DeepSeek’s AI Breakthrough Signals Major Shifts for Data CentersDeepSeek’s AI Breakthrough Signals Major Shifts for Data Centers

The emergence of DeepSeek and its efficiency improvements in AI processing could have significant implications for data centers, even as the debut of the AI model triggered a major sell-off in U.S. tech stocks early this week.

Market darling Nvidia fell more than 12% and the Nasdaq fell 2.7%, and analysts said the reaction reflects concerns over whether the massive spending on AI and its infrastructure is justified.

Meanwhile, Reuters reported U.S. power and utility stocks dropped sharply as DeepSeek's model raised doubts about the expected surge in electricity demand from AI-driven data centers.

Any shift toward cheaper, more powerful, and less energy-intensive algorithms has the potential to significantly expand AI adoption, which could ultimately fuel demand for both large-scale and distributed data center infrastructures.

"If the reports on DeepSeek are accurate, this will only push AI innovation forward," said Mitch Lenzi, vice president of sales and operations at Baxtel, an online platform dedicated to cataloging and reviewing colocation data centers worldwide.

He said lower costs for new models and deployments will allow competitors to optimize their own AI strategies, driving up demand and adoption.

Lenzi said he believes AI advancements like DeepSeek will ultimately accelerate data center growth rather than slow it.

Related:China’s DeepSeek AI App Sends U.S. Tech Stocks Reeling

"Innovation in AI doesn't reduce demand — it fuels it," he said. "As AI becomes more accessible and cost-effective, the industry will see continued expansion, maintaining the need for high-performance data center infrastructure."

Sean Farney, vice president of data center strategy at JLL, agrees that the introduction of more efficient AI models, such as DeepSeek, could reshape the data center market.

"This is good news for the industry," Farney said. "If someone has found a cheaper and more efficient way to handle AI processing, it lowers the barrier to entry, making AI more accessible for a wider audience."

Over time, this will drive increased usage and create new opportunities for data center development.

Farney notes that AI GPU-focused data centers are already the fastest-growing segment in the market, with a compound annual growth rate (CAGR) of 39%, almost double the overall data center growth rate of approximately 20%.

"Growth in AI-focused facilities dwarfs that of traditional data centers," Farney said. "With innovations like DeepSeek, we might see even faster acceleration in this space."

The financial implications of this growth are substantial: According to Farney, major hyperscale data center operators were already spending $200 billion annually on infrastructure, a figure that has now surged to $300 billion.

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"The industry is booming," he said. "If technologies like DeepSeek make AI applications faster and easier to deploy, we'll need even more data centers to support this adoption."

John Dinsdale, chief analyst and research director at Synergy Research Group, pointed out that it was generative AI (GenAI) that caused a rethink and rearchitecture of some data centers.

"If something comes along that substantially reduced the power density required, it likely is a case of moving back toward pre-GenAI designs, with more traditional cooling and power distribution," he said.

Dinsdale explained that throughout the IT ecosystem, quite a bit is invested in current GenAI technologies and products, and that isn't going to change any time soon.

"Will some technology come along that reduces the power requirements and cost of training and running AI models? Of course they will," he said. "That is the nature of technology development and life cycles."

When costs come down and capabilities increase, that often spurs a big increase in applications and usage.

"For a perfect example of that, just look at how cloud computing services have grown over the last 15 years," Dinsdale said.

Related:AFCOM: ‘Boundless Possibilities’ as AI Transforms Data Center Infrastructure

The Role of Modular and Edge Data Centers

Farney also highlighted the increasing importance of smaller, modular, and edge data centers in this evolving landscape.

While training large AI models will continue to require massive, centralized facilities, the growing focus on AI inferencing — using trained models to deliver real-time insights — may drive demand for distributed, latency-focused edge data centers.

"As we move into the inferencing phase of AI, the need for localized compute power grows," Farney said.

Inferencing often requires low latency and proximity to users, which makes smaller, edge-style facilities more practical.

"We may end up carpeting the world with small 1- or 2-megawatt data centers dedicated to AI tasks," he said.

Farney envisions a hybrid future where monolithic hub data centers and distributed edge facilities coexist to meet the diverse needs of AI workloads.

"It's not a zero-sum game," he explained. "We'll see continued growth in large-scale facilities for bulk AI training, alongside a surge in smaller data centers for inferencing and real-time applications."

The Case for Data Decentralization

Phil Mataras, founder and CEO of AR.IO, provider of a decentralized, permanent cloud network, called the way centralized data centers currently store data — hosting massive datasets in one place — "madness."

"It cannot reasonably support the growth of AI, so new solutions are needed," he said.

While Mataras said he agrees that smaller, modular, and edge data centers are a piece of the puzzle, they must be supported by a wider shift to decentralization.

"On their own, these small centers can provide low-latency processing, but they can't service the massive data storage needs as the AI sector grows — even with newer, more efficient models coming to market," Mataras said.

Kai Wawrzinek, co-founder of Impossible Cloud Network, said even with the added efficiency, the need for data storage will only increase across the globe.

"The more we use AI, the more power will be required to run the huge data centers that support it. It's inevitable," he said. "We've seen the likes of Microsoft investing heavily in renewable energy like solar, wind, and hydro to tackle the growing need of power-hungry data centers."

Like Mataras, Wawrzinek argued for decentralized solutions, claiming the age of centralized data storage is over now that AI is in the picture.

"We are going to see a data center boom — but it will be the decentralized data centers that see this huge amount of development," he said. "Centralized solutions, even from the biggest players, will struggle as data storage needs continue to increase."

A New Era of AI-Driven Growth

Farney sees the entry of DeepSeek and similar technologies as a wake-up call for the industry. "Competition is good," he said. "It pushes everyone to innovate. If DeepSeek can challenge established players and lower costs, it benefits the entire tech-driven ecosystem."

Ultimately, Farney believes that innovations like DeepSeek will fuel the next wave of data center growth.

"The impact is clear: AI adoption will increase, and with it, the demand for both large-scale and distributed data centers," he said. "This is a rising tide that lifts all boats."