Kevin Ding

Kevin Ding’s core bet on AI’s second half is that ASI will not arrive solely as a single, highly centralized supermodel, but will also take “a service form—the form of Agent swarms.” His reasoning is that the problems and scenarios AI needs to solve “are infinite in number and will keep generating new scenarios,” while the scenarios themselves are not static; “using a model with finite parameters to generalize across infinite scenarios… is still extremely challenging, at least with the Transformer architecture.” He does not rule out the first path, but believes the service path “will definitely work”—demand is already there and continues to grow.

PyroMind evolved from RL as a Service to Auto RL-powered RSI (self-improvement) after discovering that “Service only solves half the problem.” RSI is gaining attention precisely because more Agents are being deployed: “If 1 person is carrying dozens of Agents in their daily work, they can’t possibly have the bandwidth to maintain the evolution of every Agent.” Agents therefore need to iterate on their own, bringing both training and reward into the loop.

Commercially, PyroMind has “initially achieved PMF”: a single large customer pays RMB1M–10M, 2 FDEs support 10-plus B2B customers, and the workload of horizontal replication is gradually declining. Koji described the financing as an angel round; Kevin confirmed that the company completed 1 round after incorporation, with Hillhouse, Baidu Ventures, BlueRun Ventures and Vertical among the investors. The team has 20-plus people. The strongest outcome metric is in quality inspection: based on approximately 10,000 samples, the false-positive rate fell from 23% to 8%.

The key abstraction for scaling is “stateless”: instead of a stateful environment coupled to customer context, PyroMind builds a stateless Auto RL pipeline. “I can’t package Customer A’s state into the product and then sell it to Customer B”—the main exchange with customers is data in and an updated model out. The competitive field splits accordingly: Apply Compute scales vertically inside leading enterprises and takes care of everything, while DeepMind-linked Trajectory is closer to PyroMind’s philosophy—focusing on training and scaling horizontally.

The verdict on the Harness route is that enterprise demand forms an impossible trinity of cost, speed and privacy: Harness can solve speed, but “privacy definitely cannot be solved,” while methods that leave model parameters untouched have a questionable ceiling. The PCB EDA case illustrates the point: a Base Model could not have encountered such samples during pretraining, so “no matter how much Harness work you do, it will struggle to reach the ideal state.”

The new Paradigm is a collaborative inference engine pairing a 4B Worker Model with any Base Model—open or closed source, with no need for gradients—lifting benchmark performance by about 10% and cutting costs by about 20% when lambda is small. The 4B model can run on-device on a Mac, making inference costs nearly zero. Its 3-stage training process covers difficulty classification, routing and GRPO. The next step is an unreleased privacy reward: sensitive tokens will be masked before the request is routed back to the Base Model. Unlike Thinking Machines’ Tinker, whose LoRA is tied to the Base Model, Paradigm is decoupled from the Base Model and can switch models.

PyroMind fears neither foundation models nor cloud vendors: foundation models are “good enough and not enough,” and in production “you cannot just connect a model API to the site and expect it to run by itself.” The stronger the foundation model, the less pressure on the Worker Model—PyroMind is standing on the shoulders of giants, not competing with them. Cloud vendors pursue too many objectives, so their eventual products cannot be as agile or specialized as PyroMind. Citing a Hugging Face report, Kevin noted that local models above 100B account for very few downloads, while models below 100B dominate, suggesting substantial demand that must be addressed through distributed systems.

🔗 Original source & video: AI’s Second Half Won’t Leave Just One Supermodel Standing | A Conversation with Kevin Ding, PyroMind Founder/CEO