From Global-North-Centric AI to Multipolar AI: Connectivity and Data Redistribution Powered by @spacecoin
1. AI Industry, National Competitiveness, and the “AI Divide”
Today, the AI industry is directly tied to national economic power.
OECD and the World Economic Forum point out that while AI can massively boost productivity in education, healthcare, and climate response, the economic and social benefits are concentrated mainly in the so-called “Global North” (North America, Western Europe, etc.).
oecd.org/en/topics/poli…
The problem is not just “who adopts AI faster,” but that the data AI is trained on, and the worldview embedded in that data, are structurally biased toward certain regions.
Recent studies show that large language models (LLMs):
- Repeatedly reproduce narratives and framings that favor specific (mainly Global North) countries, and
- Often describe Global South countries in negative or deficit-focused terms.
In other words:
Even before we talk about “performance gaps” in AI,
the world map that AI sees is already distorted toward a few data-rich countries.
1. AI Industry, National Competitiveness, and the “AI Divide”
Today, the AI industry is directly tied to national economic power.
OECD and the World Economic Forum point out that while AI can massively boost productivity in education, healthcare, and climate response, the economic and social benefits are concentrated mainly in the so-called “Global North” (North America, Western Europe, etc.).
oecd.org/en/topics/poli…
The problem is not just “who adopts AI faster,” but that the data AI is trained on, and the worldview embedded in that data, are structurally biased toward certain regions.
Recent studies show that large language models (LLMs):
- Repeatedly reproduce narratives and framings that favor specific (mainly Global North) countries, and
- Often describe Global South countries in negative or deficit-focused terms.
In other words:
Even before we talk about “performance gaps” in AI,
the world map that AI sees is already distorted toward a few data-rich countries.
2. Structural Causes of Information and Data Bias: Infrastructure, Language, Capital
This bias does not exist simply because algorithms are “bad” or “unfair.”
The structural causes are quite clear.
2.1 Access Gap (Digital Divide)
- As of around 2023, roughly 2.6 billion people (about 33% of the global population) are still offline.
- Of these, about 1.8 billion live in rural areas.
Internet usage is around 83% in urban areas, but only about 48% in rural areas.
- Internet penetration correlates strongly with national income levels.
→ This means the people and environments that can even become “candidates” to enter AI training data are skewed toward wealthier, more urban countries.
2.2 Language and Cultural Bias
- Most of the data used for training (web content, academic papers, code, open datasets, etc.)
are concentrated in a few major languages, such as English and Chinese.
- Local languages, colloquial expressions, informal knowledge, religion, customs, and daily life patterns are treated as “low-resource” and largely ignored, and this is directly reflected in how AI understands the world.
2.3 Concentration of Capital and Infrastructure
- Data centers, cloud infrastructure, GPUs, and AI researchers are concentrated in a handful of countries in North America, Europe, and parts of East Asia.
- Low-income countries lack the capital and infrastructure to collect, clean, and train on high-quality datasets.
These three factors combine to produce:
“AI training data = data from countries that are well-connected, wealthy, and heavily use English.”
Because of this, some scholars in the Global South even describe the situation as “digital colonialism.”
This bias does not exist simply because algorithms are “bad” or “unfair.”
The structural causes are quite clear.
2.1 Access Gap (Digital Divide)
- As of around 2023, roughly 2.6 billion people (about 33% of the global population) are still offline.
- Of these, about 1.8 billion live in rural areas.
Internet usage is around 83% in urban areas, but only about 48% in rural areas.
- Internet penetration correlates strongly with national income levels.
→ This means the people and environments that can even become “candidates” to enter AI training data are skewed toward wealthier, more urban countries.
2.2 Language and Cultural Bias
- Most of the data used for training (web content, academic papers, code, open datasets, etc.)
are concentrated in a few major languages, such as English and Chinese.
- Local languages, colloquial expressions, informal knowledge, religion, customs, and daily life patterns are treated as “low-resource” and largely ignored, and this is directly reflected in how AI understands the world.
2.3 Concentration of Capital and Infrastructure
- Data centers, cloud infrastructure, GPUs, and AI researchers are concentrated in a handful of countries in North America, Europe, and parts of East Asia.
- Low-income countries lack the capital and infrastructure to collect, clean, and train on high-quality datasets.
These three factors combine to produce:
“AI training data = data from countries that are well-connected, wealthy, and heavily use English.”
Because of this, some scholars in the Global South even describe the situation as “digital colonialism.”
3. In the #Physical_AI Era, “Connectivity Bias” Becomes Even More Dangerous
Up to now, AI has been mostly web/text-centric.
But we are heading toward a Physical AI era, where robots, drones, sensors, and vehicles collect data directly from the real world.
- Autonomous vehicles, service robots, agricultural drones, and industrial robots observe their surroundings with sensors and cameras and update their own behavior or improve central models based on that data.
- In this context, the key question is: “In which cities, towns, farms, and roads is this data being collected?”
If these Physical AI systems operate mainly in:
- US, European, and Chinese megacities, and
- Wealthy areas with dense high-speed communication networks,
then AI will naturally become very good at decisions and services optimized for those environments.
Meanwhile, rural areas, low-income countries, informal markets, and non-standard infrastructure regions remain systematic blind spots.
In short:
In the Physical AI era, “who is connected” effectively determines
“whose reality exists inside AI.”
Therefore, if we don’t break connectivity bias, AI bias will only intensify.
Up to now, AI has been mostly web/text-centric.
But we are heading toward a Physical AI era, where robots, drones, sensors, and vehicles collect data directly from the real world.
- Autonomous vehicles, service robots, agricultural drones, and industrial robots observe their surroundings with sensors and cameras and update their own behavior or improve central models based on that data.
- In this context, the key question is: “In which cities, towns, farms, and roads is this data being collected?”
If these Physical AI systems operate mainly in:
- US, European, and Chinese megacities, and
- Wealthy areas with dense high-speed communication networks,
then AI will naturally become very good at decisions and services optimized for those environments.
Meanwhile, rural areas, low-income countries, informal markets, and non-standard infrastructure regions remain systematic blind spots.
In short:
In the Physical AI era, “who is connected” effectively determines
“whose reality exists inside AI.”
Therefore, if we don’t break connectivity bias, AI bias will only intensify.
4. #LEO #Satellites and Low-Cost Internet: A Hardware-Level Tool for Breaking Connectivity Bias
This is where low Earth orbit (LEO) satellite internet becomes meaningful.
- LEO satellites orbit at 160–2,000 km altitude and,
compared to traditional geostationary (GEO) satellites,
can provide lower latency and wider coverage, including areas without ground networks.
- The World Economic Forum and multiple studies regard LEO satellites as one of the core technologies for connecting unserved regions and closing the digital divide.
weforum.org/stories/2022/0…
@Starlink @EutelsatGroup @amazonnews Kuiper, and @Telesat are already using or testing LEO satellite constellations for:
- Rural and remote broadband
- Aviation and maritime connectivity
- Disaster response
- Military communications.
In other words, technically:
“Providing basic internet access to regions that might never get fiber or cell towers”
is already possible with LEO satellites.
However, current commercial LEO services:
- Still come with high equipment and subscription costs
- Are owned and controlled by a small number of large corporations (e.g., SpaceX / Starlink),
so even if they reduce the access gap, they risk leaving the data ownership and control structure largely unchanged.
Up to this point, this is the realistic picture of LEO technology itself.
This is where low Earth orbit (LEO) satellite internet becomes meaningful.
- LEO satellites orbit at 160–2,000 km altitude and,
compared to traditional geostationary (GEO) satellites,
can provide lower latency and wider coverage, including areas without ground networks.
- The World Economic Forum and multiple studies regard LEO satellites as one of the core technologies for connecting unserved regions and closing the digital divide.
weforum.org/stories/2022/0…
@Starlink @EutelsatGroup @amazonnews Kuiper, and @Telesat are already using or testing LEO satellite constellations for:
- Rural and remote broadband
- Aviation and maritime connectivity
- Disaster response
- Military communications.
In other words, technically:
“Providing basic internet access to regions that might never get fiber or cell towers”
is already possible with LEO satellites.
However, current commercial LEO services:
- Still come with high equipment and subscription costs
- Are owned and controlled by a small number of large corporations (e.g., SpaceX / Starlink),
so even if they reduce the access gap, they risk leaving the data ownership and control structure largely unchanged.
Up to this point, this is the realistic picture of LEO technology itself.
5. @spacecoin : A Token to #decentralized Both Infrastructure and Data Production
Now we introduce @spacecoin into this context.
The key idea is:
Spacecoin does not treat satellite internet as a pure telecom product,
but as a DePIN (decentralized physical infrastructure network) with token incentives.
5.1. Current Facts: Decentralized Satellite Infrastructure + Open Network
From publicly available information on Spacecoin, we can extract the following:
Satellite-Based DePIN Connected to Creditcoin
- Spacecoin is described as a DePIN project that combines LEO nanosatellites with the Creditcoin blockchain.
- The goal is to create an internet infrastructure where data can be transmitted via satellite without relying on traditional ground networks and then verified and settled on-chain.
First Satellite (CTC-0) Launch and 7,000 km Blockchain Transaction
- In late 2024, the CTC-0 nanosatellite was launched into low Earth orbit.
- In 2025, Spacecoin successfully relayed a blockchain transaction from Punta Arenas, Chile to the Azores in Portugal—about 7,000 km—purely via CTC-0, without any terrestrial backbone, and recorded it on the Creditcoin testnet.
- The partner for this mission was the Bulgarian microsatellite company EnduroSat.
Positioning as a Decentralized Alternative to Starlink
- Several media outlets, including Reuters, have described Spacecoin as a “decentralized alternative to Starlink.”
- Whereas Starlink is a fully centralized model where SpaceX controls all satellites, ground stations, and services, @spacecoin aims for an open network where developers, telecoms, NGOs, and infrastructure partners can participate as providers in the network.
Target Regions: Those Excluded from Existing Infrastructure
- Spacecoin explicitly targets regions where existing satellite or terrestrial services are
too expensive, heavily censored, or poorly developed—
essentially, low-income and/or high-censorship countries that are currently off the grid or poorly connected.
Now we introduce @spacecoin into this context.
The key idea is:
Spacecoin does not treat satellite internet as a pure telecom product,
but as a DePIN (decentralized physical infrastructure network) with token incentives.
5.1. Current Facts: Decentralized Satellite Infrastructure + Open Network
From publicly available information on Spacecoin, we can extract the following:
Satellite-Based DePIN Connected to Creditcoin
- Spacecoin is described as a DePIN project that combines LEO nanosatellites with the Creditcoin blockchain.
- The goal is to create an internet infrastructure where data can be transmitted via satellite without relying on traditional ground networks and then verified and settled on-chain.
First Satellite (CTC-0) Launch and 7,000 km Blockchain Transaction
- In late 2024, the CTC-0 nanosatellite was launched into low Earth orbit.
- In 2025, Spacecoin successfully relayed a blockchain transaction from Punta Arenas, Chile to the Azores in Portugal—about 7,000 km—purely via CTC-0, without any terrestrial backbone, and recorded it on the Creditcoin testnet.
- The partner for this mission was the Bulgarian microsatellite company EnduroSat.
Positioning as a Decentralized Alternative to Starlink
- Several media outlets, including Reuters, have described Spacecoin as a “decentralized alternative to Starlink.”
- Whereas Starlink is a fully centralized model where SpaceX controls all satellites, ground stations, and services, @spacecoin aims for an open network where developers, telecoms, NGOs, and infrastructure partners can participate as providers in the network.
Target Regions: Those Excluded from Existing Infrastructure
- Spacecoin explicitly targets regions where existing satellite or terrestrial services are
too expensive, heavily censored, or poorly developed—
essentially, low-income and/or high-censorship countries that are currently off the grid or poorly connected.
5.2. Extending From #decentralized Infrastructure to Decentralized Data Producers
To connect this to your claim about “mitigating information acquisition bias,”
we need to show how token incentives can expand from infrastructure providers to data producers.
Current Incentive Focus: Infrastructure and Bandwidth Providers
- Current token design, as described publicly, pays SPACE (and/or related tokens) to:
Operators of ground gateways/nodes, providers of bandwidth and communication resources, and participants in network governance.
- Even at this layer alone, unlike centralized satellite systems, ownership and operation of the infrastructure are distributed among many players.
Natural Extension to Physical AI and Sensor Nodes
- Physical AI and IoT devices fundamentally rely on low-latency, low-cost communication.
- @spacecoin role is to provide exactly that communication layer.
- Extending the same token mechanics to:
Agricultural sensors,
Environmental monitoring devices,
Robots, drones, vehicles,
Local commerce/traffic/weather data collection apps
yields a structure where a data #DePIN is layered on top of the bandwidth DePIN.
From here on, we are no longer describing what @spacecoin is already doing, but what is structurally possible if its architecture is extended to the data level.
From Infrastructure Incentives to Data Incentives.
If such an extension is implemented, the incentive flow could look like this:
Stage 1: “Connectivity” Incentives
- Rewards to participants who install and operate satellites, ground stations, and relay nodes.
Stage 2: “Bandwidth” Incentives
- Rewards to telecoms and community ISPs that provide affordable bandwidth to specific regions.
Stage 3: “Data” Incentives (the key to your argument)
- Rewards to local Physical AI and sensor nodes that upload AI-useful data in compliant formats and with proper privacy protections.
This progression represents a shift from “decentralizing infrastructure providers”
to “decentralizing the geographical and economic base of data producers.”
To connect this to your claim about “mitigating information acquisition bias,”
we need to show how token incentives can expand from infrastructure providers to data producers.
Current Incentive Focus: Infrastructure and Bandwidth Providers
- Current token design, as described publicly, pays SPACE (and/or related tokens) to:
Operators of ground gateways/nodes, providers of bandwidth and communication resources, and participants in network governance.
- Even at this layer alone, unlike centralized satellite systems, ownership and operation of the infrastructure are distributed among many players.
Natural Extension to Physical AI and Sensor Nodes
- Physical AI and IoT devices fundamentally rely on low-latency, low-cost communication.
- @spacecoin role is to provide exactly that communication layer.
- Extending the same token mechanics to:
Agricultural sensors,
Environmental monitoring devices,
Robots, drones, vehicles,
Local commerce/traffic/weather data collection apps
yields a structure where a data #DePIN is layered on top of the bandwidth DePIN.
From here on, we are no longer describing what @spacecoin is already doing, but what is structurally possible if its architecture is extended to the data level.
From Infrastructure Incentives to Data Incentives.
If such an extension is implemented, the incentive flow could look like this:
Stage 1: “Connectivity” Incentives
- Rewards to participants who install and operate satellites, ground stations, and relay nodes.
Stage 2: “Bandwidth” Incentives
- Rewards to telecoms and community ISPs that provide affordable bandwidth to specific regions.
Stage 3: “Data” Incentives (the key to your argument)
- Rewards to local Physical AI and sensor nodes that upload AI-useful data in compliant formats and with proper privacy protections.
This progression represents a shift from “decentralizing infrastructure providers”
to “decentralizing the geographical and economic base of data producers.”
In the Physical AI era, @spacecoin can
① Reduce connectivity bias through a LEO-based DePIN
② If its token incentives are extended to local data nodes
provide an infrastructure-level mechanism that helps mitigate the geographic and economic bias in the information AI learns from.
To be strictly accurate:
① Today, Spacecoin is still at the early stage (one satellite, testnet transactions).
② The project is far from the scale of Starlink’s thousands of satellites.
So rather than saying “Spacecoin will definitively eliminate bias,”
it is more realistic to say:
It offers a rare, structurally plausible path to reducing both connectivity bias and, in the longer term, data bias.
① Reduce connectivity bias through a LEO-based DePIN
② If its token incentives are extended to local data nodes
provide an infrastructure-level mechanism that helps mitigate the geographic and economic bias in the information AI learns from.
To be strictly accurate:
① Today, Spacecoin is still at the early stage (one satellite, testnet transactions).
② The project is far from the scale of Starlink’s thousands of satellites.
So rather than saying “Spacecoin will definitively eliminate bias,”
it is more realistic to say:
It offers a rare, structurally plausible path to reducing both connectivity bias and, in the longer term, data bias.
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