For a long time, I believed that the high cost of AI was due to the complexity of the models themselves. After all, training a single model requires massive computing power, and inference depends on expensive GPUs. This explanation sounded completely reasonable.

But as I started to use different AI services more frequently, I gradually realized one thing: the price is not only influenced by the technology itself but also by the entire structure built around it.
Many times, you don’t have a choice; the platform decides which model you use, how you are charged, and what the price is. Users can only accept it and cannot participate. This structure is more like traditional internet than an open market.
It was in this context that I began to pay attention to @dgrid_ai. What it tries to do is quite straightforward: to free AI inference from closed platforms, allowing nodes to provide computing power and models to freely participate in executing tasks.
When the supply side is no longer controlled by a few platforms, prices are no longer set unilaterally but gradually formed by the market.
This reminds me of the early development of cloud computing. Initially, computing resources were expensive and concentrated, but as more providers entered the market, costs began to steadily decline, eventually becoming the infrastructure accessible to everyone today.
At that moment, I truly understood that what determines the price is not just the technology but the market structure itself.
If this model can continue to develop, future AI inference may no longer be a high-cost service but become a fundamental resource like bandwidth and storage.
For ordinary users, this means lower barriers to use; for developers, it also means no longer being tied to a single platform.
This change won’t happen overnight, but the direction has already become clear. AI costs may no longer be controlled by a few companies forever.
@Galxe @GalxeQuest @easydotfunX @wallchain #Ad #Affiliate