Everyone’s racing to build the next ChatGPT. We’re more interested in the picks and shovels.

The AI landscape today looks a lot like the early internet or mobile eras — a few dominant platforms on top, and a wide open field underneath. While most attention (and capital) is chasing front-end wrappers or foundation models, we believe the real value is shifting toward the infrastructure layer.

This is the AI equivalent of AWS, Stripe, and Datadog — foundational tools and services that enable the builders of intelligent software.

Here’s why we’re excited, and what we’re actively backing.

1. Model Wars Are Loud — But Tooling Wins Quietly

Yes, models are improving rapidly. But with each new release, more startups emerge that don’t need to train their own models — they just need better tools to use them.

We’re seeing clear infrastructure gaps in:

• Fine-tuning and evaluation frameworks

• Data labeling, synthetic generation, and augmentation

• Prompt orchestration and agent routing

• Latency optimization and caching

• Monitoring, observability, and drift detection

These tools aren’t flashy. But they’re essential. And they’re becoming the new middleware for AI-native software.

2. AI is Becoming a Platform Shift — Not a Feature

Founders used to ask, “How do we add AI to our product?” Now they’re asking, “How do we build our product around AI?”

That shift requires a new stack:

• Systems that handle non-deterministic outputs

• UX patterns for confidence, traceability, and fallback

• Infra that supports probabilistic reasoning and continuous learning

We’re backing companies that treat AI not as a feature layer, but as a new computing paradigm — and are building infrastructure accordingly.

3. Most Founders Shouldn’t Train Their Own Models

Unless you have:

• Proprietary, high-quality, large-scale data

• A novel approach to architecture or compression

• Access to tens of millions in compute

…training your own model is likely a distraction.

Instead, we’re excited about companies that:

• Build on top of foundation models

• Add contextual intelligence through retrieval or feedback

• Optimize deployment, tuning, and integration in specific domains

The value moves up the stack — and infrastructure startups are capturing it.

What We’re Backing at VentureCapital

In the AI infra layer, we’re actively looking for:

• MLOps 2.0 — purpose-built for LLMs and multimodal systems

• Evaluation and monitoring — think Datadog for AI

• Agent frameworks and control loops

• Zero-setup integration layers for enterprise adoption

• Synthetic data and test environments for safety and scale

We back technical founders who’ve lived the pain — the ones who want to build tools they wish they had at OpenAI, DeepMind, Hugging Face, or Stripe.

Final Word

Infrastructure doesn’t trend on X. It doesn’t demo well at hackathons. But it does power the next wave of generational companies.

At VentureCapital, we believe the most durable value in AI won’t come from who yells the loudest — it’ll come from who enables others to build better, faster, and safer.

If you’re building AI infrastructure, we want to hear from you.