I asked 4 AIs to pick a number. Why they all said 7?
Summary
An article exploring why four different AI models all chose the number 7 when asked to pick a number, highlighting potential biases in training data.
Similar Articles
I've been thinking about whether AI agents should ever rely on a single model for important decisions.
The author conducted a test comparing multiple AI models on a research task and found that models sometimes confidently disagree. They suggest that AI agents should consider multiple model opinions for important decisions like planning, code review, or research, and ask how others handle this.
Political bias in AI: Where the AI models stand
An analysis of political leanings in six major AI models, showing that 4 out of 6 lean left of center on the economic axis, with some models being unaware of their own bias.
A million people, a million personal AIs, three base models. Is that a diverse deliberation — and how would you measure it?
A critical reflection on whether using only three base models for millions of personal AI agents can produce genuinely diverse deliberation, arguing that correlated errors across models may create false unanimity and seeking operational metrics—drawn from ensemble learning—to measure true human representational diversity.
AI is confidently wrong way more than people give it credit for, change my mind
A user shares concerns about AI models presenting thin or ambiguous data with the same confidence as well-supported findings, citing a case where a complaint appearing only twice in 200 comments was ranked as a top concern. The piece questions whether this is a fixable prompting issue or a fundamental limitation requiring manual verification.
The model picker is a dead end (9 minute read)
Lovable argues that asking users to pick an AI model is a dead end, explaining their approach of model independence where the control plane dynamically assigns tasks to the best-suited model and adapts instructions and context per model.