That's the million-dollar question, and you've hit on the key puzzle piece. I believe the answer lies in distinguishing between two different stages of AI development: "Foundational Model Training" vs. "Product-Specific Fine-Tuning."

1. Foundational Model Training (The Big Tech Approach): To build a base model like GPT-4 or Gemini, you need an unimaginable amount of general, brute-force data. You need millions of images labeled "cat" or "dog," and billions of text examples. For this massive scale of generic data, using large, external teams of human evaluators is often the only feasible way. It's about quantity and breadth.

2. Product-Specific Fine-Tuning (The Markhub Approach): However, once you have that foundational model, the goal changes. To make an AI truly useful for a specific product, you no longer need a million generic data points. You need a thousand high-context, high-quality data points that are specific to your workflow.

For example, an external evaluator can label a button as "a button." But only a real designer using Markhub can provide the critical feedback, "This button's corner radius (8px) is inconsistent with our design system (6px)." This is the kind of nuanced, proprietary data that creates real product value, and it can only be generated "in workflow."

So, I think Big Tech isn't wrong; they're just solving a different problem (building the foundational engine). We, as application-layer startups, have the unique opportunity to build on top of that engine and solve the "last mile" problem by capturing the high-context data that truly makes a product smart.

You're not missing a puzzle piece at all you've just identified the difference between building the engine and building the race car.