A financial services company wants to build a system that automatically classifies incoming customer emails into one of ten predefined categories (e.g., 'Account Inquiry', 'Loan Application', 'Complaint'). The company has a historical archive of 200,000 emails that have already been manually sorted by employees. Two development strategies are proposed:

• Strategy 1: Adapt a large, state-of-the-art, general-purpose language model to perform the classification task.
• Strategy 2: Use the 200,000 labeled emails to train a new, specialized classification model from the ground up, designed solely for this purpose.

Evaluate the two strategies. Recommend one over the other for this specific scenario and justify your recommendation by discussing at least two key factors that influence this decision.