That's a great insight, Paul. As someone who has been researching the data annotation space, your perspective really resonates.
I completely agree that the first-hand, contextual information you get from actual users is something an external firm can never replicate. It seems like the most effective and efficient way to spin the data flywheel at high velocity.
This leads me to a question I've been struggling to understand: If this approach is so powerful, why do you think even companies with the vast resources of Big Tech still rely on what seems to be a riskier path—using external human evaluators—instead of fully building this feedback loop in-house?
I feel like I'm missing a key piece of the puzzle. I would be very interested to hear if you have any thoughts on this.
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.
Thanks so much for that clear explanation—it really made me realize that while companies like Scale AI can thrive during the hype of the foundational-model race,
it’ll likely get tougher down the road.
If you don’t mind me asking, as someone on the front lines of AI product development, what challenges have you found to be even more difficult than data annotation?
I completely agree that the first-hand, contextual information you get from actual users is something an external firm can never replicate. It seems like the most effective and efficient way to spin the data flywheel at high velocity.
This leads me to a question I've been struggling to understand: If this approach is so powerful, why do you think even companies with the vast resources of Big Tech still rely on what seems to be a riskier path—using external human evaluators—instead of fully building this feedback loop in-house?
I feel like I'm missing a key piece of the puzzle. I would be very interested to hear if you have any thoughts on this.