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Consulting Case Interview: OpenAI Data Center Strategy (W/ BCG and A&M Consultants)

RocketBlocks
RocketBlocks•Jun 3, 2026

Why It Matters

Choosing the optimal data‑center mix will shape OpenAI’s cost structure, speed of AI service rollout, and ability to meet its carbon‑neutral pledge, influencing competitive dynamics in the AI infrastructure market.

Key Takeaways

  • •OpenAI must build three US data centers within three years
  • •Decision hinges on cost, speed to market, and sustainability
  • •Each state offers mixed strengths; no clear overall winner
  • •Portfolio approach may balance metrics across multiple locations
  • •20‑year cost model includes capex, incentives, energy and carbon fees

Summary

The video walks through a mock consulting case where OpenAI needs to locate three new U.S. data centers over the next three years. The firm must balance three strategic pillars—capital cost, speed to market, and its 2030 carbon‑neutral commitment—while evaluating Texas, Michigan and Kansas as potential sites.

The candidate structures the analysis into three buckets: economics (up‑front capex, ongoing energy and maintenance, state subsidies), resource availability (power capacity, labor, land, water) and regulatory environment (permits, ESG rules, future policy risk). Data provided shows each state excelling in different KPIs: Michigan offers the largest tax incentive but longest build time; Texas boasts abundant renewable power yet high water risk; Kansas presents low‑cost land and moderate incentives. No single location dominates across all metrics.

Key details include a 500 MW capacity per site, 90 % utilization, a uniform $10 billion capex, and a $30 /MWh carbon‑compliance surcharge for non‑renewable energy. The interviewee proposes a 20‑year total‑cost equation: capex minus incentives plus annual energy and carbon costs, highlighting the need to quantify each site’s lifecycle expense. Feedback from the coach underscores the importance of validating assumptions and using real‑world data points.

The implication is that OpenAI’s infrastructure strategy will likely adopt a portfolio mix—leveraging Texas for renewable power, Kansas for cost efficiency, and Michigan for fiscal incentives—to meet financial targets while honoring sustainability goals. This nuanced location decision sets a benchmark for other AI firms confronting massive, long‑term capital projects.

Original Description

🎥 Here’s a consulting case interview focused on a data center expansion strategy for OpenAI.
OpenAI is experiencing surging demand for AI infrastructure and must decide how to allocate three new U.S. data centers across Texas, Michigan, and Kansas. The decision involves balancing competing priorities: minimizing costs, accelerating deployment, and maintaining flexibility in the face of uncertain future demand. Your firm has been hired to help OpenAI determine the optimal expansion strategy and where to place its next generation of computing capacity.
Watch Ben Wilson (Director at Alvarez & Marsal, Darden MBA) run Abigail Doekson through this strategy consulting case interview.
🎬 Video Sections:
00:00 Start
00:03 About the case
00:44 Case question
01:38 Clarifying questions
03:50 Framework
09:44 Interviewer feedback
10:38 Charts analysis I
13:14 Interviewer feedback
13:33 Quantitative I
25:13 Interviewer feedback
26:40 Chart analysis II
30:33 Interviewer feedback
30:58 Quantitative II
34:37 Interviewer feedback
34:57 Recommendation
37:05 Conclusion
🚀 Prepping for case interviews? RocketBlocks has the best concepts, drills, and coaching to get you more consulting offers: https://www.rocketblocks.me/consulting.php?utm_source=youtube&utm_medium=video&utm_campaign=ByteSizedRealEstate-yt-mock
📝 Try this case on your own and read through sample answers with the full PDF:
#consultinginterviews #BCG #McKinsey #AI #OpenAI #datacenter

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