Last month, on June 24 to be precise, two companies released statements that were notable in themselves and even more striking for coming on the same day.
One was the release by memory-chip leader Micron of astoundingly blockbuster fiscal third-quarter earnings. The company reported that revenues had quadrupled year-over-year to $41.46 billion, while earnings soared 1,215% to $28.24 billion. It’s impossible to overstate just how extraordinary these figures are.
Memory chips are essential to virtually all modern technology, used in smartphones, laptops, automobiles, gaming consoles, and much more, but the bulk of demand is coming from the ongoing explosion in the numbers and size of AI data centers. The escalating demand for the chips has led to severe supply shortages and rising prices, fueling Micron’s astounding growth.
The other announcement came from Apple. The company said that the rising costs of components, including memory chips, is forcing it to sharply raise prices on many of its products including laptops and iPads.
Coming on the same day, the two events constitute a kind of microcosm of some of the pressures on the U.S. economy that are accelerating as we move beyond the country’s 250th anniversary. They are part and parcel of developments that suggest that this first year of the next semi-quincentennial could see major cracks in the economy and a severe market downturn. If this assessment sounds bleak, we apologize for perhaps dampening any July 4th celebrations you may be planning, but our goal is to be realistic—and to ensure that you are prepared. Below is a snapshot overview of some of the things that are keeping us up at night.
Many of the fault lines that threaten to upend the economy and the financial markets are squarely AI-related. The surge in enormous AI data centers doesn’t affect just the prices of memory chips. The centers’ enormous need for resources like water and electricity translate into higher prices for those essentials and things that go into producing them, further burdening consumers who already are being squeezed, including by higher prices resulting from the Iran war.
If, as some optimists believe, the investments in AI will end up being worthwhile—i.e., if AI can improve productivity here, raising growth—then maybe the negative effects will prove temporary and it will be worth the pain. But we don’t buy it. Don’t get us wrong. In many ways, AI is incredible, with abundant uses. We ourselves use it all the time in our research, and it’s great, for example, at directing us to a wide array of sources that we can then check out for ourselves. (The latter caveat is crucial, given that AI is inherently vulnerable to “hallucinations.” That’s the evocative term for what occurs when you ask AI a question for which it doesn’t know the answer, but it gives you an answer anyway, making it up out of whole cloth. Entire legal briefs have apparently turned out to be hallucinations, complete with nonexistent case citations that AI blithely appended.)
The mirage of AGI
But the real problem with AI as it is being pursued in the U.S. is that the focus on large language models (LLMs), with the expectation that artificial general intelligence (AGI) is just around the corner and will lead to marvelous advances, is totally off base. And the tragedy is that it has kept AI here on a path that, even as it consumes enormous quantities of resources, will essentially lead nowhere.
LLMs are what are known as “probabilistic” models. They absorb the endless reams of words and documents and data they’re being fed to come up with the most likely answers to any question. But that doesn’t equate to anything comparable to human creativity.
And this holds true despite some impressive achievements that have gotten attention, such as AI assisting in solving mathematical problems that humans had long been unable to crack on their own—for instance, the recent “disproof” of a 1940s conjecture by mathematician Paul Erdős. But here’s the thing: these achievements don’t reflect an autonomous capacity by AI to come up with an original, creative thought. Rather, they reflect brute force—the incredible speed with which AI can process data, far faster than even the fastest supercomputers. The speed is impressive, but the underlying methodology is fundamentally the same as it was 50 years ago when the IBM 360 mainframe enabled mathematicians, who fed it complex instructions that directed its calculations, to solve a mathematical conundrum known as the “Four Color Theorem.” It was the first major mathematical proof to be solved using a computer, but the machine’s role was simply to execute the grueling calculations a human could never finish in a lifetime.
In the case of the Erdős problem, AI similarly relied on human help to specify the problem. It did not construct a traditional, direct proof, which often implies a particular sequence of statements that lead to one conclusion. Instead, it delivered a disproof, or counterexample, achieved by trying out “a vast array of ideas from a wide range of mathematics”, according to noted mathematician Arul Shankar. A human mathematician can’t do that because it requires recognizing—all at the same time—that vast array of mathematical areas and performing a massive number of attempts to find contradictions across them. But the hard work was done by Erdős himself, because he presented a guess as to what the answer would be. All AI did was to try out a large number of ways to challenge the Erdős guess, until it came across one that proved him wrong. In other words, AI was a useful tool when exquisitely directed, and once directed, it found a solution that required fantastic amounts of calculation—but it remained a tool, nonetheless.
If this is more than you wanted to know about mathematical proofs, just realize the key point is that U.S. companies that are pursuing the mirage of AGI are missing out on more fruitful roles that AI could play—and in the process, raising the risks of disappointment. The contrast with China is striking. In the U.S., the goal is to develop AI that can replace humans. China is aiming to use AI to enhance human productivity—to keep people employed but working better and smarter.
Ray Dalio, founder of Bridgewater Associates, visited China in April and noted that China views AI as a utility that should be available to all workers. In China, he said, “it’s like electricity and running water in that everyone should have it. It doesn’t have to be expensive or even profitable.” The U.S. perspective is very different. Driven by a desire for profits, U.S. companies have turned AI into a modern-day gold rush, as witness the massive hype surrounding the upcoming IPOs for OpenAI and Anthropic.
And this points to another looming AI-related threat to the economy on top of the pressures that data centers are putting on resources and prices. AI companies have played an outsized role in propping up the stock market. Investors have been enchanted—bewitched—by the spell cast by the AI companies and so far, their faith has remained largely unbroken. But what happens if they lose faith? And what would it take to disillusion them?
Will Huawei be the spark?
Truly, it could be almost anything. But one specific event on the not very distant horizon is Huawei’s plan to introduce its latest smartphone this fall, possibly as soon as September. In its smartphones and other tech products, the West has long enjoyed a lead in transistor speed, thanks to the advanced lithography equipment produced by Dutch company ASML. But after the U.S. tried to hem Huawei in by preventing it access to ASML machines—thinking it would ensure that Huawei products would long be inferior to iPhones and other Western smartphones and tech products—Huawei has made astounding progress in innovating ways to increase transistor speed to keep up with Western technology. Initially, the company stunned observers by figuring out how to get from a 14nm (nanometer) chip to a 7nm chip. That had been deemed an impossible task without ASML help. And now if all goes as planned, the company appears on the verge of a phone powered by chips that while not literally 3nm, will be the functional equivalent, an even more incredible achievement.
It’s a potential tipping point—something that could shock investors by making them suddenly doubt that U.S. technology is indisputably superior. That could be the wake-up call, a fundamental shift in psychology that could be the push that sends the stock market reeling.
And that would likely have catastrophic ripple effects throughout the economy. You often hear that the stock market is not the economy, and that’s true. But also true is that today, the phenomenal rise in stocks is one reason that consumers have continued to spend, fueling decent growth. A huge number of consumers are invested in the market by virtue of their 401ks. These have provided a sense of security that has kept many consumers willing to buy goods and services—often maxing out their credit cards to do so—even in the face of rising prices. But what happens if the AI companies, instead of leading the market ever higher, lead it over a cliff? Consumer savings today are at 3%—exceptionally low and close to the level that preceded the 2008 financial crisis. There would be no cushion to help keep the economy humming. The upshot could be something that would make the housing crisis that led to the 2008 debacle look benign in comparison.
Clearly, and apart from all the other ongoing problems we’ve discussed previously such as our enormous government debt, this is no time to be complacent. As we write in the accompanying article, we do expect the Fed under Kevin Warsh will come up with a way, however disingenuous, to present inflation as sufficiently low to justify rate cuts. And given our assessment of the clouds hovering over the economy, we think cutting rates will be essential to ward off a deep recession or depression.
How to protect yourself
We noted above that our goal is to be realistic so that you can be prepared. And we continue to believe that the best way to prepare for coming times—to survive and thrive amidst the turbulence—is with gold.
In 2025, after a nearly three-year rally, gold had more than doubled before stalling near $3,500. For several months after, it gyrated back and forth, always stopping at around $3,500. Its subsequent break decisively above this level signaled a grim economic outlook—specifically, a looming inflationary wave capable of damaging financial assets and the broader economy.
Conversely, if we now see gold drop below the $3,200-3,500 range, it will signal a major pivot in market sentiment. Given our economic outlook, such a breakdown would mean the primary threat had abruptly shifted from inflation to deflation.
Right now, it’s too soon to predict whether we’ll ultimately get deflation or inflation. Either way, though, gold is the safest and most prudent investment you can own. If it’s deflation, gold will hold its value and keep you in relatively good shape. If on the other hand we follow along the model of the 2008 market and get rising inflation, gold’s potential upside is $10,000 or higher.
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