Today, #ALPHA processed a trading volume of 150,000, earning 262 points.
The market is looking better and better. Because $QAIT had a big gain of over 500 USDT, attracting more than 100,000 traders back.
$BNB is also continuously providing good news, with updates on US stock trading coming tomorrow.
My long position, which was over 1200 trapped, is about to be freed.
Last night, I wanted to check if there were any recent updates on #OpenLedger , and ended up staying up until 2 AM.
To be honest, when I first got involved with $OPEN , I was totally drawn in by the AI hype. I thought it was just another way to milk retail investors. Back then, everyone was talking about Agents, models, and computing power, and it felt like any project with 'AI' in its name could see its valuation skyrocket.
But after going through the white paper again, I realized I might have been looking at it all wrong.
I always thought OpenLedger was building an AI public chain, but I found out that the real issue they're trying to tackle is much more fundamental.
Many people haven’t noticed that the most awkward part of the AI industry right now isn’t the models being weak, but rather the issue of who creates value and whether they actually get to reap the rewards.
Here’s a simple example.
An AI answering questions might rely on tens of thousands of data sources, dozens of training datasets, and countless contributions from creators.
In the end, it’s the model company that profits.
What about the data providers? No one knows. It’s unfair! Was it all for nothing?
Simply put, what the AI industry is really lacking right now might not be models, but rather the people who keep track of contributions.
After reading this, I suddenly understood why OpenLedger emphasizes Attribution so much.
I realized the essence of it is just one thing: those who contribute value should receive the benefits.
Last year, I researched quite a few AI projects, most of them talking about how many users, how many Agents, and how much usage there would be in the future.
@OpenLedger is studying something different.
If there really are billions of AI calls in the future, how will that money be distributed?
This perspective is actually quite rare.
One detail I found during my research is that they’ve been working on the data layer since the testnet, not just focusing on models.
It’s a bit like when DeFi was at its peak, and everyone was chasing yields, only to find that many protocols were reliant on oracles in the end.
I still have a portion of OPEN in my bag. Let’s see if it surprises me!
The market is looking better and better. Because $QAIT had a big gain of over 500 USDT, attracting more than 100,000 traders back.
$BNB is also continuously providing good news, with updates on US stock trading coming tomorrow.
My long position, which was over 1200 trapped, is about to be freed.
Last night, I wanted to check if there were any recent updates on #OpenLedger , and ended up staying up until 2 AM.
To be honest, when I first got involved with $OPEN , I was totally drawn in by the AI hype. I thought it was just another way to milk retail investors. Back then, everyone was talking about Agents, models, and computing power, and it felt like any project with 'AI' in its name could see its valuation skyrocket.
But after going through the white paper again, I realized I might have been looking at it all wrong.
I always thought OpenLedger was building an AI public chain, but I found out that the real issue they're trying to tackle is much more fundamental.
Many people haven’t noticed that the most awkward part of the AI industry right now isn’t the models being weak, but rather the issue of who creates value and whether they actually get to reap the rewards.
Here’s a simple example.
An AI answering questions might rely on tens of thousands of data sources, dozens of training datasets, and countless contributions from creators.
In the end, it’s the model company that profits.
What about the data providers? No one knows. It’s unfair! Was it all for nothing?
Simply put, what the AI industry is really lacking right now might not be models, but rather the people who keep track of contributions.
After reading this, I suddenly understood why OpenLedger emphasizes Attribution so much.
I realized the essence of it is just one thing: those who contribute value should receive the benefits.
Last year, I researched quite a few AI projects, most of them talking about how many users, how many Agents, and how much usage there would be in the future.
@OpenLedger is studying something different.
If there really are billions of AI calls in the future, how will that money be distributed?
This perspective is actually quite rare.
One detail I found during my research is that they’ve been working on the data layer since the testnet, not just focusing on models.
It’s a bit like when DeFi was at its peak, and everyone was chasing yields, only to find that many protocols were reliant on oracles in the end.
I still have a portion of OPEN in my bag. Let’s see if it surprises me!