The more time I spend around AI, the more I feel we're paying attention to the wrong thing.
Everyone talks about better models.
Smarter models.
More powerful models.
But what happens when intelligence itself becomes abundant?
A few years ago, having access to advanced AI felt like an advantage. Today, new models appear almost every week. The gap between them seems to be shrinking faster than most people expected.
That's why I've started looking at a different question.
Not "Which model is best?"
But "How do we know what to trust?"
An AI response is only as reliable as the infrastructure behind it. If users can't verify where outputs come from, who runs the systems, or how decisions are made, intelligence alone doesn't solve much.
What caught my attention about @OpenGradient ($OPG) is that it makes this issue more visible. It pushes the conversation beyond model performance and toward the networks responsible for hosting, running, and verifying AI at scale.
That feels like a much bigger discussion.
Open infrastructure can reduce dependence on a handful of providers, but it also creates new challenges around coordination, incentives, and accountability.
The opportunity is clear.
The difficult part is building systems that remain open without sacrificing trust.
As AI continues to spread everywhere, the real competitive advantage may not be intelligence itself.
It may be the ability to verify it.
