Comment by lazystar

> What external factor makes the models less useful?

Life. A great example can be seen in the AI-generated baseball-related news articles that involve the Athletics organization. AI articles this year have been generating articles that incorrectly state that the Atlanta Braves played in games that were actually played by the Athletics, and the reason is due to the outdated training model. For the last 60 years before 2025, the Athletics played in Oakland, and during that time their acronym was OAK. In 2025, they left Oakland for Sacramento, and changed their acronym to ATH. The problem is that AI models are trained on 60 years of data where 1. team acronyms are always based on the city, rather than the mascot of the team, and 2. acronyms OAK = Athletics, ATL = Atlanta Braves, and ATH = nothing. As a result, an AI model that doesnt have context "OAK == ATH in the 2025 season" will see ATH in the input data, associates ATH with nothing in it's model, and will then erroneously assume ATH is a typo for ATL.