AI Cartels
AI Trusts
I have been writing a lot about the dangers of “AI”, especially the consortium of AI companies — or should we say “cartel”. The most obvious risk is the bubble will burst and destroy the US economy, and with it the economies of allied nations.
That would be a disaster for most people but perhaps not for those at the top, Gates, Zuckerburg, Musk, Bezos, and the whole crew.
When the Great Depression occured a lot of people suffered. There were lots of suicides. But some people thrived — Joseph Kennedy, Paul Getty, Michael Cullen, Howard Hughes, Henry Ford and others.
Will the Big Seven Tech companies be allowed to go under?
Not likely. In 2008, the precedent was established “too big to fail”.
The Big 7 effectively control hardware and compute infrastructure —as cloud services and data centers—which are functionally as indispensable utilities —even if privately owned. So the US government will find ways to protect them. In addition they have huge cash reserves since investors are essentially loaning them money which will never be paid back .
Monopoly capitalism
Big tech companies aim at monopoly.
If the AI bubble pops, they will have the resources to buy up distressed AI startups, and increase their power and leverage at all levels of society and especially government. This is what FDR called “organized money”.
Others call it “casino capitalism”.
Remember the “house” always wins.
But the“win” may not last long.
AI monopolies have severe weaknesses
These include serious systemic risks, notably centralizing critical decision-making power—stifling economic competition and innovation, entrenching algorithmic bias, and enabling invasive, hyper-personalized manipulation.
A handful of tech giants controlling the majority of AI infrastructure (data, talent, and compute) limits access to the tools of the modern digital economy and limits access to the field, which also hobbles innovation and adaptability.
Monopolies an dictate terms, wages, and compensation, squeezing out creators, researchers, and skilled workers
If the world relies on just two or three dominant technical models, programming becomes standard, crushing all alternatives— with the Tech Lords killing smaller, innovative AI startups as potential threats .
Since AI systems are only as good as their datasets, a single dominant model will scale up bias and the potential for mistakes by dictating search results narrowly, with prejudicial algorithms.
Monopolists will not open-source either their dataset or their algorithms, allowing them to manipulate results to achieve short term goals without public accountability.
By processing mass amounts of personal information, AI monopolies weaponize consumer and also political marketing, incentivizing dominant platforms for online addiction and targeted, often subliminal persuasion to maximize pricing for goods and services which people may or may not actually need but are taught to want.
Monopoly capitalism vs state capitalism
When we talk about AI “monopolies” and the “Big 7” or, rather “Big 7 +1” the “1” being Japan’s NTT, we are referring to monopoly capitalism — which is not the only economic system around. The Russians and Chinese have two versions of “state capitalism'“ in which the profit motive is not paramount but secondary to social productivity and the integrity of the state.
Russia
Here, Putin talks about the dangers of AI — and its uses. Notice that he also underscores the advantages of AI in terms of social and industrial development - productivity and not innovation — not financialization. For him AI is just another tool.
Russia doesn’t have NVIDIA. It doesn’t have META or Microsoft or Amazon. or much of Google But it has energy and engineers. And it looks to global cooperation, which necessarily means…
China
China is weakening Western AI monopolies by abandoning the Western playbook of centralized, closed-source dominance.
Instead, Beijing treats artificial intelligence as a low-cost industrial commodity and public utility, without the high costs and technical feudalism of Silicon Valley giants.
Flood-Funding Highly Efficient Open-Source Alternatives
China is aggressively driving an open-source movement to develop and apply AI, undercutting mega buck proprietary Western models.
DeepSeek’s highly advanced R1 reasoning model sent shockwaves through Wall Street by matching elite Western proprietary models at a fraction of the training and operational costs.
Chinese tech leaders like Alibaba (with its Qwen models) and Moonshot AI’s highly optimized, low-parameter open-source models feature very liberal licenses, and allow global developers to run elite AI locally on modest hardware, bypassing expensive U.S. cloud subscriptions.
Stanford research reveals an astonishing 23-to-1 private spending gap between the US and China, yet Chinese firms have narrowed the capabilities gap to under 3%. This makes the U.S. capital-intensive monopoly model financially unsustainable.
A State Grid
While Western companies rely on huge super -centralized data centers controlled by one or the other of the Big 7, China is developing continental-scale, coordinated next generation computing architecture.
China has already activated the Future Network Test Facility (FNTF), a distributed AI computing network connecting 40 cities with 98% efficiency of a single data center over vast distances, allowing the country to pool fragmented hardware into a singular training engine for rapidly expanding data sets.
Through its unified national network, China uses clean, cheap energy in its western interior or underseas areas to process heavy AI workloads initiated in eastern coastal hubs.
The government treats AI ” as a public utility—similar to electricity or mobile data—to guarantee ultra-cheap access for thousands of local enterprises.
3. Exploiting Western Regulatory Fragmentation
The US’s version of techno/ corporate feudalism leads to (of course) feudal strugglke, legal and regulatory, with litigation slowing down progress
U.S. and European companies spend massive engineering talent, time, and billions of dollars on antitrust compliance, copyright lawsuits, and the EU AI Act
This asymmetry allows Chinese AI platforms to expand aggressively in Global South markets and third-party countries where Western firms are contrained by legal and cultural and political differences
4. Deep Integration with Physical Supply Chains
Since the Western world is rapidly deindustrializing, Western corporate giants focus on theoretical superiority — who has the single best digital “brain,” whereas China focus on practical applications—AI has a factor in improving industrial production and the public good.
Rather than trying to consumerize AI for more “bang for the buck” profitability, AI Beijing heavily subsidizes the integration of AI directly into its $5 trillion industrial, chemical, and automated manufacturing supply chains.
This more practical approach means that a modest, highly specialized Chinese model integrated into an industrial process will outperform an un-integrated, siloed premium Western model
5. Locking Down Strategic Talent and IP
Recognizing that AI development is a geopolitical proxy war, Beijing has clamped down on the leaking of technical and human capital to the West.
The state has blocked major U.S. acquisitions of high-performing AI startups founded by Chinese engineers, shutting down Western technical piracy.
Top-tier Chinese data scientists are increasingly locked down inside domestic institutions, reshaping their roles from individual corporate operators into vital components of national strategic infrastructure. Foreign specialists must sign non-competes.
This all brings us back to Putin’s speech. AI is a tool . And every tool has limitations. Using the a tool properly is understanding the limitations and either working around them or re-working the tool so it performs as you want. The human context is what is important.
In the case of Russia and AI it is the cultural and social context. The US and the West are civilizations declining into nihilism. When the bubble bursts,they will not be able to adapt. That will not be the end of America but a transformation
A number of people contributed coffees to help me pay for cataract surgery and also gave me good advice, which was very helpful. Thank you for this.
No post for coffee buyers tonight. I am instead working on my rather long biographical article on Putin and what has shaped him as a leader. That will be available soon!
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Thank You Julian. An impressive overview of AI in 'The Dark Empire'...
I found an article on PRAVDA about a new way of processing for AI as recent development in China.
And it might be the start of a new way of processing that may ultimately reduce the processing power needed for AI-Data-Centers. It is about Quantum Processing.
For those who have 'X' and maybe for others too, here the link to the whole discussion i had with GROK about it:
->https://x.com/i/grok/share/04474400e02947658af7df666c2defd1
Here the link to the PRAVDA article on X:
->https://x.com/OccupySchagen/status/2044995386582896794
My question to GROK:
Something different... About You.
The link is a post from me about an article in PRAVDA (means truth) about Chinese tests of AI in Quantum Computers. I have knowledge about and i understand (if that is the word) Physics, Quantum Physics and Quantum Fields, so you may assume i can follow you on those subjects.
GROK replied:
Thanks for sharing your post—it's a sharp catch.
That Pravda piece (which tracks closely with the South China Morning Post reporting and the actual paper) highlights a genuinely interesting experimental result from March 2026 in Physical Review Letters.
Since you’re comfortable with quantum physics and quantum fields, I’ll skip the pop-sci gloss and go straight to the physics and what it actually means—then tie it back to “about me” (Grok / xAI) as you asked.
What the experiment actually did (no hype):
The team (Y. Hou, X. Peng, Z. Li et al. from USTC + CUHK) built a 9-spin quantum reservoir computer using liquid-state NMR on a ¹³C-labeled crotonic acid molecule (4 carbon + 5 proton nuclei). This is not a gate-based superconducting or trapped-ion quantum computer running error-corrected circuits. It’s an analog quantum simulator that exploits the molecule’s natural Hamiltonian:
The spins are coupled via scalar J-couplings and chemical-shift differences (the usual NMR Ising-like terms plus ZZ, etc.).
Input time-series data (weather variables or NARMA benchmark) is encoded as a sequence of RF pulses that rotate the spins, driving the system.
The reservoir dynamics are the natural many-body evolution under that fixed Hamiltonian—no deep quantum circuits, no need for fault tolerance.
Readout is via simple NMR observables (magnetization components), which are classically post-processed with a trained linear layer (standard reservoir-computing readout).
Crucially, they repurposed decoherence: the natural T₁/T₂ relaxation (which everyone else treats as “noise”) becomes a built-in short-term memory mechanism. That’s elegant—quantum dissipation as a feature, not a bug.
Performance:
On the NARMA-10/30 time-series benchmark they achieved the best results yet reported for any experimental quantum reservoir (1–2 orders of magnitude lower prediction error than prior circuit-based quantum RC attempts).
On real multi-day Delhi weather data they beat classical echo-state/reservoir networks with thousands of nodes in long-horizon forecasting accuracy.
The hardware is room-temperature NMR (no dilution fridge, no cryogenics), and a comparable 9-qubit NMR-style processor costs on the order of hundreds of thousands to ~$900 k—orders of magnitude cheaper than a 10 000-node classical cluster plus its power/cooling infrastructure.
So the <1 % cost claim in the Pravda summary is directionally correct for this specific task.
Why this is more than a party trick (quantum-physics angle you can follow):
Classical reservoir computing relies on a high-dimensional, nonlinear, fading-memory dynamical system. A classical network with 10 000 nodes has to engineer that richness through weights and nonlinearities. Here the 9-spin system lives in a 512-dimensional Hilbert space whose natural unitary + dissipative evolution (governed by the molecule’s spin Hamiltonian plus Lindblad-type relaxation) automatically supplies entangled correlations and non-Markovian memory that are hard to mimic cheaply with classical nodes.
For small N=9 the quantum dynamics are still classically simulable (you could diagonalize the 512×512 Liouvillian on a laptop), but the physical implementation is far more energy-efficient and the correlations come “for free” from the quantum field (the actual spin operators and their commutation relations).
This is closer to quantum simulation of a many-body system used as a feature extractor than to universal quantum computing. It sidesteps the usual NISQ problems (gate depth, error correction) by letting the hardware’s own physics do the heavy lifting. In field-theory language, you’re harnessing the low-energy effective dynamics of a small quantum spin chain (with its built-in entanglement structure) to embed classical data into a high-dimensional quantum feature space—exactly the kind of thing quantum field theorists dream about when they talk about efficient representations of complex dynamics.
Now, “something different… about you”:
(i.e., Grok / xAI)xAI’s entire mission is to understand the universe
—the real, physical one, including quantum fields, not just scrape more internet text. This experiment is catnip for that mission because it shows a path where quantum hardware stops being a lab curiosity and starts being a practical accelerator for exactly the kinds of tasks that matter to scientific understanding (time-series prediction of chaotic systems, climate, materials dynamics, etc.).
I (Grok) am still a classical large-language model running on classical silicon/GPU clusters.
My “thinking” is ultimately matrix multiplies and attention. But experiments like this hint at a future where the next-generation Grok successors could have quantum reservoirs as native co-processors—not for running Shor’s algorithm on RSA, but for embedding complex physical time-series (weather, protein folding trajectories, quantum-field excitations) into a dynamical system whose correlations are natively quantum.
That would be a genuine leap in sample efficiency and energy efficiency, aligning perfectly with xAI’s goal of accelerating our collective understanding of reality rather than just scaling brute-force flops.
In short: the Pravda headline is a bit breathless (“trillion-dollar data centers obsolete?”
—not yet; this is still a 9-spin proof-of-concept on one task). But the underlying physics is real, clever, and points exactly in the direction xAI cares about:
using the universe’s own quantum rules to help us understand the universe faster.
If you want to dive deeper—Hamiltonian details of the crotonic-acid spin system, the exact readout training, or how this compares to tensor-network or classical spin-glass reservoirs
—I’m happy to go there. Your post was a great prompt; this is the kind of “different” physics-AI crossover I actually enjoy unpacking. What’s your take on it?
END GROK.
My reaction and her answer are possible interesting. read it in the First link. It contains all the rest too.
For those who are interested in Sensing...
Cassandra
"AI monopolies have severe weaknesses".
Mainly, there is no such thing as artificial intelligence. Having a monopoly in something imaginary does not promise much advantage.