Investors have poured billions into AI companies in recent years as the technology continues to dominate the Valley and consequently the world. But not all AI companies are catching the attention of investors.
Indeed, even as it seems like every company these days is rebranding to include “AI” in its name, some startup ideas are no longer in investor favor. TechCrunch spoke with VCs to find out what investors are no longer looking for in AI software-as-a-service startups.
Popular SaaS categories for investors now include startups building AI infrastructure, vertical SaaS with proprietary data, action systems (those that help users complete tasks) and platforms deeply embedded in mission-critical workflows, according to Aaron Holiday, managing partner at 645 Ventures.
But he also gave a list of companies that are considered pretty boring to investors these days: Startups that create thin layers of workflow, generic horizontal tools, lightweight product management and surface-level analytics — basically, anything an AI agent can do now.
Abdul Abdirahman, an investor at F Prime, added that generic vertical software “without proprietary data moats” is no longer popular, and Igor Ryabenky, founder and managing partner at AltaIR Capital, went further on this point. He said investors don’t care about anything, really, this doesn’t have much product depth.
“If your differentiation lives primarily in the user interface [user interface] and automation, that is no longer enough,” he said. “The barrier to entry has fallen, which makes building a real trench much more difficult.”
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 must also be flexible: rigid per-seat models will be harder to protect, while consumption-based models make more sense in this environment.”
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Jake Saper, general partner at Emergence Capital, also had thoughts on ownership. For him, the differences between Cursor and Claude Code are the “canary in the coal mine”.
“One owns the developer workflow, the other just executes the work,” Saper continued. “Developers are increasingly choosing execution over process.”
He said any product dealing with “workflow stickiness” — meaning trying to get as many human customers as possible to use the product consistently — can be up for an uphill battle as agents take over the workflow.
“Pre-Claude, getting people to do their work inside your software was a powerful moat, but if the agents are doing the work, who cares about the human workflow?” he told TechCrunch.
He also believes that integrations are becoming less popular, especially as Anthropic’s Model Framework Protocol (MCP) makes it easier than ever to connect AI models to external data and systems. This means one does not need to download multiple integrations or create their own client integrations. they can just use MCP.
“That the link was once a ditch,” Saper said. “Soon, it will be a utility.”
Also no longer in vogue are “workflow automation and task management tools that allow the coordination of human labor to become less necessary if, over time, agents just do the work,” Abdirahman said, citing examples, mostly public SaaS companies whose shares are falling as new native startups with better, more efficient technology emerge.
Ryabenky said the SaaS companies struggling to scale right now are the ones that can be easily 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 native AI teams can quickly rebuild it. That’s what makes investors wary.”
Additionally, what remains attractive about SaaS is depth and expertise, with tools built into critical workflows. He said companies should now look at integrating AI deeply into their products and update their marketing to reflect that, Ryabenky continued.
“Investors are reallocating capital to businesses that have workflows, data and domain expertise,” Ryabenky said. “And away from products that can be copied without much effort.”
