Investors have poured billions of dollars into AI companies over the past few years as the technology continues to make an impact in the Valley and around the world. But not all AI companies attract investor attention.
In fact, it seems like every company these days is rebranding to include “AI” in its name, but some startup ideas aren’t resonating with investors anymore. TechCrunch spoke with VCs to find out what investors are no longer looking for in AI software-as-a-service startups.
SaaS categories that are now popular with investors include startups building AI-driven infrastructure, vertical SaaS with proprietary data, work systems (systems 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 came up with a list of companies that investors find pretty boring these days. It’s a startup that’s building thin workflow layers, common horizontal tools, lightweight product management, surface-level analytics – basically everything that AI agents can do today.
Abdul Abdirahman, an investor in the company F Prime, added that typical vertical software “without a dedicated data moat” is no longer popular. Igor Ryabenky, founder and managing partner of AltaIR Capital, explained this point in more detail. He said investors aren’t interested in anything that doesn’t have a lot of product depth.
“If the differentiation is primarily in UI (user interface) and automation, that is no longer enough,” he said. “The barrier to entry has been lowered, making it much more difficult to build a real moat.”
New companies entering the market now must build on “real workflow ownership and a clear understanding of the problem from day one,” he said. “A large code base is no longer an advantage. What is more important is speed, focus and the ability to adapt quickly. Pricing must also be flexible. A strict per-seat model is harder to defend, while a consumption-based model makes more sense in this environment.”
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Jake Saper, general partner at Emergence Capital, also had thoughts on ownership. For him, the difference between Cursor and Claude’s code is the ‘canary in the coal mine’.
“One owns the developer’s workflow and the other executes the tasks,” Saper continued. “Developers are increasingly choosing execution over process.”
He said any product that deals with “workflow stickiness” (meaning trying to attract as many human customers as possible to continue using the product) could find itself in an uphill battle as agents take over the workflow.
“Pre-Claude, having humans perform tasks within software was a strong moat, but if the tasks are performed by agents, who cares about the human workflow?” he told TechCrunch.
He also believes integration is becoming less popular, especially since Anthropic’s Model Context Protocol (MCP) makes it easier than ever to connect AI models to external data and systems. This means that there is no need for someone to download multiple integrations or build their own customer integrations. Only MCP can be used.
“Being a connector served as a moat,” Saper said. “It will soon become a utility.”
It is also no longer in vogue, Abdirahman said, as “over time, as tasks are executed by agents, there becomes less need for workflow automation and task management tools that enable coordination of human tasks,” citing the example of mostly public SaaS companies whose stock prices have fallen as new AI-based startups emerge with better and more efficient technologies.
Ryabenky said SaaS companies currently struggling to grow are ones that can be easily replicated.
“General 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 your product is mostly an interface layer with no deep integrations, proprietary data, or embedded process knowledge, a strong AI-driven team can quickly rebuild the product. This is what makes investors cautious.”
What remains attractive about SaaS is the depth and expertise with which tools are embedded in critical workflows. Companies need to look at deeply integrating AI into their products now and update their marketing to reflect this, Ryabenky said.
“Investors are reallocating capital to companies with workflow, data and domain expertise,” Ryabenky said. “And avoid products that can be copied without much effort.”
