Maybe I'm looking at AI from the wrong angle, but I keep feeling that the hardest problem isn't building the model itself.
It's trust.
When or any other AI gives an answer, most of us never think about what happened behind the scenes. We just see the output and move on. But as AI becomes part of more important workflows, that blind trust starts to feel like a bigger issue.
That's one reason I've been paying attention to and $OPG
What interests me isn't necessarily the models. It's the idea of verification. If AI services eventually run across decentralized networks, how do users know the computation actually happened the way it's supposed to? How do you verify an output without relying on a single company to say, "trust us"?
The opportunity seems obvious. If verification works, it could make AI infrastructure more transparent and reduce dependence on centralized gatekeepers.
But I also see risks. Verification sounds great in theory, yet theory is usually the easy part. The harder part is making it efficient, affordable, and simple enough that developers actually want to use it. If the process becomes too complex, adoption could be slower than many expect.
What I find interesting is how familiar this feels. Crypto spent years trying to solve trust problems for money and data. Now similar questions are showing up around AI.
I'm curious what others think: will AI verification eventually become a standard requirement, or will most users continue prioritizing convenience over transparency?
For now, that's the piece of and I'll be watching most closely.
@OpenGradient
#opg $OPG
It's trust.
When or any other AI gives an answer, most of us never think about what happened behind the scenes. We just see the output and move on. But as AI becomes part of more important workflows, that blind trust starts to feel like a bigger issue.
That's one reason I've been paying attention to and $OPG
What interests me isn't necessarily the models. It's the idea of verification. If AI services eventually run across decentralized networks, how do users know the computation actually happened the way it's supposed to? How do you verify an output without relying on a single company to say, "trust us"?
The opportunity seems obvious. If verification works, it could make AI infrastructure more transparent and reduce dependence on centralized gatekeepers.
But I also see risks. Verification sounds great in theory, yet theory is usually the easy part. The harder part is making it efficient, affordable, and simple enough that developers actually want to use it. If the process becomes too complex, adoption could be slower than many expect.
What I find interesting is how familiar this feels. Crypto spent years trying to solve trust problems for money and data. Now similar questions are showing up around AI.
I'm curious what others think: will AI verification eventually become a standard requirement, or will most users continue prioritizing convenience over transparency?
For now, that's the piece of and I'll be watching most closely.
@OpenGradient
#opg $OPG