The hidden battle of data annotation in this trillion dollar market has already begun. June 2025: Meta acquires 49% stake in Scale AI for $14.3 billion, making it the largest single investment in the history of the AI data field August 2025: Decentralized data annotation platform @ PerleLabs completes seed round financing led by Framework Ventures, with a total financing amount of $17.5 million (Perle's funding also includes top institutions such as Coinfund, NGC Ventures, Hashkey, etc.) In addition to direct competition in business, Perle founder Ahmed Rashad is also a former core member of Scale AI, where he served as the Head of Supply Chain and Growth from 2019 to 2022. The question arises: firstly, why is AI data annotation business so valuable? Secondly, compared to Scale AI, what are Perle's core competitive advantages? Why is AI data annotation business so valuable? From a technical perspective, the training quality of AI models directly depends on the accuracy of data annotation. Whether it's GPT, Claude, or other large models, they all require massive amounts of high-quality labeled data to 'learn'. The deeper reason is the restructuring of the business value chain. Traditional software development is code driven, where programmers write code and machines execute it. The AI era is data-driven, and whoever masters high-quality annotated data masters the "brain" of AI models. Taking Scale AI as an example, its customer base can be described as luxurious, including tech giants such as OpenAI, Meta, Microsoft, Google, as well as government agencies such as the US Army and Air Force. Scale AI's revenue in 2024 is $870 million, and it is expected to exceed $2 billion in revenue by 2025. Whoever controls the data controls the future of the AI market. What are Perle's core competitive advantages? Scale AI is not flawless. After Meta's investment, it triggered a series of vicious chain reactions: Google originally planned to pay Scale $200 million this year, but now it is cutting off the partnership directly OpenAI is gradually reducing its collaboration with Scale Microsoft and xAI suspend projects, concerned about data confidentiality issues The core contradiction is that data annotation companies need to serve multiple competitors in order to scale profits, but once deeply tied to one of them, they lose the core value of "neutrality". In contrast, Perle, as a decentralized data annotation platform, does not have similar issues. The purpose of building Perle is to support any team building or fine-tuning AI models, and this openness allows more AI teams to benefit. In addition, Perle's core advantages lie in: Innovation of incentive mechanism: In the traditional model, data annotators are often portrayed as "working people", while Perle Labs establishes verifiable work history records through token incentive mechanism, allowing high-quality work to receive due rewards. Long tail capability: As model capabilities become stronger, the key to performance often lies in the edge cases of the long tail. Perle turns data annotation into an accessible, rewarding, and community owned process, which may have advantages in handling these complex scenarios. Finally, in response to what I have said before, the current battle of wordplay has already been advanced. Everyone needs to find high-quality projects that are early and rarely written by people, and publish in advance to occupy the pit, otherwise they will only fall into meaningless internal competition.