Last night, I got into a debate with some buddies: In the second half of the AI race, is it all about the models themselves, or is it about where these models are run?
My take is a bit radical—models are going to become increasingly homogenized, and what's really scarce is "the infrastructure layer for running models." Whoever can turn inference into a pay-per-use, verifiable, and auditable service will hold the long-term access.
That's why I've been closely watching @OpenGradient . It's not just another chatbot; it's breaking down AI inference into infrastructure modules: execution goes to execution nodes, verification to full nodes, data has independent proof, and payments are settled on-chain with $OPG . When you call a model, there's a whole traceable process running behind it, not just a centralized API spitting out a result.
I personally grind on OpenGradient Chat daily, using interfaces for cutting-edge models like ChatGPT, Claude, and Gemini, and it's super smooth in comparison. But what keeps me hooked isn't just the convenience; it's that three-layer encryption architecture—local encryption, relay identity stripping, and decryption only in TEE. I can verify these guarantees are real, not just comfort myself by reading a privacy policy.
Back to investment logic. Right now, on-chain agents, DeFi risk management, and automated strategies are exploding. When these applications call on AI, who ensures the model hasn't been swapped, the output hasn't been tampered with, and the costs are clear? OpenGradient's verification and payment layers fit right into this niche.
a16z crypto and Coinbase Ventures led a $9.5 million round, betting on this very thing. My personal judgment: when "verifiable AI inference" transforms from concept to necessity, those who lay down the pipeline first will reap the rewards.
#OPG
My take is a bit radical—models are going to become increasingly homogenized, and what's really scarce is "the infrastructure layer for running models." Whoever can turn inference into a pay-per-use, verifiable, and auditable service will hold the long-term access.
That's why I've been closely watching @OpenGradient . It's not just another chatbot; it's breaking down AI inference into infrastructure modules: execution goes to execution nodes, verification to full nodes, data has independent proof, and payments are settled on-chain with $OPG . When you call a model, there's a whole traceable process running behind it, not just a centralized API spitting out a result.
I personally grind on OpenGradient Chat daily, using interfaces for cutting-edge models like ChatGPT, Claude, and Gemini, and it's super smooth in comparison. But what keeps me hooked isn't just the convenience; it's that three-layer encryption architecture—local encryption, relay identity stripping, and decryption only in TEE. I can verify these guarantees are real, not just comfort myself by reading a privacy policy.
Back to investment logic. Right now, on-chain agents, DeFi risk management, and automated strategies are exploding. When these applications call on AI, who ensures the model hasn't been swapped, the output hasn't been tampered with, and the costs are clear? OpenGradient's verification and payment layers fit right into this niche.
a16z crypto and Coinbase Ventures led a $9.5 million round, betting on this very thing. My personal judgment: when "verifiable AI inference" transforms from concept to necessity, those who lay down the pipeline first will reap the rewards.
#OPG