Venture capital investors are reallocating billions away from generic AI software-as-a-service (SaaS) startups towards companies that own critical workflows and possess deep domain expertise. This shift comes as the barrier to entry for creating AI tools plummets, making superficial products vulnerable to rapid replication.
According to Aaron Holiday, Managing Partner at 645 Ventures, popular categories now include AI-native infrastructure, vertical SaaS with proprietary data, systems that help users complete tasks, and platforms embedded in mission-critical workflows. Conversely, he states investors find "thin workflow layers, generic horizontal tools, light product management, and surface-level analytics" to be "quite boring."
The End of the 'Thin Wrapper'
Investors unanimously warn against startups offering easily copied solutions. "If your differentiation lives mostly in UI [user interface] and automation, that’s no longer enough," said Igor Ryabenky, Founder and Managing Partner at AltaIR Capital. He identified "generic productivity tools, project management software, basic CRM clones, and thin AI wrappers" as particularly vulnerable.
Abdul Abdirahman, an investor at F Prime, added that generic vertical software "without proprietary data moats" has also fallen out of favour. The consensus is that without deep integration or unique data, strong AI-native teams can quickly rebuild such products, eliminating any competitive advantage.
Workflow Ownership as the New Moat
The critical differentiator, experts say, is who owns the user's workflow. Jake Saper, General Partner at Emergence Capital, pointed to the contrast between Cursor and Claude Code as indicative. "One owns the developer’s workflow, the other just executes the task," Saper explained, noting developers increasingly choose simple execution.
This trend threatens products reliant on "workflow stickiness" to retain human users. "Pre-Claude, getting humans to do their jobs inside your software was a powerful moat, but if agents are doing the work, who cares about human workflow?" Saper told TechCrunch. Consequently, "workflow automation and task management tools that enable the coordination of human work become less necessary," Abdirahman stated.
Integrations Lose Their Edge
Another historical moat now eroding is the need for complex integrations. Saper highlighted that Anthropic's model context protocol (MCP) simplifies connecting AI models to external systems. "Being the connector used to be a moat," he said. "Soon, it’ll be a utility." This reduces the value of startups whose primary function is connecting disparate software.
The Path Forward for SaaS
For existing SaaS companies, the advice is to integrate AI deeply and update marketing accordingly. Ryabenky emphasised that "massive codebases are no longer an advantage," with speed, focus, and adaptability being paramount. He also advised moving to flexible, consumption-based pricing models over rigid per-seat structures.
The investment thesis is now clear. "Investors are reallocating capital toward businesses that own workflows, data, and domain expertise," Ryabenky concluded. "And away from products that can be copied without much effort."