Investors have been pouring billions into AI companies in recent years. AI’s influence is undeniable, but not every startup is attracting attention. While many companies are adding “AI” to their names, some ideas are falling flat with investors, especially in the software-as-a-service (SaaS) sector.
Aaron Holiday, a partner at 645 Ventures, notes that investors are keen on AI-native infrastructure and specialized SaaS with proprietary data. They want tools that help users carry out tasks effectively and are vital in businesses. On the flip side, startups offering basic tools like thin workflows and simple analytics are losing appeal. Why? Because they do what AI can already do.
Abdul Abdirahman from F Prime adds that generic vertical software without unique data is falling out of favor. Investors want more than just a flashy user interface. Igor Ryabenky from AltaIR Capital emphasizes that businesses lacking depth in their offerings aren’t likely to impress investors anymore.
“If your edge is just in UI and automation, it’s not enough anymore,” he explains. With the lowering barrier to entry, creating a solid competitive advantage is tougher.
New startups now need to focus on fully understanding their target problems and owning their workflows right from the beginning. Speed, adaptability, and flexible pricing are key. For example, rigid pricing models can deter customers today. Instead, consumption-based pricing is often more attractive.
Jake Saper of Emergence Capital believes understanding workflow dynamics is critical. He points to examples like Cursor and Claude Code, where one manages workflows while the other simply executes tasks. This shift shows that developers are leaning towards products that can handle execution, diminishing the need for heavy human oversight.
“It’s becoming less about human workflow,” Saper adds. As AI agents handle many tasks, keeping human workers engaged in software is becoming less essential.
Furthermore, with advancements like Anthropic’s model context protocol simplifying AI integrations, traditional integration tools are losing their value. Saper notes, “Using the connector as a moat is shifting. Soon, it’ll just be a standard utility.”
Abdirahman highlights that tools for workflow automation are also becoming less relevant as AI evolves. Public SaaS companies with stagnant stocks are facing competition from new AI-driven startups that provide better technology.
Ryabenky warns that many SaaS companies struggling to attract investment are easily replicated. Generic products such as basic project management tools or thin AI applications aren’t standing out. Investors are wary of anything that lacks deep integration or proprietary data.
What’s clear is that investors are prioritizing depth and expertise in SaaS. Companies must integrate AI more thoroughly into their products. Ryabenky states, “Investors want businesses that truly own their workflows and have domain expertise, steering clear of easily replicable products.”
As the landscape shifts, staying ahead means embracing the complexity and uniqueness that AI can offer, ensuring that products are not just superficially advanced but deeply valuable.
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Startups,AI,SaaS,venture
