I am not going to write another superficial “AI-first vs. data-first” comparison.
Distinction can be useful, but treating AI and data as separate foundations is misleading.
AI depends on data, although it is not merely data.
At a very abstract level, models themselves are produced through the interaction of datasets, architectures, objectives, optimization, compute, evaluation, and human feedback.
Pre-training, post-training, production AI systems all require data pipelines, quality controls, governance, monitoring, and evaluation.
In other words, AI becomes valuable only after passing through extensive data-related processes.
Without that foundational process, “AI-first” is attaching an LLM to unreliable, fragmented, or poorly governed data.