Alibaba's AI Just Shocked the World

Alibaba's AI just shocked the world. So a thousand people lined up outside a tech company in China to get software installed on their laptops, not to get a new phone, not to get a product for free, not to meet a celebrity, to get an AI agent. Engineers from the company's cloud unit helped students, retirees and office workers set up a piece of free open source software called OpenCore. And when I say a thousand people, I mean a literal queue outside the door stretching down the street in Shenzhen in March of 2026.

And here's the thing that stopped me cold when I read that. Most of those people had never written a single line of code in this. Their lives, right? They were retirees, office workers, students, regular people who heard about this thing called an AI agent and decided they needed it right now, today, enough to stand in line for it.

That's not a tech trend. It's a cultural movement. And if you're in the West, if you're building a business, running a team or just trying to figure out where AI is going, I need you to understand what's actually happened here, because this isn't a story about China. It's a story about what happens when AI becomes so cheap, so powerful and so accessible that it stops being a tool for tech companies and starts being something every single person on Earth can use.

And Alibaba is right at the center of it. Let me walk you through exactly what the feel, what it means and why you need to pay attention right now. Let's start with Quen, because that's the foundation. Quen is Alibaba's family of AI models that's been downloaded 300 million times worldwide.

And developers have created more than 100,000 derivative models of hugging face alone for Quen. It's pretty insane. Now, let me put that in perspective for you. That means 100 different thousand, 100,000 different versions of Alibaba's AI built by other people for free, customized for everything from legal documents, video games to customer service bots.

That's not a product. That's an ecosystem. Alibaba CEO Eddie Wu has said that the goal is to turn Quen into the operating system of the AI era. So you can see how fast they're moving here and how clear they are on their vision, right?

Think about what that means. When Microsoft built Windows, they didn't just sell software. They became the thing that every other piece of software runs on. They controlled the layer that everything else depended on.

That's the bet Alibaba is making with Quen. Don't sell the model, give away the model, become the operating system. And it's working. Now, here's where I need to give you some history, because the speed of what's happening right now is impossible to understand without knowing where we were.

Even 12 months ago, a year ago, Chinese AI models were considered second tier. If you were a serious developer, you were using OpenAI or Anthropic. Chinese models were cheaper, but weaker. That was a conventional wisdom.

That was the story the industry told itself. Then DeepSea dropped in January 2025 and cracked the world open. Suddenly the idea that American labs had a permanent insurmountable insurmountable lead looked much shakier. The question wasn't, is Chinese AI catching up anymore?

The question became, are they already here? And then Alibaba spent the last 12 months answering that question. Quen 3, released in April 2025, claims to match and in some cases outperform the best models from Google and OpenAI, and was trained on 36 trillion tokens, right? Double the training data of its predecessor.

The flagship model, Quen 3-235B-A22B, achieves competitive results in coding, maths and general benchmarks against some of the other models. That's a scorecard. Those are the names they're benchmarking against, right? But the raw benchmark numbers, as impressive as they are, aren't actually doing most of the story, right?

The most important part of the story is what Alibaba decided to do with these models after they built them. They gave them away. So almost all of the Quen models are released under the Apache 2.0 license. That's the gold standard of open source.

It means you can download it, use it, modify it, build a business on top of it. And you don't pay Alibaba a single model ever, right? Think about what that means. Three open source models coming out of China.

World leading and changing the world, right? The new flagship open weight model, Quen 3.5397B-A17B, packs 397 billion total parameters, but activates only 17 billion per token. And Alibaba claims it decodes 19 times faster than its previous trillion parameter flagship, whilst costing 60% less to run. 19 times faster, 60% cheaper and free to download.

Now, let me give you an analogy. Imagine you're running a car company and a competitor shows up who gives away cars, you know, good cars, fast, reliable cars for free. And they're doing it because they make money selling gas and roads and the insurance that those cars need to function. That's the play, right?

That the model is a car, the cloud infrastructure and services that enterprises need to actually run the models at scale. That's the gas and the roads. Alibaba is committed to spending at least 380 billion yuan, roughly $53 billion over three years on AI and cloud infrastructure. $53 billion.

So it's not a company hedging its bets against AI. That's a company betting the entire house. And the strategy is working. Alibaba's cloud revenue in its second quarter surged 34% year over year, driven by AI workloads, putting it on an annual revenue run rate of more than $22 billion.

AI related revenue has grown at triple digits for nine consecutive quarters, nine quarters in a row, triple digit growth. That's not a good year. That's a fundamental shift in what the business is. But here's what I find even more interesting than the cloud revenue numbers.

Here's the thing that most people are missing when they look at Alibaba's AI strategy. It's not just about models. It's about agents. During the Lunar New Year, users placed nearly 200 million one sentence orders through the Quen app, including food delivery, automated discount application and payment processing via Alibaba and Tabao.

200 million one sentence orders. Someone opened an app, typed something like, order me dinner, and the AI handled everything else. Found the restaurant, applied the discount, made the payment, confirmed the order without the person doing anything except describing what they wanted. That's not a chatbot.

That's an AI agent. And it's working right now. It's being used for millions of orders. Now, there's a crucial difference here, and I want you to make sure you really get it, right?

So chatbot answers questions. An agent takes action. So chatbot is like having a really smart librarian. An agent is like having an employee who can actually go and do the thing you need done.

The shift from chatbots to agents is the single biggest transition happening in AI right now, and Alibaba has built in toward this for years, right? Which brings us to OpenClaw. Now, OpenClaw wasn't built by Alibaba, obviously. OpenClaw is a free and open source autonomous AI agent developed by Austrian programmer Peter Steinberger, right?

It's an autonomous AI agent that can execute tasks via LLMs using messaging apps as its main user interface. So it can go off and just do stuff like, for example, I don't know, find me the top 10 best restaurants in Bangkok, or go through my email and pull out every invoice from the last 30 days. And OpenClaw bots run locally and designed to integrate with external large language models, such as Claw, DeepSeek, or one of OpenAI's GPT models, including, of course, Quentin. OpenClaw is now one of the fastest growing repos on GitHub, overtaking React, Python, and Linux in the starred list.

That's an extraordinary sentence. Three of the most foundational pieces of software in the world. Projects have been built over decades by millions of contributors, and OpenClaw, which barely existed six months ago, has more stars on GitHub than any of them. But the American story is nothing compared to what happened in China.

On a Friday afternoon in March, nearly 1000 people lined up outside of Tencent's headquarters in Shenzhen to get a piece of software installed on their laptop. Engineers from the company's cloud unit helped students, retirees, and office workers deploy OpenClaw. Experimenting with agentic AI has become a nationwide frenzy in China, driving market rallies as investors look to profit from growing AI adoption. There are hats.

There are install parties. Engineers have found a new business charging 500 yuan, about $72, to install OpenClaw on site. And then, and this is my favorite detail of the entire story, China's OpenClaw craze took an ironic turn when social media platforms were flooded with paid services offering to uninstall the AI agent after users initially paid to have it installed. People paid $43 to uninstall something they'd already paid to install.

That's how fast it's moved. That's how far ahead of this infrastructure the enthusiasm got. Some local governments are even offering subsidies worth hundreds of thousands of dollars to companies with approved OpenClaw projects. The Chinese government subsidizing an Austrian programmer's open source side project because they saw it as the on-ramp to the agentic AI future they want to build.

Now, Alibaba saw all of this happening and made a move. Alibaba launched a dedicated mobile app called JVS Claw that helps iOS and Android smartphone users without coding knowledge to install and deploy OpenClaw within minutes. Without coding knowledge. That's a key phrase here.

You don't need to know what a command line looks like or what it is. You don't need to understand what API keys are or JSON files are or any of the technical plumbing that used to be required. You open an app on your phone, you press a button, you have an AI agent. Baidu introduced an Android app for OpenClaw and other players like Tencent and Minimax also competing to offer OpenClaw services.

So now you have the biggest tech companies in China in a race to see who can most easily get AI agents in the hands of the most non-technical people. That's a competition. Not who has the best enterprise dashboard. Not who has the best benchmark marks.

It's who can get grandma set up with an AI agent fastest. That's a very kind of different ways, right? That's a very different kind of race. And I want you to sit with that a second because if you're watching this from the United States or Europe or anywhere in the West and you're thinking, okay, that's China, that's different.

I would push back on that. The underlying technology is the same. The models are the same. The open source licenses mean anyone anywhere on earth can use this.

And what China has shown us right now is what happens when you remove the friction from AI agent adoption completely. Now, let me talk about what this actually means for the model quality because I've been throwing around some big numbers and I want to make sure I'm being honest with you about what's real and what's benchmarking theater. The flagship QEM 3.5 open weight model contains 397 billion parameters, utilizing efficient architecture with 17 billion active parameters and the release introduced native multimodal capabilities with a context window of 1 million tokens in its hosted version. Let's just translate that into plain English.

397 parameters, but only in 17 billion active at any given moment. It's like a massive library where you only need to pull the specific books relevant to your question. You're not hauling the whole library at every single time. You're just grabbing what you need.

That makes it dramatically faster and cheaper to run the models that activate everything at one time. And it's free. Now, I want to be honest with you because these are Alibaba's self-reported benchmarks and every AI company's self-reported benchmarks should be looked at with skepticism. A study by ChartGen AI and 20 Data Visualization Tasks shows GPT 5.2 scoring 178 out of 200 versus 163 out of 200 for QEM 3.5, but at 10 times the cost.

10 times the cost. So for better performance at most real-world tasks, most people would never notice. That's a trade-off. And for 99% of use cases, QEM is actually good enough and free.

Airbnb CEO Brian Chesky raised eyebrows when he admitted the company used Alibaba's open-source QEM model to power its customer service agent, saying, it's very good. It's also fast and cheap. Brian Chesky from Airbnb, one of the most recognizable consumer companies on earth, running their customer service on a free Chinese AI model and saying it out loud. That's a signal.

It's not that QEM is secretly better than everything else in every way. It's that QEM is good enough, fast enough, and free. And for a business trying to build AI-powered services at scale, good enough, fast enough, and free beats slightly better and expensive every time. So that's it.

That's basically the whole of Alibaba. The QEM model is too powerful. OpenCore is accelerating it. China is loving open-source free models right now.

The question I keep asking myself and the question I'll leave you with is simple. Are you running towards AI or are you running behind it, right? Because the tools are free, the information is available. The only thing standing between you and having AI agents working for you is deciding that you're going to learn this, that you're going to spend the time to figure out.

And that decision is on you. QEM is free. OpenCore is free. The knowledge of how to use them, that's what's valuable now.

And that's what separates the people who are going to ride the wave from the people who are going to wonder what happened next. And if you want to be in the first group, you know where to find us. Join us in the AI Pro Boardroom. Link in the comments description or go to the AIProboardroom.com.

This is my AI automation community that helps you save time, scale and learn AI automation with practical, actionable new tutorials every single day. And you can connect with me personally too. Thanks for watching. I'll see you on the next one.

Cheers. Bye bye.