While US Is Chasing AGI, China is Building ASI (aka AI Plus)
When Alibaba CEO Eddie Wu took the stage at the company’s annual Apsara conference in Hangzhou last September, nobody expected fireworks.
The man’s known for reading from prepared statements like a high school student giving a book report on a novel they didn’t quite finish.
But this time? Wu came out swinging with four words beamed on a giant screen that would send Alibaba’s stock soaring: “Roadmap to Artificial Superintelligence.”
Wu’s 23-minute keynote explicitly invoked both AGI (artificial general intelligence) and ASI (artificial superintelligence)—making Alibaba the first established Chinese tech giant to publicly embrace these terms.
While Silicon Valley has been tossing around these concepts for years, this was different. This was China’s largest cloud company declaring that AGI isn’t the finish line—it’s just the starting point.
The ASI Declaration
Wu laid out an ambitious vision: “Achieving AGI—a system with human-level general cognitive abilities—now appears to be a certainty. However, AGI is not the endpoint of AI development; it is a brand-new starting point.
AI will not stop at AGI; it will progress toward ASI, surpassing human intelligence and capable of self-iterative evolution.”
Think about that for a moment.
While American tech companies obsess over reaching human-level AI, China’s biggest players are already planning for what comes after.
Wu described ASI as a system that could produce a generation of “super scientists” and “full-stack super engineers” capable of conquering medical challenges, inventing new materials, solving sustainable energy and climate issues, and even enabling interstellar travel. No pressure, right?
Wu outlined a three-phase roadmap toward ASI:
First, “emergent intelligence,” where AI develops reasoning by learning from humanity’s collective knowledge.
Second, “autonomous action,” where AI acquires tool use and programming abilities to assist humans—roughly where the industry stands today.
Finally, “self-iteration,” where AI connects to raw data from the physical world, learns autonomously, and ultimately surpasses humans.
As Georgetown Center researcher Irene Zhang noted, “This ASI narrative is definitely something new, especially among the biggest tech companies in China.”
And she’s right to be surprised. This isn’t just marketing fluff—it represents a fundamental shift in how China approaches the AI race.
The global AI arena has morphed into something far more complex than a simple sprint to AGI.
The big five AI hyperscalers are pouring billions into data centers and AI infrastructure, with spending expected to exceed $400 billion this year—roughly the GDP of Romania, for those keeping track at home. That’s a lot of server farms.
But here’s where it gets interesting: the competition isn’t just about throwing money at the problem anymore.
It’s about building complete ecosystems—what industry insiders call “full-stack capabilities” spanning hardware, software, and applications. Think of it as the difference between owning a fancy sports car engine versus owning the entire car factory, the supply chain, and the dealership network.
Capital spending by the top five US hyperscalers—Amazon Web Services (AWS), Microsoft, Alphabet, Meta and Oracle—rose 66% to US$211bn in 2024, with Microsoft alone committing over $105 billion in future data center deals.
Meanwhile, Jensen Huang, Nvidia’s leather-jacket-wearing CEO, predicted that ChatGPT maker OpenAI could become a “multi-trillion-dollar hyperscale company.”
That’s quite the endorsement, though Huang might be slightly biased given Nvidia’s $100 billion investment in OpenAI’s success.
Wu believes that to unlock this superintelligent future, large AI models will replace existing operating systems as the link between users, software and computational power, running on cloud computing networks like Alibaba Cloud.
It’s a vision where ASI and business strategy are completely intertwined—a full-stack approach that makes Silicon Valley’s piecemeal investments look almost quaint.
AGI vs. ASI: Why China Chose the Harder Path
Here’s where China’s strategy diverges sharply from America’s.
While Silicon Valley executives lose sleep over achieving AGI before China does (spoiler alert: it’s not happening next Tuesday), Beijing has taken a refreshingly different—and arguably more ambitious—approach.
Zhang Peng, CEO of Chinese AI startup Zhipu AI, predicts artificial superintelligence may arrive by 2030 but will likely only surpass humans in specific areas initially, rather than achieving full superintelligence across all domains.
That’s a more measured take than some of the breathless predictions coming from Silicon Valley, where OpenAI CEO Sam Altman suggested ASI could arrive before 2030, and SoftBank’s Masayoshi Son pegged it for 2035.
But China’s focus isn’t just theoretical. DeepSeek’s achievements highlight China’s broader goal to lead AI by 2030 under the “New Generation Artificial Intelligence Development Plan,” which combines state funding and private innovation in a centralized approach unlike the US decentralized model.
Instead of getting “AGI-pilled”—as Princeton researcher Kyle Chan puts it—Chinese policymakers see AI as something more pragmatic: a turbocharger for existing industries.
China’s strategy, dubbed “AI Plus,” focuses on integrating artificial intelligence with the country’s massive manufacturing and industrial sectors.
And the results? They’re pretty spectacular.
China showcased more than 150 humanoid robots at the World Artificial Intelligence Conference (WAIC) 2025 in Shanghai, demonstrating that the country isn’t just playing catch-up—it’s running a different race entirely.
China now leads the world in industrial robot installations, with a record 2.027 million active robots, according to the International Federation of Robotics.
In March, Beijing made a bold move by designating “embodied intelligence”—AI integrated into physical machines—as a key future industry.
In January 2025, China launched an $8.2 billion National AI Industry Investment Fund, while its broader $138 billion National Venture Capital Guidance Fund targets AI-related fields, including robotics and embodied intelligence.
While America debates how many billions to spend on the next language model, China’s robots are already assembling cars, sorting packages, and conducting power inspections.
This is where China’s ASI vision gets tangible—it’s not just about creating smarter software, but about building physical systems that can self-iterate and improve.
China’s embodied intelligence market is expected to reach 5.295 billion yuan by 2025, and the momentum is accelerating.
Companies like Shanghai-based AgiBot and Hangzhou’s Unitree Robotics are landing orders from state-owned firms, while nearly half of China’s AI fundraising this year has flowed toward embodied intelligence startups.
In the first half of 2025, total financing in the humanoid robotics sector reached a record high, exceeding 10 billion RMB ($1.4 billion). And it’s not just startups getting in on the action—tech giants like Huawei, ByteDance, and even food delivery platform Meituan are developing their own robotics solutions.
Because apparently, when you’ve mastered getting dumplings delivered in 30 minutes, building a humanoid robot is the logical next step.
As Martin Casado and Anne Neuberger from Silicon Valley venture capital firm Andreessen Horowitz bluntly put it: “China is running away with the hard-power part of AI—robotics.”
They predict a future where “intelligence embedded in the physical world” culminates in generalist robots performing tasks across manufacturing, services, and defense.
And the country betting on that future? Not the US.
If you needed more evidence that China’s AI strategy is working—and that the path to ASI doesn’t require unlimited budgets—look no further than DeepSeek.
This Hangzhou-based company, founded in 2023 and backed by Chinese hedge fund High Flyer, pulled off something remarkable in January 2025: it released an open-source reasoning model that matched OpenAI’s performance at a fraction of the cost.
By 27 January, DeepSeek surpassed ChatGPT as the most downloaded freeware app on the iOS App Store in the United States, triggering an 18% drop in Nvidia’s share price.
The company claims it trained its V3 model for just $6 million—compared to over $100 million for OpenAI’s GPT-4—and using approximately one-tenth the computing power of Meta’s comparable model.
The impact has been staggering. A US government report noted that downloads of DeepSeek models on the developer platform Hugging Face surged nearly 1,000% since January.
Over 700 community models based on DeepSeek have appeared on Hugging Face, amassing more than 5 million downloads by early 2025.
Here’s the kicker: scarcity pushed Chinese engineers to reinvent training methods and hardware stacks, while US labs—flush with top-tier GPUs—lacked similar constraints.
It turns out that having unlimited resources might not be the advantage everyone assumed. Sometimes constraints breed innovation—who knew?
Even OpenAI swiftly responded with its first open model in six years, but according to open-source AI expert Nathan Lambert, the gap may already be too wide to bridge.
“Qwen alone is roughly matching the entire American open model ecosystem today,” Lambert said at a recent industry conference.
That’s Alibaba’s open-source model, by the way—you know, the one Eddie Wu was talking about when he outlined his ASI roadmap.
America’s Spending Spree vs. China’s Ecosystem
Now, let’s talk numbers, because they’re both impressive and slightly terrifying.
US hyperscalers are expected to spend over $300 billion in 2025, up 25% from 2024 and nearly double 2023, with Amazon and Microsoft leading the charge at $96.4 billion and $89.9 billion respectively.
OpenAI alone has accumulated computing deals worth at least $1 trillion this year, including the ambitious $500 billion Stargate Project.
Alibaba, by comparison, announced a RMB 380 billion (US$53 billion) three-year investment plan in AI infrastructure.
Wu added that by 2032, compared with 2022, Alibaba Cloud’s global data center energy consumption will grow tenfold to meet the arrival of the ASI era. That’s a serious commitment, but still dwarfed by annual US spending.
But here’s the catch: China’s hyperscalers don’t need to match these astronomical figures to compete effectively.
The American “big three”—AWS, Microsoft Azure, and Google Cloud—command about 63% of the $900 billion global cloud computing market.
But in China, Alibaba Cloud leads with 36% of the domestic market, and that’s enough to build a thriving ecosystem aimed at ASI development.
The Chinese cloud market is vast enough to support multiple players—Alibaba, Huawei, Baidu, and ByteDance—each finding different opportunities while collectively pushing toward superintelligence.
The Open-Source Advantage
Here’s where China’s ASI strategy gets really clever.
While American companies guard their AI models like Colonel Sanders protects his secret recipe, Chinese tech giants have embraced open-source with the enthusiasm of a kid in a candy store.
Alibaba’s Qwen models, DeepSeek’s various offerings, and Huawei’s ecosystem are all freely available for developers to download, modify, and build upon.
By 2024, models like DeepSeek-V3 were outperforming Meta’s Llama 3.1 and Anthropic’s Claude 3.5 Sonnet on common language and reasoning benchmarks, thanks to architectural innovations such as Mixture-of-Experts and Multi-Head Latent Attention. Yeah, I know - nerd talk!
And they’re doing it in the open, where anyone can examine, improve, and build upon the work.
This open approach is creating what Zhao calls “a self-sufficient AI stack, free from Nvidia’s influence.”
When DeepSeek introduced its new programming language TileLang, Chinese chipmakers Hygon and Cambricon quickly announced “day zero” chip support, while Huawei developed core operators for the new model.
The depth of China’s open-source ecosystem spans from Big Tech giants such as Huawei Technologies and ByteDance to unexpected developers like food delivery giant Meituan and Alibaba’s fintech affiliate Ant Group, which open-sourced a 1 trillion-parameter model this year.
This collaborative approach suggests China believes the path to ASI runs through collective advancement rather than proprietary breakthroughs.
Different Races, Different Finish Lines
So, who’s ahead in this race?
Well, that depends on how you define “winning”—and whether you’re racing toward AGI or ASI.
If the goal is achieving AGI—that mythical point where AI matches human intelligence across all tasks—then the jury’s still out. Some US executives believe they’re in a sprint to reach AGI before China, leading to what Princeton’s Chan describes as
“an excessive focus on scaling computing resources and restricting Chinese access to advanced semiconductors, at the expense of developing the full US stack.”
But if the goal is ASI—systems that can self-iterate and continuously improve beyond human capabilities—China’s full-stack, applications-focused approach might have the edge.
Morgan Stanley estimates that China’s total addressable market for robots will double to US$108 billion by 2028 from US$47 billion in 2024, with hyper-growth concentrated in collaborative robots (46% CAGR), mobile robots (35% CAGR), and service robots (25% CAGR).
Alibaba chairman Joe Tsai frames it perfectly: The winner in AI shouldn’t be defined by “who comes up with the strongest AI model,” but by “who can adopt it faster.”
And in terms of actual application and people benefiting from AI—the kind of widespread deployment that could lead to self-iterating systems—China has made significant strides.
In the first half of 2025, industrial robot production in China reached 370,000 units while service robot output hit 8.82 million units, representing year-on-year growth of 35.6 percent and 25.5 percent, respectively.
Wu’s vision goes even further: “With ASI surpassing human intelligence, humans and AI will collaborate in entirely new ways. In the future, every household, factory, and company may have numerous agents and robots working 24/7, potentially requiring hundreds of GPU chips per person.”
That’s not AGI—that’s a superintelligent infrastructure woven into the fabric of society.
The Cost Advantage - China
Here’s a fun fact that keeps Silicon Valley executives up at night: It costs 2.2 times more in the US than in China to manufacture a robotic arm of similar specifications.
China’s Unitree Go2 quadruped robot starts at 1/54 the price of Boston Dynamics’ similar Spot robot. That’s not a typo. It is one fifty-fourth. There is no way, US can beat that.
This cost advantage stems from China’s well-established manufacturing system, strong industrial support capabilities, and supply chain integration.
While American companies can certainly match the technical sophistication, they can’t match the economics—at least not yet. And in a race toward ASI where you need millions of physical robots collecting real-world data for self-iteration, economics matter. A lot.
Fueling the ASI Future
Wu introduced a provocative concept: “Tokens are the electricity of the AI world,” suggesting that in the ASI era, AI agents will consume tokens like households consume electricity.
This isn’t just metaphorical—it’s a complete reconceptualization of computing infrastructure.
Wu predicted that large AI models will replace existing operating systems as the link between users, software and computational power, with natural language becoming the new programming language, agents becoming the new software, and context becoming the new memory. In this vision, reaching ASI requires not just smarter models, but an entirely new computing paradigm.
China is developing a National Integrated Computing Network that will integrate private and public cloud computing resources into a single nationwide platform optimized for AI workloads, with the “Eastern Data, Western Computing” initiative building eight national computing hubs.
By June 2024, China had 246 EFLOP/s of total compute capacity and aims to reach 300 EFLOP/s by 2025. That’s infrastructure built explicitly for the ASI era.
The Future: Five or Six Hyperscalers
At the Apsara conference, Eddie Wu made a bold prediction: there would only be “five or six hyperscalers globally” in the future, implying that Alibaba would be one of them.
Given current trends and China’s explicit focus on ASI rather than just AGI, that prediction doesn’t seem far-fetched.
What’s clear is that the AI race has evolved beyond a simple competition of computing power or model performance. It’s become a contest of ecosystems, adoption strategies, and full-stack capabilities—with China explicitly aiming for the ultimate prize of artificial superintelligence.
The most valuable tech companies have emerged as early winners in the AI era, with the five biggest companies accounting for more than 70% of the total market value of the top 20, up from 65% last year. But the Chinese challengers are proving that massive spending alone doesn’t guarantee victory in the race to ASI.
The lesson from China’s AI strategy is surprisingly simple: it takes more than chips—and more than AGI—to win the AI race.
Wu declared: “AGI will not only amplify human intelligence but also unlock human potential, paving the way for the arrival of Artificial Superintelligence. ASI will drive exponential technological leaps, carrying us into an unprecedented age of intelligence.”
While American companies pour hundreds of billions into developing ever-more-powerful language models and competing for scarce GPU supplies, China is quietly building a comprehensive AI ecosystem that spans from hardware to applications, from research labs to factory floors—all explicitly aimed at achieving artificial superintelligence that can self-iterate and evolve.
The US still holds advantages in foundational AI research, semiconductor design, and venture capital. But China’s focus on practical applications, open-source collaboration, manufacturing integration, and explicit ASI development represents a fundamentally different—and potentially more sustainable—approach to AI development.
As one researcher noted, the concept of superintelligence has long guided prominent American AI companies, with OpenAI releasing an article on safe superintelligent AI development in May 2023 stating: “Now is a good time to start thinking about the governance of superintelligence—future AI systems dramatically more capable than even AGI.”
The difference? China isn’t just thinking about it—they’re building it.
Alibaba’s commitment to full-stack development—from cloud infrastructure to open-source models to physical robotics—embodies China’s broader strategy: winning not by outspending Silicon Valley in the race to AGI, but by building a more complete, integrated, and practical path to artificial superintelligence.
In the end, the AI race might not be won by whoever reaches AGI first or spends the most.
It might be won by whoever builds the most comprehensive infrastructure for ASI—systems that can self-iterate, continuously improve, and seamlessly integrate with the physical world.
And on that metric, China’s lesson is clear: forget the hype about AGI, build the full stack for ASI, and let the robots (and the self-improving AI systems) do the heavy lifting.
Now, if you’ll excuse me, I need to go check if my food delivery app has upgraded to humanoid robot couriers powered by self-iterating superintelligent systems yet.
Given current trends, it’s probably only a matter of time.
And who knows? Maybe those robots will be smarter than us by 2030. At least they’ll probably be better at remembering to include the extra soy sauce.
About the author: Rupesh Bhambwani is a technology enthusiast specializing in the broad technology industry dynamics and international technology policy.
When not obsessing over nanometer-scale transistors, energy requirements of AI models, real-world impacts of the AI revolution and staring at the stars, he can be found trying to explain to his relatives why their smartphones are actually miracles of modern engineering, usually to limited success.