Happy Sunday and welcome to InsideAI! I'm Rob May, Founder/CTO at Dianthus. I'm an active angel investor (recent companies include Adept-ID and Thaena). If you are raising early capital for an AI company, please reach out.
This week I want to point out an interesting article from Lonne Jaffe of Insight Partners on what he's learned from ScalingAI companies. As an investor across my angel and VC portfolio in well over 100 companies, mostly AI focused, I've seen the same thing. But this point is the one that is most being missed in the market:
- AI ScaleUps need investors with very long-term time horizons.
Building an AI ScaleUp requires large up-front investments as companies struggle to define product-market fit, attract top talent and lay down a long-term foundation. Founders need to understand the relationship between their cash runway and the technical difficulty of the problem they’re trying to solve. Just as importantly, they need investors who understand the vision and commitment required for an AI ScaleUp to succeed.
Because AI is an iterative process, investors need to be willing to commit to the timeframe necessary for the engineering and data science behind the ScaleUp’s product to deliver value. As with any automation tech, the progression of an AI system is multi-step: At first the AI makes suggestions to humans, then it makes decisions with humans involved only in edge cases, and finally the automation reaches a level that doesn’t need a human touch at all.
SaaS has made us so used to getting started quick and easy, as customers, and SaaS metrics have become so predictable and well understood by investors, that it's all people want to see. AI introduces friction, and goes slower, and even if you believe (as I do) that on the other side of the curve these AI companies will be more dominant, most customers don't have the patience to adopt, and most investors don't have the patience to wait out the time to scale.
We've seen this before though. In 2009 when I was raising for my first startup, many investors still didn't get SaaS. Lots of VCs told me things like "these recurring revenue businesses will never make it - they take too much capital to get to profitability and your customer acquistion payback periods are too long - we are sticking with on-prem software." Now, I have tons of AI investments who hear the same things from other VCs. "We love your model but are sticking with SaaS." It's a huge mistake, and these VCs are missing out on the ability to learn and understand the new stuff that's coming.
AI businesses have compounding advantages that compound faster at scale. Whereas many companies slow as they get larger, that point for AI companies is going to be pushed way way out. But you have to have the guts to get to the other side of that curve.
As a former VC, I can tell you that, while VCs consider themselves the mavericks of capitalism (and yes, a few are), mostly it's MBAs pushing spreadsheets, with little vision or tolerance to explore things they don't understand - and now they are trying to fit AI companies into their SaaS mental models and it isn't working. I know a handful of really smart AI investors who are embracing these ideas and have learned these same lessons, so if you are raising, make sure your investors understand that your company might be built a bit different from all the SaaS in their portfolio. AI companies take time. Choosing the right partners is important.
Thanks for reading.
@robmay
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