AI Infrastructure Credit Research Analyst

Employment Type:

Full-Time Work Setting:
In-office/Remote

Work Location:

United States/Remote

Work Hours:

Office hours

Find out more here:

Semi Analysis is an independent research and analysis firm specializing in the Semiconductor and AI industries. Our in-depth coverage spans the entire supply chain, from semiconductor fabrication processes to cutting-edge AI Models, software, and infrastructure. We are recognized as the leading authority on the semiconductor supply chain, with the highest concentration of industry experts within one team, and a deep-rooted passion for delving into the intricacies.

• Industry Models – we develop and publish industry models on accelerator shipments, data centre demand and supply, GPU total cost of ownership, and more. We work with hyperscalers, neoclouds, many of the world’s largest hedge funds, and government agencies.
• Core Research – our public equity markets product, geared towards financial investors, distils our deep technical research and knowledge into key insights on technology and product trends.
• Consulting and Technical Due Diligence – We conduct custom research and project work to guide key strategic and investment decisions for the largest private equity funds, leading venture capital firms, companies across the AI ecosystem, and government agencies.

Semi Analysis is seeking an AI Infrastructure Credit Research Analyst to lead our coverage of how the global AI infrastructure buildout is financed. This role will examine who is providing capital, how financing arrangements are structured, the terms attached to that capital, and where the underlying financial risk ultimately sits. The analysis will combine Semi Analysis’s proprietary datacenter and semiconductor supply chain data with detailed balance-sheet, credit, and financial intermediation research.

The successful candidate will build the firm’s coverage of AI infrastructure credit from the ground up, producing definitive research for credit investors, limited partners, financial institutions, regulators, and other market participants.

• Analyze the full financing stack supporting the AI infrastructure buildout, including:
  • Hyperscalers balance sheets
  • Neoclouds and AI cloud debt
  • GPU-backed lending
  • Datacenter asset-backed securities
  • Project finance
  • Private credit funds
  • Business development companies
  • Special-purpose vehicles
  • Vendor financing arrangements
• Map financing relationships across the AI ecosystem, identifying:
  • Who is lending to whom
  • The terms and maturity profiles of the financing
  • The structures through which capital is provided
  • The parties ultimately holding the credit and asset risk
• Build quantitative models to assess credit risk across AI infrastructure investments.
• Evaluate counter party exposure, concentration risk, and dependencies across borrowers, lenders, investors, equipment vendors, and infrastructure providers.
• Model the value and resilience of collateral, including:
  • GPU depreciation and residual value
  • Power purchase agreements
  • Datacenter leases
  • Capacity contracts
  • Equipment financing arrangements
  Analyze interest-rate sensitivity, refinancing requirements, maturity walls, covenant structures, and liquidity risks.
• Develop downside and stress scenarios to assess how weaker demand, declining hardware values, delayed construction, power constraints, or tighter financial conditions could affect borrowers and lenders.
• Track the migration of lending activity from regulated banks to nonbank financial institutions.
• Assess how the growth of private credit and nonbank lending changes where financial risk is concentrated and how losses could spread during a downturn.
• Produce flagship research reports and recurring coverage on AI infrastructure financing and credit…