AI startups face different challenges than a typical SaaS company. That was the message from Rudina Seseri, founder and managing partner of Glasswing Ventures, at the TechCrunch Early Stage Event in Boston last week.
Seseri made it clear that simply connecting to some AI APIs does not make you an AI company. “And by AI native I don’t mean you call OpenAI or Anthropic with a human-like interface and you’re an AI company,” Seseri said. “I mean when algorithms and data are really at the core and part of the value you deliver.”
According to Seseri, this means there are big differences in the way customers and investors judge an AI company compared to a SaaS startup, and it’s important to understand the differences. First of all, with SaaS you can bring something into the world that is far from finished. This is not possible with AI for various reasons.
“Here’s the thing: With the SaaS product that you code, you do QA and you get the beta, so to speak – it’s not the finished product, but you can bring it to market and get started,” she said.
AI is a completely different animal: you can’t just put something out there and hope for the best. This is because an AI product needs time for the model to reach a point where it is mature enough to work for actual customers and they trust it in a business context.
“In the beginning it’s a steep curve in learning and training the algorithm, and yet it has to be good enough for the customer to want to buy, so it has to be good enough for you to add value,” she said. And that’s hard to find for an early-stage startup.
And that makes it harder to find early adopters. She says you want to avoid the long call where the buyer is just trying to learn about AI. Startup founders don’t have time for calls like this. She says it’s important to focus on your product and help the buyer understand your value proposition, even if it’s not quite there yet.
“Always articulate the problem you’re solving and what metric – how are you measuring it?” she said. Optimize what matters to the buyer. “So you’re solving a problem that drives business decisions.” It’s okay to state your vision, but always ground your discussion in business priorities and how they influence your algorithms.
How can AI startups win?
As you build your business, you need to think about how to secure a defensible place in AI, which is particularly challenging because the big players are constantly coming up with huge amounts of business ideas.
Seseri points out that in the cloud era, we had a base layer upon which the infrastructure players staked their claim; a middle class where the platformers lived; And at the top we have the application layer where SaaS lived.
With the cloud came some players like Amazon, Microsoft and Google who controlled the infrastructure. The base layer of AI is where the large language models reside, and a few players such as OpenAI and Anthropic have emerged. While one could argue that these are startups, that’s not actually the case as they are funded by the same big players that dominate the infrastructure market.
“If you’re competing for a new base layer, or you know, LLM game, it’s going to be very difficult with multi-billion dollar capital requirements, and at the end of the day there’s a good chance it’s a commodity, ” She said.
At the top of the stack is the application layer, which has benefited thousands of SaaS companies in the cloud era. She said that the big players like Amazon, Google and Microsoft are unable to take over the entire application layer business and that there is room for startups to develop and grow into large, successful companies.
There is also a middle level where the plumbing work is carried out. She points to companies like Snowflake that have managed to build successful middle-class businesses by giving application stakeholders a place to put their data.
So where does it invest when it comes to AI? “I put my money into the application layer and very selectively into the middle layer. Because I think there’s a divide around algorithms, whether they’re algorithms that you own or open source algorithms – and data. You do not have to be the owner of the data. But if I have to choose, I would like to have unique data access and unique algorithms. If I am forced to choose one, I will look for data,” she said.
Building an AI startup is certainly not easy, perhaps even more challenging than a SaaS startup. But this is where the future lies, and companies that want to try it need to know what’s coming and build accordingly.