Who's Actually Funding the AI Buildout?
In a Nutshell
Magnetar Capital, a $22B asset manager, funds AI compute buildout through innovative DDTL/SPV debt structures backed by take-or-pay contracts from hyperscalers like Microsoft and Meta, collateralizing GPUs and cash flows for 4-5 year amortization, enabling CoreWeave's scale from Ethereum mining to AI training/inference without equity dilution. AI infrastructure faces $660-690B capex in 2026 amid bottlenecks in power distribution/storage, transformers, and skilled labor, shifting from chip constraints to turning chips into reliable revenue assets, with inference demanding distributed, low-latency clusters. Capital rotates from SaaS to capex-heavy infrastructure, labs, and physical AI (robotics/defense), fueled by insatiable demand, sovereign national security builds, and efficiency gains like 90-100x from Hopper to Blackwell.
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Neil Tuari of Magnetar Capital, a $22 billion alternative asset manager, discusses their role in the AI compute buildout, financial innovation, GPU depreciation, and future of AI compute.
Magnetar, in its 20th year, has three primary strategies: private credit, venture, and systematic/quantitative public strategy. They specialize in building capital-intensive businesses using creative financing structures to optimize balance sheets.
Magnetar first invested in CoreWeave in 2021, when it transitioned from Ethereum mining to high-performance compute for visual effects (e.g., Marvel movies). This predated the AI boom. They added optionality for various GPU applications, including machine learning and AI training, doubling down as use cases expanded.
Magnetar had prior experience in real estate, energy, power, land—key data center elements—but was new to compute.
By late 2022, AI discussion intensified; in 2023, CoreWeave trained models for OpenAI, driving massive growth due to unprecedented LLM training compute needs.
CoreWeave founders' energy asset management background enabled power/energy access. They focused on scale (capital, energy, power, data centers) and reliability (managing GPU fleets at 99.9% uptime, handling failures/software challenges). Started building tech stack in 2017-2018.
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