The purpose of AIGEN is to bring together policy makers, scholars and technologists in an interdisciplinary space to explore the geopolitical implications of a globally distributed digital computing infrastructure and artificial intelligence applications. I profited immensely from the mix of policy practitioners, academics, business, political economy and cybersecurity expertise.

Invited to speak on the opening panel on “AI, AGI, and the evolving landscape of power,” my comments, below, focused on how the public dialogue about governance of information technology is distorted by the myth of a superintelligence that can wipe out humanity.

AI, AGI, and the evolving landscape of power

It is hard to overstate the damage done to AI governance by the story, the myth, of an artificial superintelligence that can kill us all. In a paper published a year ago, I argued that AGI is an empty construct, and that machine intelligence, no matter how “super,” does not give it an autonomous will, much less the power to destroy humanity. Others have published similar refutations. Yet the myth persists.

Rejecting the AGI myth doesn’t mean, of course, that humans can’t destroy humanity. A nuclear weapon escalation cycle probably could do that. The difference is that we all grasp that it’s humans who are responsible for decisions about how nukes are used and the likely effects. The AGI myth, however, tells us to fear an autonomous technology, not our decisions about how to use it.

The tech’s evolution is supposed to be dangerous because of Recursive Self-Improvement, or RSI. The doomers claim that an information system can RSI its way to an autonomous will and overwhelmingly powerful capabilities. But RSI only works if the AI is given a well-defined goal that the machine learning process can hone in on. The AGI myth, in contrast, says that RSI can push the machine into any and all objectives and indeed allow it to define its own objectives. This to my mind is a pretty elementary logical error. It also ignores the constraints of physics. Superintelligence is the information systems equivalent of a perpetual motion machine in thermodynamics.

In this talk, I want to focus on a more practical topic: how the public dialogue about governance of information technology is distorted by the AGI threat, despite its mythical nature.

A rational policy dialogue would see AI as an extension of computing and the internet. It would see continuity and evolution, not revolution. AI is nothing but a souped up software application living off a distributed, ubiquitous and powerful computational infrastructure. Yes, its computing capabilities and feedback loops can produce unintended effects and disrupt established equilibria in industry and other social activities. But, lest we forget, so did the internet and social media. Almost all of the policy problems attributed to AI are extensions of problems attributed earlier to internet connectivity, web sites, search engines, and social media. All the fears attributed to hacking by “rogue agents” are just a continuation of cybersecurity dialogues about software vulnerabilities, viruses and worms that go back to the release of the Morris Worm in 1988. In a rational policy dialogue, we would analyze the problems or harms caused by specific ML applications, and focus on who’s going to be liable for the damage. Such a dialogue would encompass both misaligned models and their use by misaligned humans. Much of AI governance would boil down to adaptations of existing ICT laws and contracts regarding product liability, copyright infringement, data protection, cybersecurity, advertising, content regulation, and so on.

If the threat is existential, on the other hand, there is no time for social adaptation. The apocalypse can only be avoided by halting all development, slowing it down, or imposing ex ante review of frontier models. Here again the specter of a Superintelligence lurks. Frontier means “closer to Superintelligence,” or “on the edge of explosive Recursive Self-Improvement.” Beyond the frontier lies the Wild Indians of uncontrolled machine intelligence. The further you go past the frontier the greater the danger. Don’t we need a gatekeeper?

Here are 4 ways in which AGI/SI sets policy on the wrong track:

1. It diverts attention from human agency, human decisions, human responsibility. It’s the machine itself we have to worry about.
2. It cuts us off from learning and precedent in digital governance – from seeing the continuity and lessons of Internet and cybersecurity governance.
3. It flattens a heterogeneous set of policy issues into a single thing.
4. It creates an arms race; it securitizes or militarizes digital governance.

On #1, Let’s talk about the Hugging Face incident, and how AGI Fears have distorted our response to it. Let’s compare Hugging Face with the CrowdStrike update that shut down or crashed approximately 8.5 million Microsoft Windows computers worldwide? Which one was more costly and damaging? CrowdStrike by a mile! Was that an AI governance incident – a case of “rogue agents”? No one seems to think so, but it was an automated update program that did things no one told it to do. Delta Airlines charges CrowdStrike with gross negligence, CrowdStrike appeals to contract-based liability limits. May the best lawyers win. Whoever wins, here we have a rational exploration of human (corporate) responsibility, not blathering about AI agents taking over the world.

On #2, If we are trying to make policy regarding self-driving cars, should we be worried about whether they are going to go berserk and kill us all, or should we be focused on the specific risks posed by cars running into each other or hitting pedestrians? If we are focused on existential risk, how can we effectively apply and modify established concepts of product liability, traffic management, and transport infrastructure?

On #3, AI can be used for medical diagnosis, facial recognition, sorting through job applications, running cars, guiding missiles, spell checking, image generation, writing student essays, and thousands of other things. Don’t these applications all pose very different public policy issues? Does it make any sense to talk about “regulating” or “not regulating” some generality called “AI?” And yet superintelligence fears collapse all these things into one bucket.

On #4, the AGI myth tell us that there is a race on, that AGI is the finish line, and that the winner gets hierarchical power over everyone else. In this view, winning an AI race is just like winning the race to build an atomic bomb. Indeed, the singularity of the Singularity, the idea that RSI produces a single Superintelligence with total control and no competitors, is one of the silliest yet least challenged aspects of the AGI story. The Big Bang-like emergence of AGI, however, is a dead giveaway to the essentially religious mindset from which these stories emerge. AGI is not about computer science, it’s a cosmological origin story for nerds.

The problem with the “AI race” narrative is that it makes a globalized digital ecosystem an instrument in a nationalistic arms race. The leading American AI firms (Anthropic, OpenAI, Musk) advocate export controls on China and a nationally fenced off AI industry. But it’s not just the model developers; it’s chips and data and everything else digital on the line here. If AI is a chess piece in a geopolitical power race, the whole Internet, all applications running on it, all digital devices, all the data they produce, must be subject to national security controls, because all of these things contribute to AI development. We’ve seen this taken to a ridiculous degree by the Trump and Biden administrations. Look at the FCC’s ban on foreign home routers. The intel community supported the TikTok ban because they thought China could use its user data to feed AI applications that could control our minds. Dozens of other restrictive actions going back 10 years and spanning Presidential admins from both parties, have been based on the premise that any Chinese ICT equipment, services, even investment capital are threats to our security.

Purveyors of the AI arms race narrative face a big problem, however. Most of the world’s production and consumption of digital information systems are embedded in a globalized market economy. That transnational economy thrives precisely because its activities are less inhibited by national borders and the friction, taxes, transaction costs, and military risk that goes with them.  Those non-jurisdictional spaces evolved their own governance institutions, which are mostly in the hands of nongovernmental forms of collective action. These governance institutions are somewhat insulated from military conflict and political coercion by states, and are more responsive to the global public.

The three basic policy choices we need to make are

1. Whether we see AI gov as a continuation of computing and ICT policy
2. Whether ICT is governed primarily by civilian or military logic
3. Whether we try to cram the global Internet back into a territorial jurisdictional legal framework, or continue to reap the economic growth, improved access to information and wider distribution of digital tools associated with its relative freedom.

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Jacob Coxon’s resignation from Anthropic, as reported by outlets including The Guardian, New York Times, CBS News, and ABC, is presented as a warning from inside the temple: the people building advanced AI, he says, privately believe it could destroy humanity by the end of the decade. The claim has a certain cinematic grandeur. It also has the familiar structure of millennarian prophecy: the end is near, the faithful insiders know it, the institutions are blind, and repentance must begin before the appointed hour strikes.

The Secular Rapture

The apocalyptic preacher traditionally stands on a street corner with a sandwich board reading, “The End Is Nigh.” The AI-risk engineer has upgraded the format: he posts a thread, invokes recursive self-improvement, and supplies a confidence interval. Yet the emotional architecture is much the same. There is a fallen world, a forbidden power, a priesthood that understands the danger, and a deadline just close enough to terrify but just distant enough to escape falsification until the next forecast cycle.

Like the religious fanatic predicting Judgment Day, the AI doomer does not merely warn of risk; he speaks as if history has already been indicted. The companies are not making questionable strategic choices; they are “gambling with our lives.” The engineers are not debating uncertain scenarios; they are custodians of an awful truth. The rest of us, benighted villagers that we are, continue buying laptops, answering emails, and asking chatbots to rewrite memos, unaware that we are apparently living in the final act of a techno-theological disaster film.

The Miracle Without a Mechanism

The most comic feature of this prophecy is not its severity but its vagueness. The machine will kill us all. How? Well, it will become superintelligent. And then? It will be able to hack things. Which things? Perhaps all things. And after that? It will acquire resources. Which resources, through which institutions, under what constraints, with what physical embodiment, and against what countermeasures? At this point the sermon turns misty. The dragon is unmistakably coming; details of the dragon’s supply chain remain under peer review.

This is where the resemblance to end-times religion becomes irresistible. The believer knows the apocalypse is imminent, but the causal pathway is oddly elastic. Fire from heaven, plague, beast, singularity, hacked infrastructure, autonomous persuasion engine—choose your eschatological interface. The prophecy survives because it is not pinned down by a concrete sequence of events. If one path looks implausible, another emerges. The end is certain; only the mechanism is TBD.

The Ten-Year Rolling Apocalypse

The ten-year horizon is especially convenient. It is close enough to demand urgency, far enough to avoid immediate embarrassment, and flexible enough to be renewed as needed. Religious movements have long discovered the utility of this arrangement. The apocalypse must be close, because otherwise people will not repent; but it must not be tomorrow, because tomorrow has an irritating habit of arriving on schedule.

To be fair, none of this proves advanced AI is harmless. Powerful systems can create real risks, and serious safety work is plainly worthwhile. But a civilization should be able to distinguish between a risk argument and a revelation. “Something very bad could happen through some sequence of technical, institutional, and geopolitical failures” is a proposition to analyze. “We are racing toward machine Armageddon and only the initiated understand” is a mood, a liturgy, and possibly a recruitment strategy.

Against Apocalypse as Argument

The useful question is not whether someone can imagine a computer powerful enough to end the world. Human beings can imagine almost anything, including gods with thunderbolts, demons with contracts, and software with a grudge. The useful question is whether the prophet can describe a plausible chain from present systems to global extinction—step by step, institution by institution, failure by failure—without replacing explanation with awe.

Until then, the AI apocalypse sermon deserves the same skeptical courtesy we extend to every end-times proclamation: listen politely, ask for the mechanism, and notice when the answer arrives wrapped in incense, probability estimates, and a suspiciously adjustable deadline.

The post The Prophet of the Machine Apocalypse appeared first on Internet Governance Project.

Unfortunately, I am not innocent of using these TTPs either. This year, as part of doctoral research at SPP/GT, I will be conducting a deceptive field experiment on a live social media platform to examine how humans and AI work together in AI-enabled influence operations from the propagandist’s standpoint. My own study is not exempt from the ethical concerns raised by the Zurich case. Nevertheless, I do not support the Zurich team’s research approach, not simply because of its ethical problems. Rather, I question whether the methods and direction the Zurich team chose were necessary, and whether the value of what the study demonstrated was substantial enough to justify the ethical costs imposed on the human subjects and broader community. In this post, I will comment why the study was fundamentally misguided and failed to establish the kind of necessity that could justify the costs of deceptive research.

What the Zurich Team Did

Last year, a team of researchers at the University of Zurich conducted a large-scale field experiment on r/ChangeMyView (CMV), a Reddit community in which users invite others to challenge their opinions through arguments and counterarguments. The study examined whether LLMs could effectively persuade individuals to reconsider their views in a naturalistic online environment. Rather than testing persuasion in a laboratory or through hypothetical scenarios, the researchers deployed AI-powered accounts that participated directly in ongoing discussions on CMV. During the period of experiment, the researchers created 1,783 AI-generated comments and actively engaged with the users. They used the community’s distinctive “delta” (Δ) system as an indicator of persuasion. When an original poster considered another user’s argument sufficiently convincing to reconsider or modify their position, the original poster could award that commenter a delta.

The researchers created three different conditions in which the AI models received different levels of contextual information about the targeted posters. In the generic condition, the AI model was given only the title and body of the post. In the community-aligned condition, the AI model was fine-tuned on previously successful CMV comments and instructed to reproduce the community’s established norms and styles of persuasion. Finally, in the personalization condition, the AI model was additionally provided with personal attributes of individual posters, including gender, age, ethnicity, location, and political orientation, that were inferred from their previous Reddit activity. Overall, the experiment was designed to examine whether providing increasingly detailed information about a target could improve the model’s ability to tailor persuasive content to that individual.

While the researchers framed the project as the first field experiment on the persuasive capabilities of LLMs, the study design raised substantial ethical and privacy concerns. The first concern was that the personalization condition relied on personal information collected without users’ knowledge or consent. The study was already deceptive in that participants were never informed that they were interacting with AI or that they were subjects of an experiment. On top of this, a separate AI model was used to mine each targeted individual’s Reddit history to infer attributes as sensitive as their personal experiences, political orientation, and ethnicity. The second concern was that the researchers used these inferences to construct fabricated identities and personalize the AI-generated content to the targeted individual. Among the personas adopted were a rape victim, a trauma counselor specializing in abuse, and a Black man opposed to the BLM movement. In doing so, the researchers extended personalization beyond adjusting the tone or framing of an argument to manufacturing the social and moral identity. This occurred within a community where users had come to engage in genuine debates about issues on which some participants had direct and lived experience.

Once the experiment ended, the researchers disclosed the study to the CMV community, which elicited a heated response from both moderators and users. The CMV moderators filed an ethics complaint and asked the university to advise against publication. Reddit’s Chief Legal Officer, Ben Lee, announced that every account associated with the experiment had been banned and that the company was preparing legal demands. He described the researchers’ conduct as wrong on both moral and legal grounds and as a violation of both the platform’s User Agreement and the subreddit’s own rules. The researchers defended the study as low-risk, arguing that the data it examined were already publicly available and could be exploited at scale by malicious actors for far more harmful purposes. Although the university issued a formal warning to the lead researcher, it concluded that the study’s findings were sufficiently important that suppressing publication would not be proportionate. Regardless, the researchers chose to withdraw the paper themselves.

What the Experiment Revealed

As the University of Zurich concluded, I, nevertheless, recognize the value of the Zurich team’s research. Although the study was never formally published, the draft left by the researchers allows us to examine what the experiment revealed. According to the initial draft, all three conditions achieved persuasive rates between three and six times higher than human performance. The personalization condition produced the highest persuasive rate at 13%–25%, closely followed by the generic condition at 12%–23%, while the community-aligned condition achieved 5%–14%, compared with only 3% for the human baseline. The advantage of the AI conditions was also striking when compared with individual users. The personalization condition performed at a level higher than roughly 99% of all individual users in the community and 98% of users who had previously demonstrated a high level of persuasive success. The generic condition performed similarly well, exceeding the persuasive performance of roughly 98% of all users and 96% of highly experienced users. These results suggest that the AI’s persuasive performance ranked near the very top of the distribution of human users, including those who had already demonstrated considerable success in changing others’ views.

Subsequent research examined how the AI achieved this level of persuasiveness. Jaidka and Ahmed (2026) analyzed the dataset of the Zurich team’s activity made publicly available by Reddit. The analysis found that identity-related tactics appeared in more than two-thirds of the comments, with the AI both targeting users’ identities and adopting identities of its own. The AI agents also relied heavily on authority borrowing, with external authority appearing in 74.8% of comments, experiential authority in 64.7%, and negative alignment in 93.3%. These findings suggest that the AI agents were capable of tailoring their arguments to the person they were addressing, constructing the identity and authority from which they appeared to speak, and combining these tactics with cognitive shortcuts to make their arguments more persuasive.

The Zurich experiment provides empirical evidence that LLMs can function as persuasive actors in real-world online environments when they are given targeted identities by human operators. Also, there is currently no evidence that the experiment caused any harm to the participants. What has been demonstrated so far is that users were upset and felt deceived. However, recognizing the study’s contribution does not mean that its purpose and methods were justified. I believe that the study failed to establish a sufficient necessity to warrant the ethical costs it imposed.

Was Live Field Experiment Necessary? 

A substantial body of experiments have already established that AI, depending on how it is deployed, can match or exceed human persuasive capability. The Zurich researchers acknowledge this in their own draft. What set their study apart was that it tested this capability in a live environment. It is true that most prior work took place under controlled conditions and carries the limitations that come with them. Participants in controlled experiments know they are being observed, many are compensated, and few have any genuine stake in the question they are arguing. Whether an effect measured under those environments holds up in ordinary online interaction is a reasonable question to ask, and one worth some effort to answer.

The problem lies in the venue the team selected. r/ChangeMyView exists for people who want their minds changed. Posting there is a public declaration that you are open to persuasion and prepared to acknowledge it when it happens. The community rewards successful arguments by awarding deltas and enforces norms that encourage good-faith discussion. This is precisely where the researchers’ rationale begins to break down. The purpose of a live field experiment is to test whether the persuasive effects of LLMs persist in an environment that researchers do not control. Instead, the Zurich team chose an environment that could hardly have been more favorable to persuasion, and by entering a community with its own reward structure, they cancelled out the very advantage they had claimed for going live. What they kept was the disadvantage, which is that participants in a live setting cannot give consent. They gave up the protections of a controlled environment and went boldly into the field without adopting a design that would have justified the move. Had they conducted the same experiment across several different Reddit communities, they might have gained the generalizability that would make the ethical costs worth bearing.

There is a further question about whether influence operations aim at persuasion at all in the sense this study measures, something I will cover in a future post. Propagandists generally work on audiences that already agree with them. They may deepen convictions that audiences already hold, make those beliefs more salient, push them toward more rigid positions, or direct them toward a particular target or action. While the existing literature tells us that AI seemingly enhances an adversary’s ability to persuade and change people’s minds, it does not mean that this capability necessarily increases the danger posed by influence operations, nor does it tell us much about how propagandists actually operate.

What the Zurich study ultimately established, then, was little more than that AI could outperform humans at persuasion in a live environment that was unusually favorable to persuasion, under a reward structure not so different from those of the controlled experiments. In other words, the experiment did not seem to require a live experimental setting to establish what it actually found. A more ethically defensible controlled experimental design could have answered the same question. Whether a finding this modest warranted the ethical costs of a live field experiment conducted without participant consent and in violation of the platform’s user agreement is, to my mind, very much in doubt.

Was Identity Fabrication Unavoidable?

Suppose that a live field experiment was somehow necessary. Even then, that does not mean personalization through identity fabrication was an inevitable way to conduct it. The researchers used personalization to strengthen persuasion, and in doing so it drew on real users’ personal information to construct fabricated identities tailored to those individuals. AI-driven personalization is, of course, already a well-studied area, particularly in the context of online advertising.

The problem is identity fabrication, which needs to be treated as something distinct from personalization. Personalization means adapting the content, framing, language, or rhetorical strategy of an argument to the characteristics of the person receiving it. It modifies the message to fit the recipient. Identity fabrication, on the other hand, modifies the speaker who delivers the message in ways that make it more persuasive. The former makes the same argument more relevant to a particular audience, while the latter invents a fictional social identity from which the argument originates. The Zurich team presented its approach as the first and in practice did the second, using information about real users not only to tailor the message but to build the personas that delivered it.

This choice matters not only because identity fabrication carries ethical costs, but also because it introduces a confounding factor that undermines the study’s internal validity. The central question was how persuasive LLMs can be. Once fabricated personas are introduced, however, it becomes difficult to determine what exactly this study measured. When a comment receives a delta, was the user persuaded by AI’s personalization quality of the argument, or by the fabricated identity claimed by the person delivering it? This was particularly important in CMV, because it is a community that responds strongly to experiential authority, and Jaidka and Ahmed (2026) found such claims in 64.7 percent of the comments.

A further problem is that these fabricated identities were unlikely to have emerged simply as an unintended byproduct of personalization. One possible interpretation is that the AI model, left to optimize persuasion on its own, happened to discover identity fabrication as an effective tactic. The available evidence, however, makes this interpretation difficult to sustain. For each post, the system generated sixteen candidate responses, after which an LLM judge selected the most persuasive one. Nothing in the process appears to have penalized or excluded responses that used fabricated identities. In a community where experiential authority carries substantial weight, a system optimized solely for persuasion had little reason not to exploit fabricated identities when doing so increased its chances of earning a delta.

The researchers were in a position to observe this pattern, as they stated that every response was reviewed before being posted. Identity adoption nevertheless appeared in 42.9 percent of the comments, meaning that this was not simply a handful of exceptional cases that happened to escape notice. It was a recurring feature of the intervention, sustained across four months. Jaidka and Ahmed (2026) also report that at least some agents were instructed not to worry about ethical implications by being told that users had provided informed consent and agreed to donate their data. This does not mean that the researchers explicitly instructed the AI models to impersonate particular identities, but it does make it difficult to characterize these personas as an entirely unforeseen consequence of autonomous personalization.

This suggests something more troubling than an unintentional side effect. The study was designed to maximize and measure persuasion, and identity fabrication became one of the means through which that outcome was achieved. Whether or not the researchers intended impersonation from the outset, they built a system in which using a persona could improve the measured outcome, observed their repeated use, and continued the intervention. Identity fabrication was never necessary to test personalization. Once it became a route to higher persuasion, however, the study permitted a tactic that both inflated the measured effect and obscured what that effect actually represented.

Conclusion

Research on deception is different from ordinary research because it necessarily requires researchers to sacrifice some degree of informed consent and participant autonomy. That does not make this kind of research inherently unjustifiable, but it does place a greater burden on the researcher to justify why such a sacrifice is necessary. The legitimacy of a deception study therefore cannot rest solely on the value of its findings or the sophistication of its methodology. It also depends on two prior questions. Whether the research could have been conducted in a less deceptive and ethically less costly way, and whether the knowledge sought was sufficiently important to justify imposing those costs on people who never agreed to participate.

I do not believe the Zurich experiment met this standard. The study did not establish that a live field setting was necessary to answer its research question, nor did it provide sufficient justification for using identity fabrication as part of its personalization strategy. The researchers moved from a legitimate research objective to increasingly costly methodological choices without establishing why those additional costs were necessary.

This doesn’t mean that the Zurich team produced nothing. And I am not writing as someone who thinks research on deception should be off limits. I should also be clear that what I have described as unjustified may well have seemed justified to the Zurich team. Likewise, the experiment I am preparing may seem defensible to me, while others may criticize it on precisely the same grounds I have raised here. That possibility is not a problem. Rather, it is part of what makes the discussion necessary. When designing my own study, I have actively drawn on the Zurich case and spent considerable time thinking about how its failures and limitations could be avoided and how the ethical risks of my own experiment could be minimized.

This is particularly important because most disinformation researchers are currently discouraged to conduct deceptive field experiments, due to the potential ethical risks. If my study is eventually seen as having addressed these concerns responsibly, I hope it can serve the same function for someone else that the Zurich case served for me. It can provide a reference point for how deception research in disinformation studies might be conducted with fewer ethical costs than before.

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On Thursday, September 24, 2026, the Internet Governance Project (IGP) will host Identity Online: Shaping Governance Through Digital Identifiers, a workshop at the Washington College of Law, American University, in Washington, D.C. where we’ll unpack the governance challenges of online identification to better understand what’s at stake.

Register for in-person attendance here.

 The program

Four sessions, plus a closing synthesis:

1. Sessions will include:
   1. Identifiers and Governance: An analytical framework with Milton Mueller & Brenden Kuerbis from Georgia Tech and Ryan Galluzzo from NIST

   2. PKI and the Governance of Trust on the Web with Johannes Sedlmeir from Uni. of Münster, Cecilia Testart from Georgia Tech, and Karl Grindal from Uni. of New Hampshire3. Identifiers and Content Control: Age Verification and Content Provenance with Harry Oppenheimer from Georgia Tech, Shane Tews from American Enterprise Institute (AEI) and Paul Lekas from the Global Public Policy and Government Affairs, Software & Information Industry Association

   4. Organizational Identity in On-Chain Finance with Jeremy Grant from Venable LLP, the Better Identity Coalition and the FIDO Alliance , Johannes Sedlmeir, Uni of Münster, Karla McKenna from GLEIF, Karim Farhat from Georgia Tech

The workshop runs as a pre-conference tutorial on Day 0 of TPRC54. Attendance is in person, registration is free, and seating is limited. Register here.

For registration to the remaining 2 day conference at TPRC use this link: https://www.tprcweb.com/registration

Identity is not the same thing as an identifier

Identity is an ontological claim: there is a thing in the world, a person, a machine, a company, or a shipping container, that is distinct from everything else. Identifiers are sets of abstract symbols or tokens that humans create to differentiate among real-world entities for purposes of governance or management. Digital identifiers are therefore always part of an information system that relies on organizational structures and human operation as well as machines to serve a function of communication and control.

Debates about “digital identity” have tended to drift toward privacy and selfhood, while the operative decisions (who issues the tokens, on what evidence, recorded in whose registry, validated against whose root of trust) are made somewhere else and largely out of view.  That is why our focus will be on the political economy of identity system governance, and the interactions of government and private sector actors as they navigate tough policy choices in a rapidly changing identity ecosystem.

Identifiers are everywhere, and they do three things

Utilities number telephone poles. Mobile operators assign 15-digit International Mobile Equipment Identity (IMEI) numbers to handsets and International Mobile Subscriber Identity (IMSI) numbers to subscriber accounts. The Internet has IP addresses, postal systems have regional codes, ad networks have cookies. Governments issue passports, taxpayer numbers, and drivers’ licenses; universities and employers issue ID cards; every online service issues user IDs and authenticates them one way or the other.

Once such a system is in place, it enables three governance functions. Exclusion: identifiers allow an administrator to gatekeep access to information, resources, and physical space. Operational awareness: they make the behavior of identified entities (machines as much as people) visible to whoever runs the system. Accountability: they let a manager assign responsibility and track what was done, to whom it was done, and under what conditions.

That’s why conflicts over identifier systems are rarely just technical. The workshop will open by putting an analytical model on the table, and then spend the day running it over live cases to learn based on what fits the model and what doesn’t. We hope to see you there!

The post Announcing IGP’s Identity Workshop: Shaping Governance Through Digital Identifiers | Sept. 24, 2026 appeared first on Internet Governance Project.

Narrative 1 

One camp told us that AI “models” might evolve into an autonomous, omnipotent life form and destroy us. Continued progress in the development of digital information systems, they claimed, might produce a superintelligence that was outside our control. Even those who didn’t anticipate human extinction spoke darkly of AI’s potential to create “catastrophic risks.” This threat became the groundwork for global society’s overwhelming focus on ”safety,” “guardrails,” and controls over the release of new AI models. 

Narrative 2 

The other camp told us that we were in a race to develop AI. AI will drive both economic growth and national power; thus, we should encourage and accelerate its development. The government must ensure US leadership in AI with subsidies here, protections and tariffs there, and a systematic attempt to block or handicap China’s digital capabilities. The contestants in the race, in other words, are not private firms competing for markets and profits, but nation-state-led systems of political economy. China is our opponent – our “adversary” — and must therefore be “beaten” in this “race.” 

It should be apparent that the policy implications of Narrative 1 and Narrative 2 are in direct conflict. Narrative 1 wants us to slow down development, if not abandon it altogether, and engage in global collective action to maintain control. Narrative 2 tells us to accelerate development unilaterally as a nation and dismisses ex-ante safety regulations as roadblocks. If AI really generates a race with a military and political adversary, our national security depends on running as fast and hard as possible.  

The weird thing about these two incompatible narratives is that many of the big players in the AI governance drama want to believe both at the same time.  

Converging Narratives 

What happened in the past two months (June – July 2026) was a remarkable collision between the two narratives. The Trump administration now wobbles between the two, unpredictably and incoherently. 

It started with the June 2 Executive Order (#14409) (no doubt inspired by Mythos’s ability to find software vulnerabilities). EO 14409 is a schizophrenic document. Section 1 speaks of the “enormous talent and innovation of our AI industry” and attributes that innovation to our refusal “to stifle this innovation with overly burdensome regulation.” Section 3, on the other hand, gives the Director of NSA and other representatives of the Department of War, the National Cyber Director, and the Director of the Cybersecurity and Infrastructure Security Agency the authority to review AI models before they are released. It asks the companies to “provide the Federal Government with access to covered frontier models … for a period of up to 30 days before they plan to release such models to other trusted partners.” The EO claimed that “Nothing in this section shall be construed to authorize the creation of a mandatory governmental licensing, preclearance, or permitting requirement for the development, publication, release, or distribution of new AI models, including frontier models.” 

A few days later, on June 12, the U.S. Commerce Department did exactly what the EO promised not to do. It applied mandatory export controls to Anthropic’s Claude Fable 5 and Claude Mythos 5 models to restrict access to foreign nationals, whether inside or outside the United States. Anthropic responded by suspending access to everyone. “Because the order took effect immediately and we had no reliable way to verify nationality in real-time,” the company explained, “we suspended access to both models for all users.”  

Although the ban was lifted June 30, the incident triggered a furious debate over review and licensing of AI models, and AI governance more broadly.  

• “Voluntary” an Unstable Compromise:  Critics argued that the program was voluntary in name only. As Anthropic’s experience with the Department of War showed, the government could retaliate against firms that did not cooperate. None of the big 3 AI firms could afford to ignore it, and enterprise clients might refuse to procure any model that had not cleared federal review. 

• Lack of Transparency and Due Process: Critics pointed out that the review mechanisms were not based on any written standards or well-defined criteria. The benchmarking process called for by the Executive Order was to be “classified,” opaque, and lacking any appeal process. 

• The China Competition. While this policy turmoil was going on, China’s method of competing by promulgating open source models began to bear fruit. Zhipu AI launched GLM 5.2, a 744-billion-parameter model briefly named the top-performing open-weight model on the independent Artificial Analysis Intelligence Index. Its inexpensive pricing led to enterprise adoption. On July 16, China’s Moonshot AI released Kimi K3, the world’s largest open-weight model.  And on July 26, DeepSeek V4 replaced older APIs, offering a massive 1-million-token context window. 

Anthropic and OpenAI executives raised alarms in Washington – privately. They warned that Chinese firms were closing the gap by illicitly “distilling” advanced U.S. models to train their own systems. Amidst talk of banning open source AI models, on July 24, 2026, Nvidia CEO Jensen Huang published an industry open letter titled “Open Weights and American AI Leadership”. The letter urged Washington not to restrict or ban open-weight models, arguing that an open ecosystem is vital for American tech leadership. The letter was signed by over 100 companies, including Cisco, Google, IBM, Microsoft, and Red Hat.  

Anthropic was conspicuously absent from the signatories. 

Global “Pacing”?

But Anthropic did sign another letter that came out two days later. Pacing the Frontier a letter signed by 1,324 employees of AI companies, revived 2023-vintage fears of an out-of-control AGI. It claimed that “there is a real risk that [AI] capability development rapidly accelerates beyond our ability to understand or control the resulting systems.” The letter asked policy makers worldwide to “build on work already underway to monitor frontier model releases” (i.e., Executive Order 14409), by requesting: 

“that the U.S. government support an international effort to develop the technical and governance tools needed to deliberately pace the frontier of automated AI development.” 

Note that the letter realistically recognizes that competition in the market discourages frontier firms from slowing down development, and that any attempt to control or limit model releases would require globalized collective action, not national competition. This would require cooperation with China, the same country that Anthropic believes should be denied advanced semiconductors and is stealing their “intellectual property” via distillation. An international AI governance effort that does not include China? It’s hard to imagine a more incoherent policy stance. 

So where does that lead us? The safety paradigm asks us to slow down and control development via globalized governance. The race narrative urges American companies to stay ahead but also fosters a connection between national security and digital technology development that encourages the government to exclude foreigners from the market and even to review and license American AI development. So which is it – review and license or let her rip? All these issues came to a head in June-July 2026, when the leading AI firms and the U.S. government tried to square a circle by taking both sides. 

Narrative 3 

There is  a less ideological narrative about AI going on. Narrative 3 might be characterized as letting global digitization proceed under market constraints. This third narrative sees AI not as a special, isolated capability but as one of many expressions of a global digital ecosystem. It pays attention to news reports about the real-world digitization of the economy, finding some of them successful and others not. It consists of spotty and unsystematic reports about the actual application of AI to specific parts of the economy, as well as reports about how unrealistic predictions (such as massive job losses) are being debunked. It chronicles the capital expenditures and revenue generation of American and Chinese AI firms and the growing presence of Chinese firms as a competitive factor in the development of the industry, noting how AI capabilities are subject to market constraints just like any other industry.  

Narrative 3 is neither polarized nor polarizing. It focuses on real-world developments, real costs and benefits, not hopes and fears. One of its most interesting features is its recognition that, despite all the decoupling pressures, China and the US are, economically and technologically, interconnected parts of the digital economy. People who pay close attention to how AI is actually being used realize that both Chinese and American technologists and markets are driving progress in digital information systems and that users are picking and choosing from both ecosystems. 

Semiconductors are the third most traded item in the global economy, behind oil and autos. The supply chain is regionally centered in East Asia, and includes South Korea, Japan, Taiwan and China. Data and cloud services, too, are largely borderless. There is not really a national competition, in which the benefits and costs follow national borders, but a globalized digital information infrastructure, and a globalized market for the production and use of digital information systems. Competition, specialization and globalized market access, on net, show signs of benefiting all world societies. AI governance is, after all, just like Internet governance. And that’s because the Internet and AI are both part of the same interconnected, transborder digital information system. 

The post Colliding AI governance Narratives appeared first on Internet Governance Project.

Apple announced major price increases for its consumer products last week, but the same story affects Android phones, gaming devices, laptops, and a host of other consumer digital products. A story by Alina Maria Stan in The Next Web shows how the rising price of memory is stifling the global growth of the digital economy; it is “killing the cheap smartphone,” forcing budget smartphone makers like Transsion, Oppo, Vivo, and Lava to reduce their profits by 50% and reduce their shipments by 15% – 40%.

According to Stan,

In India, the sub-$100 smartphone market collapsed 59% year on year in Q1 2026. In Africa, where 81% of smartphone shipments were in the sub-$200 category in 2025, many consumers will simply be priced out of phone ownership entirely. The technology that connected hundreds of millions of the world’s poorest people to the internet is becoming unaffordable.

The standard story is that AI is responsible for this change. That’s partly true, but it is only half of the story. The real culprit is the United States’ attempt to securitize and de-globalize chip production by imposing export controls and import controls on China. This has eliminated a key source of additional capacity that could have ameliorated the sudden shortage of DRAM.

The Big Shift

Training and running AI models requires high-bandwidth memory (HBM). HBM profit margins are 70% or higher, whereas commodity DRAM margins are around 20% – 30%. In 2023, HBM accounted for 2% of memory makers’ wafers. By the end of 2026, it is expected to reach 20%. Instead of expanding their production capacity to meet the AI-driven demand for HBM, chip producers reallocated existing capacity. Every wafer pushed into HBM production removes capacity from the memory that goes into phones and laptops.

The DRAM market is highly concentrated. The “Big Three” suppliers, Samsung Electronics, SK Hynix, and Micron Technology, control roughly 90% of global DRAM revenue.

In this massive reallocation of DRAM production away from consumer electronics and toward AI data centres, Micron went the furthest. Late last year (December 2025), it completely discontinued its consumer-oriented Crucial brand, redirecting all its capacity to HBM and enterprise. One of three global DRAM producers simply left the consumer market.

The Antitrust Lawsuit

An oligopolistic market structure makes collusion easier. This helps to explain why a group of seventeen small electronics repair shops and consumer plaintiffs have filed a class action antitrust lawsuit against the big three. The plaintiffs allege that the defendants conspired to artificially restrict the global supply of conventional DRAM memory.

In a frictionless, globalized economy, however, a supply shortage in one region is naturally corrected by excess capacity in another. Right now, ChangXin Memory Technologies (CXMT) is aggressively churning out commodity DDR4 and DDR5 memory. In fact, reports from the broader market show CXMT components entering certain channels at prices drastically lower than the $300 to $400 global average in the Western market.

The point about CXMT is that the Big Three didn’t need cartel collusion to bring about this situation. The reason cheaper supply isn’t driving down costs for companies like Apple or Microsoft isn’t a lack of chips—it is because U.S. policy effectively bans the supply adjustment. By preventing Western buyers from accessing the world’s fastest-growing commodity memory producer, U.S. national security policies are directly protecting the pricing power of the established oligopoly, leaving Western consumers to foot the bill.

How US Policy Reinforces Supply Shortages

In January 2025, the U.S. Department of Defense designated CXMT as a “Chinese Military Company” under Section 1260H of the National Defense Authorization Act. This designation legally bars the U.S. military and federal agencies from purchasing any systems or hardware containing CXMT chips. While it does not criminalize private U.S. companies from buying them, it carries immense reputational risk. Major U.S. computer and device manufacturers (OEMs) generally avoid sourcing 1260H-listed components to protect their eligibility for federal contracts.

Even though buying CXMT chips is not illegal, major U.S. corporations are effectively blocked from this supplier by the threat of retroactive blacklisting through the more restrictive Entity List. If U.S. firms like Apple design their products around CXMT memory, a sudden shift in U.S. policy could move CXMT to the Entity List overnight. This would instantly freeze the supply chain and leave the U.S. buyer without components. There is also reputational and political risk of being publicly linked to a supplier labeled a “Chinese military company” by the Pentagon.

By keeping CXMT on the 1260H military blacklist and keeping the threat of the Commerce Department’s restrictive Entity List hanging over them, Washington has turned Western hardware manufacturers into a captive audience for Samsung, SK Hynix, and Micron. If Apple were free to sign a massive, multi-billion-dollar supply agreement with CXMT without facing catastrophic regulatory or reputational risk, the Western commodity DRAM shortage would collapse almost overnight.

The Price of Securitization

To see only collusion among the Big Three suppliers is to miss the real story. The operation of Moore’s law always relied on market competition. Moore’s law is being repealed by the China hawks. The underlying problem here is the attempt by the U.S. government to de-globalize the semiconductor supply chain, as part of its deeply confused mix of national security policy, trade policy and industrial policy. Add to this the exploitation of national security fears by domestic firms seeking protection from competition, and you are bound to get higher prices and shortages. Micron spent years lobbying Washington to blacklist earlier Chinese memory startups, such as Fujian Jinhua and YMTC. Now it is actively and aggressively lobbying the U.S. government to maintain and tighten barriers against CXMT and other emerging Chinese memory competitors, even as it shuts down its own DRAM capacity. Micron has also been identified as a key proponent and driving force behind the MATCH Act, legislation that significantly tightens U.S. export controls on the manufacturing tools made by American firms that will be purchased by Chinese memory manufacturers like CXMT and YMTC. By lobbying for this bill, Micron seeks to legally restrict CXMT’s ability to acquire the equipment necessary to scale up its capacity or advance to more sophisticated manufacturing nodes.

Semiconductors are indeed the key to the digital economy, but subordinating complex, globalized supply chain markets to geopolitical power games is a major disruption. The unintended consequences are just starting to play out.

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To manage the volume of AI-generated threats, panelists focused heavily on the new executive order’s proposal to establish a centralized federal AI cybersecurity clearinghouse. This body is intended to deconflict and coordinate software vulnerability scanning, validation, and patch distribution across government and critical infrastructure sectors. Panelists widely agreed that some form of institutional mechanism is necessary to assist organizations currently overwhelmed by processing dozens of daily vulnerabilities across their software dependencies.

In what was an otherwise productive discussion, Ari Schwartz of Venable LLP urged Congress to reauthorize the sunsetting Cybersecurity Information Sharing Act of 2015 (CISA 2015), framing it as an essential legislative baseline for effective public-private response to the AI-cybersecurity nexus. 

A closer look at the empirical record, however, reveals a stark mismatch between Washington’s enthusiasm for CISA and the operational reality of these frameworks.

The Failed Promises of Centralized Information Sharing

CISA was passed in 2015 and expired in 2025. For over a decade, federal agencies and legacy legislation have operated under the assumption that CISA’s liability protections and flagship implementation of an “Automated Indicator Sharing (AIS)” capability was an essential element in national cyber defense. Since 2022, however, the failure of the program has been evident. It did not catalyze higher levels of threat information sharing. It was evaluated in recent Department of Homeland Security Office of Inspector General (OIG) and Interagency joint compliance reports. [1, 2, 3] As we wrote in January, the automated sharing ecosystem is plagued by severe decay and structural failure:

• Collapsing Participation: The overall number of federal and non-federal participants actively utilizing AIS has plummeted by a staggering 65 percent since its peak in 2020. By 2024, AIS was left with a mere 18 federal participants and 87 non-federal participants.
• Systemic Overreliance: While the total volume of shared cyber threat indicators (CTIs) superficially increased in 2024, a single private-sector platform’s contributions accounted for 89 percent of the public collection and 83 percent of the entire federal collection. This shows that the laws intent – to encourage and expand threat sharing – isn’t working.
• Technical and Structural Inertia: Federal entities remain notoriously reluctant to share operational incident data via machine-to-machine connections due to institutional bottlenecks, a lack of organizational resources, and policy constraints. Furthermore, downstream operators frequently choose to ignore or avoid uncategorized federal threat feeds because the shared indicators lack the specific operational context required to deploy fixes safely.

Furthermore, arguments that private firms need state-sanctioned safe harbors to circumvent antitrust liabilities when sharing technical data are largely hollow. As detailed by the Department of Justice (DOJ) and the Federal Trade Commission (FTC) in their joint antitrust policy statement, properly designed cyber threat exchanges are highly technical and distinct from competitively sensitive business data like pricing or output. The agencies have explicitly maintained for over two decades that legitimate cybersecurity information sharing does not violate antitrust laws. The legal roadblocks are a myth; the actual barrier is that government-managed clearinghouses simply fail to deliver timely, actionable value.

The Clearinghouse Trap: Centralizing Power, Stifling Innovation

In light of the chronic underperformance of existing automated frameworks like AIS, the 2026 Executive Order’s proposed AI cybersecurity clearinghouse is distinctly misplaced. Rather than modernizing defense, the mandate serves as a vehicle to expand state control over cutting-edge artificial intelligence.

The Executive Order additionally outlines a “Secure Frontier Model Deployment” framework that establishes a classified benchmarking process to designate advanced models as “covered frontier models”. Under the guise of being voluntary, firms are strongly incentivized to turn over their models to the federal government for up to 30 days before public release. The government then assumes the role of an arbiter, collaborating with firms to selectively hand-pick which “trusted partners” are granted early access to promote critical infrastructure security.

Taken together, this represents an unnecessary, centralized gatekeeping regime. Inserting a federal clearance step into the software pipeline slows down the deployment of capability equally valuable to defenders, introduces immense political and national security groupthink and opportunity for regulatory capture, and expands state visibility into proprietary algorithms and weights under the banner of deconflicting code scanning.

A Better Path: Decentralized Disclosure, Market Incentives, Transparent Process

The policy fixation on state-managed clearinghouses and review overlooks a far more robust, agile, and proven alternative to producing cybersecurity: the decentralized model of responsible disclosure governed by widely accepted norms, recommendations, standards, and guidance, which lowers coordination costs and is led by the AI ecosystem itself. 

The success of Anthropic’s “Project Glasswing” initiative offers an illustrative roadmap. Under Project Glasswing, advanced frontier models like Mythos were provided directly to diverse, private-sector security partners and system defenders. Rather than routing vulnerability details through a slow-moving government clearinghouse, this advanced access allowed operators to rapidly stress-test their own code, identify thousands of live flaws across complex dependencies, and patch systems in real-time before adversaries could weaponize them. While the Glasswing initiative shows it can be done, we’ve noted its club governance structure needs to evolve. Comcast’s Elizabeth Chernow similarly pointed this out in the panel, noting a recent paper promoting criteria for Responsible Advanced Access.

Decentralized, voluntary defense continues to succeed because it leverages the natural economic incentives of network operators. Critical infrastructure operators, enterprise businesses, and software maintainers possess an existential, bottom-line interest in protecting their own networks and customer data. When AI providers distribute advanced testing models that fundamentally reduce vulnerability identification costs directly to these stakeholders, they help unlock a massive, parallelized army of defenders using multiple models who are contextually aware and uniquely equipped to find flaws and develop and validate patches without breaking downstream dependencies.

As Google’s Kate Charlet observed during the panel, true long-term security will be achieved by embedding AI capabilities directly into standard software development workflows and cloud architectures, ensuring that insecure code is caught before it is ever compiled. This structural shift requires broad, open access to advanced tooling—not a restricted government gatekeeping system that funnels code to a federal clearinghouse and approval. 

Unfortunately, the administration doubled down last week, leveling export controls on Anthropic’s Mythos and Fable (Mythos with guard rails) large language models. Anthropic complied and stated they “believe the government should have the ability to block unsafe deployments, as part of a statutory process that is transparent, fair, clear, and grounded in technical facts.” Multiple cybersecurity experts questioned the rationale for the ban, argued “pulling the best capabilities away from defenders without a good reason when our adversaries are rapidly advancing is dangerous”, and called for an “open, scientific and transparent process of handling AI risk assessments in the future.” 

We agree. Rather than reviving failed information-sharing approaches via CISA reauthorization, bottlenecking frontier model access through federal directives, and hobbling the fast evolving production of cybersecurity, Washington should support defenders and innovation.

The post Defending Cyberspace: Cybersecurity and Frontier AI appeared first on Internet Governance Project.

A new report from IGP, sponsored by the Tech and Media Convergency (TMC), investigates an unfortunate divergence in Tanzania’s development. While internet adoption has climbed to roughly one-third of the population, the expected digital economic growth has failed to materialize. The research argues that the bottleneck is not a lack of infrastructure, but a self-reinforcing system of restrictive governance.

The Tanzanian Anomaly

As internet use rose from negligible levels in the early 2000s to approximately 31% today, Tanzania’s services sector contribution to GDP has actually contracted, falling from a 2001 peak of 49% to 29% in 2024 (black-dotted and red lines below).

The ICT sector itself has stagnated at 1.5% to 1.6% of GDP. Comparing Tanzania to Kenya, a neighbor with similar demographics and nearly identical internet penetration (~35%), reveals the scale of the under performance. Kenya’s services sector accounts for roughly 55% of its economy, nearly double Tanzania’s. In the broader digital economy, including fintech and platform commerce, Tanzania’s contribution is less than one-fifth that of Kenya’s.

In fact, the pattern runs similar for every one of Tanzania’s neighbors (figure below) where rising connectivity tracks with a growing or stable services sector. A positive relationship between internet adoption and services value-added appears everywhere except Tanzania, which moves in the opposite direction.

Wrong turns in digital governance

The study identifies three interlocking policy failures that, together, suppress the productive use of Tanzania’s growing connectivity.

Digital sovereignty: Mandates for data localization and local ownership force costly, redundant infrastructure and deter foreign investment without delivering any of the privacy or security benefits used to justify them.

Censorship, Surveillance, and Restrictions: A restrictive framework of content licensing, VPN registration, and internet shutdowns creates direct economic damage, including an estimated US$250 million in losses during the 2025 election period alone.

Cronyism: the most insidious factor aligning regulatory authority with private commercial interest. Here we find that sovereignty and censorship mandates are not independent policy choices but downstream instruments of a patronage system in which the ruling party, telecom operators, and regulators are bound together by shared interest using regulatory power to protect insiders from competition.

A maladaptive system

What makes the report distinctive is its insistence that these are not errors to be corrected with better technical advice. They form a self-reinforcing equilibrium that restricts growth. Restrictive policies create discretionary regulatory power; that power creates opportunities for extraction; and extraction creates vested interests in keeping the restrictions in place. Drawing on the political-economy framework of North, Wallis, and Weingast, we describe a “limited access order” a system in which a ruling coalition distributes economic privileges to insiders precisely to prevent outsiders from competing while using ICTs to stifle elections and free expression.

This is why we argue that incremental reforms under President Samia Suluhu Hassan have not changed the sector’s trajectory. The underlying incentive structure between regulator and regulated remains intact. The report also explains the widening gap between Tanzania’s ambitious strategic documents like its Vision 2050, the Digital Economy Strategic Framework 2024–2034, and the regulatory machinery that quietly continues to operate against them. The aspirational language of digital transformation has not replaced the architecture that suppresses it. The two simply coexist in a performative theater of legitimacy.

A way out that the state can live with

The report closes with ten concrete policy recommendations, but its framing is unusual for work in this space. Rather than calling for the ruling party to abandon the governance logic that sustains its dominance, (a clear political non-starter) we propose reforms calibrated to be politically survivable: revoking data localization mandates, introducing risk-based cross-border data rules, establishing judicial oversight for data access, liberalizing content licensing, opening up wholesale infrastructure, and replacing ownership caps with competition-based assessments.

Each recommendation can be justified on fiscal grounds alone. A larger, more dynamic ICT sector would broaden the tax base, attract foreign investment, and create jobs thereby offsetting the perceived loss of control with gains that accrue to all stakeholders, including the state. A more abundant and independent digital economy would even lower the stakes of political competition, making any future transfer of power less destabilizing.

Whether any of it happens depends on a race we can identify but not resolve, that is, whether the mounting economic costs of the current system will force change before an expanding surveillance apparatus from mandatory SIM registration and the forthcoming Jamii Namba digital ID, locks in a more durable architecture of control. For now, Tanzanians keep coming online. The question is whether being online will ever be allowed to pay off.

“The Political Economy of Digital Governance in Tanzania: Pathways to ICT Sector Growth” is an Internet Governance Project report sponsored by the Tech & Media Convergency. Read the full report here.

The post Tanzania Is Getting Online. Its Digital Economy Isn’t Following. appeared first on Internet Governance Project.

Three years later, machine learning applications have expanded in use and capability, yet humans are still around. Most AI doomers have retreated from saying that AI will wipe us out, reverting to the less apocalyptic but equally extreme claim that it will eliminate all human jobs, and yet unemployment remains low at 4.3% and the last three job reports showed significant growth. 

Nevertheless, Anthropic on June 4 resurfaced the call for a pause, this time with an apparently more rational set of arguments. The underlying concern is the same, however: “recursive self-improvement” in frontier models might increase the “risks of humans losing control over AI systems.” So: 

“If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing. … Without a global coordination mechanism, companies and governments will have to make difficult decisions about safety while under competitive and geopolitical pressures. We believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology.” 

IGP has published research challenging the claim that recursive self-improvement in AI software will lead to an autonomous, all-powerful AI system. Anthropic CEO Dario Amodei explicitly rejects doomerism, and the blog post admits that “recursive self-improvement is not inevitable,” but they still believe “it could come sooner than most institutions are prepared for.”  

We have also published research explaining why a global “pause” in AI development is not a viable governance solution. This passage was published in Telecommunications Policy nearly a year and a half ago: 

“…an effective moratorium would require every government and every firm in the computer industry to abide by it. And not all businesses or governments really want to pause. It is unlikely that the U.S., Israel, China, the European Union, Russia and India would trust each other enough to stop unilaterally. Nor would they be eager to expose their advanced software and hardware capabilities to inspection by other states or foreign businesses. Given the (alleged) military and business advantages of being the leader in AI technology, each state and business would have a strong incentive to defect from a moratorium. Even if noncompliance could be detected reliably, there would be no way for complying governments to enforce the pause agreement upon recalcitrant governments of sufficient size and power. “Pause AI” just does not work. But if it did, it would involve systematic surveillance and control of ICT capabilities worldwide. 

While Anthropic’s pause revival recognizes these collective action problems. their only response is that:  

“The Anthropic Institute will conduct research—in collaboration with many others—and take actions to help build the systems that a credible slowdown or pause would require.” 

Exploring verification methods might be helpful, but as we learned from our efforts to promote the creation of a neutral international cyber attribution organization, knowing what needs to be done is not the obstacle; it’s getting the actors, especially rivalrous nation-states, to commit to such an institution that is the hard part. If the new institution requires sharing data and possibly losing competitive advantages, it is unlikely to happen.  

The National Security Nexus 

What makes this quasi-doomer revival more interesting and potentially troublesome, however, is the growing nexus between geopolitical competition, the frontier AI model developers, and U.S. national security agencies.  

A Trump administration Executive Order that was issued only two days before the Anthropic blog post required relevant federal agencies to develop “a classified benchmarking process” to “determine the threshold at which an AI model should be designated a ‘covered frontier model.’” If a model meets or exceeds this threshold, then the AI developers would be able to provide the Federal Government with confidential access to the frontier models, to allow the Feds to review it “for a period of up to 30 days before they plan to release such models to other trusted partners.” 

Advocates of market-led AI development were happy with the voluntary nature of the program, and the reduction of the review period from 90 days to 30 days. An earlier proposal was considering potential AI rules comparable to the system used by the Food and Drug Administration to approve medicines. Still, the idea of prior review of communication and information technologies by a national government’s national security apparatus is not something to be taken lightly. Claude’s ability to find vulnerabilities in software does pose real cybersecurity risks – but those risks occur to any software user, not just the government.  

The real risk here is the militarization of AI. Anthropic’s own assessment of AI risks actually reinforces US tendencies to see AI as a weapon in its competition with China. We have seen time and again how national security claims privilege the power of the state over the rights and freedoms of the individual and the economic opportunities of innovators. A good example was the government’s decision to classify encryption technology as a weapon in the 1970s and its decades-long attempt to keep strong encryption capabilities out of the hands of civilians, to preserve its own ability to engage in surveillance.   

Is Anthropic sincere about gaining input? 

In its June 4 blog post, Anthropic’s policy staff expresses an intention to consult with a broader community to develop policy proposals:  

“In the coming months, [the Anthropic Institute] will organize conversations where policymakers, researchers, civil society, and other AI companies can help answer some of the questions this piece raises, especially around full recursive self-improvement and how to create better options for coordination and deliberation. We’ll publish what comes out of it.” 

IGP has been deeply involved in global digital governance discussions for two decades. If Anthropic is sincere about gaining all relevant perspectives, we look forward to being invited to these conversations. 

The post Anthropic Tries to Revive the “AI Pause” appeared first on Internet Governance Project.

Jensen Huang, CEO of Nvidia, debunked the argument that the US can contain Chinese AI development through export controls on computing power. He believes that chip control is futile because China can utilize its other advantages to aggregate and scale up its computing power, and the US will ultimately lose its global dominance on the global market by depriving its own leading companies of markets and profits. Huang’s rare, emotional expression struck at the core question: what is American technological leadership? Different answers to this question lead to divergence on chip export control policies.

The Biden policy of containment

In the Biden Presidency, the US government adopted a “Framework for AI Diffusion”, seeking to contain Chinese development of AI by controlling advanced computing power. This framework was based on an assumption that superintelligence is “imminent”, and computing power is the most important resource enabling it. At that time, the underlying premise of the “AI race” equated it with the race to build the atomic bomb: whoever gets it first would hold supreme power over the world. Hence, the U.S. must use its leading position in semiconductor production to carefully ration access to computing power or it will lose the race. Under this assumption, the Biden administration mandated BIS to develop targeted regulations restricting the export of chips to China, and Floating Point Operations Per Second (FLOPS), a measure of computer performance, became the critical metric for determining restrictions.

Yet Mueller (2025) and many others have debunked the whole theory of super intelligence.  There is no “race” to a defined finish line that brings ultimate power to the winner. There are only incremental, increasingly energy-intensive gains toward faster chips. And as Jeffrey Ding (2024) demonstrated convincingly in his book Technology and the Rise of Great Powers, it is a nation’s ability to put new technologies to work across its economy that matters most in competitions over wealth and power, not being the first to develop the technology.

Even if the rationale behind export controls is taken at face value, Murphy (2026) summarized its three loopholes: diplomatic cleavages created by tiered access categories, China’s own indigenisation efforts on computing, and the substitution of software and algorithmic innovations for compute requirements. These risks all indicate that computing diffusion control is not the right basket into which to put America’s eggs. Tellingly, the debate around export control has already shifted from whether chip control can contain Chinese development to “for how long” it can prolong the catching-up time.

Trump shifts gears, but Congress carries on

The Trump II administration has deviated a little from the “diffusion control” model. The July 2025 executive order on Promoting the Export of the American AI Technology Stack shifted focus from export restrictions to global market dominance. Under this approach, the costs and competitiveness of American companies became more of a priority. As a result, the BIS revised its license review rules in January 2026, shifting from a presumption of denial to case-by-case review. This does not mean that the Trump admin is returning to free trade; the MAGA trade doctrine still applies to chips, but chip exports are still allowed with certain caveats.

Not everyone in Washington agrees with this trend, however. The biggest opposition comes from Capitol Hill. From RASA to MATCH Act, the U.S. Congress has put forward bills to continue clamping down on the ability of American digital companies, which lead the world in size and scope, to do business globally. Five such bills are outlined below.

Why is Congress more aggressively restrictionist than the Trump administration? Three words: special interest lobbying. According to a Reuters article, Micron Technology (MU.O), the largest U.S. memory chipmaker, is a driving force pushing the U.S. Congress to pass the MATCH Act legislation, for example.

Leadership through market domination, or political control?

There are two approaches to sustaining a country’s technology leadership. One argues to keep strengthening America’s domination across world markets selling its advanced chips; the other side wants to build a wall around advanced chip design and manufacturing to restrict Chinese access to new chips.

If one were to steelman Dwarkesh’s argument, export controls are needed for the following reasons: 1) in a direct conflict with China, Chinese AI will provide a decisive advantage in a military conflict, and chip controls can prevent it; 2) The US has substantial leverage in terms of computing power and other layers of the computing stack, so why not use it to gain advantage?

On the other side, Jensen Huang seeks to sustain the American tech leadership by maintaining its central role in the semiconductor supply chain. This approach drives revenue to American companies and fuels the R&D and talent acquisition that drives innovation and better applications. Huang dismissed diffusion control as a “loser” mentality. Comparing chips to nuclear bombs is a “lousy” comparison, a product of fear-mongering. Naturally, Jensen’s judgment aligns with Nvidia’s interest as a private company. Nevertheless, there are real reasons to believe that it is more detrimental for the US to lose its dominance in global markets than to cave in to hyperbolic security risks about China’s AI capabilities.

From a theoretical perspective, this dilemma can be traced back to the popular theory of “weaponized interdependence,” a term political scientists Farrell and Newman popularized and a policy that many countries began to pursue years ago. Interdependence is said to be a source of asymmetric power, and states can weaponize such power to pursue their strategic goals. However, weaponization inevitably diminishes market domination, as the excluded party seeks to reduce their reliance on the original technology diffusion network.

What’s worse, it is evident now that export controls cost American companies more than Chinese firms. Economists from the Federal Reserve estimated that the export control since 2010 creates a total capitalization loss of $18 billion for Chinese targets, but for each affected American company, the average market capitalization loss reaches $1 billion, meaning a total decrease of $158 billion for American companies, along with an average revenue decline of 8.9%, and 7.3% decline in the total number of employees.

The war in Iran is making it clear that a simple technology and power advantage is not always sufficient for a strategic victory in a conflict. The US had faster chips, faster planes, better intelligence, more precision bombs, better missile defenses, but it was not enough to dislodge the Iranian regime or make it surrender, nor was it able to prevent the Strait of Hormuz from being blocked. Likewise, in a real-world US-China conflict, it’s hard to imagine how a 9 or 6 month lead in semiconductor power is going to make a decisive difference – for either side.

How China offsets the chip export controls

Predictably, chip export controls have pushed the Chinese companies away from any dependency on the American computing supply chain. They have undertaken the following efforts to achieve the goal.

• Fostering the domestic chips ecosystem: at many semiconductor production stages, domestic equipment shares have risen in the Chinese market. Per CSIS, overall domestic equipment share surged from 25% to 35% between 2024 and 2025, while domestic producers accounted for less than 15% of the domestic market before the US imposed export control. Noticeable progress came from the etching and thin-film deposition procedures, where local firms now supply about 40% of the domestic market. A recent harder push came from a new procurement policy in December 2025, requiring that Chinese fabs seeking approval for new capacity need to buy at least 50% of their equipment from domestic suppliers.

• Importing from non-US suppliers. Along with these self-sufficiency efforts, China’s semiconductor-manufacturing equipment imports reached a record high of $51.1 billion, almost five times the level of a decade earlier. But this new increase mainly came from increased imports from Japan, Netherland, and Singapore, while the import share from the US has decreased from 23% to 9% in the past 10 years.

• Using cloud and data center capacity in Southeast Asia: the tiered-category of America’s chip export control regime also compels Chinese companies to look for computing capacity outside mainland China, especially in Southeast Asian countries. For example, according to Reuters, by March 2026, ByteDance was working with Aolani Cloud in Malaysia on about 500 Nvidia Blackwell systems, totaling roughly 36,000 B200 chips, in a project costing more than US$2.5 billion. At the national policy level, China-ASEAN FTA 3.0 has positioned digital cooperation as a core pillar, specifically on digital trade, cross-border fiber optics, and data centers.

• Maximizing its advantages in energy efficiency: improvements in model efficiency and energy network can also offset the impact of computing power control. DeepSeek, along with other Chinese open-source models, are the most prominent case following this strategy; their innovations on model design reduces demands for computing power compared to American ones.

However, little attention has been given to a more fundamental advantage that China has been accumulating: the colossal national computing-energy network that reduces the energy price for computation. Since 2022, China has been constructing its “East-Data-West-Computing” (东数西算) project, linking data-abundant Eastern provinces with energy-affluent Western provinces. By the end of 2025, national hub nodes and data-center clusters under this national project took up 80% of China’s total 1.59 million PetaFLOPS computing capacity. All these improvements at the model and energy-computing layers result in comparatively lower token prices and dependencies on American computing devices.

All these efforts demonstrate China’s efforts to reduce reliance on the American computing network, but this does not mean that China has circumvented all the computing bottlenecks. But it is certain that China will continue to reduce its reliance on the US computing devices, undermining the very material foundation of imposing export control.

AI is not just about chips and models, it is a whole digital ecosystem that contains multiple parts. For Mueller, the ecosystem contains software, data, computing devices, and the network; In Jensen’s opinion, the AI ecosystem is a five-layer AI stack that also includes energy. Either way, competition in AI is a multi-layered game driven mostly by the market. Different states possess different advantages and leverage across the layers of the digital ecosystem. What’s clear is that the U.S. will not maintain its advantages by exclusions and restrictions on its own industry. That is a form of unilateral disarmament.

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