Beyond GPU Counting: Cognitive Efficiency

There is a lazy metric that economists, tech pundits, and government officials love to use: GPU Per Capita.

It’s easy to measure. You count the H100s (or B200s, or whatever silicon god we worship this month), you divide by the population, and you produce a ranking. It feels scientific. It feels like “progress.”

But it’s wrong. It’s like measuring a nation’s literacy by counting how many pencils they own. Pencils are necessary for writing, but owning a billion pencils doesn’t make you Shakespeare.

The winners of the AI era won’t be those with the most GPUs. They will be those with the best Cognitive and Talent Efficiency Per Token.

The Token as a Unit of Labor

We need to reframe our economics. Think of an AI token not as a piece of text, but as a unit of cognitive labor. A micro-second of thought. A spark of reasoning.

• If you spend 1,000,000 tokens generating spam emails, generic marketing copy, or endless bureaucracy? Your national productivity is effectively zero. You have burned energy and silicon for nothing.
• If you spend 1,000 tokens helping a brilliant engineer debug a fusion reactor simulation? You have created immense value.

The game is not “who has the most tokens.” The game is “who applies their tokens to the highest-leverage problems?”

Talent Alignment: The Great Matching Problem

This is where “Talent Alignment” comes in.

In the old world, we wasted talent on a massive scale. We had brilliant writers doing data entry. We had creative architects fighting with compliance paperwork. We had compassionate doctors typing notes into EMRs instead of looking patients in the eye.

That was low “Cognitive Efficiency.” It was a tragic waste of the human processor.

In 2026, the goal is to align every human mind with the exact AI resources they need to maximize their unique output.

• The artist shouldn’t be cropping images; they should have an image generation agent.
• The coder shouldn’t be writing boilerplate; they should have an architecture agent.
• The policymaker shouldn’t be reading PDFs; they should have a simulation agent.

Mean Time to Outcome

We need to stop measuring “Flops” (Floating Point Operations Per Second) and start measuring “Outcomes.”

We need strict discipline in our systems. We need architectures that route easy queries to small, cheap models (SLMs) and complex reasoning to the big, expensive brains (LLMs). We need to stop using a flamethrower to light a candle.

The companies and countries that master this—allocating the right amount of intelligence to the right human at the right time—will vastly outperform those who just throw raw compute at the wall.

It’s not about how big your brain is. It’s about how well you use it.