An AI model is not universal but sharply strong only where its training data is thick. In 2026, models exceed human baselines in verifiable domains like math and code where data exploded, yet produce plausible but wrong output where data is thin. Dwarkesh Patel called this "the data black hole at the center of AI." ASAP organizes model selection per task around this jaggedness.

Why capability is jagged

Capability is jagged by domain because the training data differs by domain, as Dwarkesh Patel argues. Models spike in capability in verifiable environments like math and code through reinforcement learning, and fray at the edges where such data is thin. The map of a model's strengths is the map of where data piled up.

How to choose by task

Model selection is matching a data-thick model to the nature of the task. The criteria are as follows.

The more verifiable and precise the task, the more a top model makes sense; the more simple and bulk, the more a cheap fast model does.

The most expensive model is not always the answer

Using the most expensive model for every task is pure waste, since a small model can cost 10 times less. Bulk simple work like classification, extraction, and summarization runs fine on a small model, and the cost differs several to dozens of times. Conversely, using a cheap model for a task that needs one precise inference only gets you a wrong answer faster.

Trace it back to verifiability

After choosing a model, ask "can I verify the answer to this task." Verifiable tasks are where models are strong and mistakes get caught. Tasks that are hard to verify are risky on any model, so keep human judgment in the loop — all the more where data is thin.

Wrap-up

AI model selection is a matter of matching the task's data thickness and verifiability. Reasoning and code go to top models, bulk and simple to cheap models, long context to large-context models. Rather than covering everything with one expensive model, picking the model whose strength fits each task wins on both cost and accuracy.

Source: ASAP analysis grounded in Dwarkesh Patel, "The data black hole at the center of AI" (2026; jagged capability concentrated in verifiable domains).