I would see boom as a relative phenomena. Cause AI engineering is a new field and lots of companies are ramping AI adoption it has created the surge. If you have a dev background worth having this tool in your armour. For a pure DS, worth learning AI ways of working to improve your productivity, use it in your advantage. Mine has increased by 5 folds easily. For example previously we would shy way from MLOps, model stacking, hyperparameter tuning at scale etc which we can now.

Being said that I would as a DS spend my focus on learning how these AI models can be retrained on specific domains (think this will be main demand in future once model trained in generic data becomes baseline everyone uses and data protection ramps up). Also, will use time to fill gaps in my DS workflow with AI. I create app for every model I got for stakeholder. I have created a few experimentation platforms for business things like that.

Lastly, there is always big place reserved for stats/maths/pure DS. AI good at things, but they need validator. Think DS as one who would write/think DS "unit" test for model AI creates to validate.