Something I keep coming back to is how we’ve been measuring AI value through speed and scale, while quietly ignoring something slower but more important: accumulation of context. The real shift might not be how intelligent models become, but how much they remember about people using them, and how that memory changes decision-making over time.
Every interaction with AI is leaving a trace of behavior—preferences, timing, reasoning patterns, even hesitation. Over time, you don’t just “use” AI; you start to co-adapt with it. It learns your working style, and you unconsciously adjust to how it responds. The result is a gradual convergence where decisions are no longer isolated prompts, but part of an evolving shared context. That’s the part I think most people still underestimate: intelligence becomes relational, not just computational.
This is where @OpenGradient and $OPG become interesting beyond pure compute infrastructure. If validator collateral and staking participation are required to secure the network, a portion of supply naturally gets locked, tying economic security to usage and trust. But more than that, the design around persistent memory, verifiable inference, user-owned intelligence, and privacy/data sovereignty suggests something deeper: the AI context being created isn’t disposable. It can be preserved and verified over time, turning accumulated human-AI alignment into something structurally durable.
The question is whether markets are still valuing AI mainly on compute and throughput, or if they’re beginning to price in the compounding value of human-AI alignment over time. And if so, how much of that is already reflected in $OPG
