Is anyone else watching what Qubic is doing with distributed compute and AI training? Seems underreported in AI cirles

*Qubic's Distributed Compute and AI Training: An Underreported Development in AI Circles?*

As someone who follows AI infrastructure closely, I've been intrigued by Qubic's approach to distributed compute and AI training. Despite its potential significance, I've noticed a lack of discussion about Qubic in AI-focused communities. In this post, I'll provide a brief overview of Qubic's technology and its implications for the AI industry.

Qubic's Unique Approach to Distributed Compute

Qubic uses a concept called "Useful Proof of Work," where the compute power is used for neural network training tasks, rather than solving random hash puzzles. This approach combines AI training with the validation of transactions, effectively using the same hardware for multiple purposes. The Qubic network has been independently verified to process 15.52 million transactions per second, outpacing Visa's theoretical peak throughput.

Architecture and Throughput

The key to Qubic's high throughput lies in its bare-metal architecture, which eliminates the need for a virtual machine layer. This design choice enables the network to achieve its remarkable speeds. In contrast, other distributed compute projects, like Bittensor, focus on competing AIs and subnets rewarding each other, rather than leveraging raw hardware power for training models from scratch.

Implications for AI Research and Industry

The model of mining-funded distributed AI training, as exemplified by Qubic, raises interesting questions about the future of AI development. While large companies invest billions in building massive data centers and training ever-larger language models, it's unclear whether this approach will yield true Artificial General Intelligence (AGI). Qubic's approach, which uses mining as a means to fund and power AI training, may be a more effective path to AGI.

Discussion and Community Engagement

It's surprising that Qubic's development hasn't generated more discussion in AI research circles. Is this concept considered fundamentally different from serious AI infrastructure, or is it simply underreported? The lack of engagement from the AI community is puzzling, given the potential implications of Qubic's approach.

In conclusion, Qubic's distributed compute and AI training model is an intriguing development that warrants closer examination from the AI community. Its potential to disrupt traditional approaches to AI development and its implications for the future of AGI make it an important topic for discussion and research.