Investors spill what they aren’t looking for anymore in AI SaaS companies
#AI SaaS #venture capital #startup funding #proprietary data #workflow ownership #AI agents #product depth #pricing models
📌 Key Takeaways
- Investors are avoiding AI SaaS companies with thin workflow layers and generic tools
- Products whose differentiation is mainly in UI and automation are no longer sufficient
- Generic vertical software without proprietary data moats has lost investor appeal
- New companies must build around workflow ownership and deep problem understanding
- Flexible pricing models are replacing rigid per-seat structures
📖 Full Retelling
Venture capitalists at leading Silicon Valley firms including 645 Ventures, F Prime, AltaIR Capital, and Emergence Capital revealed in a recent TechCrunch interview that investors are no longer funding certain types of AI SaaS companies, as the rapid advancement of AI capabilities has made thin workflow layers, generic tools, and surface-level analytics obsolete in the current market. Aaron Holiday, a managing partner at 645 Ventures, explained that while investors remain interested in AI-native infrastructure, vertical SaaS with proprietary data, systems of action, and platforms embedded in mission-critical workflows, they are actively avoiding startups that lack product depth or whose differentiation is primarily based on user interface and automation. Abdul Abdirahman from F Prime emphasized that generic vertical software without proprietary data moats has lost its appeal, a sentiment echoed by Igor Ryabenky of AltaIR Capital, who warned that 'if your differentiation lives mostly in UI and automation, that's no longer enough' as the barrier to entry has significantly dropped. Jake Saper of Emergence Capital highlighted the changing landscape by comparing Cursor and Claude Code, noting that 'one owns the developer's workflow, the other just executes the task,' suggesting that products focused on 'workflow stickiness' may struggle as AI agents increasingly take over task execution. The consensus among these investors is that new companies must build around 'real workflow ownership and a clear understanding of the problem from day one,' with flexible pricing models replacing rigid per-seat structures, as the market shifts toward AI-native solutions that offer deeper integration and proprietary data advantages.
🏷️ Themes
AI Investment Trends, SaaS Evolution, Venture Capital Preferences
📚 Related People & Topics
AI agent
Systems that perform tasks without human intervention
In the context of generative artificial intelligence, AI agents (also referred to as compound AI systems or agentic AI) are a class of intelligent agents distinguished by their ability to operate autonomously in complex environments. Agentic AI tools prioritize decision-making over content creation ...
Entity Intersection Graph
Connections for AI agent:
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OpenAI
6 shared
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Large language model
4 shared
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Reinforcement learning
3 shared
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OpenClaw
3 shared
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Artificial intelligence
2 shared
Mentioned Entities
Original Source
Investors have been pouring billions into AI companies over the past few years, as the technology continues to hold sway in the Valley and thus the world. But not all AI companies are grabbing investor attention. Indeed, even as it seems every company these days is rebranding to include “AI” in its name, some startup ideas are just no longer in favor with investors. TechCrunch spoke with VCs to learn what investors aren’t looking for in AI software-as-a-service startups anymore. Popular SaaS categories for investors now include startups building AI-native infrastructure, vertical SaaS with proprietary data, systems of action (those helping users complete tasks), and platforms deeply embedded in mission-critical workflows, according to Aaron Holiday, a managing partner at 645 Ventures. But he also gave a list of companies that are considered quite boring to investors these days: Startups building thin workflow layers, generic horizontal tools, light product management, and surface-level analytics — basically, anything an AI agent can now do. Abdul Abdirahman, an investor at the firm F Prime, added that generic vertical software “without proprietary data moats” is no longer popular, and Igor Ryabenky, a founder and managing partner at AltaIR Capital, went deeper on that point. He said investors aren’t interested in anything, really, that doesn’t have much product depth. “If your differentiation lives mostly in UI [user interface] and automation, that’s no longer enough,” he said. “The barrier to entry has dropped, which makes building a real moat much harder.” New companies entering the market now need to build around “real workflow ownership 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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