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Recently, stories in the AI and Web3 fields have become more and more outrageous. From capital scams to technical infighting, from hacker intrusions to benchmark data falsification, the industry ecosystem issues reflected behind these cases are really worth alerting all investors and practitioners.
**A simple AI label can scam away $1.5 billion**
British company Builder.ai once claimed to be able to generate software with AI with one click, and this concept sounded explosive. Major capital like Microsoft and SoftBank rushed to invest, and the company's valuation soared to $1.5 billion. But what happened? In May 2025, the truth was revealed—the backend had no real AI system at all, just Indian programmers manually coding. The founder also inflated revenue by three times. Once the scandal was exposed, investments were instantly frozen, and the company declared bankruptcy. As a result, early investors basically lost everything.
The lesson from this is very painful: in the AI startup boom, there are countless scams disguised as "artificial + intelligent" under the banner of AI. Many projects only showcase PPTs and concepts during fundraising; real technological implementation, practical application cases, and authentic revenue data are the key to judging whether a project can be trusted.
**A major tech company's large model ranking scandal, open-source dreams shattered**
By the end of 2025, another big scoop emerged. A leading tech company's large model was exposed for cheating in benchmark tests— to achieve better scores, they used different versions of the model for different evaluations, and even directly tampered with data. Turing Award winners had to come out and admit that "the results were slightly altered." This explosion of news caused the core technical team to all leave, and the company was forced to abandon its open-source strategy and turn to closed-source model development. The entire open-source empire suddenly started to struggle.
Such incidents occur frequently, indicating that the evaluation system of the entire industry is still very fragile. Data falsification, technical exaggeration—without solving these problems, it will be very difficult for investors and users to truly assess a project's strength.