OpenAI’s Compute Chief: We Can’t Build Fast Enough | Sachin Katti

Sachin Katti leads OpenAI's compute infrastructure strategy, describing the current effort as potentially the largest infrastructure buildout in human history. The conversation covers multiple dimensions of this challenge:

Data Center Architecture: OpenAI is building large-scale supercomputers visualized as "giant factories turning electrons into tokens." These facilities span football-field-sized areas with extensive liquid cooling systems at both the data hall and chip levels, including cooling for interconnecting cables and transformers. Liquid cooling technology, while not new, is being deployed at unprecedented scale with innovations in reliability, cost reduction, and new cooling liquids that enable chips to run hotter, directly correlating to increased performance and memory bandwidth.

Power and Energy Infrastructure: OpenAI commits to not extracting power from existing grids but rather investing in new generation and transmission infrastructure. This includes funding solar, gas, and hydro generation capacity alongside transmission lines, transformers, and substations. While today's deployments primarily use gas turbines in the US due to energy density and availability, Katti expresses strong enthusiasm for nuclear power as a future solution, noting it cannot come soon enough and represents the densest clean energy source available.

Compute Strategy and Diversification: OpenAI employs a portfolio approach rather than relying on single sources. Current compute comes from Microsoft, AWS, Google Cloud, Azure, CoreWeave, and chip partners like Cerebras. The company is increasingly taking active roles in building and designing compute infrastructure. Stargate serves as an umbrella term for this evolving strategy, involving partnerships with Oracle for data center design and operation, and collaboration with SoftBank on custom data center shells.

Custom Silicon (Jalapeno): OpenAI's recent entry into chip design represents a significant shift. Jalapeno was designed in just 9 months—exceptionally fast—from design to tape-out. This speed resulted from: an experienced team (many with Google TPU design backgrounds), strong partnership with Broadcom, the unique advantage of knowing end-workload requirements as both designer and customer, and AI assisting in chip design and optimization. The strategic thesis optimizes for tokens per watt, the critical metric in a power-constrained world.

Networking Innovation (MRC): OpenAI released a new routing protocol designed for ultra-large cluster fabrics (100,000+ GPUs). MRC uses multi-path spraying to gracefully mask network failures common at this scale, ensuring training workloads aren't interrupted by individual link failures.

Supply Chain and Bottlenecks: The team faces bottlenecks across multiple dimensions: permitting for data center construction, availability of specialized equipment (gas turbines, transformers) from industries that historically added minimal capacity, and shortage of skilled trades (electricians, plumbers) capable of executing massive construction projects. These represent the most immediate constraints rather than silicon availability.

Business Model Evolution: OpenAI introduced guaranteed capacity offerings—effectively guaranteeing customers specific token production. This treats intelligence as a critical supply unit for enterprises, allowing them to secure compute allocation and mitigate business risk in a token-constrained environment.

Demand Outlook: Katti emphasizes that demand far outstrips compute supply. Any compute brought online is immediately consumed. The company's biggest worry isn't overbuilding but rather the inability to construct compute fast enough due to physical world constraints. As AI increasingly conducts AI research itself, the number of experiments and required compute accelerates exponentially beyond what human researchers alone could execute.