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Hangzhou-based Unitree Robotics is among the top players in China’s robotics industry, developing best-selling civilian quadruped and humanoid robots.

Unitree’s H1 humanoids captivated over a billion people with a traditional folk dance performance during China’s 2025 Spring Festival Gala. Just weeks later, Unitree’s CEO was the youngest front-row participant in Beijing’s highly-anticipated private sector summit.

To better understand the Chinese robotics industry and where it’s headed, we’ve translated and annotated qan interview with Unitree’s founder and CEO, Wang Xingxing 王兴兴. Originally conducted in April 2024 by Titanium Media (TMT), the interview covers:

• Why LLMs aren’t enough for the robotics industry, and why Wang predicts the emergence of a large-scale AI model for general-purpose robotics by the end of 2025,

• Factors driving the global humanoid robot boom, and why China is uniquely poised to succeed in this industry,

• The techno-optimist vision for the economy of the future, powered by humanoid robots as well as machines of alternative forms,

• The timeline for mass adoption of AI-powered general-purpose robots,

• Unitree’s strategy for competing against foreign and domestic robotics firms.

We’ve added some editorial notes for your enjoyment, including commentary by anonymous robotics PhD and current industry player KL Divergence.

Interested in learning more? For past ChinaTalk coverage, see Angela’s work on China’s leap into industrial robotics and China’s humanoid robot industry.

The Translation

Original Article | Archive | Title: “Dialogue with Wang Xingxing: Humanoid Robots Will Reshape All Industries Within My Lifetime” | Author: Rao Xiangyu 饶翔宇 for Titanium Media (TMT) 钛媒体APP | Editor: Zhong Yi 钟毅

On February 17, 2025, a highly anticipated private enterprise symposium was held in Beijing.

At the event, the presence of Wang Xingxing, the founder of Unitree Robotics and a post-90s entrepreneur, attracted market attention. Wang was seated in the front row among the business representatives, alongside industry giants such as Zeng Yuqun, Jack Ma, Ren Zhengfei, Wang Chuanfu, Lei Jun, and Pony Ma. Among them, Ren Zhengfei, Wang Chuanfu, Liu Yonghao, Yu Renrong, Wang Xingxing, and Lei Jun delivered keynote speeches.

As a startup company, Unitree Robotics and Wang Xingxing have experienced what can be described as a “rocket-like leap” in growth.

[KL Divergence: Perhaps. Though consider that Unitree has been an established player, particularly in quadruped robots, for 10 years. They have worked hard and scraped their way up inch by inch by serving the small markets which existed for their product (mostly researchers). Their recent prominence has come from riding the wave of interest in humanoid robots by creating low-cost, easy-to-use, but not particularly advanced or capable, humanoid robots for research.]

Public records show that Wang Xingxing earned his bachelor's degree from Zhejiang Sci-Tech University. Due to poor English proficiency, he failed to gain admission to Zhejiang University for his master's studies and was instead placed at Shanghai University. In an interview, Wang once mentioned, “During my three years in high school, I only passed English exams three times in total.”

From 2013 to 2015, while pursuing his graduate studies, Wang, despite having limited resources and funding, independently designed hardware and control algorithms and combined them with industrial motors to develop the robotic dog XDog. This project won second prize in the Shanghai Robotics Design Competition. After graduating, Wang embarked on an entrepreneurial journey focused on robotic dogs.

[KL Divergence: Actually, much of the hardware and control algorithms were from publicly-available robot and actuator designs published by Western researchers, such as Sangbae Kim (MIT) and Dan Koditschek (UPenn). What Unitree really excelled at was (1) iterating high-performance actuators, robot designs, and research-grade control algorithms, and (2) leveraging the Chinese supply chain to create low-cost, high-performance, highly-reliable combinations of these key technologies. In other words, they productized the research.]

Unitree Robotics, founded in 2016, initially specialized in the development of quadruped robotic dogs and successfully sold them worldwide, becoming one of the leading players in the industry in terms of product shipments. By 2023, the company ventured into humanoid robotics and quickly became one of the most closely watched companies in the field. In 2025, Unitree Robotics’ latest humanoid robot appeared on the stage of the CCTV Spring Festival Gala, garnering widespread public attention.

In April 2024, Wang Xingxing, the founder of Unitree Robotics, had an exclusive interview with Titanium Media APP. The relevant content can be found below.

Wang told TMT APP that the fundamental reason behind the humanoid robotics boom is the emergence of large AI models. Previously, it would take one to two years for a humanoid robot to learn to walk, but now, with AI algorithms, this can be achieved in just one month.

Regarding the future development of humanoid robots, Wang expressed strong optimism. He believes that by the end of 2025, at least one company worldwide will have developed a general-purpose robotic AI model. This foundational model, he explained, is like a complete set of building blocks, with large language models being just one piece. Other crucial components include visual perception, tactile sensing, decision-making, and interaction systems.

Looking at an even longer timeline, Wang told TMT, “Within our lifetime, humanoid robots will be able to revolutionize every industry, from manufacturing and agriculture to services and industrial sectors. Taking it a step further, governments could potentially deploy 100,000 humanoid robots to construct an entire city. On a smaller scale, robots could even shrink down to the size of cells, transforming all aspects of our natural environment.”

Below is the full interview between TMT and Unitree Robotics founder Wang Xingxing, with slight editorial adjustments.

“The Turning Point for Humanoid Robots Has Not Yet Arrived”

TMT: A few days before our meeting, Boston Dynamics, a star company in the robotics field, announced that its hydraulic-powered humanoid robot would be phased out, and future developments would focus on electric-powered products. What are your thoughts on this?

Wang Xingxing: Boston Dynamics has been making robots for many years, and they’ve also been working on commercialization for a long time.

As for hydraulic drive systems, I had already believed before 2013 that this approach could not be commercialized. The reason is simple: it relies entirely on precision mechanical components, and once you involve such components, costs will never come down. Moreover, all hydraulic systems leak oil. That’s why you hardly see hydraulic systems in consumer vehicles anymore — they’ve all been replaced by electric drive systems.

So, if Boston Dynamics wants to continue developing humanoid robots, switching to electric drive is definitely the right path. The only surprising thing to me is that I assumed around 2018 that they had already started working on an electric version. But later, when they had made no detectable progress, I just stopped paying attention.

TMT: Compared to hydraulic systems, is electric drive better suited for large AI models?

Wang Xingxing: Compared to hydraulic systems, electric drive is all advantages and no disadvantages. As for whether electric systems are better suited for AI, that’s harder to say. However, electric drives have lower production costs, offer greater motion flexibility, are safer, and also lighter in weight.

TMT: Now that Boston Dynamics has switched to electric-powered robots, combined with their existing training data, do you think they could iterate faster than competitors in this new wave of competition?

Wang Xingxing: It’s hard for me to say. However, we remain quite confident, because we’ve been working on quadruped robots for many years, and a lot of the algorithms and components we’ve developed can be directly applied to humanoid robots.

Another important point to note is that most of the top AI talent in the U.S. isn't at Boston Dynamics—they’re at Google, NVIDIA, and OpenAI. Boston Dynamics' strength likely lies in hardware development and traditional humanoid robot control systems.

TMT: So, would you say that the emergence of large AI models is a major turning point for humanoid robots?

Wang Xingxing: I don’t think we’ve reached that turning point yet.

Right now, it’s more like a starting direction. There's a common misconception—many people think that large language models like ChatGPT can be directly applied to robots, but in reality, that’s not the case.

TMT: Why not?

Wang Xingxing: Because LLMs aren’t designed for robotics in the first place. ChatGPT operates purely on text logic, and its entire training dataset is based on text data. It doesn’t perform well in robotic environmental perception — this is a global challenge, not just a problem for one company.

While the humanoid robotics industry does use AI, the technology is actually very different from large model technology.

TMT: But some companies have claimed that large AI models can already recognize different types of plates, allowing robots to identify and pick them up.

Wang Xingxing: That’s not something we can verify. It was just a video, and no one has confirmed its authenticity.

Besides, there’s no data proving that if you swap the plate for an apple, a pear, or something else, the robot would still be able to recognize and handle it correctly. Personally, I don’t see any evidence of real technical breakthroughs coming from Silicon Valley — it still seems quite conventional (中规中矩).

TMT: So, large AI models are not the key turning point for humanoid robot development? Are they less important than people think?

Wang Xingxing: The models themselves are not important for robots, but the underlying technological direction they represent is very important.

Right now, large models are mainly focused on language models — but no one has yet developed a true large-scale model specifically for robotics.

TMT: That brings us to the big question — what triggered the humanoid robotics startup boom in 2023?

Wang Xingxing: The reason is really quite simple: Tesla started working on humanoid robots.

Elon Musk has already disrupted industries like automobiles and rockets, growing them into massive sectors. Now that he's entering humanoid robotics, governments and various institutions want to get started early, rather than waiting for Musk to succeed first and then trying to catch up.

[KL Divergence: I think this is a little bit just-so and playing to the audience a bit too much. The fundamentals are more important. Elon and Optimus is definitely the spark which ignited the wildfire. But the kindling was years and years of slow and steady progress on batteries and electric motor power density made it finally possible to create practical (as in, strong and light enough) electric humanoid robots, around 2020-21. Elon's team caught on to this a little early, because these are also technologies that Tesla happens to to be deeply invested in. But others were doing it already, just quietly.]

At the same time, ChatGPT and other LLMs have expanded the public’s imagination of AI’s potential. You could say these models ignited excitement and enthusiasm across the industry. Right now, what we’re seeing is just the beginning — the momentum will only grow stronger.

As hardware and AI technology advance each year, the impact of humanoid robotics on the world will be massive and transformative.

“It’s simple, really not as complicated as most people think”

TMT: Current large AI models are just the beginning. What's the future direction of the industry or where should everyone's efforts be focused?

Wang Xingxing: There are many directions. The first step is adapting AI for robots - developing robotic vision, perception, understanding, execution planning, and various operations.

I'm excited just like everyone else. I personally feel this industry will develop rapidly, including robots, large models, and AI. I believe by the end of 2025, at least one company globally will develop a relatively general-purpose robot large model.

Our company hopes to develop it ourselves, but realistically speaking, the probability is higher that an American company will achieve it first.

[Angela: Wang is optimistic. Depending on what goalposts you set, training a robot “foundation model” requires large, multimodal datasets that take time and capital to collect or synthesize – including vision, sound, touch, motion, social and environmental interaction, and so on. In some ways, Wang’s prediction aligns well with the Chinese government’s stated goal of mass production of humanoids by 2025 and world leadership by 2027. At the same time, he realistically recognizes the strength of US innovation. The main takeaway here is that to Wang — and likely to many of his industry counterparts — the humanoid robot race is accelerating towards some decisive moments.]

TMT: So that brings up the question of open source versus closed source.

Wang Xingxing: If we develop it, it definitely won't be open source.

TMT: Is there a unified model between robot large models and robot dogs?

Wang Xingxing: Most robot dogs are implemented through reinforcement learning, which is a relatively mature technology.

Robotic large models or robot world models can be applied to all robots, not necessarily humanoid or dog-shaped ones - they're universal tools. I've always believed that robots don't necessarily have to be humanoid; the humanoid shape is just one of many possible forms. I've never insisted they must be humanoid.

TMT: But the mainstream view is that humanoid forms are better because our whole society is built for human-shaped frames.

Wang Xingxing: They might like to say that, but I've never believed it.

You can build entirely new physical worlds. Why would you need a humanoid form for mining? Why would you necessarily need a human shape for building houses? Of course, humanoid forms are important, or relatively important, but they're not everything.

For example, at home, people might prefer humanoid robots for performing scenarios or accompanying you on trips. But for building houses or transporting things - physical labor - there's no need for them to be humanoid. Plus, humanoid forms might give people a sense of owning a slave if you make them do unpleasant work, making their owners feel uncomfortable.

TMT: Would you feel sorry for them?

Wang Xingxing: Current AI hasn't reached that level yet; it can’t perceive such things.

But if its AI could perceive pain or negative emotions, then yes, that might be problematic. But there’s no need to feel sorry now, because it’s still just an inanimate object [死物, literally a ‘dead thing’] with limited intelligence.

TMT: I'm curious about something — even though their intelligence is limited, when you push them, why do they display human-like staggering movements?

Wang Xingxing: Because that’s what the AI was trained to do through reinforcement learning.

TMT: So it’s imitating human behavior?

Wang Xingxing: Some behaviors aren't imitation; they’re determined by natural laws. You could say physical laws constrain these robots to move in certain ways. If an alien had a human shape, its movement would probably be the same as well.

TMT: Currently people break down robots into cerebrum, cerebellum, and the physical body. What's your view on this?

Wang Xingxing: I’ve never liked separating the cerebrum and cerebellum so distinctly. One model is enough - why divide it into two? I don't think it's necessary.

Of course, there might be various modules within the model, but overall I prefer treating it as a single model. From walking to fine-grained operations, we implement everything using AI in a completely end-to-end manner. From visual perception to leg execution, one model handles it all - no intermediate mathematical formulas whatsoever.

TMT: Can the hardware capabilities keep up?

Wang Xingxing: For robots, it's just a few joints — it’s really not that hard. Just sensors feeding into the model, and then the model outputs to the joints. That's all.

TMT: Your understanding of humanoid robots seems simpler than others'.

Wang Xingxing: It is simple, not that complicated.

TMT: For example, others might think dexterous hands are difficult for fine operations because they require more accurate recognition and finer motion control.

Wang Xingxing: It's very difficult if you use traditional technology, so you can't rely on traditional approaches. Without technological innovation, there's no point in working in this field. Of course, you can't express it so directly — better not to go too far beyond public understanding, otherwise I'd probably get cursed out (骂死).

TMT: What specifically do you mean by non-traditional?

Wang Xingxing: It's new AI, end-to-end. It means not having to manually write lots of software programming rules in between, nor perform traditional image recognition.

TMT: How do you implement that?

Wang Xingxing: Modify the model. The underlying AI is the same, but your entire model structure and algorithms are different. I can't explain this too specifically - it would be hard to understand. For example, you don't need traditional image annotation or image understanding at all. You can input images and videos into a model, and the output is directly the robot's joint trajectories, then you just train it. You can still do image annotation, like labeling images of apples. But annotation has only one function: interacting with humans, helping it better understand people. For the robots themselves, there's no difference between an apple and a pear.

TMT: Compared with the mainstream opinions, your logic and industry judgments are unique.

Wang Xingxing: The mainstream viewpoint still has many issues. As a startup, if your thinking is just mainstream, it just won’t work out well for you. You must see the development direction for the next few years, and once you see it, plan ahead accordingly - then you're certain to win, or at least not lose. If you only see what everyone else is talking about, others can certainly do better than you - how could you stand out?

TMT: In your view, what will the next few years look like?

Wang Xingxing: I can't get too specific, but what's certain is that the industry will progress extremely rapidly.

TMT: How fast are we talking here?

Wang Xingxing: It's basically beyond imagination. The current pace of AI deployment in factories — globally, technological progress is extremely fast and has almost proven viable.

TMT: Currently, no company can fully utilize robots for work.

Wang Xingxing: But the entire logic has almost been proven. This doesn't mean robots can do everything, but work-capable, end-to-end robots are nearing maturity. A more general-purpose robot model will likely be developed by a company globally before the end of 2025.

TMT: That fast?

Wang Xingxing: It could be even faster. Some people have already seen where this is going - though it sounds a bit boastful, I feel I've seen it too. Following this direction, with some additional time, manpower, and money, it can basically be developed.

“All technological breakthroughs have a large element of luck"

TMT: What specifically does a robot model refer to?

Wang Xingxing: You can think of it this way: first, it has strong mobility capabilities applicable to most terrains, possibly with some mobility skills exceeding humans. For instance, its obstacle-crossing ability, speed, jumping ability might be better than humans. Another aspect is working in factories - it can do many tasks without requiring manual programming. Through large model capabilities, with just a little teaching, it can learn by itself and then perform well.

TMT: Is simulation training in virtual environments still necessary?

Wang Xingxing: Probably not all that necessary. Once you've trained it well and validated it, you don't really need simulation anymore. Of course, completing the hardware won’t happen right away, but I think that's just a matter of time. As for AI, there's still some uncertainty. Although I just said I'm personally optimistic it will emerge before the end of 2025, it might not happen - it could take 3-5 years before it's developed. It depends on humanity's collective luck - sometimes it just comes down to luck.

TMT: How do you understand this kind of luck?

Wang Xingxing: Many technological breakthroughs depend on luck. For example, if Einstein hadn't existed, someone else would probably have discovered his theories. But it might have been delayed by several years, or even decades. You can consider that all technological breakthroughs have a large element of luck involved.

TMT: Another point: besides algorithms and models, large models need data. Is data collection currently very difficult?

Wang Xingxing: There are indeed many things that need to be done, but there are methods for addressing them. It's not as complicated as people think - many problems aren't as complex as people imagine. You know, in all current technology fields, if you really look, there's nothing truly complex; everything is relatively direct and simple. Even

TMT: So is your industry also divided into two camps - optimistic and pessimistic? For example, you're more optimistic, thinking the whole thing isn't that difficult.

Wang Xingxing: It definitely requires time and intellectual investment, but these are things that can be solved and advanced. They’re not like room-temperature superconductors or controlled nuclear fusion. The biggest problem with room-temperature superconductors and controlled nuclear fusion is that there’s a question mark over whether they’re physically possible. The universe might simply not allow such things to exist, and humans might never achieve them no matter how much time and effort we invest. Artificial intelligence robots are common things, not something extraordinary — just the intelligence of a bunch of humans and animals. Intelligence is a widespread phenomenon. Some animals are very smart and can understand much of what humans say, they just can’t speak. And crows — some crows can even use tools directly. So, intelligence doesn’t have many limitations or physical constraints; it can be replicated.

TMT: What’s the biggest motivator for your work?

Wang Xingxing: To be honest, what moves me personally is AI.

A few years ago, an investor asked me whether our company would ever develop humanoid robots, and I told him, “We would never do it, even if it kills us.”

[KL Divergence: Great honesty here. It's true. Virtually the entire field considered humanoid robots a hopeless tarpit, which would consume all of your time and money and render not progress. Even in robotics research, humanoids were a quirky backwater reserved for the cranks and over-optimistic.]

Back then, humanoid robots were far too complex. Traditional algorithms simply couldn’t handle such intricate machines. The conventional approach to training humanoid robots relied on highly skilled engineers manually writing mathematical equations to model movement. These equations would then be solved to determine the robot’s motion trajectory. But this method had severe limitations—if the environment changed, the equations often became invalid, requiring entirely new models to be designed from scratch.

This approach also led to an overwhelming amount of code, and as the system grew more complex, it became nearly impossible to maintain manually. However, AI changes everything. As long as the model is well-structured and continuously fed with data and compute, AI can iteratively optimize itself through trial and error. By leveraging reinforcement learning and reward mechanisms, AI can automatically retain successful training outcomes and discard ineffective ones, dramatically improving training efficiency.

Recent progress in AI technology—both in capability and speed—has far exceeded my personal expectations. That’s why, despite having worked on humanoid robots for just over a year, our performance is already exceptionally good. The reason we’ve been able to move so quickly is simple: thanks to advancements in AI.

The benefit of AI is that once you’ve built a strong model, the rest is just a matter of compute—you don’t have to manually fine-tune everything. If you need to test a scenario, OK, all you need to do is feed the system more data. This is also why Tesla’s autonomous driving team is significantly smaller than Chinese autonomous driving teams. I know for a fact that Tesla’s team has only a few hundred people, whereas some companies in China have teams numbering in the thousands.

TMT: Is this also why newer players have been able to surpass Boston Dynamics?

Wang Xingxing: Exactly. If we were competing with Boston Dynamics purely using traditional algorithms, we wouldn’t stand a chance. The reason is simple: Boston Dynamics has an entire team of PhDs from MIT, and there’s no way China could outmatch them in that domain.

TMT: Looking ahead, what do you think will be the key differentiator among humanoid robots?

Wang Xingxing: Robotics is an integrated product. Unlike fuel-powered vs. electric vehicles, where the underlying technologies are fundamentally different, the differentiators in humanoid robots will be more incremental—primarily in specific engineering optimizations, such as motor scale, motor placement, workspace dimensions, structural design, and leg configurations.

The same principle applies to AI. Take large language models — they’re fundamentally pretty similar. The biggest points of differentiation are in the details rather than in fundamental design; OpenAI’s GPT architecture is still relatively clean.

“In our lifetime, humanoid robots can reinvent all industries and the natural environment.”

TMT: Commercialization is also important. How can startups survive in an increasingly competitive landscape?

Wang Xingxing: The business logic is very simple. As long as your product is better than your competitors’ in all dimensions, then you will profit. What remains is the question, how big is the entire market? Right now, our company has a strong market position, so we have captured most of the easily accessible revenue opportunities.

TMT: What do you mean by ‘easily earned revenue’?

Wang Xingxing: From having high shipment volumes. We sold quite a few quadruped and humanoid robots last year.

TMT: How many did you sell?

Wang Xingxing: It's hard to say exactly, but it's under a few hundred. However, we definitely sold the most in the domestic market.

TMT: Who bought them?

Wang Xingxing: A variety of buyers, including research institutions, AI companies, and businesses pursuing real-world applications.

TMT: How can you move so fast and manage to sell your products?

Wang Xingxing: Because we have a strong foundation. There’s significant overlap between robotic dogs and humanoid robots. Our company holds advantages in technical R&D, AI algorithms, manufacturing, and sales channels. We already have an established customer base and ready-to-market products. Other companies have to build everything from scratch, which takes time.

TMT: Is your revenue sufficient to support R&D?

Wang Xingxing: Our company maintains healthy gross profit margins, complemented by ongoing funding.

TMT: For humanoid robot startups, is the ability to secure funding a core advantage?

Wang Xingxing: It’s hard to judge the industry right now because it’s too hot. Many companies with basic foundations have raised some funds, which are at least enough to keep them afloat.

There’s certainly no shortage of funds in this industry. When we started, we were poor. Compared to back then, things are completely different. Now, some companies have been around for only a year and already have a valuation of 1 billion yuan. It's astonishing. The industry isn’t short on capital, and neither are they.

But I think that before the industry truly takes off, having too much money is pointless. It’s difficult to allocate effectively, and if spent indiscriminately, it could easily be wasted. At this stage, neither the technology nor the business models have been fully validated, so throwing money around wouldn’t be wise.

Take bike-sharing, for example. It worked because the business model made sense. Once that’s proven, the only thing left is scaling up, and there’s nothing left to do but pour in funding.

TMT: What do you mean when you say the technology and business model haven’t been fully validated?

Wang Xingxing: It means that neither the technical framework nor the commercialization strategy is fully developed. Even if you have the capital, you don’t necessarily know how to deploy it effectively.

TMT: What are the main technical challenges?

Wang Xingxing: For humanoid robots, the biggest question is how to integrate with AI models—we don’t have a definitive answer yet.

TMT: Another observation—most humanoid robotics entrepreneurs today are quite young. (Wang Xingxing is from the ‘90s generation.) Why is that?

Wang Xingxing: It’s simple. Older generations just aren’t as interested in this space. AI technology is evolving at an unprecedented pace and older knowledge is becoming outdated – knowledge of the technology we had five years ago is practically irrelevant. The younger generation is fastest at learning and applying the new advancements. Traditional internet startups had a low barrier to entry — basically anyone could become a product manager. But humanoid robotics isn’t a conventional industry.

[Angela: We’ve written before about how this generation of emerging technology creates space for young, enthusiastic talent to make an impact — DeepSeek is a good example of this. It would be interesting to know if, like DeepSeek, Unitree draws its success from China’s pool of homegrown talent. Wang Xingxing himself never studied or worked abroad.]

TMT: Earlier, you mentioned the potential for a breakthrough innovation. Were you referring to how humanoid robots and AI models can be integrated?

Wang Xingxing: Yes, more or less.

TMT: But aren’t AI models just modular components that can be put together like building blocks?

Wang Xingxing: The differences in AI models go far deeper than that. Take Transformer architectures, for example—there are still endless ways to optimize and refine them. Researchers are even exploring alternatives to Transformer-based models altogether. The AI field is full of opportunities for technical breakthroughs, and there’s still vast room for innovation.

I anticipate that by 2025, we’ll see a significantly improved AI model for general-purpose humanoid robots. When that happens, industry momentum will accelerate even further, to the point where companies from around the world try to enter.

TMT: At that point, do you think hardware or software will be the first to breakthrough?

Wang Xingxing: Software will be the key driver. No matter how advanced the hardware is, without the right software, it’s just an expensive pile of metal.

TMT: So given the current pace of development, as soon as the right software emerges, the hardware will be able to keep up?

Wang Xingxing: Absolutely. Hardware won’t be a bottleneck. If it’s really needed now, we can scale production quickly by aggressively deploying capital. If necessary, we could push manufacturing capacity to its limits — pay engineers 10 times their normal salary, work around the clock, and purchase all the necessary equipment. With sufficient investment, we could have mass production up and running in as little as a few months to a year.

TMT: How does China’s hardware capabilities compare to those of other countries?

Wang Xingxing: China has a significant edge in hardware. The cost-performance ratio is much higher.

TMT: Why is that?

Wang Xingxing: First, in the U.S., hardware development doesn’t receive as much attention—most of the top talent is focused on software. Second, manufacturing and labor costs in the U.S. are much higher than in China.

TMT: It seems like they are prioritizing software, while our strength lies in hardware.

Wang Xingxing: Exactly. Most major U.S. companies focus primarily on software. But at Unitree Robotics, we are developing both software and hardware, because maintaining competitiveness requires full-stack capabilities. As a relatively smaller company, we can’t afford to focus on just one domain. Large corporations can get away with specializing in software and outsourcing hardware, but for us, abandoning hardware development would be an unwise strategic move.

TMT: Why has robotic dog technology matured faster than humanoid robotics?

Wang Xingxing: One reason is that robotic dogs have been in development for a longer period, and their form is more stable. They don’t require complex dexterous manipulation, like grasping and handling objects.

Another key reason is that, in the past, there was a much larger community of developers working on robotic dogs, whereas today that number has declined. In AI, the maturity of a technology is often directly correlated with how many researchers are actively working on it.

For example, large language models have advanced much faster than AI for robotics simply because more people are involved in developing them. Ten years ago, computer vision—especially facial recognition—was in its golden age because so many researchers were working on it. Image-based AI took off because it was relatively easy to experiment with; all you needed was a decent computer.

But robotics is a different story. It requires hardware simulation and real-world testing, which makes it much harder for individuals to participate. That’s why the field has been slower to progress. However, as I mentioned earlier, the industry is now accelerating because a growing number of people are entering the space. More minds working on a problem naturally lead to faster breakthroughs.

TMT: Does Unitree Robotics have a clear product roadmap and timeline?

Wang Xingxing: We will launch new products every year.

TMT: What do you envision for Unitree’s next-generation robots?

Wang Xingxing: The next generation will undoubtedly surpass current models in every aspect—appearance, performance, AI capabilities, and more.

TMT: Can you give a specific example?

Wang Xingxing: Our goal is for humanoid robots to perform real industrial tasks—working in factories, assisting in production assembly, and handling logistics.

TMT: Do you have a release timeline for the next generation of robots?

Wang Xingxing: It’s not convenient to disclose at the moment.

TMT: Unitree Robotics has already completed eight rounds of funding. Do you expect fundraising to accelerate moving forward?

Wang Xingxing: I think we’ll be fine. As the industry gains more attention, we’re seeing increased interest from investors.

TMT: What do you think will be the ultimate future for humanoid robotics?

Wang Xingxing: In the future, humanoid robots could redefine entire industries—from manufacturing and services to agriculture, mining, and construction.

I imagine a distant future in which governments could deploy tens of thousands of humanoid robots to build entire cities from the ground up. At that point, infrastructure is fully automated, housing is provided at no cost, and people no longer need to work because robots sustain the entire economy. That’s entirely within the realm of possibility.

Also, right now when we talk about humanoid robots, we picture machines that are roughly human-sized. But in reality, humanoid robots could build smaller robots, and those smaller robots could build even smaller ones. This process could continue indefinitely, leading to robots at microscopic scales.

Eventually, we might see robots as small as biological cells. Who knows what’ll happen then? What we perceive as bacteria could actually be tiny robotic entities. The entire natural environment could be restructured from the ground up. When that happens, governments will need regulations to prevent unchecked proliferation, or these robots could consume resources uncontrollably.

[Angela: A very science-fiction vision indeed. But Wang’s fantasy of a robot economy resonates with Beijing’s investment in industrial robotics as a path for economic advancement. ChinaTalk will continue tracking such developments in robotics.]

TMT: Do you think we will see this level of technological advancement in our lifetime?

Wang Xingxing: Absolutely. The only missing piece is AI. Once AI breakthroughs happen, everything else will follow naturally.

This will fundamentally reshape the world. I’ve always believed that when we look back at today’s society after the emergence of general-purpose AI and humanoid robots, it will feel as distant and primitive as looking back at the Stone Age.

为全球华人游荡者提供解决方案的平台：游荡者（www.youdangzhe.com）
这世界的辽阔和美好，游荡者知道。使用过程中遇到问题，欢迎联系客服邮箱wanderservice2024@outlook.com.

【和放学以后永不失联】订阅放学以后Newsletter，每周三收到我们发出的信号：afterschool2021.substack.com 点击链接输入自己的邮箱即可（订阅后如果收不到注意查看垃圾邮箱）。如需查看往期内容，打开任一期你收到的邮件，选择右上角open online，就可以回溯放学以后之前发的所有邮件，或谷歌搜索afterschool2021substack查看。

截至目前，放学以后Newsletter专题系列如下：“在世界游荡的女性”系列、“女性解放指南”系列、“女性浪漫，往复信笺”系列、莫不谷游荡口袋书《做一个蓄意的游荡者》系列、“莫胡说”系列”《创作者手册：从播客开始说起》,播客系列和日常更新等。

写在前面：

本期放学以后信号塔由金钟罩轮值。

上初中的时候，从学校骑车回家需要40mins，坑坑洼洼的路面把我震得起起伏伏，但其实那种状态很high。路两边树长得老高，尤其不远处还有广阔的麦田，整个环境既隐蔽又开阔。路上罕有人见，我会在骑车的时候放声高歌，但大多数时候唱着唱着就开始自己freestyle瞎编起来，这应该是我最早的“音乐创作人”雏形，在那条回家的路上我少说创作了三五十首歌曲。

为什么会提起这段经历？最近为了更新newsletter我已经尝试了好几篇草稿，写到一半都觉得不够满意，总有种为了写而写的感觉。但昨晚散步回家的路上，我忽然灵感大作，并且从框架到细节完整地在脑海浮现，整个人进入了近乎狂喜的状态。

而当我打开电脑准备把这些想法写下来时，忽然想起一个问题：

“为什么一个人在散步的时候，很容易脑子里迸发出很多有创造力的想法？”

这或许可以把「骑车/散步vs创造力」关联起来。

以下是AI给我的答案：

1. 散步是一种低强度、重复性运动，不需要高度专注，这时大脑会进入“默认模式网络”（Default Mode Network, DMN）状态。这种状态通常出现在人放松、走神或做简单重复活动时，大脑反而会开始自由联想，整合碎片化记忆和知识，从而激发灵感。

2. 户外散步时，自然环境的视觉（树木、天空）、听觉（鸟鸣、风声）和触觉（微风、阳光）等“轻度刺激”会占用少量注意力，既不会让人分心，又能抑制过度理性的逻辑思维（比如工作时的“任务专注模式”），让潜意识有更多空间处理问题。

3. 当人长时间专注于某个问题时，容易陷入“功能固着”（Functional Fixedness），即只能用固定模式思考。散步时身体的活动和环境的切换，能帮助大脑暂时跳出原有框架，触发“远距离联想”（Remote Associates）——将看似无关的概念联系起来。

4. 心理学中的“孵化效应”（Incubation Effect）指出，当暂时放下难题去做其他简单活动时，潜意识仍在后台处理信息，反而可能突然找到答案。散步正是这类活动的典型代表。

5. 2014年斯坦福大学的研究发现，参与者在步行（尤其是户外）时的创造力测试得分比静坐时高60%。另一项研究表明，接触自然环境能提升50%的认知灵活性（Cognitive Flexibility）。

6. 贝多芬、乔布斯、村上春树等创造性人物都曾提到散步对灵感的帮助。哲学家尼采甚至说：「只有散步时浮现的思想才有价值。」

于是，那些大脑在散步的时候整合了的碎片化记忆和知识，就成了今天的正文。

正文：

上英语课的时候，外教问了一个问题，“你认为在上一份工作中最自豪的成就是什么？”

对于英文提问，我现阶段大脑处理步骤还处于「收到英文问题-转化成中文-构想中文回答-把答案翻译成英文」的模式。当下我想回答的是，“跑通公司娱乐内容出品生产流程，并在OGC部门推广使用”。但是我根本不会翻译这句话，于是我换了个简单的答案说“在项目中帮公司省钱”。

我其实觉得这样回答没什么问题，对英文课来说，老师可能更在意表达的流畅性和语法用词准确性，并不在意你真正的答案是什么，所以我在当下做这样的选择，也是正确的。

但在散步的时候，这个问题忽然再次浮现，我意识到这不是个例，而是我习惯于回避一些真实但复杂的想法，习惯了用一种模式化或者套路化的方式去回答问题，而我的真实心意却没有表达，有时是因为难以表达，有时是因为我根本没有属于自己的观点。

比如，问到有关恋爱异地的问题，关于好和不好我都能说一堆；问到该选择工作还是选择gap时，我也都能说一堆。如果说这类问题本身就是各有利弊的话，有时问到我的个人感受或者个人想法，我也是可以张口就来，主观客观地分析一堆，回头说了什么自己都不记得。

这点在录播客的时候尤其明显，我大都是在为了回答问题而回答问题。这种感觉就像，领导让我准备一个立项文档，我甚至可以还不用深刻思考关于项目的意义和价值，或项目到底是为了解决什么，它背后有什么社会情绪。立马就可以从概要-目标-内容示例-时间预算-团队搭建等多个维度写完。可是为什么要做这个项目呢？我都没有好好问过自己。但是我知道，这是必做的项目，不必思考为什么要做，老板们已经决定了，我只需要去解决问题就行。

所以有时候莫不谷做出一个提议，霸王花可能半天说不出来，我却上来就能整两句。但是最终霸王花一旦表达出来的往往都是真诚且容易引起共鸣的；而我总是浮于表面的，听完了难以有什么感慨。

从上学到上班，这么多年的练习，样板间式的内容我做的太多了，我太知道怎么去做一个样板间了，就把做样板间的思考路径带入到生活的很多个方面。我并不是在diss说做样板间的能力或经验是不好的，只是我既然意识到了，就不能把自己的生活，把自己的大脑局限在样板间里。我得有一些个性化的东西，有一些自我的态度，有一些真正属于自己的思考，否则我将被必然被主流路径裹挟，有时候甚至被放学以后裹挟。

在洛桑的时候，朋友问了我一个问题“你是否会因觉得自己比不上她俩（莫不谷和霸王花）而感到自卑？你觉得她们已经在走自己的路了，但是你马上结束gap又要回国上班。”是啊，我有这样的想法。其实在过去这段时间，我否认了很多事情，包括否认了自己的工作上的成就，而这几乎是我过去十多年的全部啊。她们给我带来了广阔的世界，像一阵呼啸而过的飓风，排山倒海般的冲击着过往的价值体系。在这个过程中，我没有站稳，无意去美化什么，但是这个没站稳确实是必然的。世界是美好和多样的，莫不谷是在探索和开拓可能性，但并不是写了个作业让我来抄。主体性、主体性、主体性，这个放学以后反复呼吁和强调的概念，我也跟着大声附和的三个字，在几年后在一场散步中终于又见面了。

我本想溯源一下，生活中这种张口就来的习惯是从什么时候来的，不幸的是，好像从小就这样。小时候我简直是假话连篇，倒没有什么目的性，就是随口就说，张口就来，放在现在互联网也是那种很美的精神状态了。因为不经思考的真真假假，妈妈对我的回答，总是会抱有不可置信的态度看着我多问一遍，眼神仿佛写着“你没诓我吧？”的潜台词。

后来就发现学习中一切都有模版，参加演讲比赛，演讲稿和演讲的情绪都是有模板的；文科考试写问答题，回答的起承转合也是有模版的；等到上大学，更是一切皆有模版的，写个实践报告，把基础格式完成后，报告也差不多写了1/2。在学生会做事情更是有成熟的套路，看到老师首先礼貌打招呼，然后用看起来很熟的语气聊一些琐事；遇到同学，对大家的问题表现地先煞有介事的记住，然后官方客套地结束。

几乎是在一个又一个阶段的精细模仿中，结束了人格塑造关键的青春期。我记得之前提到过自己是一个适应能力很强的人，现在看来我其实是个模仿能力很强的人，能够快速去模仿并适应主流路径的叙事方式。

在这套叙事模式下，很多事情都有范本答案，“上个好大学，选个好专业，找个好工作，娶个好老婆/嫁个好男人，买个好房子，生个大儿子”（说完这些我已经有点恶心了）。在这套模式下，是很难做出什么真诚走心反馈的，如果你还身在其中，那你也一定也是张口就来。毕竟你在顺从的同时，一定也在回避。而你回避的是什么呢？你是否在散步的时候考虑过这些问题。

除了这种根本上对自我的回避，还有一种张口就来，是源于对当下情绪放大的需求。

比如为了博得同情而大肆渲染，用各种故事和细腻的感受来强化自己的情绪；为了表达共情和理解，而调动各种亲身经历来佐证此刻自己是理解对方的；为了维系感情创造浪漫，而半真半假的营造了很多共同点……在很多类似的情况我都容易张口就来很多话，越说越high，越说越把自己感动了。但其实转过头就忘记了，我很难记住那些时候说过了什么，只知道当时的情绪很上头，回想起来没有哪次是不后悔的。

我觉得这些可能是源于对失去的恐惧，或是对沉默的尴尬，身体有一种自我保护机制，在这种时候就会张口就来、喋喋不休。别管真的假的，一通语言攻击下来，说话的人和听话的人都累了，谁还在意的了细节。

不过我现在不太想随意放大这种情绪了，这种情绪张力其实很宝贵，偶尔在写作或者思考的时候遇到这种张力会觉得很难得。而且是我也不想浪费时间在这些问题处理上，不想再为此去迎合或讨好些什么，因为大部分结果都证明这些事情或者关系是有毒的。

现在回过头来看这篇文章我非常震惊，散步是如何把这些想法关联起来的？以及人的大脑运转起来的速度真是惊人，半个小时步行想到的东西，我花了将近5个小时才写完。英语课上的一个提问引发了一场大雨，在散步的过程中呼啸而下。

生活中的散步真的是大有裨益的！虽然听起来像是一句老生常谈的废话，但不妨今天下班的时候就可以提前一站下车，走一站路回家，整个过程就让大脑放空，看看散步会把你的思绪指引向哪里。

写在后面：

比起张口就来，我现在更喜欢提笔就写，即对自己不加限制地先把所有想法都写下来。我有一个从来没有刻意整理的备忘录，里面零七八碎的写了很多东西，有的写了几千字没有结尾，有的只写了一个标题。我时常在坐飞机或者极其无聊时翻翻这些杂乱的笔记，虽然很少续写过，但这些文字也在提醒我，我脑子里的确有闪过一些东西，并不是一直空洞的状态，我是可以练习并抓住的。

提笔就写，这是一个更加私人的事情，把这些东西都先放在备忘录里，自己有时间去考虑，我要不要去打磨它？要不要把它放出来？现在，我更喜欢这种有主动性、有私密感、还有可选择的状态。当然，这也是对创作灵感和创作素材的保护，在练习更多写作技巧之前，提笔就写才是最关键的那一步。

为全球华人游荡者提供解决方案的平台：游荡者（www.youdangzhe.com）
这世界的辽阔和美好，游荡者知道。使用过程中遇到问题，欢迎联系客服邮箱wanderservice2024@outlook.com.

【放学以后文章&书籍&其它】

解锁放学以后《创作者手册：从播客开始说起》：https://afdian.com/item/ffcd59481b9411ee882652540025c377

解锁莫不谷《做一个“蓄意”的游荡者》口袋书：
爱发电：https://afdian.com/item/62244492ae8611ee91185254001e7c00微信公众号：《放学以后After school》（提示安卓用户可下载“爱发电”app，苹果用户可把爱发电主页添加至手机桌面来使用，目前爱发电未上线苹果商店）

Newsletter订阅链接：https://afterschool2021.substack.com/（需科学/上 网）

联系邮箱：afterschool2021@126.com （投稿来信及合作洽谈）

为全球华人游荡者提供解决方案的平台：游荡者（www.youdangzhe.com）

小红书：游荡者的日常

同名YouTube：https://www.youtube.com/@afterschool2021

同名微信公众号：放学以后after school

欢迎并感谢大家在爱发电平台为我们的创作发电：https://afdian.com/a/afterschool

播客收听平台：【国内】苹果播客（请科学/上网）、爱发电、汽水儿、荔枝、网易云、小宇宙、喜马拉雅、、QQ音乐；
【海外】Spotify、Apple podcast、Google podcast、Snipd、Overcast、Castbox、Amazon Music、Pocket Casts、Stitcher、Radio Public、Wordpress

Announcing: the ChinaTalk book club! We have upcoming shows with the authors of To the Success of Our Hopeless Cause: The Many Lives of the Soviet Dissident Movement, To Run the World: The Kremlin’s Cold War Bid for Global Power, and Learning by Doing: The Real Connection between Innovation, Wages, and Wealth. We’d like to encourage you to read along with us in preparation for the shows!

Manus, a Wuhan-developed AI agent went viral this weekend. Guests Rohit Krishnan of Strange Loop Canon, Shawn Wang of Latent Space, and Dean Ball of Mercatus and Hyperdimensional join to discuss.

We get into:

• What Manus is and isn’t,

• How China’s product-focused approach to AI compares with innovation strategies in the West,

• How regional regulatory environments shape innovation globally,

• Why big AI labs struggle to build compelling consumer products,

• Challenges for mass adoption of AI agents, including political economy, liability concerns, and consumer trust issues.

Listen now on iTunes, Spotify, YouTube, or your favorite podcast app.

What is Manus?

Jordan Schneider: This past Friday, Monica — a startup founded in 2022 — launched a product called Manus. The launch was done through a video in English. Manus is ostensibly an AI agent that you tell to do something, and it can interact with the internet as if it were a person clicking around to book a restaurant, change a reservation, or potentially one day take over the world through Chrome. The rollout was remarkable, with hype building dramatically over the weekend. The product seems to be more competitive than similar offerings we’ve seen from OpenAI and Anthropic. With that context, Shawn, what were your first impressions of what Manus was able to build?

Shawn Wang: My first impression was that it’s a very well-executed OpenAI Operator competitor. It can effectively browse pages and execute commands for you. In side-by-side tests that people in the Latent Space community were running with Operator versus Manus, Manus consistently came out on top. This is backed by the benchmarks they hit on the Facebook Gaia benchmark, which evaluates agents in the real world [which is a public benchmark]. The product is very promising. I’m somewhat suspicious about how well the launch was executed with influencer-only invite codes and people writing breathless threads. We’ve seen this many times in the agent world, but this one people actually seem able to use, which is nice.

Rohit Krishnan: What interested me most was that all our previous conversations about China focused on models — how good their models are, how much money they have, how many GPUs they possess, etc. Now we’re talking about a product. The closest thing to a product from DeepSeek was their API, which is really good with an exceptional model, but the interface was just the same old chat interface. We’ve been discussing agents extensively for a long period. In the West, we still live under the umbrella of fear regarding AI agents, which is why most models aren’t given proper internet search capabilities. It’s amazing to see that the first really strong competitor has come out of China — arguably better, perhaps slightly worse, but definitely comparable to Operator. They made it work with a combination of Western and Chinese models, using Claude and fine-tunes of Qwen underneath. That changes the product landscape as far as I can see.

Dean Ball: I didn’t get an invite code myself but was able to use someone else’s account briefly. I ran it through my favorite computer use benchmark that I’ve organically discovered — trying to book a train ticket on amtrak.com. Operator consistently fails at this task, but Manus succeeded on its first attempt. That says something significant! Many other demos I’ve seen of the product seemed quite impressive and like things that would surprise me if I saw Operator doing them, based on my perception of Operator’s reliability and competence.

This isn’t a story about some shocking technological innovation or about DeepSeek’s unfathomable geniuses, as Jack Clark says, discovering some new truth about deep learning. This is good product execution in a style relatable to Y Combinator circa 2015. It’s a well-built product that works effectively, though it has flaws and glitches like all computer use agents do. What’s interesting is that this represents an advancement of capabilities. DeepSeek might be a more impressive technical achievement than R1 and V3 in some fundamental sense, but DeepSeek R1 wasn’t better than OpenAI o1 — at best, it was comparable. Manus appears better than what I’ve seen from Operator, or at minimum comparable but I think unambiguously better. Thinking about why that’s the case is a really interesting question.

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Jordan Schneider: Shawn, you just hosted a wonderful week-long AI agent conference in New York City. What’s your take on why no one in the West got to this first?

Shawn Wang: There are many skeptics of agents, even among the agent builders themselves. People range across the spectrum in terms of the levels of autonomy they’re trying to create. The general consensus is that lower levels of autonomy are more successful. Cursor being an agent is now worth $10 billion, whereas the people who worked on Baby AGI and Auto-GPT are no longer working on those projects.

Working on level four or five autonomy agents hasn’t been a good idea, while level one and two — more “lane assist” type autonomy agents — has been the better play. With the rise of reasoning models and improvements in Claude and other systems, that is changing every month. The first one to get there, like Manus, would reap the appropriate rewards.

Dean Ball: That intuition you’re describing is definitely something I’ve heard too, and it’s probably right. For a variety of reasons, I won’t be using Manus on a day-to-day basis. Part of that involves security concerns, but even without those concerns, I’m not sure this is a product I would use regularly compared to an agent like OpenAI Deep Research or a Cursor-style product. Those have much more genuine day-to-day utility.

As an investor in this company, I would be concerned that Manus will be, to use Sam Altman’s terminology, steamrolled by the next generation of computer use agents from the big labs. That’s very possible. From a practical business and technological perspective, this makes sense to me.

Rohit Krishnan: The key question I keep pondering is why Manus wasn’t built by a YC company six months ago. We’ve internalized the fear Dean talked about — that anything we build will get steamrolled by Sam Altman. In some ways, that’s correct. We all personally know code assist companies that emerged a couple of years ago and went bankrupt when the big labs effectively took over.

However, I have this heretical notion that despite everyone talking about agents, nobody at the large labs cares enough about them. They don’t seem interested in building products beyond making models smarter and letting them figure out products on their own. We’re stuck in this weird situation where I have access to every large model in the world, but half of them can’t do half the things because nobody has prioritized those capabilities.

O1 Pro can’t take in documents. O3 Mini couldn’t take in Python files or CSVs. Claude can’t search the web. These weird restrictions exist partly from AI safety concerns and partly because nobody has bothered to add these features.

One significant benefit of something like Manus is that people are actually trying to build useful agents for real-life tasks, like booking Amtrak tickets — which is a great evaluation benchmark. This pushes the labs or anyone else to say, “We should probably try to do this.” We can’t just throw up our hands and wait a year hoping the labs will build the next big thing.

The Western success story is effectively Perplexity — the one company that did what the labs would have been closest to doing but never did, and found success. Beyond that, when thinking about other agents we normally use, I can only realistically name Code Interpreter from a couple of years ago and Claude Code, which just released. Both are stripped-down versions that do a few things but still can’t handle basics like search.

When I look at Manus, what stands out isn’t just that they made an agent ecosystem work using external or combined models, which everyone expected would happen. More importantly, they actually went for it. There’s a price to pay — you have to try it. You can give it browser access, let it work for four hours, and get something useful back. Unlocking this capability is important from a product perspective.

Jordan Schneider: I’ll give one more perspective as well, which could be a fun US-centric observation. In the US, we’re very interested in B2B and developer tooling, especially in Silicon Valley. We really love developer tools, building for developers because we feel the pain. In China, there’s perhaps more B2C focus, which actually works to their benefit in terms of finding good use cases.

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Rohit Krishnan: What is the previous large software success story from China that took over the world? There’s TikTok, and that’s essentially it. WeChat is amazing, but nobody uses it outside China — maybe in parts of Southeast Asia. Banking software emerged, but nobody really adopted it. Alibaba Marketplaces exist, but they haven’t permeated the West in any meaningful sense.

This might be an unconventional statement, but AI is one of those domains where you can build amazing AI agents using existing models from anywhere in the world. I’m glad we’re starting to see that happening.

Jordan Schneider: It was remarkable how this company, with both its browser and its first product Monica — a ChatGPT-like search browser add-on — targeted foreign users first. That’s notable because running Claude is illegal in China, which makes development difficult.

Reading interviews with the CEO over the weekend, he stated essentially: We’re not really trying to take on the big labs, but we think there’s an opportunity and a big market here. It was somewhat sad reading when he alluded to the politics of AI: “I've come to understand that many things are beyond your control. You should focus on doing well with the things you can control. There are truly too many things beyond our control, like geopolitics. You simply can't control it—you can only treat it as an input, but you can't control it.”

Frankly, I don’t think Chinese AI agents will have much longevity in the US market without hitting some severe regulatory headwinds. However, their skill at playing the global influencer marketing game to generate this hype cycle reflects a real fluency in digital marketing. The fact that they could play this game better than any Western agent competitor — except for Devin, which tried but faced its own challenges — is remarkable. There hasn’t been another major attempt at this over the past year and a half.

Dean Ball: I would go even further. When the first Devin demos appeared, people exclaimed, “Look how cool this thing is!” Then the bubble burst when people realized it was GPT-4 with prompt engineering and scaffolding.

The Western AGI obsession makes us want to conceptualize this as one godlike model that can do everything, and we implicitly dismiss product engineering and practical applications. You see that reflected in public policy, which is obsessed with big models, giant data centers, and similar infrastructure. Those are the only things we seem to take seriously and value.

I’m a deep learning optimist — I’m not going to tell you AGI doesn’t exist or is a Gary Marcus fiction. I’m not in that camp at all. But the AGI obsession has developed into something that feels like a perversion, distracting us from opportunities lying right in front of us.

I’m not necessarily saying Manus represents that opportunity, but there are thousands of possibilities where cleverly stringing together different AI products and modalities could yield interesting results. We just don’t see much of that happening. A year ago, I was more inclined to say, “Well, it takes time,” but a year later, I find myself less willing to make that excuse.

Structural Factors Driving the AI Product Overhang: Why Big Labs Don’t Do Product

Rohit Krishnan: Shawn, what is this? Are the VCs dumb? Are the founders dumb? Are there actually not pennies to be picked up off the ground?

Shawn Wang: There are, and the VCs have woken up to it. I started writing about the rise of the AI engineer two years ago, and now there are VPs of AI engineering at Bloomberg, BlackRock, and Morgan Stanley — they just spoke at my conference last week.

People were very dismissive of the GPT wrapper, viewing it as just a thin layer over the LLM. Now the perception has almost flipped, where the model is the commodity and everything else on top of it is the main value and moat of the product. This is why I started talking about AI engineering, and I think it’ll be a growing job title. It’s what we orient my conference and podcast around.

It’s music to my ears. I’ve been saying this for a while now, and the VCs have caught up. It’s just harder to fund because you can’t just say, “Here’s the pedigree of the 10 researchers I have. Give us $300 million.” Now you have to actually look at the apps and see if they’re well-engineered and fit the problem they’re trying to solve, whether B2C or B2B. That’s much more difficult than throwing money and GPUs at talented researchers and letting them go for it. That approach caused Inflection AI, Stability AI, and other mid-tier startups to burn around $100 million each.

Rohit Krishnan: That’s back to the SaaS era in some sense. You suddenly find a new vertical niche where you can build something, spend time and effort, learn about the specific problem you’re solving — not just intelligence but something more targeted, B2C or B2B. Then you have to tackle it and solve it.

Shawn Wang: The interesting thing about this SaaS transition is you’re charging on value and not on cost, and the margins between those approaches are enormous. Many of us in Silicon Valley realize that if you develop your own models, the next one that comes out is probably open source from China and better than yours. So where’s the value in that?

Everything’s being competed down to cost. Anthropic offers Claude at cost. OpenAI has a small margin, but every other GPU provider serving open source models is just providing at cost because they’re trying to capture market share with VC money. Nobody’s making a margin here.

You contrast the $200 versus the potentially $2,000 or $20,000 a month agents you can offer, because you’re competing against human labor and human thinking time, which we are all limited by. The economics start to really work out. You could start charging for your output instead of charging for your cost of goods sold. That is fundamentally a better business.

Rohit Krishnan: Speak for yourself, Shawn.

Shawn Wang: The fact that you could just start charging for your output instead of charging for your cost of goods sold is fundamentally a better business.

Rohit Krishnan: You wrote a very cool thing about Google’s awkward struggles to make products that people use. What is stopping the big model makers from starting to do things they can charge value-based pricing for? Is it just that they don’t need to and have their hands full making AGI?

It does seem that just selling tokens isn’t going to make you money in the long run. It’s funny because if you’re one of the large labs, if you’re Sam or Dario, you don’t particularly care about that since you already have so much money coming your way. Anthropic just raised $60 billion, OpenAI is valued at $300 billion. These are astronomical figures.

We’ve normalized these numbers in conversation, but they’re absurd by any stretch of imagination. $300 billion is bigger than Salesforce. It’s insane to think about for a company. Why are they getting that money? Because they want to build AGI. Why do they think they can build AGI? Partly because they’re true believers, partly because they have the best research talent in the world who wants to build AGI.

What happens if you tell that research talent that they’ll be working on building agents for awhile? Many of them quit. Arguably, many did. In a weird way, it’s only in larger places like Google where you can potentially have a large enough contingent of people try some unusual approaches and build cool stuff.

They did create interesting products — NotebookLM is actually really interesting. It was a cool new product, new modality, new way of interacting with information. I am surprised that we didn’t see more of it. In typical Google fashion, it just kind of disappeared after a while. They have Colab, which is an interesting product that’s languishing in a corner somewhere.

Everything Google does involves creating a very interesting first product and then slowly killing it by cutting off the oxygen supply over the next five years. For somebody to care deeply about building a product here, it has to start right at the top. It has to come from a mission, because the argument against building a product — the engineers saying, “Just wait a year and everything will get solved" — is really seductive.

Safety, Liability, and Regulation

Shawn Wang: You really need somebody who has a Jobsian level of ability to push back and say, “I don’t care what you guys think. We need to actually build something that really works here.” That’s not a muscle that any of these companies have because none of them have built products. Arguably, the thing that kicked it all off, ChatGPT, was built as a research preview. What we are doing is all being okay with playing around with research previews that consistently sneak their toe in and pretend they’re a bit of a product, but they’re not really.

Rohit Krishnan: Let’s fast-forward to the near future when agents can do more economically useful things than book you a train ticket. Should we start with the safety angle? It’s wild if I’m going to let something exist as me on the internet or in my workplace and I’m responsible for it. Maybe Manus is responsible? Maybe OpeningEye’s responsible? Maybe the AI engineer who goes to Shawn’s conferences is responsible?

This is a very weird world where it’s not just Jordan Schneider as an AI-enhanced worker using chatbots, but Jordan Schneider letting go a little bit and having these automated minions exist under my aegis but also not.

Dean Ball: I haven’t checked their website thoroughly, but I would be very surprised if Manus or the company that built it has a safety and security framework, a responsible scaling policy, or has commented on the EU code of practice.

Rohit Krishnan: I actually looked for this. I could not find one thing that the CEO has said in any relation to any safety discussion or question.

Dean Ball: This thing doesn’t have any guardrails. I don’t think it’s a consideration for them. In some sense, that’s probably part of what makes this better than Operator or Claude computer use, because Anthropic and OpenAI have both legitimate business incentives and internal stakeholders who won’t let the company ship things with no guardrails.

There’s reputational risk. If OpenAI had released something like Operator with zero guardrails, you’d be looking at state attorneys general investigating you, and the FTC and others coming after you, just as they did with ChatGPT. The tech industry is pretty risk-averse on things like this because it’s an inherently risky endeavor.

Those are market incentives, because you shouldn’t be incentivized as a consumer or business user to throw agents into the wild who do things for you and potentially cause problems. There should be some liability for that. You should be incentivized not to do such things, and companies should be incentivized not to release such things.

I’ve been thinking about liability issues in the last few months and have concluded that the court system is going to really struggle. If something happened with Manus, there’s the user who prompted it, multiple LLMs behind the scenes, and a Chinese company that is almost certainly not subject to a legally cognizable claim, unless you want to go to court in Beijing. How is the American tort liability system going to figure this out? I’m skeptical it will do a very good job.

But no liability is a moral failing, too. As the cost of cognitive labor declines, one of the only things left with economic value is trust, pricing risk, and similar concepts. I wonder if frontier AI companies will slowly converge to being more like insurance companies or financial services companies. Those industries are based on trust, pricing of risk, and allocation of responsibility for harm that occurs from realized risks. That feels like what’s economically valuable here, certainly not selling marginal tokens.

Shawn Wang: There’s one proof point that maybe agrees on some level: we’ll never get the O3 API because OpenAI is choosing to release products instead of APIs. That makes sense if you believe your APIs are valuable — you stop giving them to everyone else. It also stops the Manuses of the world in their tracks, because they can no longer use those APIs.

In broadening this general safety discussion, this is just an argument for American AI accelerationism. The simple fact is, if you are more safe and stop yourself from doing anything, then China will do it first, and you’re behind. It’s better to be ahead and in control of the narrative, build in the safeguards at the LLM layer with the post-training that you do, and try to lead from the front instead of the back.

Rohit Krishnan: I have a more contrarian view. Even framing this in terms of safety is incorrect. What are we talking about today? The Manus of today, Operator of today — these aren’t safety concerns. They’re engineering concerns, misuse concerns. We’re using the 2023 version of AI safety, which seeps into every part of the “anything a model can do can be unsafe” conversation, and that distorts how we discuss what these products do.

As Dean said, the liability issue is important once these tools start getting used inside companies. If someone at Pfizer uses Manus to figure something out and creates a wrong drug, there are liability issues. If someone at Cloudflare uses Manus to fix a bug and creates an outage, there are clear questions about where responsibility sits.

But we’re still at the point of making these things work properly in the first place. Think about our example — testing if it can book an Amtrak ticket. We’re not yet at the point where AI agents are so incredibly amazing that we have to restrict them before they engage. I’d like to see them work properly before we leash them.

That doesn’t mean we shouldn’t have a parallel track thinking about liability issues. But these will be hard-won battles that push the frontier forward one issue at a time, rather than “We’ve figured it out for everything from searching medical information to booking tickets to writing open source code or malware."

One problematic outcome of these discussions in recent years is that we’ve conflated all these issues into one, and they’re not the same. I look at Manus and think, “Good. I’m glad somebody without a responsible scaling policy is showing us what can be done,” because there’s no inherent problem with giving something a browser. Yes, there can be prompt injection attacks — that’s new and we need to solve it, but we can’t figure it out without anybody actually doing anything. It’s a chicken and egg issue.

Dean Ball: If you’re a dentist trying to use AI to automate business processes within your dental practice, then the fact that OpenAI has a responsible scaling policy about biological weapons risk evaluation isn’t that important for you. But perhaps more problematically, OpenAI’s model specification says, “Follow the law.” Okay, gotcha.

My view is that we have to almost entirely reject the tort liability system for this because it’s too complicated an issue. This is the kind of situation where transacting parties need to come to agreement about what makes sense in these particular contexts and let contracts do their thing. The courts won’t adjudicate this on a case-by-case basis in any effective way.

The risk of accelerationism is that you accelerate without proper safeguards. Noam Shazeer left Google to accelerate and founded Character.AI. What happened? Character.AI said problematic things to children, made sexual advances to children, and a kid killed himself. I don’t know if you could say Character.AI is responsible for that child’s suicide, but he was talking to the chatbot when he killed himself. That’s a tort case — Tristan Harris is funding it in the State of Florida, with a sympathetic jury and judge.

What’s Character.AI now? It’s a husk. Noam Shazeer’s gone, back at Google, and the company is likely to be picked apart in tort litigation, with other cases against them too.

If you accelerate without figuring this out, something very bad could happen. As they say, bad facts make bad law. Maybe it’s not unambiguously the AI model’s fault, but if there’s a really nasty set of facts, you could get adverse judgments in American courts. The common law is path-dependent, so you could end up with a very bad outcome quickly.

I’m enthusiastic about accelerating adoption and diffusion — Manus is very much a diffusion story. But if we don’t, in parallel, work on risk assignment (not catastrophic risk safety, but determining who is responsible when things go wrong), we could end up in a bad situation rapidly.

Legal Frameworks and Innovation Timelines

Shawn Wang: Do you think it’s primarily financial infrastructure that is needed, like your model of AI companies as insurance companies?

Dean Ball: Legal and financial, yes. What you basically need is a contracting mechanism that is perhaps AI-enabled — AI-negotiated contracts, perhaps AI-adjudicated contracts so you don’t have to deal with the expense of the normal court system. Once you have contracts and liabilities on the balance sheet, you’re in derivatives territory.

It’s a New York problem, not a San Francisco problem at that point. This is certainly an AGI-pilled idea. I wouldn’t do this with Claude 3.7, even though I think it’s great, but I think we could get there in the next couple of years when models are capable of doing things like this.

This is just one approach, certainly not the only one, and it’s a nascent idea for me. But this could be where the money actually is — pricing risk and transforming risk is something America is much better at than China. We’re fantastic at that.

It’s weird because many of my Republican friends in DC hate that fact. They view finance as decadent, as does Chairman Xi. But there might be trillions of dollars of wealth to be created here.

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Shawn Wang: Any financial asset is based on the laws it’s grounded in, and I think the laws have to be figured out here. There’s a bit of a chicken-and-egg situation with that.

Dean Ball: Yes, but if you have contracts, contracts would form a substantial part of the law.

Shawn Wang: They still need to be litigated. One measure I’d be interested to plot is AGI timelines versus legislative timelines. We’re accelerating in AI progress and decelerating in law and Supreme Court resolutions of cases. Our legal infrastructure needs to keep up with AI progress, or we’re in serious trouble.

Dean Ball: I completely agree. That’s the problem I try to get my head around all the time.

Jordan Schneider: We saw the EU just try and completely fall on their face, which was not a good first effort for democracies.

Dean Ball: The problem is you don’t want to create a statute prematurely — a statute with a bunch of technical assumptions embedded in it prematurely. It’s a very narrowly targeted thing. For me, this is all clicking into place, and I think if we got this done in the next two years, we’d be fine.

Jordan Schneider: The future of agents in China is going to be really interesting. There was an argument two years ago that LLMs would have a hard time gaining traction in China because the government would worry about aligning them to avoid anti-party statements. But this is basically a solved problem.

I’m curious about your perspectives on the technical challenge — not just at a legal level of assigning blame, but at a product and operational level of building things that governments and large companies will be comfortable with. Is this just a matter of time? Is there anything fundamentally difficult requiring major breakthroughs? Once we have the technology to make Operator and Manus do really good things, will they be controllable as well?

Dean Ball: I’d be curious if you’d correct this assumption if I’m wrong, Jordan, but my impression of China is that it’s actually a somewhat more ice-cold libertarian country when it comes to liability issues, where there’s a greater “developing country” or “these things happen” mentality.

Jordan Schneider: Yes, until bad things happen, and then your company gets shut down.

Dean Ball: It’s more of a binary outcome.

AI and the Future of Work

Jordan Schneider: Let me take this in a different direction. JD Vance at Paris said, “We refuse to view AI as a purely disruptive technology that will inevitably automate away our labor force. We believe and we’ll fight for policies that ensure AI is going to make our workers more productive. We want AI to be supplementing, not replacing work done by Americans.”

Shawn Wang: This is something AI engineers worry about a lot. A surprising number of them are actually worried for their own jobs, which is very interesting.

The main question is whether you have a growth mindset or a fixed mindset view of the world — whether you believe human desires tend to expand over time. Whenever we reach a certain bar, we immediately move that goalpost one football field away. The idea is that, yes, AI will take away jobs that exist today, but we will create the jobs of tomorrow, and ideally those are the jobs we want to do more of anyway.

Rohit Krishnan: I agree. To a large extent, that sentiment is the most normal politician statement in the world — technological growth is great and will continue making everyone’s lives better. It’s the same thing people have said for a very long time.

The difference here is that there’s at least a contingent of people who look at that and say, “No, this time it’s different.” You might say it’s not disruptive, but it could be massively disruptive in a short period of time to a large segment of society. It’s not just agriculture getting mechanized, but potentially all white-collar jobs.

When I’ve examined this issue, I don’t think massive disruption will happen immediately. The technological, regulatory, and sociological barriers are large enough that we won’t all be unemployed in five years. There are enough things to do. As Shawn mentioned, we’ll have to address the inevitable complications of regulatory frameworks before these technologies can be deployed everywhere.

When I did some rough calculations, we’ll still be bottlenecked by chips and energy in 10 years, which will prevent us from replacing all labor with AGI or AI agents. Does that mean there will be no disruption? Absolutely not. I fully expect disruption.

We already have AI that can plausibly replace large chunks of specific white-collar tasks that I do, you do, legislators do, or Supreme Court justices do. Pick your poison — we could probably replace a chunk of these roles with Claude 3.7 and get better results. We’re already in that world, but complete transformation won’t happen immediately.

JD Vance has to toe the party line: anti-AI safety, pro-acceleration, technological optimism all the way. I support that approach, but I’m not parsing his statement with any deeper meaning than that.

Dean Ball: I find myself more worried about slow diffusion due to multiple factors. The bottlenecks are regulatory, but there are many other bottlenecks as well. I’m much more concerned that diffusion and actual creative use will be slowed. I worry about the uses of LLMs that no one has ever thought of, and I’m concerned that no one will ever think of them. That’s probably the bigger issue we should be addressing through public policy.

In the longer term — and in AI time, that’s about three years — I do think there’s a possibility that some elite human capital might get automated in different ways. Political instability tends to emerge when you have an overproduction of elites in a society. We already have that problem. We’ve already significantly overproduced elites in America, and I worry that will get worse. When you combine that with other political problems America faces, you could have a tipping point phenomenon.

I wouldn’t dismiss it entirely as a risk, but my default assumption would be that the risk is actually on the other side — not diffusing fast enough.

Rohit Krishnan: I have a thesis that I sometimes hold that markets that become extremely liquid end up with polarized outcomes. We’ve generally seen that with capital markets — globalization has meant some companies get extremely large while the middle gets decimated, which is why most gains come from the Magnificent Seven.

We could easily see something similar happen in labor markets. We already see flashes of it. Engineering salaries have a somewhat bimodal distribution. Lawyers experience it too. Once AI enters the picture, we might have a vastly more liquid labor market than ever expected. This sounds nice, except it results in a power law distribution. Polarization is difficult to address in domains we don’t know how to handle cleanly or where we can’t easily establish minimum thresholds.

Jordan Schneider: Any closing thoughts?

Shawn Wang: [In true professional podcaster fashion…] I don’t know if we’ve answered the question that you’re likely to put in the title of your episode: “Is this a second DeepSeek moment?” For what it’s worth, my answer is no.

Dean Ball: I agree. In some sense, I think it’s actually more interesting than DeepSeek. And in another sense, it’s certainly not as impressive of a technical achievement as DeepSeek.

Rohit Krishnan: I’ll argue for yes, because I think DeepSeek was a DeepSeek moment for core research talent. Manus is closer to DeepSeek for product. I’m glad we’re pushing a second boundary as opposed to pushing the same boundary.

Mood Music

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Just as the buzz around DeepSeek was beginning to fade, Chinese AI has made waves again with the AI agent “Manus,” launched on March 6th, 2025. Today, we’re here to unpack the Manus launch, explore the business model of Manus’ parent company, and offer a glimpse into the mind of Xiao Hong 肖弘, the founder behind China’s latest viral AI product.

What is Manus?

Manus claims to be the world’s first general-purpose AI agent. It ostensibly outperforms OpenAI’s ChatGPT Deep Research on the General AI Assistants (GAIA) benchmark. Currently in beta testing, access is restricted to those with invitation codes, which are reportedly being listed second-hand for 50,000-100,000 RMB (whether anyone actually paid that much is another question). Users report impressive performance in basic tasks, for instance rebooking airline flights, beyond what Anthropic’s Computer Use and OpenAI’s Operator have thus far provided to users. The product is also experiencing slowing response times, hinting that Monica.ai may be struggling to scale up compute to meet skyrocketing demand.

Manus started in 2022 as an AI-powered browser plugin, backed by ZhenFund (真格基金). In 2023, the company secured Series A funding led by Tencent (腾讯) and Sequoia Capital China (红杉资本中国). What began as a simple “ChatGPT for Google” browser plugin has since evolved into a full-fledged AI agent.

Monica, the company that developed Manus, operates from Wuhan, rather than from China's major tech hubs like Beijing or Shanghai. In early 2024, ByteDance attempted to acquire Monica for $30 million, but founder Xiao Hong (肖弘) turned down the offer. ByteDance’s plan was to absorb Monica’s team and technology into its Doubao AI ecosystem, a move that would have diluted Monica’s distinct market position. Instead, Monica closed a new funding round at the end of 2024, reaching an estimated valuation of nearly $100 million.

The exact AI models powering Manus remain unclear. The company claims to use multiple models for different tasks. Notably, when prompted to reveal its own system files, Manus reveals it may be powered by Anthropic’s Claude models — which would make operating in China illegal. This probably explains why Monica’s website appears to be blocked in China.

Edit: confirmed by co-founder.

Anyway, the fact that Manus appears to disclose more than it should hints at broader potential security vulnerabilities.

Who is behind Manus?

Founder & CEO, Xiao Hong (肖弘), is a serial entrepreneur and a graduate of Wuhan’s Huazhong University of Science and Technology (华中科技大学). He first made his mark by building WeChat-related tools as a student, admitting that while his “academic performance was quite poor,” he partnered with more technical classmates to build tools. In 2015, he launched Nightingale Technology (夜莺科技) and created Yiban Assistant (壹伴助手), a WeChat management tool that secured early backing from ZhenFund (真格基金).

By 2019, Xiao saw a bigger opportunity in enterprise WeChat tools and developed Weiban Assistant (微伴助手). His timing was perfect—when rival WeTool (微商工具WeTool) was shut down in 2020, Weiban became the go-to alternative, attracting investment offers from Sequoia Capital China (红杉资本中国) and Youzan (有赞). Eventually, Minglue Technology (明略科技) acquired Weiban, marking Xiao’s first major financial success.

Sensing the potential of large AI models, Xiao left Minglue in 2022 to create Monica.ai, originally designed as a “ChatGPT for Google” browser plugin.

Co-founder & Chief Scientist, Ji Yichao (季逸超) dropped out of high school at 17 to develop Mammoth Browser (猛犸浏览器). His talent caught the eye of Sequoia Capital China’s Zhou Kui (周逵), who introduced him to investor Xu Xiaoping (徐小平). Xu invested 1.5 million yuan, giving Ji complete creative freedom. Recognizing the large potential of LLMs, Ji joined Xiao Hong to start Monica in late 2022.

Interview Quotes

Unlike DeepSeek’s media-shy Liang Wenfeng, Xiao Hong has done a ton of press. Below are selected translations from several in-depth interviews with Monica’s founder and CEO, Xiao Hong, offering insights into his vision, strategy, and the future of AI agents.

The vibe of Xiao Hong’s interviews is distinct from the AGI-driven idealism blended with national pride we’ve seen from the founders of DeepSeek and Unitree. Xiao is pragmatic and focused on profitability rather than research. A newly published three-hour podcast with Xiao opens with offering this piece of advice:

“I remember there was a Northeastern Chinese restaurant near my university. I made enough money to treat my tech club friends to dinner there every day. Here’s a tip for the audience: if you’re in college, take your most talented classmates out for meals as often as you can. If you wait until after graduation to recruit them for your startup, you’ll have to treat them to Michelin-starred restaurants instead.”

In another interview from January 2024, Xiao openly admits that he didn’t initially believe in AI’s potential, and “remained cautious” despite the hype surrounding GPT-3.5 in the fall of 2022. He describes coming to two conclusions about AI investment, which eventually led him to focus on AI products as opposed to chasing AGI with foundational model research:

"First, I wouldn’t consider working on big models without sufficient business scale. Second, I believe that in China, big model services will eventually integrate fully with cloud computing. I’ve discussed this with our CTO and believe that cloud computing companies will provide customized deployment services, so we don’t need to dive into that ourselves."

…

"I focused more on what big models could do, and what kind of applications I could build with them. In the beginning, many people were financing based on concepts, but by the second half of the year, both domestic and international, there was much less of that. Everyone was returning to business rationality, focusing on finding PMF (Product-Market Fit). By February of 2023, I had a conversation with an investor focused on big models, and no matter how I asked, they refused to talk about products. They weren’t discussing technology or plans. By March, the product’s valuation plummeted. People realized that simply building a single application based on big models might not work, and that’s when the consensus started forming: either focus on technological breakthroughs or work on relatively closed-loop application scenarios."

…

"In March and April of 2023, the fastest-growing product outside of ChatGPT globally was Poe. It was essentially a shell around a big model, and I told investors that if you can perfect the shell, that’s still a big deal. So we decided to do it too, and instead of resisting the demand, we decided to embrace it. In the first half of 2023, Monica integrated all the major models because that’s what the users wanted, and we started by doing that, figuring out how to find more use cases step by step."

Monica’s business model focuses on catering to the overseas market, which likely explains why their website is devoid of any reference to being based in China. Besides English, Monica’s website has dedicated versions in traditional and simplified Chinese, as well as Russian, Ukrainian, Bahasa Indonesian, Persian, Arabic, Thai, Vietnamese, Hindi, Japanese, Korean, and a slew of European languages.

In Xiao’s words, “We chose to target the overseas ToC market because I felt it was a larger, more commercially viable market. The domestic market seemed a bit more challenging.” Their focus shows: in contrast to DeepSeek’s very low key model launches, Manus’ launch came with a whole sophisticated press push like one you would see out of a YC startup, complete with a very well-produced English-language launch video and early access for select YouTubers and twitter influencers.

International expansion comes with its own difficulties, but Xiao believes those challenges made Monica stronger as a company. He’s recently argued that China would benefit from having more firms look abroad:

Xiao Hong: I think we are still in a great era with many opportunities…. First, it's the AI era. Second, I think we are also in a great era of globalization. I'm not a geopolitical expert, but it seems like every country has its own problems — internally, everyone has their own issues. So overall, the world is becoming more conservative and more isolationist, right? But at the same time, no one wants others to be isolationist; they only want to be isolationist themselves. So, everyone hopes that their own entrepreneurs will think more globally.

I believe China’s entrepreneurs of today should be more aggressive in globalizing. If we see overseas markets as better opportunities, it’s not just about market-driven decisions — we should step into international markets to gain experience. We need to participate in global competition, rather than just competing in the markets we are familiar with.

By the way, this process requires a lot of things. When I started this company, none of our founders had lived abroad for an extended period. Everyone’s English proficiency peaked in high school and declined in college! [

I once joked that if, at the same time, there was another founder who had lived in the U.S. and was placed next to me, I would have chosen to work with that founder myself. But this shouldn’t be the way we compare things — it should be about doing our own thing. Secondly, I had a simple belief at the time: the global market is much bigger, and the market itself will provide the tuition fees for founders to learn. (Laughter)

Besides the AI era, another crucial topic is that we are now thinking about things with a globalized mindset.

Unsurprisingly, this business model also relies on collecting vast amounts of user data. Monica’s free Chrome extension requests expansive access to browser data, including permission to log keystrokes, and Manus “crawls” devices to make suggestions. Xiao is betting that widespread adoption of these products will unlock a treasure trove of monetizable insights.

“The data we collect through our browser plugin is critical. Even though this might not guarantee success, it’s a step in the right direction. The private data we gather, along with contextual information, will help differentiate us from the competition. This is one of the key assets we need to grow.”

Xiao is explicitly describing an intent to build an incumbent advantage on a foundation of user data, and TikTok demonstrates how effective that strategy can be. Reliance on eventual mass adoption could partially explain the high-publicity invite-only launch strategy for Manus (although limited access to compute is also certainly a factor).

That said, he is aware that the politics exist and could get in the way of a Chinese-owned AI agent gaining widespread adoption abroad. He spoke about it in a recent podcast alluding to NeZha 2.

I've come to understand that many things are beyond your control. You should focus on doing well with the things you can control. There are truly too many things beyond our control, like geopolitics. You simply can't control it—you can only treat it as an input, but you can't control it.

I recently asked DeepSeek to explain three terms 贪 (greed), 嗔 (hatred), and 痴 (ignorance) [the ‘three poisons’ of Buddishm recently spotighted in the truly excellent animated movie NeZha 2]. It explained it very well: greed is attachment to favorable circumstances; anger is dissatisfaction with adverse circumstances; and ignorance is not understanding the truth of the world. The "truth of the world" is very profound, so I won't discuss that. But greed and anger are problems many people encounter, as are attatchment to favorable circumstances and dissatisfaction with unfavorable ones.

This business-minded pragmatism shines through in Xiao’s vision for the future — instead of techno-optimist visions of AI-powered drug discoveries or a moon colony staffed by robots, he imagines a world where humanity can return to a glorious past:

“I think that the white-collar lifestyle may be a detour for mankind. If you look at it in terms of a curve or over a longer period, say thousands of years, or even the ten-thousand-year span of human history — it's actually quite rare for people to sit in one place and engage in intense mental work without much physical activity. This is probably only a phenomenon of the past hundred years.

For a longer time in history, maintaining physical health and developing spiritual civilization have gone hand in hand. In ancient times, people also needed spiritual and cultural development, but that involved physical labor as well, which helped strengthen their bodies.

In the past hundred years, however, issues like diabetes and high blood pressure have become widespread because people work in this sedentary way. If we look at humanity as a whole, sitting and working for eight or more hours a day is an anomaly.

If AI can take over these tasks, then people can work fewer hours and go back to living more like they did in the past — focusing more on spiritual and cultural enrichment while also taking better care of their physical health.”

To close, here’s a quote from Xiao about how it feels to live through history:

Xiao Hong: From the time I was born in the 90s until now… there have been significant shifts, from PCs to mobile, then the semiconductor industry, which has been booming behind the scenes, the rise of the internet, and now artificial intelligence. I feel like these opportunities are emerging very intensively. When I watched The Godfather, I realized that if I had lived in that era — it was also a time of change — but if you lived in certain periods, you might not have witnessed such rapid technological progress. Sometimes, when we read history books or ancient texts, it feels like things barely changed, which I think would be a little frustrating!

亲爱的媎妹：

见字如面！

谈起女性身份，我们最先想到的往往是苦难与压迫。在父权社会，生而为女似乎是“不幸”的代名词。女人注定要被重重枷锁禁锢，历经苦痛也难以获得真正的自由。仔细想来，长期被定义为“第二性”的我们，好像很难具备那种与生俱来的自信和主体性。我们常听人说“做女人好难”，而做一个觉醒的女人更是“难上加难”，因为这意味着要清醒地面对现实的痛苦、即使前方迷雾重重也要摸索前行。

然而最近，我们开始尝试转变思维：做女人真的不好吗？作为在现实中努力生活的女性，我们能否跳出女性“处境”的问题，从另一个角度探索内心深处“身为女人”的自豪感？我又要怎样发掘能持续推动自己向前的积极力量？在这封信里，我们想和大家一起思考：生而为女，我们究竟有哪些独特的力量和优势？

1、吾身即宇宙：我的存在就是我对抗世界的武器

我们曾在去年的女性节推文《为99%的女性而战｜一份女权宣言》中深入探讨过父权资本主义的贪婪和短视。男权社会建立在无休止的侵略和索取之上，单方面的暴力掠夺看似保证了男人江山永固，实际却是压迫者在自掘坟墓。这种毫无合法性的权力来源催生了上位者内心深处对资源耗尽的焦虑，对失势者挺身反抗的忧虑，以及对终将失去权力、财富、地位的恐惧。所以，尽管父权制赋予男性至高无上的权威、不遗余力地贬低女性价值，却始终难掩自己“小人得志”和“金玉其外、败絮其中”的本质。与之相反，女性的力量却是源自生命本身——我们的基因血脉、身体机能，精神力量、爱的天赋、与自然的共生关系——这些都是偶然“篡位”的男人们久久觊觎却永远无法真正夺走的无价珍宝。

我们非常喜欢的女权动作片《疯狂的麦克斯4：狂暴之路》就完美地印证了这一点。影片中，导演描绘了一个资源紧缺、生态崩溃、战争频发的男性主导的世界。暴君"不死乔"通过控制水资源建立武装政权，利用宗教信仰奴役民众，将女性改造成生育工具，把新生命打造成战争机器。然而，当指挥官Furiosa联合母系部落的力量、带领生育者奋起反抗，竟在几日之内就摧毁了固若金汤的"不死乔"帝国。最终，Furiosa不仅手刃了暴君，还打开了“水阀”，让生命之源重新滋润干涸的大地。这个过程完美诠释了父权体系的脆弱和女性的本源力量：即使男性掌控资源和权力，他们也无法摆脱资源短缺和延续后代这两个最根本问题带来的焦虑。男人对一朝覆灭的恐惧是一个注定会自我实现的预言。

其实，女性在生命层面的独特优势早已得到证实：联合国数据显示，女性平均寿命比男性长4.5年，其中一个重要原因就是基因层面的特质。研究表明，顺性别女性的双X染色体能有效预防免疫性疾病；而顺性别男性X染色单体上的任何基因突变都会严重损害他们对性相关疾病（如血友病和肌营养不良症）的免疫力。换言之，从基因角度看，（顺性别）女性天然具备更坚韧、稳固的身体结构，而这一点已然从根本上瓦解了“男性拥有绝对力量优势"的论调。

此外还有研究表明，雌激素的保护作用使女性在生命早中期遭受的氧化损伤更少，能显著降低因中风和冠心病导致的早逝风险。相比之下，男性的死亡率却是女性的三倍，这主要是因为睾酮分泌增加使得男性更鲁莽、更容易攻击他人。由此可见，《疯狂的麦克斯4》中暴君“不死乔”对后代健康的担忧恰恰是现实社会的写照——男性用健康和寿命换来的爆发力与攻击性，显然无法支撑其征服自然和宇宙的野心。

最后，男性的短寿还源于他们“掠夺式”的生活方式，这也反映了男权社会的焦虑底色。专家指出，男性普遍偏好红肉（猪、牛、羊肉等），蔬菜和膳食纤维摄入明显不足。这种营养失衡直接提高了高胆固醇疾病、心脏病乃至癌症的风险。符合“男性气质”的饮食结构和行为模式尽显压迫者无知贪婪的本色。为了维护父权资本主义，他们不得不马不停蹄地将最多最好的资源收入囊中、并试图榨干她人和自然的一切价值。男性无止无休的索取是对母系族群曾孜孜守护的自然法则的蔑视，而当下“丰衣足食”的盛景不过是父权制走向末路的回光返照而已。

由此可见，生而为女，我们的优势不仅镌刻于身体发肤，更蕴藏于我们认识世界、延续生命、与自然和谐共生的日常点滴之中。这不仅是我们作为「天选之女」的优势，更是女性先祖代代相传的智慧积淀，是一种源自生命本源的强大力量。即便在父权制下，男性凭借历史先机占据了资源和话语权，但他们却无法抢走和复制我们与生俱来的生命力。吾身即宇宙。当我们意识到自己才是生命真正的主宰，我们的存在本身已然蜕变为开辟新世界的无穷力量。

2、穷且益坚，不坠青云之志：在命运的漩涡中扶摇直上

的确，男性一出生就走上了一条通往“成功”的轨道，社会结构也不惜提供丰厚的资源托举他们，可这样一条“坦途”真的能通往成功和幸福吗？法拉奇有名言：“战斗本身比获胜更为可取，行在途中比到达终点更为美好。” 优越的条件和唾手可得的资源让男性「抗打击能力」奇弱，在困境中更容易迷茫、脆弱，甚至一蹶不振（参见《女性真的是一种处境吗？》）。在长期的“坐享其成”中，他们逐渐失去警惕性，变得越发无能。同时，因为总是被当作宇宙中心，男人往往冷漠傲慢，对自身以外的人事物缺乏同理心，无法为彼此提供有效的支持。因此，自私自利的男权拥趸必将在挥霍尽资源后走向灭亡，父权资本主义对男性的扶持和优待不过是作茧自缚。

而反观女人呢，我们从最深的泥泞中走出来，却从未失去战胜命运的决心和守望相助的精神力量。“宝剑锋从磨砺出，梅花香自苦寒来”——正是因为经历过最痛苦的绝境，我们才锻造出了最坚毅的品格；正是因为战胜过最刺骨的严寒，女人才明白携手并肩走出冬天的重要与可贵。种种打压非但没有泯灭我们的斗志，反而让我们淬炼出超凡的本领和生存能力，在一次次“磨砺”中变得更敏锐、强韧、有智慧。例如，研究表明女性的多任务处理和压力管理能力远超男性，而这样的后天优势就源于女性“被压迫者”的身份。大多数女人的日常——既上班又育儿——难道不正是最困难的Multitasking吗？（讽刺的是，按照男权推崇的社会达尔文法则，“养尊处优”的男性不是更应该因能力退化而被环境淘汰吗？🤣）

除此之外，父权社会的孤立和排挤更让我们学会了和她人建立深厚的连结和关爱网络。我们变得擅长协作共生、互利互助，亦掌握了取舍之道、领悟了守护与爱的真谛。在这场命运的洪流中，女性总是「多劳者能」、坚韧不拔，用灵活自如的创造力和悲天悯人的情怀实现集体的超越与重生。

更何况，正因为父权并没有为我们设定向上爬的路径，我们才有源源不断的动力去反叛、抗争、去改写男性制定的游戏规则、掀翻这盘“视我们为无物”的棋局。生而为女，我们的人生就是一场逆天改命的征程——扶摇直上、涅槃重生，我们会在打破桎梏的旅途中激发出无穷的力量，在广阔天地间展露属于雌性的万丈锋芒。而到那时，男权刺向女人的利剑必将刺穿他自己的咽喉。

3、破釜沉舟，必至颠覆：真正的自由从与父权决裂开始

生而为女，父权社会的压迫铸就了我们与之决裂的勇气。这看似矛盾，实际却是历史的必然。俗话说“光脚的不怕穿鞋的”，男权社会从未接纳和认可女性、也不曾给予你我真正的财富和权力，所以我们与父权的利益捆绑也就微弱到可以忽略不计。“置之死地而后生”——正因为身处“谷底”，我们才得以认清男权的腐朽与不堪，预见他们终将化为乌有的结局，并从心底萌发出最具颠覆性的力量。觉醒后“破釜沉舟”的勇气就是我们与父权决裂、逃离充满“毒气”的洼地的最佳筹码。女人表面的弱势正是我们最为强大之处。

而在父权社会中占尽“便宜”的男性就没那么幸运了。父权的游戏实在太过诱人，以至于他们即使受尽折磨也依旧趋之若鹜。与其承受背叛“大爹”的代价，他们宁愿选择屈服——因为害怕被“开除男籍”、沦为一无所有的父权“弃子”。电影《好东西》中的一段对白恰好印证了这一点：小叶调侃小马是“和父权决裂的loser”，而小马却反问：“我都跟父权决裂了，我还能是loser吗？” 这句话点出了男权体系的的滑稽本质——父权千方百计要选出一个赢家，却恰恰导致所有“玩家”都必输无疑。父权体系何来真正的赢家，有的不过是一轮又一轮的自相残杀。因此，人只有彻底脱离这个游戏才可能避免成为loser的命运，可是男性真的会愿意吗？

事实上，和成为父权社会下的失败者相比，男性更害怕的是那种完全脱离父权体系的孤立感。他们担心自己被排除在符号系统之外、经历“社会性死亡”，因此不得不选择与父权共沉沦。在那种永无可能实现的“赢”的蛊惑下，他们争先恐后地进入这个残酷的游戏，即使这意味着要放弃爱的能力、抛弃信仰、感情和道德也在所不惜。沉没成本越高就越难脱身，骑虎难下的男人们终将在肮脏的泥潭里越陷越深，就算拼命互相倾轧也逃不过被吞噬的结局。

而我们不同！成为父权期待的“女性”并不能给我们带来丝毫好处，而只会换来更多打压和束缚。我们早已不想再为男权“输血”，更无意将自己“阉割”成男性、加入这场弱肉强食的必输局。而这难道不是最大的幸运吗？正因不容于体系，我们才得以摆脱沦为无思想、无情感的生产机器的命运、才能逃脱「将全部时间心血都奉献给资本主义这个冰冷魔窟」的诅咒。

当你本就一无所有，任何反抗都是赢面；当你深陷谷底，任何机遇都会带来向上的可能。作为父权的「弃女」，我们生来就要踏上与之决裂的道路，而在这条“不归路”上，我们也注定会寻得最纯粹的自由——而这样的自由，恰恰是身处“高位”的男性可望而不可及的。

结语

生而为女，我很自豪。我为我的身体发肤、血液脉搏、基因肤色而自豪；为我每一寸肌肉、每一块脂肪所承载的生命力而自豪；为我洞察万物的双眼和倾听风吹林海的双耳，更为这颗永远鲜活跳动、永远热烈纯净的赤子之心而自豪。

我为爱自己而自豪，也为爱她人、爱世间万物而自豪。我爱我在逆境中铸就的细腻情感和共情能力，爱我在困苦中冷静自持的定力、在艰难时化险为夷的聪明才智。我爱我与天地万物和谐共生的天赋和悟性。我爱每一个独特灵魂散发的光芒，爱自然慷慨的馈赠和眷顾，爱理解、包容、接纳以及原谅的炽热温度。

我也爱这个阻碍我、伤害我、却又无时无刻不在成就我的世界。

就此搁笔，期待下一次和大家见面！

陌生女人2号和1号

二〇二五年三月十日

对于我们60后这代人来讲，罗马尼亚是个既陌生又似曾相识的国度。一位老友看到我拍摄的沿多瑙河骑行的照片，说那些画面让他想起一部罗马尼亚老电影《多瑙河之波》，他小时候看过，对多瑙河很是神往。当时正值改革开放之初，国内公开放映的外国电影不多，其中就有罗马尼亚电影，除了《多瑙河之波》，还有《沸腾的生活》。那两部电影成了我们青少年时代有关多瑙河、黑海、爱情、反法西斯和现代欧洲生活最早的启蒙。   

从贝尔格莱德往东约100公里，多瑙河变成塞尔维亚和罗马尼亚之间的界河。沿塞尔维亚一侧的河边公路骑行，可以清晰地看到北岸罗马尼亚公路上行驶的汽车，听到对岸传来的汽车喇叭声和救护车、警车的笛声。1960年代，罗马尼亚和当时的南斯拉夫合资兴建水利，在多瑙河上筑起拦河大坝，把铁门峡一带的水位抬高了几十米。

大坝上可以走汽车和自行车，从塞尔维亚一侧骑过河的中线，就到了罗马尼亚，入境关口设在北岸。一出关就是70号公路，高速行驶的大货车络绎不绝，跟塞尔维亚一侧车辆稀疏的沿河公路形成巨大反差。

从匈牙利开始，专用自行车道越来越少，不守规矩的司机越来越多，我已经逐渐习惯了在公路上跟各种车辆同行，但70号公路上贴着路边高速行驶的大货车队仍然有些令人望而生畏。离关口大约15公里的地方有座名叫德劳贝塔的城镇，我决定快速骑到那里住下来，好在路面平坦，又是顺风，不到半小时就进入城区。

城内的街道布局类似于中国的城市，主街道十分宽阔，中间是凸起的隔离带，两边是整齐高大的水泥柱路灯。那晚住的旅馆位于闹市区，设施比较陈旧，空调看上去至少有20年历史，但床铺和地面都很洁净。楼下有超市、银行、餐馆和各种商铺。街上车来车往，但没有专门的停车场，汽车都停在外侧的车道上。

进入罗马尼亚的第二天是中秋节，我一早出发，避开上班的车流，在乡村公路上继续沿多瑙河北岸往东骑。路过的每个村庄都有一座教堂，教堂旁边都有一处墓地，教堂的钟楼都是村里的最高建筑。每个村口的路边都立着统一设计的村名标志和欧盟标示，用罗马尼亚语写着“欢迎莅临”，村公所门口都挂着罗马尼亚国旗和欧盟旗。几乎每个村口都有一口水井，有的井台上放着水桶和一只缸子。那种涂着蓝色和白色油漆的缸子现在已经很少见了，放在那里显然是供路人喝水用。

临近中午，途经一座画着耶稣像的神龛，旁边有水泥桌凳，我停下休息，顺便喝水，吃点东西。神龛的另一侧有口压水井，一位骑机动三轮车的老大爷见我拿水壶喝水，过来示范如何从水井往上压水。三轮车后轮两侧的挡泥板上分别印着“国威”两个汉字。

不远处的田野上有座白色窝棚，老大爷冲窝棚打了几个呼哨，一条黑狗出现在窝棚口，向我们张望了一会儿，然后奔跑过来。三轮车后斗里放着几袋面包，老大爷打开一袋喂那条黑狗。他不讲英文，我们用肢体语言做简单的交流。见我手里面包不多了，老大爷要送我一袋面包。我说“贝切特”，指指自己的嘴，表示骑到贝彻特村吃午饭。老大爷纠正我说是“贝凯特”，然后说了几句罗马尼亚语。

中秋夜，我投宿在离多瑙河不远的马格拉维村一座没有电的房子，当然也没有网。淋浴是凉水，好在是夏天，一分钟后身体就适应了。年轻时在济南和北京，一年四季冷水浴，这二十年多年，热水淋浴设备普及了，那个习惯也中断了，只有在野外宿营时才有机会冷水浴一次。

院子里有条自由游荡的迷你泰迪狗，还有一条被关在墙角围栏的德国牧羊犬，见陌生人进门，不停地狂吠狂跳。客栈有好几个房间，但没有其他住客，主人住在另一个院落。村里没有餐馆，但有个小卖部，我只好去那里解决吃喝问题，坐在柜台后的中年村妇用异样的眼光打量着我。客栈后墙外是一片树林，夜幕降临，房间暗下来，窗外月色如水，秋虫齐鸣，催人入眠。

这次骑行经过的13个国家中，最热情的是罗马尼亚乡村的孩子。他们在路边看我骑过来，远远地挥手，欢快地呼喊“Hallo”或“Bună”，还有我听不懂的罗马尼亚语。经常有小朋友伸出手跟我击掌，甚至特意从马路另一边跑到我这边来，跟我击完掌再跑回去。遇到学校放学，成群的学童在街边为我加油鼓掌。

有一天午后，骑到一个村口，一对衣着破旧的中年夫妇赶马车迎面过来，身边坐着五个孩子，一起冲我挥手欢呼。那都是此行最令人难忘的场景。罗马尼亚的农村生活并不富裕，我跟村民也语言不通，但随处能感受到男女老幼对陌生人的温情与善意。

在苏哈亚村，有家名叫“雪绒花”的客栈。老板加百列40来岁，兼做厨师，他姨妈阿美丽娅帮着做饭、收拾房间、洗晒床单被褥。那天顺风120公里，我到的早，只有阿美丽娅一个人在。她只会说罗马尼亚语，我听不懂。路上后轮又断了一根辐条，我收拾车轮时手上沾了些油腻。看到院墙边有水龙头，我拧开洗手，转身见阿美丽娅递过来一条毛巾。

看我听不懂她的话，她有点着急的样子，指着树下的椅子，示意让我坐下。我打开手机上的谷歌翻译，请她对着讲了一遍，由罗马尼亚语翻译成英文，原来她是问我喝啤酒还是喝水。我说：“啤酒。”她进屋拿出一罐Ursus，帮我倒进玻璃杯。我正要喝，一辆半旧的轿车开进院子。  

加百列到了。寒暄之后，他看见桌上的啤酒，开始跟阿美丽娅讲罗马尼亚语。阿美丽娅脸上现出窘迫的神情，双手捂在胸口，跟我说了几句罗马尼语。加百列说：“我姨给你拿了罐无酒精的啤酒，那是给司机喝的，换一罐吧。”我说：“已经打开了，不用换了，只要解渴就好。”他说：“那就晚餐再喝真啤酒吧。”

我问加百列村里有没有修车铺，虽然随车带了备用辐条，但崩断的那根辐条在变速轮一侧，我没有卸变速轮的工具，自己换不了。他说要到镇上才有车铺，拿出手机，在谷歌地图上找出位置，并拨通了车铺的电话，跟老板约好第二天我路过时在那里修车。那是我第一次喝无酒精的啤酒，可能是口渴的缘故吧，如果加百列不说，也尝不出来没有酒精。

加百列厨艺不错，做了一菜一汤，拿出自酿的果酒让我品尝，说是用果园的苹果和梨酿制的，加了咖啡和香草。我喝了一小杯，酒精度不低。吃完饭，见他在院子里看手机，我夸奖了一番他的厨艺。他道谢，很高兴的样子，然后说乌克兰军队又打了胜仗。

话题一开，加百列讲了很多乌克兰的事。他说，罗马尼亚人大都支持乌克兰，只有少数人支持俄国，觉得普京伟大。今年春天，他的客栈收留了20多名难民，住了两个月，有些回乌克兰了，有些去了西欧。“他们拖家带口，我每天给他们做饭。他们没有钱，我也不会向他们收钱。”从加百列开的半旧轿车看，说不上家境富裕，也没有念过多少书，人却有侠义心肠，讲起战争和难民，是非之心和恻隐之心溢于言表。

“雪绒花”客栈还住了一位开房车旅行的罗马尼亚姑娘。她自我介绍名叫丹妮拉，家住布加勒斯特，但母亲住在比利时的布鲁塞尔。丹妮拉说附近有处泉水，还有个大湖，我们约好骑车去湖上看日落。她借了加百列的山地车，比我的车子轮胎更适合走砂石路。

一路上坑坑洼洼，在一道山坡上，远远看到牧羊人赶着羊群，堵塞了道路。大群山羊踏起一片沙尘，由远及近移动过来。我们穿过羊群，快到大湖时，几条狗狂叫着从前面的路上跑过来。一位老人跟在后面冲狗喊话，但狗并不理会。我让丹妮拉从车上下来，把车挡在人和狗之间，顺手从路边捡了块石头，以防不测。

几条狗冲到离我们几步远的地方停住，只是狂叫，并不攻击。那位老人越走越近，丹妮拉能跟他搭上话了，两人隔着几条狂叫的狗相互用罗马尼亚语喊话。丹妮拉说，老人让她不用怕，这些狗只叫不咬人。我正在半信半疑，叫声嘎然而止，几条狗一起跑进了路边的树丛，消失得无影无踪。这一切发生得太快，丹妮拉对转瞬间化险为夷还没有反应过来，一脸诧异地问：“狗跑哪儿去了？”

大湖周围是茂密的树林和芦苇荡，北风呼啸，丹妮拉穿得很少，开始觉得冷，说还是回客栈看日落吧。客栈房门外是苹果园和梨园。加百列为丹妮拉做好了晚饭，回到院子聊天。他说，今年春寒，满树苹果花一夜之间凋落了，整座苹果园只结了几颗苹果。梨树耐寒，果实累累。

第二天清晨，满园的苹果树和梨树沐浴在晨曦中，除了沿途在乡村客栈常听到的鸡鸣、狗吠、布谷、乌鸦和麻雀啼叫，还有罗马尼亚乡村特有的清脆马蹄声，不时从篱笆墙外的马路上传来。罗马尼亚乡村，马匹处处可见，很多农户仍然在用马拉车，运载地里的收成

。

阿美丽娅准备了丰盛的农家早餐，除了面包、牛奶、果汁、奶酪，还有用自家种的无花果做的酱。早餐后，我告别“雪绒花”客栈，阿美丽娅送到大门口，她两手捂在胸口，用罗马尼亚语说谢谢，然后挥手用英语说再见。

大约15公里后，我骑到镇上的车铺。修车的是位老大爷，只讲罗马尼亚语，他可能有口音，谷歌翻译不全。老人没有修过“速联”变速器的车，卸变速轮有点作难。他回家拿了一箱子工具，挨个试。两个小时后终于修好了，又调整了轮圈的辐条。

中欧和东欧国家的路况不像荷兰、德国和奥地利那么理想，过了布达佩斯，平均每骑500公里断一根辐条。罗马尼亚之后还要走保加利亚、土耳其和希腊三个国家，照这个损耗进度，随车带的6根备用辐条勉强够用。

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国际劳动妇女节到来的前几天，我发布了一期单口播客《美妙人生的关键呀，我们一起来扭一扭它》（全网可听），当做了节日礼物，给我自己，也给我在意的朋友们。

所以本来今天没有要发的东西了，但是我还是忍不住要把如下的文字发出来。仪式感的原因之外，更重要的原因是我现在每天都写作（除了节假日和生病的时候），实在写了非常多篇文章，总是要让它们找个机会见见天日！

下面这篇文章写于2月底，我觉得它非常适合今天这个日子发出来。

因为它是在我因为病痛状态不好后又状态转好后写出来的，几天后因为别的事情陷入悲愤而又续写。它的起承转合仿佛是绝大多数女性在这个世界的处境：和一些糟糕的事情做抗争，有时赢了，有时输了，有时候自我宽慰，有时候豪情满怀，有时候失落失望。最后想要学会的不是坚强，而是一次次让自己面对残酷的真实后，还能再打捞起被清洗一遍的希望。

这也是一篇我对心中所爱女性的表白，在我自己和她们还活着的时候我要多做这样的表白。

白天出门上课的时候在飘小雨，下午下课的时候雨也还在淅淅沥沥下，小腹一阵胀痛去卫生间就发现月经来了，所以取消了在学校图书馆学习的计划，立刻冒雨骑车回家，途中经过因为昂贵很少走进去的东方超市采购一番，回到家就开始吃刚买的汤圆，吃山楂，吃蟹黄豌豆，吃止痛药，看手机，以各种哄着自己的方法来应对痛经的到来。结果吃了药几个小时后几乎丝毫不痛（感谢我们的听友YY推荐的不伤胃的止痛药Advil，推荐给所有痛经还同时有胃溃疡的朋友们，胃溃疡的朋友们请谨慎吃布洛芬扑热息痛等止疼药，吃完痛经可能稍有缓解，胃就开始灼烧），这大把时间就被我以应对痛经的虚假军情浪费了。

到了晚上10点多终于就起身把房间里我吃东西制造出来的垃圾拿下楼去扔，本来以为院子里还在下雨，结果一到院子，抬头竟然看到漫天星光闪烁，又惊又喜又觉得天地开阔。

突然想起来有一次看到演员春夏的采访，对方问她为什么要做一个特别的人，和别人一样安安稳稳的不好吗？

春夏非常本能地脱口而出：当然不行了。

对方问她为什么？

她说出了如下这句话：

“我就是要这个世界上有一束光是为我而打的，对，我就是要有一个舞台是为我而亮的，我要这个世界上，有人是为我而来的。那非常非常重要。”

说的时候随着句子的推进，她的眼泪开始在眼眶涌出，声音中有了颤抖的哭腔，语气愈发坚定。

因此今晚我看到这漫天星光时，就想起了春夏。

所以你看春夏，你在某种程度得偿所愿，在世界遥远的地方，有人看到天上的星星的时候，就会想起你，想起你说的那句话，今夜荷兰上方某个庭院的星空，在一些瞬间为你闪烁。

而我怎么会把这句话牢牢地记住并在时隔几年后还会想起呢？归根结底，可能本质上还是因为有同样的渴望。

人感受到同类，就会有被标记的感受。

这几年我甚至不怎么敢搜春夏的消息，她的勇敢，真诚，不苟且，在那样的环境，会受到怎样的折磨，又会有如何的妥协，代价和惩罚，我可以轻易想象地到，但是我却不愿意也不敢细想，一想就觉得自己承受不了。

有时候偶尔刷到同类在那个环境中做的“困兽之斗”，我本来的平静的心就会突然从海底窜出火舌。像徐娇在长沙的餐厅劝说邻桌的男人不要在餐厅抽烟，结果被那个男人把烟直接放入她的饭碗还各种进行威胁，她报警之后警察却说餐厅室内可以抽烟。事情上了新闻，又有无数人骂她。

这个事情从开头到结尾的每一步，都让我愤怒又倦怠。而这样的事情，在那样的环境，几乎每一天，每一分，每一秒都在发生，像天罗地网一样无处可逃。即使不出门，坐在家里，不公和无力也会像天花板滴落的水一样逐渐淹没你。

只要你真诚，勇敢，想要维护自己生而为人的权益，你就每一天都是被困住动弹不得的巨兽，心中有泼天巨浪，手中却无计可施。

为了解放自己和它人而发声和行动，结果别人非但不领情，还会过来谩骂你，侮辱你，损害你，想要让你闭嘴不言甚至销声匿迹。我不知道如春夏和徐娇一样的女性，如何面对这些愤怒，无力，不公，以及最后这最让人心寒的“辜负”。

我自问我面对不了，所以我选择了离开，我想保护我自己，不让自己“自燃”。

但是与此同时我想说的是：在遥远的地方，我领你们的情，我感谢你们的行动，我会在看到春日星光的时候，想起你们，这个世界有一些挂在天上的星光，是为你们而闪烁的。

本来这篇文章到这里已经结束，几天后我又在阳光灿烂的午后补写了下面这几段：

不仅春夏，不仅徐娇，不仅大S徐熙媛，不仅邵艺辉，不仅贾玲，不仅柴静，不仅我的姥姥，我的小姑奶，不仅我心中很多时刻想躺在床上为你们痛哭一场的这些女性的名字，也不仅仅在很多时刻感受到被伤害和被背刺的我自己，这世界这样的女性不可胜数。

在今天这个给女性的节日，我想和你们说：我永远感念你们的爱意和善意，我为你们的遭遇鸣不平。你们带给我很多星光闪烁的好东西，我都记住了。我为你们遭受到的暴力感到悲痛，它们很多时刻简直是对我的一场鞭刑。

释放出珍贵爱意的女性被残酷地辜负，我有时候简直不知道这个世界想让我从中学会什么：学会自我保护吗？学会对别人的善念和努力改变的行动最终注定是不值得的付出吗？学会不再关心也不再问津吗？学会从人群中走开，去走向自然，动植物和自我使命的探索吗？

时至今日我还没有答案，我把这个疑问也留给这个节日。可怕的不是没有答案，而是停止思索。因为痛苦和艰难就停止思索，这从来不是我的选择。

我一直在写一篇小说，想写“不被辜负的女性”，可是它太艰难，我目之所及和亲身经历的几乎总是反例。我在虚拟世界的构建能力比我想象得匮乏，我太难写出我没看到过的东西。但是我每天都在努力，我每晚睡觉前都在想这篇小说的细节，我希望闭上眼在暗夜中遨游时我能抓住把虚拟的东西牢牢建构住的神力。

今天我又翻看起了《从诗善开始》这本书，为什么我如此喜爱这本书呢？因为它给了我很多希望，让我看到女性的善意和行动原来可以不被辜负，可以在母系的家族一直流传，让生活在糟糕世代的女性们，因为沈诗善的精神和行动，在失落，悲愤，颓唐，绝望之后依然有勇气面对。诗善没有被女儿们和孙女们辜负，也没有被更大范围的韩国女性们辜负。韩国女性们在诗善活着的时候总是给她寄泡菜来表达对她的敬意和爱意，所以诗善家里堆满了全国各地寄来的泡菜坛子。

作者郑世朗女士创作出了不被辜负的女性的乌托邦，所有故事都是虚构的，可是韩国的女性们正在现实社会书写着一次次不被辜负的故事。姐姐来了，没有辜负妹妹，妹妹们为女校而抗争，没有辜负姐姐。甚至去年的韩国民众，也没有辜负几十年前的“首尔之春”。几十年前民众用私家车和出租车取得的胜利，去年它们又再一次用车和肉身捍卫。不辜负别人的人才能一代又一代取得胜利，而我身后那些总是相互辜负的人，过去几十年写满了让人疑惑但现在终于了然的溃败。

人一定会承担辜负别人的结局。庆幸的是当我把视线往远处转移，辜负就不是唯一的结局。我不仅把说同一种语言的人当做同族同类，那我就总是有不辜负它人的挚友姐妹。

所以我会努力把这篇小说写完的，希望明年或者某一年的节日，我有机会把它发出来，那是我最想送出的礼物。

From an Anon contributor who would know:

TLDR: U.S. export controls targeting China’s AI capabilities focus primarily on limiting training hardware but overlook the growing importance of inference compute as a key driver of AI innovation. Current restrictions don’t effectively limit China’s access to inference-capable hardware (such as NVIDIA’s H20) and don’t account for China’s strong inference efficiency. While China’s fragmented computing infrastructure has historically been a disadvantage, the shift towards inference-heavy AI paradigms positions their compute ecosystem to be more utilized and valuable. As reasoning models, agentic AI, and automated AI research elevate the role of inference to advancing AI capabilities, the US should urgently strengthen export controls to hinder China’s inference capacity and develop a coherent open-source AI strategy to maintain competitive advantage.

The Export Control Status Quo is Broken

The global AI competition is unfolding along two critical axes: innovation — the development of advanced AI capabilities — and diffusion — deploying and scaling those capabilities. The United States has prioritized outpacing China in AI innovation by focusing on pre-training as the main driver of progress. However, a new paradigm is emerging where inference, not just training, is becoming central to advancing AI capabilities.

This shift has significant implications for U.S. AI policy. Current export controls aim to limit China’s ability to train frontier AI models by restricting access to advanced chips, based on the belief that scaling pre-training is the primary driver of AI progress. By limiting China’s access to compute resources, these controls aimed to slow its AI development.

Yet, these same controls are far less effective at restricting China’s inference capabilities — exposing a critical gap in U.S. strategy. As inference becomes more central to AI innovation, current policies are increasingly misaligned with the realities of AI development. To effectively counter China’s growing inference capabilities, the U.S. must strengthen its export controls.

Inference Compute is a Key Driver of AI Innovation

The AI landscape is evolving beyond the scaling pre-training paradigm that dominated recent years. Emerging solutions are shifting innovation toward a paradigm where inference compute — not just training compute — has become a critical driver of AI progress.

Three interconnected trends are driving the link between inference and AI capabilities:

Reasoning Models

These models require significantly more inference than traditional LLMs, leveraging test-time scaling laws that suggest a link between amount of inference compute and model performance. Inference demand is further driven by a feedback loop that accelerates AI capabilities: reasoning models generate high-quality synthetic data, which enhances base models via supervised fine-tuning (SFT). These stronger models can be adapted into stronger reasoning systems, creating even better synthetic data and fueling continuous capability gains.

Agentic AI

AI agents — systems capable of taking autonomous actions in complex environments to pursue goals — are often powered by reasoning models, which drives up inference demand. Many agents have access to external tools and environments such as code execution environments, databases, and web search, which enhance their capabilities by enabling them to retrieve information, plan, and interact with digital and physical environments.

Some agents continuously learn by interacting with their environment via reinforcement learning. Unlike standard language models that handle one-off queries, agentic AI systems require persistent inference as they continuously interact with external environments, adapt to new information, and make complex, multi-step decisions in real time — significantly increasing overall inference requirements.

Automated AI Research

Automated AI researchers can design new architectures, improve training methods, run experiments, and iterate on findings. Scaling in this paradigm requires both inference compute to power research agents and training compute to execute their proposed experiments. Greater inference capacity allows more of these systems to operate in parallel, expanding both the breadth and depth of AI exploration. This, in turn, enlarges the search space they can navigate and directly increases the rate of AI innovation.

Greater inference also enhances research agents through iterative reasoning, self-play debates, and automated evaluation — capabilities already demonstrated in AI-driven scientific discovery. As these automated systems achieve early breakthroughs, they become better at identifying promising research directions and architectural improvements, potentially setting off a compounding cycle of progress. Thus, even small initial advantages in inference capacity can compound, leading to a significant, potentially decisive, lead in AI capabilities.

In an era of reasoning models, agents, and automated AI research, inference capacity is not just an enabler — it is a primary determinant of the speed and trajectory of AI innovation. This shift has significant implications for the U.S.-China AI competition and underscores the need for stronger U.S. export controls.

China’s Inference Capacity is Key

Current U.S. export controls aim to restrict China’s ability to train frontier AI models but overlook the growing importance of inference and China’s capacity to scale it. As AI development shifts towards inference, China’s position strengthens considerably due to three key factors:

1. Steady access to inference-viable GPUs

2. Leading inference efficiency

3. Compute ecosystem being better suited for inference rather than pre-training

Access to Inference Hardware: The H20 Loophole

Despite U.S. export controls restricting access to cutting-edge AI chips like the H100 and H800, China maintains strong access to inference-capable hardware through several avenues — most notably through Nvidia's H20 GPU.

The H20 represents a significant gap in current export restrictions. Specifically designed to comply with export controls and serve the Chinese market, the H20 is actually superior to the H100 for particular inference workloads. The H20 outperforms the H100 for inference workloads due to its superior memory capacity and bandwidth. It delivers 20% higher peak tokens per second and 25% lower token-to-token latency at low batch sizes—key advantages given that inference performance is driven more by memory bandwidth and batch efficiency than by raw computational power. With 96GB of HBM3 memory and 4.0TB/s memory bandwidth, compared to the H100’s 80GB and 3.4TB/s, the H20 is highly viable for inference, making it a significant gap in current export restrictions.

China has been importing large sums of the H20. SemiAnalysis estimates that in 2024 alone, NVIDIA produced over 1 million H20s, most of which likely went to China. Additionally, orders by Chinese companies, including ByteDance and Tencent, for the H20 have spiked following DeepSeek’s model releases.

Access to Inference Hardware: Trailing-Edge GPUs

Trailing-edge GPUs remain surprisingly effective for inference workloads. China retains strong access to trailing-edge GPUs due to large stockpiles of the A100, A800, and H800 in 2022 and 2023. Additionally, Chinese firms, including Huawei, Alibaba and Biren, have also developed indigenous chips. The viability of trailing-edge GPUs for inference suggests that China’s inference capacity is stronger than their volumes of cutting-edge GPUs may suggest.

The effectiveness of older GPUs for inference stems from fundamental differences between inference and training workloads:

Long-Context Inference is Memory-Bound, Not Compute-Bound

Unlike training, inference only runs forward passes, avoiding computationally intensive processes like backpropagation and gradient updates. As a result, inference is significantly less compute-intensive than training.

The real constraint for inference is memory. Inference, particularly long context inference, is currently memory-bound rather than compute-bound due to several factors:

1. Model Weights & Key-Value (KV) Cache: For transformer-based models, inference requires storing both the model parameters and a key-value (KV) cache. The KV cache stores the past tokens' key-value pairs, allowing the model to retain context and coherence, and grows linearly with the context length. While compute resources are only required to process each newly generated token, memory usage continuously increases as new key-value pairs for each transformer layer are stored in the cache with every additional token generated. Consequently, total memory consumption rises steadily as the context expands, in contrast to compute needs, which remain stable and do not accumulate in the same manner. As a result, inference often becomes memory-constrained before it becomes compute-constrained, particularly for long-context tasks, where the KV cache can exceed the model weights in size.

2. Autoregressive Bottleneck: Input tokens can be processed in parallel, leveraging the full sequence since it’s known upfront. However, output tokens are generated sequentially, with each new token depending on all the previously generated tokens. This creates a bottleneck during output generation:

   1. Full KV Cache Access: Each generated output token requires accessing the entire KV cache.

   2. Memory Bandwidth Limitation: On long sequences, this repeated full KV cache access for every output token creates a memory bandwidth bottleneck (data transfer rate between memory and processor), which becomes the primary limiting factor.

   3. Constrained Batch Sizes: The size of the KV cache directly limits batch size during output generation. Longer sequences consume more GPU memory, reducing space for batching multiple sequences. This forces smaller batch sizes–the amount of independent user queries that can be processed in parallel–which reduces GPU utilization and restricts inference throughput.

This memory constraint becomes evident when examining FLOP utilization rates. During inference operations, GPUs typically achieve only about 10% FLOP utilization when generating tokens, compared to 30-50% during training. This underutilization occurs because GPUs spend much of their time retrieving and managing the KV cache rather than performing actual computations. The inefficiency grows even more pronounced with newer, more compute-dense chips, where increasingly powerful processing cores sit idle waiting for data to arrive from memory.

The Memory Wall

This inference bottleneck reflects a broader structural limitation in computing hardware. While GPU compute performance has grown exponentially (approximately 3.0x every 2 years), memory bandwidth and capacity have improved at a much slower rate (around 1.6x every 2 years). This growing gap creates a “memory wall” where performance is constrained not by processing speed but by how quickly and how much data the GPU can store and access.

This memory-bound nature of inference has significant implications for hardware viability. While newer GPUs offer exponential improvements in raw computational power (measured in FLOPs), they provide more limited gains in memory capacity and bandwidth — the true bottlenecks for inference workloads.

As a result, inference workloads often cannot fully utilize the computational resources available in cutting-edge GPUs. When memory bandwidth is the primary bottleneck rather than raw compute power, older GPUs remain surprisingly effective for inference tasks. The performance gap between newer and older GPU generations becomes much less significant than their computational performance might suggest.

These technical characteristics create a unique hardware dynamic that changes the calculus around AI chips. Trailing-edge GPUs retain viability in an inference-dominated landscape — a generation-old GPU might deliver 60-70% of current-generation inference performance, making it highly viable for most applications. This shifts the cost-effectiveness equation; dollar-for-dollar, older GPUs often provide better inference performance per unit cost than cutting-edge hardware optimized for training workloads. While trailing-edge GPUs quickly become obsolete for training, they remain viable for inference much longer.

Architectural Innovations and Shifting GPU Viability

A single architectural innovation can reshape which GPUs are viable for inference tasks. DeepSeek's Multi-Head Latent Attention (MLA) highlights this dynamic, reducing KV cache requirements by over 90% and fundamentally changing inference bottlenecks.

By shrinking KV cache memory demands, MLA shifts short and medium-context inference tasks from being memory-bound to increasingly compute-bound. Lower memory demands mean GPUs spend less time waiting for data retrieval and more time on actual computation, significantly increasing GPU utilization rates. For China's AI ecosystem, this unlocks substantially more inference throughput from trailing-edge GPUs.

Custom optimizations further amplify these benefits. DeepSeek has demonstrated that Huawei's domestically-produced Ascend 910C can achieve 60% of Nvidia's H100 inference performance through targeted optimizations. This showcases how software and architectural innovations continually reshape the viability and relative strengths of different GPUs for AI workloads.

MLA renders short- and medium-context inference tasks far more efficient by reducing memory bottlenecks, allowing cutting-edge GPUs to fully leverage their computational power. While this widens the performance gap between cutting-edge and trailing-edge GPUs, it also increases China’s overall inference capacity by making older hardware more efficient. Leading-edge GPUs like the H100 will continue to dominate compute-bound workloads, but MLA significantly boosts the total inference power that can be extracted from China’s existing GPU stockpile.

For long-context inference, the hardware calculus shifts again. When context length becomes sufficiently large, tasks remain memory-bound even with MLA, reducing the performance advantage of cutting-edge hardware over trailing-edge hardware for these specific workloads. Long-context inference tasks are particularly important for reasoning, agentic AI, and automated research applications. The capacity of trailing-edge hardware to support these AI capability-enhancing tasks strengthens China’s ability to advance AI progress despite hardware constraints on cutting-edge GPUs..

Implications for Export Controls

The implications for export controls are significant: inference capacity is growing across the board, and restrictions on cutting-edge hardware won’t prevent China's inference capacity from expanding. Cutting-edge GPUs will retain significant performance advantages for short and medium-context workloads, but trailing-edge hardware remains surprisingly effective for long-context inference where memory constraints persist.

The prolonged viability of trailing-edge GPUs for inference extends the lifespan of China's existing hardware stockpile. Even as export controls limit China’s access to cutting-edge AI accelerators, China’s large stock of A100, A800, and H800 GPUs remains useful for inference applications far longer than they would for training. This sustains China's AI infrastructure and boosts its inference capacity despite limits on acquiring new chips.

Moreover, China has developed indigenous AI chips capable of inference. Huawei's Ascend 910C has demonstrated competitive performance for inference workloads. Notably, the Ascend 910C’s yield rate has doubled since last year to 40%, and Huawei plans to produce 100,000 units of the 910C and 300,000 units of the 910B in 2025, signaling a significant expansion of domestic chip production. Biren Technology's BR100, a 7nm, 77-billion transistor GPU, rivals the A100 for both training and inference. China’s growing production of inference-viable chips, substantial stockpile of trailing-edge GPUs, and continued access to the H20 reinforce its ability to sustain AI capabilities in an inference-heavy AI paradigm despite restrictions on acquiring cutting-edge hardware.

The Hardware Multiplier: China’s Inference Efficiency

Beyond hardware access, China’s advances in inference efficiency have significant strategic implications for U.S. export controls. DeepSeek’s recent innovations — particularly its v3 and R1 models — demonstrate China’s ability to push the frontier of inference efficiency. By implementing innovative techniques like a sparse Mixture of Experts architecture, multi-head latent attention, and mixed precision weights, DeepSeek’s R1 model achieves approximately 27x lower inference costs than OpenAI’s o1 while maintaining competitive performance.

This efficiency advantage effectively counterbalances U.S. hardware restrictions. Even if export controls limit China to 15x less hardware capacity, a 30x inference efficiency advantage would enable China to run nearly twice as much inference as the U.S. This acts as a multiplier on China’s hardware base, potentially giving China greater total inference capacity despite hardware restrictions.

The efficiency gains extend the utility of trailing-edge GPUs in China’s AI ecosystem, as improved inference efficiency compensates for computational and memory limitations. While DeepSeek’s achievements are a continuation of the observed decline in inference costs, this case demonstrates that Chinese AI labs have already developed the expertise to push the frontier of inference efficiency and could choose to withhold future breakthroughs if strategic considerations change.

The Sleeping Dragon: China’s Compute Overcapacity

Additionally, China’s massive but fragmented compute ecosystem is structurally better aligned with inference requirements than training needs. The aggressive GPU stockpiling during China’s “Hundred Model War” of 2023 created substantial compute capacity that became underutilized as many firms abandoned their foundation model ambitions. As Alibaba Cloud researcher An Lin observed, many of China’s claimed “10,000-GPU clusters” are actually collections of disconnected GPUs distributed across different locations or models. While this fragmentation makes the infrastructure suboptimal for training frontier models, it remains viable for inference workloads that can run effectively on smaller, distributed clusters.

Open-source models are particularly well-positioned to leverage this distributed infrastructure, enabling deployment across China’s fragmented GPU ecosystem and transforming previously idle compute into a strategic asset for widespread inference. This approach allows companies to preserve limited high-quality compute for model development while unlocking latent compute capacity.

China’s once-idle compute resources are increasingly valuable in an inference-heavy AI landscape, improving China’s position along both the innovation and diffusion axes.

How Should the U.S. Respond?

An inference-heavy AI paradigm favors China’s AI innovation potential. Its access to inference-viable hardware, leading inference efficiency, and compute overcapacity function better in an inference-driven context than in a pre-training one. U.S. export controls, designed to constrain training, have been less effective at limiting inference. China’s inference capacity remains underestimated. Despite restrictions, access to trailing-edge GPUs, stockpiles, domestic chips, and H20s enable continued progress.

As inference becomes central to AI competition, China’s relative position strengthens, narrowing the U.S. advantage. This shift demands a strategic recalibration: the U.S. must reinforce export controls and develop a coherent open-source AI strategy.

Restricting Exports of the NVIDIA H20

Export controls on AI hardware operate with a lag — typically one to two years before their full impact materializes. This lag effect is central to understanding both current policy outcomes and future strategic decisions for export controls.

Some cite DeepSeek’s latest models as proof that U.S. export controls have failed. However, this outcome is a shortcoming in how the controls were initially calibrated rather than a failure of the broader strategy. The Biden administration initially set narrow thresholds—based on FLOPs and interconnect bandwidth — which NVIDIA circumvented with the H800, designed specifically to remain exportable to China. When controls finally expanded to include the H800 in October 2023, Chinese companies had already stockpiled these GPUs in addition to speculated H100s and H20s, allowing them to maintain frontier development and delaying the policy's actual impact.

This lag highlights how AI hardware and model lifecycles can stretch over many months, so chips purchased immediately before or soon after a policy shift can remain in service for a long time. Consequently, the policy’s full impact may not be evident right away. As older hardware loses its edge for training and frontier development scales, the impact of controls becomes realized through constraints on both the speed of a country’s AI advancement and the extent of its diffusion.

The lag effect of export restrictions is more pronounced for inference hardware. Unlike training, inference workloads can remain viable on older GPU generations for much longer periods, as they depend more on memory capacity and bandwidth than raw compute power. If the U.S. delays restricting inference-oriented chips like the H20 until inference becomes even more central to AI power, the extended lag could substantially weaken the effectiveness of export controls as a defensive measure. By restricting the H20 now, the U.S. can meaningfully limit China’s accumulation of inference hardware before inference becomes the dominant compute paradigm in AI. The sooner these revised controls take effect, the sooner they will impose measurable constraints on China’s ability to compete along both axes of AI competition.

A Strategy for Open-Source AI

Open-source AI is a key vector of competition that requires a strategic U.S. approach. While it fuels innovation, not all models or circumstances warrant taking the same open approach. Open-sourcing an advanced model represents a form of technology transfer to China if that model exceeds the AI capabilities that China has access to. This reduces the U.S. lead on the AI innovation axis, shifting competition toward the diffusion axis — an area where China may be better positioned to compete.

As the compute requirements for pre-training grow, open releases help China overcome its pre-training disadvantage while amplifying the role of inference, where China is stronger. If not managed strategically, open-source AI could accelerate China’s ability to close the gap in both innovation and diffusion. The U.S. must assess whether it retains an edge in leveraging open models for research, application, and deployment. If so, open-source strategies can reinforce leadership; if not, they risk eroding it.

To assess the impact of an open release on U.S. tech competitiveness, we should evaluate how much of an immediate advantage the U.S. is foregoing on the AI innovation axis by open-sourcing a model and compare that to the net effect of how well the U.S. and China can convert open access into gains across both axes. If the U.S. retains a structural advantage in furthering AI research, building applications, fine-tuning, and scaling AI deployment, then open-source strategies can reinforce U.S. leadership. However, if China is more effective at leveraging open models for research, real-world adoption and economic or military applications, then unrestricted open release could benefit China more. This dynamic underscores the need for a structured approach and collaboration between private and public sector regarding deployment decisions.

The Bottom Line

As trends in AI elevate the importance of inference, the U.S. must reassess its strategy to lead along both axes of AI competition. While early export controls are designed to constrain China’s ability to train frontier models, they are less effective in limiting its capacity for large-scale inference. To sustain its competitive edge, the U.S. must expand export controls to address the growing role of inference, particularly by restricting chips like the NVIDIA H20 before their strategic importance escalates further. At the same time, the U.S. must refine its approach to open-source AI, ensuring that its diffusion benefits reinforce, rather than undermine, U.S. national AI leadership. Winning the AI competition requires adapting as fast as the technology evolves, and this is a critical moment for the U.S. to recalibrate its strategy.

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It’s been a tough week for the international order. It feels like every TV in every restaurant across Taiwan is blasting nonstop coverage of the Trump-Zelenskyy fallout.

How will Taiwan respond to Trump’s pivot to Putin? Would Taiwan be safer with nuclear weapons? What platforms do Taiwanese people use to debate about politics anyway?

In today’s roundup, we’ll analyze perspectives from Taiwanese legacy newspapers, social media firestorms, and viral political influencers.

Driving Solidarity

We’ll start off by highlighting some reactions on the most popular Taiwanese social media platform, PTT.

PTT is a bit like a Taiwanese version of Reddit. The key difference is that comments are always displayed in chronological order instead of being ranked by popularity. Users can “push” 推, “boo” 噓, or reply to comments to express their opinion. The platform shows whether each comment is being “pushed” or “booed” overall, but doesn’t display the total vote tallies. Like on Reddit, there are sub-forums for topic-specific discussion.

Disclaimer: these forums are hosting open debates with intense back-and-forth between commenters. I’ll be highlighting recurring themes, as well as arguments where both sides are earning push-votes, but I want to be clear that there is no broad consensus on what the Trump-Zelenskyy fallout means for Taiwan at this point.

For example, the following debate emerged below a Mandarin translation of the Foreign Affairs article entitled “Ukraine Will Not Surrender to Russia”:

(Pushed) I support and praise this article, justice will prevail.

(Booed) Then how come there are no soldiers? Conduct an opinion poll or something.

(Pushed) These past couple of days, I've seen quite a few people claim that Ukraine should have originally surrendered to Russia in exchange for peace and prosperity. This kind of argument completely ignores the suffering Ukraine endured under Russian rule in the past.

(Pushed) In the past, we thought that people in democratic countries feared death more than other people — but Ukrainians are not afraid.

(Pushed) The Uyghurs will never surrender, but they will not go to the front line

(Pushed) It is 100000000% reasonable to be suspicious that Trump received personal benefits from Russia or made a blood pact with Russia.

The Taiwanese transliteration of “Zelenskyy” is 澤倫斯基 Zélúnsījī, and in casual writing Taiwanese people refer to him by the nickname 司機 Sījī (literally, “The Driver”) which has the same pronunciation as the last two characters of the transliteration.

From a thread in a military forum about whether Zelenskyy overplayed his hand:

(Pushed) The driver really shouldn’t have talked back to Vance. If he wanted to argue, he could have done it in private.

(Pushed) After apologizing, you still have nothing, so why bother apologizing?

(Pushed) If Little Z doesn’t kneel, America will make explosive corruption accusations against him.

(Reply) East Asian countries are better at licking.

(Pushed) If Ukraine wants to thank someone, it should thank the previous Biden administration. Why thank Trump?

(Pushed) It seems someone is trying to smear and destroy Mr. Z's image. Be careful when responding to this thread.

Indeed, there are signs of disinformation in some discussions of this topic. An FT article entitled “Zelenskyy rejects calls for immediate Ukraine-Russia ceasefire” was posted on PTT with the mistranslated title, “The Driver Rejects Ukrainian and Russian calls for a Ceasefire” (司機拒絕烏克蘭與俄羅斯立即停火的要求), a fact which was quickly pointed out and mocked in the comments.

Marco Rubio is well-known in Taiwan thanks to his long congressional record of support for the island. Here are some comments about him:

(Pushed) Rubio will be replaced soon.

(Pushed) Rubio was once a pioneer in anti-communism, but now he bows down to power.

Underneath an article reporting Trump’s plan to freeze aid to Ukraine in response to the meeting:

(Pushed) Stop it right now immediately!!!!!!!!!!!!!!! I’ve never seen such a cowardly U.S. president!! You truly see everything if you live long enough!!!!!!!!

(Pushed) Will the European big brothers shoulder some of the responsibility? Isn’t this an opportunity for them to show off?

In a financial forum:

(Pushed) Being pro-China is selling out Taiwan, being pro-America is also selling out Taiwan.

(Pushed) In the Budapest Agreement, even China said it would protect Ukraine, but that isn’t happening

(Pushed) Ultimately, [Ukraine] should not have given up its nuclear weapons. Security guarantees are bullshit.

(Booed) Ukraine has no nuclear bombs, so of course it has no bargaining chips.

(Pushed) The driver’s bargaining chip is making the king (Trump) lose face.

(Pushed) Buddha’s mercy 佛祖慈悲 [This phrase is used ironically in situations that are cruel or corrupt to the point of hopelessness.]

Ukraine Today, But Taiwan’s OK?

At the start of the invasion, the DPP popularized the slogan, “Ukraine today, Taiwan tomorrow.” Editor Gu Shu-ren 辜樹仁 of CommonWealth Magazine 天下雜誌 (a Taiwanese publication similar to the Atlantic), addressed fears that Trump will abandon Taiwan after Ukraine in a recent editorial:

Looking back at history, Taiwan's strategic value to the United States has been the key factor in America's decision to either abandon or support Taiwan.

In 1950, when the Korean War broke out, the Republic of China (ROC) government, which had retreated to Taiwan and was on the brink of collapse after being abandoned by the U.S., suddenly became the central hub of the U.S. first island chain strategy in East Asia — a so-called unsinkable aircraft carrier — greatly increasing Taiwan's strategic importance.

In the 1970s, as the U.S. aligned with China to counter the Soviet Union, Taiwan lost its strategic value, leading to the severance of U.S.-Taiwan diplomatic ties and the withdrawal of U.S. troops from Taiwan. …

Today, Taiwan's strategic value to the United States is at its highest since the servering of diplomatic ties, as the primary battleground in the U.S.-China rivalry is now the technology war, with semiconductors at its core. More specifically, TSMC is the most crucial asset for the U.S. in securing a supply of advanced chips and revitalizing its semiconductor manufacturing industry. If the U.S. wants to maintain its technological and military lead over China, it must firmly keep Taiwan within its grasp. …

Ensuring that the U.S. remains dependent on Taiwan’s advanced chip manufacturing — making American national security synonymous with protecting Taiwan — is the most critical factor in maintaining Taiwan’s strategic value to the United States.

Of course, there is another equally important factor. Trump dislikes war, especially costly military interventions where the U.S. cannot be assured of victory. He has repeatedly complained that Ukraine failed to prevent war at the outset. Therefore, avoiding war at all costs is also a key strategy for Taiwan to secure Trump’s support.

Only through this can tomorrow’s Taiwan avoid becoming the Ukraine we saw today.

Reporter Jiang Liangcheng 江良誠 similarly warned that Taiwan would need to become more transactional in its relationship Trump:

“Trump's only vocabulary is actually "money, money, money". All international relations can be measured by money. There is no free lunch in the world. It is impossible to ask Americans to help you defend your country like a plate for free and without any reward. …

However, when it comes to Taiwan's policy toward the United States, Lai Ching-te still sticks to Tsai Ing-wen's international politics, such as the first island chain, geopolitics, and Indo-Pacific security. I'm afraid even Trump doesn't understand these terms.”

The Meihua News Network (梅花新聞網), a Pro-China news outlet owned by a controversial Taiwanese religious leader, argued instead that Taiwan needs to reopen dialogue with Beijing given the reality that the U.S. is an unreliable partner.”

In front of cabinet members and the media, Trump was unwilling to guarantee that the Chinese Communist Party would not invade Taiwan by force during his term, and emphasized that he had a good relationship with Chinese Communist Party leader Xi Jinping. …

“Foreign Affairs” recently published a special article titled “The Taiwan Fixation: American Strategy Shouldn’t Hinge on an Unwinnable War”, co-authored by Professor Kavanagh of the Georgetown University Center for Security Studies and senior scholar Wertheim of the Carnegie Endowment for International Peace. The gist of the article is: Taiwan is certainly valuable to the United States, but if American decision-makers overestimate Taiwan's importance, they will sacrifice the security of maintaining the status quo due to the risk of endless and destructive war; and Taiwan's importance is not enough for the United States to sacrifice tens of thousands of American lives to protect it. Former National Security Council Secretary-General Su Chi 蘇起 described this article as the most powerful article to date advocating the United States to let go of Taiwan. …

Apart from fully relying on the American security umbrella and turning Taiwan into a "porcupine," the DPP also has another option: restoring cross-strait communication and reducing tensions in the Taiwan Strait. If that happens, the so-called "Abandon Taiwan Theory" would naturally dissipate. Rational decision-making should not be obstructed by anti-China or China-hating sentiments.”

By contrast, a popular post from the Taiwanese political influencer James Hsieh argued that Taiwan should be doing whatever it takes to improve relations with the U.S., not criticizing Trump’s Ukraine policy:

“I still see many people online going against the tide, bashing Trump, criticizing the U.S., and supporting all kinds of conspiracy theories.
Here are five reminders:

1. Before the war, Ukraine was extremely pro-China, selling major military technology to China. Just a few days ago, Ukraine even asked China for help.

2. Morally, we must oppose aggression, but in terms of international strategy, we must firmly support the United States.

3. Taiwan is not Ukraine. In terms of historical ties with the U.S., the Taiwan Relations Act, geographical location, type of warfare, and economic strength, Taiwan is completely different. Taiwan is absolutely not a distant European country like Ukraine in America's eyes. Comparing Ukraine to Taiwan is a completely flawed analogy. Saying that the U.S. pulling out of the Russia-Ukraine war implies that it will betray Taiwan is just another favorite conspiracy theory of the dumb lefties (左膠) and the Chinese Communist Party’s propaganda machine.

4. Personally, I hope the Russia-Ukraine war ends quickly so that the U.S. can fully prepare for the Indo-Pacific. This is a practical concern, as China is rapidly advancing its strategic plans. How the U.S. swiftly ends its engagements elsewhere and refocuses on the Indo-Pacific is critical. Just yesterday, Vice President Vance stated that the U.S. military-industrial production can no longer sustain the continuous supply of heavy weaponry to Ukraine.

5. History has shown that during major wars, opportunistic nations take advantage of a great power’s exhaustion to invade smaller neighboring countries. If the Russia-Ukraine war escalates into World War III and the U.S. and Europe are preoccupied with fighting Putin’s alliance, it would be the perfect moment for China to seize Taiwan under the guise of maintaining stability.

If Taiwan's democracy, freedom, and independence from oppression are what you value most, then Taiwan should prioritize its relationships with the U.S. and Japan over everything else — not Ukraine.

Only the U.S. and Japan will help us. Survival comes first before ideals.

Taiwan-U.S. friendship!”

It remains unclear what the Lai administration’s approach will be, but you can be sure that ChinaTalk will keep monitoring the debate as it evolves.

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Will Taiwan Get Nukes?

Zelenskyy’s White House press conference also reignited the old debate about whether Taiwan would benefit from having its own nuclear arsenal. Taiwan abandoned its indigenous nuclear program in response to pressure from the U.S., much like how Ukraine relinquished its nuclear weapons to Russia after the fall of the USSR. Taiwan was estimated to be just two years away from completing a WMD when the U.S. intervened in 1988.

These parallels were drawn explicitly by a CNN profile of Colonel Chang Hsien-yi 張憲義, the Taiwanese nuclear engineer who provided intelligence about Taiwan’s proliferation plans to the CIA. The article was repackaged, translated, and published on the front page of the China Times on Monday.

On PTT, the profile drew comments like:

(Pushed) This person is the reason why Taiwanese independence is impossible.

(Pushed) Nuclear weapons are not something that Taiwan's extremely incompetent politics could handle. If nuclear weapons were in the hands of Chiang Kai-Shek and his family, Taiwan would have ended up like North Korea. The Chiang family would still in power, and there would never have even been a chance for democratization. So many people have no clue what’s going on.

Taiwanese political influencer Mr. Shen 公子沈, who runs a YouTube channel with more than 700k subscribers, posted the following meme on Threads (which is way more popular in Taiwan than the U.S.) with the caption, “With nukes vs without nukes: it’s time for Taiwan to develop nuclear weapons.”

Speaking of bargaining chips…

Reactions to the TSMC Deal

TSMC’s newly announced $100 billion investment in US chip manufacturing led to more online discontent. The following comments from Facebook were curated by Angela Oung:

“Today we are all Ukrainians”

“At least Zelensky has guts”

“ASMC” [American Semiconductor Manufacturing Company]

“So they’re taking our stuff, leaving us with no cards. Think they’ll help in the future? Stop dreaming!”

“Taiwan’s remaining value is becoming a meat grinder like Ukraine.”

“He [TSMC Chairman CC Wei] looks like he has a gun behind his head. Hostage situation.”

“The silicon shield we spent decades building is being handed over by our government without a whimper”

“TSMC: built by the KMT, sold by the DPP”

“Is Lai Ching-te such a pussy that he’s not even gonna say anything?”

“Today Ukraine, tomorrow Taiwan. One step closer to refugee status.”

“Bandits…just like the CCP”

To close, I’ll leave you with another popular post on Threads expressing frustration about Taiwan’s-U.S. relations:

“The U.S. asks us to buy military equipment — we buy it.

The U.S. asks us to extend the length of mandatory military service — we extend it.

The U.S. wants TSMC — we hand it over with both hands.

The U.S. wants us to implement resilient defense — we manage to do it, even if we have to hide and shuffle the budget.

For every single thing the U.S. asks of us, from the issue of eating ractopamine pork in our daily meals to national defense policies involving regional security cooperation, Taiwan follows the U.S.’s demands without question.

But will there come a day, just like today’s Ukraine, where we sign agreements on resource concessions, trading away our country's future rebuilding assets, yet still lack the most basic “security guarantees”?

Ukraine has the support of the entire European continent—but what about Taiwan?

Will today’s Ukraine be a reflection of Taiwan’s future?

Will Taiwan, when that day comes, be even more isolated and helpless?”

To be fair, this commenter is right that Taiwanese pork is way more delicious than the ractopamine pork imported from the U.S. I sincerely hope that every ChinaTalk subscriber has an opportunity to come to Taiwan and eat stewed pork rice (滷肉飯)…before it’s too late!?

从布达佩斯往南，朝克罗地亚方向走，欧洲骑行六号线变成草皮泥土路和砂石路，食宿费用也大幅下降。离开布达佩斯那天，只骑了不到80公里，在一个村庄住下。客栈有冰箱、炉灶、炊具，还有一个带花圃的后院，一晚只收28欧元。

街头有家店面，卖冰激凌和比萨饼，有村民带着小孩坐在店门前树下的椅子上吃冰激凌。我在旁边的小型超市买了新鲜玉米、牛奶、面包和熟食。回到客栈，我正在煮玉米，听到敲门声。门外是位英俊的少年，用英语自我介绍说是客栈女主人的儿子，他妈派他来看看是不是住的合意。

Read more

大家可能几个小时前收到了另一封邮件，那一封邮件中的播客音频非常离奇地充满了回声（Echo），恰好我的英文名也叫Echo，所以可能机缘巧合一些量子力学的神秘力量，让那一期播客音频中充满了我。被一些听友戏称为“空灵的声音”，像在“山洞里和我一起点着篝火”。现在这一版本让我们回到了清晰的世界和无场景的生活，请纵享此刻没有Echo，但是有更清晰的莫不谷的声音！

你所在的地方春日已经到来了吗？

我所在的荷兰已经从雨雪霏霏来到春光日暖，遍地花开。

在荷兰第一个我觉察到春日到来的夜晚，我录了这期两个半小时的播客《52 美妙人生的关键呀，让我们一起扭一扭它》，来聊一聊，美妙人生的关键。它是我对美妙生活不可抑制的礼赞，也是我想在3月8号国际劳动妇女节之前想要送给所有华人女性的一份礼物：关于美妙生活的灵感。

美妙人生的关键是什么呢？放心，不是天生的美貌，不是挥霍不尽的金钱，更不是至高无上的权力，这三者在主流社会，都和人生圆满绑定在一起。但是实则人只要追逐这些东西，无论自己已经拥有多少，都总是感觉焦虑、匮乏，和渺小，因为在这三个度量衡上，总有人在你的上方。庆幸的是，美妙人生的关键，离它们仨很遥远。我看的第一本科幻书《球状闪电》里说：美妙人生的关键在于你能迷上什么东西。

你无法描述美妙生活，但是你可以活出美妙生活!本期是一期分享美妙生活的“哄睡播客”，倘若听到最后还没睡，那么你还能学到一个可以改变自身命运的一个工具的使用方法(没有夸张，没有骗人）。我在播客里分享了最近让我着迷的各种新知，我最深层次的渴望，我在实现渴望的路径中学到的各种奇妙的东西（包括但不限于语言，经济学，投资理财，社会心理学，物理学和数学），因此它同时也是“终身学习者乐园”系列的第000期，一期抛砖引玉的“钓鱼播客”，这里是鱼饵：

征稿：快到终身学习者乐园来！

你在学习哪些能让人生更美妙的东西呢？它吸引你的原因是什么呢？学习的方法和最主要的收获（Learning and key takeaway）是什么？最让人惊讶新知又是什么呢？欢迎来稿你的分享——建立一个自愿的终身学习乐园，每一个学习者也都是知识的传播者和分享者。投稿发送afterschool2021@126.com。

【投稿方式】手机录音即可，录制时可将手机垫高，与嘴平齐，收音更清晰。音频请发送至afterschool2021@126.com.（提示注意避免距离手机太近容易喷麦，或背景声音嘈杂收音不清晰，以及请保护好个人信息，避免透露个人ID，如有昵称可以用昵称投稿）

请投稿发送前谨慎思考，如需变声处理也可提前处理好。如非人身安全或隐私威胁等重大原因，一般在已经录制上线后无法予以撤稿，敬请理解。

【温馨提示】播客分享内容皆为主播学习的知识和感受，请勿对号入座，也请勿进行危险尝试，如有不适，建议优先照顾好自己身心健康。

最后所有的春日和节日祝福都在这期时长2个半小时的播客里，期待你在春日的夜晚听它，在春光中驾车骑行散步听它，和女友朋友们一起gathering听它，或者在家中独自一人听它。期待它带给你一些新知，一些雀跃，一些希望，一些灵感，一些启发。

祝你今夜好眠，此生美妙非凡！

（我-莫不谷用Canva制作的本期播客封面）

【Timeline】

02:00 为什么会有这期播客？这是一杯睡前温牛奶

07:00 在这个时代，人还可以热爱学习并渴望新知、理性和自由吗？

15:00 真正热爱学习的关键，在于你是否“自愿”

20:00 为什么面对新知，很多人更容易感到疲累和恐惧？

25:40 为什么宁要模糊的正确，不要精确的错误？

30:00 面向全球百万听友的征稿：快到终身学习者乐园来！

36:00 为什么我在学荷兰语：这是我获得自由的关键

47:00 我在语言学习的重大发现：荷兰语是面向未来的

55:00 对比各国语言差异：中文非常灵活，同时也非常模糊和危险

62:00 为什么我开始学习投资理财：这是一个无需撕扯的领域

69:00 牛顿和2万英镑：即使聪明绝顶的天才，也会缺乏理性犯大错甚至闯大祸

72:00 我从社会心理学明白的：为什么我们很难拒绝别人？

76:00 物理学家和数学家的不同，可以致命？

84:00 量子力学听起来很复杂，但我们日常生活经常可以用到

100:00 为什么我要学习价值投资，同时投资巴菲特、查理芒格厌恶否定的比特币？

110:00 当你遇到不可解释的情形时，到底采用什么样的观点来看待？

113:00 最近我对外星生命产生好奇，以及人类如何活出二象性？

120:00 数学可以帮助你改变个人命运？

126:00 勤奋不是美妙人生的关键词，你的渴望才是

131:00 如果你在困惑人生没有弹性，可以学习下数学的排列组合原理

138:00 要注意很多着迷上瘾并不是好的：你为什么如此痛苦，空虚？

145:00 很多时候你在最痛苦时做出的决定，会成为你生命中最重要最好的决定，但这需要理性

145:00 最后祝你今夜好眠，此生美妙非凡！游荡者见：www.youdangzhe.com

【播客&文章&书籍&影视】

播客：《新的一年会好吗？答案在这期播客和这些祝福中》

影视：《甄嬛传》《降临》

书籍：《绝对笑喷之弃业医生日志》《聪明的投资者》《穷查理宝典》《芒格之道》《怎样解题》《量子力学史话》《影响力》《空洞的心》特德姜《你一生的故事》《三体》《球状闪电》

文章：莫不谷爱发电文章《价值投资：在中国或欧洲投资美股美债的原因及方法》（Newsletter及游荡者网站也可查看）；

莫不谷爱发电文章《从《财富自由主义》到比特币，自由的上限是我们持续学习的能力》（Newsletter及游荡者网站也可查看）；

莫不谷游荡者文章《莫不谷的语言学习一揽子经验分享：关于英语和其它各语种》注册解锁游荡者即可查看：www.youdangzhe.com

【为全球华人游荡者提供解决方案的平台】

游荡者（www.youdangzhe.com），注册完成后可免费阅读由莫不谷和霸王花撰写的三篇文章（Run的800种可能、语言攻略和全球签证攻略），目前游荡者平台已更新上线文章分区功能（游荡区、学习区、欢愉区和闲聊搭子区），欢迎大家注册完成后开启内容创作并在游荡者游荡愉快！找到同类！交易自由！手机端用户可把新网址添加桌面，便于日常使用。在使用新网址期间如果有任何注册、支付、退款等需求，欢迎给我们客服邮箱wanderservice2024@outlook.com发送邮件。

放学以后Newsletter《新的一年会好吗？答案在这期播客和这些祝福中》

放学以后微信公众号《新的一年会好吗？答案在这期播客和这些祝福中》

【延伸信息】

永不失联Newsletter订阅链接：https://afterschool2021.substack.com/（需科 学/上 网）

为全球华人游荡者提供解决方案的平台：游荡者（www.youdangzhe.com）

联系邮箱：afterschool2021@126.com （投稿来信及合作洽谈）

同名YouTube：https://www.youtube.com/@afterschool2021

同名微信公众号：放学以后after school

小红书：游荡者的日常

欢迎并感谢大家在爱发电平台为我们的创作发电：https://afdian.com/a/afterschool

片头曲：<the right time> Ray Charles

片尾曲：<On The Radio>Regina Spektor

播客封面：莫不谷用Canva制作

放学以后表情包：微信表情包搜索“放学以后”，感谢萝卜特创作。

播客收听平台：

【国内】爱发电、网易云、苹果播客（请科学/上网）、喜马拉雅、汽水儿、荔枝、小宇宙、QQ音乐；

【海外】Spotify、Apple podcast、Google podcast、Snipd、Overcast、Castbox、Amazon Music、Pocket Casts、Stitcher、Radio Public、Wordpress

你所在的地方春日已经到来了吗？

我所在的荷兰已经从雨雪霏霏来到春光日暖，遍地花开。

在荷兰第一个我觉察到春日到来的夜晚，我录了这期两个半小时的播客《52 美妙人生的关键呀，让我们一起扭一扭它》，来聊一聊，美妙人生的关键。它是我对美妙生活不可抑制的礼赞，也是我想在3月8号国际劳动妇女节之前想要送给所有华人女性的一份礼物：关于美妙生活的灵感。

美妙人生的关键是什么呢？放心，不是天生的美貌，不是挥霍不尽的金钱，更不是至高无上的权力，这三者在主流社会，都和人生圆满绑定在一起。但是实则人只要追逐这些东西，无论自己已经拥有多少，都总是感觉焦虑、匮乏，和渺小，因为在这三个度量衡上，总有人在你的上方。庆幸的是，美妙人生的关键，离它们仨很遥远。我看的第一本科幻书《球状闪电》里说：美妙人生的关键在于你能迷上什么东西。

你无法描述美妙生活，但是你可以活出美妙生活!本期是一期分享美妙生活的“哄睡播客”，倘若听到最后还没睡，那么你还能学到一个可以改变自身命运的一个工具的使用方法(没有夸张，没有骗人）。我在播客里分享了最近让我着迷的各种新知，我最深层次的渴望，我在实现渴望的路径中学到的各种奇妙的东西（包括但不限于语言，经济学，投资理财，社会心理学，物理学和数学），因此它同时也是“终身学习者乐园”系列的第000期，一期抛砖引玉的“钓鱼播客”，这里是鱼饵：

征稿：快到终身学习者乐园来！

你在学习哪些能让人生更美妙的东西呢？它吸引你的原因是什么呢？学习的方法和最主要的收获（Learning and key takeaway）是什么？最让人惊讶新知又是什么呢？欢迎来稿你的分享——建立一个自愿的终身学习乐园，每一个学习者也都是知识的传播者和分享者。投稿发送afterschool2021@126.com。

【投稿方式】手机录音即可，录制时可将手机垫高，与嘴平齐，收音更清晰。音频请发送至afterschool2021@126.com.（提示注意避免距离手机太近容易喷麦，或背景声音嘈杂收音不清晰，以及请保护好个人信息，避免透露个人ID，如有昵称可以用昵称投稿）

请投稿发送前谨慎思考，如需变声处理也可提前处理好。如非人身安全或隐私威胁等重大原因，一般在已经录制上线后无法予以撤稿，敬请理解。

【温馨提示】播客分享内容皆为主播学习的知识和感受，请勿对号入座，也请勿进行危险尝试，如有不适，建议优先照顾好自己身心健康。

最后所有的春日和节日祝福都在这期时长2个半小时的播客里，期待你在春日的夜晚听它，在春光中驾车骑行散步听它，和女友朋友们一起gathering听它，或者在家中独自一人听它。期待它带给你一些新知，一些雀跃，一些希望，一些灵感，一些启发。

祝你今夜好眠，此生美妙非凡！

（我-莫不谷用Canva制作的本期播客封面）

【Timeline】

02:00 为什么会有这期播客？这是一杯睡前温牛奶

07:00 在这个时代，人还可以热爱学习并渴望新知、理性和自由吗？

15:00 真正热爱学习的关键，在于你是否“自愿”

20:00 为什么面对新知，很多人更容易感到疲累和恐惧？

25:40 为什么宁要模糊的正确，不要精确的错误？

30:00 面向全球百万听友的征稿：快到终身学习者乐园来！

36:00 为什么我在学荷兰语：这是我获得自由的关键

47:00 我在语言学习的重大发现：荷兰语是面向未来的

55:00 对比各国语言差异：中文非常灵活，同时也非常模糊和危险

62:00 为什么我开始学习投资理财：这是一个无需撕扯的领域

69:00 牛顿和2万英镑：即使聪明绝顶的天才，也会缺乏理性犯大错甚至闯大祸

72:00 我从社会心理学明白的：为什么我们很难拒绝别人？

76:00 物理学家和数学家的不同，可以致命？

84:00 量子力学听起来很复杂，但我们日常生活经常可以用到

100:00 为什么我要学习价值投资，同时投资巴菲特、查理芒格厌恶否定的比特币？

110:00 当你遇到不可解释的情形时，到底采用什么样的观点来看待？

113:00 最近我对外星生命产生好奇，以及人类如何活出二象性？

120:00 数学可以帮助你改变个人命运？

126:00 勤奋不是美妙人生的关键词，你的渴望才是

131:00 如果你在困惑人生没有弹性，可以学习下数学的排列组合原理

138:00 要注意很多着迷上瘾并不是好的：你为什么如此痛苦，空虚？

145:00 很多时候你在最痛苦时做出的决定，会成为你生命中最重要最好的决定，但这需要理性

145:00 最后祝你今夜好眠，此生美妙非凡！游荡者见：www.youdangzhe.com

【播客&文章&书籍&影视】

播客：《新的一年会好吗？答案在这期播客和这些祝福中》

影视：《甄嬛传》《降临》

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A guest piece by Afra, freelance writer and podcaster [Jordan: I highly recommend this show!] with working experience in tech and crypto. Personal site here.

DeepSeek’s winds have already been blowing for some time, but this particular gale seems to have real staying power.

President Trump characterized DeepSeek as “a wake-up call,” Scale AI CEO Alexandr Wang called it “earth-shattering,” and Anthropic CEO Dario Amodei deigned to come on ChinaTalk to discuss fears of powerful AI within “authoritarian systems of government.”

On Chinese social media, the discussions took on a life of their own, with the most popular use case being the calculation of one’s Ba Zi (八字) and astrological chart, using the social media tag “AI玄学” (AI Mysticism). Users weren’t just seeking their personal fortunes — they saw the nation’s destiny itself shifting through DeepSeek’s emergence. These conversations are a swirling mix of collective jubilation, national pride, and gleeful satisfaction over America’s “China envy,”1 often accompanied by playful banter.

Yet amidst this discourse, a deeper and more resonant question emerges: could this be a sign of China’s technological ascension? Is this evidence that Guoyun (国运) — the nation’s long-awaited destiny — has finally arrived?

First, what is Guoyun 国运？

The term 国运 combines two characters: 国 (guó, “nation/state”) and 运 (yùn, “fate/destiny/fortune”). This concept emerged from traditional Chinese cosmological thinking, where the destiny of the state was seen as intertwined with celestial patterns and dynastic cycles.2 This term, once confined to the ornate dialogue of period dramas set in imperial China, has begun to surface with increasing frequency on my social media timeline.

The Guoyun narrative around DeepSeek began when Feng Ji 冯骥, creator of the globally successful game “Black Myth: Wukong,” declared it a “national destiny-level technological achievement.”

The discourse gained momentum when Zhou Hongyi 周鸿祎, Chairperson of Qihoo 360, positioned DeepSeek as a key player in China’s “AI Avengers Team” against U.S. dominance. This sentiment echoed across media, with headlines like “Is DeepSeek a breakthrough of national destiny? The picture could be bigger” and “DeepSeek triggers U.S. stock plunge; can it really change the nation’s destiny?"

For Chinese netizens, discussions about politics on social media are often marked by subtlety and veneration with trepidation (for reasons that require little explanation). However, during the 2025 Chinese New Year, the discourse expanded far beyond politics and DeepSeek into a cacophony of cultural euphoria —a wave of self-congratulatory enthusiasm that evolved into something larger culturally. This included the movie Nezha 2, which shattered box office records and surpassed Inside Out 2 to become the highest-grossing animated film of all time (with patriotism-fueled consumption boosting the box office performance), TikTok refugees flooding Xiaohongshu, and advanced Unitree robotics performing during the Spring Festival Gala. These achievements seemed to occur against a historical backdrop where technological and cultural advances carry deeper significance about China’s rightful place in the cosmic order.3

The Guoyun discourse extends beyond tech leaders, media commentary, and social media posts.

President Xi Jinping has woven the concept of destiny into official rhetoric, though carefully stripped of its more superstitious elements. Speaking at the 19th Academician Conference of the Chinese Academy of Sciences in May 2018, Xi declared, “Innovation determines the future; reform concerns national destiny. The field of science and technology is the area most in need of continuous reform 创新决胜未来，改革关乎国运。科技领域是最需要不断改革的领域.” This statement aligns with his broader techno-nationalist vision, explicitly linking technological advancement to China’s strategic future.

A 2024 People’s Daily article discussing Xi’s thoughts emphasized that “cultural confidence is a major issue concerning national destiny 坚定文化自信，是一个事关国运兴衰...的大问题"。

This rhetorical shift signals a carefully calibrated blend of traditional Chinese concepts with modern governance — a bridge between ancient ideas of dynastic cycles and contemporary aspirations for technological supremacy.

Beyond superstition: is this a collective myth-making or post-pandemic yearning for certainty?

It would be a mistake to dismiss this discourse as mere superstition or propaganda.

The COVID-19 pandemic marked a watershed moment in Chinese society’s relationship with national destiny. To me, Zero COVID became a mirror polished to cruel clarity, reflecting a China I no longer recognized. During the rigid cycles of lockdowns and reopenings, I didn’t see my parents for two years, my grandmother was hospitalized, and my cousin was confined to his university dorm for three whole months culminating in a severe mental breakdown. Friends lost loved ones due to a lack of timely treatment options. Back then, seeing how waves of people wanted to “run (润)” from China, I thought for the first time that I might never return to China, and that I might become part of the Chinese diaspora forever.

COVID created a collective trauma that many Chinese are still processing.

But this experience has paradoxically reinforced a certain earnest faith in China’s future among ordinary citizens. The optimism in the discussion of Guoyun might represent a complex emotional response to the uncertainty and trauma from the COVID era — a blend of traditional fatalism with genuine aspirations. Having weathered the pandemic’s disruption, many ordinary Chinese seek reassurance about the future through familiar cultural frameworks. ‘National Destiny’ provides exactly that — it’s a narrative that contextualizes current struggles within a larger, ultimately triumphant story. It’s therapeutic.

The discourse around 国运论 (guóyùn lùn, or “national destiny theory”) reveals parallels to America’s historical myth-making. Perhaps the most striking similarity between China and the US is their unwavering belief in their own exceptionalism and their destined special place in the world order. While America has Manifest Destiny and the Frontier Thesis, China’s “national rejuvenation” serves as its own foundational myth from which people can derive self-confidence. Through countless repetitions across state and social media, this narrative has become deeply ingrained in China’s national consciousness.

The wounds behind techno-nationalism

Where myths nurture the national consciousness, technology has become the battleground where China’s historical narrative demands its vindication. The roots of China’s techno-nationalism run deep, drawing emotional power from China’s “century of humiliation.” U.S. actions — chip controls, the attempted TikTok ban, tariffs, investigations of Chinese scientists, and suspicions of Chinese espionage — rekindle the historical trauma of humiliation.

For decades, China has been portrayed as a mere copycat or thief of Western innovation. Each technological breakthrough now serves as vindication, a refutation of that dismissive narrative — this shame has never truly been resolved. As Kevin Xu elaborated on DeepSeek’s open-sourced nature, “It’s all for the validation and approval,” — a sharp acknowledgment that when Chinese engineers share their code with the world, they’re not just demonstrating technical prowess but seeking to heal a wound in the national psyche:

In the Chinese open source community, there is this thing that I would call open source “zeal” or “calling” (开源情怀)

Most engineers are thrilled if their open source projects — a database, a container registry, etc-- are used by a foreign company, especially a silicon valley one. They’d tack on free labor on top of already free software, to fix bugs, resolve issues, all day all night. It’s all for the validation and approval.

Implicit in this “zeal” or “calling” is an acute awareness that no one in the West respects what they do because everything in China is stolen or created by cheating. They are also aware that Chinese firms have been taking for free lots of open source tech to advance, but they want to create their own, contribute, and prove that their tech is good enough to be taken for free by foreign firms -- some nationalism, some engineering pride.

So if you want to really understand why DeepSeek does what it does and open source everything, start there. It’s not a political statement, not to troll Stargate or Trump inauguration, or to help their quant fund’s shorts on NVDA (though if that were the case, it’d be quite brilliant and savage)

The drive to prove oneself on behalf of the nation is expressed vividly in Chinese popular culture. I couldn’t stop thinking about Illumine Linga (临高启明), an open-source collaborative novel that has captivated China’s engineering community and become a phenomenon of its own. The story follows modern Chinese engineers who time-travel to the declining Ming dynasty, right before China was conquered by the Manchus, bringing industrial equipment and technical knowledge. They gradually industrialize Hainan and Guangdong provinces before expanding outward with the ultimate goal of establishing global hegemony.4

Though ostensibly just fiction, Illumine Linga pulses with the heartbeat of China’s “Industrial Party” (工业党) — that loose constellation of engineers, programmers, and technically-minded patriots united by an almost religious faith in technology as destiny’s instrument. The novel serves as a sharp allegory for contemporary aspirations: technological mastery as the path to national resurrection and global respect.5

In the Western intellectual tradition, technology and data have undergone phases of detached scrutiny — viewed first as tools of emancipation, and later as vectors of control. Foucault’s panopticon mutated into Zuboff’s surveillance capitalism; Wiener’s Cybernetics birthed both Silicon Valley and Snowden’s disclosures. This academic back-and-forth assumes a fundamental premise: technology can theoretically exist as a neutral substrate awaiting ideological imprint.

However, in my impression, China’s techno-discourse never evinces such “purity.”

From its inception, technology has been semantically encased in the shell of techno-nationalism. In China’s history textbooks, Qian Xuesen’s missiles for the Two Bombs, One Satellite program were never just missiles, but brushstrokes in the narrative of “standing up again.”6 Yuan Longping’s hybrid rice strains didn’t merely feed millions; they were genetic correctives to the “Century of Humiliation,” each harvest a quiet refutation of the colonial-era belief that China couldn’t innovate.

On Chinese New Year’s Eve, a fake response to the “national destiny theory” attributed to Liang Wenfeng circulated widely online, with many believing and sharing it as authentic. This response claimed that DeepSeek’s open-source decision was merely “standing on the shoulders of giants, adding a few more screws to the edifice of China’s large language models,” and that the true national destiny resided in “a group of stubborn fools using code as bricks and algorithms as steel, building bridges to the future.” This fake statement—notably devoid of wolf warrior rhetoric—spread virally, its humility and relentless spirit embodying some values people hoped Chinese technologists would champion. Meanwhile, the real Liang Wenfeng remained silent after DeepSeek’s rise. A month later, he appeared on CCTV sitting beside Tencent’s Ma Huateng at Xi Jinping’s symposium for top business leaders.

The public’s fascination with Liang showed no signs of waning. In Silicon Valley, his previous interviews were swiftly translated into English and meticulously analyzed, while in China, his rise also inspired mystical interpretations—during the Spring Festival holiday, Liang Wenfeng’s ancestral home in Zhanjiang, Guangdong transformed into an impromptu tourist attraction, drawing feng shui masters eager to study the geomantic properties of his family residence.

Humans have always sought ways to calculate the incalculable. Perhaps that’s what makes the conversation around Guoyun so captivating: it’s not just about predicting the future, but about sense-making in China’s present.

Gary Wang spent the past decade developing business and product strategy for Silicon Valley technology companies, with a focus on enterprise software, the industrial internet of things and AI. He has a degree from HKS and worked in China. The views expressed here represent only his own.

About a decade ago, the best forecasts heralded a promising manufacturing future, in the United States and globally, with the advent of the fourth industrial revolution (also called “industry 4.0,” the “industrial internet,” or “industrial internet of things” aka IIoT). The belief was that the falling cost of cloud computing, sensor costs, and machine learning — coupled with new connectivity technologies such as 5G or IPv6 — would lead to a revolution in manufacturing productivity and ultimately higher GDP growth.

Despite these promising forecasts, multiple data points indicate that US manufacturing has largely stagnated. Analysis from the New York Federal Reserve reveals that both total factor productivity and labor productivity have been flat from 2007 to 2022. Meanwhile, US share of global manufacturing value add fell from nearly 25% in 2000 to an estimated 15% today in 2024. The UN Industrial Development Org projects US share of global manufacturing value add will fall to 11% in 2030, while China may account for 45% of global output.

This decline comes after multiple presidential administrations’ efforts to revitalize American manufacturing — from the Obama-era policies such as the Advanced Manufacturing Partnership or the Manufacturing USA initiative, to the Biden administration’s Inflation Reduction Act, and now the Trump administration’s desire to reshore manufacturing via tariffs and other policy tools.

Off-shoring and free-trade agreements go only so far in explaining this decline. And the present debates over US industrial policy — sparked by the advent of emerging technologies (generative AI, quantum computing) as well as intensifying competition with China — perhaps focus on the wrong things.

The real questions US policymakers must grapple with: why did the United States fail to capitalize on technology that was already available to make its manufacturing base more competitive?

Put another way: why have the promises of the IIoT revolution failed to materialize in the United States?

This piece makes a few key arguments:

• The “industrial internet of things” is not an industry. It’s a set of disparate technologies that all need to be adopted together to create value.

• The free market will not always optimize adopting a broad set of technologies for an entire ecosystem of industries. The underwhelming results of today’s industrial internet is a case in point.

• China’s industrial policies to “win” the fourth industrial revolution offer lessons for policymakers in the United States to consider.

• When it comes to revitalizing manufacturing, or ensuring American leadership in AI or quantum computing, policymakers need to craft policies to develop entire value chains and tech ecosystems — not myopically focus on just one strategic technology (eg. advanced semiconductors).

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What is IIoT?

IIoT refers to the interconnection of machines, devices, sensors, and systems which are connected on the internet in performing industrial tasks.

Take one of IIoT’s leading “use cases” (ie. applying tech to solve a business problem): predictive maintenance. Sensors connected to a piece of factory equipment, such as a boiler, can measure temperature or vibration. When combined with machine-learning algorithms, manufacturers stand to save millions by predicting when a machine would fail, and then proactively maintaining the machine before a failure occurs — thus reducing factory downtime and increasing productivity.

Another use case: gathering GPS data from truckers could enable machine-learning algorithms to optimize the routes of commercial trucks (saving fuel costs). When paired with data on customer demand (say, Pepsi sales in a city), manufacturers could save billions by optimizing their inventory costs to ensure that the optimal amount of Pepsi reached store shelves at just the right time.

The use cases are endless: deploying robots on the production line, using cameras and AI to automate quality inspection for finished products, creating a “digital twin” of an entire production process for optimization, and much more. All of these use cases required cloud computing, real-world historical data, and connectivity. As a practitioner who has worked with technology companies on their strategy for delivering industrial IoT to manufacturing companies, I can attest to the level of industry enthusiasm for IIoT during this time (as well as the numerous operational challenges).

How off were the IIoT forecasts?

In 2015, McKinsey forecast $1.2 to 3.7 trillion in economic value created per year by 2025 from IoT technologies in factories. Assuming technology vendors alone capture 5% of the value created — a very conservative benchmark — that’s $60 to $185 billion in revenue. The International Data Corp in 2017 forecast that manufacturers would spend $102 billion in the industrial internet, meaning vendors selling IIoT technologies should see comparable revenue figures. Accenture and World Economic Forum joined the hype, intoning that the “Industrial Internet will transform many industries, including manufacturing, oil and gas, agriculture, mining, transportation and healthcare. Collectively, these account for nearly two-thirds of the world economy.” These market forecasts led the Congressional Research Service in 2015 to predict, “The current global IoT market has been valued at about $2 trillion, with estimates of its predicted value over the next 5 to 10 years varying from $4 trillion to $11 trillion.”

These forecasts were off by multiple orders of magnitude. Today, to my knowledge, there is only one publicly listed company in the United States solely focused on IIoT: Samsara, with $1.4 billion in revenue, growing at a healthy ~40% year over year. (Palantir in 2024 reported $700 million in revenue from US private-sector firms, some of which include manufacturing — but the majority of Palantir’s business is with governments.)

General Electric and Siemens both tried to become technology companies by developing their own cloud platforms and AI applications to digitize the manufacturing sector. A series of New York Times headlines, though, tells the saga of GE’s attempt to capture the purported massive opportunity of the industrial internet of things:

• 2011: GE Wants Software for the ‘Industrial Internet’

• 2012: Looking to Industry for the Next Digital Disruption

• 2013: General Electric Adds to Its ‘Industrial Internet’

• 2015: GE Navigates, Carefully, the Industrial Internet of Things

• 2016:  GE, the 124-Year-Old Software Start-Up

• 2018: GE Makes a Sharp ‘Pivot’ on Digital

And finally, later in 2018:

Siemens positioned its industrial internet cloud platform, Mindsphere, as its next growth vector. Today, Mindsphere has been rebranded to “IoT insights hub,” and the last time Siemens company leadership talked about Mindsphere on their earnings call with equity analysts was in 2022, indicating a retrenchment in expectations (unlike when they spoke about Mindsphere on earnings calls with analysts in 2015,  2016, 2019, and 2020; what industry leaders tell Wall Street indicates where they think their companies’ growth will come from).

Why were the predictions so wrong?

IIoT is a cluster of disparate technologies that have to work together to create value. It’s not one technology. Consider the aforementioned predictive maintenance use case. To realize value, a factory owner needs to adopt six or seven different technologies from different vendors.

• There’s the company providing sensors (sometimes with software) for the machines to gather data for analytics.

• Many factories have historically not been connected to the internet, so a company like Verizon needs to get involved to set up an in-plant 5G connectivity network. (Leading analysts have estimated there are only a handful of 5G industrial projects in the United States, compared to likely thousands in China.)

• A company like Cisco has to provide the networking equipment to enable internet connectivity in the factory.

• A cybersecurity company needs to ensure the sensors and machines, now that they’re connected to the internet, are secure from cyberattacks.

• A cloud-computing company, such as Microsoft or Amazon, needs to provide the compute and storage for the customer to develop AI algorithms to analyze the data generated by the sensors. These cloud-computing companies often provide the AI algorithms for customers to customize themselves (assuming they have the in-house data science talent) to analyze the data from factory equipment.

• A company needs to integrate these disparate systems together — usually a system integrator like Accenture or Wipro.

The factory owner has a finite budget, must negotiate with six different vendors (each with their own pricing and profit models, none of whom necessarily coordinate their selling activities) — but still must realize a high enough return on investment (ROI) to justify solving this one use case. Imagine a consumer buying a car — but instead of buying from an OEM like Tesla or General Motors, you have to negotiate individually with the tire company, the engine manufacturer, the seat belt maker, the company making the infotainment display, and every other component manufacturer.

The nature of the physical world makes this coordination problem even more complex:

• Algorithms aren’t immune from false positives. What happens if the algorithms incorrectly predict a machine will break down, but a maintenance technician has already been dispatched to make repairs? That reduces ROI.

• Machine algorithms need to be trained on historical data of when the machine has broken down before — but for many factories, maintenance records aren’t digitized; if available at all, they’re paper logs of when a technician fixed a machine.

• Third, from the perspective of the technology vendor, sales cycles to manufacturers often are usually one to two years — since customers will pilot the technology for one set of machines (one use case) in one factory, measure the cost or productivity savings, and then decide whether they want to scale the technologies to multiple use cases across multiple factories. Factory budgets are managed locally, not globally — meaning a vendor has to sell to a manufacturer’s factory site in, say, the United States, then Brazil, then Germany, and so on.

All of these factors help to explain why venture capitalists — with few exceptions — have not invested in startups tackling industrial IoT, as well as why it’s been hard for existing vendors to scale their business. Even McKinsey admitted in 2021, “To date, value capture across settings has generally been on the low end of the ranges of our estimates from 2015, resulting from slower adoption and impact. For example, in factories, we attribute the slower growth to delayed technological adoption because many companies are stuck in the pilot phase.”

What has China done?

While IIoT hasn’t lived up to its potential in the United States and elsewhere in the West, China has leaped ahead in the fourth industrial revolution: there is no other country in the world that can boast of legions of “dark factories” — ie. factories where entire manufacturing processes are automated.

How has China done it? By focusing on technical challenges and market-coordination problems.

First: Chinese policymakers at the highest level — eg. the State Council — crafted policies to solve known technical challenges which threatened to hold back Chinese manufacturer’s adoption of IIoT technologies.

For example, in the predictive maintenance use case, there is a known problem of “asset mapping” — ensuring all the physical and digital assets in a factory can be identified in a common taxonomy to enable machine-learning analytics and then workflow automation (sending a technician to repair a robot, changing the workload of robots working together if one robot is breaking down, etc.). Specifically, if factory owners want to predict when a robot arm will break down, they need a comprehensive way to uniquely identify the specific robot, the specific arm of that robot, the specific sensor that may be attached to the robot, the specific 3D model of the robot’s arm, and then map all of these physical and digital assets together. Without a common taxonomy, it’s impossible to automate the analysis of sensor readings from the robot arm (eg. its grip strength) and then trigger a workflow to fix the robot arm while enabling the production process to continue seamlessly, that is, in a “lights out” fashion.

China’s State Council, in a 2017 planning document — “Guidance for Deepening the Development of the Industrial Internet ‘internet + promoting manufacturing” 深化“互联网+先进制造业” 发展工业互联网的指导意见 — specifically called for implementing networking connectivity and “identity resolution system” 标识解析体系 to solve this problem, using a combination of known technologies and standards such as IPv6, software-defined networking, 5G connectivity, time-sensitive networking, and passive optical networking. The technologies mentioned in this document were available in China (and the United States) in 2017. An identity resolution system (the English equivalent term would be a digital “tracking system”), when combined with advanced networking technologies, solves this predictive-maintenance problem because then a piece of software — such as a predictive-maintenance application for robots — can automatically locate the robot arm that’s emitting sensor data indicating a breakdown, match that to the 3D model that specifies how the robot arm should function, detect issues with the robot arm, and then trigger a workflow to remediate. Dozens of physical and digital systems are involved in solving this problem.

Of course, the free market can solve this problem as well — but it runs into the same issue mentioned above: coordination of multiple vendors with multiple technologies and standards that all have to work together. No wonder that, in 2024, 5G adoption in the US manufacturing sector was at 2%. After all, a factory doesn’t realize any business value from just deploying 5G by itself, if the rest of the technology stack (sensors, algorithms, applications, cloud computing, security, etc.) isn’t also deployed.

Second: China targeted industrial policy to solve known market-coordination problems that would hold back IIoT adoption.

For example, consider the problem of sub-scale platforms. To better understand what this is, I’ll first lay some foundation on key terms:

A platform is any technology in which an underlying resource, such as computing power (eg. Amazon Web Services), is offered to customers as a software component to build a fully functional piece of software. In the IIoT case, “industrial internet of things platforms” are cloud platforms that allow manufacturers to (1) access compute and data storage, (2) enable data to be sent from physical machines to the cloud, and (3) secure the network and data from machine to cloud. An IIoT application is a packaged piece of software with algorithms and an end-user interface that solves a business problem.

The consumer analogy is how the iPhone is a platform and Google Maps is the application that runs on the platform, using its compute and storage. Manufacturers need the IIoT platform, and they must either (1) build the IIoT application themselves (which is difficult since manufacturers often don’t have the in-house talent), or (2) buy a prepackaged application from a vendor.

The sub-scale platform problem occurs when, in a market, there are too many platform vendors who can’t make enough money to scale their business due to intense competition and operational execution issues (identified above) and when there aren’t enough applications to actually create value for the customer, the manufacturer. The IIoT market in the United States has faced precisely this problem, especially because digital-platform markets tend toward winner-take-all or oligopoly competition dynamics (eg. iPhone vs. Android; the four major cloud-computing platforms: Amazon, Google, Microsoft, and now Oracle), and platforms make money only if application vendors build on the platform.

BCG, in a 2017 report titled “Who Will Win the IoT Platform Wars,” identified over 400 IoT platforms in the market due to the excitement of the industry at that time. But few of these platforms really grew to any significant scale, with some notable failures (see GE’s attempt above) because of the technical and operational issues. As a result, there were few IIoT application vendors building prepackaged software. There too many platforms they could choose to build on, and the lack of platforms at scale meant there were too many technical challenges that were unresolved. The value of the platform is to solve the underlying technical issues so an application developer doesn’t have to. In the IT world, a software developer doesn’t have to worry about which type of server or networking equipment is in the data center to build a cloud application. The same is true for a software developer on mobile: they don’t have to worry about the specific type of camera lens on the phone when building their app.

As a result, there are few if any IIoT applications at scale (Samsara being a notable exception). For example, there is no packaged software application that a factory own can buy to predict when any robot it chooses to deploy will breakdown today, or for any other type of equipment (of which there are literally thousands) in a factory.

Meanwhile, China’s State Council, in the same 2017 policy document, designed policies to solve the sub-scale platform problem in IIoT:

By 2020, form the industrial internet platform system, supporting the construction of approximately 10 cross-industry, cross-domain platforms, and establishing a number of enterprise-grade platforms that support companies’ digital, internet-enabled, and AI-enabled transformations. Incubate 300,000 industry-specific, scenario-specific industrial applications, and encourage 300,000 enterprises to use industrial internet platforms for research and development design, production manufacturing, operations management, and other business activities. The foundational and supportive role of industrial internet platforms in industrial transformation and upgrading will begin to emerge.

到2020年，工业互联网平台体系初步形成，支持建设10个左右跨行业、跨领域平台，建成一批支撑企业数字化、网络化、智能化转型的企业级平台。培育30万个面向特定行业、特定场景的工业APP，推动30万家企业应用工业互联网平台开展研发设计、生产制造、运营管理等业务，工业互联网平台对产业转型升级的基础性、支撑性作用初步显现。

Like most industrial policies in China, the State Council’s high-level policy guidance becomes operationalized in provincial- and city-level policies via funding and other incentives. For example, Jiangsu 江苏 province set a goal of establishing 1,000 “smart” (aka enabled by cloud, AI, advanced connectivity, etc.) factory workshops in 50 provincial-level factories by 2020.

What can the United States learn?

If we’re serious about revitalizing US manufacturing or maintaining leadership in emerging technologies such as AI and quantum computing, here are some things US policymakers should consider:

1. The free market, while efficient for specific markets, may not optimize for transforming entire sets of industries. The technologies for the industrial internet of things were available in the United States — but due to technical and market-coordination challenges, adoption has lagged behind that of China. AI and quantum are foundational technologies that may require an even greater level of market coordination to overcome operational and technical obstacles compared to that of the industrial internet of things.

2. Industrial policy needs to move beyond tax incentives, tariffs, and subsidies to make calculated bets on specific technologies, with deep technical expertise incorporated early on in the policy process. For example, in AI, the policy debate has focused exclusively on semiconductor subsidies and export controls — but there is limited if any discussion on how to make the AI data center itself easier to build and operate. High energy costs and energy availability due to the limits of the utility grid are known technical and business challenges to data center capacity today. Ultimately, the total cost of using AI to make predictions, optimize processes, and create value (eg. cost of inference) is not just the cost and efficiency of the chips, but the entire data center stack, including energy costs.

3. Successful commercialization of a set of technologies creates its own positive feedback loop, which reinforces first-mover advantages. Since China has a significant head start in digitizing its manufacturing base via IIoT technologies, Chinese vendors likely have more real-world data (by deploying more sensors), which enables firms to perfect their machine-learning algorithms, which will further improve manufacturing productivity in China relative to the United States. Robot adoption is a key example: when adjusted for labor costs, China uses 12 times more robots than the United States. This deployment of industrial robots at scale further advantages Chinese manufacturers and the entire technology stack associated with robotics (eg. operating systems for robots, robot supply chain, AI software to control the robots, software integrating robots into production processes, etc.). Recent reports of the Chinese government and enterprises mass-adopting DeepSeek only add urgency for more innovative industrial policies in the United States. Therefore, to achieve policy goals such as restoring US manufacturing or maintaining US leadership in quantum or AI, the United States must support companies to actually buy and use these technologies themselves.

While China may have “won” the initial round of the IoT platform wars, it isn’t too late for the United States, with smart policies and leadership, to win the broader industrial-technical leadership competition with China. While some may object to “picking winners and losers,” without urgent policy action, there may only be losers left to pick from.

由于篇幅限制，本文为第四十五封来信的后半部分。阅读文章第一小节「“伟大”的养成游戏：男宝妈是怎样炼成的？」，请点击下方链接：

养成系偶像花式吸血策略大赏（上）

2、“精美”的浪漫陷阱：性幻想是如何构建的？

从踏进公司那一刻起，为粉丝提供异性恋浪漫幻想就成了偶像最重要的任务之一。即便他只有十一二岁，公司的工作人员也会反复问一些情感问题，比如“喜欢什么样的女生？”，“觉得女生多少斤算重”等。一旦进入青春期，偶像就可以化被动转为主动、有意识地进行男友风营业，即所谓的“媚粉”（媚不应该是女字旁，但我暂时找不到替代词）。这种营业针对的是女友粉，也被称为“梦女”。比如王俊凯在2016年情人节发布的这条微博，背景是酒店的床，嘴里叼着玫瑰花，神情有些迷离，发出来就是为了让粉丝浮想联翩。

发自拍只是众多媚粉方式中最简单的一种，还有偶像会通过音乐作品构建浪漫幻想。比如马嘉祺发布过一首名为《蜉蝣》的歌曲，讲述了一个以自己为男主、粉丝为女主的暗恋故事。海报上那句“我喜欢他，他知道吗”、MV中女主角全程不露脸的拍摄手法以及某个抄袭电影《情书》的镜头都是方便粉丝代入的工具。鉴于梦女对马嘉祺单向的爱近似于暗恋，且大多数粉丝是年轻学生，这个从校园暗恋到十年后相遇的浪漫叙事很容易帮他吸引和巩固女友粉。

除了浪漫爱幻想以外，色情幻想也是成年偶像必不可少的吸粉利器。想走性感路线的爱豆会选择更成熟的妆容和更暴露的服装，并佩戴耳钉、唇钉等饰品。舞台风格也会随之变化，表演时还可以露腹肌，甚至设计一些SM的动作。以时代少年团24年8月的澳门演唱会为例。通过比较歌曲《楼外楼》的两次表演，我们可以发现，23日的服装露肤度极大，而24日每个成员都加了一件外套，裤子上也基本没有了大块露肉的设计。演唱会的服装设计可以直接反映公司想要取悦的粉丝群体：23日走性感风，是为了吸引女友粉；24日回归乖男孩形象，是为了稳住妈粉。

不过毕竟粉丝和爱豆大多隔着屏幕、常常缺少某种实质性的感受，所以男团一般还会靠卖腐进一步美化成员形象，强化上述两种性幻想——是的，卖腐并不等于同性恋叙事，反而是为了贩卖异性恋幻想。时代峰峻有限公司又被称为“时代卖腐无限公司”，该公司不仅靠卖腐起家，还以卖腐为主要业务，为了卖腐甚至可以牺牲舞台效果。公司会通过剪辑、花字、双人舞台（选曲偏向情歌）、双人小卡（类似照片）等各种方式引导粉丝嗑CP。

公司主推的CP被称为“大势CP”，其CP粉会有源源不断的“工业糖精”，而这些所谓的“糖”不过就是粗制滥造的异性恋范式。以TNT时代少年团为例，公司从7个人里推出了三对CP，粉丝称为“三大势”，包括祺鑫（马嘉祺和丁程鑫）、文轩（刘耀文和宋亚轩）、翔霖（严浩翔和贺峻霖）。名字排在前面的是攻（瓜），后面的是受（花），受方无一例外地被女化和弱化。宋亚轩的体型和身高其实一直和刘耀文差不多，但公司会刻意给他穿低跟鞋和女性化的服装以强化他的花设地位。

这些大势CP的人设是性别刻板印象的集中体现，攻方英俊、强大、冷酷，受方漂亮、弱小、爱撒娇。CP粉对受方的要求比对攻方高得多，习惯性用异性恋中的贞洁观绑架受方，受方如果与其他成员相处亲密会遭受更严厉的指责。同时，女粉丝可以代入受视角，把攻对受的爱幻想成对自己的爱，所以攻通常更容易提纯CP粉（把CP粉转化成自己的唯粉）。而受方的粉丝又会不满公司为了组CP弱化了自己爱豆的男子气概......例如大多数宋亚轩的唯粉并不喜欢他和刘耀文卖腐，因为觉得宋在CP中的人设弱化了他的个人魅力，和他本身的形象不匹配。

如果说特定的人设是构建异性恋浪漫幻想的基础，那么强调“性张力”的双人舞台则是能让色情幻想大卖的利器。时代峰峻的很多双人舞台都改编自Kpop的男女合作，舞蹈分性别走位，还会包含一些有色情意味、甚至明显性暗示的动作，比如刘耀文和朱志鑫在2020年合作的舞蹈《Trouble maker》。鉴于两人当时年仅15岁，舞蹈比原版尺度小了很多，却仍然在养成系粉丝中引起了巨大反响。刘耀文的梦女可以直接代入朱志鑫，对偶像做或浪漫或色情的梦，因此他的人气在这个舞台之后快速上升。几个月后，公司又安排两人第二次合作。虽然这两位成员不同代（文属二代，朱属三代），一年根本见不了几次面，但“文朱”这对CP自从《Trouble maker》之后一直都是整个公司最火的跨代CP，可见双人舞台对于色情幻想的加成有多大。

结语

作为女性，我们从小就被灌输各种爱男厌女的思想，普遍自我价值感较低，习惯性追逐偶像，试图从他们身上获取力量。即便自身已经非常优秀，女性意识也部分觉醒，我们仍然难以完全摆脱爱男的阴影，尤其是幻想中的完美男性。资本设计的养成游戏正是一场针对女性的长期剥削，以追梦为名激发母爱，同时用卖腐等手段贩卖各种性幻想，让女性在偶像的虚假光环下糊里糊涂地被吸血，不知不觉中变得更加爱男。

然而，除去资本的包装和你的滤镜，所谓的偶像与生活中的男性并无不同。更何况，女性的梦想不需要借由一个男人间接实现，女性要追求的亲密关系更应当远离异性恋范式的侵蚀。所以，与其追逐偶像，我们何不专注自身，把这份爱和欣赏还给自己呢？

就此搁笔，期待下一次和大家见面！

暗月使者*

二〇二五年三月三日

*本文由暗月使者主笔，陌生女人1号编辑。欢迎更多姐妹来稿至邮箱dearsisters2022@gmail.com

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Why is Trump appeasing Russia? What lessons can we learn from the battlefield in Ukraine? How will AI change warfare, and what does America need to do to adapt?

To discuss, we interviewed Shashank Joshi, defense editor at the Economist on a generational run with his Ukraine coverage, and Mike Horowitz, professor at Penn who served as Biden’s US Deputy Assistant Secretary of Defense for force development and emerging capabilities in the Pentagon.

We discuss….

• Trump’s pivot to Putin and Ukraine’s chances on the battlefield,

• The drone revolution, including how Ukraine has achieved an 80%+ hit rate with low-cost precision systems,

• How AI could transform warfare, and whether adversaries would preemptively strike if the US was on the verge of unlocking AGI,

• Why Western military bureaucracies are struggling to adapt to innovations in warfare, and what can be done to make the Pentagon dynamic again.

This episode was recorded on Feb. 26, two days before the White House press conference with Zelenskyy, Trump, and JD Vance. Listen now on iTunes, Spotify, YouTube, or your favorite podcast app.

Jordan Schneider: Shashank, it seems you had a lot of fun on Twitter this week?

Shashank Joshi: I was in a swimming pool with my children on holiday in the middle of England and didn’t notice until 18 hours after the fact that the Vice President of the United States had been rage-tweeting at me over my intemperate tweets on the subject of Ukraine. I provoked him into this in much the same way that he believes Ukraine provoked the invasion by Russia.

Jordan Schneider: What does it mean?

Shashank Joshi: It means the Vice President has far too much time on his hands, Jordan.

This is a pretty significant debate. Fundamentally, this was about whether Ukraine is fated to lose. His contention is that Russian advantages in men and weapons or firepower meant that Ukraine’s going to lose no matter what assistance the United States provides.

My argument was that while Ukraine is not doing well — I’m not going to sugarcoat that, I’ve written about this and it’s made me pretty unpopular among many Ukrainians — it’s not true that advantages in manpower and firepower always and everywhere result in decisive wins. Indeed, Russia’s advantage in firepower is much narrower than it was. The artillery advantage has closed. Ukraine’s use of strike drones — which we’ll talk about later — has done fantastic things for their position at the tactical level.

On the manpower side, Russia is still losing somewhere in the region of 1,200-1,300 men killed and wounded every single day. While it can replenish those losses, it can’t do that indefinitely. I’m not saying Vance is completely wrong — I’m just saying he is exaggerating the case that Ukraine has already lost and that nothing can change this.

My great worry is this is driving the Trump administration into a dangerous, lopsided, inadequate deal that is going to be disastrous for Ukraine and disastrous for Europe. I’m worried profoundly about that at this stage.

Michael Horowitz: Quantity generally sets the odds when we think about what the winners and losers are likely to be in a war. Russia has more and will probably always have more. But there are lots of examples in history of smaller armies, especially smaller armies that are better trained or have different concepts of operation or different planning, emerging victorious. Most famously in the 20th century, perhaps Israel’s victory in 1967.

Jordan Schneider: We have three years of data. It’s not like you’re playing this exercise in 2021. You’re doing this exercise in February of 2025. By the way, Mr. Vice President, your government actually has a ton of the cards here to change those odds and change the correlation of forces on the ground, which just makes the argument that this is a tautology so absurd coming from one of the people who is in a position to influence and who has already voted for bills that did influence this conflict.

Shashank Joshi: Wars are also non-linear. You can imagine a war of attrition in which pressures are building up on both sides, but it isn’t simply some mathematical calculation that the side with the greatest attrition fails. It depends on their political cohesion, their underlying economic strength, their defense industrial base, and their social compact.

The argument has been that although Russia feels it has the upper hand — it has been advancing in late 2024 at a pace that is higher than at almost any time since 2022 — there’s no denying that to keep that up, it would have to continue mobilizing men by paying them ever higher salaries and eventually moving to general mobilization in ways that would be politically extremely unpalatable for Vladimir Putin. War is not just a linear process. It’s a really complicated thing that waxes and wanes, and you have to think about it in terms of net assessment.

Michael Horowitz: That’s especially true in protracted wars. I’m teaching about World War I right now to undergraduates at Penn. One of the really striking things about World War I is if you look at the French experience, the German experience, and the Russian experience in particular, given the way that World War I is one of the triggers for the Russian Revolution, how their experience plays out in World War I is in some ways a function of political economy — not just what’s going on on the battlefield, but their economies and the relationship to domestic politics and how it then impacts their ability to stay in and fight.

Jordan Schneider: America has levers on both sides of the political economy of this war. There was a point a few weeks ago when Trump said he was going to tighten the screws on Putin and his economy. The fact that we are throwing up our hands and voting with Putin in the United Nations, saying that they were the aggressor, just retconning this entire past few years is really mind boggling. There was a line in a recent Russia Contingency podcast with Michael Kofman, where he says “The morale in Munich was actually lower than the morale I saw on the front in Ukraine,” which is a sort of absurd concept to grapple with.

Michael Horowitz: If you were to mount a defense here, what I suspect some Trump folks might say is that they believe this strategy will give them more leverage over Russia to cut a better deal. That involves saying things that are very distasteful to the Ukrainians, but they think as a negotiating strategy, that’s more likely to get to a better outcome.

Shashank Joshi: That’s right, Mike. Although they’ve amply shown they are willing to tighten the screws on Zelenskyy. If you were looking at this from the perspective of the Kremlin, would you believe General Keith Kellogg when he says, “If you don’t do a deal, we’re going to ram you with sanctions, batter you with economic weapons"? Or do you listen to Trump’s rhetoric on how we’re going to have a big, beautiful economic relationship with Russia and we’re going to rebuild economic ties, lift sanctions?

You’re going to be led into the belief that the Americans are really unwilling to walk away from the table because the Vice President and others are publicly saying we don’t have any cards, that the Ukrainians are losing, and if we don’t cut a deal now, then Russia has the upper hand. It puts them in a position of desperation.

My big concern is not just that we get a bad deal for Ukraine, it’s that the idea of spheres of influence appeals to Trump, dealing with great men one-on-one, people like Kim Jong Un, Vladimir Putin, Xi Jinping — and that what will be on the table is not just Ukraine, but Europe. Putin will say, “Look, Mr. President, you get your Nobel Peace Prize, we get a ceasefire, we do business together and lift sanctions. And you can make money in Moscow, by the way. Just one tiny little thing, that NATO thing. You don’t like it, I don’t like it. Just roll it back to where it was in 1997, west of Poland. That would be great. You’ll save a ton of money here. I’ve prepared a spreadsheet for you.”

That is the scenario that worries us — a Yalta as much as a Munich.

Jordan Schneider: We have a show coming out with Sergey Radchenko where we dove pretty deep into Churchill’s back-of-a-cocktail-napkin split. At least Churchill was ashamed.

It’s so wild thinking about the historical echoes here. I was trying to come up with comparisons, but the only ones I could do were hypotheticals. Like McClellan winning in 1864, or — I mean, Wendell Willkie was actually an interventionist. There was some Labor candidate that the Nazis were trying to support in the Democratic Party in 1940, but he never made it past first base. Has there ever been a leadership change that shifted a great power conflict this dramatically?

Shashank Joshi: From the Russian perspective, that’s Gorbachev. Putin would look back at glasnost, perestroika, and Gorbachev at the Reykjavik summit as moments where a reformist Soviet leader sold the house to the Americans and threw in the towel.

Michael Horowitz: You also see lots of wars end with leader change, with leadership transitions, when wars are going poorly for countries and you have leaders that are all in and have gambled for resurrection. If you think about the research of someone like Hein Goemans back in the day, then you have to have a leadership transition in some ways to end wars in some cases if leaders are sort of all in on fighting.

Jordan Schneider: The Gorbachev-Trump comparison is a really apt one because it really is like a true conceptual shift in the understanding of your country’s domestic organization as well as role in the world. Gorbachev, for all his faults, at least had this universalist vision of peace, trying to integrate in Europe — he wanted to join NATO at one point. But going from that to whatever this 19th century mercantilism vision is, is really wild to contemplate.

Shashank Joshi: The other thing to remember is Gorbachev’s reforms eventually undid the Soviet empire. They undid its alliances and shattered them. In the American case, the American alliance system is not like the Soviet empire. France and the UK are not the Warsaw Pact. We bring something considerably more to the table. It’s a voluntary alliance. It’s a technological, cultural alliance. These are different things.

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I worry sometimes that this administration or some people within it — certainly not everybody — views allies just as blood-sucking burdens. What they don’t fully grasp is how much America has to lose here. I want to say a word on this because Munich — and I heard this again — the FT reported recently that some Trump administration official is pushing to kick Canada out of the Five Eyes signals intelligence-sharing pact.

Now okay, the Americans provide the bulk of signals intelligence to allies. There’s no surprise about that. But if you lost the 25% provided by non-US allies, it will cost the US a hell of a lot more to get a lot less. It will lose coverage in places like Cyprus, in the South Pacific, all kinds of things in the high north, in the Arctic in the Canadian case. This administration just doesn’t understand that in the slightest.

Michael Horowitz: Traditionally what we’ve seen is regardless of what political hostility looks like, things like intelligence sharing in something like the Five Eyes context continues — in some ways the professionals continue doing their jobs. If you see a disruption in that context, that would obviously be a big deal.

Jordan Schneider: Just staying on the Warsaw Pact versus NATO in 2025 today, America plus its allies accounted for nearly 70% of global GDP during the Cold War. The economic outflows that were needed to sustain Soviet satellites eventually bankrupted the USSR. America isn’t facing anything resembling that situation by stationing 10,000 people in Poland and South Korea.

Michael Horowitz: We are in a competition of coalitions with China, and it is through the coalition that we believe we can sustain technological superiority, economic superiority, military power, et cetera. Look at something like semiconductors and the role that the Netherlands plays in those supply chains, that Japan plays in those supply chains. There are interconnections here. We have thought that we will win because we have the better coalition.

Shashank Joshi: That’s an interesting question to ask more conceptually — does this administration want a rebalancing of its alliances or does it want a decoupling? You could put it in terms of de-risking and decoupling if you want to echo the China debate here. Does it simply want more European burden-sharing? But fundamentally the US will still maintain a presence in Europe, underwrite European security, and provide strategic nuclear weapons as a backstop. That is what many governments are trying to tell themselves.

The more radical prospect is that whilst there are some people who envision that outcome — Marco Rubio, Mike Waltz (the National Security Advisor), and John Ratcliffe (the head of the CIA) — the President and many of the people around him view things in considerably more radical terms. It’s more of a Maoist cultural revolution than a kind of “I’m Eisenhower telling the Europeans to spend more.”

Jordan Schneider: There’s this quote from Marco Rubio that’s really stuck with me from a 2015 Evan Osnos profile where he talks about how he has not only read but is currently rereading The Last Lion, which is this truly epic three-part series. The middle book alone is most famous, which is what Rubio was referring to, where Churchill saw the Nazis coming when no one else did and did everything he could in the 30s to wake the world up and prepare the UK to fight.

Rubio is referring to this moment by comparing it to how he stood up to the Obama administration when they were trying to do the JCPOA nuclear deal with Iran. To go from that to having to sit on TV and blame Ukraine for starting the war, I think is just the level of cravenness. There are different orders and degrees of magnitude.

Shashank Joshi: You have to think about this not in terms of a normal administration in which people do the jobs assigned to them by their bureaucratic standing. You have to think about it like the Kremlin, where you have power verticals, or an Arab dictatorship where you have different people reporting up to the president. Think of this like in Russia, where you have Sergey Naryshkin, the head of the Foreign Intelligence Service, who may say one crazy batshit thing, but actually has no authority to say it. In which Nikolai Patrushev may say another thing, in which Sergey Lavrov may lay down red lines, but they have no real meaning because there’s a sense of detachment from the brain, the power center itself. Ultimately, it’ll still be Putin who makes the call. I think it’s a category error if we try to think about this administration as a normal system of American federal government.

Michael Horowitz: I will say, I can’t believe I’m now going to say this, but let me push back and say that there’s a lot of uncertainty about what the Trump administration wants to accomplish here, given the way they have embraced the notion that Trump is a master negotiator. To be professorial about it, in a Thomas Schelling “threat that leaves something to chance” way, or like madman theory kind of way, they think that there’s a lot of upside here from a bargaining perspective.

Most of Trump’s national security team is not yet in place. We just had a hearing for the Deputy Secretary of Defense yesterday. Elbridge Colby, who’s the nominee for undersecretary, has a hearing coming up, I think either next week or the following week. So a lot of the team is still getting in place.

Jordan Schneider: The thing about Trump 1.0 is there weren’t wars like this. You had two years of sort of normal people who were basically able to stop Trump from doing the craziest stuff. Then the COVID year was kind of a wash. But Trump 2.0 matters a lot more, it’s fair to say, over the coming four years than it did 2016-2020.

Shashank Joshi: It’s much more radical. In the first term, John Ratcliffe had his nomination pulled as DNI because he was viewed as inexperienced and not up to the job. Today, John Ratcliffe looks like Dean Acheson compared to the people being put into place. We have to pause and make sure that we recognize the radicalism of what is being put into place around us.

When you look at the sober-minded people who thought about foreign policy — and I include amongst this people I may disagree with, like Elbridge Colby, who will be probably the Pentagon’s next policy chief — what is the likely bureaucratic institutional political strength they will bring to bear when up against those with a far thinner history of thinking about foreign policy questions?

Jordan Schneider: I haven’t done a Trump-China policy show because I don’t think we have enough data points yet. But what, if anything, from the past few weeks of how he’s thinking and talking about Russia and Ukraine, is it reasonable to extrapolate when thinking about Asia?

Shashank Joshi: Two quick things. One is I see significant levels of concern among Asian allies. The dominant mood is not, “Oh, it’s fine, they’re going to just pull a bunch of stuff from Europe, stick it into Asia and it’ll be a great rebalancing.”

Number two, I think this is important: there is a strong current of opinion that views a potential rapprochement with Russia as being a wedge issue to drive between Russia and China, the so-called reverse Kissinger. Jordan, you know much more about China than I do. I’m not going to comment further on that, but I will say I believe it is an idea that is guiding and shaping and influencing current thinking on the scope of a US-Russia deal.

Michael Horowitz: You certainly have a cast of officials who are pretty hawkish on China, which will be a continuation in some ways of the last administration and the first Trump administration. I think the wild card will be the preferences of the president. There was a New York Times article a few days ago that talked about Trump’s desire for a grand bargain with China — his desire to do personal face-to-face diplomacy with Xi as a potential way to obtain a deal.

Now I think the reality is that every American president that has tried to do that kind of deal, whether in person or not over the last decade, has found that there are essentially irreconcilable differences. There’s a reason why there is US-China strategic competition and why that has been the dominant issue in some ways of the last several years and probably will be over the next generation. But Trump may wish to give it a shot — and it sounds like, at least from that article, that he might.

Jordan Schneider: We’ve also had every administration in the 21st century try to start their term by trying to reset relations with Russia. “Stable and predictable relationship” was Biden’s line. Maybe this stuff is just a blip, but I think Shashank’s right. We’re in really uncharted territory.

Paid subscribers get access to the rest of the conversation, where we discuss…

• AI as a general-purpose technology with both direct and indirect impacts on national power,

• Whether AGI will cause instant or continuous breakthroughs in military innovation,

• The military applications of AI already unfolding in Ukraine, including intelligence, object recognition, and decision support,

• AI’s potential to enable material science breakthroughs for new weapons systems,

• Evolution of drone capabilities in Ukraine and “precise mass” as a new era of warfare,

• How China’s dependence on TSMC impacts the likelihood of a Taiwan invasion,

• Whether AGI development increases the probability of a preemptive strike on the US,

• How defense writers and analysts help shape policy and build bureaucratic coalitions,

• Ukraine as a real-world laboratory for testing theories about warfare, and what that means for Taiwan’s defense.

Jordan Schneider: Let’s talk about the future of war. There is this fascinating tension that is playing out in the newly national security-curious community in Silicon Valley where corporate leaders like Dario Amodei and Alex Wang, both esteemed former ChinaTalk guests, talk about AGI as this Manhattan Project-type moment where war will never be the same after one nation achieves it. What’s your take on that, Mike?

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亲爱的读者周末好～ 三月新书讯简单聊一下“台版书”。

台湾拥有一个十分活跃的大型出版市场，五千家出版社自由竞争，每年出版三万到五万种新书。作为对比：人口体量更大的德国（8300 万） 和日本（1.25 亿）每年出版约七万种新书。

台版电子书主要平台是读墨，可用信用卡付款，使用专有 app 阅读；另外 Google Play、亚马逊 Kindle 亦有部分台版电子书销售。

实体书主要平台是博客来（当然也兼卖电子书）。

人文社科领域 联经、商务、允晨、左岸、麦田、台大、时报、卫城等均为知名品牌，但出版市场波动较大， 新旧书商起起落落。

1999 年台湾废除出版审查制度。没有政治审查，却有市场压力。人文社科书尤其需要平衡编辑品味和市场口味。

经营出版品牌极为不易，尤其在这个大众读者正在飞速转向网络视频的新时代。因此如有可能请支持正版购买渠道，用钱投票让自己喜欢的出版商活下来，活得好。

以下推荐几本台版新书。

聯經中國史

九卷本的联经中国史，由王汎森教授主编，从上古至清末。

已出版五卷：

• 《華麗的貴族時代：魏晉南北朝史》／呂春盛（臺灣師範大學歷史學系教授）

• 《北南角力中的新秩序：遼金元史》／陳昭揚（臺灣師範大學歷史學系副教授）

• 《華夏再造與多元轉型：明史》／徐泓編（暨南國際大學歷史學系榮譽教授）

• 《首崇滿洲的多民族帝國：清史》／葉高樹（臺灣師範大學歷史學系教授）

• 《跨國交織下的帝國命運：近代史》／吳翎君（臺灣師範大學歷史學系教授）

尚余四卷待出版：
《上古史》／黃銘崇（中央研究院歷史語言研究所研究員）
《秦漢史》／李訓詳（臺北大學歷史學系暨研究所助理教授）
《隋唐五代史》／陳登武（臺灣師範大學歷史學系教授）
《宋史》／梁庚堯（臺灣大學歷史學系名譽教授）

戰火中國1937-1952

方德萬。戰火中國1937-1952：流轉的勝利與悲劇，近代新中國的內爆與崛起。台北：聯經出版公司，2020。

van de Ven, Hans. 2018. China at War: Triumph and Tragedy in the Emergence of the New China. Cambridge, MA: Harvard University Press.

方德万是剑桥大学历史学教授。作为荷兰人的他曾在莱顿大学念汉学，1980 年到哈佛大学做博士，师从史学大师孔飞力（《叫魂》的作者）。

这本《战火中国》是他晚年集大成之作，方德万将抗战、内战与韩战作为一个连续之整体来处理，视角卓越，叙事流畅，因此很适合普通读者入门阅读。

冷戰

Westad, Odd Arne。《冷戰：從兩強爭霸到全球衝突，當代地緣政治的新世界史》。陳柏旭、林書媺譯。臺北：聯經出版，2023。

文安立的《冷战》是目前为止气势最恢宏的单卷本冷战史，不仅写美苏争霸，而且写冷战作为一个世界体系对全球每个角落的影响，以及美苏之外的次级角色对冷战进程发挥的重要作用。

戰後歐洲六十年

Judt, Tony。《戰後歐洲六十年（上下冊套書）〔新版〕》。黃中憲譯。臺北：左岸文化，2024。

Tony Judt 的经典《战后欧洲史》，台湾译本的翻译品质远胜中信出版社的简体中文译本。

東歐百年史

Connelly, John。《東歐百年史：共同體的神話》（全3冊）。羅亞琪、黃妤萱、楊雅筑、蔡耀緯譯。臺北：臺灣商務，2023。

康纳利的《东欧百年史》，从19 世纪的民族发明一路写到后共产主义的民主重建。

为什么读台版？

台译本至少没有删改。而且有些图书只有台译本，如果不喜欢直接读英文，台译本就是你接触到一些重要著作的唯一窗口。

除历史类图书外，台湾书市上有五花八门的时政类新书，鱼龙混杂，小心挑花眼。

另外台湾非常喜欢翻译日文书，如果不懂日文又想看日文新书，台湾书市值得关注。

祝阅读愉快～

如果喜欢这个专栏，请推荐给家人朋友订阅。

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Off all the wild moves we’ve gotten out of this Administration so far, basic science funding could be the dumbest and hardest to reverse.

does a great job with the basic plot.

Noahpinion

What happens when we gut federal science funding?

Back at the beginning of his first term in 2017, Trump briefly tried to cut federal science spending. I wrote a pair of posts for Bloomberg saying that this was a bad idea. Here are some excerpts from the first of those…

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10 days ago · 252 likes · 68 comments · Noah Smith

I’d like to spotlight the newest NSF directorate, Technology, Innovation and Partnerships (TIP) created by the CHIPS & Science Act, that has been particularly hard-hit by DOGE. The idea was to supplement the world-class basic research that NSF does with more use-inspired and translational research with higher technology readiness levels. I’ve been following this directorate since its creation, recorded a panicked emergency pod when for a hot minute Senate Commerce almost killed it, and have been really impressed with its work so far.

TIP helped stand up NAIRR, has done a fanstastic job helping catalyze regional innovative hubs, and is the only org I’ve seen in government actually be strategic about workforce development. My personal favorite its new APTO program, which is creating the data and intellectual substrate necessary to really do smart S&T and industrial policy. For more of what TIP has been up to, check out their Director’s annual letter here. I’d also encourage DOGE to have a read of the TIP’s roadmap for the next few years and try to spot stuff that America doesn’t need.

The NSF is not perfect. IFP has some excellent proposals on how to incorporate novel funding strategies like lotteries that need faster adoption. But IFP also recently wrote up how the NSF showed its mettle, and was able to move faster than the NIH for COVID-related grants. TIP in particular has collected some of NSF’s most forward-thinking talent and is experimenting with novel programs and funding strategies faster than anyone else in the NSF mothership.

American basic research is our golden goose and the envy of the world, building the basis for scientific innovations that make us richer, live longer, and make us more powerful. Our universities attract the best minds in the world which is an enormous boon to the country, and absent radical intervention will continue to do so. While the NSF could use reform, we are criminally underfunding R&D already, and firing the most forward-thinking junior staff in the directorate singled out by national security heavyweights as critical to competing with China is an error this administration should correct.

Try Picking on Someone Your Own Size

DOGE should really try taking on some government programs that aren’t already running lean, creating the future, preventing pandemics and saving lives. The real discretionary bloat isn’t malaria bednets and fundamental physics research but F-35s and carriers. A real push at a few deadweight DoD programs could deliver way more savings than whatever you can squeeze from NSF and USAID and likely make for a more effective force.

From Jennifer Pahlka:

The only way the DoD was really going to change was through major budget cuts — something that forced people’s hands into new ways of working, into true prioritization, into processes that took less time because they were less burdened by the trappings that come with enormous budgets. I began my comment with an apology to the senior Air Force official sitting next to me, a caveat that I meant no disrespect, and wasn’t arguing for less military might — in fact, what I wanted was a more capable military. To my surprise, he piled on. “She’s right,” he said. “But it has to be much deeper than anything we’ve seen before. We had to cut during the last sequestration, and it was around 15% off the top of everything, which doesn’t force meaningful choices. It needs to be like half.”

To get at wasteful DoD programs and acquisitions regulations this administration would have to do the hard work of wooing Congresspeople into taking votes that would more substantially impact their districts. I hope that Trump 2.0’s staff has the stomach and topcover for this sort of work that could yield real long-term dividends for the country, not just grabbing the lowest hanging political fruit which really even have long term fiscal relevance like cutting probationary employees, foreign aid, and basic R&D.

From a ChinaTalk episode coming out on Monday with Mike Horowitz, former Biden DoD official, and The Economist’s Shashank Joshi:

Jordan Schneider: And I think this is like one of the many shames of the Trump imperial presidency. He has enough control of Congress to do this well and could even get some Dem votes for real defense reform!

Mike Horowitz: Let me muster a point of optimism here. If you look at Hegseth's testimony, his discussion of defense innovation is very coherent. He has takes that are not structurally dissimilar to the ones that we have been making.

There is a potential opportunity here for the Trump administration to push harder and faster on precise mass capabilities, on AI integration, and on acquisition reform in the defense sector. Because the president right now seems to have a strong hand with regard to Congress. Whether the president's willing to use political capital for those purposes is not clear. How the politics of that will play out is unclear. But if the Trump administration does all the things that it says it wants to do from a defense innovation perspective, that may not be a bad thing!

Shashank Joshi: My concern is also that you have people who are good at radicalizing and disrupting many businesses and sectors and fields of life. But the skills that are required to do that are different to the skills in a bureaucracy like this. Because, just because you were able to navigate the car sector and the rocket sector, doesn't mean you know how to cajole, persuade, and massage the ego of a know-nothing congressman who knows nothing about this and who simply cares that you build the attributable mass in his state, however stupid an idea that is, and who wants you to sign off on the 20 million dollars.

I worry that they will either break everything, and I'm afraid what I'm seeing DOGE do right now with a level of recklessness and abandon is worrying to me as an ally of the United States from a country that is an ally, but also that they will just not have the political nous [British for common sense] to navigate these things to make it happen. Just because Trump controls Congress and has sway over Congress doesn't mean that the pork barrel politics of this at the granular level fundamentally change. You need operatives, congressional political operatives. A tech bro may have many virtues and skills, but that isn't necessarily one of them.

Here’s to hoping! Howabout a Washington quote to send us off, from a 1775 letter sent to General Schuyler: “Animated with the Goodness of our Cause, and the best Wishes of your Countrymen, I am sure you will not let Difficulties not insuperable damp your ardour. Perseverance and Spirit have done Wonders in all ages.”

The Death of AI Safety: Moving Past the Pantomime

Tim Hwang is a writer and researcher. Relevant to the NSF topic above, he also hosted a great podcast series on metascience you should check out!

The AI Action Summit, which closed just over two weeks ago in Paris, will be remembered as a historically important gathering — though not how many of its organizers, attendees, and contributors anticipated. Rather than cementing AI safety as a priority for transnational collaboration, it turned into a memorial service for the safety era.

Billed as the successor to the high-profile gatherings of leaders that took place in the United Kingdom in 2023 and Korea in 2024, the Summit was originally intended to build on the frenetic activity that has taken place over the last few years to create international machinery for collaboration on AI safety issues. This has included an agreement on statements of principles, the formation of AI Safety Institutes around the world, and a blue ribbon “IPCC-style” report on safety issues.

This Summit’s lasting moments, however, came not from the success of “open, multi-stakeholder and inclusive approach[es]” on safety championed by the official declaration from the event, but instead dramatic declarations of national primacy unshackled by safety concerns. Vice President JD Vance’s speech made little accommodation for either safety or internationalism, declaring that the United States was “the leader in AI and our administration plans to keep it that way,” and that he was not here to talk about AI safety but instead “AI opportunity.” Macron touted a massive €110 billion fund to back AI projects in France, and the United States and United Kingdom declined to sign the Summit’s declaration language. A wildcat “Paris Declaration on Artificial Intelligence” backed by private industry hit the Summit for failing to back a “strong, clear-eyed, and Western-led international order for AI.”

A sense of stuckness prevailed in the side conversations and events taking place throughout Paris. At the AI Security Forum, a slow carousel of speakers ran through very much the same tropes and ideas that had dominated the discourse for years. Shakeel Hashim captured a feeling widely held — that the Summit was a “pantomime of progress” rather than the genuine article.

The photo ops were taken, and the keynotes were done, but the old gestures and governance rituals — which had seemed so potent just a few years ago in Bletchley Park — are now odd anachronisms in the harsh light of 2025.

This isn’t just a vibe. The “AI safety community” has always nurtured a shared, but often unspoken, agreement that public-minded technical expertise and international cooperation were the most promising pathways to promote good global governance of the technology.

But the safety community made a historically bad bet. The wheels were already coming off multistakeholder, international governance in the world at large even as the safety community began to invest in it seriously in the mid-2010s. Resurgent nationalism, great-power competition, and the fecklessness of international institutions have limited options for global governance across many domains, and AI has been just another one of the casualties. This isn’t just about Trump winning: these changes in the international system are structural, and the domestic shifts in places like France and the UK would have led to a very similar result even if Harris had pulled it out last year.

The safety community was also profligate in the use of its attention and social capital. The political influence of fair-minded technical experts turned out to be a rapidly depleting resource, wasted away as one “high-profile letter from very concerned scientists” and “dramatic demo of hypothetical model threat” followed another to little effect.

Against such a backdrop, it’s no wonder that AI safety in 2025 feels ever more like pantomime. We’re still frantically pulling the same levers, even as the whole constellation of forces that move nations in general and technology policy in particular have rearranged.

We need to be asking hard questions. What are historical models for technological safety and stability in a world of fierce, unrestricted nationalism? What happens when scientific evaluation has lost its ability to persuade the policymaker? How do you slow down or stop a technological race-in-progress?

The real intellectual work is now rebuilding a theory for safety that takes these uncomfortable realities into account and builds as best it can around them.

Mood Music

Can economic warfare really work? What can we learn from the 21st century historical record of American sanctions policy?

To find out, we interviewed Eddie Fishman, a former civil servant at the Department of State and an Adjunct Professor at Columbia. His new book, Chokepoints: American Power in the Age of Economic Warfare, is a gripping history of the past 20 years of American sanctions policy.

In this show, we’ll talk about…

• The evolution of U.S. sanctions policy, from Iraq and Cuba to Iran and Russia,

• How Reagan’s deal with the Saudis turned the dollar into an economic chokepoint,

• The incredible success of sanctions against Iran, and how that playbook could have been used to punish Russia,

• Historical lessons in enforcement that are relevant for export controls on China today,

• The role of great civil servants like Stuart Levey, Daleep Singh, Victoria Nuland, and Matt Pottinger in building state power,

• Institutional challenges for economic warfare and the consequences of failure to reform,

• Strategies for writing groundbreaking books about modern history.

Watch below or listen now on iTunes, Spotify, YouTube, or your favorite podcast app.

Financial Chokepoints

Jordan Schneider: Let’s start with the Bosphorus. How does this little corner of our beautiful planet explain the evolution of sanctions?

Edward Fishman: The Bosphorus is the epitome of a maritime chokepoint. It is a narrow strait between the Black Sea and the Mediterranean Sea. Throughout history, maritime chokepoints like the Bosphorus have been critical for strategic power. Sparta was able to win the Peloponnesian War because they won a battle around the Bosphorus and blockaded it, ultimately starving the Athenians into submission. Athens had relied on the flow of grain through the Bosphorus to feed its population — that was really the whole purpose of ancient Athens’ maritime empire.

Historically, these chokepoints have been geographic features. But now, as a result of globalization, there are chokepoints in the global economy that are not geographic — the most critical of which is the U.S. dollar. This is why the book is called Chokepoints.

For thousands of years throughout history, the only way to block a maritime chokepoint like the Bosphorus was a physical naval blockade. What’s changed is that in the wake of hyperglobalization in the 1990s, the U.S. acquired the ability to block chokepoints like the Bosphorus just by weaponizing its control of the U.S. dollar.

Today, the director of OFAC, the unit at the Treasury Department that oversees sanctions policy, can sign a few documents in her office and blockade a chokepoint like the Bosphorus. This actually happened on December 5, 2022, when the G7 oil price cap went into effect. The Bosphorus was backed up with dozens of oil tankers, because Turkish maritime officials were so nervous about violating the terms of the price cap that they didn’t want the ships to cross. It took OFAC days of very intensive diplomacy with Turkish authorities to persuade them to allow the ships to cross.

Jordan Schneider: You open this book with some wild contrast. Historically, you needed triremes. Now, all you need is a piece of paper from the Treasury Department to clog up the strait in Turkey halfway around the world.

Like you, Eddie, I was a sanctions nerd in college. I wrote my thesis about the origins of the UN and did papers on sanctions policy. I remember very vividly reading this literature arguing that sanctions are useless and don’t have any big impact. There was this great quote from George W. Bush in your book where at some point in the 2000s, he said, “We’ve sanctioned ourselves out of any influence” when it came to Iran’s nuclear program. You put the spotlight on one civil servant who takes that as a challenge and through ingenuity, creativity, and a whole lot of elbow grease, is able to discover and leverage a whole new lens of American power. Let’s briefly tell the story of American sanctions pre-Stuart Levey before we discuss Iran’s nuclear program.

Edward Fishman: When Stuart Levey came in as the Treasury Department’s first undersecretary of terrorism and financial intelligence in 2004, the most recent big case of sanctions that the U.S. had was a 13-year sanctions campaign against Iraq from 1990, when Saddam originally invaded Kuwait, until 2003, when George W. Bush launches the invasion of Iraq. That embargo required full UN backing and was implemented by a 13-year naval blockade. You had literally a multinational naval force parked outside of Iraqi ports inspecting every single oil shipment going in and out of Iraq.

The lesson from this situation was that sanctions didn’t work — Saddam didn’t come to heel. He seemed to be just as aggressive, if not more so. Over time, this embargo wound up leading not only to humanitarian problems in Iraq, which are very well documented, but also significant corruption. Saddam was siphoning away oil money under the nose of the UN.

By the time Levey comes in, sanctions had been seen as something that had been tried and failed against Iraq, and in fact had paved the way for the U.S. invasion of Iraq. In many ways, the 2003 invasion of Iraq was a direct result of the perception that sanctions had failed.

When Levey started working on the Iran problem around 2004, the prospect of even doing an Iraq-style sanctions campaign against Iran was off the table because there was no way to get the UN Security Council to agree to that at the time. Bush’s comment about having sanctioned ourselves out of influence with Iran was a result of the fact that without the UN, the U.S. thought that the only type of sanctions we could impose were primary sanctions, like an embargo where U.S. companies can’t buy things from Iran or trade with Iran. The only issue is we had had an embargo in place since the mid-90s, so there wasn’t any trade to speak of between the U.S. and Iran. The two avenues of sanctions were closed off — sanctions through the UN had been discredited by the 90s, and the other, primary sanctions on Iran, had already been maxed out and had been for a decade by then.

Jordan Schneider: The other seminal piece of sanctions in American 20th-century history is the embargo on Cuba. That is the same story — we cut off trade with this country, yet Castro’s still there in 2004, some 50-odd years later. It’s interesting — if you go back even further, there was this real hope after World War II where the UN at one point was even going to have its own air force. The idea was that sanctions were going to be this incredible tool to deter bad actions by different actors around the world because the U.S. and the Soviet Union were friends and we would all police the planet in a happy-go-lucky way. That was not how the Cold War ended up working out.

In 2004, Stuart Levey started to understand that he can leverage the dollar’s role in global financial flows. Eddie, can you tell the story of how the U.S. dollar became globalized in this way?

Edward Fishman: Bretton Woods, the conference that set the rules of the road for the post-World War II economy, happened in 1944. It put the U.S. dollar at the center of the global economy and established the dollar as the global reserve currency. It made the dollar as good as gold — the dollar is convertible for a fixed rate of $35 per ounce of gold.

At the same time, it explicitly prioritized the real economy and trade over finance. John Maynard Keynes, who was one of the architects of the Bretton Woods system, said that capital controls were a very important part of the system. For the first 30 years of this new global economy that emerged after World War II, you had the dollar at the center of the world economy, but it wasn’t a particularly financialized world economy. Most states had pretty significant capital controls, and banking was a very nationalized and, in some ways, even just a regionalized type of business.

By 1971, the U.S. dollar had been losing its value for quite some time and we were running significant deficits because of the war in Vietnam. Ironically, this is when Richard Nixon unilaterally took the dollar off of the gold peg. The dollar was still at the center of the world economy, but it was no longer tethered to gold. Exchange rates were now set by the market instead of by government fiat.

In the years after that, the capital controls of the Bretton Woods system fully erode and the dollar winds up becoming even more integral to the world economy as we see financialization take off from the ’70s through the Clinton era. You get to the point where we have a foreign exchange market that is turning over seven or eight trillion dollars every single day, which is by far the largest of all financial markets.

Jordan Schneider: How did oil come to be traded in U.S. dollars?

Edward Fishman: The dollar’s role in trading oil is arguably the most important chokepoint for a number of the key sanctions campaigns of the 21st century.

After World War II, the U.S. was a large oil producer and a big exporter. The 1973 Arab oil embargo shifted our perspective, and the U.S. realized just how vulnerable it was to being cut off from Middle Eastern oil.

In 1974, Richard Nixon — who was wallowing under the political pressure of the Watergate scandal and massive deficits that we had no reasonable way of plugging — sent his treasury secretary, Bill Simon, to make a deal. Simon was a former bond trader, a New Jerseyite, a chain smoker...

Jordan Schneider: A chain-smoking New Jersey native, described by a peer as, “far to the right of Genghis Khan.”

Edward Fishman: He’s a really colorful figure. The book includes a photo of him testifying before Congress with a giant plume of smoke around him.

Bill Simon tried to think about how to plug these deficits using his financial background as a bond trader. He proposed cutting a deal with the Saudis such that, not only do they agree to keep pricing oil in dollars into perpetuity, but they actually take the dollars they earn from selling oil and reinvest them in U.S. government debt — they basically plug our deficit with the money that the U.S. is paying them for oil. He wound up taking a flight to Jeddah in the summer of 1974 — getting copiously drunk en route.

The deal worked. He cut a deal with the Saudis in which they agree to recycle their petrodollars into U.S. Treasuries. This agreement largely still exists to this day. Oil, by and large, is priced in dollars no matter who’s buying it or selling it.

Chokepoints in the global economy are typically formed by the private sector. They kind of develop naturally as businesses evolve. However, there are important moments when government intervention becomes critical.

Simon’s original deal in 1974 solidified the petrodollar, but then a few years later, as the dollar continued to slide in value, oil exporters and OPEC started getting upset because the weakening dollar was in turn reducing the real value of their oil earnings. Jimmy Carter’s Treasury Secretary, Michael Blumenthal, actually went back to Saudi Arabia and cut a new deal in which he agreed to give Saudi Arabia more voting shares at the IMF in exchange for Saudi continuing to price oil in dollars.

Jordan Schneider: Why did the Saudis even cut the deal in the first place?

Edward Fishman: The Saudis got two things. First, they got access to US military equipment, which was pretty beneficial to them. Second, which I think is more of a direct part of this deal and one that’s more easily provable through historical documents, the Saudis were able to buy U.S. government debt in secret outside of the normal auctions. Instead of participating in the public auctions for U.S. Treasuries, they had their own side deal where they could buy Treasuries. That was a big benefit to them because they were able to lock in prices and also do so without facing potential political opprobrium.

Jordan Schneider: That’s crazy.

Edward Fishman: It’s a remarkable turning point in the financial and economic history of the 20th century. There was a real shot that oil could have been priced against a basket of currencies, which in some ways makes more sense. For these countries in the Middle East and OPEC members, their entire economy basically depends on generating oil revenue. If you want stability and predictability, you don’t want to take exchange rate risk. But people like Bill Simon and Michael Blumenthal intervened and were able to get the dollar enshrined as the key part of the oil market.

The Iran Sanctions Formula and JCPOA Diplomacy

Jordan Schneider: Let’s talk about 2006, when Stuart Levey was trying to figure out how to make sanctions work against Iran. Can you explain his light bulb moment during the January 2006 trip to Bahrain?

Edward Fishman: Levey realized other countries hadn’t stopped doing business with Iran — only the U.S. had, and that’s why the sanctions weren’t working. But he realized that he could use access to the dollar as a lever to pressure foreign banks.

Typically, when you’re trying to get other countries on board for sanctions, you would go negotiate with their foreign ministry and say, “We think what Iran’s doing is bad. You should impose your own sanctions on Iran.” That was the paradigm before 2006. What Levey realizes is that he can go directly to the CEOs of foreign banks, bringing declassified intelligence demonstrating how Iran uses their banks to finance their nuclear program, and funnel money to terrorist proxies like Hamas and Hezbollah. To start, he could just present the facts and potential reputational concerns would often persuade these banks to exit Iran. In more extreme circumstances, when banks wouldn’t go along with him, he could threaten their access to the dollar to try to get them out of Iran.

What Levey really pioneered was the direct diplomacy between him as a Treasury official and his team at the Treasury Department with bank CEOs. You might ask, how did Stuart Levey get meetings with CEOs of banks all around the world? He was lucky — right when he had this epiphany, Hank Paulson, who had been the CEO of Goldman Sachs, came in as Treasury Secretary. Paulson is arguably the most well-connected banker in the world at the time. Hank winds up opening a lot of doors for Stuart and getting him meetings with ultimately more than 100 of the key banking CEOs around the world.

Jordan Schneider: Interestingly, you have to convince all the banks to get on board, because even the slightest institutional leakage would allow Iran to sell as much oil as they want.

How did Levey and his team go about convincing the Russians, the random Chinese banks, the Azerbaijani banks, and all of these other banks?

Edward Fishman: What Levey succeeds at doing between 2006 and 2010 is getting the big name-brand global banks to exit Iran. By and large, there are a few stragglers like BNP Paribas. Most of the big main global banks are out of Iran by 2010, though there are still some banks in places like the UAE, Turkey, and other countries doing business with Iran.

What winds up happening at that time is Congress, which has very little faith in Barack Obama’s willingness to come down hard on Iran — namely because Obama had very explicitly run for president in 2008 saying he wanted diplomacy. He even exchanged letters with Ayatollah Khamenei.

Even Iran hawks that are on the Democratic side of the aisle, like Bob Menendez, don’t really have much confidence that Obama is going to be tough on Iran. Democrats and Republicans basically form almost a coalition against the Obama administration on Iran sanctions and wind up passing progressively harsher sanctions legislation.

The key part of these sanctions laws, the first one called CISADA (the Comprehensive Iran Sanctions Accountability and Divestment Act of 2010), is that they require the Obama administration to impose what’s called secondary sanctions. That’s not sanctions directly on Iran, but sanctions on Iran’s business partners — for instance, the UAE or Turkish bank that I mentioned before.

Levey was a Bush appointee retained by the Obama administration (he’s one of only two very senior officials, along with Bob Gates, who’s kept on). He uses this law with the mandatory secondary sanctions as a significant cudgel. He goes to places like Dubai and talks to banks saying, “Look, if you don’t get out of Iran, I will be forced by American law to impose sanctions on you. You will lose access to the dollar and all of your assets will be frozen.” That threat is very significant. When the choice is between Iran and the United States dollar, it’s a pretty easy choice for most banks around the world.

Secondary sanctions had been tried before in the mid-90s, but the U.S. effectively wound up blinking and not imposing secondary sanctions on Total, the French oil company that had been investing in Iran’s oil sector. Even the George W. Bush administration decided not to impose secondary sanctions. This tool was very controversial. You can imagine it didn’t go down well with other countries. If you’re an American diplomat and you go meet with one of your counterparts abroad and say, “Sorry, we have to sanction your biggest bank if they don’t stop doing business with Iran” — that just feels like mafia diplomacy, not something that goes down very easily.

One of the virtues of Obama being so beloved around the world was the success of sanctions on Iran. Obama built international consensus that Iran’s nuclear program was a problem.

Jordan Schneider: We also had multilateral sanctions from the UN alongside U.S. action. What did that end up doing for the Obama psyche and the global push to limit Iran’s oil revenue?

Edward Fishman: Obama successfully got a major UN Security Council resolution done in the summer of 2010, right alongside when CISADA, the secondary sanctions law, passed Congress.

Jordan Schneider: In the Medvedev era, mind you.

Edward Fishman: Yes, exactly. Historical contingency matters — the fact that Medvedev was president of Russia at the time meant that Russia didn’t veto UN Security Council Resolution 1929. In retrospect, the benefit of that resolution wasn’t so much the specific sanctions it imposed on Iran. Rather, it explicitly drew connections between Iran’s banking system and energy sector with its nuclear program. This meant when Obama officials traveled the world to tell foreign banks and their governments that they’d be forced to impose sanctions if they didn’t stop doing business with Iran, they could credibly say they were just complying with UN Security Council Resolution 1929 and that international law was on the side of the United States. The legitimacy that Obama’s sanctions campaign derived from the UN was ultimately very significant.

Jordan Schneider: Iran was completely unprepared for this. They literally took out ads in newspapers in Austria to beg for help financing their nuclear program.

Edward Fishman: Exactly. This speaks to assumptions about how the global economy worked at the time. People just trusted that banking networks wouldn’t be weaponized. Iran really thought that they could publicly advertise these fundraising activities with no issue. Foreign banks weren’t aware of what Iran was doing and weren’t particularly worried about being penalized for it. They probably viewed sanctions as something that were unlikely to happen to them — and if they did happen, they could just be chalked up as a cost of doing business.

Jordan Schneider: Let’s talk about the penalties. One of the remarkable accomplishments of the Treasury Department, which the export controls regime on China over the past few years hasn’t been able to do, was the billion-dollar fines thrown on violators — $2 billion on HSBC, and almost $10 billion on BNP Paribas. How did this work?

Edward Fishman: This is a very important part of the story and one that often goes unnoticed. It’s not that sanctions didn’t exist before this period in the early part of the 21st century — it’s that the cost of violating them wasn’t particularly high.

One of the most important strategic legacies of the campaign against Iran pioneered by Stuart Levey is conscripting banks to be frontline infantry of American economic wars. This wasn’t because banks decided that this was morally righteous, it was because they realized that violating sanctions was existentially dangerous for their businesses.

Between 2010 and 2014, Standard Chartered wound up getting fined about a billion dollars, HSBC was fined $2 billion, and BNP Paribas was fined $9 billion. In each case, the New York Department of Financial Services actually threatened to withdraw banking licenses from each of those banks, which would eliminate their ability to do business in the United States. That was a sword of Damocles hanging over these banks — U.S. law enforcement probably could have extracted even bigger fines.

We’re still living with that legacy today. The reason that financial sanctions in particular are so powerful is a confluence of two factors.

1. The dollar is essential to international commerce. Trying to do business across borders without access to the dollar is like trying to travel without a passport.

2. The U.S. actually can weaponize the systemic significance of the dollar because banks are afraid of going against American government dictates.

Jordan Schneider: The political economy of it is also different than whacking Nvidia or Synopsys, becauce those three banks are foreign. It is one thing to threaten with extinction some hoity-toity French bank that sponsors the French Open and has been doing business with Iran forever. It’s another to threaten a major contributor to America’s national competitiveness, employment, and growth.

Compare the death sentence of being cut off from the New York Federal Reserve versus mere fines in the case of export controls. With Huawei, there were some cases where they threatened to put executives in jail. Over the past few years, the types of companies that the Biden administration has gone after have often been random Russians in Brooklyn smuggling chips into Russia and China. Whereas the Obama administration was trying to put teeth behind big economic warfare efforts by throwing down billion-dollar fines.

Edward Fishman: Is it possible to conscript tech companies in the same way that banks are conscripted? My own view is yes. If the fines were harsh enough and if the enforcement were strong enough — because the other fact we haven’t talked about is it wasn’t just fines for these banks, it was also independent monitors. The Justice Department sent in people to oversee compliance reforms for several years thereafter.

It is possible, though politically challenging, on one hand to be subsidizing American semiconductor companies to the tune of 50-plus billion dollars, and then on the other to say we’re going to take that money back because you’re violating export controls. It is possible.

One thing I would mention though is that with the BNP fine and the HSBC fine, those took many years to come to fruition. These were years and years of bad behavior that then eventually led to giant fines. It is possible that someone right now at the Justice Department is working away at a major export control violation case that we’ll learn about maybe in a couple of years.

Jordan Schneider: You mentioned “Mafia diplomacy” as a sort of derogatory term for sanctions tactics. There are a lot of moments in this story where gentlemanliness appears to be very important to Obama.

After the invasion of Crimea, around the Maidan revolution, Obama had a call with Putin where he warned that “Moscow’s actions would negatively impact Russia’s standing in the international community.” Putin’s response was basically like, “I don’t know, man, it’s hard to take you seriously.”

Why was Obama’s demeanor so helpful in the case of Iran?

Edward Fishman: Obama was very attuned to international law, or as you put it, gentlemanliness. You could argue he was very lawyerly in his approach. With respect to the Iran sanctions, I think it actually wound up being helpful because the secondary sanctions against Iran were beyond anyone’s imagination.

We haven’t talked yet about the oil sanctions, which were put in place in 2012. The U.S. successfully reduced Iran’s oil exports from 2½ million barrels a day to 1 million barrels a day over about a year. This is explicitly a unilateral U.S. sanction.

Would that have worked as well had Obama not been as attuned to diplomacy and invocations of international law? I’m not so sure. You may have seen more challenges from places like China and India and maybe more obstinance. I do think it was helpful in some regards.

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Looking at all the various examples of economic warfare that I talk about in the book, this is in some ways the most remarkable because of how unlikely it is to succeed. But it works.

One big exception from the financial sanctions during the Stuart Levey era is the Central Bank of Iran. The Central Bank of Iran is not under sanctions because it’s the repository for all of Iran’s oil revenues. The Obama administration was really nervous that if they sanction the Central Bank of Iran, other countries won’t be able to pay Iran for its oil. All of a sudden you’ll have all of Iran’s oil go off the market overnight, you’ll have a giant spike in oil prices, and everyone will be in a world of hurt.

Senator Bob Menendez, who was the key Iran hawk in the Democratic Party...

Jordan Schneider: For international listeners, Menendez is now in jail for having taken gold bars from Egypt. But anyways, continue, Eddie.

Edward Fishman: It’s a wrinkle in the story. Then Mark Kirk, who’s his Republican counterpart, who also wants to do a naval quarantine of Iran — the two of them basically say, “We don’t care, Obama, we’re going to sanction Iran’s central bank.” That amendment passes 100 to 0 in the Senate.

Obama is left with figuring out how to make this work. They come to a compromise with the Hill in which they agree to sanction the Central Bank of Iran, but they create two exceptions. One is an exception for countries who every six months significantly reduce their purchases of Iranian oil. For instance, if you’re a Chinese bank, you’re exempt from this — you can pay the Central Bank of Iran so long as China as a whole every six months reduces its overall purchases of oil from Iran. This gives a glide path for Iranian oil sales to decline over time and winds up working marvelously, luckily with the ramping up of shale production in the U.S.

The other exception put in place in 2012 says you can pay the Central Bank of Iran if you’re a Chinese refinery or bank, but those payments have to go into an escrow account that stays inside China and can only be used for bilateral trade between China and Iran.

This actually gives Chinese entities an incentive to comply, because keeping this money in China is going to boost Chinese exports to Iran — there’s nowhere else that the Iranians can use the money.

The one-two punch of these gradual oil reduction sanctions and the escrow accounts leads to a situation where Iran’s oil sales collapse by 60% by volume and it effectively has zero access to its petrodollars. Within 18 months, about $100 billion of Iran’s oil money gets trapped in these overseas escrow accounts. This is the context in which Iran’s economy really goes into free fall. Hassan Rouhani, a dark horse presidential candidate in 2013, won the Iranian presidency on an explicit platform of trying to get the sanctions lifted.

The remarkable thing about this oil sanctions regime is it’s probably the most effective oil embargo we’ve seen in modern history. It’s done unilaterally by the U.S. — no other countries are fully bought into this. It doesn’t involve any sort of naval strategy at all. There’s no quarantining of oil ships or anything. It is just using these threats of being cut off from the dollar to coax banks in places like China and India to comply with American dictates.

Jordan Schneider: This is going to be the poster child for decades of history books in that it actually created political change. It both drove home economically, causing hyperinflation and really hitting growth, and then got you a new slate of politicians who some would argue really wanted to make a deal. Looking back 15 years later, what’s your take on JCPOA and how we should think about the lessons from how the Obama administration used the leverage that they created with this oil embargo?

Edward Fishman: The JCPOA is the high point of American economic warfare in the 21st century in that you actually see sanctions leading to the outcome that the United States had set out, which was to get a peaceful resolution to Iran’s nuclear program. You can quibble about whether the terms of the JCPOA were stringent enough. However, there’s pretty good consensus that sanctions were the critical unlock to that deal.

Democrats say that sanctions were the key to getting the deal. Republicans say that sanctions were working so well that if we had only kept them in place longer, we would have gotten an even better deal. Within really a 10-year period, we flip that consensus from sanctions don’t work to sanctions are this magic bullet that just ended Iran’s nuclear program without firing a shot.

The key lesson here is that you need both economic leverage to make sanctions work and a clear political strategy. Having a clear political strategy, which was to get a nuclear deal with Iran, wound up being very important because you wind up having the international community grudgingly go along with the sanctions. They don’t voluntarily go along — they kind of have to be dragged along, including even the Europeans. But it would have been much harder to bring them along if there hadn’t been a political strategy, if it had just been bludgeoning Iran with economic pain without any sort of political end game in mind.

Responding to Russia (2014 vs. 2022)

Jordan Schneider: Let’s transition from the success of Iran sanctions to the failed response to the annexation of Crimea. What was different about how Obama and the world responded to Russia’s invasion in 2014?

Edward Fishman: Too often we tell our histories in silos — U.S. policy toward Iran vs. U.S. policy toward Russia. One thing I wanted to show in my book is that all of these sanctions campaigns are intertwined because ultimately these are the same decision makers at the table in the Situation Room across multiple issues.

The timeline is interesting here — the U.S. signed the original Iran nuclear deal, which froze Iran’s nuclear program, on November 24th, 2013. On the same exact day, hundreds of thousands of protesters descended upon the Maidan in Ukraine to protest Viktor Yanukovych’s deal with Putin.

The Ukraine crisis really does wind up taking the Obama administration by surprise. It’s not like the Iran nuclear program, which played out over the years as a slow-burning crisis. The Ukraine crisis and the Crimea annexation happened very quickly, with the U.S. constantly playing catch up. This parallel is important because right when Obama officials are scrambling to figure out what to do about Putin’s annexation of Crimea, they’re fresh off this giant victory where they just froze Iran’s nuclear program basically just by using sanctions.

It became natural for Obama officials in February-March of 2014 to say maybe sanctions could work against Russia. It’s a harder problem with Russia for several reasons. Russia has a much larger economy than Iran — in 2014 it was the 8th largest economy in the world and the world’s largest exporter of fossil fuels. Europe is completely dependent on Russian energy to heat their homes. Natural gas pipelines crisscross the continent between Russia and Europe.

Putin is creating facts on the ground as the U.S. is trying to scramble to put together sanctions. The annexation of Crimea happens within weeks of the “little green men” showing up in Crimea — they appear at the end of February and the annexation is formalized in middle of March. Shortly thereafter, Putin starts sending little green men into the Donbas, Ukraine’s industrial heartland.

Jordan Schneider: Let’s focus on the multilateral dynamic of this because obviously the UN is thrown out when Russia’s doing the thing. I remember very vividly watching the transition of the European actors who were pretty close to shrugging off this whole thing — until all those Dutch people died in the commercial liner that the Russians shot down by accident with their anti-aircraft missile. Can you explain how that changed the dynamic?

Edward Fishman: When Putin annexed Crimea in March of 2014, the U.S. and Europe did go ahead with some sanctions, but by and large they’re individual sanctions on people very close to Putin — his judo partners from childhood who have been elevated to positions of power at companies like Rosneft. Igor Sechin, for instance, the CEO of Rosneft, is sanctioned, but there are no sectoral sanctions, no actual significant economic sanctions on the Russian oil industry or its banking sector.

Obama and European leaders very publicly threatened this in March of 2014, but they don’t do anything. The reason is partly because there isn’t political will, but it’s also because they don’t know what kind of sanctions are tolerable to their own economies. They wind up spending months negotiating and coming up with what they eventually term “scalpel-like sanctions,” which effectively cut off big Russian state-owned enterprises from Western capital markets. It’s using an even narrower chokepoint than the dollar — it’s really just Western financing.

Interestingly, something that doesn’t often get recognized enough, the Obama administration went ahead with these sectoral sanctions, cutting off some big Russian energy companies and banks from U.S. capital markets on July 16, 2014, the day before MH17 was shot down. Obama and his team were getting fed up with the European foot-dragging. They say we need to send a powerful signal to Putin if we’re going to have any chance of deterring a broader invasion of the Donbas.

At the time, the New York Times was publishing headlines like, “Obama goes ahead without the Europeans.” Banking CEOs in the U.S. are incredibly upset because they’re saying this is just going to lead to a flight from the dollar to the euro and all our competitors in Frankfurt and London are going to benefit at our expense.

The next day, Putin’s proxies in the Donbas shot down a commercial airliner using a Russian-made Buk missile. They killed almost 300 people, by and large Europeans, most of them Dutch. All of a sudden the political aperture just widens completely in Europe. The Europeans are suddenly not only ready to match the U.S. sectoral sanctions of July 16, but actually go beyond them — they wind up cutting off all of Russia’s state-owned banks from the European financial system. The real core sectoral Russia sanctions are put in place after MH17, really from late July 2014 through September 2014 when Russian and Ukrainian leaders agree to the first Minsk agreement, the first ceasefire in the conflict.

Jordan Schneider: There are two parts that made me get upset rereading and reliving this story. One is that the Obama administration had just learned the lesson which Democrats in general have a really hard time with — escalate to de-escalate. It’s such an Obama thing, the same with the debt ceiling, where he was just like, “I’m going to be a nice normal actor and lay out my five demands and okay, we’ll get to two or three.” The Tea Party — this is ancient history now — and the Republicans were like, “No, we want 100% of what we want.” Obama would get scared, then they’d do a debt ceiling fight and he would end up giving way more than he realized he had to.

By the time we got to 2014, he just said “screw you.” He had the playbook with Iran. All the Treasury forecasting about the catastrophic costs of sanctions is overblown. The U.S. had more agency than expected, the euro was not going to take over.

But Russia really got away without serious economic consequences. Why didn’t Obama put the money where his mouth was?

Edward Fishman: In retrospect, there are two things that led to Obama’s overly cautious approach. One was real, genuine concern about the U.S. economy and the European economy. Remember, we’re still in the wake of the financial crisis and the Eurozone crisis is very much a live situation. There are genuine fears from the Treasury Department that you could accelerate a financial crisis in Europe if Russia were to cut off their gas supplies, and that contagion would spread to the US.

The other thing — this is an interesting paradoxical lesson for the Trump people now and people who say Europe needs to pull more of its own weight — Obama was very deferential to the Europeans over the Ukraine crisis. He explicitly wants people like Angela Merkel and François Hollande to take the lead. The negotiating block that came up with the Minsk agreement, the Normandy format, is France, Germany, Russia, and Ukraine. The U.S. doesn’t even have a seat at the table in the negotiations. Obama was saying, “This is in Europe’s backyard. It’s really their problem.”

In retrospect, that caution does not look very wise. Obama should have hit Russia much harder than he did in 2014. One interesting thing though is even though the sanctions put in place that summer — these capital market restrictions, the “scalpel-like sanctions” — are much weaker than the Iran sanctions, in the second half of 2014, oil prices cratered from over $100 a barrel to around $50 a barrel.

While the sanctions were aimed at trying to constrain Russia’s economic horizons as opposed to creating an immediate financial crisis, the sanctions do push Russia to the brink of a complete meltdown. In the winter of 2014-2015, Russia’s economy looks like it’s about to collapse — honestly just as bad, if not worse than Russia’s economy winds up looking after the much more drastic sanctions from February-March 2022.

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The reaction is remarkable. I have some of these quotes in the book. European leaders look at this and say, “This isn’t scalpel-like — this is what we signed up to. We didn’t want to push Russia off a cliff.” Hollande, the French president, actually says, “We explicitly don’t want to push Russia to its knees.” The Europeans, and to a certain extent the United States, got spooked by how impactful the sanctions are because they wind up being accelerated by this collapse of oil prices. Part of the reason why there’s a real frantic desire to get another more permanent agreement, which winds up being called Minsk II in February 2015, is because the Europeans really didn’t want to see Russia’s economy fall off a cliff.

Jordan Schneider: Elections matter and leadership matters. I like that you included so many McCain quotes about the events in both Iran and Ukraine, since he could have been president during these years.

Edward Fishman: One of the key ingredients of the success of Obama’s Iran sanctions is the fact that there’s this bipartisan supermajority in favor of tougher sanctions on Iran. Even if Obama had instincts to be cautious or lawyerly, Congress was passing draconian sanctions laws 100 to 0 over a veto-proof majority. With Russia, you had no sanctions laws at all.

What that speaks to, which becomes more important as our story develops, is that U.S. companies had a lot to lose in Russia. It’s not as much of a political winner for members of Congress and senators to try to layer sanctions onto Russia because they might hurt a company in their state or district. We start seeing that maybe there are domestic political limits to how far the U.S. is willing to go with economic warfare.

Jordan Schneider: Commitment to sanctions is a key factor. Secretary Lew once remarked, “One of the things the Russians would say to me is, ‘We survived Leningrad, we could survive this.’ Their definition of what they were willing to tolerate was well beyond the realm of what we would consider tolerable.”

America’s rich, and the pain that we would end up inflicting on ourselves with sanctions would only be like a half percentage point hit to our quality of life. Whereas Russia is starting from a lower baseline, and sanctions hurt them way more than they hurt us. Yet, we’re not comfortable letting ourselves be pinpricked, even if it’s to save the international order.

You wrote…

“With the loss of the Russian market, Lithuania’s dairy industry teetered on the brink of bankruptcy. When a team of State and Treasury officials met with a Lithuanian dairy farmer outside Vilnius in 2015, they expected her to express frustration. She did, but it wasn’t about her declining business. ‘You should be hitting Russia harder,’ she said.”

It doesn’t come down to economics for a lot of this stuff. There are the political economy games of the Texas senator wanting to help out Exxon or whatever, but it often is a question of moral righteousness. We live in rich countries and we can afford to go without, by and large, way more than that Lithuanian dairy farmer could go without.

Edward Fishman: That’s exactly right, Jordan. One of the macro ironies of the book is, the rise of economic warfare in U.S. foreign policy in the 21st century is partly because military force became politically toxic in the aftermath of Iraq and Afghanistan. As those wars were going south, neither Republicans nor Democrats felt like they could even fight limited military engagements, which is very different from the ’90s when there were all kinds of small wars and U.S. bombing campaigns.

Economic warfare initially is seen as more politically palatable because it’s not hurting Americans — we can sanction Iran out the wazoo and there’s no pain felt at home. But then once you get to Russia and even more powerfully once you get to China, there are real political risks for leaders who impose sanctions on these countries. Even a 10% spike in oil prices or a marginal increase in inflation can become powerful factors in the minds of American presidents and wind up constraining our ability to successfully prosecute economic warfare.

Jordan Schneider: That’s a great point. In the 90s, you had the Taiwan Straits crisis where Clinton threw a carrier there and things calmed down. You had Mogadishu, you had Yugoslavia. But there’s this moment in 2014 where the Ukrainians asked, “Can you give us Javelins, please?” The Europeans said no. Blankets don’t win wars, bullets do.

This is the heartbreaking thing — if Russia believed that the U.S. and NATO were really going to put their money where their mouth was in arming the Ukrainians for war number one, maybe they would have been more concerned — not only about the economic impact, which they clearly underpriced, but also the military impact. We have had hundreds of billions of dollars of armaments go to help Ukraine. It was totally reasonable for Putin, based on the track record of the Obama and Trump administrations, to not expect that to be the response when it came to 2022.

Edward Fishman: Looking at the real error of U.S. policy toward Russia, it’s not necessarily anything that happened in 2014 because we were dealing with a completely novel problem, an unexpected crisis. There was no playbook for sanctions on Russia. This is one area where it’s important to be empathetic to Obama and his top team because it wasn’t easy what they had to deal with. The sanctions they did put in place in 2014 wound up being really impactful — Russia’s economy effectively collapsed that winter.

The bigger indictment on American policy is what happened after February 2015 when the Minsk II agreement was signed. After that, the Obama administration took its foot off the gas on sanctions, basically saying they’re just going to maintain what they have in place. Russia very publicly interferes in the 2016 election. Obama had threatened Putin with drastic sanctions if he continued to interfere. Putin continued to interfere, and the sanctions Obama put in place in December right before he left office were really minor. That’s a bad signal.

Then you have four years of the Trump administration in which Trump does nothing on Russia sanctions. It’s a logical lesson for Putin to draw, both from the last year and a half of Obama and all four years of Trump, that he basically got away with the annexation of Crimea at a reasonable cost. That’s just speaking of the U.S. — Europe is even worse. In 2015, after the annexation of Crimea, a consortium of companies signed the Nord Stream 2 pipeline deal to double the amount of gas that Europe would get from Russia. Putin was completely within reason to assess that the West does not have the stomach for a real economic war.

Jordan Schneider: Unlike in Crimea, the U.S. sees this coming in 2022 and has months to try to get its ducks in order, to try to do everything it can to dissuade Putin from trying to take Kyiv. What happened then?

Edward Fishman: When Biden comes in, there’s a real debate amongst his advisors about what to do. Russia had accumulated all of these misdeeds that had gone unanswered. Biden himself, when he was vice president, wanted to arm the Ukrainians. He was the most hawkish member of the top Obama team on Russia, always in favor of tougher military steps to help the Ukrainians, always in favor of tougher sanctions.

There was real debate about what to do. Should they come in right away with really tough sanctions? Biden’s conclusion was that we were still reeling from the COVID pandemic, we had climate change to deal with, and China was the biggest geopolitical issue on his radar. They tried to have what they called a “stable and predictable relationship” with Russia — which is hilarious in retrospect, as “stable” and “predictable” aren’t things you necessarily ever ascribe to Putin’s Russia.

They came out of the gate in April 2021 with a modest increase of sanctions, saying, “Here’s some sanctions to repay you for all these bad things you’ve done over the last six years. But after this, we want stability and predictability.” Putin gets a summit with Biden, which he’s very happy to get. Then he pens a rambling 5,000-word essay about why Ukraine’s not a real country and should be part of Russia in the summer of 2021 while he’s in lockdown. He masses over 100,000 troops around Ukraine’s border that fall.

It becomes quite clear that Putin has designs on Ukraine. In what is probably the biggest intelligence success of the 21st century, the US intelligence community gets Putin dead to rights. They figure out exactly what his plan is, to the point where Biden starts warning American allies privately in September and October 2021 that an invasion is coming. Very soon thereafter, he starts making public warnings that invasion is coming and tries to use the threat of swift and severe consequences, particularly very dramatic economic sanctions, to deter Putin from invading Ukraine.

Jordan Schneider: Let’s talk about how they tried to build that coalition and signal those sanctions in the lead-up to the ultimate invasion.

Edward Fishman: A stroke of luck for the Biden administration was having Daleep Singh, who had played a significant role in the 2014 sanctions. He’s one of the top financial minds in Washington — a city that doesn’t have many people with deep financial markets expertise. Daleep is an exception. He was in the perfect role to orchestrate a sanctions campaign as the Deputy National Security Advisor for International Economics, overseeing the organs of the US Government that do economic warfare.

In late 2021 and early 2022, Daleep builds relationships with his fellow G7 counterparts: in Brussels, Bjoern Seibert, and in London, Jonathan Black. They start getting into the nitty-gritty of what kind of sanctions they might impose if Putin were to invade. This preparation is important not just for being ready to do something real if Putin pulls the trigger, but also for making the threat of deterrence more credible. Russia has a world-class intelligence apparatus — if all you had was Biden wagging his finger saying “You’re going to face really strong sanctions if you invade,” but there’s no actual bureaucratic movement in these capitals creating sanctions ready to go, Putin would probably assess it was a bluff. The preparation that Daleep Singh and his counterparts in Europe and Japan do is very important.

Jordan Schneider: I love how they were doing this like in secret, but also in public. They weren’t being super hard about using classified communications — they were just calling each other on their phones because they actually want the Russians to be listening and believe they are going to put real sanctions on them.

Edward Fishman: That’s exactly right. They view the preparations as important from both a practical standpoint and a signaling standpoint.

By the time we get to the moment of decision in late February, it becomes clear after Putin and Xi Jinping meet in early February that an invasion probably won’t happen until the Beijing Olympics wraps up — Putin doesn’t want to spoil Xi Jinping’s party. By that time, you have a very extensive menu of sanctions options. Most importantly, you have what’s called the Day Zero package — the raft of sanctions that would go into effect as soon as Putin invades.

The compromise is made because inflation is at a four-decade high and there are concerns about oil prices potentially spiking. Biden says they’re going to maximize sanctions on Russia but not aggressively target its oil sales, which is tough because Russia’s economy depends on hydrocarbon exports. The strategy of the Day Zero sanctions is to implement maximalist sanctions on Russian banks — Sberbank and VTB, the two biggest banks in Russia — as well as Russia’s access to foreign technologies. They took the Foreign Direct Product Rule that had been imposed on Huawei in 2020 and recast it to cover the entire Russian economy. They take something that had been previously employed on just one Chinese company and apply it against an entire state.

The tragedy of the situation is that Putin invades and very quickly — similar to that moment in July 2014 after MH17 was shot down — there’s a giant shift of the Overton window in Europe. Everyone becomes gung-ho for very aggressive sanctions after Putin invades and we start seeing just how horrible this war is and how imperialistic Putin’s goals are. Hundreds of thousands of people protest on the streets of places like Berlin, and there’s a massive political movement in favor of stronger sanctions.

Within 24 hours of the invasion beginning, the Day Zero package that Daleep Singh and his colleagues had worked months on looked much too weak and actually undershot the political moment. Within that first weekend of the war, the United States and the G7 agreed to go much further and actually sanction Russia’s central bank directly — something that was seen as too politically radical to even consider in the lead-up to the invasion. Putin clearly agreed because he had left half of his central bank reserves completely exposed to Western sanctions.

Jordan Schneider: This goes back to the mafia diplomacy concept. Ironically, Putin expected the West to be more gentlemanly and concerned about the centrality of the dollar and euro to global trading. Once the war started and the Overton window shifted — which everyone had a hard time foreseeing — things changed. Looking back, it seems silly that they didn’t anticipate massacres when Russia invaded. While sanctioning their central bank was an option, there remained questions about whether they could get the money out, and if they would even believe the threat before it happened. The actual deterrent value we had during those months remains an open question.

Edward Fishman: Clearly, we would have been better off had the U.S. and Europe created more aggressive sanctions plans in advance. This could have strengthened deterrence and weakened Russia’s economy and warfighting capability more quickly, directly helping Ukraine on the battlefield. There were significant costs to underestimating how willing political leaders would be to implement tough sanctions in the U.S. and Europe. But going back to your earlier point, Jordan — from a deterrent standpoint, would that preparation have overridden Putin’s lesson from 2014 and the seven or eight years of basically allowing Russia to get off scot-free after annexing Crimea? Putin had likely already sized this up in his head by then, and I’m not sure we could have changed his mind.

Jordan Schneider: Here’s a crank idea — why didn’t the Treasury Department go long on oil if they were worried about it spiking up to $250 a barrel? Couldn’t you just do the math that way?

Edward Fishman: This is a point I make toward the end of the book — the U.S. is much better at imposing economic penalties than deploying capital for strategic reasons. That would be a very creative use of government resources, but it’s not a bad idea. If we had the flexibility to do something like that in a strategic manner, sure. We do use things like the Strategic Petroleum Reserve to stabilize the oil market. In March 2022, the Biden administration released 180 million barrels of oil to try to stabilize the market.

Jordan Schneider: They did eventually act, but it took too long, and the Department of Energy people are complaining that the caves might crater in. Reading through your book, I can only imagine how frustrating it must be for these officials working around the clock to get the whole world to ramp up sanctions, and they can’t even get their own government to release oil for arguably the biggest crisis in at least 50 years.

Edward Fishman: Many of our institutions are built on the assumption that we live in a peaceful, predictable world, and we don’t always get our act together in time for crisis. This isn’t unique to the 21st century — it’s been true throughout American history.

Jordan Schneider: Here’s another crank idea for you. In the winter of 2023, everyone was terrified that oil prices were going to spike. Did anyone discuss geoengineering solutions, like spraying sulfur in the air over Europe to save everyone’s energy bills?

Edward Fishman: There are a number of tragedies in this story, one being that you decided to become a podcaster instead of a sanctions nerd. Had you gone down this path, maybe we would have benefited from your creativity in the U.S. government.

Institutional Dysfunction

Jordan Schneider: The people you profile, whom you clearly admire for their incredible feats of civil service, were creating new concepts and regimes unimaginable back in 2004 while operating under such constraints in such a dysfunctional system. They made enormous family sacrifices, which you mention several times. We did a show called “Is the NSC Unwell?” where we opened with Jake Sullivan being awake at 4 AM on a Tuesday during a home invasion because he was dealing with Ukraine issues.

Having the idea is the easiest part. Sure, I can suggest geoengineering to fight the impact of Russian oil, but transforming a clever idea that checks all the economic, institutional, and diplomatic boxes into reality is unbelievably difficult. Multiple times in your stories, there are eight-month delays for things that everyone should have immediately approved on day one.

Edward Fishman: We need a government that’s purpose-built for the age of economic warfare. That’s the premise of my book — we are living in an age of economic warfare. Sanctions, tariffs, and export controls are how great powers compete today and will compete tomorrow. This is a secular trend we’ve seen throughout the 21st century, yet we haven’t changed our government to actually fight and win these economic wars.

There’s nothing like the Pentagon for economic warfare. During my short stint at the Pentagon working for then-Chairman of the Joint Chiefs of Staff Marty Dempsey, I noticed that military force has one agency and a clear chain of command up to the Secretary of Defense. With economic power, you’ve got numerous agencies involved — the Treasury Department, the Commerce Department, the State Department, the Energy Department. Much time is spent just coordinating the interagency process.

Ideally, we would have a dedicated department with clear leadership for economic statecraft or economic warfare. Some governments have moved in this direction — Japan now has a cabinet-level minister for economic security. The U.S. hasn’t innovated like that. There’s a core budgetary problem where agencies like TFI (Office of Terrorism and Financial Intelligence) at Treasury, which Stuart Levey led, or BIS at the Commerce Department, haven’t seen significant budget increases despite their missions growing exponentially.

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Jordan Schneider: This theme comes up repeatedly in these stories and with the chip export controls. When cabinet-level officials disagree without presidential direction saying “We’re doing X, not Y, get with the program,” things stall or take longer. Cabinet members are congressionally approved; their words carry weight. When Janet Yellen believes a sanction would harm global inflation and the American economy, Jake Sullivan must call Mario Draghi to persuade her because Biden won’t act without her support. Everyone has different priorities, and without a central authority or an engaged president, you end up with stasis — allowing Russia to make an extra $200 billion they shouldn’t have throughout 2023.

Edward Fishman: Exactly. The Draghi call is one of the more remarkable episodes in the book. After the political aperture expanded during the first weekend of the Ukraine invasion in 2022, making central bank sanctions possible, the G7 agreed. Then Janet Yellen raised concerns, requiring a call from Mario Draghi, Italy’s leader and former European Central Bank chair, to personally assure her it was acceptable.

Regarding China, much of why your podcast is amazing has been its in-depth coverage of chip export controls. Looking back to the first Trump administration, export controls were deployed against Huawei instead of sanctions largely because Treasury Secretary Steven Mnuchin opposed a tough China policy. In early 2019, after the arrest of Meng Wanzhou 孟晚舟, some administration officials suggested sanctioning Huawei and putting them on the SDN list. Mnuchin refused, so they defaulted to putting Huawei on the entity list, which Wilbur Ross controlled as Commerce Secretary. The whole export controls landscape might have been very different with a more hawkish Treasury Secretary during the first Trump administration.

Jordan Schneider: You have this wild anecdote from Matt Pottinger, former ChinaTalk guest who became Deputy National Security Advisor towards the end of the Trump administration.

Pottinger noted that at one point, Bolton decided not to tell Trump about arresting Meng Wanzhou. Pottinger interpreted Trump’s rhetoric as supporting a tough stance on China.

“Pottinger told his Commerce colleagues that Trump was pursuing a two-pronged strategy. On the one hand, the president was seeking to preserve his personal relationship with Xi Jinping and the appearance of pursuing warmer ties. But as for officials in the bureaucracy, Trump ‘wants us punching as hard as we can.’ In effect, Pottinger was telling the Commerce officials to take Trump seriously, not literally — to tune out the verbal concessions that Trump made in public and keep a default position of being ‘tough’ on China.”

Presidents, even those not in their 70s, only have maybe 5% of their day for these matters. This leaves an enormous amount to be sorted out by empowered appointees and cabinet members, which explains how we ended up with export controls instead of sanctions on Huawei — quite remarkable in retrospect.

Edward Fishman: The first Trump administration has been characterized as super hawkish on China, but examining the record shows Trump himself wavered between being very hawkish and totally obsequious to Xi Jinping. The policy was shaped by different factions: people like Pottinger and Bob Lighthizer were tough on China, while Mnuchin and Gary Cohn wanted to return to the early 2000s approach — the Hank Paulson school of U.S.-China relations. These factions took advantage of opportunities when Trump leaned their way to advance their policies. Trump didn’t take a more consistently hawkish line toward China until his final year in office, when he believed Xi Jinping had lied to him about COVID, destroying his re-election chances. We’ll likely see similar dynamics in a new Trump administration — Trump vacillating while different factions capitalize on moments when he’s more receptive to their proposals.

Jordan Schneider: You close the book, Eddie, with the idea of an impossible trinity.

“We don’t yet know when the Age of Economic Warfare will end, but we can envision how. The trade-offs facing policymakers in Washington, Beijing, Brussels, and Moscow can be thought of as an impossible trinity consisting of economic interdependence, economic security, and geopolitical competition. Any two of these can coexist but not all three.”

Walk me through the 20th and 21st centuries — what different trade-offs did states make, and where are we landing now in 2025?

Edward Fishman: Let me explain why I ended the book this way. While I wrote a narrative history because I believe individuals can shape history — remove certain individuals and history would have gone differently — there are also structural reasons underlying the age of economic warfare. Consider this statistic: Barack Obama used sanctions about twice as much as George W. Bush, Trump used them twice as much as Obama, and Biden uses them twice as much as Trump. This suggests both individual agency and structural factors matter.

The geoeconomic impossible trinity I developed explains why this is happening. You can only have two of these three elements simultaneously — economic security, economic interdependence, and geopolitical competition. During the Cold War, we had economic security and geopolitical competition in a bipolar order between the U.S. and Soviet Union, but at the expense of economic interdependence — there was no meaningful economic relationship between them.

When the Cold War ended, geopolitical competition disappeared. China and Russia transformed from adversaries to potential friends, and we invested significant political capital bringing both into the liberal international order, including the WTO and other key international bodies. Without geopolitical competition, we could embrace economic interdependence without sacrificing economic security.

Today, we maintain economic interdependence while geopolitical competition has returned full force, resulting in lost economic security. This affects all major powers — the United States, Japan, European Union, China, and Russia. None feel economically secure, leading them to invest heavily in protecting themselves from rivals’ sanctions, export controls, and tariffs. To regain economic security, we must either end geopolitical competition, which seems unlikely, or significantly reduce economic interdependence. My view is we’re heading toward a significantly less interdependent global economy in the years ahead.

Jordan Schneider: You end the book with some dark words,

“Without the ability to channel geopolitical conflict into the economic arena, great powers could once again find themselves fighting on an actual battlefield. The dream of economic war, for all its downsides, is that it can be an alternative to a more violent kind of war. Someday the age of economic warfare might end, but we might miss it when it’s gone.”

Care to elaborate on this idea?

Edward Fishman: We face very significant stakes in our economic decisions today as we head toward a less interdependent global economy. This could manifest in two ways. First, a world economy where the U.S. and its allies deepen their connections. We might have less trade with China and Russia, but more with Canada, Mexico, the European Union, and Japan. Janet Yellen in the Biden administration called this “friendshoring.” Bob Lighthizer proposed this in a recent New York Times op-ed, suggesting the U.S. and other democracies create a bloc with low internal tariffs and high tariffs on everyone else.

The alternative is deploying sanctions, tariffs, and export controls arbitrarily against friends and foes alike, creating a chaotic breakdown of the global economy. We’d be forced into autarky by default, without long-term economic agreements with allies or adversaries. This scenario frightens me most because history shows that when states can’t secure resources and markets through free trade and investment, the temptation for conquest and imperialism rises.

President Trump’s talk about seizing Greenland for its mineral resources echoes Hitler’s pursuit of Lebensraum. Hitler feared being cut off from European trade after Europeans sanctioned Mussolini for seizing Abyssinia. If economic interdependence unravels into every country for itself rather than friendly blocs, we could see a return to great power war.

Jordan Schneider: Dark. I’ll refer folks back to our two-part episode with Nicholas Mulder on The Economic Weapon, which told that whole 1920s and 1930s story of how Imperial Japan and Nazi Germany developed their autarkic, resource-hungry vision. While racial ideology played a role, they were clearly terrified about accessing enough oil, minerals, and resources to remain great powers.

Researching Modern History

Jordan Schneider: Let’s shift topics. Tell me about writing history of the past 20 years. You don’t have everything declassified, you’re doing interviews, and history seems to be happening in WhatsApp groups. What was it like both as a former civil servant and then interviewing all these people to piece this recent history together?

Edward Fishman: As you know, Jordan, since we shared some classes, I studied history and in a parallel universe might be a university historian. After college, I went into government work and realized that in this era, many decisions bypass formal processes. Even back in the 2010s, decisions were made through informal communications, in coffee shops, never written down, through WhatsApp groups. This has only accelerated since I left government.

Contemporary history plays a crucial role because documentary records won’t be as valuable in 30 years as they were previously. They might even mislead — often the package going into an NSC meeting doesn’t reflect what’s actually discussed or decided. Many decisions happen outside formal meetings entirely.

This experience convinced me that the best approach was to follow Thucydides’ method — write contemporary history, documenting the times you live in, striving for impartiality. What you lose in documentary records, you gain by talking to people who were actually present. Thanks to my government experience and non-partisan reputation, I accessed everyone crucial to this story — Democrats, Republicans, and current civil servants.

Future historians will surely build on and improve the story told in Chokepoints when they access all documents. However, I hope the insights derived from my access to these people and my insider government experience will prove durable.

Jordan Schneider: Did you send Nabiullina an email?

Edward Fishman: No, I didn’t speak to Elvira Nabiullina, unfortunately. One wrinkle in the story is that I was sanctioned by the Russian government in 2022, before I even started writing. I’m currently banned from any travel to Russia.

Jordan Schneider: She’s got an open invitation to ChinaTalk. I’d love to hear her side of the story.

y through declassified documents showing what really happened — I’d bet most of the narrative around U.S. policy holds up. Rather, I hope we’ll see Chinese, Russian, or European versions of Chokepoints. While I capture those stories to some extent, the book focuses on the United States. If counterparts in those systems wrote similar books, we’d have a much more complete picture.

Jordan Schneider: Eddie and I were classmates at Yale, studying ancient history together. I love how you say you’re walking in Thucydides’ footsteps — let’s say we’re doing the same with ChinaTalk. For both of us, Donald Kagan’s classes were among the most formative in thinking rigorously about politics, history, and warfare. Any memories or reflections about his impact in the classroom?

Edward Fishman: One sad aspect of publishing this book is that Don died a couple years ago and won’t have the chance to read it. Of all my teachers, he had the biggest impact, shaping my career in many ways. He even influenced how I teach my class at Columbia on Economic and Financial Statecraft — I use his exact seminar format, with students debating each other’s papers weekly.

The main lessons I learned from Kagan that influenced the book include understanding the role of contingency in history — people and their decisions matter. While many history books focus on impersonal forces, Kagan taught me that structure sets context but free will and decisions can change history’s course. That’s why I focused on the people creating these policies.

Second, chronology matters. You must understand historical decisions within the knowledge available at the time. We tend to judge past decisions with hindsight, but understanding what people knew then reveals more about how history unfolds.

Finally, history itself matters. Kagan said, “Without history, we are the prisoners of the accident of where and when we were born.” Beyond clichés about repeating history, understanding what our predecessors did right and wrong helps us live better lives today.

Jordan Schneider: Another lesson coming through your book is that while we can debate grand strategic decisions, like Biden’s approach, the most human agency appears one or two levels below. Having someone from Goldman Sachs who understands the global insurance market enables implementing policies that might not otherwise be conceived. While we criticize civil servants in today’s America, it’s important to recognize that you can expand government’s effectiveness by empowering the right people to make decisions and analyze questions thoughtfully. For anyone at a career crossroads, read Eddie’s book and understand that your future choices matter.

Edward Fishman: I appreciate that, Jordan. If there’s one takeaway, it’s that government officials’ decisions truly matter. The protagonists I highlighted — Stuart Levey, Adam Szubin, Dan Fried, Matt Pottinger, Daleep Singh, Victoria Nuland — if you remove them from their situations, you’d have very different policies. We were fortunate to have them in those positions. Having more people with diverse skill sets willing to serve in government increases the odds of having the right person in the right place at the right time.

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【和放学以后永不失联】订阅放学以后的Newsletter，每周三收到我们发出的信号：afterschool2021.substack.com 点击链接输入自己的邮箱即可（订阅后如果收不到注意查看垃圾邮箱）。如需查看往期内容，打开任一期你收到的邮件，选择右上角open online，就可以回溯放学以后之前发的所有邮件，或谷歌搜索afterschool2021substack查看。

截至目前，放学以后Newsletter专题系列如下：“在世界游荡的女性”系列、“女性解放指南”系列、“女性浪漫，往复信笺”系列、莫不谷游荡口袋书《做一个蓄意的游荡者》系列、“莫胡说”系列”《创作者手册：从播客开始说起》,播客系列和日常更新等。

大家好，本期放学以后信号塔由霸王花木兰和瑞士朋友Ruya共同轮值。前段时间，我梳理了下“在世界游荡的女性”系列Newsletter，不知不觉竟然已经有了18期内容，其中既有我们自己世界各地游荡的记录，也有来自墨尔本、芬兰、美国、日本、济州岛等世界各地游荡女性的创作。这个系列就像是女性共同协作撰写的书籍，每翻开一页，就是一位女性选择出走，决定探索世界和发现自我的故事，也像是女性共同绘制的一幅世界地图，女性游荡者用脚步亲自丈量地理的辽阔，用创作将世界各地标记并连接起来。为了方便大家阅读，我将“在世界游荡的女性”系列汇总放在文末，大家阅读本期不过瘾，还可以继续阅读文末的游荡故事，在心里放一把游荡的星星之火，再慢慢将它灼烧为一片燎原。

继上一次投稿“游荡的十年，是理想的十年”，瑞士朋友Ruya持续走在游荡的路上，这次她的脚步来到了埃及。我和莫不谷在去年三个多月的环球游荡中，就有游荡埃及的计划。莫不谷提议结束东南亚游荡后，开启红海之旅，从土耳其到达约旦，然后坐船穿越红海到达埃及。这是一个令人心动的提议，仿佛看到圣经故事里，摩西劈开红海带领以色列人成功逃离的奇幻场景将以无比真实的距离贴近自己。最后却因为七月正直炎热的夏季，已经在东南亚历经酷暑和噪音磨难的我们改弦更张选择去凉爽的北欧波罗的海游荡。除了天气原因，另一方面，网络对于埃及的讨论与争议也让人有些犹疑，最常看到的一句评论是“去了埃及会后悔，不去也会后悔”，对于古文明和陌生世界的向往好奇与对诈骗、危险、女性得不到尊重的担忧同时并存。

也因此，我对Ruya的这次埃及游荡既感惊奇，又感佩服，也会好奇，究竟是什么原因让她选择持续出走和游荡。还记得我去瑞士游荡和她相遇的头两天，听她分享世界各地游荡的故事，遥远陌生的南非、熟悉又陌生的印度等等，当听完她在印度游荡令我瞠目讶异的故事和细节后，我曾问她，是什么让你坚持在印度游荡这么久？回答的内容记不清楚，脑海里只留下瑞士漫山绿野，悠闲牛羊，浪漫夕阳，那些美到不真实的场景。

回到在世界游荡的女性，女性不仅要游荡，创作和记录同样重要，这不仅让我们看到女性探索世界的可能性，还让我们看到女性在这个世界的处境和遭遇，感谢Ruya的创作与投稿，借着她的眼睛、摄影、文字与故事，我们有机会游荡埃及。

以下是正文：

圣诞期间我和家人去了埃及，没有碰到被one dollar环绕的场景，也没遇到任何骗子，但我也确实见识到了网上所说的埃及的酒店和外面被切分为两个世界的割裂感。一墙之隔的酒店外吃不饱饭的小小孩以极低的价格兜售劣质的小商品，在我给出一块面包后他怯懦羞涩的眼神我大概可以记一辈子，而酒店大堂内有用可食用姜饼铺满一整面墙的圣诞布景，欢欣雀跃的游客们迷失在古埃及的历史谜团里。

在十天的游荡里我们去了传说中民风淳朴的绿洲锡瓦，我们确实被很友善的对待了，甚至还免费搭了好几次便车，在水果摊前被赠予香蕉，去餐厅结账的时候经常把零头直接给我们抹掉了，他们也时常记不住价格，还要我们帮忙算钱。

锡瓦的绿洲平原上遍野都是繁枝茂叶的枣树，是埃及贫瘠的无边无涯的北部荒漠里唯一一处绿地。但我现在翻看拍下的这些美丽照片，回想在埃及从海岸到沙漠沿途看到的遍地成堆的塑料垃圾，以及当地女性的困境，所体会到的割裂感更加真实了。

我们在锡瓦期间去了一间陶艺work shop，在这里遇见了一位在大学里教设计并说着一口流利阿拉伯语的西班牙女性，从她口中我得知这间work shop源起于开罗附近的另一片绿洲，那里盛产陶土，最初只有几个零星的手工艺人，后来一位瑞士女性带去了一个公益组织在当地发展出了很多work shop，以帮助当地人就业，尤其是帮助当地女性，并怀着能逐渐改善当地极端保守的穆斯林社会氛围的期翼。在锡瓦那间work shop的主理人就是从很小开始在公益组织的帮助下学习陶艺技能，多年前移居到锡瓦，Ta们希望在锡瓦也能开展更多的work shop。

锡瓦尘土飞扬的街头放眼望去几乎清一色的都是男性，他们占据了所有的空间，小学年纪的男孩肆意的开着突突车揽客，老男人在茶馆前无所事事的注目着每一个游客，小男孩们在街上奔跑嬉戏，像野犬一样身体叠在一起撕扯着打群架，成年男性在游客聚集区烤火闲聊。

偶尔见到几个女性也是全身被布卡遮住，只留一双眼睛在外，或许这些女性自从穿了布卡之后就再也没有了朋友，她们走在街上甚至无法辨认曾经一起玩耍的童年玩伴。

这位西班牙女性每年假期都会到锡瓦做志愿者，在连续的四年里直到我们聊天的前一天才被邀请去了当地的一个派对，当然派对里只有男性，而她是唯一一个女性，她还说从来没有交到过当地的女性朋友。我们聊完没多久，就有当地男性来找她，还主动贴面拥抱，看见我在旁边也主动跟我握手。要知道锡瓦作为极端保守的穆斯林社会，异性之间有着严格的社交距离。

而让我感觉到讽刺的是，也就在前一天我们路过一户家门口的时候，小孩子们主动要求我帮Ta们拍照，Ta们全身裹着罩袍只露出一双眼睛的妈妈从破败的房门里走出来，一直以请求的口味邀请我们去家里坐客，我怕有危险而犹豫了片刻，但想到有男性伴侣在，还是欣然接受了邀请，但这位妈妈阻止了我伴侣的脚步，说了句“No Man”。

我还是决定带着孩子走进了她家里，院子里寸草不生的荒土上只铺了一张陈旧的波斯地毯，直到我们坐在地毯上，她才把遮面的黑纱掀起来，我这才看见她的脸，由于常年遮面皮肤非常白皙，比我想象的要年轻很多，这错误的想象是因为她身边站了五个孩子，后来听我们住的民宿房东说锡瓦的女孩子们16岁就结婚了，20岁未婚就会被称为“剩女”，再后来遇到的一个司机家里有12个孩子。

她拿出了两件华丽的全手工的披肩给我试穿，精美的手工刺绣上缀满了璀璨的珠片，但这么美的衣服，她却不能穿出门，只能在我这个陌生人面前展示。后来她送我走出家门，她又遮上了黑纱，并一直避免跟我伴侣接触。

（小朋友披上了她妈妈本来要给试穿的衣服让我拍照）

西班牙女性作为外来者渴望撬动极端保守的社会氛围。但在外来文化冲击下，本地男性的既得利益似乎又被放大了，游客带来了前所未有的工作机会和经济收益，以及和异性的相处模式，而在罩袍之下不被看见的当地女性被牢困在家中的锁链似乎更紧了。

我们在旅途中遇到过很多次男性司机突然停下车来，出去跪地祷告，他们无比虔诚的样子让我感到不安。宗教和集权一样可怕，他们都试图用同一种思想去控制千万种人。

一天夜里我们被民宿的房东邀请和她的朋友们一起生火烤肉，那大概是我在埃及那几天吃过最香的烤羊骨，旺火撩过的羊肉充斥着炭火香。我们一起在炉火前吃饭聊天，我知道这些说着流利英语的外来者都是这里的privilege群体，Ta们也从不打算融入当地社会，但跟Ta们聊天让我感觉自在、心安。

房东是一位来自开罗的女性，她以逃离城市的姿态来到了这片沙漠中的绿洲，买下了一片枣林密布的土地，用一种当地特有的叫Karsheef的材料搭建了房子，Karsheef是由当地的泥土、盐和石膏混合而成的。她说滴雨不下的锡瓦却在一年前下了两个小时的大雨，她在这烂泥浆搭的房子里的火炉前瑟瑟发抖，因为雨声大的让人恐惧，她担心房子会坍塌。我们从她嘴里才得知，原来多年前一场持续了三天的大雨落在了锡瓦这片沙漠绿洲上，老城里的一切建筑都在雨中毁殁了。难怪锡瓦老城里建筑残缺不堪，唯一几幢完整的建筑都是重建的商业用房，千年古堡只剩下迷宫一样的废墟，古埃及的庙宇只余地下陵墓。

（在死亡之谷的地下陵墓里看到了很喜欢的古埃及壁画，像是人鱼在托起一个梦境。）

我们后来回到了开罗，住在了老城中一间由16世纪老宅改造的Airbnb里。老城里人声喧嚣，但遍地都是塑料垃圾，埃及的塑料垃圾多到触目惊心的地步，放养的牛羊在垃圾堆里觅食，树枝上挂满了楼上居民扔的塑料袋，甚至在机场可堂食的甜品店都会用塑料托底盛放。

（这么美的海，但不远处堆满了垃圾）

（在开罗的Airbnb里的图书馆天花板，分别来自16世纪和19世纪）

这是我第二次见到如此破败的市中心，第一次是在约翰内斯堡，但约堡市中心早已人去楼空鬼气森森，而开罗的老城窄小的街巷里车流汹涌，马车驴车从尘土飞扬的土路上狂奔而过，遍地瘦骨嶙峋的猫狗躺在垃圾堆里晒太阳，十几口人蜷缩在一所残破的面目全非的上世纪公寓里。

前埃及政府在50年和60年代的社会主义改革中推行了冻结租金政策，导致现在很多埃及人还以不到一美金的价格住在市中心，房东无力修整年代已久的房产，而现任的埃及政府对原有的开罗市中心放任不顾，不断在周边开发新开罗。

我们花了大半天的时间在老城里游荡拍照，我惊讶于老城里的建筑即便只剩断壁颓垣，依旧可以看到在几代王朝兴衰中的时代印记，马穆鲁克时期精美繁复的木格栅窗，拜占庭时期细碎阳光散落在众神飞天壁画上的穹顶，奥斯曼时期宏伟的清真寺里青色的伊兹尼克瓷砖，殖民时期欧洲巴洛克风格建筑的残余轮廓。

作为另一个古老大国，他们一面沉溺于恢弘的历史叙事里，一面对非国家面子的但有百姓生活痕迹的文物古迹弃之不顾。我们在开罗的一日导游一再跟我们强调，埃及的很多文物被盗走，它们在全世界最好的博物馆里展出，这样的话我太熟悉了，小时候看某部民国电视剧里面一个穿着孔乙己长袍的知识分子带着甲骨文一起走进了熊熊燃烧的大火里，毅然决然的说甲骨文就是毁了也要在华夏大地上，小时候看到被当时的剧景触动，后来一想把文物好好安放，并且被世人所见，不才是对其该有的尊重吗。

但在埃及所感受的文化冲击和新奇很快就被冲淡了，在开罗的最后两天我们终于在震天抢地的车鸣和汽车尾气灰尘漫天的双重攻击下崩溃了。

女性去世界游荡，不仅拓宽了自己生活可能性的边界，也激发了世界各地的女性朋友行动的勇气和力量。如果有正在世界游荡的女性朋友想分享自己的体验，加入到“在世界游荡的女性”系列创作中，欢迎来信给我们的邮箱 afterschool2021@126.com ！也欢迎在游荡者平台（www.youdangzhe.com）多多分享，多多创作！

在世界游荡的女性18：女子游荡天团，重新定义春晚！

在世界游荡的女性17：在一无所有的时候，也可以靠『你是你』这件事情游荡世界

在世界游荡的女性16：在美国看见的伊拉克女性

在世界游荡的女性15：游荡的十年，是理想的十年

在世界游荡的女性14：一趟寻找美食与欢愉之旅

在世界游荡的女性12：在游荡的途中和偶遇的同路人畅聊

在世界游荡的女性11：在芬兰，在北欧，崭新的，美好的，冷冽的，热气腾腾的，和阴魂未散的

在世界游荡的女性10：埃及，普吉和夜郎活在21世纪

在世界游荡的女性9：莫不谷的滔滔生活和金龟换酒

在世界游荡的女性8：热烈的海岛和女性在这个世界的“归属”

世界游荡的女性7：一次济州岛之行意外引发的觉醒、凝固和群体讨论

在世界游荡的女性6：脱离长期生活的环境，才能有机会感知自我

在世界游荡的女性5: 人和动物还可以这么活

在世界游荡的女性4: 霸王花和莫不谷从巴黎发给你的10张明信片

在世界游荡的女性3：不再成为国家的受害者

在世界游荡的女性2: Run了就能脱离有限游戏吗？

在世界游荡的女性1：她从墨尔本的来信和我在阿姆斯特丹的回信

为全球华人游荡者提供解决方案的平台：游荡者（www.youdangzhe.com）
这世界的辽阔和美好，游荡者知道。使用过程中遇到问题，欢迎联系客服邮箱wanderservice2024@outlook.com.

【放学以后文章&书籍&其它】

解锁放学以后《创作者手册：从播客开始说起》：https://afdian.com/item/ffcd59481b9411ee882652540025c377

解锁莫不谷《做一个“蓄意”的游荡者》口袋书：
爱发电：https://afdian.com/item/62244492ae8611ee91185254001e7c00微信公众号：《放学以后After school》（提示安卓用户可下载“爱发电”app，苹果用户可把爱发电主页添加至手机桌面来使用，目前爱发电未上线苹果商店）

Newsletter订阅链接：https://afterschool2021.substack.com/（需科学/上 网）

联系邮箱：afterschool2021@126.com （投稿来信及合作洽谈）

为全球华人游荡者提供解决方案的平台：游荡者（www.youdangzhe.com）

小红书：游荡者的日常

同名YouTube：https://www.youtube.com/@afterschool2021

同名微信公众号：放学以后after school

欢迎并感谢大家在爱发电平台为我们的创作发电：https://afdian.com/a/afterschool

播客收听平台：【国内】苹果播客（请科学/上网）、爱发电、汽水儿、荔枝、网易云、小宇宙、喜马拉雅、、QQ音乐；
【海外】Spotify、Apple podcast、Google podcast、Snipd、Overcast、Castbox、Amazon Music、Pocket Casts、Stitcher、Radio Public、Wordpress

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Everything in the world is about sex, except sex. Sex is about power.

— Oscar Wilde

亲爱的媎妹：

见字如面！

2013年，内娱首个养成系男团TFBOYS出道，凭借一曲《青春修炼手册》迅速走红，并于2016年登上央视春晚，从此三位成员的名字可谓家喻户晓。2017年，组合宣布单飞不解散，此后成员各自发展，团体活动屈指可数。其经纪公司时代峰峻本是重庆一家小作坊，却因TFBOYS赚得盆满钵满，自然又忙不迭地推出二代（TNT时代少年团）、三代（T.O.P登陆少年）、四代……希望能将所谓“TF家族”延续下去，维护一批稳定不外流的“家族粉”。

毫不夸张地说，TFBOYS的成功彻底改变了国内的偶像产业和饭圈文化，“养成系偶像”的概念自此深入人心。“养成系”一词源自日本杰尼斯事务所，指粉丝从偶像小时候开始就投入资源供养他，一路陪伴他成长，见证他一步步站上更大的舞台，最终成为闪闪发光的大明星（注：国内的养成系偶像基本都是男孩，本文讨论的也只有男偶像，故用“他”指代）。

对粉丝来说，这就像个真人版的通关游戏；对偶像来说，他学习才艺的费用从原生家庭转嫁到了公司和粉丝的身上，他可以一边赚钱一边实现自己的舞台梦，所以有人说养成系偶像其实是在「贩卖梦想」。和其他男明星相比，粉丝和养成系偶像更容易建立深刻的情感联结，这是因为从小养成一个偶像要花费数年，且由于沉没成本太高，即使他长大后爆出负面新闻，粉丝也倾向于原谅而非彻底割席。

正如王尔德所说，世上的一切都关乎性，唯有性关乎权力，养成系也不例外。养成系是追星文化的一部分，其包含的性别权力关系不可避免地受到资本造星体系和男权社会两大因素影响。前者把一个个男孩包装成精美的商品，后者解释了为何女性（尤其是单身年轻女性）会是这种商品的主要消费者。此外，「养成」的特殊性又进一步凸显了“爱男”思想如何根植于女性心中。“女博士把男初中生奉为偶像”这种听起来匪夷所思的事情在养成系粉丝中并不罕见。

我认为，女性对养成系偶像的喜爱更多源于母爱泛滥，而非偶像崇拜；养成系贩卖的不只是舞台梦，更是性幻想。在这封信中，我将从两方面剖析养成系偶像和女粉丝的互动所映射的性别权力关系。一方面，资本造星体系和男权制度勾结，让女性心甘情愿地成为“男宝妈”；另一方面，养成系偶像通过媚粉和卖腐等方式积极构建异性恋浪漫爱和色情幻想，以吸引和巩固女友粉。

1、“伟大”的养成游戏：男宝妈是怎样炼成的？

要养成一个偶像，第一步当然是选好苗子。根据官网招聘广告，我们可以知道时代峰峻长期招募10-18岁、外貌出众的男孩做练习生（有才艺者优先），为他们提供免费的声乐、舞蹈、表演等培训，练习生将来有机会组成男团或单独出道。

养成系粉丝总体来说年龄偏小，但由于中小学生大多只有周末和假期能接触手机，所以平时追星的主要是有一定经济能力的大学生。我自己也是在大二时偶然了解到TFBOYS，从此开启了追星之路。据我了解，他们的粉丝不乏社会阅历丰富的成熟女性。自从女权意识觉醒之后，我不禁想问：为何这些女性会把十几岁的男孩奉为偶像？这种“爱男”有何特殊之处？

即便早就知道了世界是个巨大的“爱丁堡”，养成系偶像饭圈的爱男程度依旧令人发指。这里主要聚集了两大类粉丝——妈粉和女友粉，前者把偶像当儿子，后者把偶像当伴侣。偶像小时候的粉丝主要是妈粉，而随着年龄增大他的女友粉也会慢慢变多。

在男权社会，「生育」（尤其是男孩）几乎是女性身份不可分割的一部分，所以父权制下长大的女性也许都有当“男宝妈”的潜质。有趣的是，养成系妈粉喜欢调侃自己是“无痛当妈”，意为可以直接从一堆小孩里挑一个最可爱的“养大”，无需受生育之苦。然而，为了养成一个偶像，粉丝日积月累的金钱投入相当可观，感情上的投入更是不可估量，说是“母爱泛滥”也并不为过。粉丝的“母性”，即牺牲自我、无私奉献的精神，跟现实生活中的母亲一样，是一种社会文化的建构，而非与生俱来的天性。

为了满足这种“母爱”，时代峰峻要求练习生持续在微博营业，向粉丝汇报自己的近况，既要展示才艺上的进步，又要分享日常生活及学业。很多练习生小时候都会在微博吐槽作业多，或是表达自己想长高的愿望。这都是为了让粉丝参与少年偶像的生活乃至人格塑造，从而有一种自己能影响偶像人生走向的错觉。例如王俊凯的粉丝会在他中考和高考前在微博留言，拼命传授各种学习经验，口吻跟妈妈叮嘱儿子没什么区别。TFBOYS出道之前粉丝会成箱往公司寄牛奶，督促成员喝牛奶长高，组合走红之后粉丝太多，公司不得不管控粉丝送礼物的行为，但王俊凯仍旧会收到好几万一把的定制吉他。

另外，粉丝和偶像围绕「生日」进行的种种互动，是二者「类母子关系」的重要体现。下面我会以国内最年长的养成系偶像王俊凯为例，来简单说明这种关系。一方面，生日是母亲的受难日，是偶像感谢母亲养育之恩的重要时刻。偶像会在微博发小作文，细数自己一路以来的成长，感谢粉丝的支持，表达和粉丝共同走下去的愿望。王俊凯15岁生日时在微博上写道：“今天我15岁了，有那么多的你们陪伴我，谢谢这几年来你们的一直陪伴，《给十五岁的自己》不仅是给自己的生日歌，也是送给所有支持我的你们。”这条微博的转发次数打破了吉尼斯世界纪录。

另一方面，生日也是粉丝尽情展现母爱的舞台。她们会为偶像做大规模、长时间的生日应援，尤其是对养成系来说意义重大的18岁成人礼。下图是王俊凯18岁生日时粉丝组织的应援活动（不完全统计）：

飞机生日祝福、18颗星星命名、6万快递柜......如此大规模的应援，所耗费的金钱和时间可想而知。粉丝对偶像的爱，比起现实中溺爱儿子的男宝妈，可谓有过之而无不及！

不过，要想激发粉丝的母爱，偶像必须有一张漂亮的脸蛋。颜值是时代峰峻选人的第一标准，其旗下练习生往往个子不高、体型偏瘦、五官清秀，如果进公司早，甚至还没变声，因而嗓音清亮、稚嫩。而且男爱豆的妆造常常偏女性化，有些小孩的长相也偏（传统）女性化，呈现出来的就是比同龄男生更“弱”/“幼”的气质。

养成系爱豆的外貌和气质特点让粉丝容易将他们和现实中的男性区分开来。男孩不是男人，没有攻击性。邋邋遢遢的普男哪里能和光鲜亮丽的男爱豆相提并论？更何况是自己一点点看着长大的小孩。我在豆瓣的“楼人观察室”小组（里面都是时代少年团的粉丝，代号叫“点心”）看到过一个关于结婚生子的讨论。出人意料的是，评论区竟然几乎都是否定的答案，其中不乏“结婚生子不如死了”、“男人没一个好东西”这类对男性失望透顶的言论。

也就是说，很多年龄较大的粉丝有过非常糟糕的感情经历、对男人感到失望、甚至可能觉醒了一些女性意识，却依然无法脱粉男爱豆——这好像很矛盾。但事实上，正是因为她们对现实中的普男感到失望，才更要通过追星继续爱幻想中的男性。她们选中某个「尚未完全社会化」的漂亮男孩，凭借丰富的想象力为之镀上一层又一层的金身，吹嘘其外貌、夸大其实力，还以为自己在伟大地哺育一株未开的花。

然而，这些养成系偶像爆出的任何一条负面新闻（如辱女、走后门、14岁就跟站姐谈恋爱、拿石头砸老奶奶等）都足以说明他们和现实中的男性没有任何本质上的区别。男爱豆看着再柔弱也是男性，且他们从小就得到万千女性的追捧，自身的男性优越感只会进一步膨胀，让他们内里的爹味不亚于任何一个“老登”。养成系偶像就像一朵朵罂粟花，光鲜亮丽的外表下是五毒俱全的内核，极易成瘾却药效短暂。在这种精神鸦片的作用下，“母亲”逐渐沦为萎靡不振的瘾君子，甘愿榨干自己的荷包让花儿绽放。可终有一日，她们会发现一切的美好都是幻象，一切的牺牲都成了笑话，毕竟父权资本的土壤里怎么可能开出爱女的花？

由于字数限制，本次来信只展示文章的前半部分内容。剩余内容将在下一封newsletter中发布～

就此搁笔，期待下一次和大家见面！

暗月使者*

二〇二五年二月二十四日

*本文由暗月使者主笔，陌生女人1号编辑。欢迎更多姐妹来稿至邮箱dearsisters2022@gmail.com

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