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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.
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Was I wrong about open models & cyber risks?
Last week coined the term “Black October (https://x.com/DevMuzzammil/status/2113042127039164663)“ after an onslaught of critical vulnerability disclosures, including PHP2Shell and a long-standing GitLab bug, signified the moment when the cy
Artificial Analysis v5 and the benchmarker's dilemma
Hill-climbing is easy, knowing what to hill-climb is hard. Making scores go up is expensive, but we have a much clearer playbook for it than we do for creating meaningful benchmarks at the true frontier of AI today. Independent evaluation o
Podcast: Kimi K3.1 Pro, the new cyber reality, and Muse musings
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
Meta shakes up the frontiers, open and closed
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 (https://research.meta.ai/blog/muse-spark-open), which is now the stro
The Cyber Risk Discourse is Broken
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
Debating RSI, the US-China Gap, and Jaggedness with JS Denain of Epoch AI
This episode is with Jean-Stanislas “JS” Denain of Epoch AI, who leads their Insights Team and is one of the people I find myself debating the state and trajectory of AI with more and more. We’ve had follow-on discussions of many of my favo
The current balance of power in open models
I was recently invited to brief a group of Congressional members and staff on the state of open-weight models in the lens of U.S.-China competition. I’m sharing my prepared remarks as a state of the union on open models that is accessible t
Why I still haven’t bought into true RSI
We’re in an era where a few organizations are using thousands of concurrent agents to improve their processes and output. These organizations happen to be just the frontier AI labs, in particular OpenAI and Anthropic. In the last few weeks,
One resignation turned the embers of AI fear into a wildfire
As AI became more powerful, it was inevitable that a different, growing group would start to take AI safety more seriously – what we did not know ahead of time, is which set of views they latched onto. We have seen that some of the most ext
When will average people feel AI’s impact?
Housekeeping: Paid subscribers to Interconnects now get a permanent 40% discount on my book when purchasing at Manning.com. Access the code at the Interconnects perks page (https://www.interconnects.ai/perks). Many AI optimists tend to comp
I wrote an AI textbook — how long until AI can do it better?
There are a lot of criticisms of AI writing, but most of them are focused on more creative, high-voice writing like this blog. Those — including my own piece (https://www.interconnects.ai/p/why-ai-writing-is-mid) — often argue that it is be
Open models recap: more on Kimi K3, Qwen 3.8, Xi's WAIC speech, distillation, the open-closed gap, and what's next
Exciting news! My https://rlhfbook.com/ trying to share post-training knowledge with the world is done and shipping soon. Order on Manning (https://hubs.la/Q03TsMBq0) or Amazon (https://amzn.to/4cwCDJQ). Thanks for the support. It’s current
Kimi K3: The open-weights escalation
On Thursday July 16th, Moonshot AI released their latest flagship model Kimi K3 (https://www.kimi.com/blog/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 refle
GLM-5.2 is the step change for open agents
Housekeeping: Following my “State of the blog (https://www.interconnects.ai/p/state-of-the-blog-mid-2026)” post last week, noting a slight increase in paid features, it’s a good time to remind folks that I offer group subscriptions (https:/
Banning Open Source AI Would Be A Mistake
This post was originally an op-ed co-authored with Kevin Xu (https://substack.com/profile/9714824-kevin-xu) of Interconnected (https://interconnect.substack.com/) for a general, non-technical audience. The gatekeepers — the many media outle
State of the blog, mid-2026
As I navigate my career change after Ai2 (https://www.interconnects.ai/p/farewell-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: * Pr
Frontier post-training recipe review with Finbarr Timbers
As I’ve been recapping fundamentals of post-training to wrap up my RLHF / Post-training book (https://rlhfbook.com/) I knew I needed to get Finbarr Timbers (https://finbarr.ca/) back on the podcast to talk about the state of play. Over the
Claude Fable 5 and new AI safety fables
Edit Jun. 11: Anthropic changed (https://x.com/ClaudeDevs/status/2064949876463645026) their silent model manipulation of AI research queries to also use a classifier like the other safety domains. This addresses a key concern I had in the m
Farewell Ai2
I’m departing the Allen Institute for AI (Ai2), where I got the great privilege to work on the Olmo models, to grow, to learn, and to have broad lasting impacts. This post is an attempt to reflect on why what we did was influential, despite
Open and closed models are on different exponentials
The largest debate that’ll define the future balance of power between the open and closed AI model ecosystems is primarily economic — it’s if users of AI will continue to pay dramatically more, i.e. large margins, for the top closed models.
Some ideas for what comes next, May 2026
As the years of AI progress go by, it’s been accompanied by a slowly rising tide of consequence. Models are getting more capable, how we work is changing quickly, economics of AI are becoming real, just as real-world risks come to the foref
Notes from inside China's AI labs
Staring out the window on a new, high-speed train from Hangzhou to Shanghai I’m gifted with views of dramatic ridgelines speckled with wind turbines that are silhouetted against the setting sun. The mountains cast a backdrop to a mix of spa
The distillation panic
‘Distillation attacks’ is a horrible term for what is happening right now. Yes, some Chinese labs are hacking or jailbreaking APIs to attempt to extract more signal from model APIs — stopping this is important to maintain the U.S.’s lead in
My bets on open models, mid-2026
We’re living through the period of time when we’ll learn if open models can keep up with closed labs. The obvious answer is that no, they won’t. This answer is a form of saying they won’t keep up in every area. This framing closes off a pop
The inevitable need for an open model consortium
Recently, I was talking with Percy Liang (https://cs.stanford.edu/~pliang/), Stanford professor and lead of the Marin (https://marin.community/) project (another fully-open model lab), and it set in on me that there will eventually be a con
Claude Mythos and misguided open-weight fearmongering
With the announcement of the Claude Mythos model this week and the admittedly very strong stated abilities, especially in cybersecurity, a new (https://x.com/tenobrus/status/2041636593040236745) wave (https://x.com/stalkermustang/status/204
Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 (https
Lossy self-improvement
Fast takeoff, the singularity, and recursive self-improvement (RSI) are all top of mind in AI circles these days. There are elements of truth to them in what’s happening in the AI industry. Two, maybe three, labs are consolidating as an oli
GPT 5.4 is a big step for Codex
I’m a little late to this model review, but that has given me more time to think about the axes that matter for agents. Traditional benchmarks reduce model performance to a single score of correctness – they always have because that was sim
What comes next with open models
2025 was the year where a lot of companies started to take open models seriously as a path to influence in the extremely valuable AI ecosystem — the adoption of a strategy that was massively accelerated downstream of DeepSeek R1’s (https://
