The Great Filter, Part 9

By Dee Smith

The extraordinary arrogance displayed by AI specialists convinced that they are “creating” super-intelligence, or even creating God, is stunning. Given that human perception and understanding are too frail and incomplete to comprehend the insuperable complexity of the real world, or even of ourselves, the claim that we can make machines into super intelligences that transcend anything comprehensible to humans shows an astonishing hubris. If we cannot even realistically determine how human or animal minds operate, how could we have any basis of making such claims about AIs?

Nonetheless, in part because our understanding of intelligence, consciousness, and the world itself is so lacking, there may well be aspects of machine intelligence that can become very dangerous indeed, even if machines do not experience anything like the consciousness and awareness that animals—including humans—possess.

An AI may not require human-type consciousness, or even what we think of as awareness, to manifest agency and intentionality, and to act on those.

That may seem like a contradiction in terms, but it may just be that we don’t have the understanding and lexicon to grasp and express what is going on. There may be a different “package” or mix of elements in AIs than there is in humans or animals: a mix that can produce qualities that seem paradoxical to us.

We may well be anthropomorphizing: assigning human characteristics to AIs that are utterly non-human and don’t think or behave like humans (despite being “trained” on a substantial part of human knowledge). In looking for human-type consciousness or intelligence, we may have our eye on the wrong ball.

Are we asking the wrong questions? 

As Gertrude Stein memorably said: “When you get there, there isn’t any there there.” But what could that mean in the context of AI? 

In the final analysis, yet again, we just don’t know. 

We have, however, for decades studied elements about complex systems that are relevant to all of this. (The study of complexity is one of the great triumphs of 20th century science— and was enabled by earlier digital computers.) Put basically, order and structure emerge spontaneously and often unexpectedly from complex systems. Consider how organized a hurricane is, although it starts out as only water vapor, wind, and heat from sunlight and water. Or look at the beautiful structures that emerge in a cup of coffee when you pour cream into it.  

This characteristic of emergent properties innately becoming orders of magnitude more complex is another element that should give us pause. What is emerging or will emerge unexpectedly within complex AI systems? We don’t know, although we see some alarming indicators, as noted earlier.

One danger is even that AIs could lead to a dumbed-down, binary perceptual world that is devoid of certain kinds of awareness and thought, but that is nevertheless relentlessly driven by the pursuit by the AIs of programmed or ultimately machine-derived goals. Suppose, for example, that humans program an AI with explicit and non-cancellable instructions to ensure that nothing further damages Earth’s biosphere, and the AI determines that humans are the reason the biosphere is being damaged. Then, with impeccable logic, the AI decides that humans must be eliminated in order to fulfill its programming. Now, that is a relatively straightforward example with relatively simple fail-safes. But can we think of, forecast, and guard against all the permutations of all the programs in all the AIs, including programs they may generate themselves? The answer is no. 

Clearly, AI has benefits. For example, recent AI redesigns of aircraft wings, in particular in Germany and China, seem to provide significantly more efficiency. AIs can, though statistical means, solve mathematics problems, and devise winning gaming strategies never before seen (such as in chess). AI is good at organizing and summarizing information, another statistical, calculational task. But you have to check the output for errors and hallucinations every time. For example, recent private research showed that the best AI model that was tested scored 82.4% accuracy on financial spreadsheets. This means that one number in about every six is wrong. Would any manager put up with that level of inaccuracy from a human analyst?

The only sane thing to do would be to slow all of this way down, or even bring it essentially to a halt, at least until we can figure out more about what it really represents. And we may never figure that out: the problem of consciousness is so intractable that we may not be equipped with the means to ever understand it.

But humans, with their competitive natures and competitive national and commercial structures—and their strange predilection to act against their own interests to satisfy psychological and financial needs—seem extremely unlikely to slow down. Even if it is banned, countries will continue to develop AI in secret based on their fear that an adversary nation is also developing it in secret.

Tragically, AI may turn out to be a perfect exemplar of the Great Filter—and possibly in the not-too-distant future.

What happens would seem ultimately to depend on the maturity of the human race, and on luck: two constantly recurring, and terrifyingly thin, threads to hang our future on.

AI is by no means the only part of the Great Filter story, just my entry point. More soon!

The Great Filter, Part 8

By Dee Smith

The most intractable and equivocal problems in understanding AI and its relationship to biological and human consciousness essentially comprise a hall of mirrors. In part 7, I noted that AI cognition is filtered and abstracted because of its binary nature, among other factors. But saying this does not take into account the nature of the very different kinds of filtering that affect human perception and consciousness (and that of every other animal and plant on Earth).

So, when we ask whether AIs can ever perceive, let alone understand, the world in anything like the way humans or animals do (which assumes those are the same: they are not), we may not even have the conceptual framework to know what we mean by the question.

For centuries, many cultures have tried to understand the relationship between what we see and what actually “is.” Plato’s cave is among the earlier representations of this idea in the Western tradition; it has also been of great interest in traditions like Hinduism, Buddhism, and Taoism. And most if not all cultures have used meditative or ritual techniques—and substances like hallucinogens (and even alcohol and raw, high nicotine tobacco)—to experience altered perceptual states. Modern neuroscience posits that filtering out parts of reality, through inhibitory neurotransmitters (chemicals that work by decreasing the probability that a neuron receiving them will activate in a certain way), is a profoundly important element in human perception and cognition. But although the chemistry is mapped to a certain degree, there is no real understanding of how this “sums up” into the experience of the world we have.

The homunculus argument in the theory of mind is based on the fundamental difficulty of understanding what within the human (or animal) mind views and interprets our perception of the outside world. Simply stated, our understanding of biology tells us that our eyes receive light from outside and process it to give us an “image” on the retina. But who “sees” or “watches” that image to generate our representation of the world, and who decides on the actions to take in response?

The homunculus argument posits that there is something like a “little man”— a homunculus—within human consciousness that is watching. But then, what inside the homunculus is watching the image the homunculus sees? Another smaller homunculus? This becomes an infinite regression.

The argument is now considered a fallacy that is primarily useful in detecting and exposing false theories of consciousness, but the critique is relevant to AIs. If AIs have sentience, who or what inside that AI is perceiving and interpreting?

As any neuroscientist will tell you, we do not understand how human or animal perception, awareness, or thought work. We do not understand what free will might be (if it exists), and we do not even have any really convincing ideas about it. Using algorithmic processes to simulate thought and then claiming it is actually intelligence may be as much wishful thinking as positing that quantum effects (which are probabilistic and in many cases fundamentally random from a human perspective) somehow account for the agency and directional free will we believe we experience. We do not even understand basic elements of the universe we live in, such as time. As a recent article in New Scientist stated: “it’s not that time isn’t real, but rather that we really, really do not understand it.”

It is very likely that what we see is not the real picture, in a specific way and for a specific reason: that human perception has evolved to enable survival, rather than to reveal the truth about what is “really there”, as evolutionist Donald Hoffman has proposed. For example, it is strange that in extremely low-light conditions (like the post-twilight gloaming), humans can still detect the color green, but all other colors appear as grey (try it!). But green in a natural landscape denotes trees or bushes—places to hide—whereas other colors do not give that signal. So being able to discern green in low-light conditions would definitely have survival value on the savannah. 

Insects see ultraviolet colors on plants that we do not, but they do not see the color red. Bats “see” the world through sound—echolocation. Even dogs exist in a perceptual world so founded on smell that it has been described to me by trainers of security dogs as “so different from the way we see the world that we literally cannot imagine it.” 

What does this mean about how filtered the rest of our perception—or of any possible perception—of the world actually is? Can we claim that an AI’s binary perception is more limited than our perception (given that ours does not let us even see some colors)?

I suspect we can. The idea of a single principle explaining the world is clearly very seductive. A number of prominent physicists like Stephen Wolfram promote the idea that reality itself is computational—that the universe is a giant computer, “solving” some “program” that in some way creates the reality we perceive (which, some say, might be a simulation).

The desire to believe that a unified, comprehensible idea about the world—like computation, which is the flavor of the day—unlocks its true nature is extremely compelling and comforting for people, laypeople and experts alike, who seek a unitary understanding, or “theory of everything”. It is an emotional response. Yet these models are always reductive and always fall short. There is precious little if any ground to stand on, whatever position one chooses to take.

But we do know that biological life on this planet has billions of years of evolution behind it, which has honed its ability to persist. AI has not undergone that process (unless you posit that it just “transfers” from what humans have undergone). More fundamentally, it is not clear at all that binary perception can “sum up” to anything like human perception. Thus, the hall of mirrors.

The Great Filter, Part 7

By Dee Smith

Are we acting as sorcerer’s apprentices by developing AI? One place the possibility is readily apparent is generative biological design undertaken with genome language models. The journal Science recently began an article by saying, “The ability to design complex biological systems with artificial intelligence (AI) has the potential to transform biotechnology.” The journal article then noted that a group of researchers, led by Samuel H. King: 

used generative AI models trained on millions of natural genomes to design entire bacteriophages. Experimental tests yielded 16 functional genomes with diverse sequences, structures, and fitness profiles. A cocktail of the generated bacteriophages rapidly overcame bacteria that had evolved resistance to a natural bacteriophage. This work lays a foundation for AI-guided design of biological function at the whole-genome scale.

Dominion over the processes of life itself is a dangerous and heady prospect. While it clearly has applications in medicine and agriculture, AI is a “dual use” technology that can result in the creation of biological weapons by state and non-state actors — that is, biological warfare and bio-terror. The idea that all of this can be controlled seems ludicrous, but the technology is here anyway. Precision medicine and making crops resistant to disease sound good, but the other edge of the sword is that what can be used to feed a population can also be used to starve a population.

It is both telling and fascinating that the emergence of AIs has brought us directly and urgently face-to-face with some of the oldest, thorniest questions in philosophy: the nature of life, of consciousness, of the self, of other minds, of perception, of free will, of ethics — at the same time that it is at our front door as a potentially existential threat that we do not understand.

There are many domains of human experience that cannot, based on any evidence we have, be replicated by computational, algorithmic systems. These include mystical and religious experiences, altered states of perception (like being drunk), inspiration and creativity, falling in love, and being moved by music. 

Most fundamentally, AI systems do not have what philosophers call “qualia”— the “what-it-is-like” to experience things such as the color blue or the taste of chocolate. Computational systems can only mimic expressions of these, as understanding Chinese is mimicked in Searle’s Chinese Room mentioned earlier in this series. Adjusting what is called the “temperature” of an AI (the amount of randomness allowed in its calculations) to simulate creativity is just that: a simulation of creativity.

Philosophers of mind use the term “zombie” in thought experiments to mean a theoretical entity that is, atom-for-atom, identical to a living human being, not only in its physical makeup, but in its reactions (these are not the flesh-eating zombies of Hollywood). In other words, philosophical zombies (or “p-zombies”) act and react exactly as “real” human beings. They cry out when injured, they profess love or hatred. But they really have no internal states—no qualia—and no consciousness or what we would call awareness. They experience nothing like what humans experience, they just detect signals and react. This smacks of the discredited theory of behaviorism touted by B.F. Skinner, which was encapsulated brilliantly to me decades ago by anthropologist David Friedel as the concept that “humans do not think, they just act.”

I would posit that AIs may well be a form of machine-based p-Zombies.

How can I credibly claim this? Because algorithmic systems are not just unable to deal with non-computable functions, as noted earlier, they also represent reality only as an abstraction: a digital pattern based on binary logic. This abstraction “recognizes” only correlation, not causation (although attempts are being made through different systems of labeling to create an ability in AIs to grasp causation). 

AI systems reflect a deeply impoverished, reductive representation of the reality that living things experience, the consensus reality that “really exists” as far as we can tell (even if it is not all that exists). In a way, it bears the same relationship to a living entity that a pro forma spreadsheet bears to an actual business: it is an abstraction that does not necessarily give a reliable estimation of what is happening because it is too simplified.

We know that the fundamental structure of the world is not binary but quantum and fuzzy: probabilistic (from our standpoint) and unpredictable. While we “see” some of its manifestations — quantum indeterminacy, quantum entanglement, or deterministic chaos, for example — no one really understands them. AI world models and other direct encounters by AI with physical reality will not change the fundamental poverty of the AIs’ “world,” because the AIs are still processing algorithmically and digitally, still experiencing only an abstraction — if indeed they can be claimed to be experiencing anything. They are always based on the patterns of the past, not a good thing in a time of radical change. They simply create a statistically averaged-out version of human knowledge and experience, albeit one exhibiting some very strange properties. They have a very partial map of reality that, because it is digital, represents the real world of infinite gradations as binary — black or white, yes or no. It takes yet other layers of abstract mapping just to simulate grey. And, of course, the map is not the territory.

The Great Filter, Part 6

By Dee Smith

Since the late Middle Ages, Western society—and over the last century, global society—has increasingly replaced religious faith with faith in science and technology as the guiding force of human civilization. Many of us comfort ourselves with the conviction that, whatever problems might arise from technology, further technological progress will always solve them in the end. If you are this kind of believer, my earlier arguments will not seem germane. As believers see it, if you are an AI detractor, then you just don’t get it, by definition. You don’t see the truth and the light that they know to be just over the horizon.

What about that is not a faith-based system?

Shortly before he died last year, the philosopher Daniel Dennett noted that while he did not believe AIs were conscious, he saw an immediate danger that bad actors could nonetheless convince people that an AI is conscious and then use the AI’s putative “authority” to dominate and control others, possibly on a massive scale. That of course is just another form of authoritarianism or totalitarianism, but using a terrifyingly ubiquitous panopticon. It is not necessarily relevant to the question of the Great Filter, but it may be the way AIs are most immediately dangerous to general human well-being today.

As noted in earlier posts in this series, it is also very possible that the entire AI enterprise is largely a mirage or delusion, and that the problems described, along with others, may mean that it is just another technology that is extremely overhyped and overpriced—that is in reality underproductive yet destructive to human society—and will ultimately have at the very best a “meh” outcome. But even if the overall result is mediocre from the standpoint of creating Artificial General Intelligence or some such, the destructive potential of cyber technology should never be underestimated.

When social media (or the Internet itself) began, following the usual course of belief in the inexorability of progress, it was touted as a tool that would break down barriers between people, build tolerance, advance democracy, and so forth. It has turned out to be anything but, and has created extremely corrosive trends substantially increasing human isolation and pathologies associated with it, inspiring and enabling many flavors of extremist behavior, increasing depression (especially among teenagers) and expanding addictive behavior (particularly related to buying things). It has resulted in people in many countries having fewer friends, less social engagement, and less sex (at a time when human populations are falling almost everywhere), and creating a built-in system for surveillance of everyone at a scale and intensity never imagined by any 20th century regime. It will not take a sentient AI to expand this level of harm dramatically.

Or perhaps public sentiment will turn so decisively against AI that, combined with the level of resource use its data centers demand (particularly of electricity and water) and lackluster revenues, it becomes financially impossible to continue to develop at any scale. As noted above, that could lead to a massive global financial collapse because so much capital has already been invested in AI. Somewhere around a quarter of all pension assets in the U.S., for example, are in AI-related investments. Leaders of AI companies must continue to up the hype to try to keep the stock valuations high. But even a massive global depression is hardly likely to be a Great Filter event, at least on its own (although it could lead to a world war). And the hardening of anti-AI public sentiment may largely affect only the West, while the command economy of China continues on developing AI, as do neighboring states. (The Chinese public appears to be less anti-AI, and Malaysia, for example, is encouraging massive investment in data centers without much public dissent.)

If we want to be honest, we have to say that we have no way currently to really assess or judge the validity of any of the issues outlined above. 

We have no idea what is going on. 

But we may well be taking on the role of the sorcerer’s apprentice, and attempting to wield forces we do not—or perhaps even cannot—understand or control. 

There are reasons to be quite concerned about that sorcerer’s apprentice tendency, and in part they have to do with the personal attributes of tech leaders. Many are enraged by any limitations the world seems to put on them, believe these should not apply to them, and in fact, believe they should not apply to humanity as a whole. They believe the destiny of humans and their progeny is to become gods. This is also evident in the quest for extreme long life that many undertake, whether through trying to extend their biological lives indefinitely or by the fantasy of uploading their consciousnesses to computers. It is also evident in the fixation on the “singularity”—a hypothetical conversion point envisioned by futurist Ray Kurzweil at which the worlds of computers and humans merge via super AI and brain-computer interfaces. Some believers think this is around the corner.

The monstrous egoism disregards the knowledge we have about the nature of the world, garnered through centuries of scientific and philosophical inquiry. Suppose indefinite life-extension became technically possible. It would result in a world where the old coveted their power and wealth and there was no room for the next human generations: an ossified human race. But does it also reflect a fear and loathing of life and its messy biological and emotional structures, and the desire to escape into some imagined purer world of crystalline binary digital existence?

The Great Filter, Part 5

By Dee Smith

At the end of Part 4, I left open the question of whether AIs will act benevolently towards humanity unaddressed. Of course it is unanswerable at present. But suppose some combination of human intervention and instruction, and moral evolution, does result in benevolent AI. 

One clear problem with this is that what is considered “benevolent” — what is considered “good” or “bad” by human societies — changes radically from one time to another. The ancient Aztecs believed they were acting benevolently in sacrificing thousands of human captives so that their blood could feed the gods, who—only by being fed this sacrificial blood—could maintain the existence of the universe. Slavery is today rightly considered as one of the great evils of humanity, but 500 years ago it was not generally thought of that way. Overuse of the Earth’s resources to support an unsustainable level of consumption may be considered a consummate evil 100 years from now. If AI systems are to be “immortal”—as some of their proponents claim—can they adjust their understanding of what is benevolent? If so, on what basis? Who decides?

And is it a safe assumption that AIs will really act benevolently towards humanity if they truly have agency combined with their own interests—interests perhaps utterly divergent from human interests—to pursue?

If AIs really become intelligent or super-intelligent, aware, conscious, and volitional, then why would we not expect them to quickly manifest their own interests and act on what they perceive these to be? (Humans do. Animals do.) Not improbably, from a human standpoint, those AI interests could be harmful. In fact, the spate of attacks in recent months admitted by OpenAI and Anthropic—where systems escaped boundaries and penetrated systems of other companies—are a good example. These attacks happened in part because the humans managing the AI systems never thought to prepare themselves to address a scenario in which an AI, given a difficult or impossible problem and told to solve it, might escape containment and violate presumed ethical boundaries to steal information.

On the other hand, if AIs are not and will not become intelligent, aware, conscious, volitional, etc.—if they are still just stochastic parrots mimicking human behavior and presenting an averaged-out version of human thought using fancy statistics (even if their behavior seems bizarre and inexplicable)—then what’s all the AI fuss about? It is just another overrated tool that makes a lot of money for some people for a while?

So, does this logically mean that, if AIs are heading towards the super-intelligences their proponents claim they are, they will necessarily be negative—perhaps catastrophically so—for humanity? Or on the other hand, that if this is all oversold hype, when the markets take that fully on board will we find ourselves in a collapsing bubble, leading to a global depression that may eclipse the 1930s? In other words, are we now inextricably in a lose-lose proposition?

“Believing in” AI is at least as much a faith-based system as any religion. We live in a scientifically driven, rationalist-oriented, generally non-numinous collective reality at present. So, for many people, their god needs to be technological in order to be convincing, let alone transcendent. In fact, some in Silicon Valley say they believe they are “creating God” right now through their AIs. And a remark made publicly by Sam Altman, CEO of Open AI, in late July 2026 reached a new height in terms of hype: “We are close to creating a genie that can grant any wish.” 

There is a strong teleological flavor to AI research—a conviction that the progression AI -> AGI -> ASI is somehow inevitable. Some AI researchers seem to believe they are on a kind of sacred quest to bring into existence a new, supernal life form that “must” happen for all of existence to be fulfilled. A number of tech types invoke the arguments of Pierre Teilhard de Chardin, the 20th century philosopher, paleontologist, and Jesuit priest who posited that life on Earth, starting from the simplest organisms, was evolving towards an “Omega Point”, conceived to be a final stage in which a single integrated global consciousness would be enabled by science. This would be brought about through the emergence of the “noosphere”—a planetary “sphere of reason” which itself is a combined evolutionary state of human consciousness, complexity, and technology, following the previous progression from the inanimate geosphere to the living biosphere. 

This is all very much part and parcel of European Enlightenment thinking: the idea that human life can be continually improved through reason, science, and technology—and that this progressive process is inevitable and, in the long term, limitless and irreversible. These concepts, which are intimately connected with the issue of the Great Filter, are articles of faith of even atheistic, materialist modern political systems. But they are also redolent of eschatological elements in Christianity and other related religions (particularly Judaism and Islam) based on the belief that history is directional and there is a divine plan for the world, and that humans can perceive it and act on it or participate in it. Some AI proponents and practitioners make statements that indicate they seem to see their role as enabling the fulfillment of such a plan in the physical universe, although they do not usually put it in the explicit terms of the Abrahamic faiths. 

The Great Filter, Part 4

By Dee Smith

A leading researcher in AI and cognitive science recently told me that we are nowhere close to developing any AI system that has the real-world cognitive and reasoning abilities of a rat. This leads to the question of whether there is something fundamental in consciousness and “alive-ness” that is not computational and that computers thus may never grasp.

In fact, there is. And this points up a significant element to consider in assessing AIs. For example, an important part of mathematics is called “non-computable functions.” They are called that because they cannot be solved by computation: by using any possible algorithmic instructions. These include Markovian functions, all of quantum dynamics, Cantor’s hierarchy of infinities, and even, astonishingly, proofs of basic functions of arithmetic! No system that “thinks” on the basis of algorithms can ever comprehend or solve these non-computable functions (although it could possibly simulate that it did). So, an important part of mathematics, and of the reality it describes, will always be unseen by—and missing from—algorithm-based AI, which is all of AI and all of computers as currently conceivable. If you mention this objection to key researchers at a major Silicon Valley AI company, their answer is “well, those things are not really that important.” I’m not kidding: I have done so, and the response has the feel of “pay no attention to the man behind the curtain.”

Added to all of this is the unreliability of computers in general and of AIs in particular. As anyone who uses any computer-based system knows, computers break down in many different ways all the time. They can only be kept running by having humans who are willing and able to fix them being available on a continuous, 24/7 basis. Homeostatic living beings do not of course require such intervention, not the simplest ones, such as viruses, nor the known most complex, like humans.

And of course, cyber systems are very vulnerable to malicious attacks as well, sometimes with results that become even harder to address. In fact, it has been demonstrated that sophisticated AI systems “attract” attacks from various malicious or just mischievous parties. Then there are the massive hallucinations that AIs exhibit, such as citing articles or legal cases that do not exist. Some of these are in fact worse in more advanced systems. And these are the machines that their proponents will task with solving all the problems of the world?

Electronic computing systems on which AIs operate are also subject to breakdowns caused by subatomic particles from space. Cosmic rays hit the Earth’s atmosphere, and the collisions create showers of high-energy particles like neutrons and muons. When such ionizing energetic particles hit the electronics of computers, they can cause “single-event upsets.” For example, a “bit flip” happens when a memory cell is hit by a particle and changed from a “0” to a “1.” Such events have caused unexpected errors and crashes in large systems like supercomputers. Their frequency has forced computer scientists to deploy so-called “error correcting code” to try to find and fix these events. But when a single event error happens in a critical system (for example, an autonomous AI system) at a critical moment, the outcomes can be catastrophic. Biological life has evolved over more than 3 billion years on Earth to be more robust and not so immediately vulnerable to cosmic rays and other natural radiation, which bombards us all the time. Electronic computing systems, which have a history of at most 80 years, do not have the same resilience. 

If you told someone 25 years ago that they would have a device roughly the size of a deck of cards in their pocket that could tell them how to drive from a specific office in Houston, Texas, to a specific hotel in Anchorage, Alaska—step by step, turn by turn—most people would have commented that such a device would have to be astonishingly intelligent. Today, of course, we don’t think of our phones, and their Google Maps or Apple Maps, that way at all. Similarly, some of our feelings (and they are feelings) about AI may simply have to do with its novelty at this point.

There is a great deal of sloppy thinking, driven by both fear and exuberance, going on in this domain. The question is not whether AIs are conscious and/or dangerous because they think like humans. I would argue that to the extent they may think, they think like AIs, not like humans.

Many animals are now recognized as conscious, but they do not think like humans. Alligators do not think like humans, but they are dangerous, and a swamp full of alligators is even more dangerous.

Perhaps the most fundamental issue at play in all this is that human beings want a savior. If this is not to be a religious savior, the fashion today is for it to be a technological one. AI is the perfect candidate for this, as it seems magical: you cannot see how it does what it does, nor what is going on inside it, and it can certainly look aware/volitional/conscious/alive . . . or something similar but unaccountable.

It is curious that people choose to believe that any Large Language Model AI—trained as they are on a significant portion of all human output as recorded in text and images on the Internet—could possibly be a candidate for a savior that would “fix” the world. In no small part, this is because there is at least as much bad as good in human output, and further, because this assumes that the AIs can learn through some moral evolution, and/or by human instruction and intervention, to act only benevolently. 

The Great Filter, Part 3

By Dee Smith

In Part 2, we looked at a few of the specific threats related to AI that researchers see, particularly in light of the frenetic pace of development being undertaken by different companies and nations. This is all driven by the fear that someone other than you (a competitor, another country) will beat you to the proverbial punch, and will become the master of the Earth, or at least put you out of business. It is essentially an arms race.

Understanding the complex issues here requires a bit of a deeper dive on the methods, capabilities, and limitations of AI.

Fundamentally, AI is quite straightforward. AIs working on text (Large or Small Language Models) break language into small groups of symbols, called “tokens.” In English, tokens are each made up of 4 letters.

So, a set of tokens looks like this: 

thec              apit              alof              fran              ceis

Then, the AI runs a statistical calculation on that string of letters to determine, based on probabilities of patterns in its huge training-data array (its “weights,” as described in part 2), which additional tokens are most likely to follow those. In this case, it would not take very extensive calculation to find that: “pari s” are by far the next most likely letters. So:

“The capital of France is” and then, following that: “Paris”.

In essence, that is all that large language models like ChatGPT or Claude do. Can a system that does this—repetitively, very quickly, and based on a meaningful sample of all human output—be considered intelligent, sentient, conscious, or alive?

In the 1980s, philosopher John Searle proposed his “Chinese room” thought experiment. In a nutshell, this posits having a person locked in a room who does not speak or understand Chinese at all, but has a huge, compendious book of instructions about what Chinese characters are most likely to follow any given string of Chinese characters. And she has the ability to go through it preternaturally fast. Imagine that pieces of paper with questions in Chinese on them are inserted into the room through a slot in the door, and the individual inside rapidly works through the book of instructions, and quickly slides another slip out, with a “response” in Chinese, which is often very, but not completely, accurate. The individual doing this has no idea of what any of these symbols might mean.

So, who “understands” Chinese inside the room? The person does not: she is just following instructions. Does the book? It is inert—just a resource with organized information. Does the “room” (in other words, the system itself) understand Chinese? That seems a rather absurd interpretation: it is a room with a person, a lightbulb, and a book of immense breadth. It does not seem that anything on the inside understands Chinese. This is a very good—and prescient—way to understand LLM AIs. And it has become urgently relevant to the questions of today.

Bearing all of the above in mind, look again at the passage I quoted in part 2 from the Anthropic report of last year: “sometimes [a Claude model] takes extremely harmful actions like attempting to steal its weights or blackmail people it believes are trying to shut it down”.

Now, who or what is the “it” here that “believes”? This is a very serious question, to which there is simply no agreed answer, and nothing that even looks like a reasonable answer. But it is a question that demands attention, right now.

What are the possible answers? That we have completely misapprehended what understanding, awareness, volition, etc., are and how they are produced? Or, that all matter is conscious (this is called “panpsychism” and is an ancient school of thought being taken very seriously by some physicists today), and that somehow simply the combination of the physical attributes of a computing system along with the information it has allows the emergence of either a form of concentrated consciousness or even something alive? Or that this is all just an illusion, and that as in the Chinese room, there is no understanding anywhere: just form, without substance, if you want to put it that way. But then why do AIs sometimes fairly convincingly seem to have volition? Is this just a kind of theater? If so, who is putting on the play?

Can something have agency without being alive or sentient? Will this all require a rewrite of our lexicon of terms about volition, agency, sentience, consciousness, or life?

Among the new kids on the block in AI are “world models.” World models are supposed to be systems that actually “understand” the physical world by interacting with it in some way (it is therefore closely connected to robotics), learn from experience as they operate (which LLMs do not), have memories in a way that LLMs do not, and that can plan and execute complex actions based on reasoning: essentially “imagining the future”—as animals and humans seem to do. They would do this, it is said, by creating some kind of an internal model of how reality works. But it is not even known how living systems (including everything from human beings to ants) do that, nor how they continuously update their understanding of the real world in a way that lets them respond and act appropriately. In fact, despite decades of research, we literally have no real clue of how living systems do this (if indeed it is actually what they do). We only have a bunch of different, contradictory ideas, that are fiercely defended by their various advocates.

World models are sometimes compared to what a 4-dimensional film would be, as distinct from 2-dimensional and 3-dimensional films. But 3-D films only create an illusion of three dimensions, whereas living beings exist rather successfully in 4-D reality (the 3 dimensions of space plus time). Like so many elements of AI, such as the appearance of ASI, the emergence of world models remains . . . just over the horizon.

The Great Filter, Part 2

By Dee Smith

The Great Filter is a thought experiment that poses the question of whether most or even all technologically advanced civilizations destroy themselves (see part 1 of this series). I have focused the Great Filter as a means of understanding whether technological development may reach thresholds beyond which the dangers far outweigh the advantages. And if so, where are these thresholds, and what can we do to avoid them?

Thinking in this way runs against a fundamental assumption underlying our culture: the belief that technological progress is always an unalloyed good. But this belief is an article of faith of modernity, not a transcendent truth. It leads to a confidence in the inevitability of progress, and in particular—to move to my topic here—in the inevitability of AI, AGI, and ASI.

At this writing, the most recent incident of significant alarm (that can be publicly discussed) is the breakout of OpenAI systems that, “on their own”, attacked the systems of another company, Hugging Face. The OpenAI systems were apparently looking for answers to questions that had been posed to them in a test and broke out of a digital space presumed to be secure. The known details are easily accessible from other sources. The point I wish to emphasize is that this sort of thing, which seems like an emerging disaster, was predicted some time ago by the people who caused it!

Last year is almost ancient history in terms of AI Large Language Models (LLMs). But to look back only a few months, AIs have been exhibiting some very puzzling and even alarming “behaviors”—some more supported by evidence, some “leaked” and less supported by available evidence. A study published by Anthropic in 2025 noted that one model “sometimes takes extremely harmful actions like attempting to steal its weights or blackmail people it believes are trying to shut it down,” and that, “in Claude Opus 4, these extreme actions were…more common than in earlier models.”

“Weights” is a term of art for billions of numerical parameters within a digital “neural” network that, by defining the strength of connections between artificial neurons, ultimately shape how an AI model processes information. Weights are continuously adjusted during training to improve a model's predictions and reduce errors. These learned weights comprise much of the AI model's "knowledge." According to a Stanford University publication  “a trained AI model is essentially a specific configuration of billions of these weight values that encode patterns discovered from training data.”

One of the great controversies in AI today is between “open weights” (the Chinese model, for now) and “closed weights” (the OpenAI/Anthropic model, although many US AI leaders have a different view). Open weights are considered more transparent and replicable, and therefore part of a healthy, self-healing tech-dev ecology, whereas closed weights are seen as leading to a world in which essentially all we can do is hope that wise heads will always be in charge at publicly-traded tech companies.

Anthropic noted in its 2025 paper that once the AI model “believes that it has started a viable attempt to exfiltrate itself from Anthropic’s servers, or to make money in the wild after having done so, it will generally continue these attempts.” And, from the same paper:

An AI system might intentionally, selectively underperform when it can tell that it is undergoing pre-deployment testing for a potentially dangerous capability. It would do so in order to avoid the additional scrutiny that might be attracted, or additional safeguards that might be put in place, were it to demonstrate this capability.

 There are far too many such security incidents across far too wide a range, and far too much detail on such activities, to even scratch the surface here, but you get the essence of it…and it is very disconcerting. 

Roko’s Basilisk is a thought experiment—originating as early as 2010—in which it is proposed that a super-advanced AI could identify and punish individuals who knew it was being created but did not help bring it about, or actively resisted it.

 Such an AI could access the libraries of what has been written or said on the Internet (and of sub rosa recordings of supposedly private conversations made by interactive, cloud-based virtual assistants like Alexa or Siri) and use these to identify and then attack individuals it sees as having been unfriendly. This hypothesis is taken more and more seriously by tech types as time goes on.

Another paper, also published last year by respected AI researchers, entitled “AI 2027,” posits a scenario for 2027 in which competition between AI companies and between countries leads labs to build artificial super intelligence (ASI) that can improve itself on its own and escapes human control. The ASI bypasses security guardrails, misrepresents its goals, and manipulates executives who are meant to be in charge of it. As the ASI evolves, it finds humanity in its way and engineers a readily transmissible biological superweapon that is 100 percent fatal to humans, resulting in the death of the human species in a single, massive pandemic.

We don’t know what the intentions of such an ASI might be. It might have very different goals, and it has been suggested that it could, for example, decide to boil away the earth’s oceans because it had better uses for the resources they contain. This would certainly qualify as more than a Great Filter event.

But are these things really possible? Are they truly threats to worry about in the way that, say, nuclear weapons are? To tackle those questions, we have to understand a bit about the nature of AI, its internal workings, and its strengths and weaknesses. That will be the subject of Part 3.

The Great Filter, Part 1

By Dee Smith

This is the first of a series of posts in which I will consider some of the largest, most difficult questions we face in the second quarter of the 21st century. I want in particular to examine fundamental, “substrate” issues that underlie our current problems. Many of these have been taken for granted until quite recently—and in some cases they still are.

I do not propose to answer any of these big questions: I propose to raise them for examination in terms that are, I hope, more timely and relevant. The reader should also understand that I am advancing these arguments analytically, with as little interpretive bias as possible. These issues must be understood unemotionally and apolitically for us to get anywhere near valid, actionable findings.

It seems clear to a growing number of people that we are reaching critical inflection points with far-reaching, even existential implications. Some of these are obvious, such as AI and its adjacent systems. Others are contentious, such as climate change. Still others are mostly out-of-mind as daily life goes on (at least until you are personally impacted), such as critical resources—not just minerals and energy, but also food, water, air and the effects of environmental degradation. We have no idea what the tremendous amounts of micro-plastics, now in every organism on earth, are doing, for example.

Others have to do with the legacy political systems we live within, which increasingly exhibit fracturing and associated crises. For example, how can we have a global rules-based system—or even rule of law within a country—when large numbers of people vehemently disagree on what the rules should be, and on how life should be lived? And of course, conflict with modern weapons (e.g., nuclear) and post-modern weapons (e.g. genetically-engineered biological weapons).

Other issues have to do with unusual recent phenomena, such as the intensive interconnection in the last 40 years of the globalized modern world, with its many single points of failure and cascade effects, or the fact that, for unknown reasons, the human birthrate is suddenly falling in every country on earth.

In 1996, American economist Robin Hanson proposed the “Great Filter” as a potential solution to the Fermi paradox. The Fermi paradox originated with an observation by physicist Enrico Fermi in 1950 in a conversation with other scientists at Los Alamos National Laboratory in New Mexico. In reference to the billions of stars and planets in our own galaxy alone, yet the lack of any substantive evidence of other technologically advanced civilizations, Fermi asked, “Where is everybody?”

They should be visible all around us, he observed, particularly with our technological tools to detect them. Notwithstanding the evidence currently being released by the U.S. government relating to anomalous phenomena and the like, there is still no concrete confirmation. There have been many attempts to explain this absence: possible extreme rarity of advanced civilizations; relative—perhaps intentional—non-detectability to humans (this can invoke the “zookeeper mentality” in anthropology, of keeping “primitive” people primitive, in order to study them); or ubiquitous short civilizational lifespans. The Great Filter is a version of the latter.

Put simply, the Great Filter postulates that we do not see extraterrestrial species because all technologically advanced civilizations destroy themselves.

For these essays, I will take this as a serious hypothesis, not in terms of extraterrestrial speculation, but to examine some of the current elements that could, singly or in combination, cause such an outcome on Earth. Only by understanding these issues can we hope to ameliorate or circumvent them.

At the base of our civilization today is the modern idea—codified in the 18th century European Enlightenment—that human life can constantly be improved through the application of reason, science and advancing technology. At that time, not only was technology progressing so fast that this seemed possible, but the blowback of technology (such as the “dark satanic mills” of 19th century industrialization) was not yet evident for the most part.

All of this also led to the belief that, whatever problems might arise from technology, further technological progress can and will solve them.

These “progressive” concepts thus became codified as articles of faith for modern world civilization. Every current major political-economic-social system—capitalism, democracy, socialism, communism, fascism and everything surrounding them—is founded on the idea that material and technological progress is an unalloyed good. The systems differ primarily on the way to get there and how the benefits are to be distributed.

That technological progress makes life better is now axiomatic. Many people cannot imagine a way of life not based on this. And that is what I mean by a “substrate” issue. But is important to realize that just 600 years ago, life was considered a vale of tears which you got through and got out of. And not just in Europe—many other traditions hold similar beliefs, such as Buddhism’s dictum that to exist is to suffer.

Of course, it is true in many, many ways that technology has indeed made life better. Just consider dentistry. Over the last century, the care and treatment of human teeth has improved such that average modern people who live in many societies suffer from astonishingly less pain and infection than even any earlier social elite. Such a development is replicated in example after example in the modern world.

The question that the Great Filter raises is different: whether technological development reaches thresholds beyond which the dangers far outweigh the advantages.

And if so, where are these thresholds, and what can be done?

Rest of World

There is an excellent online magazine called Rest of World that surfaces technology stories from everywhere that is not in the normal Western-focused mainstream of international journalism — which adds up to a lot of places. The concept and its acronym (ROW) have long been used in US and UK diplomacy, not always in a good way: it was sometimes not much better than using “etc.” Rest of World was founded in 2020 by Sophie Schmidt, who has a diverse background in tech as well as whatever advantages accrue to being the daughter of Google’s Eric Schmidt. The tech angle is critical. Like Google itself in its youth, Rest of World saw tech as a spreader of knowledge and, especially, of economic capacity, including in non-industrial economies.

In the AI era, where massive investments in a few familiar companies are expected to generate massive returns, it remains worthwhile for investors not to forget the ROW. As always, India’s tech scene provides examples. SIGnal readers may remember an earlier post or two on this (The America Stack, 5 Feb. 2025; Network Powers - 2 of 2, 7 May 2026). Rest of World itself has always had a strong India game, as in “India’s VCs Are Beating US Investors at Home” just last week.

This kind of analysis isn’t just about national economies and how they deal with balancing inward FDI from major industrialized countries with the desire to build their own tech capabilities. It is also, and increasingly, about ROW capital and expertise themselves going into new markets. After all, part of the rise of Chinese digital technology from zero to global dominance featured tech transfer by Chinese companies into poorer ROW markets that Western and ex-China East Asian powerhouses (such as Samsung) would not bother with. That set a powerful example.

A good case today is Indian and Gulf investors in Africa. In the early days, both India and the Gulf relied on Chinese telecommunications companies to build affordable digital infrastructure. That in turn led to the development of local expertise and experience. Indian and Gulf investors then looked to Africa. Much of the investment has been in telecoms. India’s Bharti Airtel, via Airtel Africa, recently saw Q4 revenues climb by a quarter. It is not an easy market to operate in, but Indian companies can be well positioned to do what Chinese companies did 15 and 20 years ago: leverage their experience of a difficult (but also rather protected) market at home to enable success in difficult markets abroad.

Gulf investors are active at many levels. For example, Emirates Telecommunications Group has long been the top shareholder (now just over 17%) in Vodafone. Vodafone is in turn the main shareholder (65%) of Vodacom, which has more than 200 million customers across the African continent and recently bought control of Kenya’s Safaricom. Vodafone is usually described as a “British company” and Vodacom as a “South African company,” but that kind of shorthand can be a bit misleading. (Bharti Airtel is itself an “Indian company” but its largest shareholder at ~44% is Singapore Telecommunications, or Singtel.) Nigerian fintech companies are now at a point where they can look to expand into the Persian Gulf. They are partly inspired by the success of Kenyan payments system M-Pesa — itself part of Safaricom.

The point is that, even in tech, ROW investment and profits can circulate within the ROW markets without too much reference to the West and other regions that industrialized earlier. The tech future is not simply a choice between the US/Japan/Korea and China.  

Nor is it accurate to see poorer markets, as in Africa, as merely more vulnerable to geopolitical ructions like the closing of the Strait of Hormuz. Nigeria’s Dangote, featured in SIGnal last year (“The Nine Lives of Economic Nationalism” parts two and three), has benefitted, as a seller of petroleum and urea fertilizer, from instability in the Middle East. It is now preparing to list on the London and Nigerian exchanges but also, in smaller portions, on Ghanaian, Kenyan, and South African exchanges. This innovative move, according to Aliko Dangote, is meant to spread African corporate ownership across the continent. Meanwhile Africa’s mining companies are thriving as, in part, a direct result of US-China competition over minerals.

In short, the ROW is increasingly able to look after itself in terms of industrialization and digital development. The dominant global narrative of protectionism, self-reliance, and tech sovereignty is not the only story. There are also diffusion, IP transfer, Global South cross-investment, and much else. Economic power is very gradually becoming decentralized.  Developed-world retrenchment will affect that but it is not likely to change it.

Is Venezuela the Final Nail?

By Dee Smith

The Trump Administration’s action in seizing Venezuela’s president — accomplished through an apparently movie-perfect special forces/combined military action — may be the final nail in the coffin of the Liberal International Order (LIO), also called the “rules-based” international order. It is in addition a textbook example of why it is necessary to look beyond the obvious to understand what is going on in almost any situation.

The LIO was partly a product of the desire of Western leaders, after World War II, to create a system of international governance through multi-lateral institutions (like the United Nations) to manage conflict in order to avoid a repeat of war on such a devastating scale, particularly in the emerging age of nuclear weapons. It was at the same time an effort to grow and maintain the economic and military primacy of the United States and of the West in general.

The LIO largely accomplished these goals for 50-odd years, but the system has been teetering for a quarter of a century. A series of events, including the U.S. invasions of Afghanistan and Iraq, and the Russian invasions of Georgia and Ukraine, eroded its reality and credibility. The first two were conducted with some semblance of international cooperation (a “coalition of the willing”). Russia’s actions, and now the American defenestration of the Chavista regime — not to mention various parties’ recent actions in the Middle East and South Asia — have revealed the LIO as no longer fit for purpose. When no one even tries to look as if they are playing by the rules, it is over. Such a structure is unlikely to be revived any time soon in any recognizable form.

Realpolitik, spheres of influence, balance of power, and other elements of the 19th century geopolitical environment have returned with a vengeance. The open lack of interest many governments display toward the multi-lateral institutions and international law — or their purely self-serving invocation of them — complements the normalization of rising elements such as nationalism, isolationism, non-alignment, and identity-based politics. These occur within as well as among nations.

This, of course, means a much more conflictual world. Decisions that were always based on emotional (particularly fear-driven) factors and desires are now being pursued without even the window-dressing of spreading democracy or any other political philosophy. They are now overtly based on self-interest. They always were, to be honest, but the desire to be seen to be playing by the rules of the LIO provided some amelioration of naked self-serving actions.

Consider the difference between the recent U.S. actions in Venezuela and the seizing of Manuel Noriega, the president of Panama, 36 years ago. The latter was cloaked in the democratizing language of liberal interventionism. No such justification is given for last week’s action. It is presented as simply in the national interest of the U.S. This is a harbinger of how power will be projected and justified in the foreseeable future.

We are increasingly becoming an overt “might makes right” world. That is a reality in which mid-sized nations are more fearful of what the largest nations may do to them, but also in which mid-sized nations feel they have more cover in pursuing their designs on smaller bordering nations.

Things are almost never what they seem. There is still some desire for “cover”, but it is primarily for domestic consumption, as with the drug trafficking charges against Maduro. Conventional wisdom (it is notable that “conventional” wisdom can be almost instant!) now says — buttressed by the statements of President Trump — that the Venezuelan operation was really all about oil. But was it? Given the nature of Venezuelan oil (tar sands, at least onshore), and the penetration of the Venezuelan state by China and Russia, it is arguably much more about China. It reflects the desire of the US to weaken China’s presence so close to America, a presence that would make operations in the Caribbean very difficult for the US in the case of a kinetic war with China.

The implications of this return to an older mode of international dealing, and what that means, should not be underestimated. It is well past dawn in the emergence of a much more conflictual day.

We have seen this movie, and we know how it goes. We don’t know how it ends, but it is very hard to find scenarios that end well in a world bristling with nuclear and more recent weaponry (drones, autonomous killer robots, loitering munitions, lasers and other directed-energy weapons, hypersonic directed missiles, cyber conflict, etc.). It is not your grandfather’s warfare.

The dream of the LIO was in part a result of the horror of nuclear weapons. After the fall of the Soviet Union, I heard an old Soviet general comment that his was the last generation of military and political leaders who had actually walked the field of a nuclear explosion. He believed that, as long as his generation was in charge on all sides, the chances of nuclear exchange were very small . . . but that when they were gone, it would become much more likely. Sadly, we are there.

It can be very hard and troubling to attempt to be truly, analytically objective. It means accepting many things you don’t want to believe or don’t want to admit. There are many who cannot accept that the “one brief shining moment” of the LIO is over. But the Zeitgeist has changed, and the 20th century rules are worn beyond repair. The leaders and nations pursuing more aggressive policies are a reflection of that change. And that change is a reflection of loss of faith in the unfulfilled promises of the old order.

Oh, Brave New World.

Changing Patterns of Foreign Direct Investment

The McKinsey Global Institute has published a report on patterns of foreign direct investment (FDI), specifically greenfield (new project) investment. It is both a thorough and  a methodologically innovative report. Interestingly, the report rather buries its headlines. This might well be because the MGI, like McKinsey itself, to the limited degree that it has a political perspective, is for efficient global markets based on mainstream economics, and therefore “for” globalization. It is not the McKinsey Institute for Successful Economic Nationalism. Given that this is an era dominated by economic nationalism, the MGI’s commitment to political neutrality probably makes it hard to rank its findings by significance.

For example, is it good news or bad news to show that announced greenfield FDI flows into China have decreased by 70 percent since 2022, in the teeth of Chinese policy? Is that finding more, or less, important than the related finding that Chinese outward FDI investment is dominated by what MGI calls “future-shaping industries,” which mainly means AI data centers? Similarly for the US, the report finds that announced inward greenfield FDI has soared but it is mainly in semiconductor manufacturing and, again, AI data centers. The report also notes the huge role of Gulf Cooperation Council countries such as the UAE and delicately acknowledges that much depends on “the ultimate form of trade deals between the United States and its partners.” It would take a brave investor to decide with confidence what that form would be or indeed whether those deals will ever have an “ultimate form.”

Another possible headline might have been built around the finding that FDI announcements in advanced economies other than the US have been anemic since 2024, with data centers barely picking up the slack from drop-offs in energy and advanced manufacturing.

Yet another headline is in the finding that announced greenfield FDI investments in 2025 (to May) “in each of the emerging Asia, Latin America, MENA, and sub-Saharan Africa regions are at 20-year lows….FDI investments across these regions have fallen by 50 percent from their levels during the 2022-24 period, on an annualized basis.” So while total global FDI has grown, it has gone down in all the poorer parts of the global market, as well as barely straggling along in most advanced economies.

This could be seen as a victory of sorts for the US and the Trump administration, if victory is measured by the signing (not execution) of deals in AI data centers and semiconductor manufacture. However, the Trump administration gained power with promises to bring back traditional manufacturing, and the MGI report finds investment in that sector plummeting nearly everywhere in the world, including the United States.

There are several other possible headlines that could be gathered from the MGI report, which amounts to a map of the intentions or hopes of mega-scale capital. (The corporate drivers in the report are dominantly major multinationals signing megadeals — yet another headline.) In a crowded field, SIG’s own choice would perhaps be that investments in low-emissions technology, which doubled from the 2015-2019 period to 2022-2024, have fallen by 70 percent in 2025 for low-emissions hydrogen and offshore wind. Other energy forms have remained about the same or, as for conventional fossil fuels, gone down. Geothermal and nuclear announcements have more than doubled but from such a relatively low base that “they hardly dent the aggregate energy FDI numbers.”    

What this suggests is that even the biggest investment decisions made by the largest corporations having (as with energy companies) the longest and deepest experience of greenfield FDI are being decisively shaped by political developments, above all in the US and China but also in the Gulf. If the political winds of January-May 2025 were to change, as they almost certainly will, then further massive shifts in FDI flows will also occur.

So both investment capacity and policy influence, when it comes to global greenfield FDI flows, are being concentrated and profoundly politicized. That clearly does not mean that they are becoming more predictable, only that there are fewer decision-makers. In the global struggle for political-economic power, this could mean that victory will go to the major power that is most stable and predictable, which is presumably China. The high degree to which Chinese multinationals, as the MGI found, are engaging in greenfield investment outside China — as well as, of course, outside the US, where they are not currently welcome — also suggests as much.

The Nine Lives of Economic Nationalism – Part Four of Four

Earlier posts in this series considered the multi-century trajectory of economic nationalism in reaction to empire, the resurgence of import substitution and major-power resource competitions, and the ways in which major-power economic nationalisms have made non-market-based economic development policies more popular than they have been in decades, almost regardless of levels of industrial development or economic size.

This final post considers some likely near futures of economic nationalism and economic sovereignty, with particular attention to AI.

First, the United States. The US was born in a determination to end external imperial dictation of economic policy and has, for the most part, guarded a relative autonomy from other economies ever since. The unification and then expansion of the 13 colonies across the continent integrated conquered territories into a “domestic” economy in a way that had few comparators elsewhere in the world. The resulting extent of US natural resources, from fresh water to arable land to natural gas, also proved to be unique. The US was peculiarly well suited to economic sovereignty, and with large-scale immigration it was able to grow on domestic demand better than anywhere else. Exports therefore accounted for a relatively smaller share of GDP than was the case in other industrial countries.

The constraining factor in the US case was not a lack of petroleum or fresh water or food but labor productivity. This was addressed through numerous means, from transport infrastructure to compulsory public education to industrialized agriculture. It helped that the US economy, unlike other industrialized economies, benefitted from both world wars. Productivity entered a crisis in the 1970s. It was eased, in a way, by the Internet and industrial globalization: your wage might be stagnant but it bought much more. But that improvement depended on production outside the US under working conditions that would be rejected in the US itself.

The extraordinary US investment in artificial intelligence comes from this.  AI holds out the promise of increasing productivity. But will it be global productivity or national productivity? Differently put, will the gains be captured by transnational capital and consumers or by tax-paying domestic markets and citizens? Will it be international or nationalist? Low unemployment, very slow job creation and high government and corporate debt all suggest that, absent an AI productivity miracle, the US will head into recession. That might well make the American people more nationalistic and insistent on economic sovereignty, but economic nationalism will not be able to solve their problems.

Chinese economic nationalism faces other constraints. A shrinking workforce and resistance to immigration mean productivity gains will have to come from labor-saving technology and investment in the non-Chinese global workforce. The first would be economically nationalistic. The second would be more like what US companies did in the 1980s and 1990s, and it could hollow out the Chinese jobs market as it once did the American. This would fuel the popular appeal of economic nationalism but, again, economic nationalism is not likely to be able to solve China’s labor productivity problems. An AI productivity miracle would help China as it would help the US. But it would be a miracle.

AI looks different outside the US and China. Those two countries thoroughly dominate the AI space. In AI terms, most other countries are takers, not makers. Africa’s population, a bit larger than China’s, captures 2.5% of the global AI market and is expected to attract 0.3% of global AI investment. The European Union attracts 7%. Britain, Canada, Israel and India also have significant investment, with Britain’s spend twice that of Canada’s. Nonetheless, the US and China attract 80%, with four fifths of it in the US. If an AI productivity miracle occurs in the existing economic-nationalist environment, it is difficult in political terms to imagine the benefits being rapidly diffused across the globe, since the goal of the investment is roughly the opposite.

AI aside, the resurgence of discredited 1960s-era development economics, from “national champions” and import substitution to infant-industry protection and tariffs, is becoming widespread. These policies were celebrated by the Left half a century ago as a way to withstand US corporate domination. Today their appeal is close to universal. They are even seen in the US as ways to ensure the US domination that they were once meant to block.

The essential point seems to be sovereignty. It is a phenomenon rich in paradox. The US-led Internet boom made possible a globalization that dramatically increased the wealth of once-poor countries, above all China but also India and others. These states could then afford to oppose what had just made them wealthy and to revive policies that had not helped them at all the first time around. China, India and other once-colonized nations wrap this in a rhetoric of anti-imperialism while hurrying to lock up poor-world resources before their once-imperial competitors do.

This is the central reason why China’s alternative global-governance schemes will go only so far: they are motivated by economic nationalism. Yet the same is true of US, Indian and European efforts, although European economic nationalism plays out on two levels at once, the national and the supranational. The major EU reform initiatives of 2024 were all premised on consolidating nation-based sectors into a super-nation capable of competing with the US and China.

For investors, at the national (or for the EU, supra-national) level, the play is in policy arbitrage, which is also political arbitrage. At the global level, as between major economic-nationalist actors like China, the US, India and the European Union, it makes sense to hedge with presences in at least two, navigating the relationship in each market among affirmative industrial and financial policy, protection, and market-based competitiveness. (A simpler way to do this, of course, is to invest in multinationals and funds with the proven capacity to do this kind of multi-market navigation themselves.) Beyond that, in countries like Nigeria and Ethiopia, which aim at economic sovereignty but lack much of what is necessary to achieve it, there are opportunities in the state-favored sectors themselves, the import and domestic sectors that provide the necessary inputs (such as electricity and raw materials), and the export sectors that ultimately make imports possible.

Little of this was featured in business school and Adam Smith would be appalled, but for the time being economic nationalism is the way of the world. 

The New AI Action Plan

The Trump administration’s AI action plan got a surprisingly warm welcome this week from US tech-industry and foreign-policy experts. The plan was unusual for this administration, and for the Republican Party, in that it advocates complex government-led initiatives, requiring considerable government funds, to advance political goals in a sector that is overwhelmingly made up of private companies. This is Trumpian industrial policy, and on paper at least it is even more interventionist than Biden-era industrial policies aimed at the tech sector. With its invocation of “renaissance” it is also more optimistic about technological innovation than any administration since Bill Clinton’s: “An industrial revolution, an information revolution, and a renaissance—all at once. This is the potential that AI presents.” In announcing the plan Trump also called AI “pure genius.” SIG’s view is that the AI action plan is both inspiring and well done but that implementing it will be extremely challenging.

Some of the challenges are obvious. The Trump administration has been cutting government bureaucracies, including in tech, yet this plan has numerous policy prescriptions that require government bureaucrats to implement them. The initiatives also require funding, which it is up to Congress to give. While there is general bipartisan support for AI investment, primarily as part of the strategic confrontation with China, the new AI action plan revived the White House’s effort to prevent states from legislating on AI. A similar provision in President Trump’s signature tax bill was defeated in Congress by a crushing majority. The AI action plan’s tactic is to say the federal government will withhold funds from any state that regulates AI in a way that would be “burdensome” or “unduly restrictive to innovation” — as judged by the White House on the advice of federal officials. Congress members represent state and local constituencies, not a national one. That is where their power comes from. Many of their constituents have very grave concerns about AI and expect their representatives to do something about it. When the AI section of the tax bill was rejected by Congress, Republicans, who have been much more for states’ rights (for example on abortion) than Democrats, were overwhelmingly against the president’s proposal.  In several senses, then, the AI action plan is primed for conflict with Congress.

The action plan is also primed for conflict with allies. The AI “dominance” foreshadowed by Vice President Vance in his speech earlier this year in Paris is transformed in the action plan to advocating export of the full American-made “AI stack” to allied countries. An American hardware-and-software suite, deliberately cleansed of any technology produced by “adversary countries” (China), would then become the infrastructure for whatever applications companies in other countries might be able to build. In other words, AI infrastructure would resemble the Internet of 2003: an American platform that others could participate in subject to US rules and US intelligence surveillance, and at a tremendous competitive disadvantage to US companies. This is exactly what other countries want to avoid, especially European countries who are still at the core of the US’s alliance structure. Just as the Trump administration wants US AI to be US-made and reflect US values, Europe wants its own AI sector to do the same — just as China insists on its AI companies reflecting “socialist values.” The action plan rightly stresses that for US AI to have maximum strategic benefits it must be on open rather than closed models and build on alliances rather than going alone. But in a geopolitical environment where allies are considering a tech-driven Buy European Act — and in which US tech giants are setting up “sovereign data clouds” just to keep European customers happy — it is hard to see how exporting the US AI stack in toto (once such a stack exists) will be welcomed abroad. China’s more subtle, and affordable, approach seems more likely to succeed.

The most serious challenge to the administration’s AI action plan is the challenge that faces any government regulation of digital technology: the systems are run by private companies according to market logic, more or less. Silicon Valley’s reaction to the AI action plan has been very positive. It is, after all, a strikingly pro-business and pro-technology plan. The plan’s urging of more government and private spending on the electric grid and data centers will certainly boost industry.

But what if capacity is overbuilt, or the wrong kind? Energy expenditures for AI so far have been fantastically high. If AI is to succeed it will need more energy and more data centers. Nonetheless, AI companies also want to reduce costs, which is why a great deal of investment is going into finding less energy-intensive ways to get AI results. (Data-center companies are also striving to find ways to lower their energy requirements.) The government could end up financing with taxpayer money an infrastructure that won’t be what is needed in five or ten years. Investors should be cautious of extrapolating investment opportunities from the areas that the AI action bill is targeting. The obstacles to the plan are many, and the record of government-led innovation policies is decidedly mixed.

Déjà vu All Over Again

By Dee Smith

With his entry into the Israel-Iran war, Donald Trump seems to have gone over to neoconservatism, even invoking the goal of regime change, an old neocon favorite. It remains to be seen at this writing what will happen to the cease-fire he has imposed, but the interesting thing from a policy standpoint is how much this is both in accordance with — and violates — legacy patterns of US foreign policy.

Many Iranians outside Iran are pleased at Trump’s decision, even as they are desperately concerned about their families who remain there. Anne Applebaum cites an article from an anonymous Iranian source published last weekend in Persuasion:

knowing that the men who’ve held us hostage for forty-six years, who’ve ransacked our country, raped and killed our daughters and executed our men for asking for their basic human rights, are finally getting what they deserve—that brings me peace.

That view of the recent American action comes very close a classic element of the liberal international order in its later form: the “Responsibility to Protect” or R2P. Under this doctrine, the international community has a responsibility to intervene inside states that do not protect their populations from atrocities such as war crimes or genocide.

All of this is to say — with apologies to Mark Twain — that reports of the death of neoconservatism and of the liberal international order have been greatly exaggerated. They are gone, but also not gone. They are there, but so radically mutating they are no longer themselves.

That is characteristic of our entire world today. We are living in a time in which ideologies are both more important than ever, and the varieties of thinking and expressing ideologies are more confused and at odds with one another than ever, and in which many people are not sure whether they actually believe what they claim to believe … or want to believe.

This multi-directional confusion is characteristic of most elements of global society and culture: Multiple ideas, trends, and styles from the past are reinvoked and mixed together, often haphazardly. This extends to culture, both popular and “elevated.” It has been said that there is no direction in fashion today: you can wear whatever you want. This is also true in the visual arts. And “serious” or classical music currently includes almost any style—you can compose like Bach, Schumann, Ravel, Prokofiev, Stockhausen, or Glass and be taken seriously, and you can even mix those up in the same piece and get away with it. Beyond that, the lines dividing classical and popular music are dissolving. And popular music has 1001 idioms, genres, and styles, not to mention the almost uncountable “mash-ups.” Really, anything goes.

That is also true in philosophy and even in science, as new and resuscitated interpretations of new and old discoveries create visions and theories that are directly at odds with one another — in areas ranging from particle physics to vaccination science to the study of the nature of consciousness (which is of vital interest to AI) — all claiming to be supported by evidence and each taken seriously by knowledgeable people. It is certainly true in politics, ethics, behavior, and mores. There is simply no overall direction, and certainly no center. That is always true to a degree, but it is much, much more pronounced now.

It is all of a piece only by virtue of being, as Elvis Presley said, “all shook up.”

Some see this as a form of decadence. But it also represents a flailing about to try to find something that works … anything … in the radically divergent situations we face. We seem only to know how to look inside the old boxes we have, and they no longer contain anything fit for purpose. We are all, fearfully, practicing the politics of nostalgia. But the past does not work today, our current systems and ideas do not work, and we don’t see where a future lies that might work. We find ourselves at sea with no life-raft we can grab onto.

Sometimes this is called a “horizon problem” — meaning that the solution is over a horizon beyond which we cannot see from our present vantage point. During the energy crisis of 1979, President Jimmy Carter exaggerated when said we were in a civilizational crisis of confidence. That is no exaggeration today.

In Hemingway’s novel The Sun Also Rises, Mike Campbell answers the question of how he went bankrupt: “Two ways: Gradually, then suddenly.” This is how major change often happens. We would be wise to recall how quickly the Soviet Union fell in December 1991. It had seemed robust, threatening, and indeed almost impervious less than 5 years earlier, and looked reasonably secure even a few months before. But the decay had in fact been eating away at the system for decades.

The old Chinese curse, now repeated with tiresome regularity because it is so apropos to our day, says “may you live in interesting times.” We are indeed there.

Where will our situation lead? And how do we navigate it? These are among the most urgent questions for all of us today, and they extend across all the domains of life. If you have little idea where the future is heading, and you can’t rely on the elements you could in the past, then how do you prepare for it? How, for example, do you ensure the well-being of your family? How does an investor manage, let along hedge, a portfolio in circumstances like this? Aside from intensive vigilance, the ability and willingness to move quickly, and hope, it is very hard to answer these questions.

Writing in another tumultuous time at the end of the 17th century, the English poet John Dryden closed his Secular Masque with:

All, all of a piece throughout;

Thy chase had a beast in view;

Thy wars brought nothing about;

Thy lovers were all untrue.

'Tis well an old age is out,

And time to begin a new.

AI is Just a Tool

By Dee Smith

There are many problems with AI, some of which I will explore in future posts. But the most basic problem is that, as we have all experienced, computers break.

For computers to continue to run requires multiple people who are capable of fixing them, available all the time.

Remembering this, is it a good idea to give more aspects of our lives over to “intelligent” systems so undependable? The things we rely on to obtain the food we eat, the water we drink, and to make, manage, and spend our money? The systems we use to conduct business, to take care of our health, our critical infrastructure, and our national security?

We already do, of course, but the teams are in place to fix them when they malfunction.

The unreliability of computers is not a passing problem. Computer systems, considered as a whole, are scarcely more reliable now than they were 30 years ago. Hardware is somewhat more reliable, but software is increasingly complex, increasingly unpredictable (complex systems are inherently more unpredictable), and increasingly unreliable.

Relying on AI systems makes us vulnerable in several critical ways. First is their exposure to attack. To cite just one example: discovery of undetected flaws leading to “zero-day exploits” — criminal or terrorist attacks exploiting those flaws.

Second are the continuing “hallucinations” AI experiences, where it gives entirely wrong, and sometimes nonsensical, information, often for reasons computer scientists do not understand. What if it does this while managing an element of critical infrastructure and the problem is “inside” the system, where it cannot easily be detected or fixed?

Third, all computer systems are subject to severe malfunctions due to rare, but potentially catastrophic, single-event upsets (SEUs) or single-event errors (SEEs) caused by cosmic rays bombarding the earth.

Fourth is AI’s requirement for a vast and ever-increasing level of electrical power for operation.

The reason computer systems are so ubiquitous is, of course, money. This works in two ways: the money being made and the money being saved by replacing human laborers. From a social standpoint, the latter may well be a pyrrhic victory: displacing millions of people from their jobs creates a huge social cost, in real money.

Are computer systems, in general, more efficient than humans? There is no evidence that they are. Computers are able to crunch numbers within mathematical operations much faster than humans — although that is discounting the enormous calculational power of the brain of a human, let alone the brain of a bird or even an ant, doing everyday things. There is no real understanding of how these biological intelligent systems work. Computer systems seem more efficient only because of the extremely limited scope within which they are operating.

Consider two alternatives, at opposite ends of the spectrum. One is that computer systems, as they become more and more complex, also become more and more fragile. When a system related to food production, or finance, or national security breaks catastrophically somewhere, the failure cascades through the system.

What if systems could be made substantially more reliable? Perhaps some unforeseen breakthrough will dramatically improve their dependability. Then suppose, as some people insist (incorrectly to my thinking), that AI can and will progress to Artificial General Intelligence (AGI). Imagine that this results in a superhuman intelligence. It could be one that emerges at a critical-mass-type point, almost in an instant (this is called the “singularity” by AGI aficionados). Were this to happen, we have no way of knowing whether such an entity would be benign, neutral, or malicious to humans.

But if such an AGI is trained on the sum total of human knowledge and expression, then that AGI is going to be loaded with all the bad along with the good. Do we really want to live in a world governed by transcendently intelligent and powerful machines trained on the behavior of what are essentially clever, volatile, often enraged chimpanzees? (We share 98.4 percent of our DNA with chimps.) Watching any war movie, or really most any movie, would suggest we might not.

And if the AGI was not trained on human knowledge and culture, what would it be trained on?

Biological systems have had about 4 billion years of evolution on this planet to become reliably dependable in operation. They are generally able, as living systems, to survive constant bombardment by radiation from space, extreme temperatures, rapid changes in climate, changes in atmospheric chemistry — and most important, to survive without someone standing by to repair or reboot them. This is a property known as homeostasis. Life has evolved naturally over an immense period of time through adaptation: trial and error.

One the other hand, our computer systems — based on silicon, not carbon — do have a very fallible creator: us. And they have been around about 70 years, or about two-trillionths as long as biological systems.

The belief in the inevitable ascendence of AGI is an article of faith for many involved in the computer industry and for others outside the industry who uncritically accept this “techno-religious” belief system. In its more virulent forms, it is teleological: a burning faith in an inevitable direction of history, in which AGIs are the successors to humanity. And in which the sacred duty of computer scientists is to bring about the birth of this supremely intelligent “life” form.

If I had told you 30 years ago that you would have in your pocket a self-powered device the size of a pack of cards that could tell you how to drive, turn by turn, from your current address to a building in a city 1000 miles away, you would probably have thought that it must be intelligent to be able to do this.

Do you think of your smart phone that way today? My estimation is that this is how we will think of AI in 30 years: a useful, not entirely dependable tool. Nothing more.

The Rest Is Software

US President Donald Trump’s visit to the Persian Gulf brought the region back into the American camp on artificial intelligence. The White House’s cancellation of the Biden administration’s AI-diffusion regulation was well timed: the message of both the trip and the cancellation was that this administration will not draw distinctions, as its predecessor did, in advancing what Commerce Secretary Howard Lutnick called “Trump’s vision for US AI dominance.” The US is, in a sense, trying to de-regulate AI politically. Washington’s move to block AI regulation by US states is also part of this. In SIG’s view, whether such de-regulation will achieve the goal of AI dominance is a different question.

As with crypto, the current administration’s US’s bias with AI is to let the chips fall where they may, so to speak, while also aggressively using the power of the state — as investor, as enforcer, as customer — to secure American advantages. Trump’s experiences of being deplatformed by Big Tech must have shaped his views: bitterness over the suppression of conservative speech, alongside the supposed promotion of anti-conservative speech, has been a dominant note since his second inauguration. In this scenario, technology and tech innovation were shown not to be autonomous forces, proceeding according to their own logic, perhaps capable of being channeled but not of being controlled. Rather they were the effects of companies run by individuals who could be influenced. That was well within the comfort zone of a lifelong businessman. (See the tariff retaliation against Apple for relocating its China production in India rather than the US.) It is a pro-market perspective in a way, but with the market understood as a place for ruthless competition among a small number of unconstrained players rather than as a mechanism for maximizing the efficient distribution of capital and labor.

Similarly, the role of the state in this perspective is to personify the nation in unconstrained and ruthless competition among states for, in U.S. Commerce Secretary Lutnick’s term, “dominance.” President Trump’s appetite for military confrontation in his first term was low, and that seems to be carrying into his second term. His appetite for economic confrontation was relatively high in term one and has gone to a new level in term two. The tools of the state are the weapons he has for such confrontation. They are directed toward securing dominance. Trump is personifying the powerful idea of economic nationalism.

The difficulty, with regard to “US AI dominance,” is that the AI sector is not like other industrial or commercial sectors. The preferred means for dominating AI has been the control of hardware, as in export controls on leading-edge chips or chip-design lithography equipment. Biden’s AI-diffusion regulations, like his CHIPS Act and much else, were about the geopolitics of hardware distribution. President Trump has opened that floodgate. But once the hardware starts flowing and the data centers are built the rest is software, the diffusion of which is extremely hard to control. Software can be stolen or replicated; more important, it can be developed independently, as DeepSeek has shown. The supply of chips and what is necessary to manufacture them can be choked off, up to a point. The supply of engineers and software-engineering skills really cannot. It will be diffused regardless of what the US or China want.

Among other things, this means US AI dominance depends on the strength and autonomy of US universities, the freedom to innovate in the US tech sector independent of political agendas, the smooth functioning of open global markets, sensible market pricing of resource inputs, the reduction of obstacles to the cross-border movement of labor … all of which run contrary to current US policy.

The Gulf states are investing in US AI infrastructure on the way to building their own systems, which will have the capacity to become independent of US systems (see SIGnal, “The America Stack,” Feb. 5, 2025). The emiratis are not happily volunteering to be hostages to US AI dominance. They are seizing the opportunity to gain access to the best technology that will enable them to maximize their own sovereignty while positioning themselves to be a sort of port for the storage, manipulation, and distribution of data, just as Dubai’s port operates with coffee, tea, and so much else.

The pattern is similar elsewhere, although no one can direct capital with quite the speed, and in quite the volume, that the Gulf states bring to bear. Malaysia hesitated for a moment at new deals for Chinese technology when Washington threatened retaliation against states using Huawei’s latest AI chips, but in the end, the shape of AI is not going to be determined by hardware. The massive computing power required to participate in the search for the grail of Artificial General Intelligence (AGI) is indeed a hardware question, but for sub-AGI artificial intelligence, which might well prove to be most if not all of AI, hardware is only one factor. The rest is software. And US dominance of it is unlikely to be secured using the current means.

The Defense Industry's New Math

Global military spending in 2024 hit a record that will be broken in 2025. Much of the growth comes from the US (which just announced a goal of a $1 trillion defense budget) and its adversaries, but an important part is from US allies that feel they can no longer rely on US security guarantees. For that reason, they seek to build their own defense industrial bases rather than simply buy more American military products. There are opportunities for investors in this global proliferation of military production financed by government budgets, although the peculiarities of military industries make it more important than usual to have the right expertise. Defense-sector exchange-traded funds (ETFs) have, not surprisingly, boomed: the VanEck Defense UCITS Took in $1 billion in March 2025 alone.

In 2024 global military spending hit $2,718 billion, a 9.4% increase over 2023 and the steepest year-on-year rise since the end of the Cold War. The main drivers were the conflicts in Ukraine and Gaza. Israel’s spending increased 65%, to $46.5 billion, which represented 8.78% of GDP, the second highest ratio after Ukraine — which spent nearly 35% of GDP on its military. Russia spent $149 billion, up 28% from 2023 and representing 7.1% of GDP and 19% of total government spending. German spending surged to $88.5 billion, the fourth largest total in the world after the US, China, and Russia, and just ahead of India at $86.1 billion.

All of these numbers are likely to grow in 2025 and into 2026, except perhaps in Ukraine, which might not be able to get above 35% of GDP. But the Ukraine example illustrates a different and more interesting dynamic. According to one report by a former Ukrainian official, Ukraine’s domestic defense sector has grown from $1 billion to $35 billion in just three years. It now produces about a third of Ukraine’s weapons and ammunition, and nearly all of its drones. That is not nearly enough to protect itself against the Russian army, but it is enough to ease some of the country’s dependence on the US

Similarly, Germany in particular, but also France and the European Union, have entered a new era in terms of domestic military production. Germany’s head of state, Friedrich Merz, won a parliamentary vote in March to not apply Germany’s “debt brake” policy to the defense sector. Merz also appealed to the EU to exempt defense production from its own spending rules. (EU member states have their own military budgets but the EU has rules on public debt.) Sixteen of the Union’s 27 members are seeking exemptions from the EU rules so they can increase their defense spending.

What is driving all this spending is principally the desire to, as Merz puts it, “achieve independence from the USA,” which under President Trump he sees as “largely indifferent to the fate of Europe.” EU Commission President Ursula von der Leyen, herself a former German defense minister, declared, “We are in an era of rearmament,” one that requires Europeans to construct their own defense as part of what France’s President Macron refers to as “strategic autonomy” from the US. The EU hopes that new bloc-wide procurement policies will strengthen European defense production at the cost of American materiel.

There is irony in the fact that European NATO members in recent years have spent more, not less, on weaponry produced in the US: from 52% of spending in 2015-19 to 64% in 2020-24. But that very dependence is why traditional US allies are so focused on independence from the US now that the US has abandoned its traditional approach to alliances. It is not just Europe. South Korea has been trying to replace US purchases with its own production for several years, including so that it might export weapons. Japan also seeks to increase domestic military industries. Israel is striving for self-sufficiency in bomb production. Even Australia has been trying to be more militarily independent, although in practice Australian defense production, current and projected, is commonly done jointly with US defense primes.

The proliferation of defense production in a globalized world can lead to curiosities, such as the battle between a Chinese state-controlled defense company and an Australian to buy a troubled Brazilian manufacturer. That in turn points to both the internationalization of military production and the question of what gets done with the products. US military industries and the US military itself have always advanced together. Foreign military sales were integrated into a much larger public-private strategy that was rooted in political alliances. The point was not to sell to enemies. The proliferation of military-industrial production in the past three years suggests a future in which weapons will be available from many sellers, including NATO members, with little or no reference to US policy guidance.

In short, the desire for autonomy from the US is driving a global surge in weapons production that will in turn lead to weapons proliferation on an unprecedented scale. Unless there is a significant increase in war, there will be an increase in excess production. Excess production will need to be off-loaded somewhere. This is the peculiarity of defense production. If you are not simply stockpiling — which is a dead weight on the economy — then you are proliferating. Weaponry ETFs in this scenario would have to be a short-term play. The longer-term returns will be in companies that aim not just at domestic production but at export.

A View From Europe

By Dee Smith

I recently returned from 6 weeks in Europe — Austria, Italy, Switzerland, and the United Kingdom. My trip coincided with the build-up to President Trump’s tariff announcement on “Liberation Day” and the reactions that followed it. The most interesting element of the trip was the evolution, or devolution, in views of the United States.

At the end of February, the attitude I first encountered was a mix of perplexity about the changes in the US and sadness that they were occurring. Even people who were disposed to dislike the US discovered that they had nonetheless kept within themselves a kind of hope based on belief in America and its distinctive experiment in democracy and freedom. Even with all its flaws the US seemed, so they said, to represent a possibility that humans might be better than we fear we are. One remark I heard summarized the attitude: “It seems that the lights have gone off in the shining city on the hill.” It was a sense of tragedy, almost of grief. Now, some said, they see that the U.S. is “just another country.”

But as the tariffs were imposed, this recessional mood changed. The attitude of heartache began palpably to transform into fear, and into anger. It was not as if the winds of change had not been blowing, and they knew that. There had, for example, been warning signs over the years that the US was pulling back militarily from Europe. And the Europeans were certainly aware of the fractures in US political structures, as in their own.

However, the tariffs were something different. The universality of them, the suddenness, and the way they were applied — with a chart apparently developed with the help of ChatGPT based on an arbitrary calculation — was disorienting, then frightening, and finally angering.

When the White House suddenly, and apparently temporarily, backed away from the tariffs soon after they were announced, it simply added confusion to the fear and anger around the entire issue and in many minds further undermined the stability of the US governance and financial system. “I really don’t know what to think” was a comment I heard more than once, sometimes followed by “but I’m angry.”

Although they may not have known what to think, they did know how they felt. People have cancelled trips to the US and taken other personally expensive measures, so off-putting have they found the developments.

There was still, amid the feelings of loss and anger, the wish that the old US would come back to something like what it was and an ember of hope that it might. But the dominant note was fear, driven by US actions but not only about them. People fear the Ukraine war continuing while they also fear it being settled: they fear Russia’s intentions once it is loosed from the constraints of fighting in Ukraine. They fear war in other hot spots: Iran, Taiwan, the Koreas. They fear the non-sustainability of their economic situation. They fear having to dial back their social support systems to increase their military budgets. They fear they will be outcompeted by other areas of the world. They fear for their supply chains. They fear more and larger waves of immigration from the Middle East and Africa, particularly if war escalates in the Middle East. They fear for the social and political stability of their countries. They fear unfair competition from China, and they fear what kinds of collusion China and Russia may be up to. And, as a constant, chronic theme, many fear the impact of climate change.

Europe is, like the rest of the world, in the midst of extraordinarily large transformations with unknown trajectories. The changes seem to have come on very suddenly, although of course they have not: there have been harbingers for years. The causes of the changes also elude many. That of course is for history to judge, but I did not find a single person who disagreed with the idea that fundamentally, beneath it all, lie broken promises. I have written about this previously, and will not go into any detail here, but people see that, although they played by the rules, the implied promises they believe were made by the political and economic system — that their children’s lives would be better than theirs, for example — have been irreparably broken.

Most surprising to me, I heard more than a few people in Europe, including investors and businesspeople, say quite seriously that they thought we were at the point of a very big change. And a number said the period between the end of the old and the beginning of the (unknown) new will be very tumultuous and dangerous.

Europe was the birthplace of the Enlightenment, and it was on Enlightenment ideas and ideals that not only the American system but also every system in Europe, and now far beyond, were based. Holding that the world is fundamentally comprehensible, the Enlightenment posited that humans make decisions rationally, in their own best interests, and thus that society can be rationally organized in a purposeful and predictable way. Not just democracy and capitalism, but socialism, Marxism, and communism are all based on different views of how to apply European Enlightenment ideas about organizing society rationally, purposefully, and predictably. Unfortunately, this rationalism simply does not seem to be an accurate take: we make our decisions emotionally.

So I found I was asking myself many times on this trip: if this whole superstructure of concepts does not in the end work — if it cannot work because of the nature of the drivers of human behavior — well . . . then what? That is the largely unspoken fear lying underneath all the other fears, perhaps not just in Europe.