Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model. 🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal. 🔹 Full-project coding: games in Unreal, Unity & Godot. 🔹 Excels at parametric 3D modeling in Blender & CAD. 🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%. 🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks. Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API. Open weights by Nov 4, 2026. 🔗 API: platform.kimi.ai 🔗 Tech blog: kimi.ai/blog/kimi-k3-1-pro

There are going to be trillions of dollars of startups started out of Dogpatch Avalon.

Did not expect to be the top app on iOS for India at the peak of sale season.

Oolka will exceed 2 trillion monthly input token consumption this month. We see this getting north of 50 trillion monthly runrate by 2027. Don’t write off India AI story yet.

The smartest Indians continue to come to build in the US for the simple reason that the economic incentives are far far better. Perplexity ($20B), Skild ($14B+), Baseten ($13B), Together ($8B), Glean ($8B), Modal ($5B), Rubrik ($25B), Harness ($5B), Blacksmith, Parallel, Greptile, CodeRabbit, Composio, Distyl ($2B), Resolve, Luma, Cartesia, Wispr, dMatrix, Arize and many more - all started by at least one Indian-born founder. The US is a far easier to raise capital in with far less dilution. Most Indian companies are switching where they're domiciled to get access to the US capital markets. Every single Indian venture firm is trying very hard to invest in the US or risk not participating in the AI race (ironically, the management fees of venture firms are so high in India relatively that this is the one group that net of carry actually has incentive to be in India because they take home well north of a living wage whether or not funds perform do) While Zomato ($35B now, $8B at IPO) might be the biggest outcome in Indian venture, Cursor ($60B), Reflection (potentially $20B+), World Labs ($8B), OpenRouter ($8B), HuggingFace ($12B) and of course the $1T+ big labs quickly make those outcomes seem small and happen on a much smaller time scale, with much higher IRR. Engineers at these startups regularly get paid $1M/yr, and make $10M+ on exit. Even in BigTech, it is trivial to pull up levels fyi and see that if you're L6/L7+ with ~10 years of experience, you're making $1M/yr there too. And of course, we all know the slew of Indian CEOs of companies here, not to mention a huge bench of lesser known executives across the board in public and private companies. I say all this because every time there is a slight immigration hiccup in the US, I hear the online pleas to "build in India", I struggle to understand why most engineers, academics or founders would do that net of desire to be close to family, failure to obtain visa and devout patriotism. This is not a superiority thing or a US v India thing. The economic incentives at least for technologists to being in the US outweigh India by at least an order of magnitude.

Nvidia - making chips Google - making chips Meta - making chips Microsoft - making chips Amazon - making chips OpenAI - making chips Anthropic - making chips Alibaba - making chips Everyone’s pushing to vertically integrate chip design to reduce reliance on Nvidia. Customers are working to become competitors. Most aren’t immediate threats yet, but they’re all moving quick Only makes sense for Jensen to compete back

focus, focus and more focus

Inference is the second way to raise a massive round right now. After the labs, you can still justify massive rounds by pointing at data centers and compute, which means more inference companies keep getting funded even as the underlying product stays extremely easy to switch away from. On the flip side, many neolabs are also renting out their own compute, adding yet another source of supply into the same market. Companies that tried to verticalize around batch workloads and post-training are now pivoting back to "pure inference" and joining the same undercutting cycle. The result is a permanent reverse auction: providers undercut each other over and over: half off Fireworks, half off Baseten, 10% cheaper GLM or Kimi—and every new entrant or neolab offering capacity just accelerates the cheaper pricing At the same time, app companies have so much choice right now that they can bake off and compete prices down extremely hard. They move millions of run-rate spend overnight whenever a meaningfully cheaper option appears, because the technical switching cost is essentially "one line of code". That combination: easy capital, extra compute from neolabs, low stickiness, and continuous price pressure makes inference one of the less defensible layers in the stack even as it continues to attract large valuations. However, if you are a smart team of infra people, it is the easiest way to raise 100M seed after a neolab Researchers: neolab <> engineers: inference