分类： technology

Across major Chinese cities from Beijing to Shenzhen, crowds of ordinary people and business professionals are gathering in droves to access hands-on support installing cutting-edge artificial intelligence tools, highlighting a sweeping, rapid shift toward mass AI integration in the world’s most populous nation. More than a year after Chinese AI developer DeepSeek stunned global tech circles with its high-performance advanced model, China has evolved into the world’s largest live testing environment for widespread consumer and enterprise AI use. While U.S.-built AI models still hold an edge in raw computational power, Chinese users and companies have embraced the technology at an unprecedented pace, embedding it into nearly every sector of daily life and economic activity.

As global AI integration accelerates across workplaces and personal routines, Chinese residents are leveraging generative AI tools for an extraordinary range of routine tasks, from travel planning and food delivery to ride-hailing and professional workflow optimization. Official data from the China Internet Network Information Center reveals that as of December 2024, over 600 million of China’s 1.4 billion people were active generative AI users — marking a 142% year-over-year surge in adoption. The recent boom in demand for agentic AI tools such as the popular OpenClaw, which can autonomously complete complex multi-step tasks, has driven a corresponding spike in AI data consumption. According to OpenRouter, an AI gateway platform that tracks usage across models, the weekly share of AI data tokens (the core units of processed AI data) consumed by Chinese-developed models has now surpassed that of leading U.S. models.

The trend of mass AI adoption cuts across all demographics, from working professionals to retirees. Sixty-four-year-old Jason Tong, a retired IT engineer based in Shanghai, has used domestic AI chatbots including Doubao and Kimi for daily queries since their launch, and recently began using an AI-powered personalized blood glucose monitoring service. Tong has found the tool’s fast, customized health advice invaluable, and he views widespread AI integration as an inevitable technological shift. “Just as carriages were eventually replaced by trains, this is bound to happen,” he explained.

Chinese AI-integrated products, from smart cars with voice-activated planning capabilities to humanoid robots with advanced cognitive functions, have also seen major technical leaps in recent months. Lizzi Lee, a fellow at the Asia Society Policy Institute’s Center for China Analysis specializing in technology and economics, notes that global AI competition is rapidly shifting from standalone model development to building full integrated ecosystems. “Chinese users are basically acting as real-time testers at scale,” Lee observed.

Leading Chinese technology giants including Tencent, Alibaba and Baidu are now locked in a race to commercialize AI at scale. Tencent has already integrated OpenClaw into WeChat, China’s ubiquitous super-app that combines messaging, food delivery, mobile payments and dozens of other daily services into one platform, while Alibaba has embedded agentic AI into its internal business and merchant workflows.

OpenClaw, which was originally developed by Austrian software engineer Peter Steinberger in 2023, has exploded in popularity in China thanks to its ability to connect multiple digital tools to complete complex end-to-end tasks. For Zhao Yikang, a college student in Macao, the tool has transformed both his academic work and professional internship at a Zhuhai real estate agency, where he uses it to automatically generate promotional content and manage social media accounts. Zhao highlighted the tool’s low cost and high efficiency: he recently asked OpenClaw to build a fully functional website for his upcoming post-graduation photo services business, and the project was completed in 10 minutes for less than 0.70 U.S. dollars. “AI can understand things in a second,” Zhao said. “You just need to act as a commander and tell it what to do.”

While Chinese regulators issued temporary warnings over potential data security risks as OpenClaw installations skyrocketed, that has done little to dampen public enthusiasm for the tool. Across the country, domestic businesses are increasingly setting internal mandatory targets to scale AI adoption to boost operational efficiency. Janet Tang, a technology partner and managing director at global consultancy AlixPartners, notes that China’s unique market landscape offers an unmatched range of use cases for AI testing. Wang Xiaogang, co-founder of leading Chinese AI firm SenseTime and chairman of ACE Robotics, echoed that sentiment: “The industry is developing very fast and the people, they are very open and they are eager to try the AI in a lot of scenarios.”

Chinese policymakers have actively supported the AI boom, investing heavily in talent development and subsidizing low-cost electricity to power energy-intensive AI computing infrastructure. In the national five-year plan running through 2030, Beijing has pledged to deliver an average annual growth rate of at least 7% in national research and development spending, with AI as a core priority. A national “AI Plus” strategy lays out a roadmap to integrate artificial intelligence into every major sector from healthcare to education to public administration: a court in Shenzhen reported that it processed 50% more cases in 2024, in part thanks to AI tools that streamline judicial workflows.

However, U.S. export controls on advanced semiconductors remain a key bottleneck for China’s AI advancement. Samm Sacks, a senior fellow at New America focusing on Chinese technology policy, explained that U.S. restrictions on cutting-edge AI chips have slowed the expansion of Chinese chipmaking capacity and remain the biggest vulnerability for many Chinese AI research labs. Even so, Sacks added, the restrictions have also forced greater coordination across China’s domestic tech supply chain, uniting design, manufacturing and end-user adoption around a homegrown innovation agenda. “Over time this dynamic could fuel, not foil, China’s ambitions,” Sacks said.

The impact of that growing domestic coordination is already visible: when DeepSeek launched its highly anticipated V4 AI model preview last month, the system is partially powered by chips from Chinese tech giant Huawei, reducing the country’s dependence on leading U.S. chipmakers such as Nvidia. A recent report from Stanford University’s Institute for Human-Centered AI concluded that the performance gap between top U.S. and Chinese AI models has “effectively closed.”

While U.S. policymakers and leading AI firms including OpenAI and Anthropic have accused Chinese AI startups of intellectual property theft, Chinese officials have rejected those claims as unsubstantiated. Even with ongoing U.S. export controls and China’s domestic internet censorship regime, analysts believe the gap between the two global AI powers will continue to narrow. Lian Jye Su, chief analyst at global research firm Omdia, noted that barriers such as the Great Firewall have only had limited impact on AI development, as the technology has already been tested, integrated and scaled within China’s regulated domestic internet ecosystem. “It won’t be long before China moves from fast follower to parallel innovator,” Su predicted.

In a historic move that marks a watershed moment for global tech labor organizing, hundreds of Google DeepMind employees based in the United Kingdom have overwhelmingly voted to form a union, driven by urgent ethical concerns over the misuse of the company’s artificial intelligence technology by the U.S. and Israeli militaries in conflicts involving Iran and Gaza.

Following the internal ballot held among Communication Workers Union (CWU) members at DeepMind, official results showed a 98% majority in support of unionization. On Tuesday, workers formally submitted a request to Google DeepMind’s leadership to recognize both the CWU and Unite the Union as their official workplace representatives. If recognized, this organizing effort will become the first formal union at a major global frontier artificial intelligence research lab, according to campaign organizers.

At the core of the workers’ demands is an immediate end to the provision of Google AI tools to the Israeli military and U.S. defense entities. Additional demands include the reinstatement of a previously discarded company pledge to refrain from developing AI-powered weapons and mass surveillance tools, the establishment of an independent ethics oversight body with decision-making authority, and formal guarantees that individual employees retain the right to decline work on specific projects on moral or ethical grounds.

This unionization push is part of a broader global grassroots movement among DeepMind staff, with employees across international locations planning coordinated in-person protests and so-called “research strikes,” a work stoppage action where researchers suspend their regular work to highlight their concerns.

One anonymous DeepMind employee emphasized that no staff want their cutting-edge AI research to become complicit in violations of international law, noting that the technology is already aiding what the employee called Israel’s genocide of Palestinians in Gaza. “Even if our work is only used for administrative purposes, as leadership has repeatedly told us, it is still helping make genocide cheaper, faster, and more efficient,” the worker said. “That must end immediately, as must harm to Iranians and human lives anywhere.”

The successful unionization vote would cover at least 1,000 DeepMind employees based at the company’s London headquarters. In the formal letter submitted to management, workers have given DeepMind leadership 10 working days to voluntarily recognize the two unions or enter into mediated negotiation talks. If management fails to meet the deadline, workers will launch a formal legal process to force official recognition.

The vote comes at a time when the UK government is actively pursuing policies to attract major global technology investment, positioning the country as a leading hub for AI innovation. Already, several high-profile AI firms including Anthropic have announced plans to expand their London-based operations in recent months.

CWU national officer John Chadfield framed Tuesday’s announcement as a defining moment for tech workers around the world. “This is a really important moment where tech workers at Google’s frontier AI lab are connecting with some of the most oppressed people in communities around the world in meaningful ways, based on foundational values of solidarity and trade unionism,” Chadfield said. “By exercising their rights to collectivise they are in a strong position to demand their employer stop circling the ethical drain of military-industrial contracts, echoing the sentiment of many working people in the UK and elsewhere.”

This latest labor action is the culmination of years of growing internal unrest among Google employees over the company’s military contracts. Earlier this year, more than 600 Google staff across the company’s AI and cloud divisions signed an open letter urging leadership to cut off the Pentagon’s access to Google technology for classified military operations. Google previously drew widespread backlash after firing dozens of employees who participated in protests against Project Nimbus, a joint initiative with Amazon to provide cloud and AI services to the Israeli government amid the ongoing military campaign in Gaza. In 2023, the company also faced heavy criticism for its acquisition of Israeli cloud security firm Wiz, which was founded by former veterans of Unit 8200, the Israeli military’s elite cyber espionage and surveillance unit.

Against a backdrop of escalating wildfire risk driven by climate change, artificial intelligence is rapidly becoming a critical new tool for early wildfire detection across fire-prone regions of the western United States, already proving its ability to stop blazes before they turn into catastrophic infernos.

The technology delivered a convincing proof of concept on a March afternoon in Arizona’s Coconino National Forest, when an AI-enabled monitoring camera picked up a faint plume of smoke that did not match the signature of cloud cover or wind-blown dust. After human analysts confirmed the anomaly, alerts were immediately dispatched to Arizona’s state forest service and Arizona Public Service (APS), the state’s largest electric utility. First responders arrived on scene quickly and contained the resulting Diamond Fire to just 7 acres (2.8 hectares), a fraction of the size it could have reached if detected hours later.

The Diamond Fire interception is far from an isolated success. As record-breaking high temperatures and record-low winter snowpack stoke fears of an extreme wildfire season, state agencies, power utilities and private tech firms have been rolling out AI monitoring systems across remote, high-risk regions where human spotters or casual 911 reports often fail to catch blazes in their earliest, most controllable stages.

To date, APS already operates roughly 40 active AI smoke-detection cameras across Arizona, with expansion plans to bring the total to 71 by the end of the current summer. The Arizona Department of Forestry and Fire Management has also deployed seven of its own AI units, while Colorado-based utility Xcel Energy has installed 126 AI cameras, with goals to bring the system to all but one of the eight states it serves by the end of the year. In California, the ALERTCalifornia network operates more than 1,200 AI-integrated cameras that follow a detection model similar to Arizona’s system.

“Earlier detection means we can launch aircraft and personnel to it and keep those fires as small as we can,” explained John Truett, fire management officer for the Arizona Department of Forestry and Fire Management.

Unlike populated areas where residents often spot and report fires quickly, most high-risk unpopulated rural and remote zones lack consistent human observation. It is exactly these gap areas that AI monitoring fills, providing 24/7 surveillance that frequently outpaces 911 reports by a significant margin. According to Neal Driscoll, a geology and geophysics professor at the University of California, San Diego and founder of ALERTCalifornia, human oversight paired with ongoing AI training has kept false positive rates very low, and the technology consistently outperforms traditional 911-based detection timelines.

“It’s just the ones where we won’t get a 911 call for a long time, it is extremely helpful to have that AI always monitoring that camera,” said Brent Pascua, a battalion chief for the California Department of Forestry and Fire Protection (Cal Fire). “In many cases, we’ve started a response before 911 was even called, and in a few cases, we’ve actually started a response, went there, put the fire out, and never received a 911 call.”

Pano AI, one of the leading providers of this technology, integrates high-definition camera feeds, satellite data and artificial intelligence to scan for early smoke signs. Since launching in 2020, the company has seen surging demand for its systems, which are now deployed across 17 U.S. states plus Canada and Australia, serving forestry operators, public agencies and utilities including APS. In 2025 alone, the company’s technology detected 725 wildfires across the U.S.

“In many of these situations, we hear from stakeholders that the visual intelligence, the time, really, really gives them a head start and some of these could have taken off into hundreds if not thousands of acres,” said Arvind Satyam, Pano AI co-founder and chief commercial officer. APS meteorologist Cindy Kobold confirms the technology delivers an average 45-minute head start over the first incoming 911 call, a gap that can make the difference between a contained small blaze and a destructive megafire.

Satyam notes that the development of this technology was directly driven by the growing wildfire crisis fueled by climate change. Rising global temperatures from fossil fuel emissions have created drier, more fire-prone conditions that increase the frequency, intensity and speed of wildfire spread, and existing management tools have not kept pace. AI detection fills this gap, helping first responders act more effectively while protecting communities and critical infrastructure.

Despite its clear benefits, the technology still faces notable limitations and challenges. The most significant barrier for many agencies is upfront and ongoing cost: Pano AI charges approximately $50,000 per camera annually, a price that includes 24/7 monitoring support and fire risk analysis. False alarms remain a persistent issue, wasting valuable first responder time and resources even as training reduces their frequency. Even when the AI correctly identifies a fire, it cannot guide decision-making on response strategy – questions of when to deploy crews, whether to order evacuations, or how to prioritize resources still require human judgment and decision support systems.

In dense urban areas, where residents already report fires quickly, the technology offers less benefit, and it cannot keep pace with rapidly shifting fire behavior during extreme weather events such as the 2025 Los Angeles wildfires, when hurricane-force winds reshaped fire lines hourly. Proponents emphasize that AI is designed to complement, not replace, human firefighting teams.

“As the fire moves and shifts around, that’s where the human factor comes in and decides which tactics are best in fighting the fire. AI can only do so much,” Pascua said. “It just provides that real time information where we can make better decisions on the fire ground.”

AI’s role in wildfire management extends far beyond early detection, researchers note. The technology can already map optimal zones for controlled burns and vegetation thinning, and monitor air quality for early smoke signatures with far greater sensitivity than traditional consumer detectors. At George Mason University, professor Chaowei “Phil” Yang is leading a collaboration with California State University Los Angeles, the city of Los Angeles and NASA’s Jet Propulsion Laboratory to develop an AI system that forecasts wildfire spread and predicts which communities will face the worst smoke pollution impacts. The system will generate real-time maps to help agencies make faster, more effective decisions around evacuations, road and school closures, and public health warnings, with a target operational launch within three years.

Experts agree that AI integration in wildfire management is no longer a future concept – it is already deployed in active response, and its use will only expand in coming years. “AI in wildfires, it’s no longer just speculative. It’s really being used,” said Patrick Roberts, a senior RAND Corporation researcher who recently completed a study on innovation in wildfire management. “The future is AI everywhere, and the lines will blur between AI wildfire detection and just wildfire detection as the lines will blur in other areas of our life.”