A new vanity search tool called In the Weights is giving people an unprecedented look at their digital footprint in AI training data. The platform, created by Joey Flynn and Thomas Dimson, lets users search their names to discover how extensively they appear in the datasets powering large language models. It's essentially Google for AI weights - showing not just where you appear online, but how deeply you're embedded in the neural networks reshaping the internet.
"So what's your In the Weights score?" might become the new "What's your Klout score?" - except this time, the stakes involve how deeply your information is baked into AI systems that millions use daily.
In the Weights launched this weekend as a search tool that does something previously impossible for most people - it reveals how extensively you appear in the training data that powers large language models. Created by Joey Flynn and Thomas Dimson, the platform represents a fascinating intersection of vanity metrics and AI transparency at a moment when both topics dominate tech discourse.
The concept taps into a growing unease about AI training practices. While companies like OpenAI, Google, and Meta have scraped vast portions of the public internet to train their models, most individuals have no idea how much of their digital presence ended up in those datasets. In the Weights appears designed to change that, offering what Flynn and Dimson are positioning as a "vanity search for the AI age."
The tool arrives at a critical inflection point. Just last month, a coalition of authors and artists intensified legal challenges against AI companies over unauthorized use of copyrighted material in training data. Meanwhile, the European Union's AI Act introduced new transparency requirements around dataset composition. In the Weights could become a consumer-facing window into these otherwise opaque systems.
What makes this particularly relevant now is the shift in how people think about their online presence. A decade ago, vanity searches meant Googling yourself to see what appeared in search results. But with AI systems, the question isn't just what's indexed - it's what's been absorbed into the statistical weights of neural networks that generate responses about you, sometimes accurately and sometimes hallucinated.
The platform's creators bring credibility to the effort. While details about Flynn and Dimson's backgrounds weren't immediately available, their ability to build a tool that queries AI training data suggests deep technical expertise in machine learning infrastructure. The challenge they're tackling is genuinely difficult - training datasets are massive, often poorly documented, and the relationship between source data and model outputs is complex.
For users, In the Weights offers both curiosity satisfaction and practical value. Content creators, executives, and public figures now have a way to understand their "AI footprint" - information that could inform decisions about online presence management, personal branding, and even legal strategies around data rights. It's LinkedIn recommendations meets AI archaeology.
But the tool also raises questions. How comprehensive is In the Weights' access to training datasets? Most AI companies treat their training data as proprietary. Does In the Weights rely on leaked datasets, public documentation, or novel analysis techniques? The answers matter for assessing the tool's accuracy and coverage.
There's also the meta-irony: a tool designed to reveal AI data practices will inevitably collect user data itself. Privacy-conscious users might wonder whether searching for themselves creates new data trails, or whether In the Weights shares information with third parties. Transparency about transparency tools becomes its own recursive challenge.
The timing connects to broader industry tensions. As AI companies face mounting pressure to compensate creators whose work trained their models, tools like In the Weights could provide evidence for both sides. It might reveal that certain individuals are disproportionately represented in training data - or it might show that fears about AI "stealing" personal information are overblown.
For now, In the Weights represents an experiment in AI-age accountability. Whether it becomes a must-check vanity metric or a niche tool for researchers depends on factors beyond the technology itself - including whether people actually care how much ChatGPT "knows" about them, and whether that knowledge translates to any actionable insight or control.
In the Weights enters the market at exactly the right moment - when AI transparency is shifting from technical concern to mainstream demand. Whether it becomes a fleeting curiosity or essential infrastructure depends on execution, but the core question it answers matters deeply. As AI systems become more influential in shaping information flow, understanding your presence in their training data shifts from vanity to necessity. The real test isn't whether people will check their scores once, but whether this kind of visibility creates pressure for better data practices across the AI industry.
