Huawei is betting that the future of AI infrastructure will depend as much on energy systems as on computing power
Updated
May 19, 2026 5:43 PM
Blue light painting with a lightbulb. PHOTO: UNSPLASH
As AI companies build larger models and deploy more AI agents, the industry is running into a new constraint: electricity. The challenge is no longer just about computing power. It is increasingly about how to supply, manage and sustain the energy needed to run AI infrastructure at scale.
That was the central argument behind Huawei’s latest AI data center strategy unveiled at its Global AIDC Industry Summit in Dongguan.
The company introduced what it calls a grid-interactive AIDC strategy, focused on redesigning AI data centers around power supply, cooling systems and energy management. AIDC refers to AI data centers built specifically for large-scale AI computing workloads.
The announcement reflects a broader shift happening across the industry. As AI systems grow larger, data centers are consuming more electricity and generating more heat than traditional computing infrastructure was designed to handle. Companies are now being forced to rethink not just chips and servers, but the physical systems supporting them.
Huawei argues that future AI infrastructure will need closer coordination between computing systems and energy grids. The company says traditional data center designs are struggling to keep up with fluctuating AI workloads, rising power density and the growing use of renewable energy sources.
Hou Jinlong, Director of the Board of Huawei and President of Huawei Digital Power, said: "The booming AI industry, widely adopted large models, and numerous AI agents are creating huge energy demands, set to boost the global AIDC capacity. Electricity is essential for computing; energy is the foundation for AI long-term development. Computing and electricity will deeply synergize and empower each other, progressively building an integrated framework that brings together new power systems and AI infrastructure."
A large part of Huawei’s strategy focuses on power architecture. AI workloads can create sudden spikes in electricity demand, especially in high-density computing environments. To manage that, Huawei says it plans to develop new power systems that combine grid-friendly UPS infrastructure with energy storage technologies.
Cooling is becoming another major pressure point. AI servers generate significantly more heat than traditional enterprise systems and Huawei says liquid cooling is now becoming essential for large-scale AI deployments. The company introduced a liquid cooling system designed to improve long-term thermal management inside high-density AI environments.
Huawei is also pushing modular construction methods to reduce deployment times for AI data centers. Instead of building infrastructure entirely onsite, parts of the system can be prefabricated and tested in factories before installation.
Bob He, Vice President of Huawei Digital Power, said: "The global AI industry is booming, and the token demand surges. As such, the AIDC industry is entering the Token era."
As part of that shift, Huawei introduced a proposed measurement system called the TokEnergy Index. The company says the metric is designed to measure the relationship between energy consumption and AI computing output, rather than relying only on traditional data center efficiency metrics such as PUE.
The broader message behind the strategy is that AI infrastructure is becoming an energy engineering problem as much as a computing problem. As global demand for AI continues to rise, companies across the sector are beginning to realise that the future of AI may depend not only on better models, but also on whether power grids and data centers can keep up with them.
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Structured AI interviews and human judgment combine to address the global talent shortage
Updated
April 1, 2026 8:56 AM
ManpowerGroup World Headquarters in Milwaukee. PHOTO: ADOBE STOCK
As hiring pressures mount across global markets, ManpowerGroup is turning to technology to strengthen how it connects people to work. The workforce solutions major has announced a global partnership with Hubert, a startup focused on AI-driven structured interviews. The aim is simple: make hiring faster and fairer, without removing the human touch.
ManpowerGroup has spent decades operating at the center of the global labor market. The company works with employers across industries to fill roles, manage workforce planning and build talent pipelines. With millions of placements each year, it has a clear view of how strained hiring has become. A large share of employers today report difficulty finding skilled talent. At the same time, candidates expect more transparency, quicker feedback and flexibility in how they engage with employers.
Hubert enters this picture as a specialist in structured digital interviewing. The startup has built tools that allow candidates to complete interviews online, at any time, while being assessed against consistent criteria. Instead of relying on informal screening calls or resume filters, its system focuses on standardized questions tied directly to job requirements. The idea is to bring more consistency to early-stage hiring.
The partnership brings these capabilities into ManpowerGroup’s global operations. AI-powered interviews will now support the first stage of screening, helping recruiters identify qualified candidates earlier in the process. This does not replace recruiters. Final decisions and contextual judgment remain with experienced hiring professionals. What changes is the speed and structure of the initial assessment.
For employers, this could mean earlier visibility into job-ready talent and less time spent on manual screening. For candidates, it offers more flexibility. A significant portion of interviews on Hubert’s platform are completed outside regular office hours, allowing applicants to engage when it suits them. That flexibility can make a difference in competitive labor markets where timing matters.
The collaboration is also positioned as a step toward reducing bias. By evaluating each candidate against the same transparent standards, the process becomes more consistent. While no system can remove bias entirely, structured assessments can reduce the variability that often comes with unstructured interviews.
At its core, the partnership addresses a gap many large organizations are facing. They need scale and speed, but they cannot afford to lose the human judgment that good hiring depends on. Manual processes are too slow. Fully automated systems can feel impersonal and risky. ManpowerGroup’s approach suggests a middle path, where technology handles repetition and structure and recruiters focus on potential and fit.
The move also reflects a broader shift in the workforce industry. AI is no longer being tested on the sidelines. It is being built into the foundation of hiring operations. For established players like ManpowerGroup, the challenge is not whether to adopt AI, but how to do so responsibly and at scale.
By working with Hubert, the company is signaling that the future of recruitment will likely blend structured digital tools with human expertise. In a market defined by talent shortages and rising expectations, that balance may prove critical.