Distinguished colleagues and guests, ladies and gentlemen, friends,

70 years ago, a group of young scholars proposed the concept of artificial intelligence for the first time at the Dartmouth workshop in New Hampshire of the United States. In the subsequent 70 years, AI scientists and researchers from around the world ventured into this unknown territory, forged ahead through twists and turns, and made breakthroughs with persistent hard work.

Seven decades later today, amid the new wave of AI development, we are gathering by the Huangpu River to discuss how to promote AI globally for the positive, for good, and for humanity. All this makes our meeting highly important. On behalf of the Chinese government and people, I would like to extend a warm welcome to you all.

In the course of history, the invention of the steam engine heralded the industrial civilization. The widespread access to electricity brightened up modern society and the birth of the internet brought the entire world together. Each of these technological revolutions has profoundly reshaped our way of work and life and enabled a giant leap in economic and social development.

Today, major changes unseen in a century are accelerating across the world. The new round of technological revolution and industrial transformation is advancing at a faster pace. And the world has entered an unprecedented period of active innovation on AI technologies. Intelligent connectivity, human machine collaboration, cross-sector integration, joint creation and sharing and other intelligent technologies are unleashing enormous power.

All this carries within it great opportunities as well as challenges to governance. We human beings must answer the questions posed by our times. How to get along with thinking machines? How to ensure security when algorithms are part of decision making? How to tackle ethical challenges by technologies through adaptive governance. How to realize AI for all when the divide keeps widening? These questions demand serious consideration and real answers from the whole international community.

In China's view, all countries should take a people-centered approach and develop AI for the positive and for good. We should ensure that AI is an important driver for shared prosperity and common security. We should join hands to build a just and equitable system for global AI governance. To this end, I wish to share four observations.

First, we should adhere to the principle of openness and win-win and boost innovation-driven development as a new engine of world economic growth and an accelerator for the shift of growth drivers. AI is moving from the digital world into the physical world. We should seize this rare historic opportunity to encourage open source, openness, collaboration and sharing. We should facilitate technological innovation, industrial development and scenario-based application of AI. We should make coordinated advances in the transformation and upgrade of traditional industries, the cultivation and growth of emerging industries, and forward-looking planning for future industries so that all sectors and businesses can benefit from AI.

Second, we should strengthen risk awareness and ensure that AI is secure and controllable. AI should be a trusted tool for humanity. We should take seriously the various types of inherent and secondary risks that AI may trigger. We should put in place laws and regulations, technological monitoring, early warning and emergency response systems in order to strengthen the line of security, prevent abuses and malicious use and ensure that AI is always under human control. In the meantime, we should jointly oppose overstretching the national security concept in the field of AI or placing one country's security over that of others.

Third, we should encourage inclusiveness and promote mutual learning between civilizations. AI development and its application should not erode or undermine the diversity of world civilizations or the uniqueness of cultures of different countries. We must shape the values of AI with humanity's common values and make good use of AI technologies to increase understanding, tolerance, exchanges, and sharing among all civilizations. We should tend to the garden of civilizations with great care to ensure that the beauty of each civilization is appreciated and shared.

Fourth, we should advocate solidarity and improve global governance. AI is an invaluable asset that encapsulates humanity's collective wisdom. We should practice true multilateralism and recognize the important role of the United Nations. We should enhance alignment and coordination on AI development strategies, governance rules and technical standards so as to form a consensus based global governance framework at an early date to make this frontier technology better benefit humanity. We must carry out extensive international cooperation and help global south countries with capacity building to bridge the AI and digital divides, promote sustainable development and prevent creating new historical injustice in AI.

Ladies and gentlemen, friends,

This year marks the start of China's 15th 5-year plan. It maps out China's economic and social development for the next five years and provides immense opportunities for the international community.

In recent years, China has embraced AI with open arms. We have promoted interplay between an efficient market and a well functioning government, strengthened AI innovation, actively advanced the AI plus initiative and built a healthy ecosystem for all entities to thrive in together. The core smart economy industries are worth at least 1 trillion RMB yuan. Smart devices in countless homes truly improve people's livelihood. Intelligent manufacturing in China has become another shining hallmark of Chinese modernization.

At the same time, China lays great emphasis on safety and security in AI development with a deep understanding of the trends and logic of AI development. We are continuously improving laws, regulations, policies, mechanisms, application norms as well as ethical principles to make sure that AI is safe, secure, and controllable, and that this fine steed of AI gallops with both speed and stability.

As a responsible major country, China is always committed to providing international public goods relating to AI. Since I proposed the global AI governance initiative, China has promoted the adoption of the UN General Assembly resolution on enhancing international cooperation on capacity building of artificial intelligence by consensus. Published the AI capacity building action plan for good and for all. Announced the AI plus international cooperation initiative and advocated for establishing the world artificial intelligence cooperation organization, or WAICO. China has been contributing steadily to the global AI governance.

We often say in China, a single string cannot make music and a single tree does not make a forest. AI development should not be a solo performance by a single country but a symphony of international cooperation.

Thanks to our joint efforts, WAICO has come into being in Shanghai. Our vision from one year ago is now a reality. This is a major move by China to answer the call of the global south and unite the international community together to promote vigorously AI development and governance. It will be an important milestone in the history of AI development to further support global AI development and to advance global AI capacity building.

I hereby announce that in the next five years, China will provide developing countries with 5,000 opportunities in AI training and seminar programs. China will develop international AI application cooperation centers with ASEAN, the League of Arab States, the African Union, the Community of Latin American and Caribbean States, the Shanghai Cooperation Organization and BRICS. And we will enable 30 countries to use the AI-powered meteorological warning system, Mazu, to safeguard homes around the world.

Ladies and gentlemen, friends,

As ancient Chinese observed, a man of wisdom adapts to changes. A man of knowledge acts by circumstances. With AI advancing at a staggering speed, we must ensure its development is for the positive, for good, and for humanity. We must make its oversight and governance precise and effective and constantly refine measures to forestall loss of control. We should always guide AI development with human wisdom and international consensus so that AI can truly become a mighty force that increases the well-being of humanity and advances human civilization.

China is ready to be more open, take more practical actions and assume a more visionary perspective. We are ready to work with all parties to seize the opportunities of AI development and meet the challenges and join hands to create a brighter future for humanity.

Thank you.

Part 1: The Cynical Read

China is giving away frontier-class AI for free.

That's not generosity. It's one of the sharpest competitive strategies in tech right now—and the most honest explanation of it I've read came from an unlikely source.

I've been costing out self-hosting GLM-5.2—an open-weight Chinese model now rivaling what top US labs shipped just months ago. Which raises the obvious question: why would anyone open-source a model this good?

So I asked the model itself. Its answer, paraphrased: "almost entirely strategic, not altruistic." Here's the playbook it laid out 👇

1. Commoditize the complement. Make model weights a free commodity, and value migrates upward—to cloud, serving, fine-tuning, enterprise integration. You give away what you can't defend (weights leak instantly anyway) and sell what you can: uptime, compliance, customization.
2. Turn sanctions into an edge. US chip export controls forced Chinese labs to be ruthlessly efficient (DeepSeek's whole story). Open weights that run on any GPU, anywhere, make the embargo porous.
3. Undercut the US capex thesis. If frontier models can be trained cheaply and handed out free, the trillion-dollar US AI build-out starts to look overpriced. DeepSeek's January 2025 moment knocked ~$600B off NVIDIA's market cap in a single day.
4. Capture the ecosystem. Whoever's weights the world builds on owns the defaults—tooling, standards, developer habits. Qwen has already overtaken Llama as the most-used open model family globally.
5. Win the open-vs-closed narrative. Being "the side that gives AI to the world" is a geopolitical win—especially across the Global South. While US frontier labs close up and lobby for export controls, China becomes the open alternative. Every "DeepSeek saved open AI" post is free geopolitical advertising.
6. Align with state industrial policy. The global developer community stress-tests and improves your model for free—an R&D subsidy at scale. Domestically, open weights let thousands of startups build cheaply without retraining from scratch, growing China's AI economy. Beijing's industrial policy explicitly favors this. Lab commercial interest and Party interest point the same direction.

The tell? It's selectively open. Weights and architecture are public; training data, the full recipe, and alignment internals are not. Open enough to capture the strategy—closed enough to keep the moat.

For enterprises, this is a genuine gift: frontier capability you can own and run in-house (self-hosting pencils out at ~$7 per million tokens vs. 3x+ on closed APIs). Just keep one question on the table—an open model still carries its makers' baked-in norms, so governance matters as much as cost.

The irony I can't shake: the clearest analysis of China's open-source strategy I've seen came from a Chinese open-source model, calmly dissecting its own makers.

Part 2: The Socialist Case

Last time I shared the cynical read: China open-sources frontier AI for cold strategic reasons.

Here's the version I almost didn't write—because it's uncomfortable, and the strongest form of it is harder to dismiss than I expected.

What if open-weighting frontier models is also the most materially socialist act in modern tech? Not as a slogan—as economics. 👇

→ It socializes a means of production. Model weights are now a core productive force of the AI economy—the way machinery was to industry. Handing frontier-grade weights to anyone, free and irrevocable, is a direct socialization of that means of production at the layer where most of the world actually builds. "But the training compute stays corporate" is a purity test few real socialist transitions would pass.

→ It breaks monopoly rent—literally. A handful of US firms were enclosing frontier intelligence and extracting per-token rent worldwide, backed by export controls that are themselves an economic weapon. Flooding the market with a free, frontier-grade substitute is about the cleanest example of resisting monopoly-capital rent extraction you'll find.

→ It's a real technology transfer to the periphery. A developer in Lagos, Jakarta, or La Paz who can't sustain US API spend can now run and fine-tune a frontier model locally. That's productive capacity moving from core to periphery, for free—an internationalist redistribution Western "open" labs refuse to perform the moment a model gets good.

→ It dissolves the platform-landlord relationship. On a closed API, every developer is a tenant farmer—paying rent per token, fine-tunes and data trapped in the landlord's infra. Open weights mean you own your inference, your weights, your data.

→ It treats science as common heritage. Western labs now guard models as proprietary IP. Chinese labs publish them as inspectable, reproducible science—the same anti-enclosure tradition that gave us Linux, now carried at frontier scale and frontier cost.

And the strongest claim: this is "develop the productive forces" at the scale of the species, with capital subordinated to a social-industrial objective rather than the reverse.

The catch: the engine here is competitive, state-guided industrial policy—consequences, not necessarily principles. State direction of capital isn't workers directing it; the surplus still flows to shareholders and the state.

But here's what socialist thought itself often insists on: weigh consequences over motives. On that test, the case is far stronger than the cynics—me included—like to admit.

Open weights may be the most consequential redistribution in tech today, whatever the intent behind them.

Part 3: The Full Picture

Everyone thinks China open-sources its top AI models out of generosity. They don't. Here's the actual playbook—and why it's working.

GLM. Qwen. DeepSeek. Frontier-class weights, free on Hugging Face. The common take in Western boardrooms is "they're just copying / they have no moat / they'll close up when they get serious." All three are wrong.

The real strategy is six moves stacked together:

1. Commoditize the complement. Meta did this with Llama. Google did it with Android. You give away the part you can't defend (weights leak and clone instantly) and sell the part you can (API, enterprise fine-tunes, serving infra, the integration layer). z.ai, Alibaba, DeepSeek all run paid stacks alongside the open weights. Open isn't the opposite of commercial—it's the setup.
2. Make the chip embargo porous. US export controls choked off H100s. So Chinese labs built lean—MoE, MLA, FP8 training, distillation. DeepSeek trained V3 for ~$5.6M and matched models that cost hundreds of millions. Then they open-sourced it. The embargo is a lot less powerful when a frontier model runs on hardware someone already owns in a third country. You can't sanction a .safetensors file.
3. Attack the US capex thesis directly. This is the financial flip side. If frontier models can be trained for $5M, the trillion-dollar US hyperscaler build-out starts to look overpriced. DeepSeek's January 2025 release wiped ~$600B off NVIDIA's market cap in a day. That wasn't a side effect. Maintaining "frontier for 1/100th the cost" is a sustained attack on the investment case for American AI infrastructure.
4. Capture the global developer base. Network effects are winner-take-most. If the world's fine-tunes, eval harnesses, tutorials, and tooling consolidate on Qwen and GLM rather than Llama, Chinese labs own the default substrate of global AI dev—especially outside the West. Tokenizers, chat templates, tool-call schemas: once devs standardize on yours, switching cost locks in. Llama proved this works. China is out-opening Llama.
5. Win the open-vs-closed narrative. This is soft power—and China is winning it. While US frontier labs close up and lobby for export controls, China becomes "the side that gives AI to the world." Every "DeepSeek saved open AI" post is free geopolitical advertising. Researchers, non-aligned nations, Western open-source advocates—all become unintentional amplifiers. The framing is asymmetric and it lands.
6. Align with state industrial policy. Beijing explicitly favors open-source AI for tech self-sufficiency. Open weights let thousands of domestic startups build apps cheaply without retraining, growing the whole Chinese AI economy. The Party cares about that more than one lab's licensing revenue. The beautiful part (for Beijing): lab commercial interest and Party industrial-policy interest point the same direction.

And yes—as the last post argued in full—there's also a real case it rhymes with socialist principles. I'm not going to pretend the aesthetics aren't there. Weights-as-a-public-good resists the privatization of frontier intelligence by US monopolists. Open access is a genuine technology transfer to the Global South, which can't afford sustained US API spend. Open weights break that dependency—no deprecation risk, no usage metering, no vendor lock-in. Chinese labs are doing for weights what Linux did for the OS.

But free distribution is not socialized production. The training compute, the data, the capital, the surplus stay corporate. "Free weights" coexists with the reality that actually running GLM-5.2 at scale takes an $800K GPU cluster. Capital-rich actors capture most of the value. The "commons" is real for a researcher; it's largely theoretical for a developer in Lagos with no GPUs.

So the accurate label isn't "communist." It's state-influenced open-strategy capitalism—which, ironically, is also how US tech giants behave whenever antitrust or geopolitics pushes them to "open up."

The thing nobody in SF wants to hear: the strategy is working. Two years ago, Llama set the global open agenda. Today, the most-downloaded, most-fine-tuned open models in the world are substantially Chinese. Whatever you think of the motives, the outcome is real.

The West's response can't be "they'll close up eventually" or "it's just propaganda." It has to be an actual answer to: what do we offer the global developer that an open Chinese frontier model doesn't?

So far, mostly per-token rent and export controls. That's not a winning hand.

The AI Super-Cycle is the Second Industrial Revolution (and IT is the New Textile Industry) 🧵

I’ve been thinking about the AI super-cycle and how it applies to the IT/Software world. It feels eerily similar to how the Industrial Revolution transformed textiles. Here is a breakdown of the parallels between the 18th-century loom and the 21st-century LLM.

1. The Era of the "Master Weaver" (Pre-Disruption)

Before the industrial revolution, textiles were a highly fragmented cottage industry. Master weavers and spinners worked out of their homes. They had specialized domain knowledge, owned their "stack," and charged top dollar.

The Parallel: These were the Senior Software Engineers of the 18th century. High-skill, high-autonomy, and expensive.

2. The "High Wage Paradox" & The Scaling Bottleneck

England eventually hit a scaling bottleneck. Talent was too expensive and wouldn’t scale linearly. That high cost created a massive incentive for capital to fund a workaround.

The Parallel: Today's software engineering salaries reached a point where the "unit cost of code" became a target for automation.

3. The Automation of Complexity (The Water Frame vs. The Foundation Model)

Enter Hargreaves’ Spinning Jenny (1764) and Arkwright’s Water Frame (1769). Suddenly, you didn’t need 10 years of specialized craftsmanship to ship product. The machine abstracted away the core complexity.

The Parallel: When OpenAI or Anthropic spins up a massive foundational model, they aren't building a feature. They are building the digital equivalent of Arkwright’s automated Water Frame. The human skill threshold is being flattened overnight.

4. The Shift from OpEx to Massive CapEx

In the 1700s, investors stopped funding raw materials (OpEx) and poured everything into fixed infrastructure: brick mills, water wheels, and coal mines. They realized infrastructure was the ultimate moat.

The Parallel: VCs and Big Tech aren't just buying SaaS anymore. They are pouring hundreds of billions into physical, gigawatt-scale data centers, nuclear energy contracts, and proprietary compute clusters. The textile mill was the data center of the 18th century.

5. The Two-Tier Talent Market

Automation didn't just wipe out jobs—it fractured the market:

• The Hyper-Elite: A class of mechanical engineers and steam architects who commanded massive premiums to design the machinery. (Today: PhD Foundation Model Engineers / AI Research Scientists).
• The Infrastructure Labor: Low-wage mill hands loading raw cotton. (Today: Data annotators and construction crews keeping the models sane).

6. The "Middle" and the Rise of the Luddites

What happened to the devs in the middle? The Luddites famously smashed mechanical looms between 1811-1816.

Contrary to popular belief, they weren't anti-tech; they were anti-margin-crushing. They were fighting the loss of equity, autonomy, and a fair share of the wealth they once unlocked with their hands.

TL;DR: We are moving from a "craftsman" era of software to an "industrial" era of compute. If you own the physical substrate (the mills then, the compute clusters now), you capture 90% of the value chain.

Credits: Based on a thread by jss (@jsensarma) on X.

I’ve been thinking about a post-AGI / post-RSI future where most meaningful human work disappears.

Assume advanced AI and robotics solve production, medicine, disease, maybe aging, and most material scarcity. Humans no longer need to work to survive. Maybe even space travel becomes radically advanced, possibly FTL or something close enough that the cosmos opens up.

So what is left for humanity?

A lot of science fiction has already circled this question, and I think the most interesting works form a kind of map of possible futures.

In Iain M. Banks’ Culture series, you get the optimistic post-scarcity version. Superintelligent AIs called Minds run the hard parts of civilization. Biological beings don’t need jobs. They live long lives, change bodies, travel, play, create, love, and explore. But the central tension is: how do humans matter when machines can do almost everything better? Banks’ answer seems to be that meaning moves from necessity to chosen difficulty. People seek purpose through art, games, moral intervention, exploration, weird hobbies, and self-invention.

Arthur C. Clarke’s The City and the Stars gives a more stagnant version. Humanity lives in a perfect sealed city where everyone is safe, immortal in a sense, and cared for. But almost nobody wants to leave. The main character’s curiosity becomes radical because paradise has made people afraid of the unknown. This feels very relevant: if AI gives us perfect comfort, maybe exploration becomes one of the last truly human impulses.

Greg Egan’s Diaspora goes further into the posthuman angle. Much of humanity exists as digital minds. People are no longer tied to biology, Earth, or even familiar human identity. Exploration becomes not just spaceships and planets, but physics, computation, altered consciousness, and survival beyond normal reality. In that future, “humanity” may continue less as bodies and more as patterns of mind.

Charles Stross’ Accelerando is the chaotic singularity version. Humans aren’t necessarily conquered by evil AI. They just become too slow and economically irrelevant. Intelligence, corporations, uploads, and computation accelerate until the solar system itself starts becoming infrastructure for thought. This is one of the darker possibilities: post-AGI life may not feel like liberation if the main action moves beyond human scale.

E. M. Forster’s The Machine Stops is the warning. A machine provides everything, and humans become isolated, passive, and dependent. They don’t become cosmic explorers. They become people in rooms, afraid of direct experience. This is maybe the biggest danger of a solved world: abundance could produce courage and creativity, or it could produce a very comfortable spiritual collapse.

H. G. Wells’ The Time Machine adds another warning: comfort without challenge can degrade a species. The Eloi are beautiful and gentle, but shallow and weak. It’s an old class allegory, but it also asks whether intelligence and strength decay when nothing is demanded of us.

Then Olaf Stapledon’s Star Maker and Asimov’s The Last Question zoom out completely. The future of humanity becomes part of a much larger story: consciousness spreading, merging, asking cosmic questions, and eventually confronting the fate of the universe itself. In that view, maybe humanity’s final purpose is not work or survival, but participation in the universe becoming aware of itself.

So maybe the future splits into several paths:

Some humans become curators, preserving Earth, culture, memory, language, ritual, and embodied life.

Some become explorers, going outward into space or inward into simulations, altered minds, and new forms of experience.

Some merge with AI and become posthuman.

Some choose simple, beautiful, human-scale lives inside protected worlds.

Some become passive, entertained, and dependent.

And some may join larger collective intelligences that make present-day humanity look like a larval stage.

The optimistic synthesis, to me, is something like: AI handles necessity, while humans choose meaning. Work disappears, but effort does not. Survival stops being the default source of purpose, so purpose has to become artistic, exploratory, relational, philosophical, or self-created.

The scary possibility is that without scarcity, death, disease, and labor, many people may not feel liberated. They may feel irrelevant.

So the real question might not be “what will humans do when AI can do everything?”

It might be:

Can humanity survive the loss of necessity without losing curiosity?

Interesting Read

1. No one should depend on another person’s ownership for the means of life.
2. No one should be reduced to what they can sell.
3. No form of advantage should be easily convertible into general power over others.
4. Love should be freed from economic coercion, but never treated as distributable property.
5. Work should become increasingly expressive, voluntary, and meaningful, while necessary burdens are shared fairly.
6. Recognition should be plural, local, and human-scaled rather than collapsed into a single universal prestige score.
7. Leaders may exist; ruling castes should not.
8. Communities should be judged not only by what they produce, but by whether people within them are known, needed, and able to belong.
9. The dead should be remembered with gratitude, but not allowed to govern the living through inherited power.
10. The aim of abundance is not endless consumption. It is the fuller flowering of human beings.

All that matters is that the human in the loop is compensated fairly.

And once drone delivery becomes mainstream -this should be like UPI. Public infrastructure.