N10-3: AI Infrastructure Audit
Evaluating GPU contracts, cloud commitments, and inference cost trajectories under diligence.
🎯 What You'll Learn
- ✓ Audit GPU economics
- ✓ Evaluate cloud commitments
- ✓ Project inference cost trajectories
- ✓ Identify optimization opportunities
Lesson 1: GPU Contract Analysis
AI companies sign long-term GPU commitments ($1-10M+ annually). These contracts create fixed-cost obligations that must be evaluated like any other lease. Key questions: What's the commitment term? Can it be scaled down? What happens if GPU prices drop 50% (which they will)?
Multi-year GPU commitments at today's prices may be 2x market rate in 18 months.
What percentage of committed GPU capacity is actually used?
Training workloads should use spot/preemptible. Inference should use reserved.
Audit a GPU commitment contract: calculate utilization rate, assess pricing vs current market rates, and identify exit clause risks.
Lesson 2: Inference Cost Trajectory Modeling
Inference costs are declining 50-70% annually due to hardware improvements, model efficiency gains, and competition. A company spending $500K/month on inference today will likely need only $150-250K/month for the same workload in 18 months. This trajectory affects margins, pricing, and valuation.
Historical inference cost decline: 50-70% annually.
If usage grows 100%/year but costs decline 60%/year, net spend still grows.
Declining inference costs with stable pricing = expanding margins.
Build a 3-year inference cost projection assuming 60% annual cost decline and 80% annual usage growth. Do margins expand or contract?
Lesson 3: Optimization Opportunity Assessment
Most AI companies are running inference inefficiently: no caching, no model routing, no batching, no quantization. An investor who identifies $200K/month in optimization opportunities has found $200K/month in EBITDA improvement — which at a 10x multiple adds $24M in enterprise value.
Caching AI responses for similar queries. Typically reduces inference spend 20-40%.
Routing simple queries to cheaper models. Reduces average cost-per-query 40-60%.
Running 4-bit quantized models for 70% cost reduction with <3% quality loss.
Identify the top 3 inference optimization opportunities in a target AI company and calculate the annual COGS savings for each.
Continue Learning: Track 10 — AI Due Diligence
2 more lessons with actionable playbooks, executive dashboards, and engineering architecture.
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Module Syllabus
Lesson 1: Lesson 1: GPU Contract Analysis
AI companies sign long-term GPU commitments ($1-10M+ annually). These contracts create fixed-cost obligations that must be evaluated like any other lease. Key questions: What's the commitment term? Can it be scaled down? What happens if GPU prices drop 50% (which they will)?
Lesson 2: Lesson 2: Inference Cost Trajectory Modeling
Inference costs are declining 50-70% annually due to hardware improvements, model efficiency gains, and competition. A company spending $500K/month on inference today will likely need only $150-250K/month for the same workload in 18 months. This trajectory affects margins, pricing, and valuation.
Lesson 3: Lesson 3: Optimization Opportunity Assessment
Most AI companies are running inference inefficiently: no caching, no model routing, no batching, no quantization. An investor who identifies $200K/month in optimization opportunities has found $200K/month in EBITDA improvement — which at a 10x multiple adds $24M in enterprise value.