Data center energy use could become cheaper and more efficient if AI facilities shift electricity consumption to off-peak hours, according to a new MIT study that highlights both economic and environmental trade-offs.

As artificial intelligence fuels a rapid expansion of data centres around the world, concerns are growing over how much electricity these facilities will consume—and whether power grids can keep up.

A new study by researchers at the Massachusetts Institute of Technology (MIT) suggests there may be a way to ease the pressure. Rather than consuming electricity around the clock at fixed rates, data centres could shift a significant portion of their energy use to off-peak hours, lowering electricity costs while making better use of existing grid capacity.

The findings, published in the journal iScience, indicate that if data centres adopt more flexible electricity consumption patterns, average power system costs could fall by as much as 5 per cent in Texas, 4 per cent in the Mid-Atlantic region and 2 per cent across western U.S. states.

Data Center Energy Use: Flexible Data Centers Could Reduce Energy Costs

The researchers modelled how expanding data centres would affect electricity grids in three regions that are expected to host about 82 per cent of U.S. data centres by 2030: Texas, the Mid-Atlantic and the Western Interconnect, which covers 11 western states.

Their simulations found that shifting at least one-fifth of a data centre’s electricity use away from peak-demand periods could reduce overall system costs. In some cases, as much as half of a facility’s energy demand would need to be moved to quieter periods of the day.

“The key with data centers is: How can we add them to the network without adding a lot to our peak usage?” said Christopher Knittel, economist at the MIT Sloan School of Management and co-author of the study, in a media statement.

“One way for data centers to do that — to add to average usage but not the peak usage — is if they provide some grid flexibility during those high-cost periods. And that’s what we’ve been interested in understanding.”

The researchers note that most data centres already have some operational flexibility because they typically run below full capacity. Instead of carrying out energy-intensive computing tasks during periods of peak electricity demand, many could shift those operations to midday, when solar power generation is often highest and overall demand is lower.

AI Growth Is Putting Pressure on Power Grids

The rapid expansion of AI has dramatically increased demand for computing infrastructure, raising questions about whether electricity grids can support hundreds of new data centres without driving up costs or emissions.

The study suggests that adding more data centres does not automatically translate into higher electricity prices. Because much of the cost of running a power grid comes from fixed infrastructure such as transmission lines, increasing electricity use can spread those costs across a larger customer base—provided peak demand does not rise at the same pace.

“It’s really just math,” Knittel said.

“There are two dimensions that data centers have to make decisions about. One is how much of their load in any one time period is flexible. And two, how many hours, plus or minus, can they move that computation?”

Flexible Data Centers May Have Different Climate Impacts

The environmental picture is more complex.

The researchers found that the projected growth in data centres by 2030 could significantly increase carbon dioxide emissions if electricity demand is met through fossil fuels. Compared with a scenario without new data centres, emissions could rise by 58 per cent in Texas, 20 per cent in the Mid-Atlantic region and 24 per cent in the western United States.

However, the impact varies depending on how regional electricity systems generate power.

In Texas, where wind energy accounts for a large share of electricity generation, shifting data-centre operations to times when renewable energy is abundant could reduce carbon emissions by as much as 40 per cent.

In contrast, the Mid-Atlantic region presents a different picture. There, flexible electricity use could unintentionally keep coal-fired power plants operating for longer periods.

“When data centers provide some flexibility in that latter scenario, the data centers actually move hours to when sun and wind energy production is slowing, and that allows a coal plant to stay on,” Knittel observed. “So it doesn’t necessarily attract more renewable investment. It attracts more coal investment.”

Policy Could Shape the Future of AI Infrastructure

The researchers argue that flexibility alone is unlikely to become common unless governments and grid operators create incentives for companies.

“That’s why we have policy,” Knittel said.

One option would be to allow data centres that agree to flexible electricity use to connect to the grid sooner.

“One big concern about these data centers now is how long it takes for them to connect to the grid,” Knittel said. “One way to provide flexibility now is what’s called ‘connect and manage,’ which is, connecting you faster to the grid if you agree to provide flexibility. Tech firms would take that deal. They would rather connect a year earlier, and throttle down computation a few hours a day, than to have to wait. We do this with power plants too.”

He added that industry-wide rules would help address competitive concerns.

“Tech companies say they won’t provide flexibility alone. But if everyone in the industry has to, it’s okay.”

Balancing AI Growth With Sustainable Energy

As governments and technology companies race to build the computing infrastructure needed for the AI era, the study suggests that when data centres consume electricity may prove to be as important as how much they consume.

The researchers conclude that smarter scheduling of electricity demand, combined with supportive public policy, could lower power system costs while reducing pressure on electricity grids. At the same time, the study highlights that the environmental benefits of flexible energy use will depend on how individual regions generate electricity, reinforcing the need for location-specific energy planning.

As AI systems increasingly rely on multiple models working together to complete complex tasks, the computing infrastructure needed to run them has become more resource-intensive. Researchers at the Massachusetts Institute of Technology (MIT) and Microsoft have introduced Murakkab AI system, an automated system designed to improve how these AI workflows are built and deployed. By selecting the most efficient combination of AI models, software tools and computing resources, the system reduces computational demand, lowers cloud operating costs and cuts energy consumption without affecting performance.

Named after the Urdu word meaning “a composition of things,” Murakkab is designed to optimize the entire lifecycle of an agentic workflow, from application design to cloud deployment.

Murakkab AI System Simplifying Complex AI Workflows

Many modern AI applications are powered by agentic workflows, multi-step systems that coordinate several AI models and external tools, such as databases and programming environments, to complete tasks ranging from video analysis to code generation. Configuring these workflows typically requires developers to manually choose models, define execution sequences and allocate computing resources, making deployment both time-consuming and resource-intensive.

Murakkab, which will be presented at the upcoming USENIX Symposium on Operating Systems Design and Implementation, replaces much of that manual process. Developers only need to describe the application’s objective in plain language. The system then determines which AI models and tools are best suited for the task while selecting the most efficient hardware configuration and resource allocation for cloud deployment.

Optimizing Resources in Real Time

Beyond configuring workflows, Murakkab continuously adjusts computing resources during execution based on user-defined priorities, such as lowering deployment costs or improving response times.

In evaluations across multiple agentic workloads, the system required fewer computational units than conventional deployment approaches, reducing both energy consumption and infrastructure costs while maintaining comparable application performance.

Speaking during a press briefing, Gohar Chaudhry, lead author of the study, said the rapid growth of agentic AI systems has made resource optimization increasingly important.

“Agentic workflows are getting very complicated and quickly becoming the backbone of what cloud providers are doing. Energy usage is a huge concern, so we need to be very careful about how efficient these workflows are. It is very easy to over-allocate resources, wasting energy and money. Enabling a cloud provider to intelligently make these workflows more resource-optimal is a win for everyone involved,” Chaudhry said.

The research was conducted by Chaudhry along with Adam Belay, associate professor in MIT’s Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), Ricardo Bianchini, technical fellow and corporate vice president at Microsoft Azure, and other Microsoft Azure researchers.

Why Existing AI Workflows Waste Resources

Traditional agentic workflows require developers to make numerous technical decisions in advance, including selecting AI models, defining the sequence in which AI agents interact and choosing the hardware resources needed for deployment. Because these workflows often combine proprietary models and tools from multiple providers, introducing a newly released AI model or updating an existing workflow can require extensive reconfiguration.

The researchers said the enormous number of possible combinations makes manual optimization increasingly difficult.

“Even if you wanted to do all this manually, it is unlikely that you’ll be able to configure the workflow optimally because the space of possible configurations is so large,” said Chaudhry.

Murakkab also addresses a key challenge for cloud providers, which often over-allocate computing resources because they have limited visibility into how agentic workflows operate internally. The system automatically selects suitable hardware and adjusts resource allocation during execution, reducing unnecessary computation and energy use while maintaining performance.

Apple has announced a price hike in India for several of its products, including MacBooks, iPads, Apple TV and HomePod devices, as rising global memory chip costs driven by artificial intelligence (AI) infrastructure increase manufacturing expenses. iPhone prices remain unchanged.

The revised prices are now reflected on Apple’s India online store and come amid a global surge in demand for DRAM and NAND flash memory, essential components used in laptops, tablets and other consumer electronics.

MacBook Prices See Sharp Increase

Among the biggest revisions, the 13-inch MacBook Air (M5) now starts at ₹1,49,900, up from ₹1,19,900. The 15-inch MacBook Air (M5) has increased from ₹1,44,900 to ₹1,74,900.

Meanwhile, the 14-inch MacBook Pro now starts at ₹2,39,900, compared to its earlier price of ₹1,69,900. Premium MacBook Pro models equipped with the M5 Max chip have also witnessed price increases of up to ₹1 lakh.

iPad Prices Also Revised

Apple has also increased prices across several iPad models. The entry-level 11-inch iPad now starts at ₹49,900, up from ₹34,900, while the 11-inch iPad Air has risen from ₹59,900 to ₹74,900. The 11-inch iPad Pro now starts at ₹1,19,900, compared with ₹99,900 earlier.

Apple TV and Home Pod devices have also become more expensive, although the company has not revised prices for iPhones, Apple Watches or AirPods.

Why Has Apple Increased Prices?

According to Reuters, Apple attributed the revision to rising costs of memory components such as DRAM and NAND flash storage.

The rapid expansion of AI data centres has significantly increased demand for advanced memory chips, tightening global supply and driving up component prices. Industry analysts say manufacturers across the consumer electronics sector are facing higher production costs as AI infrastructure investment continues to accelerate.

Why iPhone Prices Remain Unchanged

Despite the latest revision, Apple has kept iPhone prices in India unchanged. Analysts believe the company may be waiting until the launch of its next-generation iPhone lineup before making any pricing changes to its smartphones. However, continued increases in semiconductor costs could influence future pricing decisions.

AI Boom Reshaping Consumer Electronics

The price hike in India highlights the wider impact of the AI boom on the technology industry. As companies invest billions of dollars in AI infrastructure and data centres, demand for high-performance memory chips has surged, increasing manufacturing costs for laptops, tablets and other electronic devices.

The development reflects a broader trend where AI is beginning to influence not only software innovation but also the pricing of consumer hardware worldwide.