Changing Trends in AI Investment
Artificial Intelligence (AI) has been a hot topic in the investment world for the past few years, with billions of dollars being poured into AI companies globally. This trend has been particularly noticeable in Silicon Valley, the epicenter of cutting-edge technology. However, not all AI companies have been able to attract significant investment. Despite the trend of companies rebranding to include “AI” in their titles, some startup ideas are no longer catching the eye of investors. A recent discussion with venture capitalist firms by TechCrunch has shed some light on what investors are no longer seeking in AI software-as-a-service (SaaS) startups. source
Shifting Preferences in AI SaaS Startup Investment
According to Aaron Holiday, managing partner at 645 Ventures, startups that are currently popular with investors are those building native AI infrastructure, vertical SaaS with proprietary data, action systems (those that assist users in completing tasks), and platforms deeply integrated into vital workflows. However, he also mentioned that certain types of companies are no longer as appealing to investors. These include startups building thin layers of workflow, generic horizontal tools, lightweight product management, and surface-level analytics — basically, anything that an AI agent can effortlessly do.
Abdul Abdirahman, an investor at F Prime, added that generic vertical software “without proprietary data moats” is also no longer popular. Igor Ryabenky, founder and managing partner of AltaIR Capital, echoed this sentiment. According to him, investors are less interested in products that lack depth. He pointed out that a primary differentiation based on user interface and automation is not enough anymore.
The Need for Mastery in AI SaaS Startups
Ryabenky emphasizes that new companies entering the market must now rely on “real mastery of workflow and a clear understanding of the problem from day one.” He added that “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.”
Changing Paradigms: The Case of Cursor and Claude Code
Jake Saper, general partner at Emergence Capital, gave an interesting observation on the issue. He pointed out the differences between Cursor and Claude Code as an indicator of changing trends. “One owns the developer workflow, the other just executes the task,” Saper explained. “Developers are increasingly choosing execution over process.”
According to Saper, any product that relies heavily on “workflow stickiness,” or the strategy of attracting as many human customers as possible to continually use the product, could find itself facing a tough battle when AI agents take over the workflow.
Abdirahman added to this by noting that “workflow automation and task management tools that enable coordination of human work become less necessary if, over time, agents simply execute the tasks.” He cited examples of primarily public SaaS companies whose shares are falling as new AI-native startups emerge with superior, more efficient technology.
Investors Warn Against Easily Replicable SaaS Companies
Ryabenky warned that SaaS companies struggling to scale are those that can easily be replicated. “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.”
What Remains Attractive in SaaS?
Despite the changing trends, depth and expertise remain attractive traits in SaaS companies. Ryabenky advised companies to consider deeply integrating AI into their products and updating their marketing to reflect that. He concluded by noting that investors are shifting their capital toward companies that demonstrate mastery of workflows, data, and domain expertise, and away from products that can be copied without much effort.
