Investors spill what they aren’t looking for anymore in AI SaaS companies

Investors are pouring billions into AI companies, but not all AI startups are capturing their attention. While it seems every company is rebranding to include “AI” in its name, some startup ideas are no longer in favor with investors. TechCrunch spoke with VCs to learn what investors aren’t looking for in AI software-as-a-service startups anymore.

Popular SaaS categories for investors now include startups building AI-native infrastructure, vertical SaaS with proprietary data, systems of action (those helping users complete tasks), and platforms deeply embedded in mission-critical workflows, according to Aaron Holiday, a managing partner at 645 Ventures.

But he also gave a list of companies that are considered quite boring to investors these days: Startups building thin workflow layers, generic horizontal tools, light product management, and surface-level analytics — basically, anything an AI agent can now do.

Abdul Abdirahman, an investor at the firm F Prime, added that generic vertical software “without proprietary data moats” is no longer popular, and Igor Ryabenky, a founder and managing partner at AltaIR Capital, went deeper on that point. He said investors aren’t interested in anything, really, that doesn’t have much product depth.

“If your differentiation lives mostly in UI [user interface] and automation, that’s no longer enough,” he said. “The barrier to entry has dropped, which makes building a real moat much harder.”

New companies entering the market now need to build around “real workflow ownership and a clear understanding of the problem from day one,” he said. “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.”

Jake Saper, a general partner at Emergence Capital, also had thoughts on ownership. To him, the differences between Cursor and Claude Code are the “canary in the coal mine.”

“One owns the developer’s workflow, the other just executes the task,” Saper continued. “Developers are increasingly choosing the execution over process.”

He said any product dealing with “workflow stickiness” — meaning trying to attract as many human customers as possible to continuously use the product — might find themselves in an uphill battle as agents takeover the workflow.

“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?” he told TechCrunch.

He also thinks integrations are becoming less popular, especially as Anthropic’s model context protocol (MCP) makes it easier than ever to connect AI models to external data and systems. This means someone doesn’t need to download multiple integrations or build their own customer integrations; they can just use the MCP.

“Being the connector used to be a moat,” Saper said. “Soon, it’ll be a utility.”

Also, no longer en vogue include the “workflow automation and task management tools that enable the coordination of human work become less necessary if, over time, agents just execute the tasks,” Abdirahman said, citing examples, mainly public SaaS companies whose stocks are down as new AI-native startups arise with better, more efficient technology.

Ryabenky said the SaaS companies struggling to raise right now are the ones that can easily be replicated, he said.

“Generic productivity tools, project management software, basic CRM clones, and thin AI wrappers built on top of existing APIs fall into this category,” he said. “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.”

Overa, what remains attractive about SaaS is depth and expertise, with tools embedded in critical workflows. He said companies should right now look into integrating AI deeply into their products and update their marketing to reflect that, Ryabenky continued.

“Investors are reallocating capital toward businesses that own workflows, data, and domain expertise,” Ryabenky said. “And away from products that can be copied without much effort.”

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