Compute Finance and Strategy

Building Open Superintelligence Infrastructure

Prime Intellect is building the open superintelligence stack — from frontier agentic models to the infra that enables anyone to create, train, and deploy them. We aggregate and orchestrate global compute into a single control plane and pair it with the full RL post-training stack: environments, secure sandboxes, verifiable evals, and our async RL trainer. We enable researchers, startups and enterprises to run end-to-end reinforcement learning at frontier scale, adapting models to real tools, workflows, and deployment contexts.

We recently raised $15mm in funding (total of $20mm raised) led by Founders Fund, with participation from Menlo Ventures and prominent angels including Andrej Karpathy, Tri Dao, Dylan Patel, Clem Delangue, Emad Mostaque, and many others.

Compute is the foundational input of the AI era. The companies, models, and capabilities that define the next decade will be shaped by who has access to compute, on what terms, at what economics, and how it gets allocated across the systems that get built on top of it. The financial and operational architecture for an asset class of this consequence is still being built, and the playbooks for navigating it don't yet exist.

The people who write them will define how the AI infrastructure industry develops over the next decade.

You will own the analytical foundation for how we understand global compute markets: pricing supply across regions and term lengths, modeling the economics of large GPU commitments, evaluating neoclouds and hyperscalers, and turning that work into provider decisions, commercial structures, and customer-facing products.

The work sits at the intersection of infrastructure, finance, and AI systems. You will evaluate questions like when an H200 cluster is the right fit versus GB200 or GB300, how networking and storage constraints affect real workload performance, how utilization assumptions change the economics of a multi-year commitment, and how regional power, colo, and capital costs flow through to GPU-hour pricing.

You will diligence providers not just on headline price, but on delivery timeline, cluster architecture, reliability, support model, contractual risk, and ability to serve frontier AI workloads.

The decisions you support will directly shape Prime Intellect’s ability to deliver high-quality compute to researchers, AI labs, and enterprises building on top of our stack.

Compute Economics

• Build and own the financial models that price our compute supply: per-cluster economics, contract structures, hardware generation comparisons, geographic and provider differentials

• Model the economics of every meaningful supply decision — reserved vs. spot tradeoffs, term length, commitment level, hardware generation, provider mix, geography

• Own margin architecture: margin by workload, customer, product, and contract, so we always know what's actually profitable and where the leverage is

• Model the long-term P&L consequences of today's supply bets under multiple demand and pricing scenarios

Strategic Bets & Capital Allocation

• Partner with leadership on the biggest decisions the company makes: which providers to commit to, which hardware generations, what geographies to lean into, how aggressively to scale

• Build the financial frameworks that turn ambiguous strategic questions into decisions we can make with conviction

• Own the long-range plan, scenario models, and capital allocation framework across compute, headcount, and product investment

Provider Engagement & Diligence

• Engage directly with neoclouds, hyperscalers, and emerging providers on economic and technical diligence

• Run the financial side of supply qualification — what we accept, what we reject, what we…