The future customer of AI may not be a human.
A few years ago, I probably would have dismissed that idea. We still think of AI as a tool built for people. We ask questions, it gives answers, and humans make the final call.
But I'm not sure that will always be the case.
As AI systems become more capable, they'll increasingly interact with other AI systems. One model may generate information, another may analyze it, and another may take action. Much of that could happen without anyone reviewing every step.
What I find interesting is that most conversations about AI still revolve around bigger models, better benchmarks, and lower costs. Those things matter, obviously.
But I keep coming back to a different question.
What happens when intelligence becomes common?
Today, we judge AI by outputs because humans are still involved. We can pause, question things, and ask for a second opinion.
Machines don't really do that.
And if AI systems increasingly rely on the outputs of other AI systems, simply trusting that everything worked as expected may not be enough.
That's partly why @OpenGradient OpenGradient caught my attention.
Maybe the next challenge in AI isn't building smarter models.
Maybe it's building systems where intelligence can be verified instead of simply assumed.
OpenGradient's focus on transparent and verifiable AI execution feels increasingly relevant as AI systems become more autonomous.
The more I think about it, the more I feel that generating intelligence isn't the hardest problem ahead.
Understanding whether that intelligence behaved the way we expected it to may turn out to be just as important.
And if that happens, the infrastructure behind AI could end up mattering just as much as the models themselves.
As AI becomes increasingly autonomous, what will matter more?