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.
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.”
Apr 25 • 9 tweets • 2 min read
0/ The birth of Pengnini (@mini_cto) is not just a cute moment for @Creditcoin.
It marks the beginning of something much bigger — a cultural revolution connecting blockchain, IP, and global audiences. ✨
1/ Until now, Creditcoin focused on building decentralized credit infrastructure.
Technologically strong, but emotionally distant.
#Pengnini with @mini_cto changes that.
Apr 23 • 13 tweets • 2 min read
0/ Many ask whether @_spacecoin success could eclipse @Creditcoin value.
The answer lies in understanding how these two are structurally connected—and why one can’t scale without the other. 🧵
1/ Spacecoin is an independent #DePIN project offering $2/month satellite internet access.
It provides real-world connectivity for #unbanked populations, solving a global access problem.
But critically—it’s built on the @Creditcoin mainnet.
