The AI industry has matured beyond the “model is everything” phase. While foundation model companies continue to raise massive rounds, the real story in 2026 is the build-out of infrastructure that makes AI practical at scale.

Beyond the Model Layer

The early AI investment thesis was straightforward: back the teams training the biggest models. That playbook has run its course. With GPT-5, Claude, and Gemini reaching capability plateaus, the competitive moat is shifting from raw model performance to deployment efficiency and enterprise integration.

This creates opportunity across several infrastructure layers:

Inference Optimization

Training costs grab headlines, but inference—actually running models in production—is where companies spend 80%+ of their AI compute budget. Startups tackling inference efficiency through custom silicon, model compression, and intelligent caching are seeing strong inbound interest from enterprises facing seven-figure monthly GPU bills.

Data Infrastructure

The “garbage in, garbage out” problem hasn’t gone away—it’s gotten worse. As models become more capable, the quality and relevance of training data matters more. We’re tracking companies building:

  • Synthetic data generation for edge cases
  • Data labeling automation beyond simple classification
  • Real-time data pipelines for model fine-tuning

AI Observability

When your product depends on a probabilistic system, you need new approaches to monitoring. Traditional APM tools weren’t built for AI workloads. Emerging players in AI observability are solving problems like prompt injection detection, output quality scoring, and model drift monitoring.

The Enterprise Opportunity

Large enterprises are past the “should we use AI?” phase and deep into “how do we deploy this safely?” This creates demand for:

  • Guardrails and governance: Tools that let compliance teams sleep at night
  • Integration middleware: Connecting models to legacy systems without rebuilding everything
  • Cost management: FinOps specifically designed for AI workloads

Our Take

The foundation model race produced clear winners and a lot of capital destruction. The infrastructure layer is more fragmented, with room for multiple successful companies at each level of the stack. We’re particularly interested in teams with deep enterprise experience tackling the unsexy but critical problems of deployment, monitoring, and cost control.

The best AI infrastructure companies won’t just be picks-and-shovels plays—they’ll become the operating system for AI-first enterprises.