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.

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.

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.

ChatWars

The showdown between the US Department of War and AI giant Anthropic over the past week has focused mainly on the private-public struggle over power: Can the federal government, as the Trump administration maintains, force a private company to work with the government on the government’s terms? Or do private companies have a final say over government use of their products? SIG’s view is that underlying this moral-political debate is a deeper question having to do with labor costs. As SIGnal has argued in different contexts over the past few years, if AI is “about” anything it is about the cost of labor. Government has long struggled with its inability to compete with Silicon Valley on wages. The AI tools built by Anthropic, OpenAI, GrokAI and Google, among others, offer a way to ease that problem, just as they offer ways for private companies to improve labor productivity. The issue is not private-sector patriotism. The issue is labor.

The struggle over who shapes the direction of digital innovation is as old as digital technology itself, and indeed dates back to pre-digital innovation during World War I around the challenges of “fire control” — improving the accuracy of munitions delivery. Many of the key figures in developing digital technology for use in war, such as Norbert Wiener and Vannevar Bush, cut their teeth on the challenges of fire control in the First World War. The commercial-academic-military cooperation that birthed the digital age in World War II has its own distinctive and peculiar history. (The venture-capital model was also rooted in wartime procurement practices, but that is another story.) The US’s extraordinary wartime spending after 1942 went on to combine with Cold War fears of Soviet technological competition. Government money financed digital development, and thereby shaped its purposes, into the 1970s.  When the government’s Internet project became commercializable beyond any expectation, the government yielded to private-sector leadership in shaping the digital landscape — and government, including the military and intelligence sectors, fell behind.

The turning point can usefully be dated to DoD’s Project Maven, undertaken in cooperation with Google. The spur for Project Maven was a labor shortage of sorts: there was too much targeting data — fire control again — for military employees to keep up with. Some Google engineers rebelled in 2018 when they discovered their engineering skills were essentially being used to crunch data for the better delivery of firepower. Google eventually canceled the contract.

The story, of course, did not end there. With China playing the old role of the Soviet Union, all branches of the US military began investing heavily in digital automation of defense processes. At the same time, many in Silicon Valley (plus Seattle) came to terms with what it actually meant to work as contractors for the military and other government agencies. Government still struggled to compete for tech talent in its own hiring, but contracting provided ways forward.

Enter AI. Even as Maven was hitting a wall, Google announced major AI innovations: the Transformer architecture (2017) and BERT (Bidirectional Encoder Representations from Transformers, 2018). OpenAI launched GPT1 (Generative Pre-trained Transformer 1) in 2018. The rest is recent history. OpenAI launched ChatGPT in November 2022 with an interface that enabled normal people to use the new tool. It could achieve computation at levels that far exceeded what Maven could do four years before. But the basic Maven challenge — there weren’t enough people to handle all the data, so they needed a machine to help them — was the same.

The massive flow of capital into AI-related investments was never based on military contracts or improved fire control. Nonetheless, at one level the military’s interest in improving targeting without hiring more targeters was identical to private companies’ interest in increasing productivity without hiring more workers. It was and is a short step from there to increasing productivity with fewer and fewer workers. This week, a Pentagon official announced that an agreement had been reached with Google to use its AI tools to automate jobs across the Pentagon’s workforce of three million. Google’s own blog noted that its AI agents would be able to help Pentagon employees automate tasks without needing to know any code. AI agents are particularly good at writing code, something that used to require expensive software engineers. Emil Michael, under-secretary of defense for research and engineering, said that a type of war-simulation exercise that took his staff six months was done through the AI portal in six weeks. Outside the military sphere, the same logic drove Jack Dorsey, CEO of Block (owner of payments system Square) and founder of Twitter, to lay off 4,000 of the company’s 10,000 workers.

The Pentagon’s attack on Anthropic has to be seen in the context of labor costs and the open competition among Anthropic, OpenAI, xAI (Grok), Google, Palantir and others for Pentagon and other US government contracts aimed at increasing labor productivity, which is to say reducing labor costs. That includes the labor cost of software talent.

But investors will need to remember that the competition among AI giants for US government contracts is not motivated principally by the search for profits. The motivation is more to gain protection from regulation or other political interference. The AI giants would develop their tools in about the same way without any government contracts at all. Their field of competition is on a vastly greater scale. That means that, in the end, however the Anthropic-Pentagon dust-up evolves, the AI sector has the upper hand because national security has become dependent on it.

The Market for Tech Containment

Recent moves by Microsoft and the Chinese government marked a new stage in the years-long process of tech decoupling, a SIGnal preoccupation. Meanwhile, the US and China are moving toward what might be significant high-level talks — and the US bull market continues, fueled by AI valuations that are dependent on American dominance of the AI future. None of these three elements seem at all stable. Even tech decoupling could be upended if US President Donald Trump decides to favor a megadeal that would bring Chinese investment into the US. The markets and the politics are both frothy indeed. SIG’s view has long been that AI technology as such will transform industrial processes and much else. A related but quite separate question is whether the massive investment into data centers, understood as the infrastructure of AI, is really necessary for the AI future. “Infrastructure” has a reassuringly solid sound, but if the much-anticipated burst of the AI bubble occurs then data-center capex is where the deflating is most likely to happen.

Microsoft’s withdrawal from China received less attention than it deserved. Bill Gates and his company have long been more pro-China than most of Big Tech. Microsoft’s China labs were crucial to China’s acquisition of AI expertise and experience. That is much of why China’s leader Xi Jinping mischievously greeted Gates as an “old friend” in Beijing in 2023, seven years into a bipartisan consensus that China was the pacing challenge for American security and the US economy. Microsoft’s withdrawal began late in 2024 and has continued through this year. The shuttering of its Shanghai AI lab early in 2025 was done very quietly but it reversed decades of company policy that had done much to create China’s AI industry in the first place. In short, when Microsoft decouples it really means something.

At the same time, China made a strategic shift this month with comprehensive restrictions placed on Chinese companies to prevent use of US silicon chips. This hit Nvidia particularly hard. Its share of the China market plummeted from 95% not long ago to 50%. Nvidia’s CEO, Jensen Huang, has done everything he can to hold on to what he still has. He is said to have the ear of President Trump. But the reprieve Huang secured in July seems to have been eliminated by China’s new moves. When China decouples at this scale, it really means something. 

Tech decoupling is a secular trend. It is the central force behind the current trade tensions, which both China and the US have been escalating, each placing the blame on the other. President Trump’s retaliatory tariffs, set to take effect November 1, responded to China’s weaponizing (not for the first time) of its tight grip on rare-earths production. All these moves revolve around the perceived centrality of AI to victory or defeat in the geoeconomic struggle between China and the US.

The two countries are nonetheless continuing talks. China hawks in Washington and elsewhere are genuinely worried that President Trump’s love of the grand gesture will combine with the influence of Jensen Huang and others to undermine the structure of tech containment built up in recent years. They might well look to Trump adviser Peter Navarro for reassurance. He has been ringing the bell about the China threat for 20 years. And indeed at the Council on Foreign Relations on Friday Navarro spoke of how the president’s tariff negotiations have already resulted in “19 trillion dollars” of promised investment: “foreigners are going to be paying to fix the vulnerabilities in our supply chain,” and once they have done so there will be a global “level playing field.” He also said that the US pre-Trump had “shipped 19 trillion dollars of our wealth” overseas. Nineteen trillion out, 19 trillion back in, and balance is restored. That is the idea. Navarro believed China’s new rare-earths policy is showing the world that China is everyone’s enemy: “The world will not go back to sleep on this.”

But if a three-year bull market, grounded in speculative bets on building data centers to set the infrastructural stage for future AI-driven productivity gains, wobbles enough, trade wars with China could lose their appeal. Tech containment and tech decoupling, though, will continue.

The Nine Lives of Economic Nationalism – Part Four of Four

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

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

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

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

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

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

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

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

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

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

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

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

The New AI Action Plan

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

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

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

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

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

The Jobs Conundrum

The US jobs numbers last week were chaotic, to say the least. The 0.1% drop in unemployment was yet another instance in which economists’ predictions were wrong. It is getting to be a habit, and the Donald Trump administration is reaping the political gains. The last few weeks have seen more and more articles attempting to explain why the predicted catastrophe after the Liberation Day tariffs announcement has not materialized. SIG’s view is that, now that the administration’s giant tax-and-spending bill has passed and members of Congress return to their constituencies for the summer recess, the real political work will concern jobs. So it is worth looking deeper into the new numbers.

Jobs in June increased by 147,000. However, the workforce itself shrank by more than that: The number of people characterized by the Bureau of Labor Statistics as “not in the labor force,” and therefore not counted as “unemployed,” grew by 490,000. The unemployment rate went down not just because jobs were added but also because the size of the workforce decreased. 

In sectoral terms, the biggest job adds (73,000) were in government. The biggest source of those jobs was growth in the public education sector, which is mainly K-12 schools. Of the 47,000 state-government jobs gained, 40,000 were in education. Of the 33,000 jobs added in local government, 23,000 were in education. Federal government employment was down by 7,000 for June and has dropped by 69,000 since the beginning of the Trump administration, in line with the president’s commitment to shrink government.

The increase in state and local education jobs should not be a surprise. The 2008 recession hit those sectors very hard. They recovered at a much slower rate than the private sector. When Covid hit, their subsequent recovery, compared to that of the private sector, was even worse. Massive federal aid got schools through the pandemic but it was always going to dry up and eventually did. States, looking to the longer term, realized they needed to increase spending. Populous states like Texas, California, and New York have recently broken records for education spending. Much of it goes into teacher salaries, which have been increasing in response to a chronic teacher shortage. (Credentialing in many states has also become much more lenient to attract more teachers.) In short, the state and local public education sector was overdue for a boost, got it, and jobs have been created.

The other major sectors driving job gains in June were “health care and social assistance” (58,600) and “leisure and hospitality” (20,000).  “Social assistance,” in the world of the Bureau of Labor Statistics, is not governmental but includes services like child care, vocational rehabilitation for the disabled, community food banks, and emergency services. The remaining major gains were in construction (15,000) and transportation/warehousing (7,500).

Overall, the private sector did not do as well as the public sector. Private payrolls were up by 74,000, the weakest growth since last October. An ADP Research study earlier in the week identified numerous indicators of weakening in the private labor market. Job losses in June were concentrated in mining and logging (down 2,000), wholesale trade (down 6,600), manufacturing (7,000), and professional and business services (7,000).

The problem, of course, is that the Trump administration’s goal has been to reduce government and favor the private sector, while the reality of the labor market so far is going in the opposite direction. Meanwhile, CEOs were spreading the word that AI would eliminate jobs on a grand scale. Ford’s Jim Farley thought that AI would “replace literally half of all white-collar workers in the U.S.” Of course, AI could also eliminate jobs in the public sector, including education. But the impetus for the current, very high levels of investment in AI is to increase productivity by making private-sector workers more efficient, not by hiring more of them. Overall, then, AI could well shift the balance of employment in the US economy further toward government.

It is possible that reducing taxes, as the new bill does, on upper-income groups could increase consumer demand, probably in the leisure category, and even free up capital for productive investment. It is also possible that a tariff program could result in increased investment in American manufacturing. However, neither of those results is going to be quick. In the meantime, Congress members will meet their constituencies as private-sector employment weakens and the federal government’s willingness or ability (given extraordinary debt levels) to solve problems, much less provide jobs, is weakening as well. Whether President Trump’s economics will work out in the end might not matter, because the end will be after the midterms, which in political terms could be too late.

Sputnik, AI, and the Nature of Victory

The US foreign-policy community has been gathering itself around the goal of winning the AI race against China. The problem is that defining “winning” is not at all easy. If winning consists of US companies, in cooperation with the US government, enjoying a monopoly on the best AI technology for some extended period — which does seem to be what is expected — SIG’s view is that winning is nearly impossible. The only way the US could come close is by sharing technology within some type of alliance. But that would entail non-American companies within the alliance having revenues and profits of their own. The US and US companies cannot “win” this alone.

As SIGnal has emphasized before, digital technology has been taking the world’s defense sectors by surprise for some 30 years. Whether it is low-earth-orbit satellite swarms, drones or navigational improvements, technology developed for one use becomes a military must-have for security uses. Proliferation is built into such a process. Military hardware needs software; software lends itself to proliferation, theft, imitation, and improvement. Artificial-intelligence software is no different.

Containment of American AI within US boundaries goes against the nature of the 21st-century technology industry. Most innovation comes from the private sector, whose ability to maximize profit and minimize costs depends on a global marketplace for products and labor. The defense sector is not the private sector but a curious public-private blend. American defense companies do sell a lot to overseas customers, but the customer whose needs shape the greater part of production is the US government. Proliferation of American defense contractors’ products, including software and data, is carefully regulated. Workers need to get government clearances. Contracts have to conform to official bureaucratic standards. There is plenty of red tape. The payoff for defense companies has been the security of long-term contracts and a relatively high level of protection from competition — notably from foreign competition.  The main downside is that profits from such quasi-public business, in the absence of corruption and favoritism, are limited by the obligation of Congress to ensure that government is not over-spending. Innovation within the defense sector thus seems to come up against natural limits. That is not the case in the private sector, which is why so much military innovation comes from outside the defense sector and commonly occurs for reasons that have nothing to do with defense.

This is abundantly true of AI innovation. If the US government wanted to make AI innovation henceforth a government-controlled process, it would amount to turning AI companies into defense companies — which would remove much of their incentive for innovation, defeating the purpose of the exercise. It would not be much of a victory in the race for AI dominance.

By contrast, operating with trusted partner countries would have some of the advantages of globalization — multiple labor and consumer markets to choose from — while preserving the goal of excluding China and other antagonists. Of course, forming some sort of digital alliance structure has been a US goal since the middle of the first Trump administration. Results have been mixed. There has been a contradiction at their core: The US wants partners but insists on being the dominant one. That kind of dominance cannot work in the case of private-sector-led technology innovation.

Fortunately US tech companies, although in their own ways just as hungry for dominance as the US government, have become accustomed in the last decade to competing in markets with foreign companies and not always winning. They have invested huge amounts in overseas markets: to pay suppliers, establish their own production, or attract customers but also to take advantage of the huge and growing innovation ecology that exists outside the United States. And foreign governments and private competitors have gotten used to them as well. The degree to which US tech companies can be profitably active in non-American markets without dominating them is an example of a type of loose alliance. The struggle with China is an important shaping factor but it does not distort everything it touches.

Learning from the success of this private-sector-led approach to the US-China tech contest could lead to a public-sector variant that could help control AI proliferation while accepting that winning the AI race with China, in the winner-take-all sense, cannot be done. A different type of victory might be possible though. After all, when the US, following the Soviets’ shocking Sputnik launch in 1957, went all out to win “the space race” against the USSR, it did not so much prevail as demonstrate its ability to continue to innovate at a pace the Soviet Union could not match. The result, in 1975, was American and Soviet astronauts living together in the International Space Station (as Russians and Americans still do) and the growth of an international scientific subculture that played an important role in bringing the Soviet experiment in oppressive governance to a close.   

AI is Just a Tool

By Dee Smith

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Rest Is Software

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

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

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

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

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

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

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

Can AI Make a Country Great Again?

Much recent commentary on artificial intelligence (AI) has focused on the prospect of a company or a country winning a race for artificial general intelligence (AGI) or more-than-human “superintelligence.” However, that goal, which seems rather more religious than technological, is both elusive and, should it ever be achieved, fragile (see SIGnal, “Mutual Assured Malfunction,” March 13, 2025). Investors are focusing instead on “little tech” and firm-level or industry-level AI that uses specific data sets to engineer specific productivity gains. In SIG’s view, this more modest course seems both economically more promising and politically much more sustainable. But it definitely does have risks of its own.

The appeal of “little tech” AI is partly that it leaves to one side the many serious questions about data privacy and other more existential matters that are posed by AGI. Smaller AI systems can run on the contained, often proprietary data sets involved in industrial processes, especially in manufacturing. The goal is not to replicate the human brain but to make industrial processes more efficient, raising productivity. It is a type of automation, using new technology yet still familiar enough from the history of industrial production.

With little-tech AI, startups can focus on specific problems whose solutions will provide a payoff in the relatively short term. In other words, AI would be monetizable. This has an obvious appeal not just to startup investors but also to industrial incumbents whose processes would be improved and whose productivity would be raised in competition with their rivals. Startups are not alone in this sphere. The German giant Siemens, for example, has put industrial AI at the core of its offering.

Politically, this approach to AI is much more appealing to most governments, only a few of which (the US, China) can have much hope of achieving global dominance by winning a race for AGI, at which point they might well regret getting what they wished for. Leaving aside the large question of AI data-center electricity demands, it offers the attractive prospect of raising productivity while reducing carbon use — because your factory in Texas, enhanced by AI, will no longer have to source so many of its components from East Asia, with all the carbon-using transport that entails. The little-AI approach also means states would not have to expose their citizens’ data to foreign tech multinationals, possibly based in hostile or overweening states, in order to participate in the later 21st century. That would be a gain for state sovereignty; and given that so many of the tensions around globalization have had to do with the way it threatens sovereignty and the democratic (or otherwise) accountability of governments to citizens, the little-AI approach could conceivably enhance global stability and the prospects for peace. Little AI, by improving productivity within a given national domestic workforce, could help states that are facing demographic stagnation — which is pretty much all industrialized states and many less-industrialized ones — to nonetheless grow on the basis of domestic labor (see SIGnal, “AI Family Values,” May 3, 2024). As Marc Andreessen and Ben Horowitz wrote in July 2024, “little tech” could make it possible “to reconstruct the American manufacturing sector around automation and AI, reshoring entire industries and creating millions of new middle class jobs” while also having green benefits. Technology could, in effect, provide the “labor” that would solve the biggest challenge facing President Trump’s vision of a more self-sufficient US: the lack of workers operating at a sufficient level of productivity (see SIGnal, “Trade Wars and US Labor,” April 11 2025).

Less carbon use, stabilization of the international sovereign-state system, a growing middle class, a renewal of rich-world domestic manufacturing but with higher wages and less grim manual work…What could possibly go wrong?

AI-enhanced production aimed at reshoring manufacturing to high-wage economies would square the circle of productivity growth and de-globalization. It would revive the pre-1975 global industrial status quo with the crucial addition of China (but not so much India or Southeast Asia). If you have the good fortune to live and work in a benefitting state, this would be a positive outcome. It could, however, also fuel techno-nationalism in the rich world (plus China) and make growth outside the AI-enhanced nations highly problematic. One key issue raised by the US-China struggle — a protected US market deprives non-American producers of consumers, while a protected Chinese economy, likewise deprived, dumps its production for the pre-tariffs US market onto the rest of the world’s economies — would be gravely worsened as the world’s two largest economies reduce their dependence on the rest of the world for both supply and demand.

AI-enhanced de-globalization could, in short, reverse the global redistribution of labor productivity that led to the greatest poverty reduction in human history. In theory, the gains from little AI could be more equally distributed. After all, the AI enhancements that would lift an underemployed person in Oklahoma or eastern Germany into the middle class of his or her domestic economy could do the same for a person in Nigeria or Thailand. But that outcome is not the goal for the people, states, and companies that are driving the growth in AI monetization. Their goal is nearly the opposite. For investors, the greatest gains will come from identifying companies and sectors best positioned to gain from AI-enhanced de-globalization.

Mutual Assured Malfunction

The past week has been a lively one for the eternal battle between digital networks and national, sovereign security. After a two-year standoff, Elon Musk’s Starlink was able to reach deals with India’s #1 and #2 telecommunications companies, Reliance Jio and Bharti Airtel, on providing satellite Internet to the subcontinent’s vast and underserved rural market. A few days earlier, Dan Hendrycks, Eric Schmidt, and Alexander Wang — respectively, director of the nonprofit Center for AI Safety, former chairman of Google, and the CEO of Scale AI — released a paper , “Superintelligence Strategy,” arguing that no one state will ever be able to win the AI race.

In the first instance, a technology company with, it is fair to say, its own distinctive geopolitical interests could potentially gain a hold over the telecommunications of the world’s second largest national market. In the second instance, tech industry leaders with, particularly in the case of Schmidt, a strong record of advocating US technology dominance in competition with China are asserting that such dominance can never be complete. Indian digital sovereignty and US digital sovereignty are rendered highly problematic if not impossible. If a state is on the networks, as all powerful states are and will be, then their sovereignty is inherently partial. Taking these two major developments together, the future of digital self-determination can be seen to be rather weak. In SIG’s view, this represents an overdue recognition of the interdependence of states even as they engage in fierce geopolitical competition.

Reliance Jio has, in the past five years, revolutionized India’s telecommunications, particularly mobile communications, bringing huge numbers of Indians online. Bharti Airtel has done a surprisingly good job at catching up, giving Reliance Jio much-needed competition. The Indian state has not been idly observing these developments. Its vigorous advocacy of an indigenous digital infrastructure, often now referred to as the “India Stack,” has become an example to others, including the European Union. (See the SIGnal post “The America Stack,” Feb. 5, 2025.) India is determined to become a major tech power. It has also, with the world’s fourth-largest defense budget after the US, China, and Russia, aggressively advanced its own space program and its own space-based navigational system to rival GPS (US), Glonass (Russia), and BeiDou (China). Balancing US and Chinese telecommunications majors over the past decade-plus, India has artfully and purposefully pursued its desire to achieve digital self-determination.

That made the Starlink deal a surprise. It appears to have been hammered out between Musk and Indian President Narendra Modi during the latter’s recent visit to Washington. The Indian government has an interest in nurturing Reliance Jio and Bharti Airtel, but it also has an interest in good relations with the US under President Donald Trump and in making sure that neither Reliance nor Airtel accumulates too much power domestically. Both the US and China have faced a similar problematic in simultaneously backing and controlling their own tech majors. The deal with SpaceX, Starlink’s parent company, provides one way for India to meet these several challenges. Indian reaction to the Starlink deal has been understandably wary and somewhat confused. After all, the Indian government, at various junctures, has humbled Facebook, Google, Amazon, Microsoft, Huawei, and ZTE, among other foreign firms eager to reach the Indian market. A recent Indian report characterized Starlink as “a technology of geopolitical control,” pointing meaningfully to Starlink’s role as the guarantor and master of Ukraine’s Internet access in that country’s struggle with Russia.

SIG’s view is that Starlink will not be able to repeat its Ukraine dominance in India, any more than its US and Chinese rivals have been able to subdue the subcontinent — not for want of trying. It is nonetheless striking that Modi, Reliance, and Airtel — the latter two have long opposed letting Starlink into the tent — now believe that the advantages of working with SpaceX outweigh the disadvantages. At the very least, Musk has dramatically proved that having the ear of the US president provides enormous business benefits.

While the “Superintelligence Strategy” has been in the works for some time, it is difficult not to read it in the context of the Trump administration’s declared determination to press the US’s AI dominance. One of Trump’s first moves was a $500 billion AI infrastructure project, and Vice President J.D. Vance later stressed in a landmark speech in Paris that the US “will ensure that American AI technology continues to be the gold standard worldwide and we are the partner of choice for others — foreign countries and certainly businesses — as they expand their own use of AI.” Vance held back from a simple declaration of hegemony but the administration’s message has clearly been that US AI should indefinitely be the parent in comparison to the efforts of other nations, especially China.

The “Superintelligence Strategy” has at its core the highly convincing argument that any large-scale AI system, even an American one, will always be vulnerable to infiltration and disruption by rivals. The strategy offers a very worldly solution, based on, but distinct from, earlier strategic arguments about nuclear weaponry. It is called Mutual Assured AI Malfunction (MAIM): the acceptance that there will be a balance of AI power, not a resolution or well-meaning regulation of it. Further, MAIM “already describes the strategic picture AI superpowers find themselves in.” A new Mutual Assured Destruction (MAD), AI version, is already with us. As in the earlier, nuclear version, there can be no victors.

There is much here for China and others to digest. The old, US-led idea of a free and open Internet, so recently repudiated, can be seen as returning, but in a much darker form appropriate perhaps for darker times. How states and companies react is the crucial question for investors. The venerable commercial goal of scaling, ideally to a global level, is not going to be achievable. AI-fueled tech companies, which increasingly means most tech companies, will face geopolitical limits. Commercial cooperation within those limits — and successful digital competition is inherently commercial — seems to be the only way forward. Musk, Modi, and the authors of the “Superintelligence Strategy” are simply ahead of the curve, and showing the rest of us where it bends.

The America Stack

Investing in technology got a lot harder in the past two weeks. Tech investors, particularly in AI, have traditionally assumed the best products would scale: pick the winner, and you will win big. That assumption will sometimes still prove valid, but it now seems fundamentally outdated. Technology markets are fragmenting for reasons that are not changing soon. That is making investors’ lives difficult.

The proximate cause for the drop in US tech stocks was DeepSeek’s launch of AI products that seemed to perform tasks that the company’s American competitors do at much greater cost. A Chinese company whose actual workings are opaque even by Chinese standards, DeepSeek surprised markets. The specific instance was indeed unanticipated, but the broader phenomenon, as SIGnal readers know, should not have been. The US has been tightening the screws on Chinese technology for years. The first Trump administration took technology containment to a new level and the Biden administration went further still. Neither administration explained what a realistic endgame was. But it was obvious that China and Chinese companies were not simply going to yield and give up. Every US sanction and prohibition has been met with Chinese innovation. The resulting products might not match their US analogues byte-for-byte, but they don’t have to. They just have to be good enough to enter the markets. Then they can win on price.

DeepSeek’s ability to do that burst the AI bubble, which was inflated by confidence in the US tech sector’s ability — supported by government spending and other encouragements — to prevail on the global scale. That confidence is now weakening, not just because a Chinese tech company can compete with America’s best but because the “global scale” has been shown to be a fiction. Neither of the world’s two largest economies is going to either give up on protecting and subsidizing its tech companies or open its digital markets to the other.

More profoundly, though, the extraordinarily tight relationship between the second Trump administration and US tech majors, symbolized by the prominent display of the leaders of X/SpaceX/Starlink, Meta/Facebook, and Google/Alphabet at the new president’s inauguration, signaled that the distinction between Silicon Valley and Washington is disappearing. The paradox is that this will make the US less dominant internationally, even if the opposite was the goal. The power of the Valley was rooted in its capacity for transcending American nationalism. Now that it has full White House backing, the Valley is losing that capacity.

Apart from China, one example of this phenomenon is the Eurostack. The term has an interesting past as it is derivative of the “India stack,” or the subcontinent’s attempt under Narendra Modi to develop domestic digital infrastructure based on control of data, payment systems, and citizen/consumer identity. This is commonly referred to by the acronym DPI (data, payments, identity). When a state can shape and integrate all three of these as the basis for a national digital infrastructure, it can control the nature of its own digital development. Historians of imperialism will savor the irony of the European Union, whose leading members all have imperial pasts of varying extent, looking to the land of the Raj and the Princely States for a model of how to gain control over its digital future. Europe has not often turned to India for geopolitical policy solutions. But that is what it is doing today.

There are counter-currents. For countries like Australia or Taiwan, which find themselves on the frontline of resistance to Chinese digital dominance, joining the US tech sphere of influence makes an immediate sense. The EU is much less sure, and the Trump administration’s indifference to European opinion can only increase its doubts. The US has inadvertently become a driver of digital non-alignment. Assuming India sticks to current policy — and there is every reason to think it will, even if Modi’s own power slips — then the world’s most populous nation and the world’s three largest economies are all pulling in the same direction, which is away from each other.

What of the rest? Consider the UAE. At the end of last year, the UAE’s position, arrived at after long debate and involving considerable discomfort, was to align digitally with what we might have to start calling the America Stack. The symbol of this was the deal last spring between G42, the UAE’s AI-investment flagship, and Microsoft. G42 is run by the UAE’s national security adviser and financed by the state’s sovereign wealth fund, Mubadala. Once the Trump administration’s tech direction became clear, however, G42 pivoted and announced (January 28) that it had become agnostic as to technology.

The demise of global scaling has been gradual over the past decade-plus, but as Ernest Hemingway said of bankruptcy, it can happen “gradually, then suddenly.” Investors now have to pick their way among the India Stack, the China Stack, the America Stack, and (if it happens) the Eurostack. It is unlikely that any invested company will be able to participate, much less thrive, in all four.