Grace Shao on What the World Should Know About Chinese AI
Episode
51 min
Read time
2 min
Topics
Productivity, Fundraising & VC, Marketing
AI-Generated Summary
Key Takeaways
- ✓Open-source as business strategy: Chinese labs initially open-sourced models not for ideological reasons but to build trust with Western developers — a branding decision. This created a collaborative ecosystem where labs share breakthroughs, congratulate each other publicly, and build on shared foundations, reducing duplicated R&D spend under tight capital and compute constraints.
- ✓Revenue model for open-source AI: MiniMax and Zhipu AI are projected to reach $1–1.2 billion ARR, with monthly revenue now matching their entire prior-year total. The profit center is managed inference services — enterprises pay for API access to avoid self-hosting costs including GPUs, deployment, security, and monitoring, mirroring how open-source software companies have always monetized.
- ✓Post-training optimization as catch-up strategy: Lacking resources for broad R&D exploration, Chinese labs wait for US frontier labs to establish direction, then concentrate entirely on post-training. They also time proprietary dataset purchases strategically — waiting out 3–6 month exclusivity windows to buy the same data at roughly one-tenth the original price paid by OpenAI.
- ✓DeepSeek V4's hardware signal: DeepSeek delayed its V4 release by 3–4 months specifically to re-engineer inference onto Huawei chips. By releasing this as open-weight, other Chinese labs could study the implementation and begin shifting inference workloads to domestic hardware — a deliberate step toward reducing ecosystem dependence on NVIDIA and CUDA.
- ✓Hybrid model deployment at application layer: US AI-native companies including Harvey and Cursor are already building on Chinese open-source models like GLM and Kimi for cost efficiency, while using closed models like Claude Opus selectively as a judge or evaluator. This hybrid approach lets application-layer companies cut token costs without sacrificing quality on critical tasks.
What It Covers
Independent AI researcher Grace Shao joins Odd Lots in Hong Kong to explain how Chinese AI labs like DeepSeek, MiniMax, Moonshot, and Zhipu AI operate under compute, capital, and talent constraints — and why their open-source, utilitarian approach is generating real revenue while closing the gap with US frontier models.
Key Questions Answered
- •Open-source as business strategy: Chinese labs initially open-sourced models not for ideological reasons but to build trust with Western developers — a branding decision. This created a collaborative ecosystem where labs share breakthroughs, congratulate each other publicly, and build on shared foundations, reducing duplicated R&D spend under tight capital and compute constraints.
- •Revenue model for open-source AI: MiniMax and Zhipu AI are projected to reach $1–1.2 billion ARR, with monthly revenue now matching their entire prior-year total. The profit center is managed inference services — enterprises pay for API access to avoid self-hosting costs including GPUs, deployment, security, and monitoring, mirroring how open-source software companies have always monetized.
- •Post-training optimization as catch-up strategy: Lacking resources for broad R&D exploration, Chinese labs wait for US frontier labs to establish direction, then concentrate entirely on post-training. They also time proprietary dataset purchases strategically — waiting out 3–6 month exclusivity windows to buy the same data at roughly one-tenth the original price paid by OpenAI.
- •DeepSeek V4's hardware signal: DeepSeek delayed its V4 release by 3–4 months specifically to re-engineer inference onto Huawei chips. By releasing this as open-weight, other Chinese labs could study the implementation and begin shifting inference workloads to domestic hardware — a deliberate step toward reducing ecosystem dependence on NVIDIA and CUDA.
- •Hybrid model deployment at application layer: US AI-native companies including Harvey and Cursor are already building on Chinese open-source models like GLM and Kimi for cost efficiency, while using closed models like Claude Opus selectively as a judge or evaluator. This hybrid approach lets application-layer companies cut token costs without sacrificing quality on critical tasks.
Notable Moment
A Hangzhou court ruled that companies cannot legally cite AI as justification for employee layoffs or contract terminations — a regulatory response that moved faster than any equivalent Western policy action and functioned as a direct public reassurance mechanism during rising automation anxiety.
Episode Transcript
Small businesses are the pulse of every community. They bring people together, create opportunities, and drive growth. Chase for business helps business owners like you with personalized guidance and convenient digital tools all in one place. With that guidance and your determination, you can take your business farther and help build a brighter future for your community. Learn more at chase.com/business. Chase for business. Make more of what's yours. The Chase mobile app is available for select mobile devices. Message and data rates may apply. JPMorgan Chase Bank, NA. Member, FDIC. Copyright 2026. JPMorgan Chase and Company. So there's a lot of noise about AI, but time's too tight for more promises. So let's talk about results. At IBM, we work with our employees to integrate technology right into the systems they need. Now a global workforce of 300,000 can use AI to fill their HR questions, resolving 94% of common questions. Not noise, proof of how we can help companies get smarter by putting AI where it actually pays off, deep in the work that moves the business. Let's create smarter business, IBM. So as a pizza genius, I know pizza shop orders come from, well, everywhere. With Genius by Global Payments, online orders actually sink straight into your kitchen. It's as simple as pie. And with digital menu boards, your specials, your prices, your brand, always front and center. It's one system ready for game night crowds. Any night of the week, really. Big league reliability for any business. That's genius. Bloomberg Audio Studios. Podcasts, radio, news. Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Weisenthal. And I'm Tracy Alloway. Tracy, I love being in Hong Kong. I love it here so much. I love it here so much. I would, like, come here a few times a year if we could. I'm sure you would. I lived here for, like, I guess, almost four years. So it's kinda weird coming back, but Hong Kong has a lot of pluses. Like, great food, great weather for most of the year, beaches. I once heard someone describe it as Manhattan meets Maui, which I think is, like, pretty accurate. Oh, it's so nice. The weather is actually not great. It's not great right now. This week. We've come during, I guess it's a monsoon season. Right? Yeah. It's the rainy season. But, oh, well. I like thunderstorms, so I'm enjoying it. Yeah. I'm enjoying it too. Anyway, one thing that has changed since the last time you were in Hong Kong, you left in 2022. Yeah. We weren't doing as many AI episodes at those days. We definitely weren't. In fact so I remember one of the big stories when I was here in Hong Kong in 2020 was China's tech crackdown. Right? That's right. That's right. Right? And, like, there was all this concern about whether or not the crackdown was gonna destroy China's entrepreneurial spirit. I'm doing air quotes on a podcast. …
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by DeepSeek
“DeepSeek delayed its V4 release by 3–4 months specifically to re-engineer inference onto Huawei chips. By releasing this as open-weight, other Chinese labs could study the implementation”
by Anthropic
“using closed models like Claude Opus selectively as a judge or evaluator”
company
“US AI-native companies including Harvey and Cursor are already building on Chinese open-source models like GLM and Kimi for cost efficiency”
“Chinese AI labs like DeepSeek, MiniMax, Moonshot, and Zhipu AI operate under compute, capital, and talent constraints”
“Chinese AI labs like DeepSeek, MiniMax, Moonshot, and Zhipu AI operate under compute, capital, and talent constraints”
“Chinese AI labs like DeepSeek, MiniMax, Moonshot, and Zhipu AI operate under compute, capital, and talent constraints — and why their open-source, utilitarian approach is generating real revenue”
“US AI-native companies including Harvey and Cursor are already building on Chinese open-source models like GLM and Kimi for cost efficiency”
“Chinese AI labs like DeepSeek, MiniMax, Moonshot, and Zhipu AI operate under compute, capital, and talent constraints”
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