Kimi K3与DeepSeek迫使行业重新审视AI竞赛规则：DeepSeek挑战了前沿AI必须依赖无限算力的假设，Kimi K3则证明顶尖AI能力并非美国闭源实验室独有。Kimi K3发布不到48小时即因需求过大暂停新订阅，暴露出从模型能力到全球规模部署的GPU瓶颈。中国AI公司已不再是追随者，但真正的挑战在于如何将前沿模型转化为可全球扩展的产品。
http://x.com/i/article/2081698130408710144
Weekly Dose of China Tech #04
China's Two AI Shocks， Inside DeepSeek's Mind， AI Infrastructure Bottlenecks， Token Factories， AI Agents + One More Thing
Hi friends，
Welcome back.
I have been thinking about a notable transition in China's AI race this week.
For the past two years， the biggest question was whether Chinese AI companies could build models good enough to compete with Silicon Valley. That question is starting to look different. Kimi K3 became one of the most talked-about AI releases of the year. Chinese models now dominate OpenRouter's global usage rankings. DeepSeek has already changed how the industry thinks about open-source AI. The debate is no longer only about capability. It is about scale.
But scale is exactly where the cracks appeared. Less than 48 hours after launch， Moonshot paused new Kimi K3 subscriptions because demand was overwhelming its GPU capacity. Building a frontier model was the first challenge. Turning it into a global-scale product is the next one. And that requires something very different： the GPUs， data centers， optical networks， and hidden supply chains that make AI possible at volume.
And underneath all of this is a bigger question： if China's AI companies can no longer be dismissed as followers， what kind of companies are they trying to become？
Let's get into it.
This Week's Features…
Why Kimi K3 And DeepSeek Are Keeping Silicon Valley Up at Night ？
For months， the AI race looked easy to understand. American companies built the frontier models， Chinese companies followed， and the competition was about who had more GPUs， bigger training runs， and deeper pockets.
Kimi K3与DeepSeek迫使行业重新审视AI竞赛规则：DeepSeek挑战了前沿AI必须依赖无限算力的假设，Kimi K3则证明顶尖AI能力并非美国闭源实验室独有。Kimi K3发布不到48小时即因需求过大暂停新订阅，暴露出从模型能力到全球规模部署的GPU瓶颈。中国AI公司已不再是追随者，但真正的挑战在于如何将前沿模型转化为可全球扩展的产品。
http://x.com/i/article/2081698130408710144
Weekly Dose of China Tech #04
China's Two AI Shocks， Inside DeepSeek's Mind， AI Infrastructure Bottlenecks， Token Factories， AI Agents + One More Thing
Hi friends，
Welcome back.
I have been thinking about a notable transition in China's AI race this week.
For the past two years， the biggest question was whether Chinese AI companies could build models good enough to compete with Silicon Valley. That question is starting to look different. Kimi K3 became one of the most talked-about AI releases of the year. Chinese models now dominate OpenRouter's global usage rankings. DeepSeek has already changed how the industry thinks about open-source AI. The debate is no longer only about capability. It is about scale.
Then DeepSeek arrived. Then Kimi K3. And suddenly， the conversation changed.
The interesting question is not why these models performed well. China has no shortage of serious AI labs - Z.ai， MiniMax， Alibaba， Tencent， and ByteDance have all built competitive systems. The question is why only two of them forced the industry to rethink its assumptions. DeepSeek challenged the belief that frontier AI progress would always require unlimited compute. Moonshot， the company behind Kimi， challenged the assumption that the hardest AI capabilities would remain concentrated inside American closed-source labs.
The market reaction revealed the difference. Some models compete inside the existing AI race. Others force everyone to question whether the race itself has been defined correctly. This week's piece explores why DeepSeek and Moonshot created a reaction that dozens of other Chinese AI companies did not .And why the biggest challenge to Silicon Valley may not simply be better models， but a different understanding of what makes an AI company valuable.
【Read the full piece →】
The DeepSeek Doctrine
Every technology company in the AI boom seems to be chasing the same things. More users. More revenue. More products. More markets. DeepSeek is doing something much stranger. It keeps saying no.
No to chasing consumer traffic. No to building the next super app. No to maximizing short-term profits. No to closing its models.
In a four-hour conversation with investors， founder Liang Wenfeng described a company that appears almost out of place in today's AI race. While everyone else is rushing toward commercialization， DeepSeek seems focused on something much harder to measure： increasing its chances of eventually reaching AGI. At first glance， the strategy sounds irrational. Why release models openly？ Why lower prices？ Why ignore obvious business opportunities？ But the logic becomes clearer when viewed through DeepSeek's own framework. Lower costs expand access. Open source creates distribution. Efficiency matters when resources are limited. And a smaller organization may have advantages precisely because it cannot afford to chase everything.
This week's piece looks at the philosophy behind DeepSeek's rise， and why its biggest competitive advantage may not be a model architecture - but a completely different definition of what winning looks like in the AI era.
【Read the full piece →】
The News…
（i） Kimi K3 Hit the World Stage. Then Reality Hit Back.
Moonshot AI's Kimi K3 launch created one of the strongest reactions to a Chinese AI model since DeepSeek. Within 48 hours， the company paused new subscriptions after demand overwhelmed its available GPU capacity. Existing users were unaffected， but new signups had to wait while Moonshot expanded infrastructure.
The irony was difficult to miss. Kimi K3 is a 2.8 trillion-parameter model that reached the top tier of global benchmarks， including strong performance on AI Arena's frontend coding rankings， and Moonshot plans to release open weights that would make it one of the largest open-weight frontier models ever released. The model itself was exactly the kind of breakthrough Chinese AI companies had been trying to prove was possible.
But deployment exposed a different challenge. Building a powerful model and serving that model at global scale are two separate problems. Several users noted that Kimi K3 felt slower than leading US alternatives - the issue is not only model quality， but inference capacity， latency， and access to advanced computing resources. China's AI labs have become much better at extracting more intelligence from limited resources， but export controls on advanced chips and semiconductor equipment mean the next challenge is no longer only building smarter models. It is building enough infrastructure to let those models run everywhere.
（ii） Chinese AI Models Are Dominating Developer Usage. The Harder Question Is Retention.
According to OpenRouter's latest leaderboard， the five most-used AI models globally last week were all Chinese. Tencent's Hy3 processed 11.8 trillion tokens， Xiaomi's MiMo-V2.5 reached 9.37 trillion， DeepSeek V4 Flash recorded 5.34 trillion， Zhipu's GLM 5.2 reached 3.57 trillion， and MiniMax M3 processed 3.46 trillion. Chinese models have now remained at the top of the ranking for 12 consecutive weeks.
The numbers are striking， but they need context. OpenRouter measures developer usage， not consumer adoption， and high token volume can reflect free trials， experimentation， or developer testing rather than long-term commercial demand，Tencent Hy3， for example， is still in a free trial phase. The more interesting signal is not simply that Chinese models are being used， but that developers globally are increasingly willing to experiment with them. For years， the assumption was that Chinese AI models would mainly serve domestic users. OpenRouter suggests the conversation is becoming more global. The next question is whether usage can turn into durable ecosystems.
（iii） The Hidden Winner of the AI Boom May Be the Company Testing the Connections.
A Chinese optical testing equipment company just reported explosive growth. Lianxun Instrument， which develops testing systems for high-speed optical modules， expects first-half 2026 net profit to increase 800% to 900% year over year - and the reason is straightforward： AI data centers need faster connections. As AI clusters become larger， the bottleneck is no longer only computing power. Moving data between thousands of chips has become equally important， and optical modules operating at 400G， 800G， and the emerging 1.6T standard are becoming critical infrastructure， with every generation requiring more advanced testing equipment.
Lianxun is only the second company globally capable of providing full testing coverage for 1.6T optical modules， behind US instrumentation giant Keysight. The company already holds the leading position in China's optoelectronic testing equipment market and ranks third globally in optical communications testing. Q1 revenue grew 142% year over year at a 66.76% gross margin， and orders on hand exceeded RMB 3 billion. The AI boom is often described as a battle between GPU companies. But every GPU cluster needs an entire ecosystem around it， and sometimes the biggest winners are the companies building the tools that make the AI factories possible.
But scale is exactly where the cracks appeared. Less than 48 hours after launch， Moonshot paused new Kimi K3 subscriptions because demand was overwhelming its GPU capacity. Building a frontier model was the first challenge. Turning it into a global-scale product is the next one. And that requires something very different： the GPUs， data centers， optical networks， and hidden supply chains that make AI possible at volume.
And underneath all of this is a bigger question： if China's AI companies can no longer be dismissed as followers， what kind of companies are they trying to become？
Let's get into it.
This Week's Features…
Why Kimi K3 And DeepSeek Are Keeping Silicon Valley Up at Night ？
For months， the AI race looked easy to understand. American companies built the frontier models， Chinese companies followed， and the competition was about who had more GPUs， bigger training runs， and deeper pockets.
Then DeepSeek arrived. Then Kimi K3. And suddenly， the conversation changed.
The interesting question is not why these models performed well. China has no shortage of serious AI labs - Z.ai， MiniMax， Alibaba， Tencent， and ByteDance have all built competitive systems. The question is why only two of them forced the industry to rethink its assumptions. DeepSeek challenged the belief that frontier AI progress would always require unlimited compute. Moonshot， the company behind Kimi， challenged the assumption that the hardest AI capabilities would remain concentrated inside American closed-source labs.
The market reaction revealed the difference. Some models compete inside the existing AI race. Others force everyone to question whether the race itself has been defined correctly. This week's piece explores why DeepSeek and Moonshot created a reaction that dozens of other Chinese AI companies did not .And why the biggest challenge to Silicon Valley may not simply be better models， but a different understanding of what makes an AI company valuable.
【Read the full piece →】
The DeepSeek Doctrine
Every technology company in the AI boom seems to be chasing the same things. More users. More revenue. More products. More markets. DeepSeek is doing something much stranger. It keeps saying no.
No to chasing consumer traffic. No to building the next super app. No to maximizing short-term profits. No to closing its models.
In a four-hour conversation with investors， founder Liang Wenfeng described a company that appears almost out of place in today's AI race. While everyone else is rushing toward commercialization， DeepSeek seems focused on something much harder to measure： increasing its chances of eventually reaching AGI. At first glance， the strategy sounds irrational. Why release models openly？ Why lower prices？ Why ignore obvious business opportunities？ But the logic becomes clearer when viewed through DeepSeek's own framework. Lower costs expand access. Open source creates distribution. Efficiency matters when resources are limited. And a smaller organization may have advantages precisely because it cannot afford to chase everything.
This week's piece looks at the philosophy behind DeepSeek's rise， and why its biggest competitive advantage may not be a model architecture - but a completely different definition of what winning looks like in the AI era.
【Read the full piece →】
The News…
（i） Kimi K3 Hit the World Stage. Then Reality Hit Back.
Moonshot AI's Kimi K3 launch created one of the strongest reactions to a Chinese AI model since DeepSeek. Within 48 hours， the company paused new subscriptions after demand overwhelmed its available GPU capacity. Existing users were unaffected， but new signups had to wait while Moonshot expanded infrastructure.
The irony was difficult to miss. Kimi K3 is a 2.8 trillion-parameter model that reached the top tier of global benchmarks， including strong performance on AI Arena's frontend coding rankings， and Moonshot plans to release open weights that would make it one of the largest open-weight frontier models ever released. The model itself was exactly the kind of breakthrough Chinese AI companies had been trying to prove was possible.
But deployment exposed a different challenge. Building a powerful model and serving that model at global scale are two separate problems. Several users noted that Kimi K3 felt slower than leading US alternatives - the issue is not only model quality， but inference capacity， latency， and access to advanced computing resources. China's AI labs have become much better at extracting more intelligence from limited resources， but export controls on advanced chips and semiconductor equipment mean the next challenge is no longer only building smarter models. It is building enough infrastructure to let those models run everywhere.
（ii） Chinese AI Models Are Dominating Developer Usage. The Harder Question Is Retention.
According to OpenRouter's latest leaderboard， the five most-used AI models globally last week were all Chinese. Tencent's Hy3 processed 11.8 trillion tokens， Xiaomi's MiMo-V2.5 reached 9.37 trillion， DeepSeek V4 Flash recorded 5.34 trillion， Zhipu's GLM 5.2 reached 3.57 trillion， and MiniMax M3 processed 3.46 trillion. Chinese models have now remained at the top of the ranking for 12 consecutive weeks.
The numbers are striking， but they need context. OpenRouter measures developer usage， not consumer adoption， and high token volume can reflect free trials， experimentation， or developer testing rather than long-term commercial demand，Tencent Hy3， for example， is still in a free trial phase. The more interesting signal is not simply that Chinese models are being used， but that developers globally are increasingly willing to experiment with them. For years， the assumption was that Chinese AI models would mainly serve domestic users. OpenRouter suggests the conversation is becoming more global. The next question is whether usage can turn into durable ecosystems.
（iii） The Hidden Winner of the AI Boom May Be the Company Testing the Connections.
A Chinese optical testing equipment company just reported explosive growth. Lianxun Instrument， which develops testing systems for high-speed optical modules， expects first-half 2026 net profit to increase 800% to 900% year over year - and the reason is straightforward： AI data centers need faster connections. As AI clusters become larger， the bottleneck is no longer only computing power. Moving data between thousands of chips has become equally important， and optical modules operating at 400G， 800G， and the emerging 1.6T standard are becoming critical infrastructure， with every generation requiring more advanced testing equipment.
Lianxun is only the second company globally capable of providing full testing coverage for 1.6T optical modules， behind US instrumentation giant Keysight. The company already holds the leading position in China's optoelectronic testing equipment market and ranks third globally in optical communications testing. Q1 revenue grew 142% year over year at a 66.76% gross margin， and orders on hand exceeded RMB 3 billion. The AI boom is often described as a battle between GPU companies. But every GPU cluster needs an entire ecosystem around it， and sometimes the biggest winners are the companies building the tools that make the AI factories possible.
