Lost in identification with venture capital firms blinders
What to do when your startup isn’t deep enough for deep-tech VCs and frighteningly deep for SaaS Venture Investors?
Spoiler — nothing, if you have the balls to do what you do and stick to your own vision.
We were lucky, after hard work on our own Automatic Speech Recognition and Natural Language Processing ML/DL over the previous year, we still did good networking and closed the tiny pre-seed round in just 1.5 months after starting fundraising in March 2021. Our cap table has a pool of wonderful investors. We call them Star Team within founders. It was fast and smooth, but we felt the first calls from VC blinders even then.
First signs of VC blinders. Several funds refused us with the words: “You cannot compete with Google, Facebook, Amazon.”
But we are not competing with the giants, because we build a vertical SaaS solution. And here we met a misunderstanding: “So if the value is in a business product, why are you still wasting resources on creating your own technology? Buy from Google, Facebook, Amazon.”
We tried to explain:
* The unit economics will not work on the prices that giants are now setting for speech to text, and this will last another 3–5 years or even longer
* Their quality of automatic speech recognition is not sufficient for what we do — we work in the complex area of phone conversations and real-time.
* When buying from other vendors, we cannot manage results, train custom models for clients and there are many other technical obstacles.
We drew charts, made spreadsheets with calculations, described so clearly (as it seemed to us) that a child would understand. Still no go.
Not all investors will understand your product, your vision, and your market — that’s okay. We just left and got support from others. But that was the beginning.
After 5 months, we achieved steep results and realized that we would start fundraising our seed round in the fall. Investors still come to us by inertia after our pre-seed. We start relationships with those who can become our seed round investors.
And on the very first investor (SaaS-focused VC), we get the picture. — Why own tech? Can you compete with giants? Deep tech requires a lot of money in R&D, we are not sure that we are the fund that can support you with such a depth of money that you succeed.
This investor’s answer should be read as follows: we are pure SaaS VC, we used to the fact that the product is made quickly, that there is no long R&D cycle, that all the results of the product roadmap are predictable — developer wrote the code, the picture came to life. Simple, fast, cheap.
Hey, we already did it. We’ve invested over a year in this and the technology is already cool enough to make a product. We have customers who pay us and they are happy with us and our MVP.
They still don’t want to take a risk.
That’s fine. We can live with it. And we go to a deep-tech fund, and they have their own agenda:
“Why do you build a vertical product? It distracts you from the technology. You need to compete with Google, Amazon, Facebook. You now have a very small R&D team. Where and how do you plan to recruit bright minds?”
Well, we plan to deepen the technology just enough to give us the required business result. And that’s all. We are not planning to sell it to other developers — we are building our own product.
“Then you are not a deep-tech.”- deep-tech VC answers.
This investor response should be read as follows: Guys, if you do not take off commercially, we want to have an asset in our hand, which could be acquihired by the tech giant and we get our money back. And in your case, there will be too few heads in AI-research to return our investment.
Here is a quote from VC we didn’t talk to but read their article: ‘For example, it has been reported that Apple buys teams at $3m per engineer.’ Fair enough. Not for us.
We were confused. And I even formulated a joke:
“For deep-tech VCs we are not deep enough, while for SaaS funds, we are frighteningly deep”.
And it made me think about what is going on.
Today the Universe blessed me with an answer!
I listened to a partner of a deep-tech fund that invests at Series A. We are seed but will start building relationships, we really liked them.
The partner in his presentation spoke about three types of startups in terms of technological complexity:
- Commodity — software that can be written by any experienced programmer: CRMs, e-commerce portal, and others — invested by SaaS funds, etc.
- Deep-tech — something that every experienced programmer cannot do, a Research Team is needed here, there will be patents and strong IP — deep-tech funds invest here
- Visionary — still in the research stage and there are still no results that can be applied for commercial purposes — deep tech visionary funds invest here, who understand that this is a long-lasting game with a huge amount of investment needed
For such a classification, our project lost in identification. What’s going on in Ender Turing.
We are preparing a patent, we have our own IP in the field of neural networks for Automatic Speech Recognition and Natual Language Processing. We have AI-researchers, who are also super strong software developers (Boom!), and we create a vertical, completely commercial product right here and now.
And this is where the biggest VC blinders lie.
Answer the question now: what happens to each deep technology over time?
You are right! It is commoditized — it becomes a mass tech. The whole question is at what point in time you look at the technology. Even 10 years ago, our field was too complicated. We are at the moment when AI in speech is going through the stage of active adoption and commercialization. Still, it is complicated enough to have a high entry barrier. But it already has predictable R&D cycles and commercializable results without all the money in the world.
Take a look at the latest Gartner Hype Cycle for Artificial Intelligence 2021 — search for Natural Language Processing, Machine Learning, and Intelligent Applications. — YES! It goes towards mass adoption.
We know that we will not live up to deep tech VC expectations by increasing research and prioritizing technology.
We build a highly demanded vertical SaaS solution with a strong technological advantage and we plan to keep this advantage for the next 5–7 years.
But it turns out that this is not obvious to everyone. Many VCs have no idea of what is happening with this or that technology at the moment. They live with the picture in mind that all neural networks are deep-tech, unpredictable, require a lot of money.
Fortunately, not all VCs think in frames. ‘The truth is grey’. Now it remains to find those who see the market as deeply as we do :) Know such guys? — let me know!
P.S. And don’t be shy to clap this article. I’d be happy to know you enjoyed reading :)
