Kimi K2.7 has just been released & it's a really good model! Open models from China are catching up very fast! By the end of the year or at the latest, early next year, we’ll likely have a Fable 5-level open-source model available at a much lower cost! AI advances cant be stopped

GLM 5.2 vs Kimi K2.7 both were tested on same settings with same prompt > GLM one shotted everything in 35 mins > Kimi needed extra prompts to fix movements and took 30 mins super surpised by how good GLM 5.2 is, while being so cheap which one do you think won?

We just announced our Fusion API: - Fable-level performance on deep research tasks, at half the cost - Better-than-SOTA performance using panels The future of AI is neurodiversity, not single-model takeovers.

While the US is cutting off access to its most advanced AI models, Mistral, the European AI champion, is raising €3bn - near doubling its valuation to €20bn in well under a year and only 3 months after it raised €830m to expand its data centre infrastructure. The company seeks to secure 200MW of capacity across Europe by the end of 2027.

Has there been anything good written about the failure of Mistral to keep up with both the Big Three and Chinese labs? They have talent and national backing, but despite being Europe’s only frontier lab (Google Deepmind’s UK lab aside), they haven’t been able to close the gap

What Europe should do right now: 1. Call all the European researchers working on AI and return them back with same salary (or they can stay but switch career). 2. Fill EU places having GPUs with money, and put those people there. 3. AI partnerships with China India.

More likely story: The "jailbreak" wasn't super serious (a situation anyone who has ever received bug reports is familiar with), Anthropic thought the demand to halt model was absurd, and Fed Gov used opportunity to punish and humiliate Anthropic for the prior sins of not bending knee. Anthropic has more credibility on such topics than Washington

Le gouvernement Trump suspend l’accès aux derniers modèles IA d’Anthropic à tout ressortissant non-américain : cette décision soudaine vient nous rappeler que l’intelligence artificielle est déjà un sujet de souveraineté nationale majeur. Les Nations qui ne développent pas rapidement leur(s) propre(s) modèle(s) dépendront toujours plus des choix des autres puissances : la France doit accélérer dans le soutien à la pépite Mistral AI et à tout l’éco-système IA.

Wall Street Journal is reporting that Amazon reported the jailbreaks to the Department of Commerce, who instituted the ban

Wait - so Amazon, one of Anthropic’s biggest investors, allegedly jailbroke Claude and then snitched to the U.S. government? This cant be real. What.

GLM-5.2 is Fully Open, Frontier Intelligence Belongs to Everyone Today, the sudden restriction of certain frontier models is deeply regrettable. At a time when access to frontier models is abruptly cut off for non-technical reasons, we are even more convinced of one thing: science should be global. The path to AGI (Artificial General Intelligence) must never be enclosed by high walls. We have always believed that AGI should be the cornerstone for all of humanity to collaboratively explore the boundaries of intelligence and solve complex challenges, rather than a privilege monopolized by a few rules and subject to revocation at any moment. In the face of external blockades and restrictions, our attitude is one of radical openness. Frontier intelligence must remain open-source, accessible, and buildable, serving every dedicated developer. GLM-5.2 is Zhipu's most capable open-source model to date. It not only supports a truly usable 1M context window but also maintains a continuous lead in the independent completion of long-horizon tasks, providing solid foundational support for building complex agent applications. It also continues to be our main engine for creating the strongest domestic coding model. Tonight at 5:21—at this special moment—GLM-5.2 will officially be available to all GLM Coding Plan users (including Lite / Pro / Max). The API will also go live next week. A step closer to frontier intelligence for everyone. The future of AI is open, and it is for the people. ModelKey: GLM-5.2

Intelligence should be open, accessible, and ready to build with, empowering every developer, everywhere. GLM-5.2 is now available to all GLM Coding Plan users, including Lite, Pro, Max, and Team plans. docs.z.ai/devpack/latest-mod… As our new flagship model, GLM-5.2 delivers powerful coding capabilities, usable 1M-context support, and continued strengths in long-horizon tasks. API and Chatbot services will launch next week. The model will also be officially open-sourced next week under the MIT License. The future of AI is open, and it belongs to the people.

This is your wakeup call. Anthropic just took down Fable 5. It's over. Here's the thing tho: no company or government will EVER be able to take away your local models. There are Opus level models you can run right now on your home GPUs, and nobody can ever stop you from using them This is only the beginning of events like this. Day 1. More government overreach will happen. This will only keep happening more and more as models get closer to AGI Become sovereign. Buy your own compute. Before even that becomes illegal

This week the most advanced AI model on the planet got switched off by a foreign government. British researchers were studying it. British companies were testing it. British hospitals were piloting it. Not any more. This isn't an AI story. It's the story of every industry we used to lead. Britain has some of the best AI talent in the world. DeepMind was built here. Our AI Safety Institute writes the rules other countries follow. We have the researchers, the universities, the standards. What we don't have is the power stations to run the data centres, the planning system to build them, or the industrial base to make the chips. So the work happens here and the value lands somewhere else. We invent. Others build. Others decide. Then we read about it on Saturday morning. Same story as the kit our soldiers don't have. Same story as the factories we used to. I spent nine months in government making this argument inside the room. I'll make it louder from outside.

JUST IN: Andrej Karpathy, a top AI scientist at Anthropic, is reportedly barred from accessing the company’s most advanced AI model because he is not a U.S. citizen.

🔥🚨LATEST: Footage has released of the moment the 21-year-old woman was accidentally killed when Entre Cordas workers forgot to attach her safety rope and and threw her off the the “Skeleton Bridge” in Limeira, a city in Brazil’s São Paulo state.

The first agentic AI infrastructure benchmark is here. An AI agent chains dozens to hundreds of AI model calls together, using tools, gathering context, and iterating until the task is done. Existing benchmarks weren't designed for that. AgentPerf from @ArtificialAnlys gives developers, enterprises and infrastructure providers a clear way to compare accelerated computing systems for agentic AI. First round of results highlight that NVIDIA Blackwell delivers 20x more agents per megawatt than NVIDIA Hopper.

New Report: From AGI to ASI. arxiv.org/abs/2606.12683 The shift from human-level AI to superintelligence will be continuous accelerating breakthroughs across science & tech. We must prepare for a series of transformative societal changes. #AGI #ASI

How do we go from AGI to Superintelligence? New report discusses four potential pathways: scaling, AI paradigm shifts, recursive improvement, and ASI emerging from large-scale multi- agent collectives. Importantly, it also looks at possible frictions and bottlenecks along these pathways. Instant classic! arxiv.org/abs/2606.12683

Google DeepMind published a 60-page paper mapping the road from AGI to superintelligence, written by Hutter, Legg, and Genewein. No hype, just a sober analysis The paper uses three levels. AGI = roughly average human performance across most cognitive tasks. ASI = a system that beats large, well-coordinated groups of human experts across virtually everything (their bar: tens of thousands of experts working ten years on one problem). Universal AI / AIXI = the theoretical ceiling, uncomputable, only approachable from below. Then they explore the question of how this could be achieved: Scaling compute, models, and data, the continuation of the trend that drove the breakthrough so far. It is the only path with historical data available for extrapolation. The core question: Does quantity transform into quality? Even if individual models plateau, the sheer act of running millions of faster AGI instances could trigger the leap. (A quick aside: that is a fascinating philosophical idea. It always reminds me of Hegel’s dialectic, the notion that quantity transforms into quality. We ought to start drawing on philosophical theories to make sense of the future.) Algorithmic paradigm shifts: a genuine break from the transformer pretraining paradigm. New architectures, new learning methods. However, hard to predict by definition. Recursive self-improvement: AI accelerates AI research, which produces better AI, which accelerates research further. Multi-agent coordination: superintelligence emerges from large collectives of AGI agents working together, like automated corporations or AI economies. Collective intelligence potentially far exceeding any individual model. The authors naturally point to what I repeatedly describe as the biggest bottleneck: energy. I recently linked to a few graphs showing, on the one hand, the extent to which energy is already becoming a problem and, on the other, how China dominates the expansion of both nuclear and solar energy in the global race. But the authors also address a profound shift in the world of work in a post-AGI era. I would say this is a reality we must face. So, it is not just about scaling, but also about whether the underlying conditions - such as energy and hardware - can be effectively established. Six things that could slow or stop all of this: The data wall. Quality training data runs out, possibly before the end of this decade. Resource demand grows too fast. Energy, chips, rare earths, investment. The physical infrastructure can't scale arbitrarily. The neural paradigm hits a ceiling. Pretrained transformers plus fine-tuning may not be enough to reach AGI, let alone go beyond it. Research gets harder. Keeping Moore's law going already needs 18x more researchers than in the 1970s. Ideas are genuinely harder to find as fields mature. The abstraction barrier. Models trained on human concepts may never invent new ones from scratch. Saturating GPQA or SWE-bench shows mastery of what humans already worked out, not the ability to go beyond it. Train only on pre-Newtonian physics and you won't reason your way to relativity. Deliberate slowdown. Regulation, accidents, public backlash. Real, but likely countered by the competitive pressure between companies and nations. I think it’s great that Google is addressing questions such as which paths they believe lead to AGI, what the road to ASI might look like, what challenges will arise, and much more. Overall, however, it sounds to me like all of this could actually succeed, making it, in that sense, a call to discuss and reflect on the consequences.