Billions continue to flow from investors into AI companies, reflecting the technology's dominant influence in Silicon Valley and beyond. However, not every AI venture is capturing that interest. Even as numerous firms rebrand to highlight artificial intelligence, certain startup concepts have simply fallen out of favor with funders.

According to Aaron Holiday, a managing partner at 645 Ventures, investor enthusiasm in SaaS currently centers on AI-native infrastructure, vertical SaaS with unique proprietary data, systems designed to help users complete specific tasks, and platforms that are deeply integrated into essential workflows. In contrast, he noted that several areas now seem unexciting to investors: startups creating thin workflow layers, generic horizontal tools, lightweight product management solutions, and surface-level analytics—essentially, any function that an AI agent can now perform.

Abdul Abdirahman, an investor at F-Prime, added that generic vertical software lacking "proprietary data moats" has also lost its appeal. Igor Ryabenkiy, a founder and managing partner at AltaIR Capital, elaborated on this point, stating that investors are generally uninterested in products without substantial depth. "If your differentiation lives mostly in UI [user interface] and automation, that’s no longer enough," he explained. "The barrier to entry has dropped, which makes building a real moat much harder."

Ryabenkiy emphasized that new market entrants must focus on "real workflow ownership and a clear understanding of the problem from day one." He continued, "Massive codebases are no longer an advantage. What matters more is speed, focus, and the ability to adapt quickly. Pricing also needs to be flexible: rigid per-seat models will be harder to defend, while consumption-based models make more sense in this environment."

The theme of ownership was also addressed by Jake Saper, a general partner at Emergence Capital. He pointed to the contrast between Cursor and Claude Code as a telling indicator. "One owns the developer’s workflow, the other just executes the task," Saper said. "Developers are increasingly choosing the execution over process."

He suggested that any product relying on "workflow stickiness"—aiming to retain human users within a software environment—could face significant challenges as AI agents take over those workflows. "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 also believes integrations are declining in strategic value, particularly as tools like Anthropic’s model context protocol (MCP) simplify connecting AI models to external data and systems. "Being the connector used to be a moat," he remarked. "Soon, it’ll be a utility."

Abdirahman further observed that "workflow automation and task management tools that enable the coordination of human work become less necessary if, over time, agents just execute the tasks." He cited public SaaS companies whose valuations have suffered as newer, AI-native startups emerge with superior technology.

Ryabenkiy identified the most vulnerable SaaS companies as those offering easily replicable solutions. "Generic productivity tools, project management software, basic CRM clones, and thin AI wrappers built on top of existing APIs fall into this category," he stated. "If the product is mostly an interface layer without deep integration, proprietary data, or embedded process knowledge, strong AI-native teams can rebuild it quickly. That is what makes investors cautious."

Overall, what continues to attract investment in SaaS is depth and specialized expertise, particularly for tools embedded in critical workflows. Ryabenkiy advises companies to deeply integrate AI into their products and update their messaging accordingly. "Investors are reallocating capital toward businesses that own workflows, data, and domain expertise," he concluded, "and away from products that can be copied without much effort."