Building AI Infrastructure for Scale

Every AI company eventually faces the same challenge: how to build, deploy, and maintain AI systems at scale. The infrastructure layer is maturing rapidly, creating massive opportunities for startups building the picks and shovels of the AI revolution.

The AI Infrastructure Stack

The modern AI infrastructure stack has several distinct layers, each with unique characteristics and opportunities:

1. Training & Compute

Optimizing training workloads, managing GPU clusters, and reducing compute costs remain critical. We're seeing innovation in distributed training frameworks, spot instance orchestration, and efficient fine-tuning methods.

2. Model Serving & Inference

Getting models into production efficiently is where many companies struggle. Solutions that reduce latency, optimize throughput, and manage costs at scale are in high demand.

3. Vector Databases & Search

The explosion of embedding-based applications has created a new database category. Vector databases that can handle billions of embeddings with sub-millisecond latency are becoming critical infrastructure.

4. Observability & Monitoring

Traditional APM tools don't work for AI systems. New platforms are emerging to monitor model performance, detect drift, track costs, and debug failures in production AI systems.

What Makes Infrastructure Defensible

Not all infrastructure businesses are created equal. The most defensible companies share these traits:

• Technical moats: Novel algorithms, proprietary optimizations, or unique architectural approaches
• Data network effects: Systems that get better as more customers use them
• High switching costs: Deep integration into customer workflows and systems
• Developer love: Best-in-class developer experience that drives organic adoption

Common Pitfalls to Avoid

We've seen many infrastructure companies struggle. Here are the most common mistakes:

1. Building features, not platforms
2. Underestimating enterprise requirements (security, compliance, SLAs)
3. Ignoring developer experience in favor of features
4. Failing to build for scale from day one

Our Investment Approach

When evaluating AI infrastructure companies, we look for:

• Technical founders with deep expertise in distributed systems and AI
• Clear understanding of the pain point and why existing solutions fail
• Strong early adopter momentum and organic developer interest
• Path to building sustainable competitive advantages

The AI infrastructure market is still in early innings. As AI adoption accelerates, the companies building the foundational infrastructure will capture enormous value. If you're working on hard infrastructure problems in AI, we'd love to talk.