He warns that while the cloud may be cost-effective for training and inference, it can lead to high costs if large amounts of data are moved in and out of data stores. He also cautions against expecting to sell a solution for a low price with unlimited queries if the AI provider is charging per token, as this can quickly lead to a failure to turn a profit.
Key takeaways:
- Investment in AI companies has become more cautious and validated, with investors looking for companies that will turn a profit.
- Building a profitable AI business poses unique challenges such as the high cost of renting GPUs, a widening talent gap, high salaries, and expensive API and hosting requirements.
- Sanjay Dhawan, CEO of SymphonyAI, shares his successful formula for building a profitable AI business, which includes focusing on specific customer needs and capturing value across a particular industry.
- One of the most important decisions for AI startups is whether to use a cloud-based AI model or host their own, as each carries significant costs and can greatly impact the financial model and revenue projections.