Editor’s Note: We do not support Artificial Intelligence, its development, or the vast majority of its potential uses. This article is intended to be informative to prepare Americans for what may be coming if we’re not able to stop it. Here’s Per Bylund from Mises…
In a recent article, we briefly summarized what it is that we today call artificial intelligence (AI). Whereas these technologies are certainly impressive and may even pass the Turing test, they are not beings and have no consciousness. Thus, this is neither the time nor the place to discuss philosophical issues of how to define a true or full AI—an artificial general intelligence—and whether we should recognize AI software legally as a person (after all, corporations are).
Economically speaking, AI as technology, whether it is used for entertainment or in production, is a good. As Carl Menger taught, what makes something a good is that it (whatever it may be) has the ability to satisfy a human need, that it must be recognized as such, and that a person—the consumer—has or can gain command over it to satisfy those actual needs. In other words, it must be scarce (there is less of it than we can use to satisfy wants) and understood as valuable (because we believe it can satisfy wants). AI certainly fits the criteria.
AI as a Consumption Good
When people entertain themselves by “discussing” with AI (try, for example, Windows Copilot) or generating quirky images using DALL-E (try it here), it is a good of the lowest order—a consumption good. As such, the economic consequences are limited to the effect this has on consumer behavior. But this may in turn have a significant impact on production.
Some consumption goods revolutionize the economy and society. Examples of such goods include the automobile (from the introduction of Ford’s Model-T) and the smartphone (starting with Apple’s iPhone). The former disrupted transportation and infrastructure and facilitated just-in-time manufacturing and urban sprawl, just to mention a few effects. The latter changed everything from how we bank to how we travel.
The point here is that as consumer behavior changes, the production structure follows along. For example, with the broad adoption of the smartphone, paper map production has all but disappeared; whereas, digital location services and intelligent logistics have seen enormous growth and development. And change leads to more change because entrepreneurs build on, add to, and challenge the new discoveries.
AI has the potential to change consumer behavior well beyond its designed functionality. Exactly how and in what ways remains to be seen. But it is safe to say that it has potential. (On the other hand, many goods have had potential to disrupt but didn’t leave a mark.) For example, we may see people produce their own stories, songs, images, and even movies. So perhaps, instead of relying on television or Netflix and Hollywood producers, we’ll make movie night into a make-a-movie night where we watch content we have generated and that fits us perfectly.
AI as a Higher-Order Good
As a tool and thus a good of a higher order, AI has already had an effect and promises to disrupt several trades. Because it is very effective at producing and presenting content, including translating and editing texts, content-related professions are threatened by AI. This includes journalists and copyeditors, as AI programs can write and edit faster than humans. After all, anyone can ask AI to produce or edit a text. Students already use AI to spice up or improve their papers—or let AI write them from scratch.
AI is similarly affecting photographers and illustrators. It only takes a minute to have DALL-E produce a new image exactly as directed, or to have an AI algorithm remove or add things in a picture you snapped. Whereas, having an illustrator create something takes much longer (not to mention the cost).
Programmers and system developers are also seeing the effects of AI, which has no problem both generating new code (without bugs!) or checking already written code. Legacy software written in dated and ineffective programming languages can be run through an AI to make the coding more efficient—and converted into a modern language.
AI is also affecting academia. Why have an instructor tell students about some subject matter instead of letting AI do it? After all, the AI can easily present content in a way that the student prefers. For example, make a movie to explain, say, biology or chemistry in an entertaining way. And it can answer all kinds of questions without ever getting bothered or cranky—and it has nowhere else to be. In research, AI can analyze data more effectively and run thousands of different regressions on data to find something that is significant and important (so-called HARKing, which is very poor research practice—but who will know?). It can write up the paper too, with citations and everything, in just seconds.
AI as Production Capital
All of this means AI can and will be used in production. In fact, it already is and we have only started to see the effects.
AI is best categorized as capital, which is used to make labor more productive (more value output per hour of labor invested) through facilitating more roundabout (but more effective) production structures. Capital goods in general have one (or both) of two functions: it makes existing production processes more effective by increasing productivity, or it makes possible types of production that were not previously possible. AI checks both boxes.
We have already seen how people working in several types of content-based professions can easily be made more productive or replaced entirely by AI. It can also do things that people may have been unable to do—or never thought of doing. This of course can cause so-called technological unemployment as people lose their jobs because AI can do them better (and cheaper). But this is a dystopian way of describing something quite normal and highly useful: that we relieve people, with all their ingenuity, from comparatively simple tasks so that they can create much more value elsewhere.
It is of course problematic for any person losing their source of income, but it is highly beneficial to consumers (and therefore society at large) that these (and other) professions are “creatively destroyed.” The economic point of employment is not to provide people with an income so they can pay taxes (although politicians seem to think so) but to produce goods that can satisfy consumer wants—to make our lives better. Just like there are very few stable boys or buggy-whip producers since the automobile revolution, the future will see fewer people doing news reporting, copyediting, or coding.
Note also that this revolution is not nearly as sudden and disruptive as it may at first seem: the news media, for example, have for many years reduced the number of journalists doing reporting (most outlets nowadays merely republishing standard articles from AP or Reuters). And software development already uses increasingly effective development environments that correct and predict commands, allow for WYSIWYG and drag-and-drop development, and can debug code and suggest solutions to bugs.
AI is only another step in this process. But the threat is greatly exaggerated. We tend to overestimate the impact of technology in the short term but underestimate it in the long term.
Limitations to Overcome
There is a problem, however, and it has to do with how large language models work and what responses they generate. When used in a setting that is strictly rules-based, such as in computer programming, the AI “understanding” of code can greatly improve the productivity of coders (or replace them). AI will not introduce bugs in software unless the specifications are incomplete or contradictory, and it will not make errors.
The same is true for AI’s language generation: it draws from large troves of text data and has a good “understanding” for how humans use language. But there are no rules-based ways by which it can distinguish fact from fiction. Instead, AI draws from what statistically is more likely to be a human-sounding response. For this reason, it produces content that can be entirely wrong.
For example, I asked AI to summarize the content of my 2022 economics primer, How to Think about the Economy. Since it has access to the text, it did a pretty good job summarizing what is in the book. But it also added comments on content that is typically in economics books but that is not in the primer (such as equilibrium theory, perfect competition, and mathematical equations). The AI is correct that economics books typically discuss such things and thus it is statistically probable that my primer would do the same. But it doesn’t.
There is a difference between statistical probability and truth. We will look at this problem and the potential threat that AI poses to human society in a future article.
Two Storms, One Harvest
Every food crisis in living memory has been a one-shock event. The 2008 price spike was a commodity bubble. The 2020 shortages were a logistics failure. The 2022 grain scare was a war on one exporter’s ports. Each time, the system bent, adjusted, and recovered, and each time the experts assured us afterward that global markets are simply too big and too diversified to fail.
What nobody in Washington seems eager to discuss is that 2026 is shaping up to be something the modern food system has never actually faced. Two independent shocks, one climatic and one geopolitical, are converging on the same harvest cycle at the same time. Not sequentially. Simultaneously.
Start with the weather. The Pacific Ocean is currently building toward what forecasters now openly call a record event. NOAA’s Climate Prediction Center puts the odds of at least a strong El Niño near 88 percent, with roughly two in three odds it reaches “very strong” status, the tier reserved for perhaps three or four events in the entire satellite era. Every major global model now projects a median peak in Super El Niño territory, and most of them project it exceeding the 2015-16 event, which until now held the modern record. Sea surface anomalies were already brushing the super threshold in mid-July, months before these events normally peak. The atmosphere has already shifted into El Niño mode, and the event is forecast to crest in late fall and early winter.
This is not about “climate change.” It’s about the standard cycles of weather, and the cycle we’re currently in is one that has likely devastated societies in the past. We’re better prepared as a society today, but not all Americans are equally prepared.
Serious households have started doing the quiet math on their own. Grocery bills tell part of the story, and the forecast maps tell the rest, which is why long-term food storage has moved from fringe hobby to mainstream line item in the family budget, with established suppliers like Heaven’s Harvest seeing demand from people who five years ago would have rolled their eyes at the idea. That instinct is not paranoia. It is pattern recognition, and the pattern is worth walking through carefully.
Editor’s Note: Heaven’s Harvest IS a sponsor, but the warnings of this article are real and would be written even if we didn’t have a survival food sponsor. With that said, those who take advantage of what they offer can use promo code “Patriot” for 15% off.
The Fertilizer Clock Is Already Running
While the Pacific warms, the second shock has been unfolding in the Strait of Hormuz. The conflict with Iran turned the world’s most important energy chokepoint into a contested waterway, and the consequences reach far beyond the gas pump. Roughly a third of global fertilizer trade moves through Hormuz, and the disruption sent urea prices up 86 percent year over year by March, with a 53 percent jump in a single month.
The World Bank projects energy prices rising about 24 percent in 2026 and fertilizer about 31 percent. By its own accounting, fertilizer prices ran 35 percent higher in the first five months of this year than the same period last year.
Here is the mechanism the nightly news will not explain. Fertilizer is not a grocery item. It is a time-delayed input. The nitrogen a farmer in Iowa or Punjab could not afford to apply this spring does not show up as a problem this spring. It shows up as a thinner harvest six to twelve months later.
The World Bank’s own food security brief concedes that the effects of reduced applications earlier this season “are likely to become visible only later in harvest outcomes.” Translate that from institutional language into plain English and it means this. The damage is already done, it is already in the ground, and we are simply waiting for it to arrive on the shelf.
Now check the calendar. Six to twelve months from the spring planting season lands us squarely in late 2026 and early 2027. Which is precisely when the strongest El Niño in the instrumental record is forecast to peak, bringing its signature droughts to Southeast Asia, Australia, southern Africa, northern Brazil, and South Asia, the very regions that grow the world’s rice, sugar, and oilseeds.
The World Bank warns openly that a strong El Niño “could disrupt multiple crop belts simultaneously” on top of the conflict-driven input costs. Their baseline projection assumes the Middle East disruptions ease by autumn. What in the last two years of Middle East history suggests that assumption is safe?
The System Has No Slack Left
The comfortable answer is that global markets always adjust. But adjustment requires slack, and the slack is gone. Global cereal production is expected to decline from last year’s records even before El Niño does its work. The UN World Food Programme, hardly a den of right-wing preppers, is calling this the most significant disruption to its supply chains since Covid and the invasion of Ukraine, and its supply chain director put the stakes bluntly.
Today’s supply chain challenges are tomorrow’s hunger crisis.
There is also a political dimension that markets cannot price. When food gets scarce, governments do not behave like economists. They behave like politicians. Export bans, hoarding mandates, and panic buying at the national level turned the modest rice shortfall of 2008 into a global crisis, and analysts are already warning that import-dependent nations are the first dominoes.
The 2015-16 Super El Niño, a far weaker event than what is now forecast, threw tens of millions into food stress across Africa and Asia. This one is projected to be stronger, and it arrives with fertilizer already rationed by price and shipping lanes already contested by missiles.
What Joseph Knew
Scripture does not treat preparation for lean years as faithlessness. It treats it as wisdom delivered in advance to those willing to act on it.
Behold, there come seven years of great plenty throughout all the land of Egypt: And there shall arise after them seven years of famine; and all the plenty shall be forgotten in the land of Egypt.
Joseph did not respond to that warning with a hashtag or a committee. He stored grain during the years of abundance, and when the famine came, Egypt stood while its neighbors begged. The lesson is not that famine is certain. It is that the time to prepare is precisely when preparation still looks optional.
Nobody who filled a pantry in a year of plenty has ever regretted it, and nobody standing in an empty aisle has ever been glad he waited for certainty.
None of this calls for panic, and panic is the enemy of sound judgment anyway. It calls for the same unglamorous prudence our grandparents considered ordinary. Keep some cash margin, know your local growers, and put real food in deep storage while it is cheap and available, because the entire arc of this story is that cheap and available is a closing window.
Families looking for a straightforward place to start can visit Heaven’s Harvest and use promo code Patriot for 15 percent off long-term storable food. The forecasts may yet soften, the strait may yet reopen, and we should pray they do. But hope is a fine thing to hold and a foolish thing to eat.
