Maybe not for a developed, but for an AI based startup: 1. Generate synthetic data that is well aligned to your needs. With careful prompting + ensembling + af

Maybe not for a developed, but for an AI based startup: 1. Generate synthetic data that is well aligned to your needs. With careful prompting + ensembling + after-fact human filtering you can generate a lot of very particular human-like data that you can then used to train/etc your product. 2. Generate labels. gpt-3 can give pretty good NLU results through appropriate prompting. You can do multiple prompts + ensembling to get very good labels on free text (sentiment, entity linking, intent, etc). In both above use cases you can actually avoid deploying gpt-3 as part of client facing product, but instead leverage gpt-3 to train smaller "on-rails" models/rules/etc.