Investment in AI companies has now entered its cautious phase. Following a year when the money directed at AI startups far outpaced any other sector, investments have recently become more sound or validated. Investors are more wary about the AI hype and are looking for companies that will turn a profit.
Building a profitable AI business poses unique challenges beyond those faced when launching a typical tech startup. Systemic issues like the high cost of renting GPUs, a widening talent gap, towering salaries, and expensive API and hosting requirements can cause costs to quickly spiral out of control.
The coming months could be daunting for AI company founders as they watch their fellow leaders struggle or even fail in new businesses, but there is a proven path to profitability. I applied these steps when I joined SymphonyAI at the beginning of 2022, and we just wrapped up a year in which we grew 30% and approached $500 million in revenue run rate. The same formula worked at my previous companies (Cerence, Harman, Symphony Teleca and Aricent, among others): focusing on specific customer needs and capturing value across a particular industry. All along the way, here are the considerations that formed the foundation for our successful efforts.
Build a realistic and accurate cost model
Let’s begin with one of the most important upfront decisions: Is it more cost-effective to use a cloud-based AI model or host your own?
Startups face many challenges, but AI businesses have some unique factors that can skew financial models and revenue projections, leading to spiraling costs down the road. It’s easy to miscalculate here — decisions on big issues may have unintended consequences, while there’s a long list of non-obvious expenses to consider as well.
Let’s begin with one of the most important upfront decisions: Is it more cost-effective to use a cloud-based AI model or host your own? It’s a decision that teams must make early because as you head down your chosen path, you’ll either go deeper into the custom capabilities offered by the AI giants or you’ll begin building your own tech stack. Each of those carries significant costs.
Defining your answer begins with determining your particular use case, but generally, the cloud makes sense for training and inference if you won’t be moving vast amounts of data in and out of data stores and racking up huge egress fees. But be careful, if you expect to sell your solution for $25 per user per month with unlimited queries — and OpenAI is charging you per token behind the scenes — that model will fall flat pretty quickly as your unit economics fail to turn a profit.