Japanese Prime Minister Sanae Takaichi begins her three-day visit to India, technology has emerged as the defining pillar of the India-Japan partnership, with both countries looking to collaborate in semiconductors, artificial intelligence, critical minerals and advanced manufacturing.

The discussions at the 16th India-Japan Annual Summit come as governments worldwide race to secure critical technologies and reduce dependence on fragile global supply chains. For India, Japan brings decades of expertise in high-precision manufacturing. For Japan, India offers a rapidly expanding market, a skilled workforce and ambitions to become a global manufacturing hub.

Semiconductors Take Centre Stage

One of the most significant areas of cooperation is semiconductors. While Japan no longer dominates chip manufacturing, it remains a global leader in semiconductor materials, manufacturing equipment and precision components—essential building blocks for producing advanced chips.

India, meanwhile, is investing heavily in developing a domestic semiconductor ecosystem through the India Semiconductor Mission. In 2023, the two countries signed a Memorandum of Cooperation on the Japan-India Semiconductor Supply Chain Partnership to strengthen collaboration in manufacturing, research, design, talent development and supply-chain resilience.

As artificial intelligence, electric vehicles, 5G networks and data centres fuel global demand for advanced chips, semiconductor cooperation has become a strategic priority for both governments.

Beyond Infrastructure to Technology Transfer

The India-Japan relationship has traditionally been associated with infrastructure projects such as the Mumbai-Ahmedabad High-Speed Rail corridor. However, the partnership is increasingly shifting towards technology transfer and industrial capability.

The bullet train project, built using Japan’s Shinkansen technology and financed largely through concessional loans from the Japan International Cooperation Agency (JICA), has introduced advanced signalling, train control systems and specialised engineering expertise to India. The project also includes training for Indian engineers, helping develop domestic capabilities in high-speed rail technology.

Broader Technology Ecosystem

Technology cooperation now extends well beyond transport. India and Japan are expanding collaboration in artificial intelligence, digital infrastructure, clean energy, robotics and critical minerals—sectors that are expected to underpin future economic growth and industrial competitiveness.

Japan’s strengths in advanced manufacturing complement India’s focus on scaling electronics production and attracting global technology investment. Together, the two countries are seeking to build resilient supply chains that are less vulnerable to geopolitical disruptions.

Why the visit matters

The significance of Takaichi’s visit lies in the changing nature of the India-Japan partnership. What began as a relationship centred on infrastructure and investment is increasingly becoming one driven by technology, innovation and economic security.

As countries compete for leadership in next-generation industries, the outcomes of this summit could help shape India’s ambitions to become a global manufacturing and technology hub while reinforcing Japan’s role as one of its most important strategic technology partners.

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.