Templar's Sam Dare argues the perceived GPU scarcity is misunderstood. The actual bottleneck is the limited supply of the latest, well-connected GPUs in data centers. His project aims to create algorithms that can effectively utilize the vast, distributed network of consumer-grade and older enterprise GPUs, unlocking a massive new compute resource.

Score addresses the high cost of AI vision by using a decentralized network of miners to "distill" massive, general-purpose models (e.g., 3.4GB) into hyper-specialized, tiny models (e.g., 50MB). This allows complex vision tasks to run on local CPUs, unlocking use cases previously blocked by prohibitive GPU costs.

While AI inference can be decentralized, training the most powerful models demands extreme centralization of compute. The necessity for high-bandwidth, low-latency communication between GPUs means the best models are trained by concentrating hardware in the smallest possible physical space, a direct contradiction to decentralized ideals.

The "AutoResearch" paradigm can be extended to a decentralized model like Folding@Home. Because verifying a good solution is cheap while finding one is expensive, this "swarm" could harness enough untrusted global compute to potentially out-innovate centralized, well-funded labs.

Platforms like BitTensor allow subnet creators to fluidly adjust their incentive mechanisms. For example, the Hippias storage network can increase rewards for speed to encourage its distributed 'miners' to improve network throughput on demand.

Instead of solving arbitrary math problems, BitTensor's blockchain incentivizes miners to contribute to building and improving AI products on its subnets. This shifts from proof-of-work for security to proof-of-work for tangible product creation, funded by token emissions.

Templar's Sam Dare clarifies that BitTensor (Tau) abstracts the blockchain to its most fundamental layer: incentives. Instead of focusing on smart contracts or value transfer, it provides a framework for creating "incentive games" where self-interested miners are compelled to produce valuable outputs, like training an AI model, to earn rewards.

Bittensor subnets operate like continuous, global competitions where miners constantly strive to solve challenges set by subnet owners, and validators score their performance. This "hackathon that never sleeps" model creates a relentless, decentralized engine for innovation and optimization across diverse AI applications like drug discovery and social media.

Sam Dare of Templar frames decentralized AI's mission not as direct competition with giants like OpenAI, but as creating optionality. It enables a new market for those who cannot afford massive, centralized training runs, such as nations seeking "Sovereign AI" or researchers exploring niche pre-training, thereby expanding the market.