| I empathize completely with this team because my company was in a very similar situation in 2018. We had two very technical computer vision products, had found traction and a growing enterprise user base but revenues didn't grow fast enough and all of the major companies were entering the market. We were lucky enough to find an acquisition off ramp last year but all of the feels are the same. The big takeaway I learned is, if your differentiating product/service could be classified as a feature (which most ML or CV products are) inside a platform or application, you'll be run over by the major platforms who rebuild your product/services inside their platform. Your only hope is that your team/data/IP is so far ahead and the acquiring company can't build what you're doing in-house more cheaply than what you're willing to sell for. Unfortunately it seems like there are fewer and fewer cases where major players can't rebuild your work more cheaply. Second, it's excruciatingly difficult to prove the value of your product/service to a potential acquirer because you don't know their metrics, and if you do, you don't know their acquisition strategy. We did an intensive integration of our product with a Fortune 50 retailer, and based on their own numbers showed (using their own A/B tests) that our service provided a statistically significant lift in a core metric that they cared about, in this case paid conversions. Their CEO even talked about it at a public summit. However their acquisitions strategy didn't include small companies that aren't major strategic partners (Only >$200M+ acquisitions). The worst part here is that, the founders (like I was) are absolutely in love with the technology and how amazing it is. The problem is, from a business perspective, that basically doesn't matter. You could be doing the most amazing work in NLP token inference, but if the product doesn't fit perfectly as an acquisition and it's not so compelling as to build a huge platform around, it's probably going to fail. I wish it weren't the case, but it leaves me questioning what the value of doing really hard technology is as a startup. It seems clear that the most financially successful startups aren't solving fundamentally hard technology problems until they get to scaling something with broad product market fit. |
Totally agree with this sentiment. I think this is why most "hard technology" problems are left to huge R&D departments or the government. Both of which aren't particularly nimble or profitable. There are a few notable exceptions (Oculus comes to mind), but most unicorns don't generally deal with solving tough problems. It's mostly about product-market fit and the balance sheets.