Americans Favor Decentralized AI Over Centralized Systems, Survey Shows
A recent Harris Poll commissioned by Digital Currency Group (DCG) reveals growing public support for decentralized AI. Among 2,036 American adults surveyed, 77% said decentralized artificial intelligence is more beneficial to society. Meanwhile, 56% expressed a clear preference for decentralized AI over centralized alternatives.
According to Julie Stitzel, DCG’s VP of Policy, “Three-quarters of Americans agree that AI’s benefits should not be concentrated in the hands of a few large tech firms.”
Public Concern About Centralized AI Is Growing
The survey also found that:
- 67% believe centralized AI is more likely to be biased.
- 63% think it benefits Big Tech more than everyday people.
- Many respondents worry about misuse of personal data by centralized AI models.
These concerns reflect a broader sentiment that innovation should be democratized and transparent. The findings suggest a shift in public expectations as AI continues to shape industries and society.
Decentralized AI Gains Industry Support
Decentralized AI isn’t just gaining support from the public—it’s also becoming a priority among tech leaders. Chris Miglino, co-founder of DNA Fund, said decentralized AI could even surpass Bitcoin in global influence.
Similarly, Ben Goertzel, CEO of SingularityNET, has long advocated for decentralization in AI. He believes that artificial general intelligence (AGI) can only benefit humanity if it’s free from centralized control.
Several projects are already working toward this goal:
- Tether plans to launch an AI platform with built-in support for BTC and USDT payments.
- Thinkagents.ai, an AI agent protocol, recently released an open-source framework for decentralized AI agents. Over 70 crypto firms, including Magic Eden and Yuga Labs, have adopted the system.
Why Decentralized AI Needs Better Model Training
For decentralized AI systems to succeed, high-quality AI model training is essential. Models must be trained on diverse, curated datasets to avoid the very biases users fear in centralized AI. Achieving this requires robust infrastructure powered by specialized tools like AI hardware-including GPUs, accelerators, and edge devices. These tools support scalable and efficient training environments that drive accurate, ethical, and secure AI performance across decentralized networks.
