Disagree and Commit: Why "Get More Data" is a Waste of Money?

The scale is not a strategy. It is an outcome. The same is true in AI.

The common mandate is to "get more data". This is a high-cost, low-rigor approach. It misunderstands the fundamental mechanism.

Here is the model that works:

Your AI model is a factory. Data is the raw material. A giant, complex factory (large model) sitting idle with insufficient raw material (small data) is a massive capital drain. It doesn't build anything valuable. It just costs you money.

Conversely, a small, simple factory (small model) overwhelmed by a mountain of raw material (large data) is inefficient. It can't process it all. You leave value on the table.

The research is clear. The Chinchilla Law proves that for a given budget, the optimal path is a right-sized factory fed by the right amount of material. Not the biggest factory you can possibly build.

The business implication is binary:

You understand this balance: You build a reliable, efficient, and cost-effective AI product. Your margins are better. Your inference costs are lower. You win on price and speed.

You don't: You burn capital on compute. You ship an overfit, unreliable product that fails your customers. You are forced to price higher. You are competitively nonviable.

This isn't a technical debate. It's a capital allocation decision. It's day one thinking.

The companies that will win in AI will be the ones obsessed with efficiency, not just volume. They will build a right-sized factory for their raw materials.

That is how you build something that matters for customers.

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