When AI gets geometry, today's data centers will look like a civilization burning forests to light a candle.
The impact across economics, industry, labor, and daily life will be brutal, and most of the people writing about AI today have not begun to price it.
Current AI is a snake-oil pitch
If you are one of those who have tried to use current AI for a problem that scales, you already know that the AI solution they sold you as the hammer for every nail is just the modern tech version of the old snake oil scam.
In fact, the numbers back your experience: ninety-five percent of enterprise AI returns nothing, and most pilots collapse somewhere between demo and operations. But let’s not jump to conclusions. If we look closer at what is actually failing, it is not a model-quality problem, nor a regulation problem; it is a problem of brittle workflows, missing context, and systems that do not learn inside the daily life of an actual business.
Now the other side of the story. The appetite for a functional AI is unstoppable, and how unstoppable? The best foxes sniffing this market put the ballpark at $2.6 to $4.4 trillion annually (McKinsey), a 7% lift to global GDP and roughly 300 million jobs exposed to automation (Goldman Sachs).
You want even more vivid? The energy alone will be around 945 TWh by 2030, with AI as the major driver — slightly more than Japan’s total electricity consumption today!
So here is the scenario we are facing with current flat-AI: trillions in projected upside, hundreds of millions of jobs in the crosshairs, continental-scale electricity demand… all for a miserable flat AI that fails 95% of the time.
Too much ado about nothing.
Is there a way out? An AI to put an end to all this ludicrous, criminal waste of energy and economic resources?
If you have read our articles before, you already know the answer. The architecture that replaces FLAT-AI has a name too. Call it GEO-AI: systems whose metric varies with context, where curvature itself carries the meaning that flat embeddings (tokens as vectors) can only approximate.
