# @activeprogrammer on Instagram

- **Type:** Image
- **Original URL:** https://www.instagram.com/p/DVE-mW8jMCb
- **Gondola URL:** https://gondola.cc/posts/63372423-activeprogrammer-instagram
- **Thumbnail:** https://img.gondola.cc/tr:w-,h-,fo-auto/postThumbnails/02897a7e87.jpg
- **Posted:** 2026-02-22T22:57:56.000+00:00
- **Account Owner:** AI News | Robotics | Future Tech (@activeprogrammer) — https://gondola.cc/activeprogrammer

## Caption

People often discuss the amount of electricity required to train an AI model.
But we almost never compare it to the energy required to “train” a human.

Sam Altman recently made that comparison.

Becoming an intelligent human takes roughly 20 years —
the food consumed over a lifetime, education systems, buildings, teachers, transportation, and the global infrastructure that supports learning.
Not to mention centuries of accumulated knowledge that every generation builds on.

So the real comparison isn’t:
AI training vs. a human answering one question.

It’s:
A fully trained AI model answering a question
vs.
A fully trained human brain doing the same task.

Once models are deployed, the energy per query may already be reaching similar — or in some cases lower — levels of consumption.

This shifts the conversation from “AI uses too much energy.”
to how intelligence — artificial or biological — actually scales.

We’re shifting from a debate over training costs to one about efficiency.

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Do you think AI will become more energy-efficient than humans over time — or is this the wrong way to compare intelligence? Share your perspective 👇

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## Tags

techdebate, aitrends, machinelearning, artificialintelligence, futureoftech

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