The decision by Anthropic and OpenAI to moderate the pace of artificial intelligence development has cast uncertainty over their potential upcoming IPOs, but its most immediate impact is being felt elsewhere. A broader audience, extending well beyond Silicon Valley and Washington, is now taking notice of AI's potential risks—a shift that has tangible consequences for the startup ecosystem.

Several investors have indicated that this heightened scrutiny is unwelcome news for young companies striving to demonstrate AI's practical value to consumers and businesses. For instance, one venture capitalist noted that a startup providing AI applications to small and medium-sized enterprises has observed growing hesitation among potential clients, influenced by extensive media coverage of potentially catastrophic AI risks.

The investor also suggested that future government regulation or industry self-policing could ultimately advantage the largest AI startups. These well-funded incumbents possess the financial resources, tools, and personnel necessary to ensure their systems do not cause significant harm. As reported by colleagues over the weekend, leading AI labs are already discussing the formation of an industry standards body. On Tuesday, OpenAI's global policy chief, Chris Lehane, confirmed that representatives from Anthropic, OpenAI, and Google have been meeting regularly since July to explore this possibility.

This development, according to the first investor, could make the competitive landscape even more challenging for emerging AI labs striving to catch up with the frontrunners. However, a second investor focused on early-stage software companies holds a different view, arguing that intense scrutiny of frontier labs could benefit startups building applications and supporting technologies on top of existing models. They added that venture capital is likely to flow heavily into model safety and AI infrastructure sectors.

This trend is already visible in recent funding rounds. Gimlet Labs, which schedules AI computing tasks to appropriate chips based on workload requirements, recently completed a financing round. Meanwhile, safety startup Neo, which aims to protect devices from threats posed by AI agents, secured $100 million in July from investors including Andreessen Horowitz and Bessemer Venture Partners.

Furthermore, a third investor with stakes in multiple application-layer AI startups believes that a slowdown in frontier model development could provide a valuable window for these companies to refine their technology. If leading labs decelerate their iteration cycles, application-focused businesses can use this period to rapidly improve their offerings. This scenario would be particularly advantageous for AI agent startup Instinct, which is currently raising a new funding round and competing against financially formidable rivals like Meta.