This video provides a sharp reality check on AI’s physical footprint, proving that the future of intelligence depends as much on thermodynamics as it does on code. It effectively demystifies how immersion cooling and hardware optimization are the unsung heroes keeping our digital ambitions from melting down.
Deep Dive
Prerequisite Knowledge
- No data available.
Where to go next
- No data available.
Deep Dive
From Air Blowers to Liquid Baths: How AI's Oven Problem Got SolvedAdded:
AI is becoming an electricity story. The real cost is power.
Every question you ask triggers data centers >> [music] >> to move huge amounts of data and run massive matrix math.
So why does talking to an AI translate into a power bill?
Let's break it down from the ground up.
AI models are built through training phases.
This is like making a kid read every book in human history in a few months.
To do this, thousands of GPUs need to run at full power.
GPT-3 used about 1,287 megawatt hours for training.
That's like hundreds of US homes for a year.
GPT-4 is much larger by scale, 1.8 trillion parameters.
>> [music] >> It's training costs over 40 times more.
Training is expensive once, but the reasoning process that serves users is the real cost.
Research shows 80% to 90% of AI's total energy use goes to inference.
One short query to GPT-4 uses about 0.42 watt hours.
Now multiply that by billions of requests per day for all over the world.
The meter spins [music] like crazy.
But even for the same task, some AIs consume massive energy while some are efficient because different models have different appetites.
Old school idea of bigger is better [music] made energy use went out of control.
But efficient models like a llama 3.18b use only 0.43 watt hours to process a long prompt. That's because same AI shows different efficiencies in different centers.
Deep Seek R1 ran on a cloud platform.
It used 70% less energy than on its own servers.
So an AI's carbon footprint isn't just about the algorithm.
It's about the data centers power usage effectiveness and cooling.
The future isn't just who is smarter.
[music] It's who is greener.
With the AI boom, data centers now run hot.
Really hot.
The power density of racks packed with top AI chips now uses 100 [music] to 200 kilowatts or even higher.
Just a single rack.
That's 39 times more than a normal home.
Pack thousands of those racks into one building, you get a giant oven that never cools down.
Air has a physical limit.
>> [music] >> It can't pull heat away fast enough anymore.
Traditional cooling can't keep up with AI. So cooling technology went through three stages.
>> [music] >> Stage one, air cooling pushed to the limit.
Engineers tried everything.
They sealed hot and cold aisles.
Cold air goes straight to the front of servers.
Hot air exits the back.
Prevent mixing.
This pushed power usage effectiveness [music] from 1.8 down to about 1.4.
But when one rack passes 40 kilowatts, running full tilt for 24 hours uses about 960 kilowatt hours.
That's more than one US home uses in a month.
At that level, even the strongest fans can't pull the heat out.
>> [music] >> Stage two, cold plate liquid cooling.
Air cooling hit limit, >> [music] >> then liquid cooling appears.
A cold metal plate sits on top of the chip. [music] Cool water runs through it. Heat gets carried away.
Liquid conducts heat way better than air.
It easily handles hundreds [music] of watts per chip.
Power usage effectiveness can drop to about 1.3.
But it's partly cooling.
Other parts still need fans and there's a leak risk.
Stage three, immersion [music] cooling. The most complete solution is dunk the whole server in non-conductive liquid. The liquid touches every component, 360°. [music] Heat transfer efficiency is over 1,000 times better than air.
If take single phase immersion, liquid just circulates and [music] carries heat away. Alibaba used it. Total power dropped 36%. Power usage effectiveness hit 1.07.
>> [music] >> If take two phase immersion, liquid boils into vapor when hot. That absorbs huge amounts of heat.
>> [music] >> Power usage effectiveness can go low as 1.02 to 1.05, the theoretical limit.
>> [music] >> This cuts hardware failure rates by 53%.
And the waste heat comes out at 60 to 80° C.
>> [music] >> That's enough to heat city buildings.
AI's fever warms your radiators.
Real efficiency gains don't just come from cooling.
They come from everything working together.
>> [music] >> Google proved it. Optimize every layer, hardware, model, system. The effects are amazing.
It needs three tricks. First trick, custom hardware. Build your own AI accelerators, low power from the ground up. Second trick, model slimming.
Use quantization to lower accuracy.
Keep answer quality, cut energy use.
Third trick, use the right tool.
A small specialized model is 15 to 50 times more efficient than a giant general model.
Less waste.
Through a combo of hardware, cooling, compression, >> [music] >> and smart distribution, Gemini cut its energy use to 1/33 of what it was.
The revolution in cooling and algorithms reflects the growth in computing power.
The future isn't just who is smarter, but also about who is greener and cooler.
For this, different countries give us different answers.
China sets up the East Data West Computing project. Build compute centers west.
Target 80% renewable by 2025.
And at peak power times, by compute energy synergy, compute centers power down.
EU law says the PUE of data centers must be no more than 1.3.
>> [music] >> US depends on market driven.
But some grids are already stressed.
Microsoft even signed to restart Three Mile Island nuclear plant.
We talked about how hungry AI is. Next time, let's talk about how dumb it can be.
It will confidently make up papers that don't exist.
These aren't random bugs. They're built into how AI works. See you next time.
Related Videos
OpenHuman VS Hermes AI: Who Wins?
JulianGoldieSEO
285 views•2026-05-29
BREAKING: Microsoft’s New Image Generating Model Beat Out GPT 1.5 and Nano Banana 2
aimmediahouse
122 views•2026-06-03
Long-Running Agents — Build an Agent That Never Forgets with Google ADK
suryakunju
142 views•2026-05-30
This computer is made from real human brain cells. And you can buy it.
Talktmsmedia
3K views•2026-05-28
I Made the Same Anime Fight Scene in Every AI Video Generator
NobleGooseAnime
295 views•2026-05-30
Nvidia Bets Big On AI PCs | New Chip To Power Windows Laptops | Technology | AI Updates | N18S
cnnnews18
3K views•2026-06-01
I Tested NEW Opus 4.8 on Four Projects (Updated LLM Leaderboard)
AICodingDaily
298 views•2026-05-29
3D Platformer Update - NO CAPES
SolarLune
294 views•2026-05-30











