In the past two years in the crypto scene, I've seen no less than a hundred AI projects, but only a few are worth digging into at the foundational level. @OpenGradient is one of them; it's not just another AI concept coin, but a long-awaited AI Layer 1 in the crypto space. $BEAT
Recently, I did a little exercise, picking around twenty self-proclaimed AI+Web3 projects, combing through GitHub commits and node structures until my fingers went numb. Most couldn't even run a single inference node, and the rest just slapped a model API on a contract. The buzz around AI infrastructure has been going on for three years now, mostly stuck at the token design level, while the real work of running models, generating proofs, and executing transactions is almost non-existent.
To put it in perspective, this is just like the early days of autonomous driving; a bunch of companies were talking about smart driving, but only a few actually laid down the control chassis and gathered real vehicle data. It wasn't until Tesla tackled the tough problems that the industry had to shake things up. The crypto space is now stuck at the PPT stage, waiting for someone to take on the role of the control chassis.
I used to think this kind of infrastructure was mostly storytelling, but after digging into OpenGradient, I had to rethink. It directly created an EVM-compatible Layer 1, positioning itself as a Network for Open Intelligence, with model hosting, inference, and verification all closed-loop on-chain. The underlying zkML combined with TEE, over 4,500 models are already linked to the network, running more than 2 million verifiable inferences, and it even got into NVIDIA Inception. EVM compatibility means that existing dApps and agents don’t have to rewrite their stacks to use AI. This is infrastructure, not just a concept.
I'm not vouching for it. The biggest cost of Layer 1 is that network effects build slowly, and it’s still in testnet. Model quality varies, the ecosystem is thin, and early developers have to bear the dirty work of an immature toolchain; the costs of zkML and the attack surface of TEE haven't disappeared; they've just been moved to a new chain. $BTW
The lines I've drawn for observation are pretty straightforward. I want to see what the real mainnet launch of OpenGradient looks like, whether it can stabilize the volume of daily real inference calls, and I also want to see when three flagship AI applications from teams other than their own are willing to put their stakes on it. Until these three things happen, don’t rush into positions; first, let’s see if it can mine effectively.
#opg $OPG
Recently, I did a little exercise, picking around twenty self-proclaimed AI+Web3 projects, combing through GitHub commits and node structures until my fingers went numb. Most couldn't even run a single inference node, and the rest just slapped a model API on a contract. The buzz around AI infrastructure has been going on for three years now, mostly stuck at the token design level, while the real work of running models, generating proofs, and executing transactions is almost non-existent.
To put it in perspective, this is just like the early days of autonomous driving; a bunch of companies were talking about smart driving, but only a few actually laid down the control chassis and gathered real vehicle data. It wasn't until Tesla tackled the tough problems that the industry had to shake things up. The crypto space is now stuck at the PPT stage, waiting for someone to take on the role of the control chassis.
I used to think this kind of infrastructure was mostly storytelling, but after digging into OpenGradient, I had to rethink. It directly created an EVM-compatible Layer 1, positioning itself as a Network for Open Intelligence, with model hosting, inference, and verification all closed-loop on-chain. The underlying zkML combined with TEE, over 4,500 models are already linked to the network, running more than 2 million verifiable inferences, and it even got into NVIDIA Inception. EVM compatibility means that existing dApps and agents don’t have to rewrite their stacks to use AI. This is infrastructure, not just a concept.
I'm not vouching for it. The biggest cost of Layer 1 is that network effects build slowly, and it’s still in testnet. Model quality varies, the ecosystem is thin, and early developers have to bear the dirty work of an immature toolchain; the costs of zkML and the attack surface of TEE haven't disappeared; they've just been moved to a new chain. $BTW
The lines I've drawn for observation are pretty straightforward. I want to see what the real mainnet launch of OpenGradient looks like, whether it can stabilize the volume of daily real inference calls, and I also want to see when three flagship AI applications from teams other than their own are willing to put their stakes on it. Until these three things happen, don’t rush into positions; first, let’s see if it can mine effectively.
#opg $OPG
做 web3 第一 AI 链
75%
AI 链有点太多了
25%
4 votes • Voting closed