Typically, there are three big concerns that we talk about:
- The worry that terrorists will use AI to create doomsday viruses
- Worries about job displacement, human obsolescence, and economic dislocation
- The worry that superintelligent AI is a new dominant species that will disempower and possibly destroy humanity
But if the industry really does become dominated by a few giant companies, we have a fourth big thing to worry about — extreme inequality. If AI’s economic benefits are highly concentrated, we could end up with a comparatively small number of people controlling most of the purchasing power in our economy. In the extreme scenario, this could lead to a small number of people holding all the power in the world.
Something I had never considered. I use only ChatGPT. Does anyone have any experience using Claude vs ChatGPT vs Meta? Any qualitative difference?
Short video here: https://x.com/ianbremmer/status/2047745979479146516?s=20
Full video here: Ian Bremmer: Who Is Actually Running the World?
Ian Bremmer: Geopolitical Fragmentation and the Rise of “Techno-Polarity”
I. Executive Summary
The core thesis posits that the post-Cold War global order, characterized by American hegemony and integrated trade, has reached its termination point, yielding to a G-Zero world. This environment is defined by a vacuum of global leadership where neither the United States nor any other sovereign entity possesses the will or capacity to drive a collective agenda. The traditional “Pax Americana” has fragmented into three distinct, non-overlapping sectors: a security-focused global order dominated by the U.S. and its allies; an economic order characterized by deep U.S.-China interdependence and emerging protectionism; and a burgeoning digital order where technology companies are evolving into sovereign actors.
Ian Bremmer argues that in Western democracies, the state has lost its monopoly on governance to algorithm-driven platforms that intermediatize human interaction and social stability. Unlike China, where the state maintains a stranglehold on data and AI development, Western technology firms now exercise “techno-polarity,” functioning as geopolitical actors that determine national security outcomes and social cohesion without electoral accountability. This shift is accelerated by a “political revolution” within the United States, where systemic distrust has transformed domestic politics into a grievance-based struggle for institutional control, effectively rendering the U.S. a “divided state” rather than a unified global leader.
The “Strategic Thinking” framework presented emphasizes two critical phases: the objective assessment of the “terrain” (the factual environment) before reaction, and the deliberate allocation of “time” as the primary scarce resource. Bremmer concludes that the survival of global stability depends on whether fragmented powers can establish “guardrails” for transformative technologies—specifically AI and biotechnology—before a systemic “crisis” necessitates forced cooperation.
II. Insight Bullets
- The G-Zero Reality: We have transitioned from G7/G20 leadership to a state where no country or group of countries has the influence to direct the global agenda, leading to increased volatility.
- Techno-Polarity: In the West, technology companies have become sovereign actors that write their own regulations via the algorithms they deploy in real-time, often superseding state influence.
- US Internal Revolution: The election of Donald Trump and the rise of figures like Bernie Sanders are symptoms of a systemic belief among the populace that democratic institutions are “sclerotic” and broken.
- Grievance-Based Politics: US elections have shifted from policy debates to grievance-based contests where political adversaries are viewed as existential enemies, eroding checks and balances.
- Social Media as a Border Guard: The US government is increasingly mirroring authoritarian states by requiring 5-year social media histories for visa applicants, a move verified by Customs and Border Protection (2025).
- Reverse Technology Transfer: European nations are now conditioning Chinese investment on technology transfers to Europe (specifically in EV and battery tech), reversing the historical flow of intellectual property.
- Algorithm Intermediation: Human beings are becoming “hybrid entities” whose social, commercial, and political interactions are entirely filtered through proprietary AI models.
- Information Tribalism: The fracturing of shared information ecosystems prevents “Step 1” strategic thinking (understanding the terrain), as different demographics cannot agree on basic facts.
- The “U-Curve” of Openness: Bremmer’s original “J-Curve” theory (openness leads to stability) has shifted to a “U-Curve,” where authoritarian states (China) use technology to consolidate power more effectively than democracies.
- Australian Digital Guardrails: Australia has implemented a mandatory minimum age of 16 for social media accounts to protect adolescent mental health, as documented by the eSafety Commissioner (2025).
- Resource Randomness: Patriotism and religion are viewed as “random” accidents of birth; strategic thinkers must separate personal identity from these variables to assess global shifts objectively.
- Time as the Final Equalizer: Regardless of wealth or power, time remains the only immutable constraint; strategic success is determined by how one allocates time relative to long-term trends.
- The Disrespect Variable: Russia’s geopolitical actions are driven less by economic gain and more by a “sense of lost empire” and perceived disrespect from the West post-1991.
- AI Arms Control Gap: Unlike the Cold War’s nuclear “hotline,” there currently exists no global framework or “hotline” for AI or bioweapon containment between the US and China.
- Geopolitical Buddhism: Effective strategy requires a clinical detachment from one’s own ideas; holding onto an idea as “part of yourself” prevents the necessary pivot when the environment changes.
IV. Actionable Protocol (Prioritized)
High Confidence Tier (Level A/B Policy Evidence)
- Digital Age-Gating: Implement strict digital boundaries for minors. The Australian “Online Safety Amendment” serves as a verified template for reducing algorithmic exposure in the under-16 demographic to mitigate mental health decline UNICEF (2025).
- Sovereign Data Auditing: Entrepreneurs and institutions must treat social media history as a permanent public record. Mandatory disclosure for international transit is now a formalized US entry protocol CBP (2025).
Experimental Tier (Strategic Framework)
- Terrain-First Assessment: Adopt Bremmer’s “Step 1” protocol: Suspend all “good/bad” judgments until the terrain is mapped using divergent data sources to counteract algorithmic bias.
- Localized Resilience: In a G-Zero world, rely on local “city-state” dynamics (e.g., the “New York effect”) rather than national stability, as urban centers often maintain higher social cohesion and economic integration than the states that house them.
Red Flag Zone (Safety Data Absent)
- Unregulated AI Proliferation: Claims that “AI will eventually regulate itself” lack empirical safety data. The current absence of US-China AI arms control suggests a high-risk window for “black swan” bioweapon or economic marketplace events.
- Institutional “Memory” Bias: Relying on the stability of 20th-century institutions (UN, IMF) as a guarantee for future stability is dangerous; these entities are currently in a “cycle of decay” and lack the enforcement mechanisms for 21st-century techno-threats.
You have to wonder… how long before curious high school biology students start developing novel viruses while playing around after school in the lab …
Source: https://x.com/heygurisingh/status/2047736841437348326?s=20
and in another news story …
“That night in the scientist’s home office, the chatbot explained how to modify an infamous pathogen in a lab so that it would resist known treatments. Worse, the bot described in vivid detail how to release the superbug, identifying a security lapse in a large public transit system … The bot outlined a plan to maximize casualties and minimize the chances of being caught.” NYT (Gift Article): A.I. Bots Told Scientists How to Make Biological Weapons.
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With the help of AIs, they could maximize the viral danger. It is a matter of optimization, lethality must be high but not too much, whereas infectivity must be high, the incubation time should be long enough, with host tissue tropism, mutation rate, immune evasion being other factors to optimize.
It turns out that SARSCOV2 was close to this creepy optimization, had the IFR (infection fatality rate) been higher, it could have meant total social disruption.
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The pushback we are seeing in San Francisco:
I define AI populism as a worldview in which AI is viewed not only as a normal technology but as an elite political project to be resisted. It regards AI as a thing manufactured by out-of-touch billionaires and pushed onto an unwilling public to achieve sinister aims like “capitalist efficiency” (layoffs) and “population management” (surveillance). AI populists don’t really care whether ChatGPT is personally useful, or if Waymos eke out some safety gains: AI’s utility as a tool is immaterial relative to the unwelcome societal change it represents.
Among the public, AI populism shows up as individual attempts to block AI encroachment; for example, data center NIMBYism, AI witchhunts among creatives, and in the extreme, assassination attempts like what happened to Altman this week.
I don’t know what exactly motivated Altman’s assailants, of course, just as I don’t know what specific thing radicalized Luigi Mangione or Tyler Robinson. But the 20-year-old Molotov-thrower had joined a Pause AI Discord and penned a Substack post on existential risk, writing that AI executives are “sociopaths/psychopaths” and “gambling with your future and the lives of your children… These people are almost nothing like you.” We know less about the second set of attackers, except that they are also young: 23 and 25.
What seems likely is that the anti-elite and nihilistic attitudes that have dominated US political culture in the last few years are transmuting into anger at AI billionaires. Young people are particularly incensed. Gen Z already grew up in a world that they felt was shrinking, where grift and shitcoins and sports gambling looked like the only paths up. Now, they’re being told AI is the reason they can’t get a job—and potentially never will. Just as the United Healthcare CEO seemed like a justified target to many disillusioned and radicalized young people, so will AI executives be to many more.
Obstructionism, cancel culture,
terrorismdirect action: this is what politics looks like when faith in democratic institutions has collapsed.
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Read the full article here (gift link):
Silicon Valley Is Bracing for a Permanent Underclass
Whether you talk with engineers, venture capitalists, founders or managers, or with doomers, accelerationists, lefties or libertarians, the so-called San Francisco consensus on the impact of A.I. for workers is bleak. Many are convinced that advanced A.I. will soon surpass human capabilities. This would produce tremendous growth and scientific achievement, but it would also displace millions of jobs as fewer humans are needed to make the economy run. The technology will depress economic mobility and exacerbate inequality, while ferrying power and wealth to the A.I. companies and the existing owners of capital.
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Meanwhile, in China:
Article: Chinese Courts Rule Companies Cannot Fire Workers Simply to Replace Them With AI - Caixin Global
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There has been some more news on AI models producing novel solutions to old problems that people were not able to solve. Previous examples of this boiled down to models finding solutions that already appeared in the literature that nobody noticed. But more recent ones are cases where models produced truly novel solutions to problems people had worked on for several years. e.g.:
We introduce a new method for bounding Erdős sums of primitive sets, suggested from output of GPT-5.4 Pro, based on Markov chains with von Mangoldt weights.
(I added emphasis to the word “new”.)
If it happens in math, it can happen in other disciplines, as well. In some ways math is harder than other fields, and in some ways it’s easier. In math you tend to have long chains of logic where an error at any point in the chain completely invalidates the argument. (More nuance: proofs often do have errors, but they can also often be repaired.) My impression of biomedical fields, say, is that there is a higher tolerance for errors in logic than there is in math – the chains of logic tend to be much shorter and less rigid in biomedicine; though, you tend to use more ad hoc facts and what seem like deus ex machinas in movies (Why does such and so medical outcome occur? Well, it’s because of the XYZ gene, case closed.) and you rarely have a sense of finality with arguments proved. However, AI models are getting better at handling all this ambiguity and things.
Another interesting sign is this tweet from a U.C. Berkeley professor:
https://x.com/tonylfeng/status/2050576299123679299#m
While I agree in principle, in practice I think AI raises tough questions about what we even mean by “good science” in the context of mathematics. There’s an infinite number of true mathematical statements, many of which we can but do not bother to prove because we consider them “routine” and therefore uninteresting. What counts as “routine” is subjective, but I think it approximately means “doable using (only) well-known existing techniques”. If AI becomes consistently stronger than humans at certain mathematical skills (for example, “combining already-existing techniques in a new way”), then certain types of previously non-routine problems will become routine in a higher level sense: doable by the well-known technique of querying an AI chatbot. At that point, are those problems still interesting?
He’s starting to worry that AI will be able to solve harder and harder problems – even using new methods, or new combinations of old ones – which could cause people to question whether what they are doing is worthwhile anymore. A response to this tweet was given by the mathematician Ken Ono, who wrote:
https://x.com/KenOno691/status/2050719804924064056#m
One interesting line from that long tweet:
If AI had been around then, it likely would have solved it, because AI isn’t constrained by our human silos.
He seems to be implying that models are getting good enough to actually solve research-level problems from back in 1996. Imagine what it means for physics, chemistry, biomedicine, etc.
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