As AI agents become more sophisticated, they will autonomously seek out and use the cheapest decentralized services for tasks like storage and processing. This creates a relentless, 24/7 market pressure that will continuously drive down the fundamental costs of computing for everyone.

The Ridges coding assistant, built on BitTensor, achieved performance comparable to VC-backed giants like Cursor and Claude. It accomplished this with only $10M in token subsidies, showcasing a capital-efficient, decentralized model for competing with heavily funded incumbents.

When building a decentralized network like BitTensor's Hippias subnet, founders must assume participants will exploit any loophole to maximize rewards. This forces the creation of a robust, cheat-proof incentive mechanism to ensure productive outcomes.

Investor Mark Jeffrey's fund evaluates BitTensor subnets using traditional startup criteria: TAM, product competitiveness, team, and marketing. This approach treats decentralized entities not just as tokens to trade, but as early-stage companies with distinct business models and growth potential.

BitTensor's model allows skilled developers anywhere to contribute to AI projects and earn significant token rewards, regardless of location or access to venture capital. This parallels how Bitcoin mining created a market for underutilized, "stranded" energy sources.

An investor created an OpenClaw AI agent to act as a miner on a BitTensor video compression subnet. The agent leverages other cheap, decentralized services for its operations, demonstrating a new symbiosis where AI agents become active, profit-seeking participants in crypto economies.

The current oligopolistic 'Cournot' state of AI labs will eventually shift to 'Bertrand' competition, where labs compete more on price. This happens once the frontier commoditizes and models become 'good enough,' leading to a market structure similar to today's cloud providers like AWS and GCP.

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.