Kimi

includes Kimi K2, Kimi K2.5, Kimi K1.5, Kimi 2.5, Kimi K2.6, Kimi k 2

4 statements across 3 episodes · 1 bullish · 1 bearish · 3 people on the record · first statement Feb 24, 2026 by Doug O'Laughlin · said 57 times in 18 episodes since 2025 · across every show →

Mentions by year, the whole family

brought up most by Philip Kiely (10), Mark Bissell (7), Doug O'Laughlin (7), Kyle Kranen (6), Shawn Wang (4), Elie Bakouch (4), Alessio Fanelli (3), Dylan Patel (2)

tap a year for its mentions
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2026 48 mentions in 13 episodes 4 per episode
2025 9 mentions in 5 episodes 2 per episode

every mention, scene by scene, with the transcript →

Everything said about Kimi, oldest first

Feb 24, 2026 neutral
Assertion Contradicted
O'Laughlin: Running Kimi agent swarms requires 16 Nvidia H100 nodes
“To just run the swarm, I think it's like a 16 node of H-one hundreds.”
Doug O'Laughlin Feb 24, 2026 ▶ 35:31 Claude Code for Finance + The Global Memory Shortage: Doug O'Laughlin, SemiAnalysis
Jun 21, 2026 negative
Opinion
Malde: Western open-source AI lags Chinese models at trillion-parameter scale
“I think America or the Western world has some work to do still. Like obviously having one trillion parameter models like Kimi or like an amazing models like GLM and DeepSeq, I don't think we're quite there yet for that size of model.”
Ronak Malde Jun 21, 2026 ▶ 14:41 ⚡️Every product of the future will be a living system — Ronak Malde, Trajectory.ai
Aug 3, 2026 positive
Assertion Partly supported
Baseten's vision-retrofitted GLM-5.2 scored 56% on MMLU Pro without text degradation
“It's not, you know, it got to a 56% on MMLU Pro, I think, so not, not quite Frontier, but if you're running this model, you haven't suffered any loss on your GLM-Five-II quality.”
Philip Kiely Aug 3, 2026 ▶ 19:22 Next 100x in AI: Inference, Networking, & Self-Optimizing Models — Philip Kiely & Ali Taha, Baseten
Aug 3, 2026
Assertion Supported
Fitting a 2.8-trillion parameter model on one node requires eight GB300s
“You need GB 300 to fit it on a single node. It's simple math. NVFP four, 2.8 trillion parameters 1.4 terabytes. The GB 300 have 288 gigabytes each. So across eight of those you have enough room For the model”
Philip Kiely Aug 3, 2026 ▶ 1:11:37 Next 100x in AI: Inference, Networking, & Self-Optimizing Models — Philip Kiely & Ali Taha, Baseten
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