Why Inference Infrastructure Is the New Cloud
The model training race is largely over. The real game is serving — and the companies building that layer are sitting on a gold mine that most people still don't see.
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Betting on AI Agents: What We Actually Look For
Most agent demos are theater. Here's how we separate the orchestration layers that will matter from the ones that won't survive their first enterprise pilot.
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The MLOps Maturity Gap and Why It Matters
Most ML teams have a training script. Almost none have a system for what happens after the model ships. That gap is worth a lot of money to the right infrastructure provider.
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Context Window Economics: A New Moat in LLMs
Long context changes everything about how you architect retrieval, memory, and cost. We walk through what that means for infrastructure bets over the next three years.
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AI Safety Is Not a Brake — It's Infrastructure
The public debate about AI safety is mostly noise. The real action is in the engineering — red-teaming tools, interpretability frameworks, and constitutional methods that belong in every production pipeline.
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Vertical AI: Why We Back Focused Models Over General Ones
GPT-4 is impressive. A model trained on five years of cardiology notes and ER outcomes is more useful to a hospital. That's not a niche opinion — it's a pattern we keep seeing in our portfolio data.
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The Data Layer Thesis: Curation Is the New Compute
Everyone is chasing model scale. The quieter insight is that the quality of training data is now the primary differentiator — and the tooling to create that quality barely exists yet.
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From Research to Product: The Gap That Kills AI Startups
A lot of AI companies die not because the research was wrong, but because the team couldn't make the transition from benchmark performance to something a real customer would pay for.
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Vector Databases and the Memory Problem in AI
Embeddings solve one problem and create three more. We dig into where vector database architecture is heading as retrieval becomes the critical path for most production AI systems.
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Enterprise AI Adoption: Why It Stalls at the Pilot Stage
Pilots succeed and then nothing happens. We talk to enterprises weekly and the pattern is consistent — the problem isn't the model, it's the integration, security review, and change management that no one planned for.
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What RAG Actually Is, Beyond the Hype
Retrieval-augmented generation became a buzzword so fast that most implementations skipped the hard parts. Here's what real RAG architecture looks like when the stakes are high.
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The NYAD AI Capital Thesis: Why Now, Why This Team
When we started NYAD, we made a deliberate bet that the infrastructure moment was arriving and most investors wouldn't move fast enough. Here's the full reasoning behind the fund.
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