As the global AI industry surges forward—breaking new ground in language models, robotics, and reasoning—one challenge looms large on the horizon: Artificial General Intelligence (AGI).
AGI refers to AI systems capable of performing any intellectual task a human can do—reasoning, problem-solving, adapting to new environments. It’s the holy grail of AI development. But while funding and progress continue to accelerate, many researchers now believe we might be missing a crucial piece of the puzzle: decentralization.
🔄 A Shift Toward AGI
Recent developments in AI have demonstrated impressive progress. Companies like Anthropic have raised billions, and startups like Mistral AI have soared to unicorn status seemingly overnight. But beneath this momentum lies a sobering truth: most models still operate in narrow domains and require massive, centralized infrastructure.
This is where Himanshu Tyagi, co-founder of Sentient and professor at the Indian Institute of Science, offers a fresh perspective.
In a recent interview, Tyagi emphasized that truly achieving AGI will require rethinking how we build and train AI systems—starting with decentralization.
🧠 Beyond Internet Data: The Human Strategy Gap
According to Tyagi, the data currently used to train large AI models—web content, documents, code repositories—is insufficient for developing human-level intelligence.
What’s missing? The deep, strategic heuristics that humans use in real-world scenarios:
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Advanced sales techniques
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High-stakes decision-making
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Creative brand-building
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Negotiation and persuasion skills
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Problem-solving under uncertainty
These are not skills easily scraped from websites or harvested from Reddit threads. They often exist in experiential, high-performance contexts, such as competitive interviews, real-world business settings, or creative environments.
Collecting this type of data is challenging—and according to Tyagi, trying to gather it through centralized, siloed systems won’t cut it. Instead, he proposes “decentralized, open, and incentivized mechanisms” as the solution.
🛠️ Building AGI with Decentralized Models
Tyagi's argument extends beyond data collection. He also emphasizes the need to decentralize model development itself.
In the current landscape, training and aligning large AI models demands resources that only a handful of tech giants can afford. But in a decentralized framework:
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Individuals can contribute specialized, skill-aligned models
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Communities can share training pipelines and datasets
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Developers can access compute resources at scale through distributed networks
This ecosystem would allow anyone to contribute to the evolution of AGI—not just engineers in corporate labs. Incentives could encourage participation, while open governance could keep the system transparent and aligned with human values.
“Decentralized model ownership with incentives and decentralized training solves these problems,” Tyagi asserts.
🌐 Why Decentralization Could Be Key
The decentralization of AI development is part of a much broader trend. Web3, blockchain, open-source software—all of these movements aim to distribute power, foster collaboration, and accelerate innovation by removing gatekeepers.
AI, Tyagi argues, should be no different.
By distributing ownership and access, we can tap into a wider range of human experiences and skills—something centralized models may struggle to achieve. This could be the edge that helps us finally cross the threshold into AGI territory.
🔮 A New Frontier for AI Collaboration
As we stand on the edge of what could be AI’s most important leap, one thing is becoming clear: AGI may not be born in isolation. Instead, it could emerge from a globally connected, community-driven effort—where individuals, not just corporations, shape the future of intelligence.
Tyagi’s vision challenges us to rethink how we approach the development of advanced AI: not as a race between companies, but as a collaborative mission that mirrors the diversity, creativity, and complexity of human intelligence itself.
📌 Final Thoughts
The path to AGI will demand more than just bigger models and faster GPUs—it will require a paradigm shift in how we gather knowledge, build systems, and collaborate. Decentralized AI could be that shift.
As the AI landscape continues to evolve, one thing is certain: the question is no longer whether decentralization matters—but how soon we’ll embrace it.
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