AI is devouring the planet’s electricity ... already using up to 2% of global energy and projected to hit 5% by 2030. But a Spanish-Canadian company, Multiverse Computing, says it can slash that energy footprint by up to 95% without sacrificing performance.

They specialize in tiny AI: one model has the processing power of just 2 fruit fly brains. Another tiny model lives on a Raspberry Pi.

The opportunities for edge AI are huge. But the opportunities in the cloud are also massive.

In this episode of TechFirst, host John Koetsier talks with Samuel Mugel, Multiverse’s CEO, about how quantum-inspired algorithms can drastically compress large language models while keeping them smart, useful, and fast. Mugel explains how their approach -- intelligently pruning and reorganizing model weights -- lets them fit functioning AIs into hardware as tiny as a Raspberry Pi or the equivalent of a fly’s brain.

They explore how small language models could power Edge AI, smart appliances, and robots that work offline and in real time, while also making AI more sustainable, accessible, and affordable.

Mugel also discusses how ideas from quantum tensor networks help identify only the most relevant parts of a model, and how the company uses an “intelligently destructive” approach that saves massive compute and power.

00:00 – AI’s energy crisis

01:00 – A model in a fly’s brain

02:00 – Why tiny AIs work

03:00 – Edge AI everywhere

05:00 – Agent compute overload

06:00 – 200× too much compute

07:00 – The GPU crunch

08:00 – Smart matter vision

09:00 – AI on a Raspberry Pi

10:00 – How compression works

11:00 – Intelligent destruction

13:00 – General vs. narrow AIs

15:00 – Quantum inspiration

17:00 – Quantum + AI future

18:00 – AI’s carbon footprint

19:00 – Cost of using AI

20:00 – Cloud to edge shift

21:00 – Robots need fast AI

22:00 – Wrapping up