Andreessen Horowitz, widely known as a16z, has closed one of the largest venture fundraises in recent years, bringing in $15 billion in new capital. A notable portion of that raise, $1.7 billion, has been set aside for the firm’s AI-focused infrastructure group, signaling a sustained push into the foundational technologies supporting the AI ecosystem.
This infrastructure team has already played a central role in backing several high-profile AI companies, including OpenAI, ElevenLabs, Ideogram, Fal, Black Forest Labs, Cursor, and many others operating across model development, tooling, and platform layers. The latest capital allocation reinforces the firm’s belief that long-term value in AI will increasingly be built at the infrastructure level rather than solely at the application layer.
Jennifer Li, a general partner within a16z’s infrastructure practice, has been closely associated with shaping this strategy. She oversees investments such as ElevenLabs, which recently reached an $11 billion valuation, along with Ideogram and Fal. Li has articulated a clear view of how the next phase of the AI expansion is likely to unfold and where the firm intends to focus its investments.
According to Li, two structural dynamics are beginning to shape outcomes across the AI landscape. One is an escalating shortage of experienced AI talent, a challenge that is already constraining AI-native startups. The other is the growing importance of search infrastructure, an area she believes is still undervalued despite its central role in how AI systems retrieve, organize, and reason over information at scale. She has also outlined the types of AI-native companies that are currently succeeding in securing venture funding, offering insight into where investor priorities are converging.
These themes were explored further during a recent episode of Equity, where Venture and Startups editor Julie Bort spoke with Li about how Andreessen Horowitz views the current AI super cycle and the structural pressures shaping it.
The scale of capital flowing into AI infrastructure carries implications beyond venture-backed startups or large technology companies. It offers a directional signal for how AI capabilities are likely to mature and diffuse across industries. For small and mid-sized businesses, understanding where firms like a16z are concentrating resources can provide valuable context for long-term technology planning.
The talent constraints highlighted by Li are unlikely to remain confined to early-stage startups. As competition for skilled AI practitioners intensifies, organizations of all sizes may face delays or limitations in adopting advanced AI capabilities. Similarly, the emphasis on search infrastructure points to a broader shift toward improving how data is indexed, accessed, and operationalized—an area that will increasingly influence the effectiveness of AI systems in real-world business environments.
For business leaders evaluating their AI strategies, these developments suggest the need for a longer-term, more foundational approach. Rather than focusing solely on individual tools or short-term gains, organizations may need to reassess their AI roadmaps, invest in workforce upskilling or external partnerships, and strengthen their underlying data and search capabilities. At the same time, the rapid evolution of AI infrastructure introduces new efficiencies alongside new competitive pressures, making continuous assessment of risk and opportunity essential.
