Investors have poured billions into AI companies over the past few years as the technology continues to dominate the Valley and therefore the world. But not all AI companies are attracting investor attention.
Indeed, even though it seems like every company is changing its name to include “AI” in its name, some startup ideas are simply no longer popular with investors. TechCrunch spoke to venture capital firms to find out what investors are no longer looking for in AI software-as-a-service startups.
SaaS categories popular with investors now include startups building native AI infrastructure, vertical SaaS with proprietary data, action systems (those that help users complete tasks), and platforms deeply integrated into critical workflows, according to Aaron Holiday, managing partner at 645 Ventures.
But he also gave a list of companies that are considered pretty boring for investors these days: startups building thin layers of workflow, generic horizontal tools, lightweight product management, and surface-level analytics — basically, everything an AI agent can now do.
F Prime investor Abdul Abdirahman added that generic vertical software “without proprietary data moats” is no longer popular, and Igor Ryabenky, founder and managing partner of AltaIR Capital, went further on this point. He said investors aren’t really interested in anything that doesn’t have a lot of product depth.
“If your differentiation lies mainly in unemployment insurance [user interface] and automation, that’s no longer enough,” he said. “The barrier to entry has fallen, making it much more difficult to build a real moat.”
New companies entering the market must now rely on “real mastery of workflow 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, general partner at Emergence Capital, also had thoughts on the property. 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 task,” Saper continued. “Developers are increasingly choosing execution over process.”
He said that any product dealing with “workflow stickiness,” that is, trying to attract as many human customers as possible to continually use the product, could find itself in an uphill battle when agents take control of the workflow.
“Before Claude, getting humans to do their work in your software was a powerful obstacle, but if the agents do the work, who cares about the 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 that someone does not need to download multiple integrations or create their own client integrations; they can just use the MCP.
“Being the connector used to be a divide,” Saper said. “Soon it will be a utility.”
Additionally, “workflow automation and task management tools that enable coordination of human work become less necessary if, over time, agents simply execute the tasks,” Abdirahman said, citing examples, primarily public SaaS companies whose shares are falling as new AI-native startups emerge with better, more efficient technology.
Ryabenky said the SaaS companies that are struggling to scale now are the ones that can easily be replicated, he said.
“Generic productivity tools, project management software, basic CRM clones, and lightweight AI wrappers built on top of existing APIs fall into this category,” he said. “If the product is primarily an interface layer without deep integration, proprietary data, or knowledge of the embedded processes, strong AI-native teams can rebuild it quickly. That’s what makes investors cautious.”
Overall, what remains attractive about SaaS is depth and expertise, with tools integrated into critical workflows. He said companies should now consider deeply integrating AI into their products and update their marketing to reflect that, Ryabenky continued.
“Investors are reallocating capital toward companies that have workflows, data and domain expertise,” Ryabenky said. “And away from products that can be copied without much effort.”
