Venture capitalists are turning their attention to emerging opportunities in the field of artificial intelligence (AI) as they seek to break out of a slowdown in deal activity and exit values. Pitchbook’s latest Artificial Intelligence and Machine Learning Report highlights the challenges faced by VCs, including dropping deal activity and exit values. However, the report also identifies three areas of potential growth that could help VCs maintain growth and deliver returns: AI data centers, local large language models (LLMs), and domain-specific foundation models.
The report reveals that AI and machine learning deal activity has plummeted by 19% in just one year, with deal values and counts also falling. However, there are exceptions to this trend, such as AMD’s acquisition of Nod.AI in machine learning operations (MLOps), IBM’s acquisition of Manta in database management, and ServiceNow’s acquisition of UltimateSuite in predictive analytics. Additionally, semiconductor startup Astera Labs’ upcoming IPO is expected to reinvigorate deal values.
Despite the challenges, there are signs of long-term growth in the AI sector. Generative AI leaders raised $6 billion in Q4 2023 alone, largely supported by tech giants like Microsoft and Google. Momentum in horizontal platforms also grew, with $33 billion raised in 2023, setting a new VC record. However, investments in vertical applications plummeted to levels not seen since 2020.
The report highlights Nvidia as a primary driver of the AI market and identifies opportunities to capitalize on the company’s innovations. Nvidia reported impressive revenue growth, with $22.1 billion in revenue for Q4 FY 2024, a 265% increase year-over-year. The data center segment saw significant growth, increasing by 409% from the previous year. AI data centers, designed to optimize high-performance servers, storage, networking, and specialized accelerators, are well-positioned to benefit from Nvidia’s momentum.
AI data centers have the potential for breakout growth. These centers are built from the ground up to support AI-intensive workloads and are optimized for high-performance GPUs, while also focusing on sustainability. Startups in this space are offering cost-effective solutions and significant savings on GPU hours. For example, some startups are offering 50%-70% cost savings on GPU hours for advanced Nvidia A100s and providing unique access to the latest H100 chips. Lambda, a leading startup GPU cloud provider, has built the largest cluster of H100 chips among all public clouds, surpassing Google and Oracle.
VCs are considering opportunities to create and partner with ecosystems of colocation providers. Specialty cloud providers have already carved out a $4.6 billion market from the internet-as-a-service market, with their ability to differentiate themselves based on AI chip availability, local presence, multicloud support, and support for legacy hardware.
Despite the challenges faced by VCs in the AI sector, there are promising opportunities for growth. As the industry continues to evolve, venture capitalists are looking to capitalize on emerging areas such as AI data centers, local LLMs, and domain-specific foundation models. With the potential for breakout growth and opportunities to partner with innovative startups, venture capitalists are positioning themselves to drive long-term success in the AI market.