AI startups face unique challenges compared to traditional SaaS companies, according to Rudina Seseri, founder and managing partner at Glasswing Ventures. Speaking at the TechCrunch Early Stage event in Boston, Seseri emphasized the importance of truly integrating algorithms and data into the core of an AI company’s value creation.
Seseri highlighted the differences in how customers and investors evaluate AI companies versus SaaS startups. Unlike SaaS products that can be released in beta form, AI products require time for the model to mature and gain customer trust in a business context.
Early-stage AI startups face difficulties in finding early adopters and must focus on articulating their value proposition and problem-solving capabilities. Seseri advised founders to ground their discussions in business priorities and metrics that matter to buyers.
In the competitive landscape of AI, Seseri suggested that startups should aim to establish a defensible position by focusing on unique data access and algorithms. While the foundation layer of AI is dominated by big players like OpenAI and Anthropic, there are opportunities for startups in the application and middle layers.
Seseri recommended investing in the application layer and selectively in the middle layer, where companies like Snowflake have found success. Ultimately, building an AI startup requires a deep understanding of the challenges and a strategic approach to navigating the evolving AI landscape.