Turning computing heat into a practical heating solution for greenhouses.
Updated
January 8, 2026 6:27 PM
Inside of a workstation computer with red lighting. PHOTO: UNSPLASH
Most computing systems have one unavoidable side effect: they get hot. That heat is usually treated as a problem and pushed away using cooling systems. Canaan Inc., a technology company that builds high-performance computing machines, is now showing how that same heat can be reused instead of wasted.
In a pilot project in Manitoba, Canada, Canaan is working with greenhouse operator Bitforest Investment to recover heat generated by its computing systems. Rather than focusing only on computing output, the project looks at a more basic question—what happens to all the heat these machines produce and can it serve a practical purpose?
The idea is simple. Canaan’s computers run continuously and naturally generate heat. Instead of releasing that heat into the environment, the system captures it and uses it to warm water. That warm water is then fed into the greenhouse’s existing heating system. As a result, the greenhouse needs less additional energy to maintain the temperatures required for plant growth.
This is enabled through liquid cooling. Instead of using air to cool the machines, a liquid circulates through the system and absorbs heat more efficiently. Because liquid retains heat better than air, the recovered water reaches temperatures that are suitable for industrial use. In effect, the computing system supports greenhouse heating while continuing to perform its primary computing function.
What makes this approach workable is that it integrates with existing infrastructure. The recovered heat does not replace the greenhouse’s boilers but supplements them. By preheating the water that enters the boiler system, the overall energy demand is reduced. Based on current assumptions, Canaan estimates that a significant portion of the electricity used by the servers can be recovered as usable heat, though actual results will be confirmed once the system is fully operational.
This matters because heating is one of the largest energy expenses for commercial greenhouses, particularly in colder regions like Canada. Many facilities still rely heavily on fossil-fuel-based heating and policies such as carbon pricing are encouraging lower-emission alternatives. Reusing computing heat offers a way to improve efficiency without requiring a complete overhaul of existing systems.
The project is planned to run for an initial two-year period, allowing Canaan to evaluate real-world performance factors such as reliability, system stability and maintenance needs. These findings will help determine whether the model can be replicated in other agricultural or industrial settings.
More broadly, the initiative reflects a shift in how computing infrastructure can be designed. Instead of operating as energy-intensive systems isolated from everyday use, computing equipment can contribute to real-world applications. Canaan’s greenhouse pilot highlights how excess heat—often seen as a by-product—can become part of a more efficient and thoughtful energy loop.
In doing so, the project suggests that improving sustainability in technology is not only about reducing energy consumption, but also about finding smarter ways to reuse the energy already being generated.
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HKU professor apologizes after PhD student’s AI-assisted paper cites fabricated sources.
Updated
January 8, 2026 6:33 PM
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The University of Hong Kong in Pok Fu Lam, Hong Kong Island. PHOTO: ADOBE STOCK
It’s no surprise that artificial intelligence, while remarkably capable, can also go astray—spinning convincing but entirely fabricated narratives. From politics to academia, AI’s “hallucinations” have repeatedly shown how powerful technology can go off-script when left unchecked.
Take Grok-2, for instance. In July 2024, the chatbot misled users about ballot deadlines in several U.S. states, just days after President Joe Biden dropped his re-election bid against former President Donald Trump. A year earlier, a U.S. lawyer found himself in court for relying on ChatGPT to draft a legal brief—only to discover that the AI tool had invented entire cases, citations and judicial opinions. And now, the academic world has its own cautionary tale.
Recently, a journal paper from the Department of Social Work and Social Administration at the University of Hong Kong was found to contain fabricated citations—sources apparently created by AI. The paper, titled “Forty Years of Fertility Transition in Hong Kong,” analyzed the decline in Hong Kong’s fertility rate over the past four decades. Authored by doctoral student Yiming Bai, along with Yip Siu-fai, Vice Dean of the Faculty of Social Sciences and other university officials, the study identified falling marriage rates as a key driver behind the city’s shrinking birth rate. The authors recommended structural reforms to make Hong Kong’s social and work environment more family-friendly.
But the credibility of the paper came into question when inconsistencies surfaced among its references. Out of 61 cited works, some included DOI (Digital Object Identifier) links that led to dead ends, displaying “DOI Not Found.” Others claimed to originate from academic journals, yet searches yielded no such publications.
Speaking to HK01, Yip acknowledged that his student had used AI tools to organize the citations but failed to verify the accuracy of the generated references. “As the corresponding author, I bear responsibility”, Yip said, apologizing for the damage caused to the University of Hong Kong and the journal’s reputation. He clarified that the paper itself had undergone two rounds of verification and that its content was not fabricated—only the citations had been mishandled.
Yip has since contacted the journal’s editor, who accepted his explanation and agreed to re-upload a corrected version in the coming days. A formal notice addressing the issue will also be released. Yip said he would personally review each citation “piece by piece” to ensure no errors remain.
As for the student involved, Yip described her as a diligent and high-performing researcher who made an honest mistake in her first attempt at using AI for academic assistance. Rather than penalize her, Yip chose a more constructive approach, urging her to take a course on how to use AI tools responsibly in academic research.
Ultimately, in an age where generative AI can produce everything from essays to legal arguments, there are two lessons to take away from this episode. First, AI is a powerful assistant, but only that. The final judgment must always rest with us. No matter how seamless the output seems, cross-checking and verifying information remain essential. Second, as AI becomes integral to academic and professional life, institutions must equip students and employees with the skills to use it responsibly. Training and mentorship are no longer optional; they’re the foundation for using AI to enhance, not undermine, human work.
Because in this age of intelligent machines, staying relevant isn’t about replacing human judgment with AI, it’s about learning how to work alongside it.