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AI data centers are often blamed for straining power grids, heavy water usage and driving up electricity demand.
This may be one of the biggest issues we face when it comes to AI, but computer scientist Ayesha Joshkun says instead of competing with AI data centers over invaluable natural resources, we should be asking a different question.
How can we leverage AI to actually help stabilize the grid?
In this talk, Aisha shares how to turn the facilities seen as energy hogs into flexible grid-supporting assets, a move that could prevent blackouts, lower costs and even accelerate the adoption of clean energy.
She shares why a sustainable future for AI exists if we're bold enough to try.
Right now, the world is in an AI race.
Companies governments, universities are all racing to build bigger models, smarter systems, and behind the scenes, they are racing to build more data centers to power AI.
But there's a problem.
We are running headfirst into the limits of our infrastructure.
The power grid includes all the infrastructure, power plants, transmission lines and all to generate and deliver power to our homes, our businesses and now to AI data centers.
In the United States, the grid operators are reporting that new AI data center projects are requesting power loads equal to entire cities.
In some regions, utilities simply can't keep up.
So when you hear AI data centers, what comes to mind?
For many, it's one thing, energy hogs.
And they are not wrong.
AI is dramatically accelerating the electricity demand of data centers.
Just training.
GPT-4 is estimated to have consumed around the annual electricity use of thousands of US homes.
In another striking example in Ireland, nearly 20 of the nation's electricity is drawn by data centers.
Today,
And these are not just statistics.
They are also community stories.
In the data center alley in Virginia, residents recently saw higher electricity bills 20 higher already compared to just a few years ago as utilities scramble to serve massive new AI facilities.
So energy hog label seems well-deserved, but that's only half the story.
Here is the new view.
These facilities are not just energy-hungry brains.
They can also be the muscles of the grid flexing on demand.
Unlike our homes or hospitals, AI data centers run jobs that are predictable, controllable and often delayable.
That makes them ideal to help balance supply and demand on the grid.
By making AI data centers power flexible, we can connect them much more rapidly to the grid, while at the same time making electricity more affordable and resilient.
What's more, the AI boom is arriving just as the renewable boom is also taking off.
Wind and solar don't follow our schedules, but data centers can, which means we can align the rise of AI with the rise of clean energy, if we are bold enough to rethink their role.
All this transformation to power flexibility didn't just come out of thin air.
It builds on decades of research on energy efficient computing, scheduling optimization and many others.
I have lived this journey myself.
Early in my career, I asked a question that many found unrealistic.
Could computer systems adapt their behavior depending on power grid needs, but without breaking their performance promise to their users?
At the time, this sounded radical.
Because why would we ever design a system that would slow itself down on purpose?
But then came the breakthroughs.
First, we discovered not all computing tasks are urgent.
Some can wait for minutes or hours, and some can be slowed down without anyone really noticing it.
For example, A researcher analyzing hundreds of medical images with AI may be okay with waiting just a little longer.
Or if you are fine-tuning your AI model over the course of the next few days, you may be okay with slowing it down for just a few hours.
This inherent flexibility in computing gives us the flexibility we need to manage power.
Second, we reframe the problem.
Instead of asking how do we compute as fast as possible, we asked how do we make computer systems meet the constraints of the power grid while at the same time still delivering on user performance agreements?
This shift led to new strategies capping power, shifting workloads and provisioning the data center as a flexible reserve to the grid.
A key aspect here is that we do keep the performance promise to users, so it's not arbitrary.
User experience remains as a key target, and better yet, it becomes more predictable.
So we built prototypes on real data center servers, and they worked.
Systems that could follow a power target. while still delivering results.
But all this journey wasn't smooth.
There were paper rejections, funding rejections, colleagues telling me this would never work.
Well, since I was a kid, I was told I'm a persistent person, perhaps stubborn at times.
And bold ideas require persistence, because change almost always looks impossible before it looks obvious.
So you take that feedback, you reframe it again and again, and you keep building, you keep proving.
So what began as scribbles on a whiteboard 12 years ago is now running on real AI data centers.
Why does this matter now?
Because the power grids challenge isn't just to generate more power.
It's about timing.
Solar gives us a glut of electricity at noon, but demand might peak in the evening.
Wind might be abundant one day and scarce the next.
Nuclear takes decades and billions of dollars to build and is often hard to locate in urban areas.
Batteries are critical, but scaling them is costly, slow, and often not environmentally clean.
Meanwhile, AI data centers themselves face five to seven-year wait times just to connect to the grid in places like Virginia.
In AI time, where technology is shifting a major way every six months, five to seven years is an eternity.
So here's the opportunity.
With the right orchestration, AI data centers can be flexible today.
No waiting, no new massive power infrastructure construction.
They can soak up excess solar in the afternoon, scale down at peak times and act as virtual batteries.
Today,
And the stakes are real.
Take Texas, August 23.
During a brutal heat wave, the rising electricity demand pushed the grid to its limits.
Wholesale electricity prices spiked over 800% in a single afternoon.
So flexible loads, if they were widely available, could have reduced the costs and could have prevented the emergency alerts that went to the consumers.
So we have two opportunities here.
One, we can make current data centers flexible and help prevent blackouts and reduce electricity costs.
Two, and perhaps the more significant by making future data centers power flexible, we can connect them much earlier, without waiting for major power grid upgrades.
If we ignore this opportunity, we are not just wasting renewable energy And we are not just raising our electricity bills.
We are also slowing AI adoption, making it delayed, more expensive and less accessible to society.
But there's a catch.
Orchestrating this flexibility is not easy.
Prices change hourly.
Workloads may arrive unpredictably.
Grid rules change across states, across countries, so no human operator and no single fixed data center management policy can keep up.
This is where AI itself comes back into the story.
The very technology driving this unforeseen demand is also probably the only thing smart enough to tame it.
AI can learn patterns, anticipate grid needs and coordinate across data centers, across utilities, even nations, in real time.
Imagine a data center or a whole network of them as an orchestra, with hundreds of instruments all playing at once.
Left on their own, it can sound like chaos.
But bringing a conductor, suddenly all that noise turns into music.
The conductor in this case, is AI.
AI can direct data center operation so that the data center can precisely match power constraints, depending on what the grid needs, what power is available and what users demand.
The result is harmony, reliable electricity, efficient computing and a system that works beautifully together.
And that's exactly what we built.
We built software that slows down, speeds up or pauses workloads in a data center or shifts workload among data centers.
Our Conductor platform tunes performance and power at real time, all the while respecting user and cloud provider performance needs.
In this way, by flexing when needed, we can connect AI data centers much faster to the grid, make better use of the available power in the power grid and enable faster AI adoption.
I've been inside the story, from an idea that once seemed impossible to prototypes in a lab, to systems now running in the field.
And I believe this is just the beginning.
AI is already reshaping how we compute, but it could also reshape how we power the world.
So the question isn't how much energy AI consumes.
The real question is, how much flexibility, resilience, and clean power can AI unlock?
If we are bold enough to rethink AI data centers, the very machines that now seem like a burden could be our greatest assets in building a sustainable AI future.
That was Ayesha Joshkun speaking at TED AI in San Francisco in California in 2025.
If you're curious about TED's curation, find out more at TED.com slash curation guidelines.
And that's it for today.
TED Talks Daily is part of the TED Audio Collective.
This talk was fact-checked by the TED Research Team and produced and edited by our team Martha Estefanos, Oliver Friedman, Brian Green, Lucy Little and Tansika Sungmarnivong.
This episode was mixed by Christopher Fasey-Bogan.
Additional support from Emma Taubner and Daniela Balarezo.
I'm Elise Hugh.
I'll be back tomorrow with a fresh idea for your feed.
Thanks for listening.
