Author: Scott Buchanan

Bears and Bulls Battle Over Nvidia Stock Price

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Nvidia is a huge battleground stock – – some analysts predict its price will languish or crash, while others see it continuing its dramatic rise. It has become the world’s most valuable company by market capitalization.  Here I will summarize the arguments of one bear and one bull from the investing site Seeking Alpha.

In this corner…semi-bear Lawrence Fuller. I respect his opinions in general. While the macro prospects have turned him more cautious in the past few months, for the past three years or so he has been relentlessly and correctly bullish (again based on macro), when many other voices were muttering doom/gloom.  

Fuller’s article is titled Losing Speed On The AI Superhighway. This dramatic chart supports the case that NVDA is overvalued:

This chart shows that the stock value of Nvidia has soared past the value of the entire UK stock exchange or the entire value of US energy companies. Fuller reminds us of the parallel with Cisco in 2000. Back then, Cisco was a key supplier of gateway technology for all the companies scrambling to get into this hot new thing called the internet. Cisco valuation went to the moon, then crashed and burned when the mania around the internet subsided to a more sober set of applications. Cisco lost over 70% of its value in a year, and still has not regained the share price it had 25 years ago:

… [Nvidia] is riding a cycle in which investment becomes overinvestment, because that is what we do in every business cycle. It happened in the late 1990s and it will happen again this time.

…there are innumerable startups of all kinds, as well as existing companies, venturing into AI in a scramble to compete for any slice of market share. This is a huge source of Nvidia’s growth as the beating heart of the industry, similar to how Cisco Systems exploded during the internet infrastructure boom. Inevitably, there will be winners and losers. There will be far more losers than winners. When the losers go out of business or are acquired, Nvidia’s customer base will shrink and so will their revenue and earnings growth rates. That is what happened during the internet infrastructure booms of the late 1990s.

Fuller doesn’t quite say Nvidia is overvalued, just that it’s P/E is unlikely to expand further, hence any further stock price increases will have to be produced the old-fashioned way, by actual earnings growth. There are more bearish views than Fuller’s, I chose his because it was measured.

And on behalf of the bulls, here is noob Weebler Finance, telling us that Nvidia Will Never Be This Cheap Again: The AI Revolution Has Just Begun:

AI adoption isn’t happening in a single sequence; it’s actually unfolding across multiple industries and use cases simultaneously. Because of these parallel market build-outs, hyper-scalers, sovereign AI, enterprises, robotics, and physical AI are all independently contributing to the infrastructure surge.

…Overall, I believe there are clear signs that indicate current spending on AI infrastructure is similar to the early innings of prior technology buildouts like the internet or cloud computing. In both those cases, the first waves of investment were primarily about laying the foundation, while true value creation and exponential growth came years later as applications multiplied and usage scaled.

As a pure picks and shovels play, Nvidia stands to capture the lion’s share of this foundational build-out because its GPUs, networking systems, and software ecosystem have become the de facto standard for accelerated computing. Its GPUs lead in raw performance, energy efficiency, and scalability. We clearly see this with the GB300 delivering 50x per-token efficiency following its launch. Its networking stack has become indispensable, with the Spectrum-X Ethernet already hitting a $10b annualized run rate and NVLink enabling scaling beyond PCIe limits. Above all, Nvidia clearly shows a combined stack advantage, which positions it to become the dominant utility provider of AI compute.

… I believe that Nvidia at its current price of ~$182, is remarkably cheap given the value it offers. Add to this the strong secular tailwinds the company faces and its picks-and-shovels positioning, and the value proposition becomes all the more undeniable.

My view: Out of sheer FOMO, I hold a little NVDA stock directly, and much more by participating in various funds (e.g. QQQ, SPY), nearly all of which hold a bunch of NVDA.  I have hedged some by selling puts and covered calls that net me about 20% in twelve months, even if stock price does not go up.   Nvidia P/E (~ 40) is on the high side, but not really when considering the growth rate of the company. It seems to me that the bulk of the AI spend is by the four AI “hyperscalers” (Google, Meta, Amazon, Microsoft). They make bazillions of dollars on their regular (non-AI) businesses, and so they have plenty of money to burn in purchasing Nvidia chips. If they ever slow their spend, it’s time to reconsider Nvidia stock. But there should be plenty of warning of that, probably no near time crisis: last time I checked, Nvidia production was sold out for a full year ahead of time. I have no doubt that their sales revenue will continue to increase. But earnings will depend on how long they can continue to command their stupendous c. 50% net profit margin (if this were an oil company, imagine the howls of “price gouging”).

As usual, nothing here should be considered advice to buy or sell any security.

Government Makes Quasi-Nationalization Deal to Assure Supply of Critical Rare Earths for Defense 

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If top government officials were regular readers of this blog, they would have been warned by a headline here more than two years ago, “China To Squeeze West by Restricting Export of Essential Rare Earths “.  For the last few years, the U.S. has been trying to limit Chinese access to the most powerful computing chips, which are largely made by American company Nvidia. But China has some high cards to play in this game. It produces some 90% of refined rare earths and rare earth products like magnets.  These super-powerful neodymium-containing magnets are utterly critical components in all kinds of high-tech products, including wind turbine generators and electric motors for electric vehicles and drones, and miscellaneous military hardware.

It has been painfully obvious at least since 2010, when China put the squeeze on Japan by unofficially slowing rare earth exports to Japan over a territorial dispute, that it was only a matter of time before China played that card again. But the West slumbered on. There is a reasonable amount of rare earth ores that are mined outside China, but nobody wanted to build and operate the expensive and environmentally messy processes to refine the rare earth minerals (carbonates, oxides, phosphates) into the pure metals. Unlike the esoteric and hard-to-imitate processing for cutting edge computing chips, anyone can gear up and start refining rare earth ores. It mainly just takes money, lots and lots of it, to build and operate all the processing equipment for the multiple steps involved*. There was little free market incentive for a Western company to invest in expensive processing, since China could readily bankrupt them by cutting prices as soon as they started up their shiny new process line. Reportedly, the Chinese used this tactic twice before (in 2002 and 2012) to kill nascent refining of the rare earth ores at Mountain Pass mine in California.

As of April of this year, in response to ongoing U.S. export restrictions on chips, China threw its latest rare earth card down on the table, requiring export licenses and imposing other restrictions that throttled rare earth exports. Western manufacturers were soon howling in pain. As of early June:

Global automakers are sounding the alarm on an impending shortage of rare earth magnets as China’s restrictions on the material vital for the automotive, defence and clean energy industries threaten production delays around the world.

German automakers became the latest to warn that China’s export restrictions threaten to shut down production and rattle their local economies, following a similar complaint from an Indian EV maker last week. U.S., Japanese and South Korean automakers warned President Donald Trump on May 9 car factories could close.

The Trump administration quickly caved on chips and in July permitted boatloads of high-end H20 Nvidia chips to ship to China, in return for resumption of rare earth exports from China. Score one for the CCP. As of mid-August, rare earth shipments had climbed back to around half of their pre-May levels, but China ominously warned Western companies against trying to stockpile any reserves of rare earths, or they would “face shortages” in the future.

After this ignominious face-slapping, the administration finally did something that should have been done years ago: they gave an American company a solid financial incentive to buckle down and do the dirty work of refining rare earth ores at large scale. The Defense Department inked a deal with MP Materials Corp, the current operator of the Mountain Pass mine and the modest refining operation there to quickly ramp up production:

The Department of Defense is investing capital in MP across several fronts. This includes a $400 million convertible preferred equity, struck at a fixed conversion price of $30.03. The government gets 10-year MP stock warrants also set for a $30.03 price. As planned, this would get the Department of Defense to about a 15% ownership position in MP Materials. In addition, the Department of Defense will lend MP Materials $150 million at a highly competitive interest rate to help the company expand its heavy rare earth element separation capabilities.

It’s not just a financing deal, however. This arrangement also provides a striking level of influence over pricing and profitability for MP Materials going forward.

For one thing, the Department of Defense will provide a price floor of $110 per kilogram for NdPr. NdPr is a product that is a combination of neodymium and praseodymium. This is a generous floor price…

The Department of Defense’s involvement now gives MP Materials the runway necessary to build what’s being dubbed the 10X magnet manufacturing expansion plant. The Department of Defense is committed to buying the output of this plant with a controlled cost-plus pricing structure. And there will be a profit split with the DoD getting a significant chunk of the upside above certain EBITDA thresholds.

This is being billed as a private-public partnership, but it is akin to nationalization. The government will be heavily involved in planning output and setting pricing here, as well as sharing in profits.  Fans of laissez-faire free markets may be understandably queasy over this arrangement, but national security considerations seem to make this necessary.

I predict that further “private-public” deals will be struck to subsidize Western production of vital materials. Let’s be clear: massive subsidies or similar incentives, in one form or another, will be needed. And this means that Americans will have to devote more resources to grinding out industrial materials, and less to consumer goods; hence, we will likely live in smaller houses, perhaps (gasp) lacking granite countertops and recessed lighting. Economics is all about trade-offs.

Due to its vast, lower-paid, hard-working and highly-capable workforce, the whole Chinese supply chain and production costs run far, far cheaper than anything in the West. We don’t have to produce 100% of what we use, even say 40% might be enough to keep from being helplessly squeezed by another nation. How to do this without descending into unproductive rent-seeking rip-offs will be a challenge.

Some other materials candidates:  China has as of December 2024 completely shut off exports to the U.S. of three key non-rare earth technical elements, gallium, germanium and antimony, so those might be a good place to start. China mines or refines between half and 90% of global supply of those minerals. Also, China has instituted export regulations of for more key metals (tungsten, tellurium, bismuth, indium and molybdenum-related products), so these may be further subjects for squeeze plays. Finally, “China is the world’s top graphite producer and exporter, and also refines more than 90% of the world’s graphite into a material that is used in virtually all EV batteries,” so that is yet another vital material where the West must decide how much it is worth to break its dependence on an unreliable trading partner.

A Modern-Day Pirate Seeks to Recover Up to Ten Billion Dollars of Gold from Republic Shipwreck Off Nantucket

Scott Buchanan4 Comments

Arrrr, me hearties! What think ye of a venture to raise a gigantic hoard of sunken treasure?

The story begins with the last voyage of RMS Republic. This was a luxurious passenger steamship of the White Star Line, which sailed between Europe and America.

Wikipedia

Republic was a large vessel (15,000 tons displacement) for her day, and was known as the “Millionaires’ Ship” for the number of wealthy Americans who sailed back and forth on her. A number of such magnates were aboard on her last voyage. In January, 1909 Republic left New York City with  passengers and crew, bound for Gibraltar and Mediterranean ports. In thick fog off the island of Nantucket, Republic was rammed amidships by the Italian liner Florida. Florida’s bow was crumpled back, but she stayed afloat. The damage to Republic was fatal. The engine rooms flooded, the ship began to list, and it was clear that the passengers needed to be evacuated.

Using the new-fangled Marconi “wireless” apparatus, a CQD distress signal was broadcast by radio operator Jack Binns. This was the first wireless transmission that resulted in a major life-saving marine rescue. (Binns had to scramble and improvise to get this done, since his apparatus had been damaged and the ship’s power was lost as a result of the collision, so he was a technology nerd turned hero, duly lauded by a ticker-tape parade). It was hard for other ships to locate Republic in the fog, but eventually nearly all the passengers and crew from Republic and from the damaged Florida were safely transferred to other ships.

As was the custom of the time, she did not carry enough lifeboats to hold all the passengers, but only enough to ferry them to some other ship; it was assumed that on the busy Atlantic route there would always be other large ships around.  (That scheme played out well with the Republic, but when sister White Star liner Titanic sank four years later, the dearth of lifeboats helped doom some 1,500 people to a watery grave.) Despite efforts to save her, Republic went down stern-first on January 24. She was the largest ship ever to sink at the time.  There were reports at the time that she was carrying some $3 million (1909 dollars) of gold, which went down with the ship. That would translate to hundreds of millions of dollars today for that gold.

But wait, there’s more, maybe much more. Enter a modern-day pirate, Martin Bayerle:

Vineyard Gazette

Bayerle looks like a pirate, sporting a genuine eyepatch covering an eye lost in an explosives accident. He killed a man who was fooling around with his wife, which seems like a piratical thing to do, and he is after a ship’s gold.   His salvage enterprise is even formally described in legal court papers as “modern day pirates”. 

His company, Martha’s Vineyard Scuba Headquarters, Inc. (“MVSHQ”), acquired salvage rights to the wreck of the Republic. In 2013 he published a book, The Tsar’s Treasure, detailing his thesis that Republic carried far more gold than was publicly acknowledged. He notes that there was no formal inquiry regarding the sinking of Republic, which was highly unusual and is suggestive of a cover-up. Cover-up of what?

Well, Europe at the time was a tinder box of potential conflict, which did in fact erupt five years later in World War I.  Czarist Russia was a key part of the European military equation. Britain was counting on Russia to help contain the emerging militaristic Germany. Russia had incurred huge debts in its disastrous war with Japan in 1905. Russia was about to issue a new round of bonds in 1909, to roll over its debt from 1905. It was critical that that bond issuance would go forward.


Bayerle believes that a large amount of gold was stashed in the hold of the Republic, destined for European banks, to support the Russian bonds of 1909. The revelation that that gold was lost would have jeopardized this crucial financial transaction, perhaps leading to Russia’s collapse, which is something Britain could not afford. Hence, the cover-up. Bayerle estimates that the value of this trove is up to $10 billion in today’s money. Shiver me timbers!

This geopolitical speculation, together with stories of failed previous salvage attempts on Republic, all make for a rollicking yarn. Is it for real? Nobody knows, but Bayerle is offering investors a chance at a slice of the booty. If you are inclined to “Dare to dream the impossible” (per the website), you have the opportunity to invest in his Lords of Treasure enterprise as they make a dive on the site this summer.


I don’t happen to have that much risk appetite, but it should be an interesting story to follow.

UPDATE

According to the June 2025 Lords of Fortune Newletter, salvage operations originally slated for 2025 are being put off till 2026, as funding is still being developed. We note the technical challenge of picking through hundreds of tons of steel plate and girders, deep underwater, in search of a smallish volume of gold. On the other hand, Capt. Bayerle’s recent researches suggest the gold trove may be even larger than earlier estimated, up to some $30 billion. So high risk meets high reward here. It seems ironic that VC’s will throw say $300 million into dubious tech unicorns or the latest crap-coin, but eschew a pretty sure bet of at least breaking even here (if only the lowest estimates of the Republic gold pan out) with a good shot at 10X-ing their investment. We will stay tuned.

Are Managed Futures Funds Worth Including In Your Portfolio?

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Back in February, 2023 I wrote an enthusiastic plug for including managed futures funds in an investment portfolio. That was based on several observations. First, bonds have become often positively correlated with stocks, so the traditional 60/40 stock/bond portfolio provides less hedging or diversification than earlier. Second, during the long grinding bear market of Jan-Oct 2022, managed futures funds shot up, nicely hedging stocks. Third, I had only recently discovered managed futures, so they were for me a shiny new toy.

Managed futures funds hold both long and short positions in futures contracts for a variety of commodities (e.g., oil, gas, metals, cattle), stocks (e.g., domestic vs. international) and other financial instruments (domestic and foreign bonds, currencies, interest rates, etc.). Fund managers usually base their positioning on momentum or trend-following. Historical data shows that if a commodity moves up steadily for, say, a month, there is greater than 50% odds that it will continue moving up for some additional time.  If the fund’s positioning is correct, it makes money the next week or month. If it is incorrect, the fund loses money.

Historically, a good managed futures fund will trade fairly flat or slightly up during a stock bull phase, then step up to give positive return during a stock bear market, to counter the drop in equities prices. We can see below how that worked for managed future (MF) ETF KMLM around 2022. It rose slowly in 2021, then fell back at the end of the year. However, in Jan-Oct 2022 while stocks (and bonds) were painfully grinding down to a 22% loss, KMLM ripped higher by a huge 40%. That seems like a great hedge:

KMLM quickly gave back those gains, for reasons we will discuss. But if you had been consistently rebalancing your portfolio, you would have captured much of those gains.

This sort of performance is why some advisors recommend moving much of your non-stock holdings out of bonds and into managed futures. What’s not to like here?

It turns out that MF funds struggle if there are not fairly long, strong trends in commodity prices. If trends reverse quickly, and then reverse again, then the fund’s positions will lose money over and over. We can see this in the above plot. The story for most of 2022 was interest rates going up and up and up. MF funds were rock stars as they rode that trend for many months. But there was a surprising break in futures trends in November, 2022, as markets suddenly started pricing in an early Fed pivot towards easing in 2023, and so interest rates rose, and bonds and the U.S. dollar tumbled. All the managed futures funds took a sharp hit Nov-Dec 2022. KMLM then went roughly flat for 2023; other MF funds fared worse.

So far, so good. However, it seems like there has been a sea change in futures markets. Before around 2010 or so, there is reason to believe that much of the futures price action was driven by the underlying commodities themselves. For instance, cattle or soybean producers wanted to protect themselves against changes in cattle or soy prices, and so they would buy or sell futures to lock in prices say eight months out. In these situations, there would naturally and normally be months-long trends in futures prices. Wall Street took the other side of those trades. But now it seems to me (can’t give proof reference) that it’s speculators on both sides of the trades, leading to trade algos constantly trying to outguess each other and higher volatility.

For whatever reason, normal trend-following MF has been a bad business for the past 2 years. Here is a continuation of the chart above, showing mid Aug 2023- mid Aug 2025 for KMLM (orange line) compared to S&P 500 stocks (blue line):

The scale is not shown here, but KMLM lost some 30% of its value during that time period. That is NOT the kind of hedge you want to hold.

So, should we forget about MF funds? It turns out that not all MF funds perform the same. My informal research suggests that most MF funds have performed similar to KMLM in the past two years (=abysmally). Since my 2023 article, though, (a) an improved MF ETF (CTA) has appeared, and (b) I became aware of a superior MF fund (AQMNX) of the old-style (non-ETF) mutual fund format. Below is a 3-year chart of KMLM, SP500, and the ETF CTA and the mutual fund AQMNX:

We can see that both the new contenders are up instead of down in the past three years, and both were uncorrelated enough to SP500 to cushion the big Feb-April stock drawdown this year. They handily outperformed bonds (e.g. BND, not shown) during this time period.

There are fundamental reasons why those two funds would behave differently than plain vanilla trend-following KMLM. CTA adds a factor called carry (which I will not try to define) to its algo, and also takes large concentrated bets. AQMNX draws on the very sophisticated quantitative resources of the AQM fund family. It also takes long/short bets on equities (e.g. S&P 500 index), which are not in KMLM.  AQMNX is not available through all brokerages (it is at Fidelity).

As the months roll by and plain stocks soar effortlessly up and up, it may seem pointless to consider any portfolio hedges. But for those who value diversification, these two funds may merit consider consideration. (As usual, nothing here should be considered advice to buy or sell any security).

Bureau of Labor Statistics Under Siege

Scott Buchanan1 Comment

Thousands of keyboards were likely drenched four days ago as coffee spewed from thousands of nostrils upon reading the headlines that President Trump fired the head of the Bureau of Labor Statistics because he (the prez) didn’t like the July 2025 job numbers that were reported. Apparently, the job stats were not as great as we had been led to expect for the new regime of tariffs and deportations. (Someone should inform the politicians that businessmen need predictability for making any expansionary plans). So, shoot the messenger, that will fix it.

The First Ire was apparently kindled especially by the truly massive downward revisions to the May (-125,000) and June (-133,000) job figures, which reduced the combined employment gain for those months by 258,000. That made for three anemic employment months in a row, which is a different picture that had been earlier portrayed. For those unfamiliar with past BLS reports, that could seem like manipulation or gross incompetence. For instance, whitehouse.gov published an article titled, “BLS Has Lengthy History of Inaccuracies, Incompetence”, excoriating the “Biden-appointed”, now-fired Erika McEntarfer who “consistently published overly optimistic jobs numbers — only for those numbers to be quietly revised later.”

But massive overestimations of jobs creation, followed a month or two or three later by massive downward revisions are pretty standard procedure for the BLS in recent years. Fellow blogger Jeremy Horpedahl has noted prior occurrences of this, e.g. here and here. There is no reason to suspect nefarious motives, though. The understaffed and overworked folks at BLS seem to be doing the best they can. It is just a fact that some key data simply is not available as early as other data. There are also rational adjustments, e.g. seasonal trends, that must first be estimated, and only later get revised.

Bloomberg explains some of the fine points of the recent revisions:

The downward revision to the prior two months was largely a result of seasonal adjustment for state and local government education, BLS said in earlier comments to Bloomberg. Those sectors substantially boosted June employment only to be largely revised away a month later.

But economists say the revisions also point to a more concerning, underlying issue of low response rates.

BLS surveys firms in the payrolls survey over the course of three months, gaining a more complete picture as more businesses respond. But a smaller share of firms are responding to the first poll. Initial collection rates have repeatedly slid below 60% in recent months — down from the roughly 70% or more that was the norm before the pandemic.

In addition to the rolling revisions to payrolls that BLS does, there’s also a larger annual revision that comes out each February to benchmark the figures to a more accurate, but less timely data source. BLS puts out a preliminary estimate of what that revision will be a few months in advance, and last year [2024], that projection was the largest since 2009.

Perhaps it would be wise for the BLS to hang a big “preliminary” label on any of the earlier results they publish, to minimize the howls when the big revisions hit later. Or perhaps some improvements could be made in pre-adjusting the adjustments, since revisions there do seem to swing things around outrageously. I expect forthcoming BLS reports to be the subject of derision from all sides. We all know which parties will scoff if the job report looks great or if it looks not great. Presumably the interim head of the Bureau, William Wiatrowski, is busy polishing his resume.

And POTUS should be careful what he wishes for – “great” job growth numbers would, ironically, strengthen the case for the Fed to delay the interest rate cuts he so desires.

Warren Buffett Quotes on Gold as a Bad Investment; Was He Right?

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To say Warren Buffett is not a fan of gold would be an understatement. His basic beef is that gold does not produce much of practical value.  His instincts have always been to buy businesses that generate steady and growing cash by producing goods or services that people need or want –  – businesses like railroads, beverage makers, and insurance companies.

Here are some quotes on the subject from the Oracle of Omaha, where I have bolded some phrases:

“Gold … has two significant shortcomings, being neither of much use nor procreative. True, gold has some industrial and decorative utility, but the demand for these purposes is both limited and incapable of soaking up new production. Meanwhile, if you own one ounce of gold for an eternity, you will still own one ounce at its end” — Buffett, letter to shareholders, 2011

“With an asset like gold, for example, you know, basically gold is a way of going long on fear, and it’s been a pretty good way of going long on fear from time to time. But you really have to hope people become more afraid in the year or two years than they are now. And if they become more afraid you make money, if they become less afraid you lose money. But the gold itself doesn’t produce anything” — Buffett, CNBC’s Squawk Box, 2011

This from when the world’s 67-cubic foot total gold hoard was worth about $7 trillion, which by his reckoning was the value of all U.S. farmland plus seven times the value of petroleum giant ExxonMobil plus an extra $1 trillion:

“And if you offered me the choice of looking at some 67-foot cube of gold … and the alternative to that was to have all the farmland of the country, everything, cotton, corn, soybeans, seven ExxonMobils. Just think of that. Add $1 trillion of walking around money. I, you know, maybe call me crazy but I’ll take the farmland and the ExxonMobils”  – – Cited in https://www.nasdaq.com/articles/3-things-warren-buffett-has-said-about-gold

And my favorite:

“Gold gets dug out of the ground in Africa, or someplace. Then we melt it down, dig another hole, bury it again and pay people to stand around guarding it. It has no utility. Anyone watching from Mars would be scratching their head“. – – From speech at Harvard, see https://quoteinvestigator.com/2013/05/25/bury-gold/

One thing Buffett did NOT say is that gold is “barbarous relic”.  That line is owned by John Maynard Keynes from a hundred years ago, referring to the notion of tying national money issuance to the number of bars of gold held in the national vaults:

“In truth, the gold standard is already a barbarous relic. All of us, from the Governor of the Bank of England downwards, are now primarily interested in preserving the stability of business, prices, and employment, and are not likely, when the choice is forced on us, deliberately to sacrifice these to outworn dogma, which had its value once” –  Monetary Reform (1924)

Has Buffett’s Berkshire Hathaway Beaten Gold as an Investment?

 Given all that trash talk from the legendary investor, let’s see how an investment in his flagship Berkshire Hathaway company (stock symbol BRK.B) compares to gold over various time periods. I will use the ETF GLD as a proxy for gold, and will include the S&P 500 index as a proxy for the general U.S.  large cap stock market.

As always, these comparisons depend on your starting and ending points. In the 1990s and 2000s, BRK.B hugely outperformed the S&P 500, cementing Buffett’s reputation as one of the greatest investors of all time. (GLD data doesn’t go back that far).  In the past twelve months, gold (up 41%) has soundly beaten SPY (up 14 %) and completely trounced BRK.A (up 9%), as of last week. A couple of one-off factors have gone into these results: Gold had an enormous surge in January-April as the world markets digested the implications of never-ending gigantic U.S. budget deficits, and the markets soured on BRK.A due to the announced upcoming retirement of Buffett himself.

Stepping back to look over the past ten years shows the old master still coming out on top. In this plot, gold is orange, S&P 500 is blue, and BRK.A is royal purple:

Over most of this time period (through 7/21/2025), BRK.A and SP500 were pretty close, and gold lagged significantly. Gold was notably left behind during the key stock surge of 2021. Even with the rise in gold and dip in BRK.A this year, Buffett’s company (up 232%) still beats gold (198%) over the past ten years. BRK.A pulled well ahead of SP500 during the 2022 correction, and never gave back that lead. In the April stock market panic this year, BRK.A actually went up as everything else dropped, as it was seen as a tariff-proof safe haven. SP500 was ahead of gold for nearly all this period, until the crash in stocks and the surge in gold in the first half of 2025 brought them to essentially a tie for the past decade.

Meta AI Chief Yann LeCun Notes Limits of Large Language Models and Path Towards Artificial General Intelligence

Scott Buchanan1 Comment

We noted last week Meta’s successful efforts to hire away the best of the best AI scientists from other companies, by offering them insane (like $300 million) pay packages. Here we summarize and excerpt an excellent article in Newsweek by Gabriel Snyder who interviewed Meta’s chief AI scientist, Yann LeCun. LeCun discusses some inherent limitations of today’s Large Language Models (LLMs) like ChatGPT. Their limitations stem from the fact that they are based mainly on language; it turns out that human language itself is a very constrained dataset.  Language is readily manipulated by LLMs, but language alone captures only a small subset of important human thinking:

Returning to the topic of the limitations of LLMs, LeCun explains, “An LLM produces one token after another. It goes through a fixed amount of computation to produce a token, and that’s clearly System 1—it’s reactive, right? There’s no reasoning,” a reference to Daniel Kahneman’s influential framework that distinguishes between the human brain’s fast, intuitive method of thinking (System 1) and the method of slower, more deliberative reasoning (System 2).

The limitations of this approach become clear when you consider what is known as Moravec’s paradox—the observation by computer scientist and roboticist Hans Moravec in the late 1980s that it is comparatively easier to teach AI systems higher-order skills like playing chess or passing standardized tests than seemingly basic human capabilities like perception and movement. The reason, Moravec proposed, is that the skills derived from how a human body navigates the world are the product of billions of years of evolution and are so highly developed that they can be automated by humans, while neocortical-based reasoning skills came much later and require much more conscious cognitive effort to master. However, the reverse is true of machines. Simply put, we design machines to assist us in areas where we lack ability, such as physical strength or calculation.

The strange paradox of LLMs is that they have mastered the higher-order skills of language without learning any of the foundational human abilities. “We have these language systems that can pass the bar exam, can solve equations, compute integrals, but where is our domestic robot?” LeCun asks. “Where is a robot that’s as good as a cat in the physical world? We don’t think the tasks that a cat can accomplish are smart, but in fact, they are.”

This gap exists because language, for all its complexity, operates in a relatively constrained domain compared to the messy, continuous real world. “Language, it turns out, is relatively simple because it has strong statistical properties,” LeCun says. It is a low-dimensionality, discrete space that is “basically a serialized version of our thoughts.”  

[Bolded emphases added]

Broad human thinking involves hierarchical models of reality, which get constantly refined by experience:

And, most strikingly, LeCun points out that humans are capable of processing vastly more data than even our most data-hungry advanced AI systems. “A big LLM of today is trained on roughly 10 to the 14th power bytes of training data. It would take any of us 400,000 years to read our way through it.” That sounds like a lot, but then he points out that humans are able to take in vastly larger amounts of visual data.

Consider a 4-year-old who has been awake for 16,000 hours, LeCun suggests. “The bandwidth of the optic nerve is about one megabyte per second, give or take. Multiply that by 16,000 hours, and that’s about 10 to the 14th power in four years instead of 400,000.” This gives rise to a critical inference: “That clearly tells you we’re never going to get to human-level intelligence by just training on text. It’s never going to happen,” LeCun concludes…

This ability to apply existing knowledge to novel situations represents a profound gap between today’s AI systems and human cognition. “A 17-year-old can learn to drive a car in about 20 hours of practice, even less, largely without causing any accidents,” LeCun muses. “And we have millions of hours of training data of people driving cars, but we still don’t have self-driving cars. So that means we’re missing something really, really big.”

Like Brooks, who emphasizes the importance of embodiment and interaction with the physical world, LeCun sees intelligence as deeply connected to our ability to model and predict physical reality—something current language models simply cannot do. This perspective resonates with David Eagleman’s description of how the brain constantly runs simulations based on its “world model,” comparing predictions against sensory input. 

For LeCun, the difference lies in our mental models—internal representations of how the world works that allow us to predict consequences and plan actions accordingly. Humans develop these models through observation and interaction with the physical world from infancy. A baby learns that unsupported objects fall (gravity) after about nine months; they gradually come to understand that objects continue to exist even when out of sight (object permanence). He observes that these models are arranged hierarchically, ranging from very low-level predictions about immediate physical interactions to high-level conceptual understandings that enable long-term planning.

[Emphases added]

(Side comment: As an amateur reader of modern philosophy, I cannot help noting that these observations about the importance of recognizing there is a real external world and adjusting one’s models to match that reality call into question the epistemological claim that “we each create our own reality”.)

Given all this, developing the next generation of artificial intelligence must, like human intelligence, embed layers of working models of the world:

So, rather than continuing down the path of scaling up language models, LeCun is pioneering an alternative approach of Joint Embedding Predictive Architecture (JEPA) that aims to create representations of the physical world based on visual input. “The idea that you can train a system to understand how the world works by training it to predict what’s going to happen in a video is a very old one,” LeCun notes. “I’ve been working on this in some form for at least 20 years.”

The fundamental insight behind JEPA is that prediction shouldn’t happen in the space of raw sensory inputs but rather in an abstract representational space. When humans predict what will happen next, we don’t mentally generate pixel-perfect images of the future—we think in terms of objects, their properties and how they might interact…

This approach differs fundamentally from how language models operate. Instead of probabilistically predicting the next token in a sequence, these systems learn to represent the world at multiple levels of abstraction and to predict how their representations will evolve under different conditions.

And so, LeCun is strikingly pessimistic on the outlook for breakthroughs in the current LLM’s like ChatGPT. He believes LLMs will be largely obsolete within five years, except for narrower purposes, and so he tells upcoming AI scientists to not even bother with them:

His belief is so strong that, at a conference last year, he advised young developers, “Don’t work on LLMs. [These models are] in the hands of large companies, there’s nothing you can bring to the table. You should work on next-gen AI systems that lift the limitations of LLMs.”

This approach seems to be at variance with other firms, who continue to pour tens of billions of dollars into LLMs. Meta, however, seems focused on next-generation AI, and CEO Mark Zuckerberg is putting his money where his mouth is.

Meta Is Poaching AI Talent With $100 Million Pay Packages; Will This Finally Create AGI?

Scott Buchanan3 Comments

This month I have run across articles noting that Meta’s Mark Zuckerberg has been making mind-boggling pay offers (like $100 million/year for 3-4 years) to top AI researchers at other companies, plus the promise of huge resources and even (gasp) personal access to Zuck, himself. Reports indicate that he is succeeding in hiring around 50 brains from OpenAI (home of ChatGPT), Anthropic, Google, and Apple. Maybe this concentration of human intelligence will result in the long-craved artificial general intelligence (AGI) being realized; there seems to be some recognition that the current Large Language Models will not get us there.

There are, of course, other interpretations being put on this maneuver. Some talking heads on a Bloomberg podcast speculated that Zuckerberg was using Meta’s mighty cash flow deliberately to starve competitors of top AI talent. They also speculated that (since there is a limit to how much money you can possibly, pleasurably spend) – – if you pay some guy $100 million in a year, a rational outcome would be he would quit and spend the rest of his life hanging out at the beach. (That, of course, is what Bloomberg finance types might think, who measure worth mainly in terms of money, not in the fun of doing cutting edge R&D).

I found a thread on reddit to be insightful and amusing, and so I post chunks of it below. Here is the earnest, optimist OP:

andsi2asi

Zuckerberg’s ‘Pay Them Nine-Figure Salaries’ Stroke of Genius for Building the Most Powerful AI in the World

Frustrated by Yann LeCun’s inability to advance Llama to where it is seriously competing with top AI models, Zuckerberg has decided to employ a strategy that makes consummate sense.

To appreciate the strategy in context, keep in mind that OpenAI expects to generate $10 billion in revenue this year, but will also spend about $28 billion, leaving it in the red by about $18 billion. My main point here is that we’re talking big numbers.

Zuckerberg has decided to bring together 50 ultra-top AI engineers by enticing them with nine-figure salaries. Whether they will be paid $100 million or $300 million per year has not been disclosed, but it seems like they will be making a lot more in salary than they did at their last gig with Google, OpenAI, Anthropic, etc.

If he pays each of them $100 million in salary, that will cost him $5 billion a year. Considering OpenAI’s expenses, suddenly that doesn’t sound so unreasonable.

I’m guessing he will succeed at bringing this AI dream team together. It’s not just the allure of $100 million salaries. It’s the opportunity to build the most powerful AI with the most brilliant minds in AI. Big win for AI. Big win for open source

And here are some wry responses:

kayakdawg

counterpoint 

a. $5B is just for those 50 researchers, loootttaaa other costs to consider

b. zuck has a history of burning big money on r&d with theoretical revenue that doesnt materialize

c. brooks law: creating agi isn’t an easily divisible job – in fact, it seems reasonable to assume that the more high-level experts enter the project the slower it’ll progress given the communication overhead

…

7FootElvis

Exactly. Also, money alone doesn’t make leadership effective. OpenAI has a relatively single focus. Meta is more diversified, which can lead to a lack of necessary vision in this one department. Passion, if present at the top, is also critical for bleeding edge advancement. Is Zuckerberg more passionate than Altman about AI? Which is more effective at infusing that passion throughout the organization?

….

dbenc

and not a single AI researcher is going to tell Zuck “well, no matter how much you pay us we won’t be able to make AGI”

…

meltbox

I will make the AI by one year from now if I am paid $100m

I just need total blackout so I can focus. Two years from now I will make it run on a 50w chip.

I promise

Economic Impact of Agricultural Worker Deportations Leads to Administration Policy Reversals

Scott Buchanan1 Comment

Here is a chart of the evolution of U.S. farm workforce between 1991 and 2022:

Source: USDA

A bit over 40% of current U.S. farm workers are illegal immigrants. In some regions and sectors, the percentage is much higher. The work is often uncomfortable and dangerous, and far from the cool urban centers. This is work that very few U.S. born workers would consider doing, unless the pay was very high, so it would be difficult to replace the immigrant labor on farms in the near term. I don’t know how much the need for manpower would change if cheap illegal workers were not available, and therefore productivity was supplemented with automation.

It apparently didn’t occur to some members of the administration that deporting a lot of these workers (and frightening the rest into hiding) would have a crippling effect on American agriculture. Sure enough, there have recently been reports in some areas of workers not showing up and crops going unharvested.

It is difficult for me as a non-expert to determine how severe and widespread the problems actually are so far. Anti-Trump sources naturally emphasize the genuine problems that do exist and predict apocalyptic melt-down, whereas other sources are more measured. I suspect that the largest agribusinesses have kept better abreast of the law, while smaller operations have cut legal corners and may have that catch up to them. For instance, a small meat packer in Omaha reported operating at only 30% capacity after ICE raids, whereas the CEO of giant Tyson Foods claimed that “every one who works at Tyson Foods is authorized to do so,” and that the company “is in complete compliance” with all the immigration regulations.

With at least some of these wholly predictable problems from mass deportations now becoming reality, the administration is undergoing internal debates and policy adjustments in response. On June 12, President Trump very candidly acknowledged the issue, writing on Truth Social, “Our great Farmers and people in the hotel and leisure business have been stating that our very aggressive policy on immigration is taking very good, long-time workers away from them, with those jobs being almost impossible to replace…. We must protect our Farmers, but get the CRIMINALS OUT OF THE USA. Changes are coming!” 

The next day, ICE official Tatum King wrote regional leaders to halt investigations of the agricultural industry, along with hotels and restaurants. That directive was apparently walked back a few days later, under pressure from outraged conservative supporters and from Deputy White House Chief of Staff Stephen Miller. Miller, an immigration hard-liner, wants to double the ICE deportation quota, up to 3,000 per day.

This issue could go in various ways from here. Hard-liners on the left and on the right have a way of pushing their agendas to unpalatable extremes. It can be argued that the Democrats could easily have won in 2024 had their policies been more moderate. Similarly, if immigration hard-liners get their way now, I predict that the result will be their worst nightmare: a public revulsion against enforcing immigration laws in general. If farmers and restaurateurs start going bust, and food shortages and price spikes appear in the supermarket, public support for the administration and its project of deporting illegal immigrants will reverse in a big way. Some right-wing pundits would not be bothered by an electoral debacle, since their style is to stay constantly outraged, and (as the liberal news outlets currently demonstrate), it is easier to project non-stop outrage when your party is out of power.

An optimist, however, might see in this controversy an opening for some sort of long-term, rational solution to the farm worker issue. Agricultural Secretary Brooke Rollins has proposed expansion of the H-2A visa program, which allows for temporary agricultural worker residency to fill labor shortages. This is somewhat similar to the European guest worker programs, though with significant differences. H-2A requires the farmer to provide housing and take legal responsibility for his or her workers. H-2B visas allow for temporary non-agricultural workers, without as much employer responsibility. A bill was introduced into Congress with bi-partisan support to modernize the H-2A program, so that legislative effort may have legs. Maybe there can be a (gasp!) compromise.

President Trump last week came out strongly in favor of this sort of solution, with a surprisingly positive take on the (illegal) workers who have worked diligently on a farm for years. By “put you in charge” he is seems to refer to the responsibilities that H-2A employers undertake for their employers, and perhaps extending that to H-2B employers. He acknowledges that the far-right will not be happy, but hopes “they’ll understand.” From Newsweek:

“We’re working on legislation right now where – farmers, look, they know better. They work with them for years. You had cases where…people have worked for a farm, on a farm for 14, 15 years and they get thrown out pretty viciously and we can’t do it. We gotta work with the farmers, and people that have hotels and leisure properties too,” he said at the Iowa State Fairgrounds in Des Moines on Thursday.

“We’re gonna work with them and we’re gonna work very strong and smart, and we’re gonna put you in charge. We’re gonna make you responsible and I think that that’s going to make a lot of people happy. Now, serious radical right people, who I also happen to like a lot, they may not be quite as happy but they’ll understand. Won’t they? Do you think so?”

We shall see.

Central Banks Are Buying Gold; Should You?

Scott BuchananLeave a comment

Anyone who reads financial headlines knows that gold prices have soared in the past year. Why?

Gold has historically been a relatively stable store of value, and that role seems to be returning after decades of relative neglect. Official numbers show sharply increased buying by the world’s central banks, led by China, Poland, and Azerbaijan in early 2025. Russia, India and Turkey have also been major buyers. There is widespread conviction that actual gold purchases are appreciably higher than the officially-reported numbers, to side-step President Trump’s threatened extra tariffs on nations seen as de-dollarizing.

I think the most proximate cause for the sharp run-up in gold prices in the past twelve months has been the profligate U.S. federal budget deficit, under both administrations. This is convincing key world actors that the dollar will become increasingly devalued over time, no matter which party is in power. Thus, it is prudent to get out of dollars and dollar-denominated assets like U.S. T-bonds.

Trump’s erratic and offensive policies and statements in 2025 have added to the desire to diversify away from U.S. assets. This is in addition to the alarm in non-Western countries over the impoundment of Russian dollar-related assets in connection with the ongoing Russian invasion of Ukraine. Also, there is something of a self-fulfilling momentum aspect to any asset: the more it goes up, the more it is expected to go up.

This informative chart of central bank gold net purchasing is courtesy of Weekend Investing:

Interestingly, central banks were net sellers in the 1990s and early 2000s; it was an era of robust economic growth, gold prices were stagnant or declining, and it seemed pointless to hold shiny metal bars when one could invest in financial assets with higher rates of return. The Global Financial Crisis of 2008-2009 apparently sobered up the world as to the fragility of financial assets, making solid metal bars look pretty good. Then, as noted, the Western reaction to the Russian attack on Ukraine spurred central bank buying gold, as this blog predicted back in March, 2022.

Private investors are also buying gold, for similar reasons as the central banks. Gold offers portfolio diversification as a clear alternative from all paper assets. In theory it should offer something of an inflation hedge, but its price does not always track with inflation or interest rates.

Here is how gold (using GLD fund as a proxy) has fared versus stocks (S&P 500 index) and intermediate term U. S. T-bonds (IEF fund) in the past year:

Gold is up by 40%, compared to 12.6% for stocks. That is huge outperformance. This was driven largely by the fact that gold rose strongly in the Feb-April timeframe, while stocks were collapsing.

Below we zoom out to look at the past ten years, and include the intermediate-term T-bond fund IEF:

Gold prices more than doubled from 2008 to 2011, then suffered a long, painful decline over the next two years. Prices were then fairly stagnant for the mid-2010s, rose significantly 2019-2020, then stagnated again until taking off in 2023. Stocks have been much more erratic. Most of the time stock returns were above gold, but the 2020 and 2024 plunges brought stocks down to rough parity with gold. Since about 2019, T-bonds have been pathetic; pity the poor investor who has been (according to traditional advice) 40% invested in investment-grade bonds.

How to invest in gold? Hard-core gold bugs want the actual coins (no-one can afford a full bullion bar) to rub between their fingers and keep in their own physical custody. You can buy coins from on-line dealers or local dealers. Coins are available from the U.S. Mint, but reportedly their mark-ups are often higher than on the secondary market. 

An easier route for most folks is to buy into a gold-backed stock fund. The biggest is GLD, which has over $100 billion in assets. There has long been an undercurrent of suspicion among gold bugs that GLD’s gold is not reliably audited or that it is loaned out; they refer derisively to GLD as “paper gold” or gold derivatives.  The fund itself claims that it never lends out its gold, and that its bars are held in the vaults of the custodian banks JPMorgan Chase Bank, N.A. and HSBC Bank plc, and are independently audited. The suspicious crowd favors funds like Sprott Physical Gold Trust, PHYS. PHYS is claimed to have a stronger legal claim on its physical gold than GLD. However, PHYS is a closed-end fund, which means it does not have a continuous creation process like GLD, an open-end ETF. This can lead to discrepancies between the fund’s share price and the value of its gold holdings. It does seem like PHYS loses about 1% per year relative to GLD.

Disclaimer: Nothing here should be taken as advice to buy or sell any security.