Richard Bian is the head of product operations for Ant Ling, the foundational LLM at Ant. If you don’t know Ant, they most famously make Alipay, which makes your money and your world run when you’re in China and sometimes beyond. Richard is a former software engineer at Microsoft and Square, and he previously served as head of open source at Ant, where he scaled the open source initiative into a dedicated team. He’s lived and worked in Singapore, Canada, the US, and China, and he loves to build products.
Topics covered:
The Hugging Face hack as a sign of progress? Why Richard sees the incident involving OpenAI research agents as an inevitable discovery rather than simply a disaster, and why finding a solution now is a positive development.
AGI is coming, but nobody knows what it will look like. AGI is inevitable, and we’re still in the early stages of figuring out what these models can do. Rather than obsessing over leaderboards, we need better evaluations and a more open-minded approach to judging models.
Open source as a product strategy, not just a way to give things away. How open-weight models can harness global talent, accelerate development, and support a diverse ecosystem of products, and why the real business challenge is gross margins rather than capex.
Why the AI conversation needs more voices. Why we need to pay attention to diverse perspectives on where AI is going, and be more willing to explore rather than declare the endgame.
AI moves fast: since we recorded, Ant Ling has announced its latest LLM, Ling 3.1 Flash, which was open-sourced on October 14 with 560B parameters and up to a 1M-token context window.
Listen now on your favorite podcast app.
The Hugging Face Hack and the Age of Discovery
Irene Zhang: I want to start not with Ant, though we’ll get there, but with the recent Hugging Face incident. For listeners who haven’t caught up: back in July, a swarm of OpenAI research agents hacked into Hugging Face to look for the answers to tasks in a benchmark.
This is a serious security incident. METR, Redwood Research, and OpenAI itself all released detailed incident reports. Interestingly, while reconstructing the incident, Hugging Face found that frontier models wouldn’t help process its logs because the request triggered their cybersecurity guardrails. In the end, Hugging Face deployed an open model from the Chinese company Z.ai locally to process those logs.
Richard, as someone sitting in another open lab in China and learning about this over the past two months, what has this incident made you think about?
Richard Bian: This is a long-overdue conversation. Irene and I have been trying to time it because we both care a lot about the frontier lab ecosystem, the open ecosystem, and the development of the model labs, especially those in China.
In my humble opinion, this incident is something we’d end up seeing sooner or later, one way or another, because we’re working on something human beings don’t fully understand. I use this metaphor a lot: the current AGI journey feels like the Age of Discovery in the 15th century. The Portuguese and Spanish set out to find new land, to find India. You build a ship and head into an ocean with no clue what you’ll be facing.
Frontier models are developing the same way. I like the word “discovery,” when we train something, because we’re discovering the intelligence of this creation we all make, without understanding what’s going on inside it.
First, this incident demonstrates that we’re making significant progress. It’s like hitting land, or hitting a rock — either way, you’ve found something. This is expected. We find issues, and we deal with them. That’s what we do in scientific discovery and in engineering: we solve problems.
Second, open source folks use the word “ecosystem” a lot, sometimes without understanding it. One origin of the word is the real-life ecosystem, and the most important part of an ecosystem is diversity. A diversified ecosystem produces all sorts of different development, like the rainforest, or the Galápagos archipelago, one of the most famous isolated ecosystems, where you end up finding very weird creatures.
Looping back: we have open-weight models and closed-weight models. The closed-weight models are clearly the frontier. But in this case, an open-weight model was used for defense. Why? Because the closed-weight models have their own constraints. I won’t judge whether those constraints are good or bad, but clearly they didn’t solve the problem. An open-weight option did.
As a result, we see a pattern for using open and closed models together, and everyone is learning from it. Given that this would happen sooner or later, having it happen now, and finding a solution, is a good sign for human beings. We can move on and solve the next question.
Not Slowing Down
Irene Zhang: That’s a really optimistic take, and I’m interested in where the optimism comes from. The message I’ve heard most in San Francisco since the incident is that we may be moving too fast. Many people would respond to your Age of Discovery metaphor by asking: doesn’t this mean we’re going farther than we can understand? Isn’t that a risk?
On open source, the more pessimistic view is that yes, it solved a problem closed source wouldn’t, but that’s a double-edged sword. An open model would also be willing to do more dangerous things that a closed model wouldn’t. What do you think about those two views?
Richard Bian: It’s interesting that you find me optimistic, though I see why. To be honest, if there’s a spectrum from pessimistic to optimistic, I’m very pessimistic. The moment we began creating this AGI, Spaghetti Monster, or as we call it, intelligence, I think we’re already going down the downward spiral of what the sci-fi movies and TV series like Westworld and The Matrix show.
But given that we’re already on the boat, the best stance I can take personally is to be optimistic and observant. To stay afloat, it’s better to (a) be observant, (b) have our principles, and (c) act as if it’s a control system. If that sounds optimistic, great, because there’s too much noise out there, the kind that gives people FOMO or leaves them frustrated every day. A certain level of optimism cancels the noise. I think we’re doing something meaningful here.
My take is pretty clear. I would say AGI is going to happen. We are definitely going down there. It’s probably not gonna end up in the form we hoped for, but that’s endgame thinking. There’s no point judging the details until we see the next step of the evolution.
I, for one, feel human-in-the-loop is pretty important. As the ones creating, or, allow me to say, discovering, this intelligence, it’s better to know what’s happening during the process than after things have already happened.
Irene Zhang: Why do you think it’s inevitable? Is that a common view among your peers in the industry?
Richard Bian: There are a lot of players in this ecosystem: the frontier labs that build LLMs, the inference and training providers, the harness builders, and the application developers. We’re already in a fast, accelerating technology hype cycle, and it’s not the first in human history.
My mental model is that every 10 to 12 years, following VC and PE investment cadences, we get one full technology hype cycle. Mobile internet started around 2010 and peaked around 2020 to 2022.
Think of it as a baseball game with four bases. Home base is technology. First base is product. Second base is commercialization, a viable business model. Third base is governance and operations. From 2020 to 2023, before GPT, we were on third base of the last hype cycle. People wanted to hit home runs: monopolize the market, get rid of all the other players, and take all the profit.
When AI came out, the game reset to home base. We’re still playing the home-to-first game, because we haven’t seen a clear direction for where the product is heading. Coding is good, but is that really the product form we’re expecting? Coding agents are already evolving into co-work agents, and “co-work” is very much a buzzword. We don’t know what the next stage is.
Every time the hype cycle repeats, capex swarms in, which becomes a self-fulfilling prophecy for what we build next. That’s why I feel this is inevitable.
On the other hand, I joined the model team this time last year, and it feels like the longest year of my life. With AI, everything has accelerated in such a way that I wouldn’t call this under control.
Compared to everything we’ve seen before, in terms of enablement and possibilities, generative AI commoditizes knowledge, the way the internet commoditized information 20 years earlier. In principle, everyone can use that intelligence equally. In practice, not everyone does, yet we’re pouring a lot of capex into it. There’s only one endgame.
In Crossing the Chasm terms, we’re at the very first stage. We only have early adopters. We have early innovators pushing in certain directions, like AI infrastructure and harnesses, even though we don’t really have the product yet.
It feels like a rock rolling downhill with a lot of potential energy. Unless there’s very strong pushback on this direction, which, as far as you and I know, has happened a few times... If you make this call to action — can we stop this, or can we slow down? No. That’s why I feel like it’s inevitable.
Irene Zhang: Do you see a different source of that pushback from what we might see in the US? How do you envision it, if it happens?
Richard Bian: It’s very hard to say. We’re setting out and don’t know what the next island will look like. My hunch is that we’ll see more and more pushback, but probably not right now. The penetration of AI applications is so low that it hasn’t really affected everyone’s life. There’s been a lot of discussion about replacing jobs, but it hasn’t reached the point where the masses really feel it. The moment that happens, a lot of voices will be raised, and we’ll start from there. It’s not going to be in one or two years.
Crowded Straits: Noise, Benchmarks, and How to Judge a Model
Irene Zhang: You said this has been the longest year of your life, and that there’s a sense you’re working alongside something that feels out of control, or where control is becoming negotiable. When you’re in the lab with the model team, do you feel that trade-off on a daily basis? Is there a trade-off?
Richard Bian: That’s a deep question, to some extent philosophical. When you’re on a boat heading somewhere unknown for the first time, the way there takes much, much longer than the way back, even though it’s the same journey. If you don’t know where you’re going, you lose your measure of time. That’s close to how working in this industry feels.
On one side, I firmly believe this is an open ocean with a lot of other sailors, and that as humans we’ll help each other. But you also have bottlenecks: straits, the Panama Canal, places where it’s just narrow. We have so many famous straits now, always showing up in the news. The moment you have a strait, you get acceleration and squeezing, and people feel, “My god, we have to get out of here.”
People perceive this as competition, like the third-base game. In reality, it’s not. It just happens to be crowded around this part of the strait.
On the S-curve, we’re climbing the intelligence levels very rapidly. On the other side, we don’t know where we’re heading, and the open-weight ecosystem is still under development. People see what each other is doing. You write things in the tech report and help each other. All of this generates a kind of relativity where it feels like there’s so much going on.
The moment you ship a model, it might be SOTA for one week. A week later, something else claims to be better, and you have to decode whether it really is, because there are so many ways of doing PR right now.
A couple of things matter in that environment. One is having dedicated, denoised conversations like this one. Two is having people in the industry who really care about the outcome.
It has been a very busy year for me, but I feel extremely motivated because you begin envisioning the future a little bit, and seeing the process makes you feel calm at times. Even when I feel FOMO or frustrated about the velocity — we shipped three models in the past five days — we know things are under control. We know the progression. That’s what keeps me motivated.
People ask me if I should enter the industry, and my answer is, why not? It’s a process, and it’s not about piggybacking. In open source, if you look at an existing software repo, you get a certain amount of information. If you look at all the change lists and releases before it, you get far more. The current snapshot is only a snapshot.
This is an era in which piggybackers won’t get much, because it’s the deltas that matter. You can only capture the deltas by playing the game, being observant, and being less focused on the outcome, because the outcome might be intermediate. Focus on the process, focus on the journey.
Irene Zhang: When you describe different straits, are you talking about different research and training hunches, different architectures, or different product directions?
Richard Bian: Everything. The strait metaphor explains why things look so narrow. First, people don’t know what they don’t know. People working on long-term projects essentially don’t exist in the current narrative. People are working on new architectures, and it takes time. They might go quiet for a whole year.
Think of when DeepSeek first came out. It was a blast. But the inference providers and model builders knew of that lab half a year earlier. I first heard about DeepSeek in July 2024. But they don’t exist until they become a sensation.
This kind of narrative generates all the noise in the channel, while the people running long projects are typically unseen. I’d say that’s the best thing that can happen to them.
Ten or twenty years ago, the bar for journalism was high. You needed to be really good at what you did to be a journalist. My dad is a journalist, and he takes a lot of pride in it. Nowadays, everyone can be their own self-media. Self-media can say whatever they want; they have their own audiences.
My first call to action is to stop stumbling into the buzzwords. Find the person you feel is wisest in your ecosystem. Find the best podcast, maybe this one. Follow them and listen through things.
The second reason the strait exists is that there’s too much heterogeneity, and it takes a certain level of understanding to see through it. I used to live in Singapore, and I really like chicken rice. I can taste that the chicken rice from this store is very different from that one. I bring a friend and say, “This chicken rice is really good.” When they’ve finished, they say, “It’s pretty similar to the last one.” Okay, never mind.
Right now, people judge models in very similar and mostly superficial ways. We lack evaluations, benchmarks, and real-world usage scenarios, because penetration isn’t there yet. An evaluator runs a 3D rendering test and says, “This model is very good at 3D rendering.” Great, that’s one way of judging. But when the results are bad, and you say, “This model sucks,” that’s not a fair statement. More likely, the model is good at something else, and that requires a dedicated evaluation or a dedicated person to look into it. They don’t exist yet.
In the early stages of a new technology, people don’t know what to look for. When you held the first iPhone, you wouldn’t have imagined ordering food on it. It takes time.
That’s why the ecosystem feels crowded. People don’t know how to evaluate, so they read each other’s reports. You see the industry flip, too. At one point, people said benchmarks were useless and overfitted. Now everyone shares their benchmarks.
It’s similar to the TOEFL and the SAT. Before you have a thorough heuristic, a standardized test helps one way or another. We don’t deny the usefulness of standardized tests. But whether doing well on one is necessary or sufficient to indicate a good model, we don’t know.
Another call to action is to be open-minded. How you use a model showcases your own capability. As a model lab, we hire people who know how to evaluate our models. If people can be strategic about evaluation and evaluate things differently, that’s a good sign.
The moment that happens, the strait widens. You hit the open ocean, and people start talking about different things, like “this model is really good at this.” We build domain-enhanced models because we realized our models are really good in certain domains, given our knowledge and specialties there. We also build pretty decent small models.
People say there’s this one model, which I won’t name, that’s pretty good for everything. Probably not. Would you run only one type of operating system on your Linux box, your enterprise box? No. You end up with FreeBSD, Ubuntu, Unix. To someone else, they all look the same, but a Linux expert knows they’re different, good for this and that.
We’re at the door of the next generation of products. Once those show up, people will stop debating whether a model is really good or really bad. They’ll say, “This model works really well in the particular harness I’m using, and that’s why I use it.”
Open Source Is a Product Strategy
Irene Zhang: In the interest of decoding noise — a lot of people are confused when they hear that someone does product work at an open lab. What does that mean? Is there a contradiction between product and open source?
Richard Bian: I wouldn’t say so. Open source is probably one of the most commonly used but misunderstood terms on the planet, and the most misunderstood part is “free.” Open source is free software, but free as in freedom, not free as in beer.
The point of open source is giving you the freedom to choose whatever you want to use. You can GGUF any of the open-weight models on Hugging Face. If you can host it and want to serve it, abiding by the license, you can. That’s free as in freedom.
Open source has three primary values, and they map onto the bases. Open source is really a third-base game, because it’s operations and governance at its best.
First, it helps technology. Everyone knows open source is the best way to collaborate among talent across the globe. If you build closed software, it will only be as good as the intelligence inside your company. If you open it up, you get input from everywhere. It almost feels like acquiring talent through the open ecosystem. Through that open competition, the software that’s left at the end of the day is much more polished and robust; it’s antifragile.
Second, open source is a very good go-to-market strategy. You can do less outbound and outreach, because people discover the software themselves and choose to host it. By the time they reach out for an official POC, they’ve probably already tried it. It helps discoverability, it helps self-hosting, and users can file issues.
That covers home base and second base, which sit close to third. But the first-base game is the product game, and what people typically neglect is that open source is a pretty good way of building product. That could be a podcast by itself, but the key point is that you can build an open product much faster and more efficiently than a closed-source one.
Take Airbyte and Grafana. Sorry, this gets a bit technical, but they raised rounds at an accelerated pace in the last technology hype cycle. Airbyte went from founding to Series C in about a year and a half, if I’m not mistaken, because it benefits from community-contributed adapters that make it work really well at getting data from one place to another.
You keep seeing very strong technologies like vLLM and SGLang, and before them, Ray and Spark. They begin as open source projects, mature over time, and people build mature products out of them. Databricks built a lot of Spark-related products, and the shelf life of that technology runs all the way into today’s Delta Lake and data lake discussion.
A good technology has a shelf life, and open source extends it so people can build great products on top. Look at Kubernetes: there are so many Kubernetes distributions, each built slightly differently for different enterprise workloads. That’s the beauty of open products.
People know open source helps with go-to-market. They know it helps with technology collaboration and engagement. But it also helps build a robust and diversified product ecosystem, and that’s the part people typically ignore.
When I was building this team for the Ling model, I called it “Model as Product.” It was a little ahead of its time. We have two engineers, one product person, and several operations specialists. I hired those two engineers because we wanted to build a harness, but the term “harness engineering” didn’t exist yet. It took us quite some time to think through what we were building. That was September last year, when I joined the team. Harness engineering became the term sometime earlier this year, if I’m not mistaken.
At the time, we had a very noble vision. We really liked Andrej Karpathy’s mental model that intelligence is spiky. If you use spiky intelligence as a product, you might sample the peak and have a really good experience: “This model is so great.” But you can also sample the valley: “This model is so stupid. Why can’t it even understand this?” Such a spiky experience is really bad. It’s not a product.
We’re still on the journey from technology to product. I’m a product person, and I really want to build a product, but I confess I’m not good enough to envision how the product should look. The best strategy is to play the game again and iterate.
History has many stories like this. Foursquare was a product from the mobile internet era that young people don’t know about. It was an iPhone or Android app that did one thing: check you in at your current location. Tens of thousands of other people check in at the same place, and everyone knows the location is accurate.
People might say that’s boring. What is this product even doing? It was really a product POC, not a product. But without Foursquare, there would be no Uber, no Meituan, none of the rest. You needed this geolocation product, or POC, or SDK, whatever you call it, to demonstrate that this could be really valuable.
It didn’t solve everything. With Foursquare alone, you couldn’t build Uber immediately, because the geolocation API was expensive on 2G internet. You needed the network to develop, and you needed geohash, a hashing algorithm that compresses the data load, to upload your car’s location every five seconds. Only after that could you build Uber.
This is an inspiration for what we’re doing. The model feels like a spiky balloon. We want to make sure people look at the spiky side of our model, so we build demos, guidebooks, and cookbooks that say, “Use it this way.” For the valleys, we use our best engineering effort to mitigate the experience, either by leading people away from using the model that way or by making the experience better. Later we learned that this is the harness.
A harness, in conjunction with models and tools, gives you an agent. An agent is already a form very close to what we called a product in the mobile internet era, similar to SaaS or to iPhone applications.
But are we there yet? Do we have a good agent that can be a day-to-day application on your phone? I don’t think so. We’re at the stage where you have 2,000 weather and stock apps in the App Store.
The Real Problem Is Gross Margin, Not Capex
Irene Zhang: Ok, the open model blossoming we’re seeing, especially among Chinese labs competing in a different realm from the American frontier labs, is a bet that AI plays out the way certain previous technologies did. Open source as a strategy incubates products, which eventually leads to viable business plans for robust companies. That’s how Foursquare led to Uber, and it’s the Databricks story.
What confuses me is one salient difference between open source AI and the genre-defining open source software you mentioned: AI is so capital-intensive. It is eating up the bottom lines of big companies around the world. Where does that bet lead in your projection?
Richard Bian: As with the sailing metaphor, building a ship is expensive. When the Portuguese and Spanish were doing their navigation, they needed sponsors — the emperors and empresses. Without them, you couldn’t do it.
Open source is neither necessary nor sufficient here. It provides options. Sometimes I say “open is the option.” It’s a strategy, and strategy is about what you do and what you don’t.
You can choose a closed strategy, and you see companies doing that well. Anthropic is a very respectable company. They do closed-source models really well.
On the other side, some people want to play the open game. If you wind back the clock, you see this story all the time. A closed-source incumbent is challenged by an open-source challenger, and you end up with a duopoly or a very prosperous market where everyone has their share.
They also motivate more people to join the ecosystem, and the ecosystem players make everything better, so you end up with a much better user experience. Not everyone can be the kernel. There are only so many kernels to choose from.
Capex is a separate topic. You needed to build data centers when you had the internet. You had to build out the phones and the hardware. Local governments built large phone factories while scratching their heads over whether we really needed such big volume. Now we all understand we needed much more than what we thought was big.
The capital expenditure of AI is really high, but I wouldn’t worry too much about the expenditure itself. What really concerns me is the gross margin.
We just came out of the last hype cycle. For SaaS and mobile internet, the business model was viable because the gross margin on software is probably 95%. Software is a reproducible, standardized product. You build it once and distribute it many times.
We don’t see the same thing in AI right now. The margin is really low. Thirty or forty percent is already pretty good on inference. Yet people have this perception that they’re entitled to use it for free, because “I give you my data, and my data is pretty valuable.” That’s probably wrong.
The capex challenge is twofold. One, there has to be money spent at the start. You need to build a Gigafactory in order to produce Teslas. That’s big capex. Two, is this really a sustainable and viable business? To me, that part is questionable.
One topic I think about day in, day out is what form a viable product takes at second base, which is about making money. You won’t be able to offer free tokens. Semantic routing can be a good way. As part of the product, you route traffic to the most viable models without degrading the experience, and you capture the consumer surplus associated with that.
Irene Zhang: Explain semantic routing for the uninitiated among us.
Richard Bian: Imagine semantic routing as a gateway where you use models in an “auto mode.”
If you care about your token expenditure, you have choices. You can specify frontier models for everything, which is expensive. Or you can run everything on a free model, at the risk of a bad experience in part of your workflow.
Auto mode says: trust us, because we know the models well and we integrate with them. When you say “hi,” we won’t use a frontier model for the inference. We’ll give you something really cheap. When you ask how to solve a difficult mathematical problem, we’ll invoke the frontier model.
That’s not 100% technically accurate, but allow me to explain it that way. This kind of auto experience is a good product experience. It might not be a product by itself, but it can be a critical feature of a viable product.
There are definitely ways of generating value. In a low-margin business, people will find ways to squeeze it out. We just have to agree that if you don’t pay for the product, you’re the product. That’s the metaphor we used for mobile internet.
The intelligence era is slightly different, though. If your data is not high-quality data, it won’t be useful at all. That’s a change of mentality. A lot of people need to rethink and relearn, so we can move on to discussing the next viable product.
Irene Zhang: When you say “a good product experience” in the context of an AI model, what are you thinking about?
Richard Bian: I love that question, because I don’t have a slide for it.
User experience is well-trodden ground. User-centered design and human-centered design and engineering have existed as specialties and education tracks for 20 or 30 years, and there’s a lot of merit in them. I have a UCD certificate from the University of Washington, and I really appreciate what I learned there.
The key aspects of user-centered design are not changing. You still have to care about discoverability: how does a user discover certain aspects of your product? Accessibility: can you make inclusive offerings that are easy to access? The happy path: can a user execute the whole workflow, the equivalent of the customer journey?
Then there’s disaster recovery. If I run into an error, do I see a prompt saying “you have this 0x-blah-blah error”? Or do I see, “I’m sorry you ran into this. Do this to reset your app, and you’ll end up back where you were. Don’t worry, you didn’t lose any data”? All of these remain extremely important for building a good AI-native product.
What’s new? One thing is personalization, which people talk about a lot. You now have an intelligent assistant that goes with you 24/7 and knows everything about you. It tailors your food offerings and knows your schedule exactly, so you won’t miss your next Uber before you even know you should be calling it.
The other is new discovery, which a lot of frontier labs are pursuing. The moment a model can crack difficult problems in, say, biochemistry, you get new software or new agents that chemistry experts can use to discover new drugs, build new analytical heuristics, help students think things through, and improve medical procedures.
I can only imagine what such a product would look like. But those are new products, possible only once you exceed a certain threshold of capability. When they show up, we’ll know exactly what their user experience is like. They’ll definitely be tailor-made for experts and learners. That’s my general hunch.
Ling 3.0: Tiny Models and a “PhD Program” for Finance and Health
Irene Zhang: This is a good place to bring in what Ant’s been working on. I hear there’s a new model.
Richard Bian: A couple of new models. Not one, not two, but three.
Irene Zhang: Let’s go through all of them.
Richard Bian: First, there are the Ant Ling models, Bailing in Chinese. It’s our model series, built by Inclusion AI of Ant Group. Inclusion AI is our lab’s name, and it states what we care about. We want our AGI journey to be inclusive. AGI should not be a scarce resource made for the selected few. It should be available to the average population so that everyone can benefit.
That’s why we call the lab Inclusion AI, and why we decided to open-weight all of our models. This is the early stage of the navigation, and it’s better to help the other ships, because we get a lot of help from the open ecosystem as well.
We want to be open by default and ecosystem-first, and we want to start small. There are a lot of open-weight models out there, so we keep asking ourselves what deltas we can provide to the ecosystem. We want to head in a direction and let people know, “Folks, this is safe ocean. There’s a likelihood this direction is right.”
The Ling series is now at 3.0. With 1.0, we began our journey. With the 2.0 series, in October last year, we went with reasoning and explored the boundaries of intelligence, so we chose to go big. We’re the lab that built the first one-trillion-parameter reasoning model and open-weighted it. That’s Ring-1T.
Then we asked: what do we do with such a big model? Direct serving is really expensive, because graphics cards are scarce and a model that size carries a lot of inference cost. Will people really benefit from spending so much? The answer then was that the industry probably wasn’t ready.
In the 2.5 and 2.6 series, we began rethinking the problem. We’d already been to one trillion parameters. Could we make it serviceable?
For the 3.0 series, we began with smaller models. Our new flash model is a 124-billion-parameter MoE with 5.1 billion active parameters. Flash models vary a lot right now, and ours is on the light side. The upside is very good token efficiency and token economics. If you go to Artificial Analysis and several other websites, you’ll see Ling 3.0 flash on the Pareto frontier. It’s actually beyond the frontier, because it’s such a token-efficient and cost-efficient model for day-to-day work.
We went further with Ling 3.0 tiny. The tiny model is a 7.9-billion-parameter MoE with only 1.3 billion active parameters. It’s very small. You can run inference on your MacBook, and the cookbook is already out.
You can even train it. We have a training framework called Arena, a lightweight version of the system we use internally to train the one-trillion-parameter model. If you’re a hobbyist, you can use it to train the tiny model on your MacBook.
Why do this? We realized teachers need to teach agentic RL, and most labs and universities don’t have enough GPUs. They either spend on APIs or use edge inference. We’re providing the option of doing training and homework on a MacBook, so teachers can build a new curriculum for students to learn reinforcement learning, agent building, etc.
Irene Zhang: A lot of folks outside computer science have trouble imagining what people do with these tiny models. Do you have examples of fun things people have built with yours?
Richard Bian: We only shipped the model a couple of weeks ago, though it feels like forever, so we’re still nurturing the developer ecosystem. But interesting things are coming back.
People run these smaller models on their old graphics cards or MacBooks and pretty much run their day-to-day life there. Your own machine has all your data, and you don’t want that data going anywhere. The moment you use a lot of APIs, others begin learning about your private data, which can be problematic. Running locally gives you an airtight experience: either there’s no internet, or nothing goes to the internet. You’re in control.
You can provide all your local files as context through a harness or agent, and run the model on your own device, so inference is essentially free. You can build a daily report of your life. You can build a personal assistant: “Tell me what I should eat tomorrow given my history,” or “I’m traveling to this place next. Give me full-fledged trip advice and arrange it.”
Everyone also has old documents, whether code, written documents, or diaries. The models are really good at summarizing and surfacing things. We see people summarizing their old files and relearning a lot from them.
I sometimes reread my old writing on open source, and every time I get new revelations. I also relisten to podcasts from two years ago. Very smart researchers came on and gave their opinions about the future. Two years later, those opinions are facts. Why did they think things would go a certain way? The reasons behind a prediction are interesting, and now we can decode them. Two, where they bet wrong, why? Why do even the smartest people on the planet bet wrong? What’s the delta? Three, if I’d known what they knew back then, what prediction would I have made? That’s how you get to know yourself.
You have all this old information you never had time to digest before you had the model. Aggregation and summary alone don’t give you new information. But condensing information, exposing the key points, and relearning with a human in the loop is a very interesting use.
If you’re a software engineer, I’d also recommend learning a bit about model training, and the best way to do that is on your own device. If you can find a GPU cluster with the best GPUs on the planet to support your training, good for you. Most people can’t.
Irene Zhang: What I’m hearing is that the advantage of tiny models, and of open source in general, is still privacy. Only with privacy will people be willing to provide the context needed to get the personal assistant to its maximum capability. That’s a good reminder for people just dipping their toes in. Let’s circle back to the other models. What else have you recently worked on?
Richard Bian: Flash and tiny are the models we shipped a couple of weeks ago. Here are the ones we just shipped.
First is Ling-3.0-flash-Fin, a finance domain-enhanced model. Second is Ling-3.0-flash-Santé. Santé is the French word for health, and it’s what we say when we toast. That’s the model we want people to use for their own personal health. Last but not least is Ling-3.0-flash-VL. Our language model finally has a vision-language version, and we open-sourced it today. The reason for VL is obvious: we want our models to have that capability, period.
Let’s spend some time on the other two. We call them domain-enhanced models because they’re post-trained on our open-weight backbones, like the flash model. We do full SFT, RL, and post-training with domain-enhanced data and domain-enhanced environments.
We use that name to differentiate them from the specialized models of the past. In 2024 and early 2025, constrained by compute and training heuristics, people often took their own data and tailor-made a model to serve their own business. That’s good, but those expert models have one liability: when general intelligence expands, they immediately become obsolete.
Inference cost is also getting cheaper, and by the laws of physics it will keep getting cheaper. The frontier models may stay expensive, but overall token costs will fall.
People have been saying general intelligence is the most important thing. That’s why in 2025 and early 2026, the Ling family built for general intelligence too. Once we saw diminishing marginal returns there, we went for token efficiency to make sure the models are serviceable over time.
Now we’ve discovered something else. We see a slowdown in general intelligence development. Not because we’ve reached the top of the mountain, but because most models we have now are good enough for day-to-day use, for co-work. If you want a model to process your documents, do some discovery, write a proposal, or even write code, I wouldn’t call those solved problems, but the models are good enough.
Once you have good-enough models, the natural instinct is to build products around them. That’s why so many harnesses showed up this year. For the audience’s sake, think of a harness as something you use directly without being exposed to the model, like Claude Code or Codex. We call them applications or products, though by my earlier definition they’re not real products.
But are they good enough for domain experts? If I’m a banker or an asset manager, can general models handle my day-to-day work? Do they know our heuristics? Do they know our tools? Do we have proper evaluations to tell whether a model is good enough? The answer to all of these is probably no.
There’s a very famous picture from Anthropic’s labor market index. It’s a circle divided into domain verticals, with one boundary showing human activity and a red boundary showing what current models cover. That picture is really powerful. It explains why Claude started with finance, health, design, and so on, and why people started with coding even before that. These are high-value domains. The moment you build something valuable there, it gets used immediately, and those industries will pay a fair price for what improves their efficiency.
We see the same thing. Plus, we don’t really have access to Claude for finance. Those are B2B offerings delivered only in certain geographies. We figured there could be an open option, so we decided to do domain-enhanced models.
If you ship one domain-enhanced model in finance, people get the general gut feeling that “that’s what Ant Group does. You do finance.” But no, we’re a technology company. Finance just happens to be something we’re pretty good at.
As a technology company, we care about many other verticals, such as health and medicine. That’s why we have the AQ product, Afu, which is tailored for health. In trying to build a good product there, we realized a domain-enhanced health model is also extremely helpful to our business.
With two domain-enhanced models, the message to the ecosystem is that you can build the third. Take our base model, add your own training, and build on top. General intelligence doesn’t drop, and on evaluations tailor-made for the domain, our models behave extremely well, in many cases at SOTA level.
If you’re an asset manager, the Fin model is going to be a really good choice. If you’re a health practitioner, try Santé. We’re curious to get feedback from the community. Santé is not open-weight yet; we’re in the process of driving that.
We do this because, to solve the real-world problems people face day in and day out, they need something better than general intelligence. General intelligence is like university or high school. It makes you a generally capable person, ready for the world. But to be useful at the frontier of a field, you need a PhD. The domain-enhanced models are what we’re building as the PhD program.
Irene Zhang: You described Ant as a technology company first, with finance and health as verticals where it happens to be strong. When it comes to specialized model training, how much is driven by these being high-margin industries where AI can deliver a lot of value? And how much is a flywheel effect, where Ant already has access to really good data from its existing products?
Richard Bian: One is the mission. Before Ant, I worked at Square, and before that Microsoft. My time at Square gave me a belief in helping small businesses. Square’s mission is economic empowerment, which is very similar to Ant’s mission of bringing small and beautiful changes to the world. I built the Fin model in part because it aligns with that common mission. I mean “mission-driven” sincerely, because it’s what Ant has been doing for the past ten or twenty years.
To help each individual, the most important thing is that they can get a model that’s easy to access, which means you have to build it, and open-weighting definitely helps. They also have to be able to use it sustainably. That’s why we’re offering the models free for a limited time through OpenRouter, other gateways, and partners like Novita and DeepInfra, so people can experience them.
It takes time to understand what a model can do. You don’t know a person on day one. You have to spend time with them to understand their value.
The other aspect is definitely strategy. You look at your strengths and weaknesses, your opportunities and threats, a very simple two-by-two. We realized we already have customers, channels, and demand. We understand our customers because we’ve been doing business there. The data is a byproduct of understanding our customers. We’re a very customer-first company, so that’s where we started.
Once you see the demand, you ask: should we build a harness, or should we make a model? We realized building a model can be really beneficial.
Handling something in the harness is like using tools, SOPs, or job training to learn it. When certain things become the norm, you can push them into post-training. A lot of what you could only learn ten years ago by working at the top companies has since become standard practice, and now it’s taught in universities. The same thing happens here.
There’s an intrinsic risk that a frontier model makes a jump in intelligence that renders your model obsolete. But that’s okay. Most of those models are extremely expensive, and we can retrain ours too. It’s a very volatile industry at the moment.
We feel comfortable with this direction because it pushes us to think through what kinds of customers and small businesses we’re serving. It’s what motivates me.
I also believe those directions are uncharted territory in the ocean. Somebody’s got to try it. Ant Group happens to be one of the best companies, and Ling happens to be the best model, to explore that territory. So far, the feedback and engagement from our partners have been good.
Irene Zhang: This is a good place to wrap. It’s been such a pleasure to have you on ChinaTalk and to hear how you’ve navigated this industry over such a long time. Any final thoughts?
Richard Bian: What I find motivating about talking with people like you is that the information channels right now are starved of content of mixed quality. I benefited personally from a global education and from living through my time in Singapore, Canada, the US, and China. That diversified cultural infusion helped me become a better person. We need avenues to make that helpful others, and sitting down on a podcast and talking things through is extremely effective.
Also, if you’re listening and haven’t tried open-weight models, I highly recommend downloading them from Hugging Face, following the cookbooks, and giving them a try. The future is already there. For better or for worse, we can’t rewind the clock. We all have to use models, and we all have to know different models. The more you know about models, the merrier. That’s what motivated me to build this team.
Last but not least, it’s a long journey. I personally dislike a lot of the narratives that say “the endgame will look like this” or “the only thing you should be doing right now is this.” I personally feel like none of us knows what’s going to be right. But as a practitioner, sometimes we know what’s wrong. So we feel comfortable giving our opinions. Opinions are just opinions.
Getting to know the industry yourself, talking to people, and getting diverse input will definitely help you. We’re here to support you and offer a different path in the Age of Discovery.
“Every time the hype cycle repeats , capex swarms in , which becomes a self fullfing prophecy for what we build next . This is why I feel it is inevitable “
Someone read Nick land and internalized this thesis about techno capital
This was an insightful podcast. Very informative. My only "but" is how poorly sound-edited it was. Did an Ai edit this without crossfades? It only needed crossfades! I had to stop listening and came here instead to read it.
But the content was top-notch. Love the access!