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Interconnects

Interconnects

著者： Nathan Lambert
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Audio essays about the latest developments in AI and interviews with leading scientists in the field. Breaking the hype, understanding what's under the hood, and telling stories.

www.interconnects.aiInterconnects AI, LLC 科学
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  • Open models recap: more on Kimi K3, Qwen 3.8, Xi's WAIC speech, distillation, the open-closed gap, and what's next

    Open models recap: more on Kimi K3, Qwen 3.8, Xi's WAIC speech, distillation, the open-closed gap, and what's next
    2026/07/22
    Exciting news! My book trying to share post-training knowledge with the world is done and shipping soon. Order on Manning or Amazon. Thanks for the support. It’s currently the #1 AI book on Amazon :).Nathan and Florian sit down to discuss everything happening with open models. Following the Kimi K3 release last week, it feels like everything is accelerating — geopolitics of US v China, economics of open vs. closed models, security at the frontier of AI, and so on.Chapters:00:00 Welcome & context04:38 Living with / using Kimi K308:53 GLM 5.2’s continued role12:47 How are the Chinese models this good?17:41 Data, environments, and a tour of the Chinese labs19:47 Roundup of Chinese providers: Qwen, DeepSeek, MiniMax…24:08 The US open-model ecosystem30:25 Frontier vs. near-frontier, and the cybersecurity case against bans34:58 Distillation and the Ben Thompson debate44:12 Predictions and a frontier tier list48:36 Wrap-upListen on Apple Podcasts, Spotify, and where ever you get your podcasts. For other Interconnects interviews, go here.For more educational post-training videos, see the course I’m putting together.Transcript00:00:06 Nathan Lambert: Okay, welcome back to Interconnects. We’re doing our quarterly open model roundup, which is mostly us just making fun of or explaining, not making fun, why so many distillation takes are bad and understanding the state of where things stand. I think last Thursday was when Kimi K3 was released. I think we will see much much more in the near future. It seems pretty inevitable. Like over the weekend, Xi gave his speech where he directly committed to openness and open source as a strategy. It wasn’t a detailed layout state of affairs.Qwen announced their next big model is going to be open weight, which is a big change of things. I think there’s just so much to get into. I think Flo you kind of were already going off on some of the performance gap and distillation takes. So we could probably start there and then as I go I have a little bit a little list and we could always go through the topics and the blog that I wrote which all are very nuanced. So I think we have infinite to talk about. So continue rant kind.00:01:17 Florian Brand: Yeah, I think, or the biggest thing at every model release at least at every open model release is how much or how many months it is behind the closed frontier. Um and people love to put a definite uh definitive number onto this uh which is really really mudding because we have so many different benchmark providers these days and such uh so many different benchmarks as well that every site and I’m not innocent in that either um pulls up their favorite benchmarks to show that the current model or the newly released model is at the frontier which is then counted by the other side pulling up another benchmark and showing oh it’s actually a year behind or something. Um and it like a lot of it seemingly hinges on that question how many months we open models are behind.00:02:26 Nathan Lambert: Yeah. So I my provocation is that some of the benchmarks are actually reasonably correlated with what people are doing and this is agentic coding and agentic computer use tasks and some of the benchmarks are correlated with the long tail which is where I think Claude and GPT is so valuable. But if it’s it’s like what is the market for Claude Code and Codex right now and if it is software engineering then like the fact that the models are say a couple months behind on that can be a very, very big deal and then I suspect that this model will be okay disclaimer the model weights aren’t out yet supposedly on 20 July 27th and a lot of the discussion will impinge on the assumption that they come.But like people could post-train this model to very likely match Opus and GPT in many of these kind of niche domains that people want I think watching I mean we both have different views into the post-training open model industry, but there is a ton ton of excitement in progress on making these models like fine-tuned for specific high-value tasks and this has been historically done on a mix of like Qwen and GLM and GLM 5.2 really accelerated this and I I curious on the first person that puts out a blog post like we fine-tuned Kimi K3 on our task because I bet you could get big gains. I think even the you use Kimi K3 more than I do, but my hunch is that it would be a bit of a um rough edged post-training just by how big of a scale up it is and that normally means there’s a lot of performance that could still be extracted from it. No.00:04:03 Florian Brand: Yeah. Running, running, running and especially post-training that one will be super hard because you need like one node of B300s to just load the weights which is crazy in terms of scale. So will probably take some time and uh a lot of engineering I’ve heard to actually get this into a state where it’s fine-tunable. Um but people you want to talk about using the model like you actually ...
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    49 分
  • Kimi K3: The open-weights escalation

    Kimi K3: The open-weights escalation
    2026/07/20
    On Thursday July 16th, Moonshot AI released their latest flagship model Kimi K3. K3 is a 2.8T parameter MoE model which will have its weights released on July 27th. Much of this article follows as a reflection on the state of the ecosystem, under the assumption that Moonshot keeps their promise of the weights release date. This is a more extreme view of the equilibrium, and many of the results end up in a middle ground if the state of affairs is that China has similarly powerful, but closed models (i.e. K3 is never released). The key fact is that either the open-to-closed or American-to-Chinese model performance gap has been reduced from the debated 6-9 months to something shorter, say 3-5 months.From the release materials, it is clear that K3 is a true frontier model. It will be the closest open models have been to the frontier since DeepSeek R1. DeepSeek R1 was a different story. This was a Chinese lab being extremely quick to pivot to reasoning models and release one faster than many American companies. Kimi K3 an example of a Chinese lab executing on scaling the known areas: data, algorithms, architecture, tools, environments, etc. Kimi K3 comes in at #2 overall on the Vals AI index, #3 overall on Artificial Analysis’s Intelligence Index (only beaten by Claude Fable and GPT-5.6 Sol Max while being cheaper), #1 overall in Frontend Code Arena, and more impressive results. Moonshot AI is going toe to toe with Anthropic and OpenAI with far, far fewer resources.It is clearly the strongest open model ever released. It should be clear looking at this model that if adversarial distillation from the closed frontier models in the U.S. contributed, it is at most to a relatively small degree. AI observers who followed the distillation panic and came away with the wrong conclusion that Chinese AI labs are only producing good models due to IP theft are in for an awakening – that Chinese companies are extremely good at building models in the same way the leading American companies are. Moonshot AI is solving many of the same problems that folks at OpenAI or Anthropic are solving. I’m confident there will be more distillation discussion, and pressure, but the evidence is now out that Chinese companies can do more than just fast following.Meeting some of the core Kimi team on my trip to China, it was clear to me that they had incredible culture, some would say aura, and a freedom to express it – within the constraints of a GPU-limited environment. Where building models is so much of a scaling game, much of the ability to build a good model still comes down individual execution, motivation, and expression. Having visited them, this result is less surprising. Having visited many AI companies, very few have a culture that you can immediately pick up like this.At the same time, China’s AI adoption trends started later than those in the U.S. So, while all the Chinese labs have way less compute than their counterparts in the U.S., more of it can certainly go to training. When I joked around about how much compute an average researcher at OpenAI could have – say a few thousand H100 equivalent machines – the researchers at Kimi were shocked. The org chart and approach to building the Kimi models surely reflect this, but it is difficult to tease out what this looks like without substantial proprietary information.The state of affairs on peak model performance is roughly as follows:* Anthropic – Claude Fable 5* OpenAI – GPT 5.6 Sol* Moonshot AI – Kimi K3 (open weights*)* SpaceXAI – Grok 4.5* Zhipu (Z.ai) – GLM 5.2 (open weights)* Meta – Muse Spark 1.1* DeepMind – Gemini Flash 3.5* Alibaba – Qwen 3.7 Max (3.8 announced, also to be open-weights, when writing)It is astonishing to see DeepMind, and some of the other American giants this low. In many ways, the X AI team deserves more credit. A visual summary from Artificial Analysis is below:This release and other recent events have caused a major change in direction for the most likely outcomes in the balance between open and closed models. I’ll unpack them individually.In many ways, it feels like the start of a new era. An era with much more competition, but also a much higher need for coordination, as we rollout incredibly powerful technologies around the world.1. China’s recommits to open-source AI – showing a different read on near-term risksMany people started following China’s AI scene relatively recently, so they can reach the conclusion that releasing models openly is their core strategy. In fact, I think most labs have a core strategy far closer to Anthropic or OpenAI – build the best intelligence possible. Having followed and engaged with the Chinese labs for years now, the best explanation for their original turn to releasing their models openly is practicality. They needed to release the models openly to get adoption, attention, and feedback (especially in the high-value, Bay Area market).For a long time, there had been very ...
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    20 分
  • GLM-5.2 is the step change for open agents

    GLM-5.2 is the step change for open agents
    2026/06/22
    Housekeeping: Following my “State of the blog” post last week, noting a slight increase in paid features, it’s a good time to remind folks that I offer group subscriptions with larger discounts proportional to the number of seats. I also released a new paper today on open RL recipes for terminal agents, read more here.A bit over a week ago, when the AI world was still reeling from the shocking export restriction, and effective banning, of Claude Fable 5, Z.ai released their latest model, GLM-5.2. This model was rolled out unusually on a Saturday, June 13th, to GLM Coding Plan members. This is an unusual release practice, normally when an AI model is released on a weekend it’s for a weird reason (most famously, Llama 4). In this case, it seemed like Z.ai was excited to capitalize on the zeitgeist of “Anthropic being anti open-science” with their silent safeguards on AI researchers. For the past year or two, the Chinese open-weight labs have taken every opportunity they have for easy marketing wins like this.GLM-5.2, in a common naming convention across the industry, looked potentially like an incremental update following the popular GLM-5.1 model. At this point, Moonshot AI, makers of the Kimi models, and Z.ai, makers of the GLM models, have consolidated the top of the reputational market with the most beloved open-weight models among AI researchers. What unfolded is a common lesson in tracking AI models that often minor version numbers can have AI models crossing meaningful user experience thresholds. A small change in benchmarks and training can open a wide range of new use-cases.What has followed is a slow, groundswell of hype for GLM-5.2. The official, MIT-licensed model weights and release blog dropped three days after the initial rollout, on June 16th. One could ramble many technical details, such as the strong benchmark scores, the very popular RL framework that Z.ai uses (SLIME), the recommendation of always using the model on Max thinking effort, and so on, but the initial release blogs usually aren’t the thing to focus on. You can wait and read the ecosystem reaction to know if it’s the real deal. Benchmarks are half dead these days, anyways.What followed on the 16th was a slew of community benchmarks showing better-than-expected results for GLM-5.2. Arena’s agent leaderboard had it as the only open model mixing it up with OpenAI and Anthropic’s latest models (notably matching Opus 4.8’s no-thinking effort to GLM-5.2’s max mode). This is one of many evals GLM-5.2 is crushing Gemini on, but that’s a topic for another time. A benchmark that has mixed perception in the community (particularly among actual designers), Design Arena even had GLM-5.2 besting Claude Fable itself — the recently banned hype machine!Pretty much everyone I respect among the AI commentariat and researcher class has praised the model after using it personally. Such a focal point of discussion among the community has only been so clear with an open model release once before — DeepSeek R1. This is not a comparison I make lightly, and when I compared Kimi K2’s release to a “DeepSeek Moment,” GLM-5.2 has well exceeded that. What made Kimi K2 impressive was that big steps in open model performance could seemingly come from anywhere in China. The step that GLM-5.2 has taken is more of a one way door for AI progress.Anthropic’s record revenue growth rate on the back of Claude Code is heavily driven by being the best model, and the only model that can really do this. GLM-5.2 is the first of many (coming soon) open weight models to offer credible alternatives. The parallel is very clear, to when DeepSeek R1 showed that open-weight labs, with far fewer resources, could also replicate the chain-of-thought reasoning models that OpenAI championed with o1. As AI systems get more complex and far more expensive to build, with tools, integrated harnesses, and scaled model weights, it was not a given that this GLM-5.2 moment would happen at all.The key point is that GLM-5.2 is the open weight model that feels right in coding harnesses as a general agent. It’s the first one. I was personally overdue in trying some of the recent peer models, such as Kimi K2.7 or GLM-5.1, but the hype was too much for me to ignore. I put it to work helping make content for my post-training course with Fireworks’ API in Claude Code (setting this up was very easy). There were some minor knife cuts, such as the Claude Code harness / my repo documentation trying to send images to the model, which would brick Fireworks API for the session — forcing a manual context clear. Overall, the model capabilities immediately felt right, and I still have some tinkering to do in which harness and inference provider to use. For more hype, you can sample the Z.ai founder telling Elon that “open-weight Fable capabilities will be here sooner than Q1 2027,” the CEO of Vercel saying “Genuinely impressed, almost shocked, at how good ...
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    9 分
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