Ben Horowitz: AI's 'Recursive Self-Improvement' Has Begun
Venture capitalist Ben Horowitz stated on a recent podcast that Recursive Self-Improvement (RSI), which he views as the trigger for a technological singularity, "happened a while ago." He argued that the structural shift to an AI-first compute era is already well underway, though societal changes will lag behind the technological advances.
- Venture capital firm Andreessen Horowitz (a16z), where Ben Horowitz is a co-founder, has earmarked $1.7 billion for AI infrastructure investments as of February 2026, signaling a strong conviction in the foundational technologies of AI. The firm's thesis focuses on areas from semiconductor design to developer software stacks, viewing infrastructure as a creator of durable competitive advantages. - The "build vs. buy" decision for AI compute is a critical consideration for hyperscalers, with many developing their own custom application-specific integrated circuits (ASICs) to optimize for specific AI workloads and reduce reliance on third-party vendors like NVIDIA. This trend is driven by the need for greater performance, power efficiency, and cost-effectiveness at scale. However, for uncertain or rapidly increasing demand, hyperscalers often lean on third-party providers to maintain flexibility. - The cost of AI inference, the process of using a trained model to make predictions, is a significant and recurring expense that can surpass the initial cost of model training. To manage these costs, teams are employing techniques like quantization, which reduces the numerical precision of a model's weights, and sparsity, which removes unnecessary connections within the neural network. - A growing ecosystem of AI-powered Go-To-Market (GTM) tools is emerging to help sales and marketing teams automate tasks, improve lead prioritization, and personalize outreach at scale. Platforms like Demandbase, Highspot, and Apollo.io are being integrated into GTM workflows to enhance efficiency and revenue growth. - The venture capital landscape for AI hardware is experiencing a resurgence, with AI hardware funding more than doubling from 2023 to 2024 and continuing to grow in 2025. Since 2018, AI chip startups have raised a total of $11 billion. - Strategic partnerships are becoming increasingly important in the semiconductor and AI ecosystem. For example, Siemens and GlobalFoundries are collaborating on AI-driven semiconductor manufacturing, and OpenAI has partnered with AMD to build out its AI computing infrastructure, signaling a move to diversify its chip supply beyond Nvidia. - The total cost of ownership for AI infrastructure extends beyond the initial chip purchase to include power consumption, which is a significant factor. Training a single large AI model can consume as much energy as multiple households in a year. This has led to the development of more energy-efficient custom silicon and techniques like scheduling training during off-peak hours. - While hyperscalers offer attractive credits and managed services for early-stage startups, costs can increase significantly once those credits are exhausted. This has led some startups to consider bare metal or Virtual Private Server (VPS) providers for more predictable and lower-cost GPU capacity, though this approach requires more in-house operational management.
