One post tagged with "podcast"

Originally published on Bilibili — Content in Chinese with English subtitles available

I just finished listening to a deeply insightful podcast with Yang Zhilin, the founder and CEO of Moonshot AI, the company behind the Kimi chatbot. I had to sit back and process it. In a field that moves at a bewildering pace—where an "AI day" truly feels like a human year—Yang’s perspective cut through the noise. He framed his team's work not as a race to a finish line, but as an ascent up a vast, unknown, and perhaps infinite mountain.

This metaphor, which he returned to throughout the conversation, has fundamentally reshaped how I think about the future of artificial intelligence. A year ago, he explained, the journey felt like standing at the base of this mountain. Today, after the launch of their Kimi K2 model, they've climbed higher. The path below is clearer, but the view above remains unchanged: endless, unknown peaks stretching into the clouds.

They've made progress that would have been unimaginable just two years ago. As Yang pointed out, models that once struggled with basic writing can now execute complex coding tasks over several hours. Yet, he emphasized that the summit feels no closer. For every problem solved, the solution reveals a new, more complex set of challenges. This constant cycle of discovery is the core of their philosophy.

It’s a way of thinking he borrows from David Deutsch's book, The Beginning of Infinity, which argues two core principles: problems are inevitable, and problems are solvable. This, Yang believes, is the engine of progress. We solve for one capability, only to be confronted by a new, more intricate frontier. And in this endless climb, the destination we call "AGI" isn't a single peak to be conquered, but a direction of travel. The journey itself is the point.

The Two New Paradigms: The Brain in a Vat vs. The Worldly Agent​

The most powerful part of the discussion was Yang’s breakdown of the two new paradigms that are redefining the frontier of AI. He explained that the biggest leaps in capability now come from "test-time scaling"—using far more computation not when training a model, but when it's actively solving a problem. He described two distinct paths for this.

The first he analogized to a "brain in a vat."

Imagine a brain in a tank, totally disconnected from the world. It can’t act, only think. This, he explained, is like the new generation of powerful reasoning models. They solve problems through a process of intense, self-contained introspection. The model proposes a hypothesis, internally reflects on it, critiques it, and generates a new one, repeating this cycle until it arrives at a solution. It's a method of pure, deep thought, allowing the model to explore a problem's solution space without any external interaction.

The second path is the complete opposite: taking the brain out of the vat. This is the paradigm of the AI agent.

An agent doesn't just think; it acts. It interacts with the world through tools—running code, browsing the web, calling APIs. As Yang described it, an agent’s process is an iterative dialogue with its environment. It takes an action, observes the feedback, and uses that new information to decide its next move. This mirrors how humans solve complex problems: not through pure, isolated thought, but through a loop of action, learning, and reaction.

These two approaches, the introspective reasoner and the interactive agent, are what enable an AI to move beyond single-shot answers and take on tasks that last for hours. It’s a monumental shift from building a machine that talks to building one that does.

The Story of K2: Bets, Bugs, and Breakthroughs​

Where the conversation became truly fascinating was when Yang pulled back the curtain on the creation of Kimi K2, revealing how these high-level philosophies translated into hard engineering decisions and unexpected crises. The entire project was a series of deliberate, often contrarian, bets.

The first was a fundamental shift in their R&D focus away from supervised fine-tuning (SFT) and toward reinforcement learning (RL). This was their commitment to the agentic paradigm, a move that reshaped their internal teams and technical infrastructure long before the K2 training began.

Their primary goal, he explained, was to build the best possible base model. This led them to confront the industry’s biggest bottleneck: the scarcity of high-quality data. Their solution was radical efficiency. Yang detailed a particularly bold bet here: moving away from the "ADAM" optimizer, the algorithm that has been the unchallenged standard for training large models for a decade. Instead, they poured resources into scaling a novel optimizer called MeiON. The payoff was huge. He claimed it gave them a two-fold efficiency gain, meaning their model could extract the equivalent of two tokens' worth of intelligence for every one token it processed. In a data-constrained world, this is a superpower.

But the climb wasn't smooth. Yang shared a gripping story about an unexpected crisis during the massive K2 training run. They encountered a "max logit explosion"—a form of technical instability where the model's internal calculations spiral out of control, threatening the entire multi-million dollar training process. This was a "bug" that, crucially, never appeared in their smaller-scale tests; it was an emergent problem of scale, a perfect example of the unknown dangers that appear high up on the mountain. The team had to react in real-time, diagnosing the issue and developing a novel "clipping" method to stabilize the training and save the model.

This specific, in-the-trenches story highlighted a key theme for me: you can't map the entire mountain from the base. You have to climb and solve the problems that only appear at altitude. While the final training run for K2 might have been a matter of months, Yang noted that the R&D for its core technologies, like scaling MeiON and developing the agentic data, was a patient, year-long process. It was a powerful reminder that these breakthroughs are not sudden flashes of insight, but the result of sustained, long-term effort.

The Mountain is the Meaning​

Listening to Yang, I was struck by his calm, persistent focus. In a world of hype cycles and overnight sensations, his philosophy is about long-term accumulation of knowledge—becoming a "friend of time." When asked about commercialization, his answer was clear: the biggest variable is still the raw intelligence of the model. As capabilities cross new thresholds, the commercial value follows. The primary task is to keep climbing.

AI, in his view, is the ultimate amplifier for human civilization, a meta-science that can accelerate discovery in every other field. The risks are real and must be managed with care, but to stop climbing would be to abandon the pursuit of knowledge itself.

The biggest lesson I took away is that the frontier of AI is not a fixed destination but a dynamic, ever-expanding process. The journey is an infinite one, where each new peak scaled reveals a dozen more. The most exciting part is not the dream of a final summit, but the endless, fascinating, and profoundly important climb itself.

Originally published on Bilibili — Content in Chinese with English subtitles available