A frequently overlooked issue is that the cost structure of AI is actually uncontrollable. The same request can have different prices and effects at different times and on different platforms.

This might be acceptable for individuals, but for applications, it introduces significant uncertainty.
@dgrid_ai is trying to address this instability by implementing a market-based mechanism that fosters competition among models, making price and performance no longer fixed parameters but dynamic outcomes.
Developers are not faced with a single quote but a set of options to choose from.
It may sound like a detail optimization, but fundamentally, it turns the cost problem into a market problem. Once integrated into the market mechanism, efficiency will be continuously compressed, and prices will gradually converge.
As a result, user experience becomes more stable—not necessarily the cheapest, but more predictable.
From a broader perspective, this structure is paving a longer road for AI, not just short-term explosive growth but sustainable long-term use.
When costs, performance, and supply can be dynamically balanced, AI truly has the potential to become foundational infrastructure.
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