A quarter of Xinjiang’s annual generation is exported east across transmission lines, capturing generation revenue but little else for the province. Much of the renewable surplus beyond that is curtailed: the transmission lines are at capacity and local demand is too low to absorb it.

Renewables account for over three-quarters of Xinjiang’s installed power capacity but only a quarter of its actual generation: wind and solar produce intermittently, and when they do, there is often nowhere for the power to go. Of what is exported east, less than a third is green.

A policy framework introduced in China’s 2026 government work report proposes capturing some of that surplus by converting electricity into AI compute. The framework is called 算电协同 (”compute-power coordination”) and Xinjiang is the first pilot region.

If all goes to plan, Xinjiang aims to reach 60 exaflops of compute capacity by the end of this year, with construction underway across four cities.

AI’s power problem

For most of the past few years, AI infrastructure headlines centered on chip supply, but that constraint is easing. Nvidia has scaled production, hyperscalers are deploying custom silicon, and China’s Huawei shipped the Ascend 950PR in Q1 2026 with roughly 2.8x the inference throughput of Nvidia’s H20 and its own domestically produced HBM. Government mandates require at least 80% domestic AI chips across the planned data center buildout.

Memory remains tight globally: SK Hynix and Samsung still control over 95% of HBM supply outside China, and Chinese memory maker CXMT’s placement on the Pentagon’s 1260H list has complicated supply chains enough that Apple is lobbying the White House for guarantees it can continue sourcing from the company. But the harder constraint is power.

In the US, new grid connections can take years. At the World Economic Forum in Davos in January, Elon Musk warned that the industry would soon be “producing more chips than we can turn on.” He put it bluntly: “The limiting factor for AI deployment is fundamentally electrical power.” He added: “except for China.”

In the US, over 2,000 GW of generation and storage capacity is waiting to connect to the grid. The median project that reached commercial operation in 2025 spent over five years in the queue. The International Energy Agency estimates that around 20% of planned data center projects could be at risk of delays. FERC responded in June with orders to fast-track data center grid connections, but new facilities may still need to bring their own power or pay for grid upgrades.

Pent-up energy

China generates more than twice as much electricity as the United States. BloombergNEF projects it will add almost six times US electricity generation capacity over the next five years. A lot of that will be renewables. In 2025 alone, China increased wind and solar capacity by over 430 GW, more than half of all global renewables additions that year.

Much of this capacity is concentrated in western provinces, where land is vast, populations are sparse, and wind and solar resources are among the strongest in the country. Xinjiang’s geography makes the case: 1.66 million square kilometres of mostly arid basin and steppe, flanked by the Tianshan mountains, with some of China’s highest solar irradiance and wind corridors like Dabancheng that rank among the country’s best. There is plenty of room for turbines and panels, and plenty of wind and sun to power them. But local demand is thin.

Xinjiang crossed 100 GW of wind and solar installed capacity at the end of 2024, a first for Northwest China. Including hydro and other renewables, total clean energy capacity reached 173 GW within a total grid capacity of 219 GW by mid-2025. But the province’s ability to use all that power locally has lagged behind its ability to generate it. Conditions have improved from historical lows, but not enough: as of early 2025, only 67.6% of available renewable generation was actually used, meaning a third still went to waste.

The traditional solution has been to export the surplus east via ultra-high-voltage DC transmission lines: Hami-Zhengzhou (commissioned 2014), Changji-Guquan (2019, at 3,293 km and 12 GW capacity the world’s longest), and Hami-Chongqing (2025). Changji-Guquan alone has delivered 84 billion kWh of green electricity since commissioning, all consumed at the far end in Anhui.

At projected 2026 curtailment rates, Xinjiang’s renewable fleet will leave untapped the equivalent of more than eight times the annual electricity consumption of Elon Musk’s xAI Colossus supercomputer.

Building compute where the power is

China’s first attempt to build compute in the west was 东数西算 (”East Data, West Compute”), launched in 2022. The program designated 8 national compute hub nodes, including Inner Mongolia, Guizhou, Gansu, and Ningxia, and built real infrastructure: 633 hyperscale and large data centers delivering 268 exaflops of national computing power by 2024.

The program built real capacity, but utilization lagged. The Oxford Institute for Energy Studies found energy savings of 4 to 12% from moving data centers west, though western centers often lacked the fiber infrastructure to serve eastern clients in real time, and the companies, talent, and customers remained concentrated on the coast. Western data center utilization rates settled at 20-30%. Over 100 data center projects have been scrapped nationally in the last 18 months, and the government imposed a moratorium on new large-scale facilities in cities where existing centers operate below 50% utilization.

Compute-power coordination, the successor framework, takes a more focused approach. Rather than trying to solve those demand-side problems directly, it asks whether energy economics alone can pull enough latency-tolerant AI workloads west. It bypasses the moratorium by granting energy-rich provinces the same policy benefits as the original 8 hub nodes based on their power resources, not administrative designation. All new data centers in hub regions must source at least 80% of their electricity from renewables, a target western provinces already meet by default.

Xinjiang, which was not one of the original 8 nodes, was granted the same pilot status. It then launched what its authorities describe as China’s first multi-user 绿电直连 (”green electricity direct connection”) pricing, bypassing the traditional grid to connect power generation directly to the data center at 0.31 yuan per kWh (~$0.045/kWh). That is 0.04 to 0.07 yuan below the standard grid-delivered tariff and cheaper than Inner Mongolia’s 0.35 yuan per kWh (~$0.051/kWh), which Xinhua describes as one of the lowest among the national data center clusters.

Despite frequent claims that China has an energy cost advantage over the West, the Oxford study found that China’s industrial electricity prices are broadly comparable to those in the US. A data center in Guangdong or Jiangsu faces delivered costs of RMB 0.60-0.75 per kWh (~$0.08-0.14/kWh), near or above industrial rates in Virginia, the largest US data center market, because coastal Chinese provinces layer grid charges, capacity fees, and cross-subsidy surcharges on top of generation costs. Western green direct connection pricing at 0.31 yuan is roughly half the eastern rate, which is the real incentive for moving workloads west.

In April 2026, four central agencies led by the National Energy Administration jointly published a 29-item action plan targeting a “safe, green, economical energy guarantee system” by 2027 and “world-leading” clean energy supply for AI compute by 2030. The plan calls for million-kW-scale AI compute facilities with coordinated energy systems, multi-year green power contracts, and infrastructure REITs as a financing vehicle for compute facilities.

The NDRC is drafting a plan to spend 2 trillion yuan (~$295 billion) over 5 years on a nationwide AI data center grid, funded by sovereign debt and ultra-long special government bonds, with China Mobile and China Telecom as primary operators. The total could exceed 5 trillion yuan (~$740 billion) if power grid upgrades are included. Compute-power coordination is one of the frameworks that determines how energy-rich provinces participate in this nationwide buildout.

The demand wave

The infrastructure is being built to meet a compute demand curve that is still steepening. Deloitte estimates that global demand for AI compute is rising four to five times per year through 2030, driven primarily by inference rather than training. Inference workloads now account for roughly two-thirds of all AI compute, up from a third in 2023 and half in 2025. The market for inference-optimized chips alone is projected to exceed $50 billion in 2026. In China, computing-related electricity consumption rose from 82.4 billion kWh in 2019 to 196 billion kWh in 2025. The China Academy of Information and Communications Technology projects it could reach 500 to 700 billion kWh by 2030, up to 5.3% of total national demand.

China’s domestic AI ecosystem is generating much of that demand. Chinese platforms processed 140 trillion tokens per day in Q1 2026, a more than 1,000-fold increase from 100 billion per day in early 2024. Chinese open-source models surged from 1.2% of global usage in late 2024 to nearly 30% by mid-2025, and by June 2026 accounted for roughly 48% of token usage on OpenRouter, overtaking US models, with DeepSeek, Qwen, and Zhipu’s GLM leading the expansion.

Goldman Sachs estimates that China’s top internet firms will invest over $70 billion in data center capital expenditure in 2026, and reports that Chinese data center power demand rose 25% in 2025.

The growing dominance of inference might seem to work against Xinjiang’s case, since inference typically runs close to the end user. But much of the fastest-growing inference demand is not real-time. User-facing inference (search, voice assistants) needs to run close to the user and will stay in eastern cities. Training is less latency-sensitive but faces its own constraint: as one executive told Caixin, the datasets are so large that “companies sometimes have to physically transport hard drives” rather than transmit them east to west. Planned network infrastructure investment aims to close that bandwidth gap. But once the data arrives, training runs are exactly the kind of sustained, power-hungry workload that suits cheap western electricity. The same is true of batch processing, agent workflows, and offline industrial analytics, and these workloads are growing fastest. In January 2026, eight ministries set a target of 1,000 industrial AI agents deployed across manufacturing, logistics, and public services by 2027. Some of these, like real-time robotics control, need to run locally. But many, like logistics optimization, demand forecasting, and document processing, are batch workloads that need cheap, reliable power and a network connection, not proximity to the end user. That is the market compute-power coordination is designed to serve.

The funding landscape

In April 2025, China launched a ¥60 billion (~$8.3 billion) state-backed AI fund to invest across the full AI industrial chain, with its first-year focus on semiconductors and computing infrastructure. ByteDance’s 2026 capital expenditure alone is ¥160 billion, over half of it on AI chips. Alibaba has committed ¥380 billion over three years to cloud and AI hardware infrastructure.

Below the hyperscalers, venture capital is filling the infrastructure stack. Some notable raises this year across the energy and cooling layers of AI compute:

Qingqi Power (清启动力): micro gas turbines for distributed data center power. Pre-A round, nearly ¥100 million (~$14 million), ¥500 million valuation. Facilities that cannot wait on the grid use on-site turbines to generate power where the connection has not yet arrived.

Xinhan Intelligence (芯寒智能): cooling systems for AI compute clusters. Pre-A round, hundreds of millions of yuan, ¥1.5 billion valuation. AI accelerators run hot and pack tightly, and at cluster scale the heat load outstrips what air cooling can handle. Cooling also sets PUE, the main lever on a facility’s operating cost.

Afalight (埃尔法光电): co-packaged optics. D round (sixth total), ~¥100 million, ¥1.2 billion valuation. Supplies a 2.4 Tbps optical engine to Alibaba’s AI data centers. As clusters scale, the copper links between racks and the network become the bottleneck on data movement, and co-packaged optics moves the transceiver onto the switch package to push interconnect bandwidth past that limit.

MetaPWR (沛塬电子): power modules for AI compute. Strategic round, undisclosed amount, ¥500 million valuation. AI racks pull very high current at low voltage, and the rack’s power conversion stage has to deliver that cleanly and reliably. Generic modules are not rated for the load profile.

Four cities, one value chain

Four cities are dividing the work: Karamay runs the most efficient facilities, Changji is building new capacity with major tech partners, Hami dispatches compute to Chongqing, and Urumqi connects westward to Central Asia.

1. Karamay (克拉玛依) has some of the most energy-efficient data center infrastructure in China. Carbon and Network Tech runs facilities with a PUE under 1.1 (PUE measures how much energy goes to compute vs. cooling and overhead; a perfect score is 1.0, and typical facilities run 1.3-1.5), with cooling and overhead energy one-fifth that of a conventional data center thanks to liquid cooling and Xinjiang’s cold, dry climate. The company has used 100% green power via green electricity certificates since 2024 and was listed on the 2025 National Green Compute Facilities roster. Karamay dispatches compute to Shanghai under a cross-regional framework.

2. Changji (昌吉) broke ground in May 2026 on a green direct-connect photovoltaic project paired with a China Unicom 16,000P intelligent compute infrastructure build at the Xinjiang Information Industry Park. Alibaba, ByteDance, and Zhipu have all reached cooperation agreements, with scheduled completion in November 2026.

3. Hami (哈密) has been designated by the National Data Bureau as an implementation city for both the Xinjiang-to-Chongqing compute corridor and compute-power coordination. A dedicated all-optical network link delivers under 20 ms one-way latency to Chongqing across 4,000 km, with 20,000P of compute already operational and a target of 40,000P by year-end.

4. Urumqi (乌鲁木齐) is building a national-level internet backbone direct connection point alongside a BRICS digital industry cooperation network city node, positioning itself as a compute hub serving Central Asia and Europe. Xinjiang borders Kazakhstan, Kyrgyzstan, Tajikistan, Pakistan, and Mongolia, and has more than 20 cross-border international fiber cables connecting to those neighbors, recently upgraded to 1.2 terabits (China-Kazakhstan) and 800 gigabits (China-Pakistan). The provincial government describes its cross-border compute services as “算力出海” (”compute going overseas”). So far, the only confirmed services crossing these borders are satellite remote sensing and smart agriculture from Xinjiang’s Space-Air-Ground Integration Lab. The fiber infrastructure exists; commercial AI compute demand from Central Asian markets has yet to materialize. Eight intelligent compute center projects are under construction, and companies including Beijing Wanjie Data and Hefei-based Leinao have established operations. The Xinjiang Daily describes Leinao’s facility here as the first compute center using domestically produced chips. Leinao’s Bitahub platform aggregates self-built and external compute resources and reports a utilization rate above 90%, but that is a platform-level figure, the result of a marketplace that resells compute across multiple facilities. Facility-level utilization in western hubs remains at 20-30%. Whether a platform model can close that gap at scale beyond a single marketplace is untested.

Electricity to compute to tokens

Jin Qian, president of Carbon and Network Tech in Karamay, told the Xinjiang Daily: “Xinjiang is thinking about how to convert electricity into compute, compute into tokens, and serve the world via the internet.”

China does not need to export chips, models, or hardware to serve AI demand beyond its borders. It can deliver tokens over fiber, keeping the infrastructure, energy, and jobs domestic. Each step retains more value locally than the last: raw electricity captures generation revenue, compute adds infrastructure and operations, tokens add the service layer. Geopolitical and data-sovereignty constraints will shape which markets those tokens can realistically reach, but the economic logic of the conversion chain holds regardless.

The electricity-to-tokens framing has taken hold in other compute hubs too. Inner Mongolia’s Ulanqab, just over 200 km from Beijing with 4.2 ms fiber latency, has branded itself China’s “Token之都” (Token Capital) under a three-year action plan (2026-2028), with over 5 million standard server racks signed and 165,000P of running compute. But Ulanqab’s success leans on proximity to Beijing. Xinjiang’s pitch is different: the cheapest green power in the country, direct-connect pricing that undercuts even Inner Mongolia, and cross-border fiber to Central Asia that no eastern hub can match.

What has to go right

SMIC’s co-CEO Zhao Haijun warned on the company’s Q4 earnings call that rushed AI data center capacity could sit idle, describing the pace as “10 years of capacity crammed into two.”

Caixin, citing expert sources, describes compute-power coordination as being in an “early exploration period,” and draws a technical distinction between 电量 (”electricity volume”), the total energy available, and 电力 (”electrical power”), managing instantaneous demand. Xinjiang has plenty of 电量, but the real challenge is 电力. AI training clusters draw power in sharp, irregular spikes that solar and wind cannot match on demand. The green direct connection model that bypasses the grid also bypasses this buffer, so facilities need either substantial battery storage or a robust grid fallback. Some are already building this: a State Power Investment Corporation facility in Xinjiang operates entirely off-grid with on-site solar, wind, and battery storage, and CATL committed over $1.5 billion to data center energy infrastructure in two months, including $577 million into data center power supplier Zhongheng Electric in April and up to $942 million into data center operator VNET Group in May.

Data centers still account for only 1.68% of China’s total electricity consumption, projected to reach 3% by 2030, a narrower scope than CAICT’s broader “computing-related” estimate of up to 5.3%, which includes telecom and edge infrastructure. Compute-power coordination will not solve the curtailment problem in volume terms. The case rests on value density: a kilowatt-hour converted into AI tokens captures far more economic value than one exported as raw electricity, even if the total kilowatt-hours consumed remain a sliver of the surplus.

The timeline mismatch compounds the challenge: large computing centers can be built in 8 to 24 months, but traditional power substations require 3 to 5 years for planning, approval, and construction. Green direct connection lets data centers start operating before the substation arrives, but the grid fallback they will eventually need still takes 3 to 5 years to build.

The Xinjiang Daily itself acknowledges the challenges: “算力基础设施投资大、回报周期长” (”compute infrastructure investment is large, payback period is long”), along with the need for better cross-regional networks and talent.

Conclusion

The question this framework has to answer is the same one East Data, West Compute could not: whether workloads will actually move. Compute-power coordination addresses the cost side of that equation with 0.31 yuan/kWh green direct-connect pricing and equivalent hub-node status. What it does not address is the commercial gravity of the eastern seaboard, the dense concentration of developers, customers, and corporate headquarters that resisted the first wave of westward migration.

If the framework succeeds, it establishes a model with implications well beyond Xinjiang: that energy-rich, compute-poor regions can compete for AI workloads on electricity price rather than proximity, provided they target latency-tolerant workloads, whether training or batch inference, rather than real-time services. That would reframe western China’s renewable surplus as upstream infrastructure for a national or even global AI services layer. The energy still reaches demand, but as tokens over fiber rather than electrons over high-voltage lines, and the bottleneck shifts from the rigid capacity of a transmission corridor to the elasticity of a network. At the token layer, remoteness stops being the obstacle and becomes the advantage: stranded power converted into tokens, carried over fiber to markets the grid was never built to reach.

It was here that CATL had built a lab to burn, freeze, overload and electrocute batteries, then document exactly how they die.

A Surge in Demand

The world adds more than a Japan’s worth of electricity demand every year. Global use is rising by about 1,100 TWh annually, up from ~700 a year over the previous decade, the fastest sustained growth in more than 10 years. The cause is the electrification of almost everything: industry, air conditioning, electric vehicles and computing.

One of the fastest-growing drivers is the AI data center.

A decade ago, a large data center drew about 50 MW. Today a single AI campus is designed for 10-20x that, and the biggest are planned for 1 GW or more, the output of a nuclear reactor, on one site. A single rack of Nvidia’s latest AI chips draws up to 140 kW, against 10-15 kW for a rack of conventional servers. A 500 MW facility running flat out uses about as much electricity in a year as 360,000 American homes, roughly the number of households in San Francisco.

OpenRouter, a marketplace that routes requests across most of the major AI models, now processes more than a quadrillion tokens a year, the units of text and data a model reads and generates, roughly 15x the volume of a year earlier. Goldman Sachs expects token consumption to rise another 24x by 2030.

The obvious counterargument is efficiency: each new generation of chips and models does more per watt. But usage is growing far faster than efficiency, so total power demand climbs rather than falls. The International Energy Agency expects data centers worldwide to roughly double their electricity use by 2030, to about 945 TWh, growing four times faster than demand from everything else.

Even so, data centers at that scale would still account for less than 3% of the world’s electricity. The total energy is not the problem. The problem is concentration: hundreds of megawatts of demand arriving at a single point on the grid, with load profiles that swing by the second, faster than any grid was designed for.

That power still has to reach the chips.

Power Struggle

Between the data center and the power it needs sit three problems, each distinct, each getting worse.

1. Power delivery is years behind demand. In the United States alone, more than 2,600 GW of generation and storage capacity sits in the interconnection queue, waiting for approval to connect to the grid. The median wait to commercial operation is about five years. Google has reported potential delays of up to 12 years. Building a data center takes 12 to 24 months. Connecting it to the grid can take three times longer. In Ireland, EirGrid restricted new data-center connections in Dublin because the transmission network could not absorb them. The queue is not just long: more than 90% of interconnection applications arrive with technical deficiencies needing multiple rounds of revision before a grid operator will even begin a study.

2. The demand itself is a new kind of problem. A conventional data center drew steady, predictable power. An AI training facility does not. Training runs ramp consumption up and down by megawatts in seconds as jobs start, checkpoint, and restart. Gas turbines, the primary source of flexible generation, take minutes to ramp. The grid was engineered for loads that change slowly and predictably. The load profile of an AI data center is unlike anything it was built to serve. It is also more expensive to run: electricity pricing penalizes peaks, and a facility whose consumption swings unpredictably pays for the spikes, not the average.

3. The cost of the mismatch does not stay inside the facility. US electricity prices rose about 13% in 2025. In Georgia, residential bills climbed roughly sixfold in two years to about $175 a month while Georgia Power proposed $15 billion in expansion driven largely by data-center demand. More than 20 data-center projects across the country have been delayed or canceled amid local opposition. In March 2026, AWS, Google, Meta, Microsoft, OpenAI, Oracle and xAI signed the White House Ratepayer Protection Pledge, committing to cover generation and grid-upgrade costs rather than pass them to households.

Batteries Lead the Charge

Batteries do not generate electricity. They store it, release it, and do both faster than any other asset on the grid. That combination solves several of the problems above at once.

1. Time-to-power. A battery system can be installed in months. Paired with on-site generation, it lets a facility begin operating while the grid connection is still years away. It also raises effective capacity beyond what the connection alone can deliver: if the grid provides 50 MW but the facility needs 200 MW at peak, the battery charges when load is light and discharges when it exceeds the connection’s limit. At the extreme, a facility goes fully off-grid with solar and batteries, bypassing the queue entirely.

2. Shock absorption. When a training run spikes or drops demand by tens of megawatts in seconds, batteries match it instantly. Turbines take minutes. The rest of the power system sees a smoother load instead of raw spikes. That smoothing saves money: batteries store cheap off-peak energy and discharge during spikes, reducing peak charges.

3. Grid services. A facility with co-located storage can provide frequency regulation and voltage support back to the grid, turning it from a burden into a resource.

4. Protecting the compute. A power cut mid-training means a lost checkpoint, potentially days of wasted work. Batteries bridge outages in milliseconds, far faster than a turbine can start, and deliver cleaner power through solid-state inverters than rotating generators produce. For racks of GPUs sensitive to voltage fluctuations, that matters.

Batteries have a hard constraint. They store hours of energy, not days. They cannot sustain a facility through a prolonged outage or replace the grid as a primary power source. Gas, grid, and eventually nuclear provide that sustained generation. But batteries are the fast-responding layer that makes the rest of the stack viable.

The Trust Problem

If batteries can do all of this, the obvious question is why the storage market is not larger. The answer is not technology, cost, or capacity.

Storage batteries and EV batteries began developing in parallel around 2009. Today the storage market is only about a fifth the size of the power-battery business. The bottleneck is proving they work. Vendors claim cycle lives and efficiencies that nobody can independently verify. Some equipment underperforms in the field. Buyers cannot tell good from bad, so they price everything as if it might be bad.

That shows up directly in what people will pay. Tesla’s Megapack sells at several times the per-Wh price of comparable Chinese storage products overseas. Tesla has five years of deployed operating data, a track record asset managers can underwrite. Chinese manufacturers, many of them technically competitive, do not. Someone has to close that trust gap.

Back to the Lab

That burned-out shipping container in Xiamen was not a failure. It was a test. The hall I walked through is one of five laboratories in what CATL and the Xiamen municipal government have jointly built as the world’s largest energy storage validation platform: the Empirical Energy Storage Technology Research Institute (实证储能科技研究院). Ten hectares, roughly RMB 3 billion, and built from the start as industry-shared infrastructure (行业共享的基础设施), open to any manufacturer willing to put its equipment through the process.

Chen Xiaobo (陈小波), the institute’s dean, describes it as a physical exam center plus hospital (体检中心加医院): install a prototype, run the full test suite, identify failures, fix them on site, iterate. Another expert called it a wind tunnel (风洞).

The five labs test a storage system the way the real world would break it:

Grid connection. A 35kV/100MVA grid simulator that tests more than 10 large containers simultaneously, simulating a 1,000-node grid topology across 15 to 60 Hz to cover domestic and international standards. A system destined for Fujian’s grid gets tested against Fujian’s actual grid behavior. The lab serves a joint State Grid (国网) / China Southern Power Grid (南网) storage innovation consortium.

High-voltage safety. Testing from 1kV to 500kV, tracing the root mechanisms of ignition and explosion under extreme voltage. The engineering goal: equipment that does not catch fire and does not explode (不起火、不爆炸).

Thermal safety and combustion. The hall from the opening scene. A 20MW calorimeter, the first of its kind, inside 100,000 cubic meters of indoor burn space. Nine large storage containers can be deflagration-tested simultaneously. This is where batteries are documented failing under thermal stress.

Environmental adaptability. Five chambers (climate, salt spray, rain, dust, environment) covering minus 50°C to 100°C, altitudes up to 7,200 meters, and full-grade dust and water protection. A container bound for the Gobi Desert or a Norwegian coast gets tested under the conditions it will actually face.

Electromagnetic compatibility. The only EMC lab in the world that can take a full 40-foot shipping container as a single unit. A 65-ton turntable, 5MW power supply, and an anechoic chamber running real high-power charge/discharge cycles. EMC testing at the scale the equipment actually ships at, not on a downsized sample.

Chen names three beneficiaries:

1. Equipment makers. Faster iteration, fewer field failures, deeper knowledge of their own products.

2. Long-term asset investors. Returns depend on uptime (带电率) and how quickly a station moves from grid connection to revenue. Validation shortens that timeline. “Real money they can actually earn” (实实在在可以赚到的钱).

3. The grid. Confidence that when a major event hits and storage needs to hold the grid up, it can.

The institute is already in talks with a European insurer about recognizing validation data. A second expert predicted a national grading body will eventually rate storage equipment on safety, reliability, and grid functionality. The chain: validated asset, rated asset, insurable asset, financeable asset class. Solar walked this path over the past decade. Storage is at the stage solar was before institutional capital flooded in.

CATL’s strategy: make the lab open, but the investment unrepeatable. The consortium model follows GB 38031, the power-battery safety standard that became a national standard through industry collaboration. Chen insists the intent is “not to block others” (不卡别人). Another expert: “it will be hard for anyone to invest in a facility this complete again.” Both are true. To reinforce the platform’s independence, CATL brought in TÜV Süd, TÜV Rheinland, CSA, and CGC (鉴衡) as co-supervisors. Storage is ~20% of CATL’s business. The lab’s value is raising the bar for the entire industry.

The Money Is Moving

Capital is already flowing. Nextpower acquired Prevalon Energy for up to $365 million in May 2026, buying into BESS and AI data-center infrastructure. Prevalon, spun out of Mitsubishi Power in 2024, has over 6 GWh deployed globally with firm contracts supporting hyperscaler data centers.

In Oregon, Aligned Data Centers and Calibrant Energy deployed a 31 MW/62 MWh battery system at a Pacific Northwest campus, the first US project where a battery was purpose-built to accelerate interconnection and bring a data center online years earlier. Operational in 2026.

LG Energy Solution signed a $1.6 billion deal with Michigan utility DTE Energy for 1.5 GW/6 GWh of battery storage across eight projects, using LFP cells manufactured in the US and Canada. LG shares surged 15% on the announcement.

In Croatia, the Pantheon AI project backed by KKR-owned Greenvolt is building a €50 billion hyperscale data-center campus with 500 MW of solar and 8 GWh of battery storage behind the meter, designed to operate fully off-grid to bypass interconnection constraints. It is the largest private US investment in Europe.

Google acquired Intersect Power for $4.75 billion, gaining 2.2 GW of operating solar and 2.4 GWh of battery storage co-located with data centers, targeting $20 billion in renewable infrastructure by the end of the decade. In Minnesota, Google partnered with Xcel Energy to deploy a 300 MW/30 GWh iron-air battery from Form Energy, the world’s largest battery by energy capacity, capable of discharging for 100 hours. Iron-air stores energy by rusting iron and releases it by reversing the reaction, a technology that directly addresses the “hours not days” constraint of lithium-ion.

Jefferies estimates that hyperscalers represent a ~20 GW BESS opportunity through 2035, with ~9 GW through 2030. BloombergNEF raised its US storage projection to 204 GW by 2035 even while cutting solar and wind forecasts.

In China the capital is flowing even faster, though across the storage ecosystem broadly rather than into named data-center deals. Chinese venture databases tracked more than 50 energy-storage-related fundraises in the first five months of 2026 alone, spanning cells, materials, systems, inverters, testing, and AI-driven power management. Sunwoda Power (欣旺达动力), one of the companies building AI-data-center-specific storage solutions, closed a RMB 1.68 billion (~$230M) C round at a RMB 26.7 billion valuation. Haier New Energy (海尔新能源) raised RMB 1 billion at a RMB 6 billion valuation. Saudi Aramco and French PE firm Eurazeo co-led a B round valuing 碧澄能源 (Bicheng Energy) at RMB 10 billion.

One startup, 京清数电 (JingQing Digital Power), a storage-inverter maker, raised three rounds in five months. 新能先锋 (New Energy Pioneer) is being funded to build battery testing services, the same layer CATL’s Xiamen lab targets. The data-center-specific market in China is forming through CATL itself: a $600 million acquisition of Zhongheng Electric for data-center power (Zhongheng’s HVDC systems already run in facilities operated by Alibaba, Tencent, ByteDance, and Baidu), a $942 million stake making it the largest shareholder of 21Vianet (世纪互联), one of China’s biggest carrier-neutral data center operators, and a 60 GWh sodium-ion order with HyperStrong (海博思创), the largest sodium deal globally. Together the Zhongheng and 21Vianet deals total roughly $1.5 billion and put CATL on both sides of the data center power equation: supply infrastructure and demand.

What This Changes

The economist George Akerlof called it a market for lemons: when buyers cannot verify quality, they price everything toward the worst case, and good products get punished. The energy storage market has exactly this problem. Akerlof’s fixes are certification, warranties, brand, and licensing. CATL’s lab is reaching for nearly all of them.

The dean called the price gap with Tesla “enormous” (巨大) and framed the lab as infrastructure for 出海 (going overseas). A credible validation record gives Chinese integrators like Sungrow, HyperStrong, and Envision bankability they currently lack. CATL’s numbers show where value is heading: ESS gross margin 26.7% against 23.8% for EV batteries, overseas revenue at 31.4%. The premium is in the validated system, not the cell.

The lab is also a competitive move, and not the only one. In EVs, CATL expanded steadily from cells into materials, components, financing, and partnerships with automakers. It did not need to control the entire industry to gain influence over it. The data center investments suggest a similar pattern: Zhongheng extends its reach into power systems, 21Vianet connects it directly to demand, and the validation lab sets the quality standard. Integrators are diversifying cell supply as the market matures. Cell leadership alone is not enough at the system layer. If validation becomes an industry norm, the early mover that built the infrastructure and co-opted the certifiers has a structural advantage, even under an open-access model. Standards may ultimately be set by neutral bodies, and the geopolitical risk on Chinese exports remains regardless of validation quality. But the lab exists, and nobody else has built one.

The burned-out shipping container is still sitting under the test structure in Xiamen, in 38-degree heat, inside a hall the size of a small aircraft hangar. It was not a failure. It was a product that went through the worst conditions anyone could engineer, and the data from its death was recorded. Before batteries become infrastructure, they have to become trusted assets. CATL is building that trust.

Looking around, most tables had people on their phones. Not scrolling social media. Trading. Stock tickers, candlestick charts, portfolio dashboards. An older woman two tables over was comparing notes with her friend on a position they both held. A younger guy in a polo shirt was toggling between a brokerage app and what looked like a crypto wallet. They all had smiles on their faces after riding the stock bump since FTSE Russell confirmed Vietnam’s upgrade from frontier to emerging market in its April 2026 review.

Most of the people in that coffee shop probably couldn’t tell you what FTSE Russell is. It doesn’t matter. Trillions of dollars in passive funds are benchmarked to FTSE’s indices: index ETFs, pension funds, sovereign wealth allocations. When Vietnam moves from frontier to emerging market, those funds are required to buy Vietnamese equities to maintain their index weighting. Active managers follow, chasing the inflows. The retail investors caught the front of an institutional wave.

After my coffee, my new friend Trung Pham (Gavin) and I hopped on a scooter for a short ride along the riverfront north to his office at the new International Financial Centre, Da Nang. It was here that Da Nang’s government-licensed regulatory sandbox had recently approved Vietnam’s first pilot for converting USDT to Vietnamese dong (VND). The country already has a massive retail investor base trading equities, crypto, everything. The IFC is the government’s bet that it can build new financial infrastructure on top of that energy.

I’ve been in China over a decade. I went to Vietnam to test a specific hypothesis: that the country would be following a pattern similar to China’s, where infrastructure and regulation get built simultaneously, and fast. What I found was even more impressive than the China parallel I had in mind. Vietnam already has full, open internet. Its IFC operates a specialized court with common law principles and English-language proceedings (it even has international judges), and is closer to Dubai’s DIFC than to anything in mainland Asia, borrowing selectively from the best jurisdictions globally and building something distinctly its own.

Vietnam is borrowing selectively from the best jurisdictions globally and building something distinctly its own.

The Financial Laboratory

Gavin works at the Da Nang Fintech Lab (DFL), the government-licensed regulatory sandbox operator sitting inside VIFC-DN, Vietnam’s International Financial Centre in Da Nang. The -DN designation is to distinguish it from its sister organization in Ho Chi Minh City.

DFL holds the actual regulatory rails and the API infrastructure (banking, e-wallet integration, KYC/AML stack) that lets ventures land and operate inside the sandbox. It even provides funding to startups in the sandbox.

Gavin is also an entrepreneur and the leading case study. His team is building a crypto accounting platform that does deep transaction-level tracing across inputs and outputs, producing compliance-ready reports. That kind of tooling matters because of what’s sitting just offshore.

Bringing $220 Billion Home

Vietnam recorded over $220 billion in crypto asset flows in 2025, up 55% year-on-year. Some 21 million adults hold or have used crypto, and Chainalysis ranks the country fourth or fifth globally in adoption.

Almost all of that activity happens on offshore exchanges, outside Vietnamese regulation, untaxed and unsupervised. The government wants to bring it onshore, and it has been moving fast at the national level.

The Politburo’s Resolution 57 (December 2024) named digital transformation a national strategic priority. The Law on Digital Technology Industry followed in June 2025, passed 441 to 4, covering semiconductors, AI, and digital assets in a single framework. A five-year crypto sandbox launched in September with plans for five licensed domestic exchanges, and the Ministry of Finance began accepting applications in January 2026 with a minimum capital requirement north of $400 million. OKX and HashKey jointly invested in a Vietnamese entity earlier this month to meet the bar, though foreign ownership is capped at 49%, keeping control local. The IFC is where much of this gets built.

The Tokenization Opportunity

Once the exchanges are licensed and the capital comes onshore, the question becomes: what does it invest in? Not just trading crypto back and forth. Regulated infrastructure makes tokenization viable: representing real-world assets (bonds, equities, fund units) on-chain. A retail investor buys a fractional slice of a government bond for a few hundred dollars instead of needing a brokerage account and a high minimum. Equities trade 24/7 instead of within exchange hours. Fund units settle instantly instead of T+2. Compliance rules get embedded in the token itself, so KYC and transfer restrictions travel with the asset.

Globally, this is already moving fast. BlackRock’s BUIDL fund, a tokenized money market product issued through Securitize, holds over $2 billion in assets. JP Morgan’s Kinexys platform has completed tokenized Treasury trades with Ondo Finance and Chainlink, and bought $100 million in tokenized commercial debt from Singapore’s OCBC. The SEC issued a joint taxonomy of tokenized securities in January 2026, and US banking regulators published guidance on capital treatment the following month. McKinsey projects $2 to 4 trillion in tokenized assets by 2030. BCG puts the bull case at $16 trillion. Closer to Vietnam, Singapore’s MAS has 40-plus financial institutions in its Project Guardian tokenization program and is testing tokenized government bills for interbank lending. Thailand issued a 5 billion baht tokenized government bond for infrastructure investment.

Vietnam has something most of these jurisdictions don’t: a large, active retail base, a government writing the rules in real time, and a sandbox where new financial products can be tested under license. People are already building for it.

Cathal Donnellan, a fellow Irishman who has spent years building crypto and fintech platforms across Southeast Asia, put it simply in a conversation we had before the trip: the old speculation model is, thankfully, mostly dead, and the future is real-world assets. He’s now driving asset tokenization in Vietnam with NexStox.

Gavin’s compliance platform, already operating inside the sandbox, is built for exactly this kind of market. Tokenized assets need transaction-level tracing and compliance-ready reporting baked into the infrastructure, not bolted on after the fact. The fact that DFL and VIFC have ventures building real products inside a sandbox that barely existed a year ago suggests the model is working as designed: stand up the infrastructure, and the builders show up.

Ten years from now, the retail investor sitting in that Highlands Coffee won’t just be trading stocks and crypto on separate apps. They may hold tokenized government bonds, fractional fund units, and international securities in a single wallet, settled on-chain, regulated onshore.

A Tale of Two Cities

The IFC itself operates as a dual-hub model, formalized in Decree 323/2025. Rich McClellan, CEO of VIFC-HCMC and Vice Chair of the Da Nang hub, walked me through the structure before my trip. Rich’s side of the house focuses on traditional capital markets, corporate finance, and asset management, and he’s been landing partners fast. Nasdaq is one of seven founding members alongside VinaCapital, Sovico Group, and others. HDBank and the London Stock Exchange signed a partnership in April 2026 for cross-border fundraising, connecting Vietnamese issuers with global institutional investors. Rich’s team also launched an Asia-Pacific Aviation Financial Hub at the Singapore Airshow with Airbus and Boeing as strategic members, targeting $6.1 billion in initial capital mobilization. HCMC climbed 11 places to 84th in the GFCI 39 rankings and was named one of 15 centres likely to become more significant.

VIFC-DN handles the experimental side: fintech, digital assets, trade finance, specialized lending, and new exchange models. I only had time to visit Da Nang this trip, so that’s where most of this piece is focused, but the two hubs are complementary.

The VIFC-DN sandbox can issue licenses for up to five years through its own executive authorities, not national regulators. Ventures can test things inside Da Nang that would be blocked anywhere else in the country. The first project through the door, Basal Pay, runs a controlled USDT-to-VND conversion under a non-custodial model on a 36-month pilot. Tether signed an MoU with Da Nang’s People’s Committee in November 2025 to support blockchain-enabled governance, digital asset policy, and tokenization of real-world assets. Corporate tax inside the IFC is capped at 10% for up to 30 years, half Vietnam’s standard rate.

This follows the path Dubai’s Virtual Assets Regulatory Authority (VARA) carved starting in 2022. VARA didn’t try to squeeze crypto into existing securities or banking law. It built a new framework around what was already happening. Vietnam is applying the same logic to a much larger market.

The Industrial Base

Chips Next Door to Fintech

Right next door to DFL inside Da Nang’s IFC, VSAP Lab is building a $69 million chip packaging technology facility in Da Nang Software Park. Construction runs through Q4 2026, with capacity for 10 million semiconductor products annually and a 50-year operational license.

Chip packaging is where individual chips get assembled, stacked, and connected into finished products. VSAP Lab is targeting advanced packaging (fan-out, 2.5D, and 3D), the same technology used to build AI accelerators like NVIDIA’s H100. Vietnam already has serious semiconductor assembly investment from multinationals: Amkor is building a $1.6 billion plant in Bac Ninh, Hana Micron has committed around $930 million. But virtually all of that is conventional packaging. VSAP Lab would be the first domestically-led facility attempting advanced packaging, and advanced packaging is increasingly the bottleneck for global AI chip supply. Vietnam’s share of global assembly, test, and packaging is projected to rise from roughly 1% in 2022 to 8-9% by 2032.

The same Law on Digital Technology Industry that created the digital assets framework also covers semiconductors and AI. Da Nang is assembling financial infrastructure, semiconductor capacity, and regulatory sandboxes in the same district. That concentration is deliberate, and it’s the kind of stacking that compounds.

The Factory Floor

The IFC and the sandbox are the financial layer, and Vietnam’s industrial policy treats them as part of a larger stack. The four Politburo resolutions issued since August 2024, covering tech self-reliance, international integration, legal reform, and private sector development, are a coordinated push to move the country up value chains.

Samsung produces roughly half of all Galaxy smartphones in Vietnam, spread across factories in Bac Ninh and Thai Nguyen provinces. Intel’s largest chip assembly and test facility globally sits in Ho Chi Minh City. Vietnam’s electronics exports overtook garments as the country’s top export to the US for the first time in 2025.

Disbursed FDI hit $27.6 billion in 2025, a five-year high, with 81.7% of newly registered capital flowing into manufacturing and processing. Q1 2026 registered FDI surged 42.9% year-on-year to $15.2 billion. Singapore and China were the top two sources of new investment. China hitting number two is worth pausing on: Chinese manufacturers are doing their own China+1, moving production lines into Vietnam to diversify supply chains and hedge against trade friction.

The tariff situation almost put the kibosh on the whole plan. On April 2, 2026, the White House announced a 46% reciprocal tariff on Vietnamese goods. General Secretary To Lam called Trump directly and offered zero-for-zero tariffs. A 90-day pause followed, and negotiations are ongoing. Where the final rate lands is the single biggest near-term variable for Vietnam’s export-oriented manufacturing. But the rest of the industrial buildout (chip packaging, financial infrastructure, rail) isn’t waiting on that answer, and some of it exists precisely because the world is diversifying away from China, not toward it.

A $75 Billion Rail Network

On April 12, Vingroup’s VinSpeed broke ground on Vietnam’s first high-speed rail line, the $5.6 billion Hanoi-Quang Ninh route, 120 kilometers at 350 km/h using Siemens Velaro Novo trains, cutting travel time to 33 minutes by 2028. The line stitches together the Red River Delta’s industrial spine: Hanoi, Bac Ninh (where Samsung’s factories sit), Haiphong port, and Ha Long Bay, collapsing a 2-hour drive into a commute and opening a major tourism corridor at the same time.

It’s also explicitly designed as a pilot for the national network: Vietnam is using this line to build the operational experience before tackling the $67 billion North-South HSR approved by the National Assembly in November 2024, which is also moving into construction. Separately, in February 2025, the National Assembly approved an $8 billion rail link to China: 427 kilometers connecting the border at Lao Cai to Hanoi, Haiphong, and Ha Long City, targeting completion between 2026 and 2030. During Xi Jinping’s state visit in April 2025, the two countries agreed on feasibility studies for two additional intermodal railways, from Guangxi to Hanoi and from Shenzhen to Haiphong, with China funding the research phase.

High-speed rail restructured where capital, talent, and industry clustered inside China over the past two decades. Cities that were too far from economic centers to compete became viable manufacturing and service hubs almost overnight. Shanghai, Hangzhou, and Suzhou were always nearby, but HSR turned them into the Yangtze River Delta megaregion. The same thing happened with Shenzhen and Guangzhou in the Greater Bay Area, and Beijing and Tianjin in Jing-Jin-Ji. The same dynamic is available on the China-Vietnam corridor. A functioning HSR link between the two countries would do for cross-border economic integration what China’s domestic network did for its internal geography.

But there are limits to what government-led investment can build.

Fresh Ideas and Spoiled Food

Vietnam has gaps at every layer of the stack, and the biggest ones aren’t in regulation or capital. They’re cultural and structural. Coincidentally, conversations about agriculture showed the clearest examples of both.

The Fruit Nobody Brands

The government can build chip plants, lay rail, and write sandbox rules. What it can’t do is manufacture entrepreneurial instinct. Trung’s view on Vietnamese entrepreneurship was blunt. Most Vietnamese, he said, are raised with an employee mindset. The few who go entrepreneurial tend to chase quick cash.

His example was fruit. Take Vietnamese tropical fruit, dry it, cut it, brand it, and the price quadruples. Almost nobody does it. I pushed back: why doesn’t someone just import the processing machines from China and start capturing that margin locally?

Chinese entrepreneurs, he said, have a more scientific approach to business: doing deep market research, finding what specific areas produce what, and have decades more experience building supply chains. They pull raw inputs into China, process them domestically, and control distribution from there. Vietnamese operators trying to capture that value-add locally are competing against that experience gap. For now, most keep shipping raw product.

What Rots Before It Ships

This isn’t limited to fruit. Across the Mekong Delta’s perishables, the same gap appears at every stage. The delta produces roughly 24 million tons of rice annually; post-harvest losses from harvest to milling run 12 to 14 percent, over $600 million in lost value each year. For fruit and vegetables, losses reach 35 to 45 percent, mostly from bruising during transport and the absence of on-site cold storage. Seafood loses 15 to 20 percent of its value if it isn’t processed and frozen within hours of harvest. Nationally, food losses total 8.8 million tons a year, around $3.9 billion, roughly 12 percent of Vietnam’s agricultural GDP.

None of this is exotic to fix. Cold storage between farm and processor, basic processing capacity, a brand on the output. The gaps are real and the fixes are known. Vietnam has the volume; what it doesn’t yet have is capital and operators willing to deploy it.

Conclusion: The Concrete is Still Wet

Vietnam is not the next China. No country is. But after a week in Da Nang, I think it’s the most interesting place in Southeast Asia to be building right now. The people I met there aren’t waiting for the framework to be finished. Gavin is building compliance rails inside the sandbox, the DFL team is operating actual regulatory infrastructure, the VSAP Lab team is putting semiconductor capacity next door. They’re building alongside the framework, not after it.

None of this is guaranteed. HSR programs blow budgets, the grid hasn’t been tested at data center scale, and the sandbox is months old. But Vietnam broke ground on high-speed rail on April 12, its financial sandbox approved its first crypto conversion pilot four months ago, and FTSE Russell just confirmed the emerging market upgrade. That’s a lot of momentum landing in the same window.

What I’m Watching

Vietnam as Southeast Asia’s fintech sandbox. This is the big one. Vietnam has 21 million crypto holders, a government writing regulation around activity rather than ahead of it, and a sandbox where ventures can test products under license that would be blocked anywhere else in the country. Singapore’s framework is more mature, but Vietnam’s domestic market is twenty times larger and the cost of building there is a fraction. If the sandbox model works, and the early signs suggest it does, Vietnam could be where you build and test fintech products before scaling them across ASEAN’s 700 million people.

Institutional digital asset products. Once the exchanges are licensed and the capital comes onshore, the infrastructure is there for tokenized bonds, equities, and fund units to trade on regulated rails. The IFC’s common law court and English-language proceedings make it viable for cross-border products: a Vietnamese investor buying tokenized international bonds, or foreign capital accessing tokenized Vietnamese government debt. One interesting experiment further out: Vietnam doesn’t have freehold property. All land is leased from the state under time-bound use rights, 50 years for commercial, up to 70 for residential. Those rights are standardized, time-limited, and already in a government registry. Fractional tokenized land use rights could make one of Vietnam’s largest asset classes tradeable without requiring a change in property law.

Private infrastructure. The government is building rail, semiconductor parks, and financial architecture. What’s missing is the boring stuff in between: cold storage near farms, processing capacity near source, logistics that don’t destroy a third of the product before it reaches a buyer. None of this needs a sandbox or a license. It needs capital willing to build warehouses instead of apps.

Vietnamese brands. Vietnam exports volume. The margin goes elsewhere. Agriculture is the starkest example, but the gap applies more broadly. The entrepreneurial shift Trung described, from employee mindset to building something, is what turns a country that grows and assembles into one that sells finished products under its own name. The government can’t manufacture that. It either emerges or it doesn’t.

In closing, Ho Chi Minh declared independence from France in 1945, quoting Jefferson as he did it. Today, his bust overlooks a fintech sandbox running common law courts in English, licensing crypto exchanges, and writing the rules for tokenized securities. Eighty years from independence to a fintech sandbox with common law courts.

Not bad.