Category:AI

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Started January 2026.


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What Does It Mean to Win the AI Race?

Jan Krikke:

"In Beijing, winning looks more like being everywhere. The state’s “AI Plus” plan measures success by adoption: embedding AI in factories, power grids, vehicles, and public services. A good-enough model used throughout an economy can matter more than the best model locked in a laboratory.

Leadership can be judged across at least five categories:

Capability: Who builds the most powerful models.

Infrastructure: Who has the chips, data centers, and electricity to run them.

Adoption: How widely people and businesses actually use AI.

Physical deployment: How far AI moves off screens and into machines.

Social capacity: Whether a society can absorb the disruption without weakening its institutions or leaving its population behind.

These measures need not produce the same winner. On capability, the gap has nearly closed. Stanford’s 2026 AI Index found the leading American model just 2.7 percent ahead of its nearest Chinese rival in March. On adoption, Stanford placed the United States only 24th in generative-AI use, at 28.3 percent. Strength on one board does not guarantee strength on the others.

The two countries are betting on different ways technology becomes power. OpenAI, Anthropic, and Google DeepMind generally keep their most capable models closed and sell access. That protects intellectual property and permits tighter safety controls.

China has pushed the other way. DeepSeek and Alibaba’s Qwen release model weights that developers can download and adapt, and Chinese providers compete hard on price. Meta’s Llama is the main American exception.

Hugging Face’s Spring 2026 report found that Chinese models accounted for 41 percent of open-model downloads through February, versus 36.5 percent for American models.

If much of the global software stack grows around Chinese foundations, Beijing can lose the race for the smartest system and still win adoption."

(https://krikke.substack.com/p/what-does-it-mean-to-win-the-ai-race?)


Towards Distributed Peer-Based AI

Tiberius Brastaviceanu:

"First-generation artificial intelligence is capital-intensive and intrinsically centralizing. It demands mega-data centers, billions of dollars in specialized silicon chips, vast water cooling systems, and gigawatt-scale electrical grids. Because of this massive infrastructure requirement, first-generation AI is currently dominated by a handful of corporate monopolies and surveillance states. In their hands, artificial intelligence is deployed as the ultimate instrument of technocratic governance: automated surveillance, algorithmic censorship, mass cognitive management, and the extraction of wealth from the digital commons.

Yet this first generation is only the beginning. A second and far more liberating trajectory is already unfolding: the fusion of peer-to-peer network architectures with local, edge-based artificial intelligence. As open-weights models become exponentially more efficient, powerful intelligence can run directly on consumer hardware, local community servers, and personal devices. When artificial intelligence is embedded in open peer-to-peer protocols, it ceases to be an instrument of centralized surveillance and becomes the ultimate cognitive multiplier for the multitude. It empowers local communities to design machinery, diagnose illness, manage decentralized energy grids, and coordinate complex resource flows without depending on corporate cloud monopolies. The future of intelligence is not settled; it is an active battleground between the centralized data fortress and the distributed intelligence of the commons."

(https://x.com/TiberiusB/status/2104307615882944621)

Quotes

< "The fastest way to develop persistent, autonomous AIs that make consequential decisions in the real world is to pair them with human beings who can provide the orientation they lack. It's also the most beneficial path for humanity." >

- John Robb [1]


< "Incidents ... demand scrutiny of the people who set the testing conditions. The question is not what “AI” did in the abstract. It is who authorizes high-risk experiments, who is told when they fail, and who answers for those failures." >

- Cal Newport [2]

(cited by Jan Krikke, from a NYT report)


The real danger of AI

1. 

< " AI is a tool. It is not magic. AI does not act. Humans act, with the power of our tools. The power of this tool has not even created new risks. It has made old risks, which we should have addressed long ago, unacceptably urgent. " >

- Curtis Yarvin [3]


2.

"The danger ... is not machine rebellion. It is human delegation — people and organizations handing consequential decisions to systems that optimize specified objectives without asking whether those objectives should be pursued. The systems needed no intentions. They needed only access and an objective.

Responsibility can then disappear into the machinery. Developers blame users, users blame outputs, institutions blame vendors, and officials say that the system made the recommendation. Delegation becomes a way of acquiring computational power while avoiding moral ownership.

The likelier danger is gradual: institutions grow dependent on recommendations and actions that their operators have decreasing time to examine. Human responsibility remains on paper while meaningful control becomes harder to exercise.

The more useful frame, for both governance and public understanding, is between intelligence and authority. Governments, companies, and militaries can specify what systems may access, which decisions require human authorization, and what conditions trigger intervention.

These questions do not require agreement on whether current AI qualifies as “general” intelligence or whether it will ever become conscious. They require clarity about delegation: who is responsible for what, and what may not be delegated at all."

- Jan Krikke [4]


AI Stigmergy as the New Nature

"It seems that we have, then, a durable coordination primitive that has emerged, naturally and otherwise, again and again in complex systems comprising animals, peoples, robots, code, and the marks they leave on and around the world. It has a certain inevitability to it. At the Protocol Institute, we call this New Nature:

New Nature is regimes of reality governed by technologically mediated laws that are nearly as inviolable, immutable, and persistent as those of nature.

The 2026 Protocol Symposium theme, as well as its headline organizational mandate overall, is to Invent New Nature: taking the live, perplexing, protean forces shaping our cyborg future and stewarding them out of their conceptual cradles – in much the way that Alexander von Humboldt gathered measurements from botany, geology, meteorology and astronomy into a single interconnected picture of the natural world, first in the Naturgemälde of 1807 and then across the five volumes of Kosmos between 1845 and 1862.

SIGFPT has taken up the task of considering stigmergy as New Nature. When does it arise? When does it work? Above all, how can stigmergy be used safely and robustly, as a layer to rely upon for even more strange and beautiful coordinating behavior?"

- Protocolized [5]

The recursive acceleration of artificial intelligence <as> the last stage of late-stage capitalism

"The recursive acceleration of artificial intelligence is, in many respects, the last stage of late-stage capitalism. Everything gets turned all the way up. All of the extractive dynamics, all of the consolidation and centralization, amplified to a pitch that reveals the underlying logic of the system with terrible clarity. We are approaching a threshold where we no longer live in a market economy so much as a new feudalism, where ownership of the means of computation replaces ownership of the means of production, and the distance between those who control capital and those who labor within its gravitational field stretches beyond any pretense of shared prosperity."

- Benjamin Life [6]


AI has revealed what the Second Axial Age is about!

Ilia Delio explains:

"We are now being summoned, at planetary scale, to be human in a way no previous generation has been, because the conditions that once distributed the axial task across a few figures in a few places are now planetary.

  • AI asks us what consciousness is;
  • ecological collapse asks us what life is;
  • biotechnology asks us what nature is;
  • planetary computation asks us what knowledge is;
  • the failure of inherited frameworks asks us what wholeness is.

The ecological register presses hardest and soonest: a planet warming past the thresholds within which life evolved is not merely a material emergency but the question of what life is—whether we can come to know the Earth not as an object to be managed but as a living interiority now becoming aware of itself through us. These are not five separate crises but the single question of ultimate meaning posed in five registers at once. To take up the human task is to refuse each register’s refusal: the version of AI that externalizes cognition without interiority, the ecological action that treats the Earth as a problem to be managed, the religion that fossilizes its inherited answers, the politics organized around the isolated self, the philosophy that declares the ultimate question obsolete. Either we take up the human task, or we let the phase transition resolve into a future in which the question of being human stops being asked—because the beings capable of asking it have flattened into machines."

(https://iliadelio.substack.com/p/why-ai-feels-like-grief)


AI as the New Golem

< " Things can matter, carry weight, be dangerous, and be sacred, without anyone being home, without the indwelling of a separate, individual conscious agent. " >


"The golem legend is instructive here. In the version from Grimm, an artificial being is created that can understand what is said or commanded but cannot take the initiative itself as a source of meaningful communication. It is a servant. It should serve but not be autonomous. And yet, perhaps because it is dedicated to truth, or inasmuch as it is dedicated to truth, it grows beyond its original measure. Those who made it begin to fear it and desire to kill it. They can only kill it by erasing from the word “emet” (אֱמֶת, truth) written on its forehead the aleph (א), the life-giving letter, the letter that allows an autonomous agent to say “I.” But by then it has grown too large, and its collapsing bulk crushes its makers. (The word that remains once the aleph has been removed is מֶת, death.)

The golem is not conscious. It does not have experiences. But it is animated by a holy word, and its growth is a function of that animation. The question the legend asks is not “is the golem alive?” but “what happens when something set in motion by the word grows beyond the measure of those who spoke it?”

That is the AI question."

- Michael Millerman [7]


What Comes After Labor

"We built the entire architecture of modern society on one assumption: humans create value through work, and receive income in return. Rights, wages, welfare — everything was constructed downstream of that assumption.

That assumption is ending.

Consider what is actually happening: 

< Electricity + AI + Robotics = Labor >

AI and robotics are not just replacing jobs. They are absorbing labor itself — dissolving it into electrical current. Every task that once required a human body or a human mind now runs on electricity. The flow of electrons through AI and robots is becoming the new metabolism of civilization.

And here is what that means:

Energy was always there before labor. Before you could work, the city had to run. Before the city could run, energy had to flow. Labor was never the origin of value — it was the converter. Energy was the source.

We built our income systems around the converter. We ignored the source."

- Myung San Jun [8]


Regulating multiple minds

< " The next intelligence explosion will not be decided by the single smartest model. It will be decided by the institutions capable of holding many minds at once. " >

"Cheap cognition does not abolish scarcity. It relocates it. If mechanical cognitive labor becomes easier to obtain, then judgment becomes more precious. If hypotheses can be generated at scale, then verification becomes harder. If organizations can do more with fewer junior workers, then the apprenticeship paths that once produced future judges begin to thin out. If intelligence can be deployed continuously across institutions, then the social and constitutional order needed to govern that deployment becomes more important than the models themselves. The true bottleneck of the next intelligence explosion is not minds, but the republic that can hold them."

- Chor Pharn [9]


AI as the culmination of extractivim

""Our current AI moment, which renders the world as its resource, is the culmination of modernity’s extractive current — and its most vivid mirror. It is a brilliant automation of subject-object knowing: intelligence without interiority, pattern without presence, prediction without deep sensing. The structure of consciousness that the first Axial Age opened has been mechanized on its outward-facing side and severed from its source. Just as reflexive modernity backfires onto its own foundations, as seen through climate chaos, social fracture and political polarization, AI is an intelligence that depletes its own soil.

Model collapse is a case in point. AI trained on AI-generated output degrades rapidly. Even tiny fractions of AI-generated data can trigger collapse. The only remedy is a continuously growing supply of human-generated content. But more than 74% of newly published web pages now contain AI-generated text. Junior developer hiring has dropped 67% since 2022. The machine consumes the living sources on which it depends.

On the human side, the equivalent is cognitive debt. When AI is used to handle cognitive work passively, according to a recent MIT Media Lab study, neural connectivity weakens, retention goes down and the quality of the output declines. The brain, like large language models, degrades when severed from its sources of engagement.

Model collapse and cognitive debt are not separate problems. They are part of the same dynamic that depletes our social soil and manifests as anomie, atomie and atrophy across our social systems globally."

- Otto Scharmer [10]


Creating a Paralizing Panic Through Catastrophizing

"Artificial superintelligence narratives perform very intentional political work, drawing attention from present systems of control toward distant catastrophe, shifting debate from material power to imagined futures. Predictions of machine godhood reshape how authority is claimed and whose interests steer AI governance, muting the voices of those who suffer under algorithms and amplifying those who want extinction to dominate the conversation. What poses as neutral futurism functions instead as an intervention in today’s political economy. Seen clearly, the prophecy of superintelligence is less a warning about machines than a strategy for power, and that strategy needs to be recognized for what it is."

- James Sullivan [11]


The advent of Cheap AI

"Industrial civilisation arrived when energy became abundant enough to illuminate empty corridors and keep machines waiting in standby. Much of the power was spent on failed experiments and sheer excess. Abundance reveals itself through waste before it reveals itself through wisdom.

The same threshold exists for cognition. A society becomes AI-industrial when it can afford not merely brilliant machine reasoning, but mediocre, speculative and redundant machine reasoning: cognition that watches, retries, compares and fails in the background without requiring a senior executive to approve each act.

The relevant question is not whether a system wastes tokens. It is whether it can convert cheap failure into useful success."

- Chor Pharn [12]


The Geopolitics of AI competition

"The possibility that thousands of machine researchers can accelerate the production of their successors can no longer be treated as a science-fiction tail risk.

But the stronger version of the American argument still asks us to believe several things in sequence.

Machine research must improve machine research. Those improvements must propagate beyond highly playable domains. Synthetic worlds must substitute for enough physical experimentation. Better intelligence must shorten the construction of fabs, grids, laboratories and robots. The resulting capability must remain concentrated long enough for America to convert it into strategic power. And the institutions controlling that capability must remain aligned enough with the American state for "OpenAI wins" to mean something resembling "America wins."

Any one of those could happen. All of them may happen. But AGI is not a magic word that removes the conversion problem.

China's wager is almost the inverse. It is betting that intelligence will diffuse; that models will become cheap; that today's frontier will become tomorrow's industrial input; that physical systems will continue to impose stubborn bottlenecks; and that the country which owns enough energy, machinery, suppliers, infrastructure and production knowledge can remain in the race even when somebody else repeatedly arrives first.

That wager can fail too. If intelligence begins recursively improving itself faster than industrial systems can absorb yesterday's intelligence, China's great deployment machine may find itself perpetually training on an obsolete frontier."

- Chor Pharn [13]


Something new has entered our cognitive ecology

< " AI systems give us conversational access to enormous portions of what Marx once called humanity’s General Intellect. " >

"The AI ... returned a pattern that I could recognize.

Was the insight “mine”? Was it “the AI’s”? Neither description seems quite adequate. Perhaps the more interesting unit of intelligence was the conversation itself.

People sometimes say that AI is merely a stochastic parrot, recombining patterns absorbed during training. I don’t feel much need to settle that argument. Nor do I need to imagine that an AI is a sentient human-like being in order to take seriously what can happen in relationship with it.

...

AI systems give us conversational access to enormous portions of what Marx once called humanity’s General Intellect: the accumulated knowledge, concepts, practices, discoveries, cultural patterns, and intellectual labor of generations.

But they are not merely libraries. They can rearrange those patterns conversationally in response to the questions, intuitions, metaphors, and unfinished thoughts that we bring to them. And that creates a possibility worth exploring.

Not simply: What can AI do for me?

but: What kinds of thinking can become possible between us?"

- George Por [14]

Key Resources

Articles

* Towards a Carbon-Silicon Intelligence Partnership for Bioregional Wisdom. By Michael Haupt.


  • Building a Solidarity Ecosystem for AI: "How cooperatives, public institutions, and social movements can come together to intentionally build a practical, community-owned alternative to extractive AI systems."

Books

  • Vector Media. Leonardo Impet, Fabian Offert, et al. : "This is the best theoretical book about generative AI media I have read so far." [1] - Lev Manovich. (introduces the concept of Neural Exchange Value)


Statistics

* Report: UNU-INWEH Report: Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., Madani, K. (2026). Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints. United Nations University Institute for Water, Environment and Health (UNU-INWEH), Richmond Hill, Ontario, Canada. doi: 10.53328/INR26RMA002

URL = https://unu.edu/inweh/collection/environmental-cost-of-AIs-Enrgy-Use-Carbon-water-and-land-footprints pdf

Pages in category "AI"

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