised its key interest rate target from 0-0.1% to around 0.25%, and announced they would scale back their QE bond-buying, in an effort to address inflation. As may be expected, that raised the value of the yen on Thursday and Friday, though not by much. But the yen made a surge up at the end of Friday’s trading.

Apparently, that caused enough angst in the carry trade community that participants in the carry trade started running for the exits, selling dollar-denominated assets (including stocks) and scrambling to buy yen. Naturally, that shot the price of yen up even more, so on Monday, Aug 5, we had a disorderly market rout.

Bad news sells, and so all the finance headlines on Monday were blaring about the stock price collapse and start of an awful bear market. However, nothing substantive had really changed. By Friday, the S&P 500 had recovered from this big head-fake.

As usual, investors sold stocks (at a low price) on Monday, and presumably bought them back at a higher price later in the week. This is why the average investor’s returns fall well below a simple buy and hold. But that is another subject for another time.

Will the Huge Corporate Spending on AI Pay Off?

Scott Buchanan2 Comments

Last Tuesday I posted on the topic, “Tech Stocks Sag as Analysists Question How Much Money Firms Will Actually Make from AI”. Here I try to dig a little deeper into the question of whether there will be a reasonable return on the billions of dollars that tech firms are investing into this area.

Cloud providers like Microsoft, Amazon, and Google are building buying expensive GPU chips (mainly from Nvidia) and installing them in power-hungry data centers. This hardware is being cranked to train large language models on a world’s-worth of existing information. Will it pay off?

Obviously, we can dream up all sorts of applications for these large language models (LLMs), but the question is much potential downstream customers are willing to pay for these capabilities. I don’t have the capability for an expert appraisal, so I will just post some excerpts here.

Up until two months ago, it seemed there was little concern about the returns on this investment.  The only worry seemed to be not investing enough. This attitude was exemplified by Sundar Pichai of Alphabet (Google). During the Q2 earnings call, he was asked what the return on Gen AI investment capex would be. Instead of answering the question directly, he said:

I think the one way I think about it is when we go through a curve like this, the risk of under-investing is dramatically greater than the risk of over-investing for us here, even in scenarios where if it turns out that we are over investing. [my emphasis]

Part of the dynamic here is FOMO among the tech titans, as they compete for the internet search business:

The entire Gen AI capex boom started when Microsoft invested in OpenAI in late 2022 to directly challenge Google Search.

Naturally, Alphabet was forced to develop its own Gen AI LLM product to defend its core business – Search. Meta joined in the Gen AI capex race, together with Amazon, in fear of not being left out – which led to a massive Gen AI capex boom.

Nvidia has reportedly estimated that for every dollar spent on their GPU chips, “the big cloud service providers could generate $5 in GPU instant hosting over a span of four years. And API providers could generate seven bucks over that same timeframe.” Sounds like a great cornucopia for the big tech companies who are pouring tens of billions of dollars into this. What could possibly go wrong?

In late June, Goldman Sachs published a report titled, GEN AI: TOO MUCH SPEND,TOO LITTLE BENEFIT?.  This report included contributions from bulls and from bears. The leading Goldman skeptic is Jim Covello. He argues,

To earn an adequate return on the ~$1tn estimated cost of developing and running AI technology, it must be able to solve complex problems, which, he says, it isn’t built to do. He points out that truly life-changing inventions like the internet enabled low-cost solutions to disrupt high-cost solutions even in its infancy, unlike costly AI tech today. And he’s skeptical that AI’s costs will ever decline enough to make automating a large share of tasks affordable given the high starting point as well as the complexity of building critical inputs—like GPU chips—which may prevent competition. He’s also doubtful that AI will boost the valuation of companies that use the tech, as any efficiency gains would likely be competed away, and the path to actually boosting revenues is unclear.

MIT’s Daron Acemoglu is likewise skeptical:  He estimates that only a quarter of AI-exposed tasks will be cost-effective to automate within the next 10 years, implying that AI will impact less than 5% of all tasks. And he doesn’t take much comfort from history that shows technologies improving and becoming less costly over time, arguing that AI model advances likely won’t occur nearly as quickly—or be nearly as impressive—as many believe. He also questions whether AI adoption will create new tasks and products, saying these impacts are “not a law of nature.” So, he forecasts AI will increase US productivity by only 0.5% and GDP growth by only 0.9% cumulatively over the next decade.

Goldman economist Joseph Briggs is more optimistic:  He estimates that gen AI will ultimately automate 25% of all work tasks and raise US productivity by 9% and GDP growth by 6.1% cumulatively over the next decade. While Briggs acknowledges that automating many AI-exposed tasks isn’t cost-effective today, he argues that the large potential for cost savings and likelihood that costs will decline over the long run—as is often, if not always, the case with new technologies—should eventually lead to more AI automation. And, unlike Acemoglu, Briggs incorporates both the potential for labor reallocation and new task creation into his productivity estimates, consistent with the strong and long historical record of technological innovation driving new opportunities.

The Goldman report also cautioned that the U.S. and European power grids may not be prepared for the major extra power needed to run the new data centers.

Perhaps the earliest major cautionary voice was that of Sequoia’s David Cahn. Sequoia is a major venture capital firm. In September, 2023 Cahn offered a simple calculation estimating that for each dollar spent on (Nvidia) GPUs, and another dollar (mainly electricity) would need be spent by the cloud vendor in running the data center. To make this economical, the cloud vendor would need to pull in a total of about $4.00 in revenue. If vendors are installing roughly $50 billion in GPUs this year, then they need to pull in some $200 billion in revenues. But the projected AI revenues from Microsoft, Amazon, Google, etc., etc. were less than half that amount, leaving (as of Sept 2023) a $125 billion dollar shortfall.

As he put it, “During historical technology cycles, overbuilding of infrastructure has often incinerated capital, while at the same time unleashing future innovation by bringing down the marginal cost of new product development. We expect this pattern will repeat itself in AI.” This can be good for some of the end users, but not so good for the big tech firms rushing to spend here.

In his June, 2024 update, Cahn notes that now Nvidia yearly sales look to be more like $150 billion, which in turn requires the cloud vendors to pull in some  $600 billion in added revenues to make this spending worthwhile. Thus, the $125 billion shortfall is now more like a $500 billion (half a trillion!) shortfall. He notes further that the rapid improvement in chip power means that the value of those expensive chips being installed in 2024 will be a lot lower in 2025.

And here is a random cynical comment on a Seeking Alpha article: It was the perfect combination of years of Hollywood science fiction setting the table with regard to artificial intelligence and investors looking for something to replace the bitcoin and metaverse hype. So when ChatGPT put out answers that sounded human, people let their imaginations run wild. The fact that it consumes an incredible amount of processing power, that there is no actual artificial intelligence there, it cannot distinguish between truth and misinformation, and also no ROI other than the initial insane burst of chip sales – well, here we are and R2-D2 and C3PO are not reporting to work as promised.

All this makes a case that the huge spends by Microsoft, Amazon, Google, and the like may not pay off as hoped. Their share prices have steadily levitated since January 2023 due to the AI hype, and indeed have been almost entirely responsible for the rise in the overall S&P 500 index, but their prices have all cratered in the past month. Whether or not these tech titans make money here, it seems likely that Nvidia (selling picks and shovels to the gold miners) will continue to mint money. Also, some of the final end users of Gen AI will surely find lucrative applications. I wish I knew how to pick the winners from the losers here.

For instance, the software service company ServiceNow is finding value in Gen AI. According to Morgan Stanley analyst Keith Weiss, “Gen AI momentum is real and continues to build. Management noted that net-new ACV for the Pro Plus edition (the SKU that incorporates ServiceNow’s Gen AI capabilities) doubled [quarter-over-quarter] with Pro Plus delivering 11 deals over $1M including two deals over $5M. Furthermore, Pro Plus realized a 30% price uplift and average deal sizes are up over 3x versus comparable deals during the Pro adoption cycle.”

Tech Stocks Sag as Analysists Question How Much Money Firms Will Actually Make from AI

Scott Buchanan4 Comments

Tech stocks have been unstoppable for the past fifteen or so years. Here is a chart from Seeking Alpha for total return of the tech-heavy QQQ fund (orange line) over the past five years, compared to a value-oriented stock fund (VTV), a fund focused on dividend-paying stocks (SDY) and the Russel 2000 small cap fund IWM.

QQQ has left the others in the dust. There has been a reversal, however, in the past month. The tech stocks have sagged nearly 10% since July 11, while the left-for-dead small caps (IWM, green line) rose by 10%:

Some of this is just mean reversion, but there seems to be a deeper narrative shift going on. For the past 18 months, practically anything that could remotely be connected with AI, especially the Large Language Models (LLM) exemplified by ChatGPT, has been valued as though it would necessarily make every-growing gobs of money, for years to come.

In recent weeks, however, Wall Street analysts have started to question whether all that AI spending will pay off as expected. Here are some headlines and excerpts (some of the linked articles are behind paywalls):

““There are growing concerns that the return on investment from heavy AI spending is further out or not as lucrative as believed, and that is rippling through the whole semiconductor chain and all AI-related stocks,” said James Abate, chief investment officer at Centre Asset Management.”

www.bloomberg.com/…

““The overarching concern is, where is the ROI on all the AI infrastructure spending?” said Alec Young, chief investment strategist at Mapsignals. “There’s a pretty insane amount of money being spent.
Jim Covello, the head of equity research at Goldman Sachs Group Inc., is among a growing number of market professionals who are arguing that the commercial hopes for AI are overblown and questioning the vast expense required to build out infrastructure required for the computing to run and train large-language models.”

www.bloomberg.com/…

“It really feels like we are moving from a ‘tell me’ story on AI to a ‘show me’ story,” said Ohsung Kwon, equity and quantitative strategist at Bank of America Corp. “We are basically at a point where we’re not seeing much evidence of AI monetization yet.”

https://finance.yahoo.com/news/earnings-derail-stock-rally-over-130001940.html

Goldman’s Top Stock Analyst Is Waiting for AI Bubble to Burst

Covello casts doubt on hype behind an $16 trillion rally

He says costs, limited uses means it won’t revolutionize world

https://finance.yahoo.com/news/goldman-top-stock-analyst-waiting-111500948.html

Google stock got dinged last week for excessive capital spending, even though earnings were strong. Microsoft reports its Q4 earnings after the market closes today (Tuesday); we will see how investors parse these results.

How to (Almost) Double Your Investing Returns 3. “Stacked” Multi-Asset Funds

Scott Buchanan2 Comments

Two weeks ago we described a simple way to achieve roughly double investing returns on some asset class like an S&P 500 stock basket, or on some commodity like gold or oil, by buying shares in an exchange-traded fund (ETF) whose price moves up or down each day two times as much as the price of the underlying stocks or commodities. For instance, if the S&P 500 stocks go up (or down) by 2% on a given day, the price of the SSO ETF will move up (or down) by 4%.  And last week we noted that buying deep in the money call options can also result in an investment which can move up or down by twice the percentage of the underlying stock. These call options side-step the volatility drag implicit in the 2X funds, but require some housekeeping on the investors part to roll them over once or twice a year.

Today we present a third approach for multiplying the return on your investment dollars. This is to buy shares of a fund which holds two different asset classes, in a leveraged form. As an example: if you buy $100 worth of the fund PSLDX, you are buying the equivalent of $100 worth of S&P 500 stocks PLUS about $100 worth of long-dated US Treasury bonds. (PSLDX happens to be an old-fashioned mutual fund, not an ETF, but no matter). It works like this: The fund takes your $100 and buys a bucket of bonds. It then uses those bonds as collateral, and uses futures to get around $100 worth of exposure to the price movements of the S&P 500 stocks. There is not quite a free lunch here, since there is a “carry” cost on the futures, which is about equal to the LIBOR/SOFR short term interest rates (currently ~ 5%).

PSLDX does not promise exactly 100/100  stock/bond exposure, but it comes out pretty close much of the time. A similar product is NTSX which is leveraged x1.5. It gives 90/60 stocks/mixed-term bonds. NTSX has outperformed PLSDX in recent years, since the price of long-term (10-20 year) bonds has been crushed due to the rise in interest rates. RSSB is a recent entry into this space, offering 100/100 exposure to global stocks/laddered Treasuries.

Another reason these leveraged stock/bond products have done relatively poorly in the past two years is that the cost of leverage is actually higher than the bond coupons, due to the inverted yield curve.  This problem will go away if the Fed lowers short-term rates back down to near zero, as they were prior to 2022, but lingering inflation makes that prospect unlikely.

That said, if I have $200 to invest and want $100 stock and $100 bond coverage, I can put $100 into one of these 100/100 funds, and still have $100 left to collect interest on or to invest in some other, hopefully higher-yielding venue. So, these stock/bond funds have their place.

Where this so-called asset stacking shines even more is combining stocks or bonds with something like managed futures. Managed futures are an excellent diversifier for equities (see here). Moreover, since managed futures are typically held in both long and short positions, there will be less financing (carry) cost associated with them. When both stocks and bonds cratered in 2022, managed futures went up. Thus, funds like BLNDX (50 global stocks/100 managed futures) and MAFIX (stocks plus managed futures) went up in 2022, and then continued to rise as stocks recovered. Thus, the returns for these two funds have been steadier and higher than plain stocks (SP 500) over the past three years:

Total returns for past three years, for BLNDX (50 stocks/100 managed futures), SP500 stocks, BND broad US bonds, and MAFIX stacked multi-asset.

BLNDX and its sister fund REMIX are readily available at most brokerages (I hold some), while MAFIX may have daunting minimum investment requirements. RSST is a recent 100/100 stock/managed futures ETF that is easily invested in, and seems to be performing well.

Disclaimer: As usual, nothing here should be considered advice to buy or sell any investment.

How to (Almost) Double Your Investing Returns 2. Buy Deep in the Money Calls

Scott Buchanan6 Comments

Last week we described a simple way to achieve roughly double investing returns on some asset class like an S&P 500 stock basket, or a narrow class of stocks such as semiconductors, or on some commodity like gold or oil. That way is to buy one of the many exchange-traded funds (ETFs) which use sophisticated derivatives to achieve a 2X or even 3X daily movement in their share prices, relative to the underlying asset. For instance, if the S&P 500 stocks move up by 2% on a given day, the SSO ETF will rise by 4%.

Of course, these leveraged funds will also go down two or three times as much. They also have a more subtle disadvantage, which is that when the markets go up and down a lot, they tend to lose value due to their daily reset mechanism.

In this post we describe a different way to achieve roughly double returns, which does not suffer from this volatility drag issue. This way is to buy long-dated deep in-the-money call options on a stock or a fund.

Say what? We have described how stock options work here and here. The reader who is unfamiliar with options should consult those prior articles.

A stock option is a contract to buy (if it is a call option) or to sell (if it is a put option) a given stock at some particular price (“strike price”), by some particular expiration date. Investors generally buy calls when they believe that the price of some stock or fund will go up.  For a call option with a strike price far below the current market price of a stock, the market price of the option will move up and down essentially 1:1 with the market price of the stock.

For instance, as I write this the market price of Apple is about $230. Suppose I think Apple is going to go up by say $40 in the next six months. One way for me to capture this gain is to invest $230 in buying Apple stock. The alternative propose here is to instead of buying the stock itself, buy, say, a call option with a strike price of $115 and an expiration date of January 17, 2025. The current market price of this option is about $119.

Other things being equal, we expect that the market value of this call option will go up by $40 if Apple itself goes up by $40. But we have invested only $119, rather than $230, so our return on our investment is roughly double with the option than by buying the stock itself.

There is a subtle cost to this approach. At a stock price of $230 and a strike price of $115, the intrinsic value of this call option is $115. But we pay an extra $3 of extrinsic value when we buy the option for $118. This extrinsic value will gradually decay to zero over the next six months.

Thus, if Apple went up by $40 within the next month or so, we could turn around and sell this call option for nearly $40 more than our purchase price. But if we wait for six months before selling it, we would only net $37 (i.e., $40 minus $3). This is still fine, but it illustrates that there is a steady cost of holding such options. This annualized cost is about equal to or slightly higher than the prevailing short term interest rate (5% /year). This option pricing makes sense, since an alternative way to control this many shares would be to borrow money at current interest rates (5%) and use those borrowed funds to buy Apple shares. Options and futures pricing is generally rational, to make things like this equivalent, or else there would be easy arbitrage profits available.

As a side comment, the reason I am focusing on deep in the money calls here is that the extrinsic premium you pay in buying the call gets lower the further away the strike price is (i.e. deeper in the money) from the current stock price. A deeper in the money call does cost you more up front, but net net its dollar movements up and down more closely track (1:1) the movements of the underlying stock. So, if I am not trying to guess right on any market timing, but simply want to get the equivalent of holding the underlying stock but tying up less money to do so, I find buying a call that is about 50% in the money generally works well.

How I Use Deep in the Money Call Options

I consider the technology-oriented stock fund QQQ to be a core holding in my portfolio, so I would like to stay exposed to its movements. But I might as well do this on a 2X basis, to make better use of my funds. I do hold some of the 2X ETF QLD. But if we experience a lot of market volatility, the price of QLD will suffer, as explained in our previous post.

As a more conservative approach here, I recently bought a deep in the money call on the QQQ ETF. As usual, I went for a call option with a strike price roughly half of the market price, with an expiration date 6-12 months away. When this gets close to expiration (May-June next year), I will “roll” it forward, by selling my existing call option, and buying a new one dated yet another 6-12 months further out. This takes little work and little decision making. I will pay the equivalent of about 5% annualized cost on the decay of the extrinsic option premium, but I come ahead as long as QQQ goes up more than 5% per year.

This is a little more work than just holding the 2X QLD ETF, but it gives me a bit more peace of mind, knowing I have done what I can to smooth out some of the risk there. Of course, if QQQ plunges along with the markets in general, I will be looking at double the losses. For that reason, I am taking some of the money I am saving by using these leveraged approaches, and stashing it in safe money market funds. In theory that should give me “dry powder” for buying more stocks after they drop. In practice, I may be too frozen with fear to make such clever purchases. But at any rate, I should not be appreciably worse off for having used these leveraged investments (2X funds or deep in the money calls).

Disclaimer: As usual, nothing here should be considered advice to buy or sell any investment.

How to Roughly Double Your Investing Returns 1. 2X (or 3X) Leveraged Funds

Scott Buchanan7 Comments

Most years, stocks go up, by something like 9%. Wouldn’t it be nice to invest in a fund that went up double those amounts? Such funds exist. They use futures or other derivatives to move up (or down!) by double, or even triple, the percentage that the underlying stock or index moves, on a daily basis.

For instance, a common unleveraged fund (ETF) is SPY that roughly tracks the S&P 500 index of large U.S. stocks is SPY. SSO is a 2X fund, which gives double the returns of SPY, on a daily basis. UPRO is a 3X fund, giving triple the returns. 2X funds exist for many different asset classes, including semiconductor stocks, treasury bill, and crude oil – see here. And similarly for 3X funds.

Since all the action in stocks these days seems to be in large tech companies, I will focus on the NASDAQ 100 index universe. The leading unleveraged fund there is QQQ. The 2X version is QLD, and the 3X is TQQQ. Let’s look at how these three funds performed over the past twelve months:

QQQ is up a respectable 36%, but QLD is up by 70%, and TQQQ by a mouth-watering 106%. You could have doubled your money in the past twelve months simply by investing in a 3X fund instead of holding boring 1X QQQ. 

These leveraged funds can be utilized in more than one way. One approach is to just put the monies you have allocated for stocks into such funds, and hope for higher returns. Another approach is to put, say half of your speculative funds into a 2X fund (to get roughly the same stock exposure as putting all of it into a 1X fund), and then use the remaining half to put into other investments, or to keep as dry powder to give you the option to buy more equities if the market crashes.

What’s not to like about these funds? It turns out that a year of daily doubling of returns does not necessarily add up to doubling of yearly returns. There is “volatility drag” associated with all the exaggerated moves up and down. As an illustration of how this works, suppose you held a stock that went down by 50% one day, say from a price of $100 to $50. The next day, it went back up by 50%. But this would only get you back to $75, not $100.

It turns out that with these leveraged funds, as long as stocks are generally going up, the yearly returns can match or even exceed the 2X or 3X targets. But in a period with a lot of volatility, the yearly returns can fall far short. And in a down year, the combination of the leverage and the volatility drag lead to truly horrific losses. For instance, here is what 2022 looked like for these funds:

QQQ was down by 31%, which is bad enough. But imagine your $10,000 in TQQQ melting down to $3,300 that year.

And here is the chart from January 2022 to the present:

QQQ is up 27% in the past 2.5 years, 2X QLD is up only 16%, while 3X TQQQ is actually down by 6%, as it could not recovery from 2022.

This was a kind of a worst-case scenario, since 2022 was an exceptionally bad year for QQQ, coming off a fabulous 2021. A chart of the past five years, which includes the 2020 Covid crash and recovery, and the 2022 crash and subsequent recovery still shows the leveraged funds coming out ahead over the long term:

The net returns on QLD (321%) were about double QQQ (158%), while the more volatile TQQQ return (386%) was plenty high, but fell well short of three times QQQ.

In my personal investing, I hold some QLD as a means to free up funds for other investments I like. But if I smell major market trouble coming, I plan to swap back into plain QQQ until the storm clouds pass.

There are some other ways to get roughly double returns, which suffer less from volatility drag than these 2X funds. I will address those in subsequent posts.

Disclaimer: As usual, nothing here should be considered advice to buy or sell any investment.