1. Many startups claiming AI are simply doing things with data; research shows two-fifths have no AI programs in their products [1]. 2. Round-closing announcements trail the actual closing by several months. Not to mention the actual raising and diligence takes months (even more so for larger/later rounds). Thus the $ reported here does not factor in covid19 and the current economic crisis, as implied. [1] https://ww…
The first point gets used a lot to support a narrative that all a startup needs to do is say "ML" in their pitch deck and secure funding. The reality is, almost all investors are aware of the prevalence of AI snake oil, and the good ones are pretty sophisticated when it comes to vetting it.
There is an also an attitude, related to this point, that startups who use ML in ways that aren't "new"—as in, finetuning a state of the art model, using well-known techniques, or otherwise "simply doing things with data"—aren't doing something worthwhile. I actually think the opposite is true. These startups represent the most exciting change in ML, in my opinion, in a very long time: They're actually building things with it.
We have a tendency to judge all ML announcements relative to our most extreme projections (autonomous vehicles, AGI, etc.) In that sense, yeah, nothing measures up right now. But lost in the back forth over the hype is the fact that there are a ton of companies building really cool, valuable products that couldn't exist without ML. Recommendation engines, speech-to-text, real-time prediction services (think Uber's ETA prediction), image analyzers, etc. Many of these are built by startups who aren't doing anything fundamentally new on the data science side, but I'm fine with that. Most SaaS companies aren't pushing basic technical boundaries either, and we find them pretty valuable.