This post is a personal reflection penned by a Kimi team member shortly after the launch of Kimi K2. I found the author’s insights genuinely thought-provoking. The original Chinese version is here—feel free to read it in full (and of course you can use Kimi K2 as your translator). Here’s my own distilled summary of the main points:

• Beyond chatbots: Kimi K2 experiments with an “artifact-first” interaction model that has the AI immediately build interactive front-end deliverables—PPT-like pages, diagrams, even mini-games—rather than simply returning markdown text.

• Tool use, minus the pain: Instead of wiring countless third-party tools into RL training, the team awakened latent API knowledge inside the model by auto-generating huge, diverse tool-call datasets through multi-agent self-play.

• What makes an agentic model: A minimal loop—think, choose tools, observe results, iterate—can be learned from synthetic trajectories. Today’s agent abilities are early-stage; the next pre-training wave still holds plenty of upside.

• Why open source: (1) Buzz and reputation, (2) community contributions like MLX ports and 4-bit quantization within 24 h, (3) open weights prohibit “hacky” hidden pipelines, forcing genuinely strong, general models—exactly what an AGI-oriented startup needs.

• Marketing controversies & competition: After halting ads, Kimi nearly vanished from app-store search, yet refused to resume spending. DeepSeek-R1’s viral rise proved that raw model quality markets itself and validates the “foundation-model-first” path.

• Road ahead: All resources now converge on core algorithms and K2 (with hush-hush projects beyond). K2 still has many flaws; the author is already impatient for K3.

From the entire blog, this is the paragraph I loved the most:

A while ago, ‘Agent’ products were all the rage. I kept hearing people say that Kimi shouldn’t compete on large models and should focus on Agents instead. Let me be clear: the vast majority of Agent products are nothing without Claude behind them. Windsurf getting cut off by Claude only reinforces this fact. In 2025, the ceiling of intelligence is still set entirely by the underlying model. For a company whose goal is AGI, if we don’t keep pushing that ceiling higher, I won’t stay here a single extra day.

Chasing AGI is an extremely narrow, perilous bridge—there’s no room for distraction or hesitation. Your pursuit might not succeed, but hesitation will certainly fail. At the BAAI Conference in June 2024 I heard Dr. Kai-Fu Lee casually remark, ‘As an investor, I care about the ROI of AI applications.’ In that moment I knew the company he founded wouldn’t last long.