This is an interesting observation. In line with the ideas around innovation. While this is an example of SQL-based companies versus the Graph-Model, there are key takeaways here. It highlights the impact of the narratives of conviction spun out while making a pitch.
Series B startups and onwards should be judged purely based on their traction. However, this is not the case with database startups — which continue to get a multiple on their revenue based on which market they fall in (conviction). Hence proving that conviction continues to play a pretty big role even in later stages, even when traction should take over (money raised vs. est. revenue chart above).
Should you fund a proven model of returns or “bet on the wild horse for returns”? This question requires careful deliberation and understanding of game theory, especially on the bet part. Each grant committee requires an outsize return (like the VC’s), but is cagey to fund the upstarts. Scientific progress is in limbo, precisely for this reason.
Here’s something more:
It’s also hard to assess what makes a “great team”. It’s as subjective as a “great product”. A proxy for a great team (and the resulting great product) would have to be traction. And SQL companies I analyzed that got more funding didn’t necessarily end up with more traction than their Graph/Multi-model counterparts.
If you extrapolate it to research (papers published/citations for paper) while ignoring the publishing rings and citation loops, then you have a similar scenario of what makes a company “great”. The proxy for assessment is the number of paying customers, revenues and “product-quality”. For example, Microsoft Word is default across many desktops. Does anyone consider it as a great product? Yet, it makes them boatload of revenues.
Challenge your assumptions by breaking out of your echo chambers, and push the idea of innovation and thoughts. It helps connect the dots.