A lot of folks see AI as just an upgraded search engine, but I'm starting to think the biggest difference isn't efficiency—it's trust.
Back in the search era, we’d open multiple tabs to cross-verify info; now, in the AI era, more and more people are just accepting the answers the models spit out. The issue is, as users lose the habit of verifying, the reliability of data sources becomes super crucial. A seemingly reasonable answer, if built on faulty data, could have repercussions that far exceed what we saw in the traditional search era.
That's why I'm keeping an eye on @OpenGradient . While many projects are busy flexing their model capabilities and parameter sizes, OpenGradient is more focused on the connection between AI and trustworthy data. My biggest takeaway from the experience with @OpenGradient isn't how flashy its answers are, but its attempt to tackle a deeper challenge: once AI becomes the gateway to information, how do we know where that info comes from and why it’s worth trusting?
In the Web3 space, this issue becomes even clearer. Whether it's on-chain data analysis, market research, or project evaluation, the quality of decisions often hinges on the quality of data. In the past, we worried about not being able to access information; now, the bigger challenge is that there's just too much info, and it's hard to judge what's real and valid. If AI can establish a verifiable data network in the future, the value it brings might not just be about boosting efficiency, but helping users cut down on cognitive costs.
I’ve always felt that the real competition in the AI space won’t just stay at the model level forever. Once everyone can access powerful model capabilities, the product ceiling will likely depend on data quality and credibility. The ones who can make users feel more secure using AI will have a better shot at becoming the next phase of infrastructure.
From this angle, the direction explored by @OpenGradient might be worth more long-term observation than a lot of short-term hype.
#opg $OPG $SPCXB
Back in the search era, we’d open multiple tabs to cross-verify info; now, in the AI era, more and more people are just accepting the answers the models spit out. The issue is, as users lose the habit of verifying, the reliability of data sources becomes super crucial. A seemingly reasonable answer, if built on faulty data, could have repercussions that far exceed what we saw in the traditional search era.
That's why I'm keeping an eye on @OpenGradient . While many projects are busy flexing their model capabilities and parameter sizes, OpenGradient is more focused on the connection between AI and trustworthy data. My biggest takeaway from the experience with @OpenGradient isn't how flashy its answers are, but its attempt to tackle a deeper challenge: once AI becomes the gateway to information, how do we know where that info comes from and why it’s worth trusting?
In the Web3 space, this issue becomes even clearer. Whether it's on-chain data analysis, market research, or project evaluation, the quality of decisions often hinges on the quality of data. In the past, we worried about not being able to access information; now, the bigger challenge is that there's just too much info, and it's hard to judge what's real and valid. If AI can establish a verifiable data network in the future, the value it brings might not just be about boosting efficiency, but helping users cut down on cognitive costs.
I’ve always felt that the real competition in the AI space won’t just stay at the model level forever. Once everyone can access powerful model capabilities, the product ceiling will likely depend on data quality and credibility. The ones who can make users feel more secure using AI will have a better shot at becoming the next phase of infrastructure.
From this angle, the direction explored by @OpenGradient might be worth more long-term observation than a lot of short-term hype.
#opg $OPG $SPCXB