We have all seen the pattern.
A new AI model gets released, social media fills with benchmark charts, traders start debating winners and losers, and investors try to figure out which part of the AI stack will end up capturing the most value.
The usual conclusion is pretty simple: the smartest models win.
For a while, I looked at it that way too.
But the longer I think about it, the more I wonder if intelligence is only half the story.
Imagine a world a few years from now where AI models are everywhere. Running inference is cheap, deploying models is easy, and new versions appear faster than anyone can keep up with. At that point, finding intelligence may not be difficult at all.
Knowing what to trust might be.
That's why projects like @OpenGradient caught my attention not because they make AI more powerful, but because they make me think about a different problem. In a future filled with machine-generated outputs, how will users, businesses, and markets verify what actually happened behind the scenes?
If intelligence becomes abundant, confidence may become the scarce resource.
And if confidence becomes the scarce resource, are we spending too much time measuring model performance and not enough time measuring proof?
#opg $OPG @OpenGradient
A new AI model gets released, social media fills with benchmark charts, traders start debating winners and losers, and investors try to figure out which part of the AI stack will end up capturing the most value.
The usual conclusion is pretty simple: the smartest models win.
For a while, I looked at it that way too.
But the longer I think about it, the more I wonder if intelligence is only half the story.
Imagine a world a few years from now where AI models are everywhere. Running inference is cheap, deploying models is easy, and new versions appear faster than anyone can keep up with. At that point, finding intelligence may not be difficult at all.
Knowing what to trust might be.
That's why projects like @OpenGradient caught my attention not because they make AI more powerful, but because they make me think about a different problem. In a future filled with machine-generated outputs, how will users, businesses, and markets verify what actually happened behind the scenes?
If intelligence becomes abundant, confidence may become the scarce resource.
And if confidence becomes the scarce resource, are we spending too much time measuring model performance and not enough time measuring proof?
#opg $OPG @OpenGradient