Fair point regarding the unlabeled/unstructured data. One could also argue that labeled data isn't going to be a prerequisite forever (see https://ai.facebook.com/blog/the-first-high-performance-self...). We see a very sharp rise in unstructured data use for ML (especially a large spike caused by large language models like Dall-E 2 and Stable Diffusion). In my opinion, the majority of the novel use cases are outside of big tech, and we also see a trend in "legacy" companies like media, manufacturing, etc. start building dedicated ML teams. The industry is still nascent, but it is growing fast. Frankly, we see the pain points we're solving resonate with so many more companies than just a year ago.
Agree re Snowflake/Databricks, they are partners rather than competitors. We sit on top of S3/GCS or other blob storages and currently are competing with various in-house solutions that ML scientists built themselves. I do see your point regarding large foundational models that would be only fine-tuned on the tail end for various use cases. I believe there still would be still companies building foundational models from scratch (currently at 5 billion images) so they can serve more application-specific products and unstructured data generators that partner with those companies creating a good enough market for the tool.