AI is making the leap from "technology" to "profitable asset"
When you study @OpenGradient's working principle You will understand how we make money with AI
That's why I am long-term optimistic about this project!
#OpenGradient is: Breaking down AI that used to run only on big factory servers, and handing it over to a group of "computers on the chain" to do the work, After completing the work, it can also verify authenticity on the chain and automatically settle payments.
How does it achieve this step by step?
① Training: Not a single server, but a group of people computing together
Traditional AI:
Training a model = rent AWS, one or a bunch of servers, expensive, black box
OpenGradient:
Break the model training task into many small parts Who has idle GPU / computing power, come and take orders to compute a part
• Computing power comes from decentralized nodes • Submit results after computation • On-chain verification: Was the computation real? Was there any laziness?
👉 Computing power is like "miners," AI training is like "mining"
② Deployment: Model not on cloud, but on-chain
Traditional AI:
Model stored on company servers, only accessible via API
OpenGradient:
Models are "registered" on the chain like smart contracts
• Models have addresses • Version records • Who calls, who pays, who profits—all on the chain
👉 Model = a "public asset" on the chain
③ Invocation: Trust results, not the person
Traditional AI:
You ask the model a question You can only "trust it didn't lie to you"
OpenGradient:
You invoke the model Multiple nodes compute together Automatically verify the consistency of results on-chain
👉 Not "trust the model," but "the computation is done and you can't cheat"
④ How does the money flow? Fully automatic • Use the model → pay • Run computing power → earn money • Provide good models → keep earning rent
No platform cuts, no manual settlements.
👉 AI pays itself a salary
A metaphor from daily life makes it instantly clear
What is traditional AI like?
A black box factory, you pay, it gives results, you don't know what happens inside
What is OpenGradient like?
A transparent marketplace • Who farms (models) • Who provides computing power • Who pays (invokes) All recorded on the ledger
Why is this important?
Because it solves a fundamental problem 👇
When AI becomes infrastructure, why should we trust it?
OpenGradient's answer is: • Don't trust companies • Don't trust authorities • Use on-chain rules to force it to be honest
What OpenGradient does is simple: Transform AI from a "proprietary tool of big companies" Into a "public machine that everyone can use and verify"
View Original
This page may contain third-party content, which is provided for information purposes only (not representations/warranties) and should not be considered as an endorsement of its views by Gate, nor as financial or professional advice. See Disclaimer for details.
"Used to work for AI, now AI pays you"
AI is making the leap from "technology" to "profitable asset"
When you study @OpenGradient's working principle
You will understand how we make money with AI
That's why I am long-term optimistic about this project!
#OpenGradient is:
Breaking down AI that used to run only on big factory servers, and handing it over to a group of "computers on the chain" to do the work,
After completing the work, it can also verify authenticity on the chain and automatically settle payments.
How does it achieve this step by step?
① Training: Not a single server, but a group of people computing together
Traditional AI:
Training a model = rent AWS, one or a bunch of servers, expensive, black box
OpenGradient:
Break the model training task into many small parts
Who has idle GPU / computing power, come and take orders to compute a part
• Computing power comes from decentralized nodes
• Submit results after computation
• On-chain verification: Was the computation real? Was there any laziness?
👉 Computing power is like "miners," AI training is like "mining"
② Deployment: Model not on cloud, but on-chain
Traditional AI:
Model stored on company servers, only accessible via API
OpenGradient:
Models are "registered" on the chain like smart contracts
• Models have addresses
• Version records
• Who calls, who pays, who profits—all on the chain
👉 Model = a "public asset" on the chain
③ Invocation: Trust results, not the person
Traditional AI:
You ask the model a question
You can only "trust it didn't lie to you"
OpenGradient:
You invoke the model
Multiple nodes compute together
Automatically verify the consistency of results on-chain
• Inconsistent results → penalize malicious nodes
• Consistent results → automatically settle rewards
👉 Not "trust the model," but "the computation is done and you can't cheat"
④ How does the money flow? Fully automatic
• Use the model → pay
• Run computing power → earn money
• Provide good models → keep earning rent
No platform cuts, no manual settlements.
👉 AI pays itself a salary
A metaphor from daily life makes it instantly clear
What is traditional AI like?
A black box factory, you pay, it gives results, you don't know what happens inside
What is OpenGradient like?
A transparent marketplace
• Who farms (models)
• Who provides computing power
• Who pays (invokes)
All recorded on the ledger
Why is this important?
Because it solves a fundamental problem 👇
When AI becomes infrastructure, why should we trust it?
OpenGradient's answer is:
• Don't trust companies
• Don't trust authorities
• Use on-chain rules to force it to be honest
What OpenGradient does is simple:
Transform AI from a "proprietary tool of big companies"
Into a "public machine that everyone can use and verify"