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

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

Investors have been pouring billions into AI companies over the past few years, as the technology continues to carry sway in the Valley and thus the world. But not all AI companies are grabbing investor consideration.

Indeed, even because it appears every company these days is rebranding to incorporate “AI” in its identify, some startup concepts are just not in favor with traders. TechCrunch spoke with VCs to be taught what traders aren’t looking for in AI software-as-a-service startups anymore.

Popular SaaS classes for traders now embody startups constructing AI-native infrastructure, vertical SaaS with proprietary data, programs of action (those serving to customers full duties), and platforms deeply embedded in mission-critical workflows, according to Aaron Holiday, a managing accomplice at 645 Ventures. 

But he also gave an inventory of companies that are thought-about fairly boring to traders these days: Startups constructing skinny workflow layers, generic horizontal instruments, light product administration, and surface-level analytics — mainly, something an AI agent can now do. 

Abdul Abdirahman, an investor at the agency F Prime, added that generic vertical software “without proprietary data moats” is not in style, and Igor Ryabenky, a founder and managing accomplice at AltaIR Capital, went deeper on that point. He said traders aren’t in something, actually, that doesn’t have a lot 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 getting into the market now have to build round “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.” 

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Jake Saper, a basic accomplice at Emergence Capital, also had ideas on possession. To him, the variations 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 coping with “workflow stickiness” — that means attempting to draw as many human prospects as attainable to repeatedly use the product — may discover themselves in an uphill battle as brokers 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 have gotten less in style, particularly as Anthropic’s mannequin context protocol (MCP) makes it simpler than ever to attach AI fashions to exterior data and programs. This means somebody doesn’t have to obtain a number of integrations or build their very own buyer integrations; they can just use the MCP. 

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

Also, not en vogue embody 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 shares are down as new AI-native startups come up with better, more environment friendly technology. 

Ryabenky said the SaaS companies struggling to lift proper now are the ones that can simply 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 stays enticing about SaaS is depth and experience, with instruments embedded in important workflows. He said companies ought to proper now look into integrating AI deeply into their merchandise and update their advertising to mirror 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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