Data is everywhere, but what kind of data is more useful to AI companies to create an efficient model or sustainable agentic system ? Before the advent of AI models, early founders of computer like Ada Lovelace, manually wrote long lines of programmes to the hardware of the computer for one purpose only , that purpose is "instruction". AI agents needs direct instructions from humans, not just a script generated by another AI model. Just as it is impossible for a kid at middle grade school to teach another kid in the same class, so does the need for direct impaction of the source information (Real-World data) arises. But how are we sure that these data is truly from the real world source, we can only find out by platform such as @datafdn , which ensures that there is a verifiable record of where the data came from and under what terms it would be distrubuted. Not to mention that this verified data is actually licensed. Data Network has now been backed by Sui native protocol, in partnership with @SuiNetwork and a funding of $30 million to enable AI labs to get a guaranteed source of rights-cleared AI training data with verified provenance. On this cause, $DATA will continue to be the token of the Data protocol and @WalrusProtocol will serve as a underlying storage system for these licensed and verified Real-World Data. #DATAonSuiQuest