The New Digital Status Symbol

If you want to know how much you matter in 2026, don't check your Google search results. Check your weight.

That is the premise behind In the Weights, a new project from former OpenAI engineers Thomas Dimson and Joey Flynn. The site doesn't crawl the web for your LinkedIn profile or your latest byline. Instead, it queries a suite of large language models—including GPT-4o, Claude 3.5, Gemini, and Grok—to see if you exist within their internal parameters. If the models can recall who you are without reaching for a live search tool, you are officially "in the weights."

It is a digital vanity search for the post-Google era, and it is already turning into a high-stakes game of social comparison.

How the Scoring Works

The mechanics are deceptively simple. When you enter a name, the site prompts a variety of LLMs with a request: "Who is [Name]? Give up to 10 results, each with a short description and confidence." The system then clusters these responses and assigns a "strength score" based on how consistently and accurately the models identify the subject.

For the average user, the results can be jarring. A score of 641 might place you in the top 6 percent of names, but a quick search for a colleague might reveal they are sitting comfortably in the 900s. The leaderboard is currently dominated by cultural titans like Macaulay Culkin and Luciano Pavarotti, who hover near the 988 mark.

But the real value isn't just the score; it is the exposure of model bias. The platform highlights which models hallucinate, which ones are confused by ambiguous names, and which ones simply have no idea who you are. It is a raw look at the "knowledge" contained within the black boxes of modern AI.

Why the Old Vanity Search is Dying

Dimson and Flynn, who joined OpenAI following the acquisition of their startup Global Illumination, built the tool to capture a shift in how information is consumed. As traffic moves away from traditional search engines and toward LLM-based interfaces, the "canonical" source of truth is no longer a blue link. It is the model's training data.

"Google vanity searches are the wrong objective in 2026 as more traffic moves to LLMs," Dimson told TechCrunch. "So many lives are encoded somehow in a bunch of floating point numbers inside the AI brain."

Critics, such as AI researcher Anthony Moser, have pointed out that the tool is essentially just a wrapper for asking 13 chatbots the same question. Yet, the project has struck a nerve. It taps into a primal human desire to see if we have been "remembered" by the machines that are increasingly shaping our reality.

What This Means for Your Digital Footprint

For the average person, being "in the weights" is becoming a proxy for relevance. If you aren't in the model, you are effectively invisible to the next generation of AI-powered assistants.

Dimson plans to expand the project to analyze why certain models favor specific demographics and to identify individuals who are culturally significant enough to warrant a Wikipedia article but remain absent from the training sets of major models.

Key Takeaways

  • Beyond Google: The project highlights that LLM training data is becoming the new canonical source of truth for public figures and professionals.
  • Statistical Reality: Your "strength score" is a measure of how deeply your digital footprint has permeated the training sets of major AI models, not your actual popularity.
  • Model Bias: The platform exposes significant discrepancies between models, showing that "knowledge" is not uniform across different AI architectures.

The Next Frontier of Relevance

For now, In the Weights is a curiosity—a Nintendo-inspired, retro-styled experiment that turns existential dread into a leaderboard. But as these models become the primary interface for information, the question of who gets encoded into the "AI brain" will move from a fun party trick to a serious concern for public figures, journalists, and anyone concerned with their digital legacy.

Watch for the next update from the team, which promises to dive deeper into the specific biases of individual models. By then, the question won't be whether you are in the weights, but how much the models actually know about you.