#openledger $OPEN
In the AI game, the easiest targets for replacement might just be the "low-quality data workers." Recently, I had a long chat with a team that does AI annotation outsourcing. They used to pull in a few hundred folks for data cleaning and manual tagging on a project, but this year, orders have suddenly tanked. The reason? It's pretty straightforward.
Many companies are realizing that low-quality manual data is turning into the biggest hidden cost in the AI industry. A lot of teams, in a rush to hit deadlines, have started using AI to churn out "pseudo-manual annotations" in bulk. On the surface, it looks like there’s human review, but in reality, a lot of the content isn’t checked closely at all.
The result? No issues during model testing, but once it goes live, the error rates start skyrocketing. Especially in finance, customer service, and healthcare scenarios, companies are now more afraid of AI not being smart enough, but rather: AI learning faulty logic from data that "looks right."
This made me take another look at @OpenLedger regarding DataInf and the design of data attribution. Many are still caught up discussing #OpenLedger , focusing on AI + blockchain, but I think the key takeaway is that what will really hold value in the future AI industry might not just be the "amount of data."
Instead, it's about: which data truly influences model outcomes. For instance, if a financial model can pinpoint: which part of the training corpus genuinely affected this inference, and which data is stable and effective over the long haul, then companies can gradually weed out low-value data sources.
OpenLedger's DataInf is essentially trying to establish this "data influence identification." In the past internet era, it was a race for who had more data. In the future AI landscape, it might be a competition for whose data is more useful. And $OPEN feels like the settling fuel within the entire data circulation system.
However, there's a real issue here: if in the future only a handful of high-value data sources are consistently utilized, will the AI industry end up forming a new "data resource monopoly"? That’s a question the whole industry might not have an answer to right now.
#OpenLedger $OPEN
In the AI game, the easiest targets for replacement might just be the "low-quality data workers." Recently, I had a long chat with a team that does AI annotation outsourcing. They used to pull in a few hundred folks for data cleaning and manual tagging on a project, but this year, orders have suddenly tanked. The reason? It's pretty straightforward.
Many companies are realizing that low-quality manual data is turning into the biggest hidden cost in the AI industry. A lot of teams, in a rush to hit deadlines, have started using AI to churn out "pseudo-manual annotations" in bulk. On the surface, it looks like there’s human review, but in reality, a lot of the content isn’t checked closely at all.
The result? No issues during model testing, but once it goes live, the error rates start skyrocketing. Especially in finance, customer service, and healthcare scenarios, companies are now more afraid of AI not being smart enough, but rather: AI learning faulty logic from data that "looks right."
This made me take another look at @OpenLedger regarding DataInf and the design of data attribution. Many are still caught up discussing #OpenLedger , focusing on AI + blockchain, but I think the key takeaway is that what will really hold value in the future AI industry might not just be the "amount of data."
Instead, it's about: which data truly influences model outcomes. For instance, if a financial model can pinpoint: which part of the training corpus genuinely affected this inference, and which data is stable and effective over the long haul, then companies can gradually weed out low-value data sources.
OpenLedger's DataInf is essentially trying to establish this "data influence identification." In the past internet era, it was a race for who had more data. In the future AI landscape, it might be a competition for whose data is more useful. And $OPEN feels like the settling fuel within the entire data circulation system.
However, there's a real issue here: if in the future only a handful of high-value data sources are consistently utilized, will the AI industry end up forming a new "data resource monopoly"? That’s a question the whole industry might not have an answer to right now.
#OpenLedger $OPEN