After digging deep into the $OPG mixed TEE+GPU node architecture, I'm increasingly skeptical about whether this computing power network incentive model can hold up long-term. It's hiding an irreconcilable supply-demand conflict. The project aims for large-scale decentralized AI inference, with a hard requirement that all inference nodes must simultaneously host high-performance GPUs and TEE trusted hardware, making it impossible for a single device to connect to the network. To me, this rule raises the operational threshold for nodes to an extremely high level, leaving ordinary retail traders and small computing power businesses out of the game entirely. They have to bear the hefty hardware investment for high-end graphics cards and TEE servers, as well as ongoing expenses for the data center, electricity, and bandwidth, plus the extra manpower to maintain the TEE remote proof chain. The initial capital expenditure and long-term operational costs far exceed those of typical distributed GPU computing projects on the market. Additionally, node participants must stake a large amount of OPG as collateral, creating dual constraints of hardware costs and token funds, significantly shrinking the potential node supply pool, making it hard to quickly roll out enough computing power to support the touted large-scale commercial inference. Looking at the network incentive model, its sustainability is also in doubt. Node earnings come from just two sources: the OPG inference fees paid by users and the staking rewards released linearly over 96 months. Currently, there are very few real paid inference orders across the network; most interactions are just early airdrop tasks, resulting in meager fee streams that barely cover fixed operational expenses for nodes. The staking rewards are released monthly over eight years, which dilutes individual node earnings in the long run. If the market for #opg weakens, the returns converted to fiat will shrink rapidly, causing many heavy asset nodes to choose to go offline. Moreover, the protocol lacks a dynamic adjustment mechanism; incentives for nodes won't increase during power shortages and there are no guaranteed minimum earnings during periods of low demand. In contrast, centralized cloud providers can offer AI services relying solely on GPUs, without the extra premium for TEE hardware, meaning OPG nodes have no long-term competitive advantage @OpenGradient . They paint a grand narrative of vast enterprise inference while simultaneously raising the entry barrier for nodes with dual hardware requirements, and the incentive system is heavily dependent on token prices and scarce paid orders. This high threshold locks in the supply of computing power, and the thin earnings struggle to keep operators engaged. I don't see a feasible path for this model to maintain a long-term balance between supply and demand for computing power.