After MAGA

The focus of attention in American politics for the next weeks will naturally be on whether the Republican party can keep control of the Senate or the House. But in one way the election is probably already over: MAGA has lost its magic. What effects this will have on policies that investors care about is a different question.

Donald Trump began using the slogan Make America Great Again at the end of 2011, in the context of rumors that he would run against Barack Obama in the 2012 election. After Obama defeated Mitt Romney, the phrase quickly became central to the Trump political brand. He applied to the Patent and Trademark Office within a week requesting an exclusive right to use the slogan for political purposes. MAGA was born.

Although Ronald Reagan (and even Bill Clinton) had used the phrase, it became inseparable from Trump’s political ambitions, all the more so as the acronym derived from it also became a powerful brand. By the time of Trump’s first successful presidential campaign in 2016 the slogan had proved its value. It focused attention intensely on him rather than on the Republican party that had nominated Mitt Romney — not at all a MAGA figure — just four years before, and John McCain four years before that.

Over the course of one and a half Trump presidencies it became clear that MAGA and the president were like two phases of a single phenomenon. Whatever MAGA might actually mean was clearly up to the president to decide, including to the point of changing his mind. MAGA without Trump was hard to imagine. So was MAGA without executive power. The extraordinary way in which the White House clawed powers from Congress after the 2025 inauguration was about personalizing power in President Trump, whose control over his own branch of government has been very strong in his second term. Budget Director Russell Vought and others certainly do have arguments for strengthening the presidency generically, but their success in strengthening Trump specifically was their real achievement and it depended on having an executive who wanted as much power as he could get. Whether future presidents will want the level and forms of executive power that Trump has acquired is not an easy question to answer.

Whatever the answer to that might be, for now the key fact is that MAGA and Trump have become inseparable. This is the key fact because the president’s favorability rating is extremely low — even as low as 31%. Republican candidates have been quietly distancing themselves from the president for many weeks, and the president’s campaigning stops have often been far from the battleground races.

For both the Republican and Democratic parties the landscape after MAGA is unmapped. SIG’s view, advanced with all due caution, is that Congress will gradually reclaim some of its powers. If nothing else Republican legislators will not want a prospective Democratic president to have the overpowering dominance over Congress that Trump has built for himself and deployed against the Democrats (and disloyal Republicans).

But where will this take policy?  In terms of the reach of executive power, which has dramatically affected policy areas like tariffs, foreign policy and Federal Reserve independence, the likely result will be a gradual shift of power back toward Congress and agencies. What is left of the old “permanent government” will slowly revive. But this is not going to lead to bold policy initiatives. Much of the ground that MAGA and Trump have cleared will remain cleared. The US political system was weak enough that it had few defenses against White House initiatives, and if Trump is now weaker himself the broader system has not rebuilt its defenses, much less shown a coherent will for taking new initiatives.

Electorally, at least for the Senate, the battleground races are in mostly Republican states. The results will be determined by independents and Republicans who are no longer willing to stand by Trump. This is the same slice of the population in these states that put Trump and MAGA in office in 2024. So the policy effects will probably not be that great in 2027, because in policy terms that swing vote is as center-right as it was two years ago.

The US after MAGA will then not look as different as might be assumed, at least not at first, even if the president and his family become the targets of investigations. The part of the population that put them in power will be the one that takes them out, and their own policy preferences are not likely to change.

The Lonely Civilizations

It has been roughly a decade since civilizational ethno-politics emerged as a significant geopolitical and geoeconomic issue. It has gradually built momentum, and in 2025-26 new landmarks appeared: Russia’s new Migration Policy Concept, China’s Law on Promoting Ethnic Unity and Progress, and the Trump administration’s worries about European “civilizational erasure” and “reverse colonization” by non-European immigrants. While some of these moves — not in China, of course, which like most East Asian nations tightly restricts immigration — can be attributed to increases in immigrant populations, that phenomenon is disappearing. Even the absorbent US seems to have hit the point of negative net immigration (more people leaving than arriving). Civilizational identity politics could become a lonely pursuit.

What makes this geoeconomically significant is that China, Russia, the US and Europe all have stagnating working-age populations, sometimes with high unemployment (China, Russia, EU), sometimes with low (US). Population stagnation makes economic growth more difficult, particularly with an ever-higher median age. SIG’s view is that stability and prosperity will depend on a new wave of globalization that can improve productivity in demographically stagnant countries, particularly those capable of large-scale war. This will be very challenging indeed given the current tendency of large powers to turn in on themselves.

One could argue that tendency began in India and Europe. In 2019, India passed the Citizenship (Amendment) Act, which fast-tracked citizenship applications by nearly everyone except Muslims. The government of Narendra Modi has stressed Hinduism as the core of India’s “civilizational state.” Also in 2019, EU president Ursula von der Leyen created a post of vice-president for “Protecting our European Way of Life.” This person was responsible for migration, integration and security. The controversial title was later changed. But the trend did not really weaken. Anti-immigrant parties have continued to prosper in Germany, France, Spain, Italy, Austria and elsewhere. Modi-style Hindu majoritarianism is not waning; it may even be spreading into Nepal. 

Russia began, in a foreign policy concept paper, to describe itself as “a unique country-civilization” in 2022, in tandem with its invasion of Ukraine, which was premised in part on the idea that Russia was intervening on behalf of ethnic Russians in Ukrainian territory. Russia’s “Migration Policy Concept 2026-2030” aimed at reducing immigration by Central Asians and attracting foreigners who shared “traditional moral and spiritual values.” China’s move toward assimilation of minorities to the dominant culture of the Han majority began soon after Xi Jinping became Communist Party general secretary (2012) and culminated in 2026 with the Law on Promoting Ethnic Unity and Progress.

American policy moves toward similar goals have been scattered and contradictory, but immigration policy in the current Trump administration has aimed fairly openly at returning to the Europeans-only template that ended with the Immigration and Nationality Act of 1965. The administration has granted refugee status almost exclusively to white South Africans and plans to do the same in 2027. The administration’s rhetoric about threats to European civilization is informed by an assumption that the US itself is among Europe’s “civilizational allies” and part of what Vice President Vance has called “our civilization.”

This isn’t just about immigrants. While the EU, US and Russia have brought about a shrinkage in their immigrant populations through policy changes and enforcement, neither China nor India has had significant immigration (although India has hosted large refugee populations). China and India are using domestic policy to create an ever-stronger core by assimilating domestic populations outside that core. The EU, US and Russia have been more exclusionary or expulsive, as through deportations. In all cases the result is internal, domestic pressure toward a type of conformity. The other result is a failure to achieve population growth.

It’s the demographic stagnation that points toward the coming challenge for these emerging ethno-civilizations: how to grow. AI is the dream-machine solution, promising productivity growth through substitution of machine thinking for human thinking and electric power for human power. The other solution is expansion abroad. But the enthusiasm for expansion beyond borders is not great. Enthusiasm for investment abroad is a different matter. The political pressure for keeping domestic capital domestic contrasts with the market pressure to invest outside markets that are running short on both supply and demand. The self-sufficient ethno-civilization model is clearly very attractive to many people and governments, but market forces, especially if the AI genie fails to deliver, will nonetheless push capital outward.

The Final Frontier

The great-power race to protect sovereignty in space has reached a new stage in 2026. Low-Earth orbit (LEO) connectivity systems like Starlink are converging with higher-orbit navigation systems like the US’s Global Positioning System (GPS). Starlink is still by far the dominant global player in this game, but as in earlier Internet chapters the market built by an American company is fragmenting into multiple pieces, each highly inflected by local politics. That creates investment opportunities, though of a highly geopoliticized kind.

Looked at one way, the extraterrestrial world should be able to ignore sovereign boundaries. Satellites orbit regardless. The International Telecommunications Union (ITU) is the forum for negotiations over satellite use of radio frequencies and over permission to use orbital slots. One country cannot prevent another from launching satellites (except through physical force).

However, nation-states do control a number of levers. The use of frequencies within a country is controlled by that country’s government. Countries normally license the terminal dishes used by consumers to grab a signal. Current systems need terrestrial gateway stations that act as a two-way bridge connecting satellites in space with Internet fiber on the ground. Gateways in turn need land, permits, electricity, fiber access, and so on. A satellite-signal company like Starlink also has to comply with local laws about content, taxation, data interception, and so on. (Starlink discovered this when it crossed the government of Brazil in 2024.) Satellite communications systems do indeed fly around above Earth’s puny borders, crossing national borders at will. But states on the ground still have plenty of leverage.

In the current geopolitical configuration, major players are all acting about as you would expect. Russia has a GPS alternative, GLONASS, that has been beset with maintenance problems, much like Russia itself. Its broadband constellation, Rassvet, has managed to put something like 40 satellites in orbit this year, although its target was 156. Like Russia itself, Rassvet tends to privilege national security over consumer services.

Canada, interestingly, aims to launch a system, Telesat Lightspeed, aimed mainly at government and enterprise users, including on military Ka-band spectrum, but the first launches won’t be until the end of this year or early 2027. Similarly, India’s JioSpaceFiber runs on others’ satellites — those of SES, which is minority-controlled by Luxembourg state entities with a majority of shares held by institutional investors — but hopes to have some test satellites of its own aloft in about a year. India’s NavIC positioning system, however, has been a disappointment. Taiwan has B5G, with plans for an entirely Taiwan-built payload by 2030. All of these smaller players (and much larger SES) are worth watching.

Europe has long sought an independent space capacity, basically as a sovereign hedge against the US — a motivation that has burgeoned under the Trump administration. Eutelsat OneWeb is a mature service, but it is there for governments and enterprises, not consumers, and is mainly keeping itself sound until the transition to IRIS. But IRIS will continue as a security-first sovereignty play, one the EU is likely to need if current difficulties with Russia and the US continue. IRIS’s first launches are currently scheduled for 2029. It is not meant to be consumer facing. Europe’s Galileo positioning system can at least be considered a success, with superb accuracy and a global customer base.

That leaves, as so often, the US and China. The US, typically, has a combination of slow-to-innnovate public infrastructure (GPS) on one hand — to take care of geospatial positioning — and extremely vital but also uncontrollable and erratic private efforts (Starlink above all, but also Amazon’s Leo, with 396 satellites currently in low-Earth orbit). Starlink has a staggering 11,130 satellites in orbit and 12 million customers.

China’s GPS alternative, BeiDou, has been consistently strong and compares favorably with GPS. Its LEO effort, however, got off to a slow start, mainly because its initial focus was security-driven and buried within China’s massive complex of state-controlled military enterprises. The result is called Guowang, and it has had a number of slow years. So much so that in 2023 state regulators ended Guowang’s monopoly, clearing the way for a consumer-facing rival: Qianfan. This was more in keeping with the Chinese government’s approach to innovation: creating a space for domestic rivals to battle with each other. It is still an intra-governmental game. Qianfan is essentially controlled by the government of Shanghai with participation by China Telecom, a central state company. But at least there is competition underway.

If Guowang continues to right itself, China will have a dual-track offering, with a strong sovereign-military component (Guowang) and a consumer-facing Starlink alternative (Qianfan), as well as a superb higher-orbit positioning/navigation system (BeiDou) which Qianfan is integrating with its own satellites. No other state will get close to this dynamic, integrated capacity. BeiDou already exports to more than 140 countries. Qianfan is aggressively pursuing service deals, famously so in Brazil, where Starlink first had to kneel to national sovereignty, but with signed arrangements with Kazakhstan, Turkey and Malaysia and soon others.

The United States will get wherever it gets by grace of its private tech sector. The likely near-term map shows Europe slowly but surely developing a positioning-connectivity-infrastructure system that will keep it from dependence on the US and China but might not prosper commercially in world markets. China will have a government-dominated offering that will essentially provide services comparable to the American alternatives but with an unknown number of dependencies that would leave its effects on national sovereignty (outside China itself) highly debatable. The US will have private, for-profit companies dominating development and leading innovation but disconnected from the public interest. (Starlink actually shut down its positioning feature this year.) And other ambitious countries like Taiwan and India will carve out mini-sovereignties and, at least with India, look for markets abroad as well. Businesses that depend on these emerging infrastructures — and they will number in the millions — will need to balance revenue with the not-so-obvious costs of infrastructural dependencies. Investors will need to understand those choices to allocate capital well.

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.

Europe’s Chaotic Future

The decision by France’s poll-leading politician Marine Le Pen to contest the presidency has revived, once again, the prospect of a national-populist challenge to the dominant European orthodoxy of pro-EU centrism. This challenge has been regularly underestimated for about 20 years, with each populist setback looked to as evidence of a turning of the political tide back toward centrist normalcy. The defeat of the self-described “illiberal” Hungarian leader Viktor Orban in April was the most recent instance. SIG’s view is that European right-wing populism is as close to a durable trend as one can find in politics. Investors cannot afford to ignore it.

In the past decade and a half, Marine Le Pen has run for president three times. Her electoral performance has increased each time: 17.9% of the first-round results in 2012; 21.3% in 2017 (with 33.9% of the runoff vote in the second round); 23.2% in 2022 (and 41.5% in the runoff). With her declaration last Tuesday, Le Pen is on course for her fourth run and leads French polling for the next election, scheduled for April 2027.

Meanwhile Nigel Farage, leader of the Reform UK party, has called for a by-election in his constituency of Clacton. Farage was leader of the United Kingdom Independence Party (UKIP) from 2006 to 2016, with a short break in 2010 to run, unsuccessfully, for Parliament. UKIP agitated for Britain’s exit from the European Union, which occurred in June 2016 with the support of 52% of the British electorate. Farage co-founded the Brexit Party in 2018, and the next year it became the dominant party in the European Parliament. (The process of Britain exiting the EU lasted from the 2016 election will into 2020.) The Brexit Party was renamed Reform UK in 2021. Current polling shows Reform as the dominant British political party at 25%, trailed by Farage’s former political home, the Conservative Party (21%), and Labour (20%). Britain’s mainstream parties have so far refused to engage in the Clacton by-election, calling it a “circus” (Labour) and a “gimmick” (Conservatives). There was some hope that the UK’s chancellor, who must certify a by-election, would block Farage’s plans. That hope disappeared on Thursday, July 9. The by-election will probably take place in mid-August.

Germany’s far-right party Alternative für Deutschland leads the most recent polls at 27%, followed by the conservative CDU/CSU coalition at 22%. Italian Prime Minister Giorgia Meloni’s center-right party Brothers of Italy continues, at 27.8%, to dominate polls, but is facing a surge to its own right from the months-old Futuro Nazionale, which appears to be displacing the League (5.9% vs. 5.8%) as the far-right alternative. Spanish politics also seem to be moving rightward. In Austria the ruling far-right FPO party remains dominant at 37%, followed by the center-right OVP at 20%.

Centrist or center-right coalition governments trying to avoid being pulled further right have become a European norm. The countries in question are all democracies; the rightward trend is a result of voter preferences. When the center-left London Guardian framed Le Pen’s “resurrection” as “a wake-up call” it did raise the question how many wake-up calls there can be before some new descriptive term is required.

What does this mean for investors? The main question is productivity growth, or the lack of it. GDP per capita growth rates in Europe have been declining since the 1960s. Rates in the rest of the world surpassed Europe’s in the mid-1990s. European fertility has been declining over the same period, from a high of 6.8 million births in 1964 to 3.55 million in 2024. Population growth is persistently well below replacement level. This is important because weak domestic demand is one of the few explanations for low European productivity that economists agree on — and opposition to non-European immigration is the main point of agreement among Europe’s center-right and right-wing political forces. Strikingly, 24% of European births in 2024 were to foreign-born mothers, although “foreign-born” includes intra-Europe migrants as well as those from outside the EU.

Reconciling  declining population with economic growth and productivity growth is not easy. The European economy’s response has been to look for demand outside Europe while increasing openness to trade. Euro-area trade openness has doubled since 1999 while it has stayed stable in the rest of the world. Europe has had a trade surplus over the same period as producers find demand overseas — particularly in the US. The EU’s goods trade surplus with the US has doubled over the past decade. In short, more globalization has eased the problems posed by weak domestic demand and low productivity.

The proposed centrist solutions to productivity decline in Europe have been to deregulate labor protections, encourage intra-European population movements, improve technology, support European entrepreneurship, and consolidate markets across European national borders. Much of this comes under the slogan of “more Europe” and from many points of view is inarguable. However, these solutions can also be understood as more globalization within Europe itself —an internal liberalization.  In either case, such policies run against the national focus of most center-right and far-right political groupings.

If the growth of such groupings is stable, as it has appeared to be for some time, then Europe will be at war with its own economies. The most likely policy solutions are at odds with the most likely political configurations as validated by the preferences of European voters. Investors would prefer the centrist reforms associated with Mario Draghi and Enrico Letta, but the political trends point in the opposite direction, with enduring negative implications for European growth.

Is AI a Uniter or a Divider?

Two articles in very different places carried a similar message: foreign-language learning is being defunded in education. Stefan Collini, writing in the London Review of Books, and Carol Yang in the South China Morning Post, reported that languages, and the cultural knowledge that they embody and give access to, are no longer priorities in the United Kingdom and China, respectively. In both places, the shift has been to more technical education, which is seen as more likely to lead to students gaining employment. Don’t learn about languages (or art or the humanities); learn about whatever will be materially useful in the AI era.

It was not that long ago that to have some skill in one or two non-native languages was a basic requirement in being considered “educated.” Post-World War II globalization was built by people educated under these expectations. But then English became a lingua franca of ever increasing importance as globalization ramped up in the 1980s and after the Cold War. It became possible to function internationally with only English. Digital technology further solidified the grip of English.

But if English was being successfully spread across the planet it was not thanks to Shakespeare. English, or “globish,” was itself being lifted from its cultural contexts, whether they had been in England, India or California. English became a thin language in service of a thin (in cultural terms) globalization. People of most social classes traveled so much more freely in 2016 than in 1965, but this was equally the period when US college foreign-language-study enrollments dropped by 59%. Then they dropped another 17% from 2016 to 2021.

This would appear to underscore a SIGnal theme: the fragmentation of markets into national units. The decline of foreign-language study would seem inevitably to lead to ever more mutual incomprehension. Certainly this is true in terms of literature. In one way, it is true in terms of AI as well. Most national communities have one dominant language. Large Language Models operate in languages as well, which means an LLM can police expression in the language it is using. When the Chinese Communist Party developed regulations for ensuring that LLMs would uphold “socialist values” it was possible to enforce them because the language being used was Chinese. The models were training on Chinese-language data. The pre-LLM strategy was censorship: the words “Tiananmen” and “massacre” were never to appear together. With an LLM, the goal is rather to shape the understanding of a word like “freedom” or “economic development.” As LLMs proliferate in different languages, AI becomes more localized even as it spreads internationally. In such a situation, we can anticipate a world in which globalization will continue to expand in strictly technological terms while that same technology makes the world fundamentally more provincial.

True, AI is also superb at translation. Many of us now work every day in languages that we do not actually know. Google Translate has been surpassed by DeepL AI. Claude Code is happy to labor away in multiple languages at astounding speed. Markets are being created: Spotify listeners are now consuming more than half of their music in non-English languages, and artists on Spotify are finding that more than half of their listeners are outside the artist’s home country. Cultural diffusion is hardly dead.

Nonetheless, so far the trend of AI LLM development is toward globalization via localization, and that includes localization in terms of the language being used — not least because a “national AI” gives governments a greater prospect of political and speech control as well as fiscal power. The fit between the nation-state and a national language is likely to grow tighter with AI. The range of disincentives to learning another language (as opposed to having your phone translate one) will likewise grow. Digital translation technology, accelerated by AI, will, however, also make it possible to access more markets, both as producers and consumers. Investors will need to understand these markets in order to penetrate them, but the extraordinary decline in language education combined with easy translation will tend to make that understanding shallow. Any deeper understanding will still require old-school linguistic and cultural immersion, skills that are not valued as they once were but will be at a premium when they are truly needed.

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?

Huawei and the Next Generation

Huawei founder Ren Zhengfei has been at the center of US-China technology competition since the US House committee on intelligence singled the company out as a national-security threat in 2012. Still at the head of his company, he will turn 82 in October. How the US-China tech competition might change when the Ren era ends is an important question, for which his and his company’s past can serve as a guide.

Ren’s company came to prominence building mobile-phone (and thereby mobile-Internet) infrastructure in the protected Chinese market, competing mainly with state-owned ZTE. As the mobile, radio-based Internet replaced the hard-wired desktop Internet, the resulting networks of machines and data became something like remote-access battlegrounds. China and the US alike wanted to protect their domestic networks from manipulation, which resulted in the US blocking Huawei and ZTE from US markets and compelling allies to do the same. Huawei circa 2015 was the test case for how much US political pressure could harm a Chinese tech multinational.

Huawei was indeed excluded from most of the world’s wealthier markets. The extraordinary thing is that under Ren’s leadership it did not matter all that much. There are many reasons for this, but arguably the main ones were Huawei’s emphasis on fundamental R&D and its management of the relationship with the Chinese Communist Party.

Ren had a People’s Liberation Army background.  (He joined the PLA’s engineering corps in 1974.) He also had a vision for the CCP’s relationship with technology. The ownership structure of Huawei is famously odd: active employees own most of the private company’s equity, but they must sell it back to the employee union when they leave. Ren himself owns a small share of the company, but he does actually own it and in effect continues to control all major decisions. So Ren’s view on how to handle the company’s relationship with the Chinese state is crucial.

Ren has always phrased the company’s mission as one of bringing China up to the technological level of the US. Along with his PLA roots, his explicitness on this topic is one reason why his company became such a focus for the US. It also meant the CCP could be confident Huawei was on its side. When in 2020 the CCP engaged the Chinese tech sector in a prolonged struggle session — the Party feared the tech giants were getting too powerful and cut them down to size — Huawei did not suffer.

But Huawei’s success was not due only to conformity and managing up in an authoritarian society. Huawei was adept at identifying the CCP’s tech pain points and addressing them, and the CCP knew that it could count on Huawei. The company pivoted into semiconductor design when US policy made chips hard  to buy. It pivoted into the auto business in 2021 as US sanctions against Chinese cars bit. In the same year it began work on its own AI LLM. It pivoted into AI data centers. In short, when the CCP saw a competitive problem caused by the US (usually), it could count on Huawei to help solve it.

The auto-industry intervention is especially interesting. Alongside private auto startups like BYD (2003, although it had been a battery company since 1995), there were numerous state-owned legacy auto companies. Initially Huawei ventured into the auto business itself with a partner, but it changed direction in 2023, launching the Harmony Intelligent Mobility Alliance (HIMA). In this alliance, state-owned auto companies gathered under a Huawei tech umbrella. Huawei became the software designer and provider for a large part of the Chinese auto market, in effect preserving a share of that market for continued state control and investment. Chinese state-owned enterprises are not often market-leading innovative companies. Huawei and HIMA provided a way around that for the domestic auto industry, so that CCP-controlled auto companies could survive and continue to compete with private Chinese auto companies like BYD. Arguably none of the top Chinese technology firms fulfill this type of role in the state-market relationship like Huawei does.

Huawei’s special position with regard to the Party was one advantage, at least in Ren’s hands. The other was its emphasis on fundamental as well as applied R&D spending. This is not unique to Huawei. The battery giant CATL under founder Robin Zeng (born 1968) has likewise stressed basic research, with spectacular results. But Ren staked out a commitment to fundamental research early on and has kept it even into the current era, when China’s AI industry as a whole, for example, has gotten into a hurry to go to market. Ren has long seen fundamental research as indispensable for the mission of Chinese national greatness and catchup with the US.

When Ren’s time as Huawei chief ends, will the company’s two special characteristics — CCP relationship management and fundamental research — survive the transition? The first might not. Ren has been a master of this game from the beginning. He turned his status as a top target of the American superpower into an advantage, not just for him but for his company. And his ownership structure made that possible to do. The ownership structure is not likely to survive him, nor is there likely to be a younger Ren capable of replicating his role.

The emphasis on fundamental research might not survive either. If Huawei after Ren becomes more like a state-owned enterprise, it will struggle with innovation. If it becomes more like a normal private company, it will struggle to accommodate CCP directives while also serving its shareholders.

This is important for investors, not just because Huawei is a huge (as yet uninvestable) company. It is important because it shows how dynamic and unpredictable the commanding heights of the Chinese economy  are going to become in the next decade. Something as large and seemingly permanent as Huawei, which has in a way anchored the CCP-tech relationship for 15 years, will inevitably be going through major changes, and so will the tech relationship between China and the US.

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.

Are We Sleepwalking Into an Energy Disaster?

By Dee Smith

The Iran war, like many things in the world these days, is full of contradictions and cognitive dissonance. For example, multiple expert voices have, since early in the war, been predicting a dire energy supply crisis from the closing of the Strait of Hormuz, the narrow strip of ocean between Iran and Oman though which about 20 percent of the world’s supply of oil passes in “normal” times.

Such concerns have not abated. The chief economist of Rystad Energy told Fortune magazine on 6 May: “We’re still kind of sleepwalking into this approaching disaster. There is little doubt there is going to be a disaster.” Numerous other informed observers have made similar points.

But where is the disaster? Why have we not yet really started to feel it?

Some places have. South and Southeast Asia, for example, are already buckling under the price increases and shortages. And many companies—first and foremost airlines—are rapidly feeling such pressure that they are curtailing operations. Lufthansa has cancelled over 20,000 flights.  Spirit Airlines went out of business entirely, with a sudden loss of 17,000 jobs.

Nevertheless, the world as a whole and the West and China in particular are not yet visibly reeling. While prices are substantially up, oil markets have not shot to and stayed at the heights of over $140 per barrel that were predicted if the war continued this long. The prices of West Texas Intermediate and Brent crude hover at this writing between US$105 and $110 per barrel on the spot market (for immediate purchase of oil) and around US$80 to $85 on the futures market. The latter is a more reliable indicator of what traders are willing to bet money on. Notably, the divergence between the spot and future markets has been narrowing recently, reflecting what some are calling a “mini-glut” at present.

The reasons for this have been perplexing a number of observers. A few factors are invoked to account for it:

·      The reduction in imports by China (over 4 million barrels a day lower than a year ago), which is probably both price-driven demand destruction among consumers in China, and Chinese government policy since the start of the war allowing drawdowns of stocks and prohibiting exports.

·      The surprising increase in U.S. exports of petroleum and its products, which is nearly 4 million barrels per day above previous-year levels (much of this reflecting the drawdown of the U.S. Strategic Petroleum Reserve).

·      Rationing in the Global South, which has created demand destruction. The Philippines, for example, went to a 4-day work week shortly after the war started.

·      Oil stocks had been at or near a record high at the start of the war, with a similarly high level of oil in transit on the seas at that time.

But this reprieve is short-term, and it may end quite soon and quite abruptly. The U.S. administration, for example, may suddenly come to terms with how much of America’s stocks are being drawn down, what this is doing to gasoline and food prices, and do an about-face. An oil export ban is already being quietly discussed. China, similarly concerned about stock drawdowns, may start importing more oil. The war itself is at risk of turning into a “frozen” conflict, where each side essentially holds the other hostage. But even if hostilities ended today, it would take months to regularize the situation for reasons ranging from de-mining the Strait to physical destruction of various energy facilities in the Gulf, and simply the re-start-up time faced by closed facilities.

If—or perhaps when— a longer-term reconfiguration of energy markets happens, the consequences may indeed be dire. Prices could start to seriously rise again. Some informed estimates predict oil above $200 a barrel, perhaps significantly above.

But serious shortages loom even more threateningly than price increases.

The most alarming aspect of this for both social stability and for everyday life everywhere is the food-petroleum nexus. Food production is overwhelmingly dependent on fossil fuels. Diesel fuel is essential for transporting food from farm to processor to market, whether by truck or rail. But diesel is also essential to farming machinery. And shortages of urea and other fertilizer ingredients from the Persian Gulf will also affect farming.

Global supply chains are now so intrinsically intertwined that this could well evolve into an “everything crisis,” as CNN has put it. From plastic containers for food and water, to bags, solvents, industrial lubricants, medical equipment, cosmetics, footwear, microchips, and even condoms, so much is utterly dependent on petroleum byproducts or other resources of which a significant percentage comes from the Persian Gulf. It is a single point of failure.

When could this materialize? It is hard to say, due to the vagaries outlined above, but the best estimates are by mid-summer. Some sources are quietly saying we could start to see rising alarm again in the next 2 weeks.

Some areas, like Europe and California (which imports about 60 percent of its crude, 20 percent from the Persian Gulf), will be affected before others, but if the status quo continues, all will be affected, everywhere.

It is worth noting that this war—intended by some accounts to keep Iran from acquiring nuclear weapons—has provided Iran with another weapon even more actionable: the ability to close the Strait of Hormuz and essentially hold the whole world hostage. This is not lost on the Iranian regime.

We are suffering again from our recency bias—the conviction that the near future will be like the recent past—and the closely related problem of induction, which makes people discount the possibility of fast, radical change.

These potential events have huge social-stability, business, and geopolitical implications. It is worth restating the obvious point that when people have nothing to eat, they have nothing to lose.

Network Powers - 2 of 2

The turn to digital sovereignty, and now somewhat more plausibly to AI sovereignty, is an attempt to impose some framework of purpose on a technological and economic stage of development that threatens otherwise to reduce national and supra-national (as in the West) self-determination to a memory. The corporate reactions from OpenAI, Palantir, DeepSeek, Mistral, and others are attempts to ride this wave, giving political meaning to business activities. But by seeking to acquire public missions that advance sovereignty rather than destroy it, corporations are hitching their fortunes to one state (or a collection of states) that puts them in opposition to another state and the competitors who serve it.

What are the counter-vailing trends? One option that makes more sense than might be obvious is sovereignty-as-a-service. Major US tech companies insisted for many years that what they were doing was beyond the reach or understanding of mere nation-states. That changed for a host of reasons, including strong Chinese competition, the Indian mode of playing foreign tech multinationals off each other, European digital regulation, and a much stronger economic-nationalist cast to US tech policy beginning in the first Trump administration. Tech multinationals eventually learned that sovereignties created a market they could sell into, for example with sovereign data clouds.

A combination of the Indian model of digital public infrastructure (DPI) and data localization, together with some regulatory requirements in the European manner and security-oriented foreign-investment rules in the American one, can create a rough version of a national sovereign “stack.” AI corporations, and others, can then create products that service this stack.

It may seem almost paradoxical that multinationals should offer national sovereignty as a service. But it seems to be the political price that must be paid to have a transnational product. It at least preserves the possibility of the multinational selling across barriers of national values or social missions.

Another counter-vailing trend that operates against LLMs implementing social missions is theft. Anthropic, like OpenAI, is not available in China (or Russia, Iran, North Korea, Afghanistan, Cuba). But when Anthropic, which does not enforce socialist values as Chinese LLMs are required to do, accidentally leaked the source code to its Claude Code product, Chinese developers seized the opportunity anyway. Anthropic and OpenAI also both believe there has been very substantial theft of their IP by Chinese companies.  So whatever social mission Chinese AI companies are meant to pursue does not keep them from using non-Chinese AI product, including LLMs, even if these are developed under a rubric of “democratic AI.” This occurs at the consumer level as well, as seen in the ferocious adoption of Austrian AI product OpenClaw in China earlier this year — to the annoyance of the Communist government.

A third counter-vailing trend to the assignment of national missions to AI companies is the tradition of open source. The leading open-source LLMs are Chinese (DeepSeek, Mimo, Kimi) alongside Google’s Gemma. The Chinese government has long advocated open source as a catch-up (to the US) strategy with a values veneer. But because Chinese AI firms do need to make money, and Chinese venture-capital markets do not have anything close to the size of their US counterparts, the open-source window might be closing, which would tend to work in favor of AI nationalism. We shall see.

A fourth counter-vailing trend has to do with how companies actually use LLMs. For most, AI is a design tool. Designers use LLMs to develop software methods for doing existing processes differently and to design new processes. It’s an iterative approach involving multiple pieces of software from various sources; the selection of components for the resulting software stack is part of the process. And over time those components can change. The .md files that accumulate as a project takes shape are part of the process content. Those files change too as the process is refined. The data being used is often proprietary and sitting on the AI user’s machine or the company’s machine, and the data can change. The final result also is subject to change. This is not using a chatbot. It’s using an LLM as a design partner to make something that is unique — something that the LLM could not have conceived “on its own” — to meet a particular commercial purpose. All of the design files and documentation and even data of the process can be transferred from one LLM to another, or exposed to one rather than another, with some tweaks. So there is a real limit to the vendor lock-in of an LLM, at least in the context of corporate product design. That means that “AI-generated” products can travel across borders as software. This will frustrate nationalist designs because it limits the power of LLMs and therefore the power of states to shape products built with LLMs to their values.

And finally there is cross-border competition for customers. Chinese AI companies may have to advance socialist values at home, and American AI companies might want to advance democratic values in the US or the West, but none are going to therefore abandon customers outside their preferred borders because the customers are not socialists or democrats. Anthropic only stays out of half a dozen countries, which is far from an exhaustive list of authoritarian states. American programmers definitely use Chinese AI products and Chinese programmers use American ones, even when their respective states don’t want them to. No AI company shows any signs of wanting to be limited to the home market, although they are very happy to have their home market protected. AI companies will continue to press beyond the borders of their own social-mission statements as long as they can get away with it because there is money to be made.

For investors, the key things are to identify companies that have the capacity to adjust to nationalist and other values demands without sacrificing commercial vigor, and to identify sectors (like advanced manufacturing rather than media or edutech) where AI can do incredible things with minimal political exposure, including exposure on job loss.

Network Powers - 1 of 2

Artificial intelligence has rapidly come to be seen as a threat to national sovereignty. Accordingly, it is bringing forth state responses. Because digital technology, even in China, is for the most part developed by private companies seeking profit, state responses have to accommodate and even enhance market forces. At the same time, AI companies are expected to grapple with the non-market goals of states — and, in democracies, of the people those states represent. Unlike their predecessors in search and social media, AI companies from the beginning have had to ponder their own legitimacy, social and political, which is very different from explaining their prospects of profitability to investors. Underneath it all there is still a genuine business justification for identifying a social mission: if the AI companies cannot gin up a plausible social purpose of some kind, their businesses could be targeted by regulation that would hurt or eliminate profits. So, for better or worse, large corporations are getting into the legitimacy game.

This is not altogether new. In the late 1990s, browser companies were required by government to find ways to defend their systems from abuse by the “Four Horsemen of the Infocalypse”: drug dealers, money launderers, terrorists, and child pornographers. (The idea that Internet companies were “content-neutral” was always a fiction.) Email providers had to control spammers and financial fraudsters. The digital-tech industry has always shouldered some social burdens as costs of business lower than the costs of being more actively regulated.

However, AI is qualitatively and quantitatively different. The training data for AI large language models (LLMs) is so vast, and the computational capacity at once so extraordinary and accessible, that AI can create worlds which we are then invited to inhabit. Each world is different, both actually and, more important, potentially. DeepSeek world is not the same as Claude world or ChatGPT world or Mistral world or Grok world. Chinese LLMs are required to carry out censorship along lines set by the government. It is easy to imagine an LLM offering a world in which most of reality is reflected accurately except that every battle your nation fought was a victory and every leader a hero.

The LLM and other AI companies know this and are trying to get ahead of the social-regulatory curve. So Alex Karp, CEO of Palantir, talks about his company as having at its core a mission to defend the values of the United States, as he understands them, and of the West.  Elon Musk, of Grok and much else, talks increasingly of a mission to save the white race from decline. OpenAI sees its role as advancing “democratic AI” as against China’s “authoritarian AI.”  Chinese AI companies meanwhile must adhere to “socialist values” and advance the cause of the Chinese Communist Party — which, interestingly, is acquiring a more ethnic cast. European anxiety about digital platforms that don’t respect European values is being made much worse by AI. The situation has become so pronounced that the invaluable Center for a New American Security (CNAS) just launched a “Sovereign AI Index: Tracking the Global Push for AI Self-Reliance.” The “self” being referred to here is, with partial exception for the EU, a nation-state self.

In short, AI is being positioned as a booster for nationalism, perhaps even ethno-nationalism. This must be about the opposite of what AI’s early visionaries imagined they were working toward. But it is a clear trend. Every state is jealous when calculating its powers, and states are working hard to make AI submit to sovereignty.  

Are there counter-trends? There are, and we will look at those in the second and final post.

America’s Unwritten Constitution — 2 of 2

The first post in this pair established the backdrop for the current contest between the president and Congress over control of the federal state. This post assesses how that struggle is progressing.

The ongoing contest between the executive and legislative branches — a contest being mediated by the judicial branch — has mostly concerned encroachments on Congress’s “power of the purse.” (The other core question has been the power to declare war.)  One theater of conflict has been federal revenues. The written US Constitution gave Congress the power to impose tariffs on imported goods, as did the unwritten constitution. One result was an incoherent series of vast tariff bills in the 19th and early 20th centuries, taking up a staggering amount of Congressional negotiating time. A major reason for the institution of an income tax (1913) was to escape this chronic legislative morass. Over a century later, President Trump revived tariffs as a revenue source, partly as a way to make possible the reduction of income tax. This of course put the executive in conflict with Congress. A sharply partisan Congress was unable to defend its own powers, but the Supreme Court partly did so in February of this year (see “Peak Trump? – 4 of 4,” 21 Feb. 2026).

A second theater of conflict between Congress and the executive over the power of the purse has been “impoundment” via “rescission.” This obscure strategy, based on a 1974 law, also aims at the “administrative state” discussed in the last post. Most administrative-state activities are financed by “discretionary spending” — federal spending that goes through Congress’s appropriations process. Discretionary spending is nearly 30% of federal spending. Half of it goes to defense. The other half goes to food safety, science research, homeland security, education, and so on, most of which the White House’s proposed 2027 budget would cut by a further 10%. “Mandatory” spending, like Social Security and Medicare, is separate and accounts for 60%. The balance goes to pay interest.

Since the Thomas Jefferson administration, “impoundment” — withholding some amount of appropriated, discretionary spending — has occasionally been used by a president to manage expenditures, for example when money had been appropriated for a purpose that later ceased to exist. This was an example of a feature of an unwritten constitution. It worked for almost two centuries until President Nixon tried to use impoundment much more expansively to advance his agenda; Congress retook its authority via the Impoundment Control Act (ICA) of 1974. This included a process by which a president could send a special message to Congress requesting “rescission” of appropriated monies.

President Trump, in his first administration, asked Congress for 34 rescissions totaling $14.8 billion. Congress did not allow any of them. He asked again, in the last days of that term, for 73 rescissions totaling $27.4 billion. Again the request was refused. In his second term, the president asked for $9.4 billion in rescissions. These were the initial cuts to USAID, public broadcasting, support for “color revolutions around the world” and the Green New Deal, and other administrative-state, discretionary expenditures. Congress passed $9 billion of the cuts and the Rescissions Act of 2025 became law on a narrow party-line vote. The Trump administration went on to make an additional $5 billion in cuts to foreign aid and international organizations in August through a “pocket rescission,” a maneuver that allowed it to eliminate Congressionally appropriated funds without any approval from Congress at all.

By such means the current administration has taken some budgetary authority away from Congress, effectively altering the constitutional balance of domestic governing power. It is all pretty murky and has occurred without much public debate. The same is true of the justification for it, the “unitary executive theory.” This theory has nothing to do with foreign aid, public broadcasting, color revolutions or Green New Deals. It only has to do with power. The narrow basis for it is the assertion that the executive branch has complete power to fire anybody the executive might care to fire — for example, anyone in the administrative state. The broader basis is the notion that it is up to the president to decide whether or not he has adequately carried out the will of Congress. It is agreed that Congress has the power to approve hires; but does it have the power to approve firings? The answer has been controversial since the beginning of the republic and has taken different forms for 250 years. They are all part of America’s unwritten constitution.

It is easy to underestimate this administration’s determination to assert the power of the unitary executive to dismantle what Congress has mandated by trimming Congress’s ability to control both the raising of revenue and the spending of it. An unwritten constitution seems at times to be overtaking the written one. The Supreme Court appears to be appreciating the magnitude of this, which is why the president made the extraordinary assertion that the courts really should not be independent at all. 

The risk to investors is that the underlying rule of law, which has stabilized markets since the 18th century, will be weakened. While, as every prospectus says, past performance is no guide to future earnings, it should be remembered that one enduring result of England’s Glorious Revolution of 1688 was precisely — by making Parliament permanent and in a position to restrain the spending of the monarch as executive — to stabilize property relations so England could invest in long-term projects like infrastructure, be restrained from expensive wars, and enable the birth of modern industrialization. That example was very much in American minds when the US system was being designed 250 years ago. Limiting the power of the executive was part of the foundation of modern government and modern prosperity.