ChinaTalk

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.

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Saif Khan makes his ChinaTalk debut. He is perhaps my favorite example of posting-to-policy that I’ve seen over the course of my time following Washington. Saif wrote some truly excellent deep dives into AI and semiconductors during his time at CSET, then went into the NSC and Commerce in the early-to-mid 2020s, across both the Biden and Trump II administrations, to help manifest the export control regulatory regime we have today. He is now out of government and launching the Center for Technology and Statecraft, an IFP-affiliated think tank, which is already producing excellent, grounded, technical, forward-looking work that will certainly shape many an AI policy discussion in the years to come.

We discuss:

Why Saif started a new think tank, and the value of deep, forward-looking policy research in an AI world moving at breakneck speed;

CTS’s landmark DUV report, which projects that stopping tool exports today could leave China producing roughly 100x fewer AI chips than the US by the mid-2030s, making DUV controls the US’s most consequential lever over the AI balance;

How rigorous research actually translates into government policy, and why think tank experience is valuable before working in government;

Robotics as the next frontier of US-China AI competition, and the security vs. diffusion mindsets shaping AI policy;

How AI is changing policy research itself, the challenge of bringing AI into government, and what AI-driven economic and social change could mean for humans.

Plus, why everyone should read Lu Xun, why AI might socialize like insects, and AI-nannying.
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I join Matt and Jerusalem for an unhinged conversation (most entertaining of the year?) about China, AI, corruption, politics, and various other things.

Check out their show The Argument, it's good.
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Kevin Roose, formerly of the New York Times and Hard Fork, joins ChinaTalk to discuss AGI Chronicles, his new history of OpenAI, Anthropic, DeepMind, and the people who believed in AGI long before it was cool and could make you trillions. Kevin is launching his own show, Machine Gods, later this October.

We discuss…

Why AGI was built on belief, not just money, GPUs, or talent, and how Google had every piece of the puzzle except the conviction.

What drives Sam, Dario, and Demis, some of the most competitive people alive.

Whether the Manhattan Project analogy holds, and why the government can’t just take the AI over from the scientists who built it.

Why Dario is the most hawkish lab leader on China, and what may have changed during his time at Baidu.

How Washington became the second most AI-pilled city in America, and why politicians fear the machine god for their own power.

Whether people actually hate AI, and what recursive self-improvement in 2027 could mean for everyone else.

Plus: Noam Shazeer’s “divine benevolence,” why Dario can’t lie to save his life, and Lenin singing the outtro music
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Everyone wants AI that wins wars. Very few people want to collect, label, store, and test the data it runs on.

Bharat Patel is currently Accenture’s AI and data lead for its defense portfolio, a former enlisted Navy sailor, and a veteran of the Army acquisition world, with experience at MITRE and across the Department of Defense. We’re here today to talk about why data is the hard part in making AI work to win wars for you — and what standards, integration processes, and governance you need to turn fun tech and cute little language models into things that can actually serve the warfighter.

We’re also going to talk about the dark side of data — how poisoning can happen, the enemy’s vote in the datasets you build to find things on the battlefield, and what it’s like to be a civil servant trying to make big programs happen inside the acquisition bureaucracy.

Our conversation covers:

Why data is the real bottleneck for military AI, why “AI-ready data” is a myth, and why autonomous tanks are further off than you might think,

Ukraine’s autonomous systems as a case study: how years of collecting, labeling, and learning from battlefield data created the foundation for autonomy,

The boring side of AI: how pipelines, standards, governance, and testing and evaluation determine whether models ever make it into the field,

The adversary’s AI toolbox — including camouflage, deception, electronic warfare, and poisoned data — and how to test against those threats.

The 4D chess game of building programs inside the Pentagon: navigating requirements, funding, acquisition rules, and the people who make or break a program.

Thank you to Accenture Federal Services for bringing us this episode.
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