『Interconnects』のカバーアート

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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  • Podcast: Kimi K3.1 Pro, the new cyber reality, and Muse musings

    Podcast: Kimi K3.1 Pro, the new cyber reality, and Muse musings
    2026/10/25
    We — Nathan and Florian — wanted to discuss all the messy trade-offs of a busy week in AI and ask all the pressing questions like:* Why did Kimi K3.1 Pro have a mixed reaction with such incredible benchmarks? Will it be adopted?* Were we wrong about the risks of open-weight models and cybersecurity?* Why do cyber experts discuss recent events so differently than AI experts?* What comes next in the few months? What will change our minds?We will write more about this subject, but wanted to bring you behind the curtain as we aspire to quickly learn more about the cybersecurity industry and trends across the last few years that have been leading us today. We conclude the podcast with some lighter topics, from Muse-maxing to excitement in still hiring junior technical talent. Please send us questions and topics, as we plan to record more of these podcasts going forward.Listen on Apple Podcasts, Spotify, and wherever you get your podcasts. For other Interconnects podcasts, go here. YouTube ↓Chapters* 00:00 Kimi K3.1 Reaction* 08:15 Kimi’s open-weight cyber principles* 11:23 You can hack everything era* 15:54 PHP2Shell, LiteLLM, and the new internet* 24:00 Abliterated APIs, safeguards, and trusted access* 31:41 Anthropic not hiring juniors employees* 36:16 Florian’s Muse addiction* 45:30 Wrap-upLinks we discuss* My thread with Dean Ball about the “boy who cried wolf” story of the frontier labs* Kimi K3.1 Pro announcement blog* Kimi K3.1 Pro on Artificial Analysis Intelligence Index* Kimi K3.1 on Artificial Analysis defensive cyber benchmarks* Program Bench* Open-weight Mythos would’ve been fine take* PHP “hasn’t caused a disaster yet” update* Abliteration.ai serving abliterated models API* Morph serving abliterated DeepSeek via API* Joshua Saxe’s Substack on AI and cybersecurity* Azeem Azhar, “The internet’s Wuhan moment”* Anthropic, “How employment is changing at Anthropic”* LiteLLM security incident reportTranscript00:00:00 Nathan Lambert: We are back with another Interconnects podcast. We are with Florian, eval guy, recently moved to SF. Florian, is Kimi K3.1 Pro, name is a mouthful, going to live up to the benchmark scores, for example on Artificial Analysis, or where is it going to land?00:00:17 Florian Brand: In the past, the Kimi models always lived up to the expectations, but this release is kind of weird compared to the others. I think it is also partially because they are becoming more professional, and the previous model drops were kind of out of nowhere. This time, which also got some criticism, they launched a bigger social media campaign, which allegedly also involved paying people to post about the model without disclosing.00:00:56 Nathan Lambert: I think a lot of companies do this and tell you not to mention it, but it’s a mess. It’s bad form. No company should do it. Essentially, one person tweeted a screenshot of the press instructions for Kimi that said, “Don’t say this is a paid promotion.” And to my knowledge, no one has corroborated it after that.00:01:15 Florian Brand: Yeah, and there are right now a few of these model launches, including by American companies, that are just very obviously astroturfed. We have seen launches where it’s thousands of retweets and a few hundred likes, because it works so well to get the attention. Some of them disclose that it is a paid ad while others don’t. But in general, we have seen the professionalization of model launches over time, and pretty quickly. A year ago, everyone just dropped the model, then patched vLLM afterwards, so no one was actually able to run any of the open models for a while. Now you get all the inference frameworks on day one, you get all the inference providers on day one. Or, which is also what we see more and more, the model creator has a few weeks of exclusive access before they release the weights. And that’s also what’s happening with K3.1 Pro. I think it’s two weeks of exclusive access before they release the model weights.00:02:38 Nathan Lambert: They said the weights would come out by November 4th. And the headline things... I don’t know if I’m going to be able to share screen. I don’t have this set up on this computer. But the headline is this Artificial Analysis number where it’s ahead of GPT-6 Astra. And then if you look at the Kimi K3.1 blog, they have their headline numbers, which are really strong, and then they also have some numbers later on which seem a bit more jagged. I think it was Terminal-Bench Science, which is 20 points behind GPT-6 Astra. I think there were a few others, like ProgramBench. Do you know Terminal-Bench Science and ProgramBench? Are any of these things that people think are important benchmarks for frontier models? Or how do you explain the fact that on Artificial Analysis the model is top, what, three, four? It’s the two latest Claude 5.5 models, Muse Wonder Full, and then Kimi K3.1, which looks crazy. It just looks like ...
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    47 分
  • Meta shakes up the frontiers, open and closed

    Meta shakes up the frontiers, open and closed
    2026/10/17
    This has been a week where the balance of American open-weight models took a step change back toward the frontier. Meta took the lead of this charge with Muse Spark Open, which is now the strongest American-made open model by quite some margin, but the addition of Reflection’s first model Beam is also relevant. Without these two models, there would be 20+ open-weight models built and released by Chinese companies better than the best Western models. At the time of writing this, Mistral has also announced a strong model, Mistral Large 4, with an index score of 38 (see below), but its weights are not yet released. There are rumors of a few more strong American open-weight models coming from other organizations too!In many ways, the gap between the best American and Chinese models has been reduced roughly by half. We’ve gone from the best American open models being largely irrelevant to consumers who require the cutting edge of performance, to them being behind but relevant.This paints the picture of the cut-throat, competitive nature of the frontier model business. Reflection and Mistral announced their model scores and Meta dropped the weights of a smarter model before these other two labs could get their own weights out. All of these models are competing with Chinese models released months ago, where the industry is collectively asking — where have Kimi and Zhipu AI been since the releases of GLM-5.3 and Kimi K3 on Aug. 18th and Jul. 16th, respectively?That is a very nuanced question, which I’ll dig into another time. Today’s piece is focused on Meta’s turnaround, what Muse Spark Open means for the ecosystem, and the balance of power across Western frontier labs.Why Muse Spark Open should take off — and why it may notOne thing we see again and again at Interconnects through our open model analysis is that Western open-weight models get astonishingly more adoption than Chinese models with the same performance-to-size footprint (even though the Chinese models usually come out first)! Muse Spark Open is landing into this environment, at the same time as when open-ended LLM agents are finally real (~GLM 5.2 capabilities and on). Riding off a rapid increase in performance of Chinese models, the open ecosystem has been living through an acceleration in demand. The inference providers are rapidly scaling, the open post-training economy is forming, and countless enterprises are looking to own their intelligence. Muse Spark Open’s performance falls somewhere above GLM 5.2, near DeepSeek V4.1 Flash, and behind GLM 5.3 or Kimi K3. It should be squarely “good enough” to slot right into this.For some background, from the Muse Spark Open model card on Hugging Face:Muse Spark Open is an 816B total/42B active parameter open weights model with a context window of 1M tokens. Built for coding and agentic work, Muse Spark Open is natively multimodal. This open weights version has additional safety post-training to make it more robust against misuse as a standalone model without system guardrails. We recommend deploying Muse Spark Open as part of a broader AI system, with additional guardrails suited to the specific use case.Here’s the distribution of the various Muse models on the Artificial Analysis Intelligence Index.This is included to loop back to the original model card snippet. It was shared that this open-weight model underwent additional safety training to prepare for this open release. Many observers saw this release as potentially a model that was “broken” during the safety training, as it came out after Muse Spark 1.3. In reality, this was much more likely a safety tune of Muse Spark 1.2, which would only be a 1 point drop on Artificial Analysis to shore up safety behaviors. When Meta released Muse Glimmer, the 30B agentic model, Alex Wang said that they were preparing to release the Muse Spark 1.2 model as open weights, which fits with the above.If anything will hold Muse Spark Open back, it’ll be the specific license they designed for it. In summary, this custom license has pretty much all of the restrictive license behaviors we’ve seen (and criticized here) in the last few years of building with open models. They are as follows:* Users of the models in products must display “Built with Muse” on their products. Meta did this with Llama, and Kimi has tried it, but almost no one complies.* There is a revenue-gating factor, where companies that make more than $20M per month across their entire business deploying the model need a separate license with Meta. Other prominent open model providers are doing this too, in order to set up revenue share agreements for re-selling inference.* Muse Spark Open makes you agree to an acceptable use policy, which in the true “open source software” corner of the world makes this model extremely not open-source.* All of the above applies to outputs from the model as well, and derivatives, so there is legal ambiguity if you can safely use the ...
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    12 分
  • The Cyber Risk Discourse is Broken

    The Cyber Risk Discourse is Broken
    2026/10/06
    I’ve had one too many discussions on the cyber risks of open models — where someone assumes that either banning open models happens in a silo (and bad actors will somehow actually be stopped) or that China is a threat and doesn’t care about AI safety — that I feel we’re going to end up making policy decisions that both limit American AI competitiveness and increases long-term cyber risk. I feel like we’re locked in on a path of lose-lose situation, unless people embrace nuance, trade-offs, and scary realities.Much has been written about the views of various players in this debate, which I summarize as follows:* Open weight models pose an untenable risk to society’s functionality, occupied by frontier lab leadership and the national security community in the U.S.* Western voices saying open weight models are necessary for defense and banning them will make the world less safe, occupied by AI risk moderates to different extremes. I include myself in this bucket, along with Hugging Face’s perspective after the OpenAI incident, and others like Joshua Saxe.* Chinese companies continuing to release open-weight models with strong cyber capabilities, who are making decisions based on the risk assessments of their society and government.This list is also organized by volume or clarity of message in the Western AI ecosystem. The risk-focused discussions have been disproportionately visible, including pieces of work which I view as very detrimental to potential of engaging across assumed party lines on this issue. There’s also very little substantive engagement trying to understand how Chinese companies assess risk — rather, mostly arguments that border on ad hominem attacks like “Chinese labs don’t care about safety.” Interconnects AI is a reader-supported publication. Consider becoming a subscriber.The delusions of the anti open-weight allianceThe most recent publication from the “open-weights are dangerous” crowd was the report by Anthropic on the risks of GLM-5.3 as an offensive cyber tool. The problem with this is not the technical research they did, which is largely reasonable, but the failure to engage on more cross cutting questions like: What happens if we ban open models due to cyber risks or why do Chinese companies deem these models safe to release?These questions, which I’ll come back to, are the ones we need to answer to understand the ecosystem perspective on current AI risks. All policy actions happen in a multi-piece puzzle, where the shape of risk can be changed, but it is rare that you get a universally better outcome. They are all trade-offs.This sort of solipsistic writing by Anthropic fits very closely to another type of discourse I’m hearing about regularly that is determining the fate of open models and AI broadly — classified briefings. I’ve had many discussions with people who say something like “Look, I’m pro open models, but if you’re seeing what I’m seeing, it’s an onslaught out there due to [XYZ Chinese open-weight model] and we need to act.” This perspective flies in the face of public information, where to date closed models have been documented as the cause of most existing cyber attacks. As someone who tries to make grounded predictions, the evidence of numerous attacks from OpenAI models is the only data I have on the shape of cyber risks (or, you can broaden the scope to include all of FelonyBench). There are a few possible explanations, and it’ll take years to get to the true answer: * Maybe open model weights and closed model APIs (with some safe guards) are both far closer to being easy to mis-use, rather than API models being closer to safe. The trope "Open Dangerous, Closed Safe" may be closer to "Open Unsafe, Closed Unsafe."* Maybe there are far fewer bad actors who are willing to use AI models for “loud” cyber attacks that target crucial infrastructure in the US or powerful and underprepared institutions. Closed models have stronger capabilities and stronger safeguards (in theory), but the stronger capabilities part could matter more in net harm if the safeguards on both are porous. * I have a lot more to learn about how the cybersecurity ecosystem works, so I won’t support this full-heartedly as one of my current recommendations, but I think it’s at least a reasonable argument that open weight models as a diffusion tool for cyber defense could be the best tool we have to prevent harm over the next few years. There are many domains, e.g. sensitive government agencies, where open-weight models are the only tools that can be deployed in the near-term on air-gapped networks.Lots of this situation seems like the messy reality being hard to pin down, where the logical limits are clearer. I may agree with the simple reality that closed models are far safer in the limit, as a technical tool with more layers of protection, but they may actually be causing more harm in the near term because of how accessible model APIs ...
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    9 分
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