Americans oppose huge AI data centers in their towns. Tiny ones in their homes may be a different story

Maine’s legislature recently passed a data center ban in the state (but failed to override the governor’s veto). According to the National Conference of State Legislatures, 14 states spanning the political spectrum from Oklahoma to New York are considering legislation that would ban or pause new data centers, as public opinion on AI has increasingly shifted to the negative.

Still, despite the qualms of the public and politicians, there’s a torrent of capital for building new data centers. The biggest technology companies in the U.S. are on pace to spend as much as $1 trillion annually by 2027 on AI, according to recent Wall Street estimates. Globally, a recent McKinsey report forecasts spending on data centers will hit $7 trillion by 2030.

At the same time, the idea of putting data centers closer to consumers, even onto and into their homes, is gaining traction in real estate circles. Major players in housing, including homebuilder PulteGroup, are in early testing with Nvidia and California-based startup Span to install small fractional data center “nodes” on the exterior walls of newly built homes, according to recent reporting from CNBC’s Diana Olick.

The question of whether that model can scale, and whether homeowners, HOAs, and regulators will approve it, is up for debate. Experts point to some benefits to home-based data centers, with the home-based grid allowing for less construction needed on new ones and greater energy efficiency.

“It is technically possible and already being explored,” said Balaji Tammabattula, chief operating officer at BaRupOn, a U.S.-based energy and technology company currently building out a data center campus in Liberty County, Texas. He said just as a home computer can contribute processing power to a distributed network, a home can host compute hardware that feeds into a larger data processing system.

“Feasibility depends on available power, internet connectivity, heat management, and the type of workload. For batch processing and non-time-sensitive tasks, the home environment works surprisingly well,” Tammabattula said, though for high-density AI training or real-time workloads, residential constraints are harder to overcome.

Real-world examples are unfolding now as proof of concept, as heat waste from data centers as an issue receives more attention in Europe. For instance, a UK-based startup called Heata installs servers in people’s homes that process cloud computing workloads while channeling the heat generated directly into the home’s hot water cylinder, effectively giving homeowners free hot water in exchange for hosting the hardware. British Gas has backed a trial of this model.

At a larger scale, operations have just commenced for heat pumps that route waste heat from Microsoft data centers in Finland to warm approximately 250,000 local residents’ homes.

“These examples show the concept working at both the household level and the community level,” Tammabattula said.

The home data center brings with it a ledger of pros and cons. On the positive side, the residential model reduces land and infrastructure requirements that are becoming serious bottlenecks, distributes compute closer to end users, and creates a natural incentive for homeowners through energy savings, said Tammabattula. He added that home computing also has a strong sustainability angle since waste heat gets repurposed rather than cooled away at great expense.

But your questions for ChatGPT or Claude aren’t likely to be generated from a server in someone’s walk-in closet or basement soon, with those deep interactions with AI still require sprawling data centers. Residential environments currently lack the power density, redundancy, physical security, and environmental controls that enterprise workloads require. And if you can’t get a signal for your own WiFi or phone call, you can’t power a data center.

“Connectivity quality varies across households, creating reliability issues at scale. There are also regulatory and insurance questions around hosting commercial equipment in private homes,” Tammabattula said.

Currently, the economics only work for specific workload types like batch processing, rendering, and research computation. “Anything requiring guaranteed uptime or low latency is not a good fit for this model yet,” he added.

Home-based data center vs. the hyperscaler

The home data center is far more likely to become a niche layer of future infrastructure than a replacement for hyperscale data centers given the limitations. The home data center models also typically involve a third party owning and operating the equipment, so the homeowner does not need to manage anything technically.

“Homes are not going to replace hyperscale data centers, especially for large AI training clusters that need dense power, high-speed networking, specialized cooling, and tightly controlled environments,” said Gerald Ramdeen of Luxcore, a company developing next-generation optical networking and decentralized cloud infrastructure. He says a more realistic opportunity would be to turn homes into professionally managed edge compute nodes, useful for AI inference, low-latency workloads, flexible/batch compute, cloud gaming, and certain heat-reuse applications.

This approach has implications for everyday life as it increasingly intersects with, and through, AI.

“It can be used to sort the seven bazillion photos your teenage daughter has,” said Sean Farney, vice president of data center strategy for the Americas at JLL, a U.S.-based global professional services and commercial real estate firm that manages 4.4 GW of data center space globally from over 340 data center sites.

Farney noted your smartphone has more computing capacity than the first data center ever built, so while the idea of a home data center hasn’t taken off at scale yet, it probably will. “It’s hard to compete with a hyperscaler because it’s expensive operationally to maintain a super distributed footprint. But it can be done, and the company that gets it right is looking at a nice-sized valuation,” he said.

There are still some technical limitations to home data centers before success would be possible at commercial scale. For one, the home would need to have a supply of electrical and mechanical resources that are fairly reliable, since Farney says that a data center will exceed residential power supply really fast. “A 20-kilowatt residential generator doesn’t even give you a cabinet of AI servers,” he said.

But if technology is able to address these issues, would homes be able to overcome the scale effect of data centers? Farney thinks the answer is yes.

AI cybersecurity and physical security are issues

Aimee Simpson, director of product marketing at Huntress, a global cybersecurity company, says one reason to be skeptical of home-based data centers catching on is the cybersecurity vulnerabilities.

“A collection of home-based micro data centers creates the need for a more robust network security approach,” Simpson said. While there are potential decentralization benefits from a home-based network that is operating at scale — more sites means more redundancies in case any one data center goes down — expanding the footprint also makes security more complex.

“Each site’s hardware and software would need to be secure, and carefully monitored, to avoid any vulnerabilities,” Simpson said. Physical security of the site, meanwhile, “would be almost impossible to guarantee,” she said. “There’s a reason that mega data centers run by the likes of Amazon and Microsoft are surrounded by high fences and guarded 24/7.”

“I can’t imagine a world where end users with data security and compliance obligations would be comfortable with the idea of their sensitive, confidential information being processed and managed by servers that are potentially sitting in someone’s garage,” Simpson said. Still, she knows of legitimate networks of micro data centers that use tamper-proof physical containers. If these could be located in residences, that could temper some security concerns.

According to Arthur Ream, a computer information systems lecturer at Bentley University, the home-as-data-center model is plausible, already happening, and a sensible answer for inference workloads, if not training.

“The interesting question isn’t whether residential compute works. It’s whether the security, reliability, and regulatory story holds up at gigawatt scale or whether the industry has quietly figured out that the cheapest place to put the operational risk of AI is in someone else’s utility room,” Ream said.

Span is pioneering the model, according to Ream, with examples like the work with Nvidia and PulteGroup where Span owns and installs liquid-cooled Nvidia RTX PRO 6000 Blackwell GPUs in residential homes, then sells the compute to hyperscalers and AI cloud providers while the homeowner gets a Span smart panel, battery backup, and discounted rates for electricity and internet. Homeowners pay a fee of roughly $150 month covering electricity and internet; installation is free while SPAN sells the compute to AI customers.

“The economic argument is the one to take seriously: a 100 MW data center costs roughly $15 million/megawatt and takes three to five years to build. Span claims it can match that capacity by deploying XFRA nodes across 8,000 new homes in about six months at $3 million/megawatt. Even haircut that aggressively for marketing math, the speed-to-power gap is real,” Ream said.

Other experts are less circumspect and say the concept won’t work.

“Infrastructure for AI isn’t infrastructure for crypto. You don’t run data centers in basements,” said Sviat Dulianinov, the chief strategy officer of Bright Machines, a San Francisco-based software and robotics company. Modern AI runs on “AI factories” of thousands of GPUs working together, requiring complex engineering, precision manufacturing, and tightly integrated supply chains: from server and rack build to deployment. “It also demands industrial-scale power and cooling. Compute will move closer to the edge, but it will be standardized, engineered systems versus crowdsourced home data centers,” Dulianinov said.

And with data centers drawing the ire of communities from coast to coast, real estate professionals are paying close attention to the developments, but have their own reservations about how residential communities will react.

“HOAs would absolutely go to town on this idea,” said Jeff Lichtenstein, president and founder of Echo Fine Properties in Palm Beach Gardens, Florida. “I can’t even imagine our Facebook community page. Fighting between data companies and cities and homeowner associations would make typical Republican versus Democrat fighting look like child’s play,” Lichtenstein said.