Regarding the integration of AI and blockchain, many discussions focus on the intelligence level of the models, but this is actually asking the wrong question. The real bottleneck lies in: Are the sources of training data reliable? Has it been tampered with? How to hold parties accountable if issues arise?

Once the data source is untrustworthy, even the most powerful models are just packaging incorrect information more convincingly.

Recently, I came across an idea worth deep consideration—essentially building a "foundational but limiting" infrastructure: making data storage, transfer, and usability verifiable, provable, and traceable. In other words, "data has not been tampered with" is no longer an empty promise but a fact supported by cryptographic evidence.

If you want to evaluate any AI+ blockchain project, I suggest clarifying these three questions first:

**First, where is the data stored?**
**Second, how to prove it hasn't been replaced?**
**Third, if something goes wrong, who takes responsibility?**

Projects that can thoroughly address these three points will truly have the confidence to discuss sustainable ecosystem narratives. Instead of chasing the hype, it’s better to first solidify the foundation of a "trustworthy data chain."