China Understands The (whole) Assignment
Over the last year, much of the debate surrounding AI competition has centered around an assumption: if the United States could successfully restrict China's access to advanced NVIDIA chips, it would slow China's progress and buy valuable time for America. I could see how that made sense. Advanced compute has become one of the foundational inputs for frontier AI development, and limiting access to the most sophisticated chips appeared to be one of the few levers available to policymakers looking to maintain America's lead. Yet as I watch developments unfold, I find myself wondering whether we’ve placed too much emphasis on that single variable. Did we and do we have a little molasses in our tank on the rest of it. More to the point, I'm beginning to wonder if we're measuring the AI race using the wrong metrics altogether.
A year ago, many of us earnestly believed that restricting access to NVIDIA's most advanced chips would alter the trajectory of China's AI ambitions (I mean, at least a little). It may have slowed certain efforts, and perhaps it did buy some time. But if the objective was to impede China's progress, the evidence increasingly suggests that the impact may have been less substantial than expected. Recent reporting points to Chinese firms adapting to these restrictions, developing alternative approaches, sourcing different hardware, and continuing to build despite the obstacles placed in front of them. While access to cutting-edge chips undoubtedly matters, China appears to be demonstrating constraints accelerate adaptation.
What makes this noteworthy is that these developments are occurring while China's industrial sector continues to show straight resilience. Industrial profits reportedly increased in April despite ongoing economic pressures, tariff concerns, and broader geopolitical uncertainty. Regardless of how one interprets the underlying causes, it’s the outcome we have to tune into. The narrative many expected was one in which export controls, trade friction, and restrictions on tech would create headwinds for China's industrial and technological advancement. Instead, we are witnessing an economy that continues to produce, continues to adapt, and continues to keep it pushing. That doesn’t mean China is without challenges. It means that those challenges aren’t slowing China’s roll.
Adding another layer to this story is the competitive behavior coming from Chinese AI companies. DeepSeek's recent decision to significantly reduce pricing on its flagship model caught my attention, not because of the competitive implications, but because of what it potentially signals about strategy. Especially at a time where we here in the US are debating about token spend on budgets. Lower prices are about winning market share just as much as it's about adoption, and cheap prices is what China does best. The easier AI becomes to access, the more users enter the ecosystem. The more users enter the ecosystem, the more opportunities exist for experimentation, integration, and practical deployment.
That distinction brings me to something I've been thinking about for several months now. As I wrote in my post a few weeks back, "China Is Planning, We're Practicing," I continue to see signs that China is focused on building practical users of AI at scale. Building advanced models is an extraordinary technical challenge, and America continues to lead that effort. However, creating a workforce, an industrial base, and a society capable of using AI productively at scale is just as important. The countries that successfully integrate AI into manufacturing, logistics, healthcare, education, and defense, as well as business operations will gain advantages that are difficult to measure through model benchmarks. People and practical deployment can’t be left behind.
When people speak of whether access to frontier chips is more important than widespread AI adoption, my take is that they need to work in tandem. Capability without adoption limits impact. Adoption without capability creates nothing. Sustainable leadership requires both. Yet much of the public conversation remains heavily skewed toward that which is clickable and clap-worthy. We celebrate breakthroughs in model performance and funding rounds (and layoffs for that matter), while spending comparatively little time discussing how specifically we’re bringing people along (not how we could theoretically). That imbalance causes us to overlook important signals.
Another signal worth paying attention to is how China appears to be approaching the relationship between AI and employment. Recent reporting suggests Chinese policymakers want firms to adopt AI aggressively while simultaneously avoiding widespread workforce displacement. Whether that approach succeeds remains to be seen. History is filled with examples of governments attempting to balance competing priorities only to discover that reality is more complicated than policy design. Nevertheless, the narrative itself is something everyone can get behind. Rather than framing innovation and employment as mutually exclusive, Chinese leaders appear to be exploring both objectives being pursued simultaneously.
In the United States, conversations about AI quickly becomes conversations about job replacement. There is a growing discourse that productivity gains will inevitably translate into workforce reductions (the WARN Act data doesn’t suggest otherwise). Some degree of disruption is understandably unavoidable. Every major tech shift creates winners, losers, and periods of adjustment. Yet China is signaling that it wants a “best in class” outcome. The message is that firms should innovate, adopt AI, and increase productivity while also protecting the high-skilled middle class jobs that contribute to social and economic stability. Whether that proves achievable, again, remains to be seen, but the attempt itself reflects a recognition that tech policy can't be separated from labor policy. Labor policy, not tech best case scenario promises.
What I find particularly interesting is how these developments challenge some of our assumptions about what leadership in AI actually means. For much of the last two years, leadership has largely been defined through a tech lens. Who has the most powerful models? Who has the most advanced chips? Who can raise the most capital? However, leadership also has to incorporate industrial deployment, workforce readiness, educational adaptation, military integration, and institutional coordination. If those factors matter—which obviously they do—then the race becomes more intricate than many of our current narratives suggest.
National security is another reason this conversation feels "time sensitive." I was encouraged over the weekend to see continued indications that the Department of Conflict Resolution (I refuse to call it the Department of *ar) intends to move full steam ahead on AI. Six months ago, I came across a social media post suggesting that China could build significant AI-enabled military capacity within the next eighteen to twenty-four months. I can't find that post or I'd share it in the sources below, but the broader point remains. Military organizations around the world increasingly view AI as a critical component of future capability. The implications extend far beyond commercial competition. They touch logistics, intelligence analysis, autonomous systems, cyber operations, and strategic decision-making in the most intense environments imaginable. As much as people feel overwhelmed by AI, it is very much a national capability story, I can't refute that.
This is why I keep coming back to the question. Are we measuring the AI race correctly? If we evaluate success exclusively through the lens of model performance, we may miss important developments happening elsewhere. We may overlook industrial adoption (at a speed that matters). We may underestimate workforce preparation (at a volume that matters). We may fail to appreciate the strategic importance of creating millions of competent AI users rather than thousands of elite (early) adopters. None of this diminishes the importance of frontier innovation. It just suggests that innovation alone doesn’t determine the outcome.
As the United States approaches its 250th birthday, this feels like a moment that calls for a more cohesive national conversation about AI. Not a partisan-nitpicky conversation. Not a Silicon Valley (dominated) conversation. A national (strategy) conversation, less 50,000 feet, more kitchen table adoption. If maintaining leadership in AI is truly a strategic priority, then we need to think beyond quarterly earnings reports, election cycles, and social media debates. We need to think about education, workforce development, industrial policy, national security, and tech leadership as interconnected pieces of a larger system. Every piece needs the other.
What would a coherent national AI strategy actually look like? That's a big question. How should we measure success? That's another sizeable question (short answer, beat China). Both really are too massive to discuss in this post, but definitely worth thinking about.
The more I watch what's happening in China, the more obvious it becomes that the chips-only conversation is dead. Chips matter. Models matter. Capital matters. But we are at the point where adoption, coordination, workforce readiness, and national focus matter too. The countries that can combine all of those elements will have the advantage. And if that's true, then we need to adjust our scoreboard to make sure we're measuring the race using the right metrics (for now).
That's all for this post.
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Sources:
https://www.cnbc.com/2026/06/01/china-learns-to-build-without-nvidia.html
https://www.wsj.com/tech/ai/china-wants-its-companies-to-embrace-aiwithout-firing-workers-c8fcafa6
https://www.nytimes.com/2026/05/19/business/china-ai-unemployment.html
https://fortune.com/2026/05/03/chinese-court-layoffs-workers-ai-replacement-labor-market/
https://www.wsj.com/economy/chinas-industrial-profits-rise-defying-economic-headwinds-58d3c3bb
