Every major economy is staring at the same problem right now. Artificial intelligence is consuming electricity at a pace that grids were never designed to handle. In the US, capacity market prices in PJM, the country’s largest grid operator, have risen more than tenfold in two years, with data-centre growth identified as a primary driver. In Europe, utilities are scrambling to upgrade transmission infrastructure fast enough to keep pace with hyperscalers’ demand.

The International Energy Agency (IEA) projects global data-centre electricity consumption could approach 1,000 TWh by the end of this decade. Renewable energy is largely there, but the ability to coordinate it, through AI energy grid mapping at national scales, is what most countries still lack. But China just built it.

A study published in Nature this week by researchers from Peking University and Alibaba Group’s DAMO Academy has produced something that no country has managed before: a complete, high-resolution, AI-generated inventory of an entire nation’s wind and solar infrastructure, with the analytical framework to coordinate it as a unified system.

Using a deep-learning model trained on sub-metre satellite imagery, the team identified China’s 319,972 solar photovoltaic facilities and 91,609 wind turbines, processing 7.56 terabytes of imagery to do so.

AI energy grid mapping

Prior research into solar-wind complementarity – the idea that two sources can offset each other’s variability in time and geography – has largely relied on hypothetical or modelled deployment scenarios. How complementarity manifests under real-world infrastructure, and how it shapes system-level integration outcomes, has until now remained unclear.

The researchers show that solar-wind complementarity substantially reduces generation variability, with effectiveness increasing as the geographic scope of pairing expands.

In practical terms, the further apart the facilities being coordinated are, the more reliably they achieve balance. A cloud that covers solar farms in Gansu does not darken wind corridors in Inner Mongolia, for example. The study’s findings point to a structural inefficiency in how China currently manages its grid: coordination happens at a provincial rather than national level.

Transitioning to a unified national scale, the researchers argue, would make it easier to pair complementary energy sources, stabilise the grid, and avoid curtailment – the wasting of generated renewable power that has long been one of China’s most costly clean-energy problems.

Liu Yu, a professor at Peking University’s School of Earth and Space Sciences, described the inventory as allowing China to see its new-energy landscape from a “God’s-eye view,” a phrase that carries more operational weight than it might first suggest. Grid operators cannot optimise what they are not aware of – until now.

China is in the middle of an AI-driven electricity demand surge that is straining its grid. The rapid proliferation of data services and massive computing facilities have pushed the sector’s power consumption up 44% year-on-year in the first quarter of 2026, reaching 22.9 billion kilowatt-hours, according to the China Electricity Council.

That is an extraordinary rate of growth for a sector whose demand was already great. This has accelerated data-centre expansion in China’s northern and western provinces, where land is cheaper, wind and solar resources are more available, with commensurately lower electricity prices. The provinces being targeted for new data centres are the same regions with the highest solar-wind complementarity.

Behind the model

The technical achievement behind this is worth understanding in its own right. DAMO’s deep-learning model was trained to identify solar photovoltaic facilities and wind turbines from sub-metre resolution satellite imagery, a task complicated by the sheer diversity of installation types, terrain conditions, and image quality.

The resulting dataset covers installations in 1,915 Chinese counties, spanning everything from rooftop panels in coastal cities to utility-scale wind farms on the Mongolian plateau. Processing 7.56 terabytes of imagery to produce a nationally consistent, county-level inventory is a demonstration of what large-scale geospatial AI can do when applied to infrastructure problems, and a template that other countries could, in principle, replicate.

China’s clean energy sector generated an estimated 15.4 trillion yuan (US$2.26 trillion) in economic output last year, equivalent to Brazil’s entire GDP, according to the Finland-based Centre for Research on Energy and Clean Air. Managing an asset base of that scale without a national-level visibility tool was always going to be a limiting factor, a limit that’s now gone.

The study’s dataset and code have been made publicly available via Zenodo.

(Photo by Luo Lei)

See also: Inside China’s push to apply AI in its energy system

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The ceremony was scheduled. The CEOs were on the guest list. And then it wasn’t happening.

On Thursday, US President Donald Trump scrapped a planned AI executive order, which had already been delayed multiple times, citing concerns that it might erode America’s competitive edge over China.

“We’re leading China, we’re leading everybody, and I don’t want to do anything that’s going to get in the way of that lead,” Trump told reporters in the Oval Office. What he did not say was that the order had been effectively killed by the very industry it was meant to oversee.

Lobbied out in one night

According to Semafor, which first reported the backstory, the White House’s plans were halted after Elon Musk of xAI, Meta CEO Mark Zuckerberg, and venture capitalist David Sacks, who, until recently, was Trump’s AI and cryptocurrency tsar, all spoke directly with Trump between Wednesday night and Thursday morning.

The argument that landed, according to US media, citing sources, was an appeal to the “accelerationist” faction in the administration, including officials at the National Economic Council and staffers in the Vice President’s office.

The order itself was not a sweeping regulatory framework. It would have established a voluntary mechanism for AI developers to engage with federal agencies and submit advanced models for security review up to 90 days before their public release. No licensing regime. No mandatory hold periods. Voluntary.

That was apparently still too much. Trump said he postponed it “because I didn’t like certain aspects of it,” declining to specify which ones. He added that he worried it “could have been a blocker,” a telling phrase from a president who has otherwise positioned AI as a jobs and national security priority.

A vacuum with consequences

The US has yet to pass comprehensive AI legislation. What governance architecture exists has been assembled piecemeal, through executive orders, agency guidance, and voluntary agreements. Earlier this month, the federal Centre for AI Standards and Innovation announced evaluation agreements with Google DeepMind, Microsoft, and xAI, allowing the government to assess models before public availability. That programme continues regardless of Thursday’s non-signing.

But the broader picture is one of regulatory drift. In early March, the Trump administration released a National AI Legislative Framework urging Congress to preempt state-level AI laws that “impose undue burdens,” arguing for a single national standard over what it called “fifty discordant ones.” Congress has not acted on it.

The contrast with China is sharp and increasingly difficult to ignore. Beijing’s State Council issued a 2026 legislative work plan in May outlining plans to accelerate comprehensive AI legislation, deploying language on AI governance in formal planning documents for the first time. The National People’s Congress has listed AI legislation for review for the third consecutive year.

In April, Beijing issued new rules requiring AI companies to establish internal ethics review committees. China is writing rules. Washington is cancelling ceremonies.

Who shapes US AI policy

Thursday’s episode clarified something implicit for months: in the current administration, the effective veto on AI regulation sits with a small group of industry principals who have direct access to the president.

Musk, whose xAI is a direct competitor to OpenAI and Anthropic, has a structural interest in keeping the regulatory field open. Zuckerberg’s Meta has similarly positioned itself as a champion of open-source AI development. Sacks, despite having formally left his White House advisory role in March, evidently retains enough influence to shape executive action.

Separately, Semafor reports that OpenAI has secured White House backing for a parallel effort to push AI regulations at the state level, an interesting manoeuvre given that Trump’s earlier executive order threatened states that enacted AI laws the administration disliked. That the administration appears to be simultaneously discouraging state regulation and endorsing OpenAI’s state-level strategy suggests the policy coherence problem runs deeper than one postponed signing.

The China frame does real work, but in both directions

Trump’s stated reason for pulling back, protecting the US lead over China, is the same logic that has driven every major AI policy decision since he returned to office, from the H200 export licence framework to the Stargate infrastructure programme. It is also the logic that China is watching closely.

At the Trump-Xi summit in Beijing earlier this month, the two leaders agreed to launch an intergovernmental dialogue on AI, according to the Chinese Foreign Ministry. Beijing will have noted that Washington’s internal debate about even voluntary AI oversight was resolved not by policymakers, but by the companies that stand to profit most from the absence of guardrails.

In a report by the South China Morning Post, Lizzi C. Lee, a fellow at the Asia Society Policy Institute’s Centre for China Analysis, noted that both the US and China are grappling with the same underlying question: where should the regulatory frontier sit for frontier AI, particularly as models become more capable of autonomous action and more relevant to cybersecurity.

“I think a separate, potentially more important race is on governance and safety: not about who has the most advanced models, but who can govern powerful AI without choking off innovation,” she said.

The same report highlighted what Kyle Chan at the Brookings Institution put it more simply: “AI safety and regulation can be done in a way that doesn’t compromise innovation.”

Neither argument was enough on Thursday. Whether it becomes enough next time, assuming there is a next time, remains unclear.

(Photo by White House)

See also: The US-China AI gap closes amid responsible AI concerns

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The Nvidia Vera chip is rarely the headline when earnings beat estimates, but it should be. When Nvidia reported Q1 revenue of US$81.62 billion on Wednesday, beating analyst estimates of US$78.86 billion, and guided Q2 at US$91 billion–well above Wall Street’s US$86.84 billion forecast–the numbers did what Nvidia numbers always do: dominate the room. 

But buried in CEO Jensen Huang’s conference call with analysts was something more strategically interesting than another quarterly beat. Huang told analysts that Nvidia’s new Vera central processors unlock access to a US$200 billion market, one that sits entirely outside the US$1 trillion the company has already forecast from its Blackwell and Rubin AI GPU lineup between 2025 and 2027. 

He expects Vera chip revenue to hit US$20 billion by the end of this fiscal year. “I expect (Vera) to be the second largest” sales contributor, Huang said during the call.

That’s not a footnote. That’s a second front.

The Vera chip and the inference pivot

The reason Nvidia needs a second front is straightforward: its biggest customers are building their own. Google, Amazon, and Microsoft–collectively expected to pour more than US$700 billion into AI infrastructure this year, up sharply from around US$400 billion in 2025, are simultaneously pouring funds into custom silicon to run AI models. Intel and AMD are also touting CPUs as a credible play for inference workloads. 

The narrative in the chip industry has shifted from who can train the biggest model to who can serve it cheapest and fastest. Inference is where Nvidia’s GPU dominance is most exposed. Training large models is still firmly Nvidia territory, but inference, generating answers at scale, in real time, is increasingly where custom chips from Google’s TPU line, Amazon’s Trainium and others are making their case.

Nvidia’s answer is Vera. The chip, developed in part using technology from Groq, a startup specialising in inference that Nvidia licensed in a deal reportedly worth around US$17 billion, targets exactly this workload. The full Vera Rubin platform, which combines the Vera CPU with Rubin GPUs, is set to launch later this year.

Supply is already the constraint

Huang was candid about one problem: supply. “My sense is that we’ll be supply-constrained through the entire life of Vera Rubin,” he said on the call. It’s a telling admission for a product Nvidia is positioning as a major growth pillar. To get ahead of disruptions, Nvidia is spending heavily on the supply chain. The company disclosed that its supply commitments rose to US$119 billion in Q1, up from US$95.2 billion the previous quarter, a significant jump that reflects both confidence in demand and anxiety about a global memory chip crunch.

Nvidia also announced a US$80 billion share repurchase programme and raised its quarterly cash dividend to 25 cents per share, from 1 cent, moves that signal financial confidence even as Huang warned of tightening supply.

The question investors are asking

Despite the beats, Nvidia shares fell 1.6% in extended trading after the results. eMarketer analyst Jacob Bourne captured the mood: “Nvidia delivered another beat, but at this point that’s essentially priced in as it keeps beating quarter after quarter. The lingering question is whether it can convince investors the AI buildout has durability into 2027 and 2028, especially as the narrative shifts toward inference workloads and competing silicon from Google, Amazon, AMD, and Intel.”

Huang pushed back with numbers of his own. He pointed to a growing sub-segment of AI-specific cloud customers whose spend is now roughly equal to the hyperscalers, but growing faster quarter-over-quarter. “We should be growing faster than hyperscale capex,” he said.

The Vera chip is central to that argument. Whether the supply chain cooperates is a different question entirely.

(Image source: Nvidia’s Newsroom)

See Also: The Nvidia H200 China deal survived the Trump-Xi summit–just not in the way anyone expected

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Alibaba has unveiled a new AI processor built specifically for AI agents, pairing the chip announcement with a multi-year silicon roadmap and a new large language model, signalling that the company is building an integrated AI stack rather than just filling a gap left by US export controls.

The Zhenwu M890, developed by Alibaba’s semiconductor subsidiary T-Head, delivers three times the performance of its predecessor, the Zhenwu 810E, according to the company, as per Reuters report. But the performance jump is less notable than the architectural intent behind the chip: the M890 is purpose-built for AI agents, where software systems must retain long stretches of context, coordinate with other models in real time, and execute complex multi-step tasks with limited human intervention. 

Those demands, heavy on memory bandwidth and inter-model communication, are meaningfully different from what standard inference chips are optimised for. The difference matters because it tells you something about where Alibaba thinks AI compute is heading. The company isn’t designing around today’s dominant use case; it’s building for the workload profile it expects to define enterprise AI over the next several years.

Built for AI agents, not just inference

More significant than the chip itself is the roadmap Alibaba put alongside it. The M890 will be followed by the V900 in the third quarter of 2027, expected to deliver another roughly threefold performance gain, followed by the J900 in the third quarter of 2028. That’s a deliberate, sustained cadence of in-house silicon upgrades that mirrors the kind of tick-tock product cycles Nvidia has used to maintain its lead in AI accelerators.

The parallel to Huawei is worth noting. Huawei laid out a similar chip roadmap for its Ascend line last year, and both announcements reflect the same underlying reality: Chinese technology companies have concluded that depending on foreign silicon, even in scenarios where export restrictions might ease, is a structural risk they cannot accept. The response has been to treat semiconductor development as a long-term capability-building exercise rather than a procurement problem.

Alibaba’s commitment to that exercise is not shallow. The company pledged more than 380 billion yuan, roughly US$53 billion, on cloud and AI infrastructure over three years last year, its largest-ever investment commitment to the sector. The M890 and its successors are downstream of that spending.

Traction that predates the announcement

T-Head said it has shipped more than 560,000 Zhenwu units to date, with over 400 external customers across 20 industries deploying the chips, including automakers and financial services firms. That is a material production footprint, not lab hardware, and it provides Alibaba with real-world deployment data at scale ahead of the M890’s rollout.

The new chip will be available to Chinese enterprise customers through Alibaba Cloud’s domestic model platform, Bailian, packaged inside the Panjiu AL128, a server system that stacks 128 M890 accelerators into a single rack.

The software side of the stack

Alongside the hardware, Alibaba announced Qwen 3.7-Max, the latest version of its flagship large language model, described as engineered for advanced coding and long-running agent tasks. The company said the model can operate continuously for up to 35 hours without performance degradation, a capability specification that only makes sense if you are designing for extended autonomous operation.

The timing is deliberate. Releasing a chip and a model optimised for the same workload class on the same day is a platform play. Alibaba is building a closed loop: its own silicon in T-Head, its own model in Qwen, its own cloud delivery in Bailian. Each component reinforces the others, and the combined stack is designed to reduce enterprise customers’ dependence on any external vendor.

More than half a million chips have been shipped. A successor is arriving in 2027, with another planned for 2028. T-Head is not hedging. At some point, building around US export controls stops being a workaround and starts being a strategy. Alibaba appears to have crossed that line.

(Image source: The White House)

See Also: Alibaba Qwen is challenging proprietary AI model economics

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

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President Trump flew to Beijing, brought Jensen Huang along at the last minute, and left two days later, telling reporters that “something could happen” on chip exports. Nothing did. Not a single Nvidia H200 has shipped to China since Trump first authorised the sales in December 2025, and US Trade Representative Jamieson Greer told Bloomberg that semiconductor controls were not even on the bilateral agenda. 

The summit theatre obscured a more interesting development underneath it. The H200 isn’t stuck because Washington won’t allow it. Washington already has allowed it. Roughly 10 Chinese firms, including Alibaba, Tencent, ByteDance, and JD.com, hold approved US export licences for up to 75,000 units each, with Lenovo and Foxconn authorised as distributors. The chips aren’t moving because Beijing won’t let its own companies take delivery.

Two frameworks, one deadlock

The mechanics of the stalemate are worth understanding clearly. US rules require that all H200 chips ordered by Chinese clients be used only in China. Beijing, meanwhile, has instructed Chinese tech companies to limit their use of Nvidia chips to overseas operations while supporting domestic manufacturing. The two requirements are mutually exclusive. 

Chips cleared for export cannot legally be deployed where Beijing wants to deploy them, and Beijing won’t authorise the domestic use the US licences require, according to Implicator.

Commerce Secretary Howard Lutnick stated at a Senate hearing last month that Chinese firms are trying to keep their investment focused on domestic suppliers, including Huawei. Beijing’s State Council has also ordered a supply-chain security review aimed at cutting dependence on US semiconductors. 

The policy contradiction is not accidental. That is the point.

What Huawei gained while diplomats talked

The days around the summit produced several data points that matter more for the long term than Trump’s parting comment. DeepSeek confirmed its latest model had been optimised to run on Huawei processors. Tencent’s chief strategy officer said Chinese GPU supply would increase progressively through 2026, and an Alibaba executive said its T-Head proprietary GPUs had achieved scaled mass production. 

This follows the April launch of DeepSeek V4, which adapted the model for Huawei’s Ascend chips – the first major Chinese frontier model to do so in training, not just inference. What the summit week confirmed is that the shift is no longer experimental. It is now a supply-chain policy. Nvidia’s China revenue has fallen to roughly 5% in recent quarters, down from above 20% before export controls tightened. The company’s own guidance for the current quarter assumes zero revenue from China. 

Huang’s last-minute inclusion in the delegation – Trump called him directly after seeing media coverage that he had not been invited – suggested urgency. The outcome suggested the limits of what CEO diplomacy can achieve when the obstruction is structural, not procedural.

The read for the AI industry

The stalemate matters beyond bilateral optics. Chinese AI platforms are now operating under a domestic mandate to build on Huawei’s compute stack. The question of which AI hardware architecture becomes dominant in the world’s second-largest AI market is being answered not by technical benchmarks but by government directive.

Beijing steering platforms toward Huawei Ascend chips rather than Nvidia H200S is not just a trade posture. It is a structural bet that the performance gap will close fast enough that being locked into the domestic stack is manageable. DeepSeek V4’s results suggest it may be right, at least for inference workloads. 

Trump said something could happen. Greer said the decision is sovereign for China. Both are true, and neither changes the current position: the H200 deal is approved, licensed, and frozen, with Huawei filling the space it leaves behind.

(Image source: The White House)

See Also: Can China’s chip stacking strategy really challenge Nvidia’s AI dominance?

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

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