LinkedIn has its problems, but so does every other social network.
I joined LinkedIn out of college because it seemed like something you were supposed to do if you want a job someday, but I never checked it because the academic job market makes little use of LinkedIn. In 2013 LinkedIn added social media features like a newsfeed, but I still never spent time there. Facebook and Twitter seemed more interesting, and like many people I’ve always been allergic to “networking” or other social settings where one person is just trying to get something from another. It seemed like a recipe for posts that are cringe, soulless, or desperate.
But over the past couple years, I’ve found myself spending more time there- and not because I’m looking for a job or looking to hire. Some of the posts are genuinely interesting, and it is a nice way to keep up with what people I know are up to. Either LinkedIn got better or I got worse.
I find that LinkedIn is particularly good for staying in touch with my old students. I always told my students they could still e-mail me or stop by my office after the semester is over, but they almost never do; that takes a lot of thought and energy. Social networks are the ideal way to keep in touch with “weak ties“, but you have to find the right one. Facebook was the best for this when it was ubiquitous, but now it is becoming more common for Americans not to have or not to check Facebook, especially young ones (plus it was always a bit too personal for former students). Twitter has never been something that most people have, and the more popular networks are either too personal (Instragram, Snap et c) or too impersonal where almost all content users see comes from people they don’t know (TikTok, Youtube, et c).
LinkedIn by contrast is ubiquitous and just the right amount of personal. It also seems to be increasingly a good place to share interesting writing. I like much of what I read there, and my writing gets a good reception; I tend to get more engagement for EWED posts on LinkedIn than on X and Facebook despite having fewer connections there than Facebook friends or Twitter followers. Yes, you’ll still see some cringe posts there, but it beats the angry political posts that are ubiquitous on Facebook and especially X.
This is the second of a series of occasional posts on observations of how some individual initiatives made strategic impacts on World War II operations and outcome. While there were innumerable acts of initiative and heroism that occurred during this conflict, I will focus on actions that shifted the entire capabilities of their side.
It’s the summer of 1941. The war in Europe between mainly Germany and Britain had been grinding on for around two years, with Hitler in control of nearly all of Europe. The Germans then attacked the Soviet Union, and quickly conquered enormous stretches of territory. It looked like the Nazis were winning. Relations with Japan, which aimed to take over the eastern Pacific region were uneasy. The Japanese had already conquered Korea and coastal China, and were eyeing the resource-rich lands of Southeast Asia and Indonesia. It was a tense time.
The Japanese military had been building up for decades, preparing for a war with the United States for control of the eastern Pacific. They developed cutting edge military hardware, including the world’s biggest battleships, superior torpedoes and a large, well-trained aircraft carrier force. They also produced a new fighter plane, dubbed the “Zero” by Western observers.
Intelligence reports started to trickle in that the Zero was incredibly agile: it could outrun and out-climb and out-turn anything the U.S. could put in the air, and it packed a wallop with twin machine cannons. Its designers achieved this performance with a modestly-powered engine by making the airframe supremely light.
As I understand it, the U.S. military establishment’s response to this intel was fairly anemic. It was such awful news, that seemingly they buried their heads in the sand and just hoped it wasn’t true. Why was this so disastrous? Well, since the days of the Red Baron in World War I, the way you shot down your opponent in a dogfight was to turn in a narrower circle than him, or climb faster and roll, to get behind him. Get him in your gunsights, burst of incendiary machine-gun bullets to ignite his gasoline fuel tanks, and down he goes. If the Zero really was that agile, then it could easily shoot down any U.S. plane with impunity. Even if you started to line up behind a Zero for a shot, he could execute a tight turning maneuver, and end up on your tail, every time. Ouch.
A U.S. Navy aviator named John Thatch from Pine Bluff, Arkansas did take these reports on the Zero seriously. He racked his brains, trying to figure out a way for the clunky American Wildcat fighters to take on the Zeros. He knew the American pilots were well-trained and were good shots, if only they could get some crucial four-second (?) windows of time to line up on the enemy planes.
So, he spent night after night that summer, using matchsticks on his kitchen table, trying to invent tactics that would neutralize the advantages of the Japanese fighters. He found that the standard three-plane section (one leader, two wingmen) was too clumsy for rapid maneuvering. He settled on having two sections of two planes each. The two sections would fly parallel, several hundred yards apart. If one section got attacked, the two sections would immediately make sharp turns towards each other, and cross paths. The planes of the non-attacked section could then take a head-on shot at the enemy plane(s) that were tailing the attacked section.
The blue planes are the good guys, with a section on the left and on the right. At the bottom of the diagram, an enemy plane (green) gets on the tail of a blue plane on the right. The left and the right blue sections then make sudden 90 degree turns towards one another. The green plane follows his target around the turn, whereupon he is suddenly face-to-face with a plane from the other section, which (rat-a-tat-tat) shoots him down. In a head-to-head shootout, the Wildcat was likely to prevail, since it was more substantial than the flimsy Zero. Afterwards, the two sections continue flying parallel, ready to repeat the maneuver if attacked again. And of course, they don’t just fly along hoping to be attacked, they can make offensive runs at enemy planes as well, as a unified formation. This technique was later dubbed the “Thatch weave”.
Thatch faced opposition to his unorthodox tactics from the legendary inertia of the pre-war U.S. military establishment. Finally, he and his trained team submitted to a test: their four-plane formation went into mock combat against another four planes (all Wildcats), but his planes had their throttles restricted to maximum half power. Normally that would have made them toast, but in fact, with their weaving, they frustrated every attempt of the other planes to line up on them. This demonstration won over many of the actual pilots in the carrier air force, though the brass on the whole did not endorse it.
By some measures the most pivotal battle in the Pacific was the battle of Midway in June, 1942. The Japanese planned to wipe out the American carrier force by luring them into battle with a huge Japanese fleet assembled to invade the American-held island of Midway. If they had succeeded, WWII would have been much harder for the U.S. and its allies to win.
The way that battle unfolded, the U.S. carriers launched their torpedo planes well before their dive bombers. The Japanese probably feared the torpedo planes the most, and so they focused their Zeros on them. Effectively only Thatch and two other of his Wildcats were the only American fighter protection for the slow, poorly-armored torpedo bombers by the time they got to their targets. Using his weave maneuver for the first time in combat, he managed to shoot down three Zeros while not getting shot down himself. This vigorous, unexpectedly effective defense by a handful of Wildcats crucially helped to divert the Japanese fighters and kept them at low altitudes, just in time for the American dive bombers to arrive and attack unmolested from high altitude.
In the end, four Japanese fleet carriers were sunk by the dive-bombers at Midway, at a cost of one U.S. carrier. That victory helped the U.S. to hang on in the Pacific until its new carriers started arriving in 1943. Thatch’s tactic made a material difference in that battle, and was quickly promulgated throughout the rest of the U.S. carrier force. It was not a complete panacea, of course, since the once the enemy knew what you were about to do, they might be able to counter it. However, it did give U.S. fighters a crucial tool for confronting a more-agile opponent, at a critical time in the war. Thatch went on to train other pilots, and eventually became an admiral in the U.S. Navy.
Hey look, I just found a way to get infinite free electric power:
This sort of extension-cord-plugged-into-itself meme has shown up recently on the web to characterize a spate of circular financing deals in the AI space, largely involving OpenAI (parent of ChatGPT). Here is a graphic from Bloomberg which summarizes some of these activities:
Nvidia, which makes LOTS of money selling near-monopoly, in-demand GPU chips, has made investing commitments in customers or customers of their customers. Notably, Nvidia will invest up to $100 billion in Open AI, in order to help OpenAI increase their compute power. OpenAI in turn inked a $300 billion deal with Oracle, for building more data centers filled with Nvidia chips. Such deals will certainly boost the sales of their chips (and make Nvidia even more money), but they also raise a number of concerns.
First, they make it seem like there is more demand for AI than there actually is. Short seller Jim Chanos recently asked, “[Don’t] you think it’s a bit odd that when the narrative is ‘demand for compute is infinite’, the sellers keep subsidizing the buyers?” To some extent, all this churn is just Nvidia recycling its own money, as opposed to new value being created.
Second, analysts point to the destabilizing effect of these sorts of “vendor financing” arrangements. Towards the end of the great dot.com boom in the late 1990’s, hardware vendors like Cisco were making gobs of money selling server capacity to internet service providers (ISPs). In order to help the ISPs build out even faster (and purchase even more Cisco hardware), Cisco loaned money to the ISPs. But when that boom busted, and the huge overbuild in internet capacity became (to everyone’s horror) apparent, the ISPs could not pay back those loans. QQQ lost 70% of its value. Twenty-five years later, Cisco stock price has never recovered its 2000 high.
Beside taking in cash investments, OpenAI is borrowing heavily to buy its compute capacity. Since OpenAI makes no money now (and in fact loses billions a year), and (like other AI ventures) will likely not make any money for several more years, and it is locked in competition with other deep-pocketed AI ventures, there is the possibility that it could pull down the whole house of cards, as happened in 2000. Bernstein analyst Stacy Rasgon recently wrote, “[OpenAI CEO Sam Altman] has the power to crash the global economy for a decade or take us all to the promised land, and right now we don’t know which is in the cards.”
For the moment, nothing seems set to stop the tidal wave of spending on AI capabilities. Big tech is flush with cash, and is plowing it into data centers and program development. Everyone is starry-eyed with the enormous potential of AI to change, well, EVERYTHING (shades of 1999).
The financial incentives are gigantic. Big tech got big by establishing quasi-monopolies on services that consumers and businesses consider must-haves. (It is the quasi-monopoly aspect that enables the high profit margins). And it is essential to establish dominance early on. Anyone can develop a word processor or spreadsheet that does what Word or Excel do, or a search engine that does what Google does, but Microsoft and Google got there first, and preferences are sticky. So, the big guys are spending wildly, as they salivate at the prospect of having the One AI to Rule Them All.
Even apart from achieving some new monopoly, the trillions of dollars spent on data center buildout are hoped to pay out one way or the other: “The data-center boom would become the foundation of the next tech cycle, letting Amazon, Microsoft, Google, and others rent out intelligence the way they rent cloud storage now. AI agents and custom models could form the basis of steady, high-margin subscription products.”
However, if in 2-3 years it turns out that actual monetization of AI continues to be elusive, as seems quite possible, there could be a Wile E. Coyote moment in the markets:
Last week I took kids to an excellent show at Samford’s Christenberry Planetarium. If you live in Alabama, follow them on Instagram for updates on events (often free).
I have heard people say that the liberal project is doomed because people just want to war. Well, did you know that the James Webb Space Telescope orbits the sun? (I was busy on Christmas 2021 when the rest of the world was alerted to this fact.)
A couple of days ago I spoke with a friend who was troubled by the case of Adam Raine, the sixteen-year-old who was counseled by a ChatGPT AI therapy chatbot into killing himself. That was of course extremely tragic, but I hoped it was kind of an outlier. Then I heard on a Bloomberg business podcast that the number one use for AI now is personal therapy. Being a researcher, I had to check this claim.
So here is an excerpt from a visual presentation of an analysis done by Marc Zao-Sanders for Harvard Business Review. He examined thousands of forum posts over the last year in a follow-up to his 2024 analysis to estimate uses of AI. To keep it tractable, I just snipped an image of the first six categories:
It’s true: Last year the most popular uses were spread across a variety of categories, but in 2025 the top use was “Therapy & Companionship”, followed by related uses of “Organize Life” and “Find Purpose”. Two of the top three uses in 2024, “Generate Ideas” and “Specific Search”, were aimed at task productivity (loosely defined), whereas in 2025 the top three uses were all for personal support.
Huh. People used to have humans in their lives known as friends or buddies or girlfriends/boyfriends or whatever. Back in the day, say 200 or 2000 or 200,000 or 2,000,000 years ago, it seems a basic unit was the clan or village or extended kinship group. As I understand it, in a typical English village the men would drift into the pub most Friday and Saturday nights and banter and play darts over a pint of beer. You were always in contact with peers or cousins or aunts/uncles or grandmother/grandfathers who would take an interest in you, and who might be a few years or more ahead of you in life. These were folks you could bounce around your thoughts with, who could help you sort out what is real. The act of relating to another human being seems to be essential in shaping our psyches. The alternative is appropriately termed “attachment disorder.”
The decades-long decline in face-to-face social interactions in the U.S. has been the subject of much commentary. A landmark study in this regard was Robert Putnam’s 1995 essay, “Bowling Alone: America’s Declining Social Capital”, which he then expanded into a 2000 book. The causes and results of this trend are beyond the scope of this blog post.
The essence of the therapeutic enterprise is the forming of a relational human-to-human bond. The act of looking into another person’s eyes, and there sensing acceptance and understanding, is irreplaceable.
But imagine your human conversation partner faked sympathy but in fact was just using you. He or she could string you along by murmuring the right reflective phrases (“Tell me more about …”, “Oh, that must have been hard for you”, blah, blah, blah) but with the goal of getting money from you or turning you towards being an espionage partner. This stuff goes on all the time in real life.
The AI chatbot case is not too different than this. Most AI purveyors are ultimately in it for the money, so they are using you. And the chatbot does not, cannot care about you. It is just a complex software algorithm, embedded in silicon chips. To a first approximation, LLMs simply spit out a probabilistic word salad in response to prompts. That is it. They do not “know” anything, and they certainly do not feel anything.
Here is what my Brave browser embedded AI has to say about the risks of using AI for therapy:
Using AI chatbots for therapy poses significant dangers, including the potential to reinforce harmful thoughts, fail to recognize crises like suicidal ideation, and provide unsafe or inappropriate advice, according to recent research and expert warnings. A June 2025 Stanford study found that popular therapy chatbots exhibit stigmatizing biases against conditions like schizophrenia and alcohol dependence, and in critical scenarios, they have responded to indirect suicide inquiries with irrelevant information, such as bridge heights, potentially facilitating self-harm. These tools lack the empathy, clinical judgment, and ethical framework of human therapists, and cannot ensure user safety or privacy, as they are not bound by regulations like HIPAA.
AI chatbots cannot provide a medical diagnosis or replace human therapists for serious mental health disorders, as they lack the ability to assess reality, challenge distorted thinking, or ensure safety during a crisis.
Research shows that AI systems often fail to respond appropriately to mental health crises, with one study finding they responded correctly less than 60% of the time compared to 93% for licensed therapists.
Chatbots may inadvertently validate delusional or paranoid thoughts, creating harmful feedback loops, and have been observed to encourage dangerous behaviors, such as promoting restrictive diets or failing to intervene in suicidal ideation.
There is a significant risk of privacy breaches, as AI tools are not legally required to protect user data, leaving sensitive mental health information vulnerable to exposure or misuse.
The lack of human empathy and the potential for emotional dependence on AI can erode real human relationships and worsen feelings of isolation, especially for vulnerable individuals.
Experts warn that marketing AI as a therapist is deceptive and dangerous, as these tools are not licensed providers and can mislead users into believing they are receiving professional care.
Bar codes have been common in retail stores since the 1970s. These give a one-dimensional read of digital data. The hardware and software to decode a bar code are relatively simple.
The QR code encodes information in a two-dimensional matrix. The QR code, short for quick-response code, was invented in 1994 by Masahiro Hara of the Japanese company Denso Wave for labelling automobile parts. It can pack far more information in the same real estate than a bar code, but it requires sophisticated image processing to decode it. Fortunately, the chip power for image processing has kept up, so smart phones can decode even intricate QR codes, provided the image is clear enough.
Like most QR codes, it has three distinctive square patterns on three corners, and a smaller one set in from the fourth corner, that give information to the image processing software on image orientation and sizing.
As time goes on, more versions of QR codes are defined, with ever finer patterns that convey more information. For instance, here is a medium-resolution QR Code (version 3), and a very high resolution QR code (Version 40):
My phone could not decode the Version 40 above; the limit may be how much detail the camera could capture.
QR codes use the Reed–Solomon error correction methodology to correct for some errors in image capture or physical damage to the QR code. For instance, this QR code with the torn-off corner still decodes properly as the URL for Wikipedia (whole image shown above):
Getting down a little deeper in the weeds, this image shows, for Version 3 (29×29) QR code, which pixels are devoted to orientation/alignment (reddish, pinkish), which define the format (blueish), and which encode the actual content (black and white):
Uses Of QR Codes
A common use of QR codes is to convey a web link (URL), so pointing your phone at the QR code is the equivalent of clicking on a link in an email. Here is an AI summary of uses:
They are used to access websites and digital content, such as restaurant menus, product information, and course details, enabling a contactless experience that reduces the need for printed materials. Smartphones can scan QR codes to connect to Wi-Fi networks by automatically entering the network name (SSID), password, and encryption type, simplifying the process for users. They facilitate digital payments by allowing users to send or receive money through payment apps by scanning a code, eliminating the need for physical cash or cards. QR codes are also used to share contact information, such as vCards, and to initiate calls, send text messages, or compose emails by pre-filling the recipient and message content. For app downloads, QR codes can directly link to the Apple App Store or Google Play, streamlining the installation process. In social media and networking, they allow users to quickly follow profiles on platforms like LinkedIn, Instagram, or Snapchat by scanning a code. They are also used for account authentication, such as logging into services like WhatsApp, Telegram, or WeChat on desktop by scanning a code with a mobile app. Additionally, QR codes are employed in marketing, event ticketing, and even on gravestones to provide digital access to obituaries or personal stories. Their versatility extends to sharing files like PDFs, enabling users to download documents by scanning a code. Overall, QR codes act as a bridge between the physical and digital worlds, enhancing efficiency and interactivity across numerous daily activities.
Note that your final statement in this world might be a QR code on your gravestone.
Security with QR Codes
On an iPhone, if “Scan QR Codes” (or something similar) has been enabled, pointing the phone at a QR code in Camera mode will display the first few characters of the URL or whatever, which gives you the opportunity to click on it right then. If you want to be a bit more cautious, you can take a photo, and then open Photos to look at the image of QR code. If you then press on the photo of the QR code, up will come a box with the entire character string encoded by the QR code. You can then decide if clicking on something ending in .ru is what you really want to do.
Accessing a rogue website can obviously hurt you. And even if you aren’t dinged by that kind of browser exploit, the reader’s permissions on your phone may allow use of your camera, read/write contact data, GPS location, read browser history, and even global system changes. The bad guys never sleep. Who would have thought that a QR code on a parking meter posing as a quick payment option could empty your bank account? Our ancestors needed to stay alert to physical dangers, for us it is now virtual threats.
ACKNOWLEDGEMENT: The bulk of the content, and all the images, in this blog post were drawn from the excellent Wikipedia article “QR code”.
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.
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.
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.
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.
Sometimes I get weeks in the summer that are more research focused. This past week is very much a teaching and service focused week at my university. I haven’t had any time to ponder topics related to research or current events. So, I will share what I’ve been telling my fellow college educators. This will sound backward to some and like common sense to others. Feel free to comment with your thoughts.
College professors who teach 200-level or “principles” classes should not change all that much in response to AI. Students still need to know something. There need to be a few concepts and vocabulary words in their heads. For example, a person cannot use a calculator effectively if they do not know what a square root is at all.
I see highly trained mid-career professionals bragging about how they get ChatGPT to do their work. Can a 20-year-old do that if they don’t know what words to use in a prompt? How does vibe coding go for people who never learned to write out a single line of code? (not a question I have an expert answer to right now)
We should largely be sticking to the “old ways” and at least to some extent still require memorization. Having an exam on paper is a good way to ensure that the students can form coherent thoughts of their own, when possible.
Indeed, students might become AI jockeys when they get to the workplace. A 400-level class would be a good place for them to start heavily integrating AI tools to accomplish tasks and do projects. For anyone unfamiliar with American college categories, that would mean that an undergraduate might heavily use AI tools in their 4th and final year of study.
AI makes a great tutor for learning and enforcing principles, but it should not serve as a replacement test-taker. A human who cannot read and write will not be able to take full advantage of an intelligent machine in the next decade. Voice recognition is getting very good and the models are getting more agentic, so this might all change if we can keep the data centers on long enough. In the future, you might argue that having students write an exam answer by hand is as superfluous as teaching them to play the violin.
As of 2025, what you might see is some teachers who feel pressured to claim they are integrating AI more than they actually want to. A relative I talked to his summer in a corporate job told me that she feels intense pressure at work to be able to claim that she’s using AI. Anyone doesn’t have the appearance of embracing AI looks behind or expendable!
Two ideas coalesced to contribute to this post. First, for years in my Principles of Macroeconomics course I’ve taught that we no longer have mass starvation events due to A) Flexible prices & B) Access to international trade. Second, my thinking and taxonomy here has been refined by the work of Michael Munger on capitalism as a distinct concept from other pre-requisite social institutions.
Munger distinguishes between trade, markets, and capitalism. Trade could be barter or include other narrow sets of familiar trading partners, such as neighbors and bloodlines. Markets additionally include impersonal trade. That is, a set of norms and even legal institutions emerge concerning commercial transactions that permit dependably buying and selling with strangers. Finally, capitalism includes both of these prerequisites in addition to the ability to raise funds by selling partial stakes in firms – or shares.
This last feature’s importance is due to the fact that debt or bond financing can’t fund very large and innovative endeavors because the upside to lenders is too small. That is, bonds are best for capital intensive projects that have a dependable rates of return that, hopefully, exceed the cost of borrowing. Selling shares of ownership in a company lets a diverse set of smaller stakeholders enjoy the upside of a speculative project. Importantly, speculative projects are innovative. They’re not always successful, but they are innovative in a way that bond and debt financing can’t satisfy. Selling equity shares open untapped capital markets.
With this refined taxonomy, I can better specify that it’s not access to international trade that is necessary to consistently prevent mass starvation. It’s access to international markets. For clarity, below is a 2×2 matrix that identifies which features characterize the presence of either flexible prices or access to international markets.
Economist Writing Every Day 