『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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  • 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
  • Banning Open Source AI Would Be A Mistake

    Banning Open Source AI Would Be A Mistake
    2026/06/19
    This post was originally an op-ed co-authored with Kevin Xu of Interconnected for a general, non-technical audience. The gatekeepers — the many media outlets we pitched it to — passed on publishing it. Luckily, we have our own platforms to get the message out. Please help us forward this op-ed to any one you know who is on the fence about open source AI or new to the topic and want to learn more. Thank you.The energy to regulate AI is in the air in Washington. With the recently signed executive order to review AI models, a congressional proposal to legislate AI further, the government possibly taking shares of frontier AI labs, and last Friday’s action prohibiting foreign nationals anywhere from accessing Anthropic’s most advanced models, this may be the opening salvo of more AI regulation to come.We are afraid future actions could inadvertently or intentionally regulate or even ban open source, a much maligned and misunderstood topic in AI. That would be a grave mistake.Open source – simply a process that allows technology to be shared, built, and distributed publicly and transparently – is safe, secure, and drives economic growth. More than 90% of the world’s software was already built on open source and produced more than 8 trillion dollars worth of economic benefits, long before AI entered the picture. Today, open source technology is quietly training, improving, deploying, and securing AI everywhere.For more than three decades, open source has been powering three trends, and upholding three values, which the American society holds dear – education, competition, and innovation.Open source is pro-education because its origin was rooted in academic institutions trying to make technology free and open, not held hostage to the profit-maximizing zeal or the menacing lawyers of large corporations.The precursor of open source is the free software movement, which started in 1983 on the campus of MIT. It was a time when every small act of using software, whether it was teaching students or doing research or improving a printer’s performance, meant paying or dealing with big corporations like AT&T or Xerox. After this struggle gave birth to open source, every student in every university, community college, and coding bootcamp in America now taps into the freedom that open source enables to learn how to program, engineer, and build. Open source is at the heart of technical education everywhere.Open source is pro-innovation because it essentially provides a set of tools plus a community of other users to help anyone turn an idea into reality, for free. Combined with its role in education, it has watered most of the seeds of innovation in recent memory. Some of these seeds stayed as hobbies that brought joy and personal learning to the hobbyists. Others blossomed into huge companies, like Meta, where the initial version of Facebook was built entirely on a stack of open source software.Every day, new ideas or solutions are being coded up in a dorm room, garage, or basement, all because open source lets innovators create without fear of a lawsuit or an expensive bill.Open source is pro-competition because it helps the underdogs challenge and compete with the large incumbents, keeping monopolistic threats at bay. Linux, the open source operating system that now runs more than 90% of the world’s cloud computing infrastructure, was the antidote to the Windows monopoly (so much so that former Microsoft CEO, Steve Ballmer, called Linux “cancer”). Android, the open source mobile system, fostered a long string of competitive smartphones before Apple’s iPhone could control the market. Many other examples exist in the more niche, but no less important, segments of self-driving, databases, and semiconductor design.Without the equalizing and democratizing nature of open source, we would all be living with the rent-seeking consequences of more monopolies and less free market competition.Does AI change any of this? No.The duopoly of Anthropic and OpenAI are rapidly concentrating power between them with their closed, proprietary models. Anthropic, in particular, has flexed its monopolistic muscle recently by reducing its most advanced model’s capability when it is being used to improve someone else’s model. While the capabilities of their models are undeniable, so are their price tags and market concentration. Open source AI, mostly in the form of open weight models, has been the only counterweight for startups, educational institutions, and enterprises looking for alternatives.Does open source lead to more safety or security concerns? Not quite.We acknowledge it is worth monitoring the security implications of open source models that may reach frontier capabilities. But for the most part, the transparency that is inherent to open source makes them safer and more secure, because more engineers and researchers can tune out unwanted model behaviors, like censorship, or fix bugs in the software that ...
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    7 分
  • State of the blog, mid-2026

    State of the blog, mid-2026
    2026/06/17
    As I navigate my career change after Ai2, I wanted to share my views of how this blog relates to my missions and broader work. In my farewell post, I summarized my three goals right now as:* Provide clarity in the evolution of frontier models. * Create a vibrant and diverse open (model) ecosystem.* To build institutions that make these goals possible.Within this, Interconnects is at its core a bit different than many of the highly-polished, professional newsletters on this platform – and this is becoming intentional.How Interconnects fits into my career goalsInterconnects is the tip of the spear of all of my missions in AI. It is meant to start a conversation and to let the reader into the mind of someone at the frontier. This insight makes the writing sometimes a bit raw, sometimes a bit too technical, but it is the map of how I progress my thinking in the ever changing world. This style of writing has helped me create very strong relationships with the core group of readers, many of who listen to the voiceovers I do for these posts. The plan is to keep operating and refining the Interconnects experience around those loyal fans. These are to a large part people building the frontier AI ecosystem — researchers at labs, top investors, policymakers obsessed with the frontier, and students aspiring to have one of those roles.I’m very happy with this sort of raw, high-voice outcome for the blog. It is not something I sought out, but rather accepted as I saw it coming and realized it would be disproportionately successful in a near-future of vast AI slop media. With years of trying to squeeze writing into a busy schedule, the only sort of writing I had time for was that which had a style very closely matching how I think.I’m also very happy to be an independent voice. As a person I don’t do well with some power structures like having a boss, and I think there are very few people without extreme financial conflicts of interest that are willing and allowed to write. Through a wide job search, few companies were genuinely excited about me continuing writing.Over the past few months, I considered taking Interconnects in more of a direction like SemiAnalysis or Stratechery, where it is my full-time gig and number one priority, but it didn’t seem like the right fit for what I am trying to achieve. I’m trying to build an open ecosystem and a movement for true open-science at the frontier of AI. These areas are very narrowly populated and trying to influence them with only commentary, analysis, and related research products wouldn’t work for me.These sorts of full-time outcomes are definitely still one of my dreams, and I will do it at some point. The dream of this is also one of the reasons I take conflicts of interest seriously. Though, in this era of AI I can’t be fully on the outside.In this vein, I wanted to disclose two advising agreements I recently signed. I don’t view them as a compromise of the above independence, as I’ll happily quit if I feel like I can’t speak my mind, but as a form of support in accomplishing my missions. If I want to make a true open-science ecosystem I have some catching up to do with how the frontier labs approach post-training. The two companies I’m advising, whose leadership I’ve become friends with, are Arcee AI and Mercor. Arcee should be fairly obvious as the no-nonsense player building open-weight models. Mercor will make more sense over time, but they’re a close ally to a lot of my goals in transparent evaluations, open post-training, and neutrality with respect to the leading labs. These advising agreements are based on me wanting to learn more, and I don’t suspect I will ever engage in the very cursory advising roles that are more of name-stamping.I keep an up-to-date disclosures statement at the end of the Interconnects about page: https://www.interconnects.ai/about.Otherwise, my full-time job should still be in the non-profit sector as long as I get the next few months of logistics right.Interconnects AI is a reader-supported publication. Consider becoming a subscriber.Some operations & audience notesInterconnects has cultivated an excellent, niche, and largely technical audience with representatives of all the top companies and labs (recently crossed 70K subscribers). I intend to protect this niche audience rather than trying to expand to bigger pastures. I think this success in audience alignment is reflected in my ~900 paid subscribers supporting it with infrequent paywalled content. I appreciate the support greatly, as the money has let me expand Interconnects operations and quality over the last 18 months.I created Interconnects AI, LLC last January along with business bank accounts. Since then I’ve made some money, but I’ve reinvested it (and more) back into the business and the various AI services I need to try to write these articles. So, at this moment going full-time on Interconnects is a pretty risky financial proposition ...
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    6 分
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