A stablecoin arbitrage bot finds a spread worth about $0.80.
The opportunity may only exist for a few seconds. If the bot acts immediately, it captures the trade often enough to make the strategy profitable. If it pauses to request verified inference, it pays an extra cost and loses precious time. The expected return shrinks, so eventually the bot stops asking for verification.
That outcome doesn't feel surprising. It's simply what a system optimized for profit would be expected to do.
I kept thinking about this while reading about @OpenGradient . Most discussions describe it as decentralized infrastructure for AI—running inference, storing models, and verifying execution. At first, I saw inference payments mainly as a smarter pricing model: pay only for the compute you actually use instead of maintaining subscriptions or unused capacity. I still think that's a compelling idea.
What keeps sticking with me is something different. Once verification carries a measurable cost, it becomes part of the strategy's economics. It's no longer just about security or trust; it's another line item in the PnL. And strategies naturally optimize around costs.
Maybe verification stays cheap enough that nothing changes. Maybe it remains worthwhile in most cases. But if agents optimize for incentives rather than ideals, it's worth asking whether trust alone is enough—or whether economics will ultimately shape their behavior.
