Investors have been pouring billions into AI companies over the past few years as the technology continues to hold sway in Silicon Valley and the wider world. However, not all AI companies are grabbing investor attention. Even as many firms rebrand to include “AI” in their name, some startup ideas are no longer in favor with venture capitalists.
Popular SaaS categories for investors now include startups building AI-native infrastructure, vertical SaaS with proprietary data, systems of action that help users complete tasks, and platforms deeply embedded in mission-critical workflows. But there is also a list of companies considered quite boring to investors today. These are startups building thin workflow layers, generic horizontal tools, light product management, and surface-level analytics. Essentially, this includes anything an AI agent can now do.
Generic vertical software without proprietary data moats is also no longer popular. Investors are not interested in anything that lacks significant product depth. If a company’s differentiation lives mostly in user interface and automation, that is no longer enough. The barrier to entry has dropped, which makes building a real competitive moat much harder.
New companies entering the market need to build around real workflow ownership and a clear understanding of the problem from day one. 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.
The differences between certain developer tools highlight this shift. One owns the developer’s workflow, while the other just executes the task. Developers are increasingly choosing the execution over the process. Any product dealing with workflow stickiness, meaning trying to attract as many human customers as possible to continuously use the product, might find itself in an uphill battle as AI agents take over the workflow. Before, getting humans to do their jobs inside your software was a powerful moat, but if agents are doing the work, the importance of human workflow diminishes.
Integrations are becoming less popular as well, especially as new protocols make it easier than ever to connect AI models to external data and systems. This means someone does not need to download multiple integrations or build custom ones; they can just use a standard protocol. Being the connector used to be a moat, but soon it will be a utility.
Also no longer in vogue are workflow automation and task management tools that enable the coordination of human work. These become less necessary if, over time, agents just execute the tasks. This is reflected in public SaaS companies whose stocks are down as new AI-native startups arise with better, more efficient technology.
The SaaS companies struggling to raise funds right now are the ones that can easily be replicated. Generic productivity tools, project management software, basic CRM clones, and thin AI wrappers built on top of existing APIs fall into this category. 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.
Overall, what remains attractive about SaaS is depth and expertise, with tools embedded in critical workflows. Companies should look into integrating AI deeply into their products and update their marketing to reflect that. Investors are reallocating capital toward businesses that own workflows, data, and domain expertise, and away from products that can be copied without much effort.