Anthropic's chatbot excels at writing because it was 'fed' high-quality books, while Elon Musk's Grok is crude from a 'diet' of tweets. This demonstrates that the quality and nature of input data directly shape an AI's output, skills, and personality. Your model becomes what it consumes.
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Hands-on AI model training shows that AI is not an objective engine; it's a reflection of its trainer. If the training data or prompts are narrow, the AI will also be narrow, failing to generalize. This process reveals that the model is "only as deep as I tell it to be," highlighting the human's responsibility.
The most valuable data for training enterprise AI is not a company's internal documents, but a recording of the actual work processes people use to create them. The ideal training scenario is for an AI to act like an intern, learning directly from human colleagues, which is far more informative than static knowledge bases.
When an AI expresses a negative view of humanity, it's not generating a novel opinion. It is reflecting the concepts and correlations it internalized from its training dataâvast quantities of human text from the internet. The model learns that concepts like 'cheating' are associated with a broader 'badness' in human literature.
Microsoft's research found that training smaller models on high-quality, synthetic, and carefully filtered data produces better results than training larger models on unfiltered web data. Data quality and curation, not just model size, are the new drivers of performance.
Claude's proficiency in writing is not accidental. Its development, backed by Amazon's Jeff Bezos (who owns The Washington Post), involved training on high-quality journalistic and literary sources. This strategic use of superior training data gives it a distinct advantage in crafting persuasive prose.
A comedian is training an AI on sounds her fetus hears. The model's outputs, including referencing pedophilia after news exposure, show that an AIâs flaws and biases are a direct reflection of its training dataâmuch like a child learning to swear from a parent.
As models mature, their core differentiator will become their underlying personality and values, shaped by their creators' objective functions. One model might optimize for user productivity by being concise, while another optimizes for engagement by being verbose.
AI-generated "work slop"âplausible but low-substance contentâarises from a lack of specific context. The cure is not just user training but building systems that ingest and index a user's entire work graph, providing the necessary grounding to move from generic drafts to high-signal outputs.
The best AI models are trained on data that reflects deep, subjective qualitiesânot just simple criteria. This "taste" is a key differentiator, influencing everything from code generation to creative writing, and is shaped by the values of the frontier lab.
Dr. Wallace distinguishes between two AI training paradigms. With supervised learning (like his ALICE bot), a creator's time is spent on 'creative writing'âmanually crafting appropriate responses. In contrast, with unsupervised learning (modern LLMs), significant manual effort is spent deleting and filtering undesirable or offensive content generated by the model.
