Why a Chinese AI Lab’s Product Lead Thinks AGI is “Inevitable” | Richard Bian

Richard Bian (0:00)

I wouldn't call this under control. So my take is pretty clear. I would say AGI is going to happen. Can we stop this or can we slow down?

No, we're kind of already going down the downward spiral of what the sci-fi movie is.

Irene (0:13)

Do you think there's a bit of a contradiction between product and open source?

Richard Bian (0:18)

I personally feel like none of us knows what's going to be right. But as a practitioner, sometimes we know what's wrong.

You know, like if you don't pay for the product, you're the product. For mobile internet, you're actually having a business model viable because the gross margins of building a software is probably 95 percent. We don't see the same thing happens in AI right now.

Irene (0:39)

Hi everyone, it's Irene reporting for duty today and I'm really excited to have a really special guest. Richard Succumbian is the head of product operations for Ant Ling, the foundational LLM at Ant.

If you don't know what Ant is, they make most famously 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 successfully scaled their open source initiative into a really dedicated team. He's lived and worked in Singapore, Canada, the US, and China, and he just loves to build products. Richard, welcome to ChinaTalk.

Richard Bian (1:19)

Glad to be here. Thanks for having me.

Irene (1:20)

Thank you so much. We're very, very thrilled. So I wanted to start today not with Ant, though we'll get to there, but with the recent Hugging Face incident. So if some of our listeners haven't caught up on this yet, basically back in July, there was a swarm of OpenAI's research agents that was employed for research purposes that ended up hacking into Hugging Face to look for the answers to tasks in the benchmark. This was a really serious security incident. METER, Rebel Research, and OpenAI itself all released detailed incident reports. And interestingly, while they were trying to reconstruct the incident, Hugging Face realized that Frontier Models wouldn't help them process their logs because it triggered their cybersecurity-related guardrails. So in the end, Hugging Face actually deployed an open model by the Chinese company ZitaoAI locally to process those logs.

So 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 recently?

Richard Bian (2:26)

Sure.

Well, I mean, first thing first, thanks so much for having me here. This is a very long overdue conversation. I know that Ray and I have been talking about having this conversation because we both care a lot about Frontier Lab ecosystem, open ecosystem, and the development of the model labs, especially the model labs in China overall. Yeah. So this particularly, as we call incident, is a very, in my opinion, is something you will end up seeing regardless sooner or later, one way or another, while we're working on something human beings don't fully understand. I use this metaphor a lot. I feel the current AGI initiative, or as we call it, AGI initiative journey, it feels like the age of discovery, or as a great navigation back in the 15s, when the Portuguese and Spanish, they're sailing out to find their new land, and they're going out to find India. So when you're actually heading out, you build a ship, you go to the ocean, which you have no clue whatsoever what you'll be facing.

I kind of feel models are essentially developing that way. We are decoding, or to some extent, I really like using the term discoverability or discovery.

So we are training something, because we're essentially discovering the intelligence of this creation that we all make. But at the same time, we don't understand what's going on there. So I would say first thing first, this incident actually demonstrates we're making some significant progress. Almost feels like you're hitting a land, or you're hitting a rock, a new sinker ship, but you find something. And so this is expected. We find issues, we deal with them. That's what we do in science discovery. That's what we do in engineering. We solve problems. On the other side, the second point I would love to make is, when we actually use the word ecosystem, open-source folks use the ecosystem word a lot. But sometimes people use it without understanding it. The original word of ecosystem comes from multiple originations. One of them is referencing to the real life ecosystem. For example, in Amazon, right? So in the Amazon forest, they will have this kind of ecosystems, which are flourishing like big giant trees, but because it covers the light, and henceforth, you will not having much of the plants on the ground. So this is essentially ecosystem. The most important part of ecosystem as a keyword is diversity.