Instead, xAI seemed fixated on a range of alleged conduct of former employees. But in assessing xAI's claims, Lin said that xAI failed to show proof that OpenAI induced any of these employees to steal trade secrets "or that these former xAI employees used any stolen trade secrets once employed by OpenAI."
This approach requires sourcing and maintaining accurate information, which means you can't fabricate numbers or exaggerate metrics. AI models increasingly cross-reference claims across sources, and inconsistencies damage credibility. The data you include must be truthful and, where relevant, attributed to primary sources. But when you consistently provide specific, accurate information, you build a reputation as a reliable source that AI models return to repeatedly.
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