The whole internet is talking about Kimi K2 Thinking. Here’s my simple take: don’t get distracted by headlines like “Surpassing GPT-5.” What really matters is the feedback from frontline developers during real-world testing. In complex software engineering (SWE) tasks, its performance hasn’t reached the level of GPT-5 Codex. Some even pointed out that it missed build targets during code refactoring, leading to incomplete solutions. But what’s truly alarming isn’t its performance—it’s the cost. Rumor has it that the training cost was only $4.6 million, yet it achieved nearly 90% of SOTA on paper and about 75% in real-world capabilities. In other words, they’ve built a “good enough” model at an extremely low cost. This is a game-changer for the entire landscape. In the past, OpenAI and U.S. AI companies had the upper hand due to their fundraising abilities and compute scale—the essence of this model was “using money to build barriers.” But if an API call costs just $0.53, while the same task on Claude costs $5, those barriers start to crumble. This is a replay of *The Innovator’s Dilemma*: when the “small steel mills” produce steel at 90% of the quality of the big mills, the high-profit, high-cost giants are the first to collapse. Kimi K2 Thinking uses a 1T MoE architecture, activating only 32B parameters, with shockingly high cost efficiency. If this approach becomes mainstream, capital-intensive AI models might turn from an advantage into a burden. The real battlefield is no longer about who runs faster, but who can run fast enough with fewer resources. This isn’t just a “vibration”—it’s the prelude to an industry earthquake. — *Manzi Talks AI* #XueManzi #AI #KimiK2 #TheInnovatorsDilemma #ComputeWars