Investors have been heavily investing in AI companies in recent years, with the technology playing a significant role in the tech industry. However, not all AI companies are attracting investor attention. JS recently spoke with venture capitalists to understand what investors are no longer interested in when it comes to AI software-as-a-service startups.
According to Aaron Holiday, a managing partner at 645 Ventures, popular SaaS categories for investors now include startups focusing on AI-native infrastructure, vertical SaaS with proprietary data, systems of action, and platforms deeply embedded in mission-critical workflows. On the other hand, companies that are considered unappealing to investors are those building thin workflow layers, generic horizontal tools, light product management, and surface-level analytics – essentially, tasks that can now be performed by AI agents.
Abdul Abdirahman from F Prime mentioned that generic vertical software without proprietary data moats is no longer in demand. Igor Ryabenky, the founder and managing partner at AltaIR Capital, emphasized the importance of product depth. He stated that investors are looking for companies with real workflow ownership and a clear understanding of the problem they are solving from day one. He highlighted that differentiation based solely on UI and automation is no longer sufficient, as the barrier to entry has decreased.
Jake Saper, a general partner at Emergence Capital, discussed the significance of workflow ownership in products. He noted that products focusing on workflow stickiness may face challenges as AI agents take over tasks previously done by humans. Saper also mentioned that integrations are becoming less popular, with Anthropic’s model context protocol simplifying the connection of AI models to external data and systems.
In conclusion, the key to attracting investors in the current market is depth and expertise. Companies should integrate AI deeply into their products and emphasize their domain expertise. Investors are now looking for businesses that own workflows, data, and domain expertise, moving away from products that can be easily replicated. It is essential for companies to adapt quickly, focus on product depth, and offer flexible pricing models in the evolving landscape of AI software-as-a-service startups.
