The energy in AI is electric right now — and rightly so. We’re seeing breakthroughs in model performance, product velocity, and public imagination like never before. But with great energy comes great distortion.

At our fund, we’re excited about AI — but we’re also quietly contrarian about where the real opportunity lies. If you’re a founder, this post is a map: here’s what we think is overhyped, what’s quietly underfunded, and where we’re placing long-term bets.

🚨 What’s Overhyped

1. Wrapper apps with no moat

There’s a sea of startups built on plugging OpenAI into a frontend. Some are clever, but most are undifferentiated. The moment GPT-5 drops or Claude 3.5 opens up new API routes, these products risk being leapfrogged — or commoditized by incumbents overnight.

2. Shiny demos with shallow depth

It’s never been easier to build an impressive demo. But it’s also never been harder to build lasting value in AI. Many tools look smart but can’t handle edge cases, maintain reliability, or scale beyond a few workflows. Users notice — fast.

3. Model-building for the sake of it

Unless you have a novel approach to architecture, a proprietary data advantage, or $100M in GPU spend — you’re better off building on top of someone else’s foundation model. We’re skeptical of startups whose value hinges solely on “training our own LLM.”

🧠 What’s Underfunded (and Where We’re Betting)

1. The Infrastructure Stack

Most people see ChatGPT. We see the stack beneath it — and that’s where the magic (and bottlenecks) are. Data labeling, model evaluation, monitoring, observability, fine-tuning orchestration, agent frameworks — these are the plumbing that will power the next 1,000 AI-native apps. And they’re deeply defensible.

2. AI for the real world

Beyond docs and pixels: AI is beginning to touch logistics, manufacturing, field ops, and supply chains. These are complex environments, but they have massive upside — and high pain. We’re excited about startups solving physical-world bottlenecks with intelligent systems.

3. Hybrid systems (human-in-the-loop)

Despite all the AGI noise, most high-value workflows will remain semi-automated for the next 5–10 years. That’s not a weakness — it’s a feature. We love products that embrace human-AI collaboration with clear UX, confidence scores, override logic, and traceability.

4. Developer tools for AI-native software

Traditional dev tools weren’t built for multi-modal input, hallucinations, or probabilistic outputs. We’re backing startups building version control, CI/CD, debugging, and security tooling for a world where AI is part of the runtime.

📍 Final Thought

AI is not just a model race. It’s an era-defining platform shift — like mobile, cloud, and internet combined. But the opportunity isn’t in chasing headlines. It’s in building where others haven’t looked yet.

At VentureCapital, we’re backing founders who are early to the real edges — infrastructure, robotics, workflows, and AI-native design. If that’s you: let’s talk.