Just like during the peak crypto hype, people were doing things that clearly had no obvious business model. Some figured it out, maybe pivoted a few times, and are successful. Most silently disappeared.
The AI space is really hard to have a long term business model strategy because the market is evolving insanely fast. Your long term business plan could easily be killed by 1 feature release from OpenAI without notice. Perhaps that's anothe reason people are focussing more on use adoption rather than revenue.
The reality is user adoption in AI has WAY more value than revenue growth. You can always monetize later. But first you need users. Most companies prioritize user adoption and worry about revenue later (that's what VCs want from early seed stage AI startups... users are gold, revenue is just a cherry on top).
Obviously thats not sustainable. But that's ok... most companies don't fail because they didn't generate enough revenue. They fail most commonly because they couldn't build a product that could attract enough users (which made it impossible to generate revenue). So it kind of makes sense to stress out about user adoption, because if you can't get that, your business model doesn't matter.
Ideally these solutions bridge to engineering teams better, who DO have budget and get the best tools. But again they don't have this problem (they can build & deploy web apps)
Snowflake's acquisition of Streamlit was similar to Google Cloud's acquisition of Kaggle. They bought a large community of end-users that spent money on their cloud databases.