AI Strategy Convergence: US and China Are Meeting in the Middle
A balance of profitability and technological pursuit; capital pressure and policy demand
China’s and the U.S.’ AI strategies are converging.
Only six to twelve months ago, I was writing about two fundamentally different playbooks: American labs chasing AGI and Chinese firms threading “good-enough” AI into everything from search and shopping to education and factory lines. Now that narrative has finally gone mainstream, I feel like the vibe has actually started to shift.
There’s a genuine inching toward each other moment. U.S. AI entrepreneurs are realizing the research team can keep pushing the frontiers, as they should, but capital still asks the fundamental questions: What’s the product? Where’s the value to customers and society? What’s the ROI? Meanwhile, Chinese companies are discovering that to improve the user experience, they can’t skip the frontier; they need a top-tier model to power the next tier of apps and compete for AI dominance.
“We’re seeing AI move from labs to living rooms, with real-world adoption accelerating as tools become intuitive for everyday users.” — Sam Altman, Oct 22, Bloomberg panel.
But why the convergence now?
Diffusion as an economic driver is becoming more of a reality. ROI pressure and FOMO are real.
Blurred lines of enterprise vs consumer playbook in the AI era.
Policy pressure in both systems to turn AI into a real growth/ diplomacy tool. Mandate is real.
Physical constraint started differently, but solutions look the same.
I. Frontier vs. Diffusion: The Balance
For years, the U.S. AI labs defaulted to “frontier first,” with business coming second, primarily focused on winning in research, and assuming the rest would follow. Silicon Valley was also dominated mainly by this pursuit of AGI, and it seemed that achieving whatever that may entail would then end all competition or higher calling. But suddenly, there appears to have been a shift in mentality.
A year ago, the U.S. was indeed laser-focused on AGI moonshots (think OpenAI’s early GPT scaling bets). Meanwhile, China prioritized embedding AI into manufacturing, e-commerce, education, healthcare, and more.
And previously, Chinese big tech, especially, has treated AI as a market share and distribution game from day one, as I wrote about for Azeem Azhar’s Exponential View.
We saw that through ByteDance Doubao’s hundreds of millions of MAUs (April 2025), and the never-ending number of consumer apps popping up and crowding the space, according to the national registry.
And of them all, I thought the most brilliant consumer strategy was demonstrated by Tencent’s integration of DeepSeek R1 at launch (no spending on courting users over to a new app like ByteDance). The thinking was evident: if 1-plus-billion users can invoke competent AI where they already type, search, pay, and share, “diffusion” stops being a strategy slide and becomes daily behavior.
The vibe shift in the U.S.
But things have changed recently. It’s not the ambition but finally separating scientific pursuits from business objectives. Investors, business leaders, and founders are now discussing how to better integrate AI into the real economy and create AI products that can capture a larger market share. Something that sounds familiar.
Now, everyone I meet (or hear on podcasts/ interviews) in the U.S. is talking about consumer products and asking, sometimes with a sideways glance at China, why diffusion seems faster there.
Dan Wang’s recent book, which succinctly weaves in personal experiences with interviews and rigorous research to explain the meticulous planning that went into China’s economic rise. Particularly zooming in on how pragmatism underlies all decisions, even if some policies are now seen as mistakes, while on the other hand, America, largely led by lawyers over the last few decades, has built up walls internally and externally that have hindered the ability to build. Though too many laws have stifled domestic production, they have also built up a society that values copyright, ideas, and empowered innovation from zero to one. He uses this catchy framework that China is built by engineers, and America is fenced by lawyers, and this has prompted conversations across Silicon Valley and Washington, DC. Zooming in on a few questions: how did China plan its economy, and more importantly, how does it plan to turn AI into a core economic driver?
Then we can see how the vibe shift is also evident among industry leaders. From OpenAI’s Sam Altman’s “labs to living rooms” to Nvidia’s Jensen Huang’s “agentic AI markets,” these comments in recent weeks seem to implicitly nod to China’s diffusion playbook, where we saw “AI” integrated across sectors rapidly. America is catching up on China’s playbook.
And lastly, capital is demanding ROI. Amid rising concerns about frothy valuations and AI bubbles, the core of this fear is that there hasn’t been enough proof that AI is profitable. As much as I believe we need to give companies time to figure out clear business models, it hasn’t even been three years since the ChatGPT moment, yet the pressure to deliver in the real economy is real.
China’s R&D focus
Meanwhile, on the other side of the world, vibes have shifted, too. Moonshot CEO Yang Zhilin’s dreams of landing on the moon (AGI), or DeepSeek Liang Wenfeng’s open-source dreams of propelling China’s AI capabilities to the forefront, have made them national heroes. Even big tech players like Alibaba CEO Eddie Wu’s ASI roadmap and the grand ambitions announced at the Apsara Conference echo America’s AGI ambitions.
In recent months, investor lenses have shifted. Instead, it has been the combination of continued frontier pushes —think Moonshot’s K2 or DeepSeek’s OCR engineering breakthroughs —and Tencent’s quiet gray-scale test of AI Search inside WeChat that has really been capturing eyeballs and headlines.
And just announced in the latest Five-Year Plan that concluded last week, China will aim to “greatly increase” its capacity for self-reliance and strength in science and technology over the next five years, according to Bloomberg’s report.
The announced policies mean that public and private sectors will work together to invest trillions of yuan in science and technology development over the coming years. Acknowledging that to continue to be relevant (and lead) in the long haul, it won't be consumer apps that help the economy leapfrog, nor will it be enough to rely on just engineering tricks. Frontier science will still pave the way for the future of further tech development, so now, the focus in China is on R&D and tech self-sufficiency.
The shared realization is simple: you need frontier models to build the best products, and you need distribution and workflow to turn them into an economic engine. This is now a balancing act—technological advance and commercialization, not one or the other.
The U.S. is no longer ONLY obsessing over the most high-performance model, nor is China solely focused on capturing the most market share. The strategy is coming together to balance technological advancement and tech diffusion while finding the most cost-effective way.
II. Blurred Lines: Enterprise vs. Consumer
Nearly a year ago, I suspected enterprise adoption in China would lag because of the country’s internet-era SaaS legacy.
But a new vertical has clearly formed: as the relatively new knowledge-worker class has risen in China with the internet, the willingness to pay has increased, especially among prosumers and enterprises. If a software developer’s salary is 200k USD a year and he’s asked to pay 200 dollars a month, but that can increase his productivity by 2-3 fold, I dont think he’s holding back.
And suppose that thinking is applied to the enterprise level, well, it’s proven that whether it's ByteDance or Alibaba, they’re happily shelling out money for tools like Curser to promote productivity and capabilities.
Consider the underlying infrastructure. In the 2000s, enterprise SaaS struggled to scale in China largely because mature cloud and network foundations weren’t in place. That constraint has mostly disappeared. Cloud platforms are widespread, 5G is pervasive, power and data-center capacity have expanded, and domestic compute has improved. Practically, this means many companies already have data in the cloud, security and compliance workflows are established, and integrating an AI tool or agent into existing systems will be far more straightforward than it was a decade ago for a company’s notebook/ pen system to switch/adopt Salesforce.
And companies like Alibaba are perfectly positioned to lean into this opportunity in the AI era. With its full-stack “one-dragon” offering, it is tapping into end-to-end: Qoder for copilot coding, Accio for ads and inventory, DingTalk for AI chat and workflows, Qwen for models and APIs, and cloud infrastructure and services for developers.
What I’m realizing is that the old internet-era split doesn’t map cleanly to AI. double-clicking on this.
In the U.S., SaaS is typically monetized through the enterprise, and thus, a year ago, I assumed U.S. AI firms would do the same. Earning primarily from managed inference on hyperscale clouds and from copilots inside productivity suites with strong margins. In China, I expected monetization to flow through consumer distribution, with AI lifting ads and services inside super-apps. That neat divide is fading as both markets cross over on go-to-market and revenue.
In 2025, that so-called consumer/ enterprise wall came down. Microsoft put Copilot squarely into consumer subscriptions—Word, Excel, PowerPoint, Outlook—collapsing the gap between “work” and “home” AI and turning Office into a mainstream diffusion channel. Then it pushed “agentic” experiences into Windows itself, so the assistant shows up at the moment of intent.
Meanwhile, OpenAI moved harder into consumer with Sora and the app-within-app launch, essentially building a WeChat-Mini-Program-style interface that can capture users now and serve as the entry point for future agents. It’s making frontier capability productized at the moment of user intent, not parked behind an API for specialists.
OS Strategy
Taking a step back, you can also think of it this way. The overall vision is converging. Once you accept that the intent layer matters, distribution is key; both sides are playing the OS game, and it doesn’t matter if that’s through enterprise or consumer. Think of OpenAI (ChatGPT) as the entry point and default browser for AI agents; it’s not that different from what WeChat is doing, which is the entry point for all mini programs and eventually AI tools. The goal now is to determine who can become the next Google/Chrome or macOS/iOS.
Ultimately, everyone is realizing that it’s not a consumer vs enterprise game anymore. Rather, consumer-grade distribution and enterprise-grade monetization can codevelop. What we’ve seen over the last few months is that the U.S. is becoming more consumer-focused, and China is no longer neglecting enterprise potential. They’re both just trying to capture it all.
III. Hybrid of Open-Weight & Proprietary
A third axis of convergence is what some say is philosophical: openness. (I’ve argued that it’s both personal and pragmatic)
China’s momentum in open-weight models that are efficient, “good-enough,” and cheap to run has lowered the cost curve and widened access for companies worldwide. The U.S. big tech and leading labs still lean proprietary at the frontier, but have become increasingly pragmatic about open options for reach and total cost. Meta’s Llama family is the emblem of that hybrid logic: source-available models to seed ecosystems, lean mobile/edge variants for on-device work, and larger models when the task truly demands it.
The thing is, even American startups are choosing Qwen over others because of its efficiency and the reality of price considerations. Famously, even Brian Chesky, co-founder and CEO of Airbnb, said that the firm “relies heavily” on Alibaba’s Qwen models to power its AI-driven customer service agent, and the models are “very good” and “also fast and cheap”, according to a Bloomberg report.
The debate has moved from ideology to unit economics.
Companies are thinking of cost. So are investors.
While private investment remains concentrated in the U.S., business usage of AI accelerated materially worldwide in 2024 and 2025, according to Stanford’s report. Stanford’s AI Index pegs it at roughly 78% of organizations using AI in 2024, up from 55% the year before—evidence that diffusion is no longer a vibes story. The flip side: value capture remains uneven, which is why both ecosystems are steering toward agentic workflows and unit economics discipline.
Policy push
Then there’s also a policy practicality you can feel on both sides. Open models drive adoption, especially for cash-constrained firms and countries. While AI Czar David Sacks is fanning the flames by saying that U.S. export controls must curb the deployment of Chinese LLMs in the global south, he’s overlooking one critical point: the Alibaba and DeepSeek models, which are widely popular, are open source.
Tech diplomacy is another reason for the push to open-source models. You heard versions of this at WAIC from Chinese tech diplomats, and you have heard this from D.C. AI policymakers: open source/ weight will accelerate diffusion and ecosystem capacity. Whether companies want to do so is another question, or a hybrid model will likely triumph - and be the best model for all.
IV. Physical World: Same Constraints, Same Playbook
The convergence is spilling into the physical world. AI has become a physical problem. The question is not only how many GPUs, but where to put the capacity, and how to power and cool them.
To note, though the two have the same constraints, they had different starting conditions. Nevertheless, on both sides of the Pacific, the bottlenecks are somewhat the same: power, land, grid interconnects, cooling, and time-to-permit.
Once inference shows up as a real cost, operators everywhere are focused on the same three levers: the price of electricity, the distance to the user, and where data is allowed to live.
Sovereign AI
Thus, what we’re seeing is that the policy responses on both sides are looking increasingly similar. Data centers are treated like strategic infrastructure. Governments are speeding permits, transmission, and clean-power hookups because compute capacity and grid capacity now move together. The industry narrative also converges. And the goal for both is to have sufficient sovereign capacity, meaning owning enough compute and power close to your data and users to handle demand spikes.
Physical integration follows the same pattern. Models are moving closer to users and machines to cut latency, reduce cost, and protect privacy. Phones and PCs run lightweight models with cloud backup. Vehicles, stores, warehouses, and robots run efficient models on or near the device and call bigger models only when needed. The result is a common architecture.
Heavy training lives in large campuses near abundant, low-cost power.
Latency-sensitive inference lives at the edge, closer to users and machines.
Small, frequent tasks live on devices in phones, PCs, cars, and robots.
What I’m saying is that the two may not necessarily mean identical conditions/ starting points. But the constraints match, the strategy is looking to match. Optimize efficiency per watt. Place each workload where it fits, based on latency and data sensitivity. Drive down cost per task with smaller models and routing. Build enough sovereign capacity to ride demand spikes without service risk. End of day, it’s all same, same, but (slightly) different.
Can’t Predict the Future, But Things To Look Out For
Expect SDK-to-store flywheels. Suppose OpenAI’s Apps SDK grows into discovery and monetization, and similar directories appear inside enterprise suites. In that case, the U.S. distribution story begins to look a lot like Mini Programs—intent surfaces that can discover, invoke, and compose capabilities in one place.
Assume “AI factory” decisions will be power decisions. Watch where new builds land, how quickly interconnect queues clear, and the cost of megawatt-hours over your planning horizon. If inference is becoming a material share of your COGS, power procurement is now a first-order lever.
Budget for layered inference: device → edge → cloud. On-device and near-edge will take a rising share of requests for latency, cost, and privacy. Cost-efficiency takes front seat. Reserve frontier-class calls for the small set of tasks where they change outcomes. (Apple)
Distribution as a moat, not an afterthought. Whether your surface is OS-native, super-app-native, or suite-native, the winner’s pattern is the same: reduce distance between intent and action—and own the moment where the user decides. Big tech with existing distribution continues to have huge advantage for general use AI.
Relevant Reads
Divergent Approaches to AI Commercialization: Comparing the U.S. and China
OpenAI Is Becoming an Operating System: The WeChat Mini Program Blueprint
Integration at the Speed of Light: Tencent’s WeChat Embeds DeepSeek
Why Tencent’s Integration of DeepSeek Into its AI App is a BIG DEAL
DeepSeek’s Ascent Reshapes China’s Burgeoning AI App Landscape
DeepSeek’s Open Source Week: Sharing the Future of AI Efficiency
DeepSeek V3 puts China AI on the global map: consumer use and capital expenditure implications
Everybody Losing Sleep Over DeepSeek: Industry Implications to LLMs and AI Infrastructure
The Jevons Paradox in AI Infrastructure: DeepSeek Efficiency Breakthroughs to Drive Energy Demand
Hey, great read as always. This convergence you're describing feels so real. It’s a truly significant shift, watching both sides adjust their playbooks. The market and ROI pressure are realy shaping the next phase of AI. It makes you introspect about the future of tech.