Explore the current AI SaaS investment landscape, preferred categories for investors, and trends that no longer excite them. Learn from venture capitalists
Current State of AI SaaS Investment Landscape
In recent years, billions have been invested in artificial intelligence (AI) companies, particularly those in the Valley and beyond. However, not all AI startups are receiving such attention. As every company rebrands to include "AI" in their name, certain startup ideas are deemed less attractive by investors. TechCrunch spoke with venture capitalists (VCs) to understand what no longer excites investors in the realm of AI software-as-a-service (SaaS).
Preferred Categories for Investors
According to Aaron Holiday, a managing partner at 645 Ventures, popular SaaS categories for today's investors include startups building AI-native infrastructure, vertical SaaS with proprietary data, systems of action, and platforms deeply embedded in mission-critical workflows. However, some areas are less favored.
Disfavored Categories and Trends
Startups focusing on thin workflow layers, generic horizontal tools, light product management, and surface-level analytics—basically anything an AI agent can now do—are considered quite boring by investors, as noted by Abdul Abdirahman from F Prime. Additionally, Abdul explained that generic vertical software without proprietary data "moats" is no longer popular.
Igor Ryabenky of AltaIR Capital further elaborated, stating that new companies must have substantial product depth to attract investor interest. He emphasized that mere user interface and automation differentiation is insufficient. Ryabenky also highlighted the reduced barrier to entry in the tech market, making it harder for startups to build a competitive moat.
Key Takeaways
Jake Saper from Emergence Capital drew attention to the difference between Cursor and Claude Code, suggesting that ownership of developer workflow versus just executing tasks is changing behavior among developers. He predicted an increasing trend where agents are replacing human workflow, which may pose challenges for products focusing on workflow stickiness.
Integrations are also becoming less popular due to Anthropic's model context protocol (MCP), making it easier to connect AI models to external data and systems without the need for multiple integrations. This shift is seen as a utility rather than a moat, according to Saper.
Abdirahman pointed out that generic productivity tools, project management software, basic CRM clones, and thin AI wrappers built on top of existing APIs are no longer attractive to investors due to their ease of replication by strong AI-native teams. Instead, depth and expertise in critical workflows remain highly valued by investors today.
In conclusion, startups focusing on building around real workflow ownership, understanding the problem from day one, and embedding AI deeply into products will find greater favor with investors who are reallocating capital towards businesses that own workflows, data, and domain expertise.
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