Who is OpenGradient really solving for?
I checked out OpenGradient's white paper, and after reading it, I had a question.
It addresses a real pain point: AI inference is a black box. You don’t know how the model arrives at its results, you don’t know if the data is tainted, and you don’t know if the inference process has been tampered with. The project places inference in TEE or zero-knowledge proofs, generating verifiable proofs, making everything traceable. This sounds like the right answer. The AI industry indeed needs transparency and auditability, especially in fields like healthcare, finance, and justice. But the question isn’t “is the solution correct?” but rather “who is going to pay for this solution?”.
I asked a friend who develops AI applications. After hearing about OpenGradient’s proposal, his first reaction was: “It does sound useful. But TEE is expensive, and zkML is even pricier. Right now, my users pay a penny for each inference; if we switch to OpenGradient, it would at least double or triple that cost. Who's going to cover that price difference?” I checked the official website and couldn’t find any pricing info, no cost comparisons, no model calculations. The team has developed a world-class solution, but the question of “how much are users willing to pay for it?” hasn’t been validated. @OpenGradient
If we classify technical difficulty into several levels, inference verification is at the highest tier. If they can pull this off, it shows the project has serious technical chops. But the logic of commercialization is another beast altogether; the most challenging tech parts are often not what users are most willing to pay for. Typically, users are willing to fork over cash for things that are cheap, useful, and hassle-free, not the most complex. And what OpenGradient aims to do is precisely complexity itself.
One user joked online about OpenGradient's tech architecture: “It’s like building a maglev train in the city only to find out most people still take the subway. It’s not that the maglev is bad; the subway is just good enough.” It may sound rough, but it’s true. OpenGradient's technical standards are sky-high, but does the market actually need such high specifications? This question remains unanswered in the white paper.
The tech direction is spot on, but the longer you’re in the industry, the clearer it becomes: there’s a long road between having the right tech path and achieving commercial success.
#opg $OPG
I checked out OpenGradient's white paper, and after reading it, I had a question.
It addresses a real pain point: AI inference is a black box. You don’t know how the model arrives at its results, you don’t know if the data is tainted, and you don’t know if the inference process has been tampered with. The project places inference in TEE or zero-knowledge proofs, generating verifiable proofs, making everything traceable. This sounds like the right answer. The AI industry indeed needs transparency and auditability, especially in fields like healthcare, finance, and justice. But the question isn’t “is the solution correct?” but rather “who is going to pay for this solution?”.
I asked a friend who develops AI applications. After hearing about OpenGradient’s proposal, his first reaction was: “It does sound useful. But TEE is expensive, and zkML is even pricier. Right now, my users pay a penny for each inference; if we switch to OpenGradient, it would at least double or triple that cost. Who's going to cover that price difference?” I checked the official website and couldn’t find any pricing info, no cost comparisons, no model calculations. The team has developed a world-class solution, but the question of “how much are users willing to pay for it?” hasn’t been validated. @OpenGradient
If we classify technical difficulty into several levels, inference verification is at the highest tier. If they can pull this off, it shows the project has serious technical chops. But the logic of commercialization is another beast altogether; the most challenging tech parts are often not what users are most willing to pay for. Typically, users are willing to fork over cash for things that are cheap, useful, and hassle-free, not the most complex. And what OpenGradient aims to do is precisely complexity itself.
One user joked online about OpenGradient's tech architecture: “It’s like building a maglev train in the city only to find out most people still take the subway. It’s not that the maglev is bad; the subway is just good enough.” It may sound rough, but it’s true. OpenGradient's technical standards are sky-high, but does the market actually need such high specifications? This question remains unanswered in the white paper.
The tech direction is spot on, but the longer you’re in the industry, the clearer it becomes: there’s a long road between having the right tech path and achieving commercial success.
#opg $OPG