Fearing RSI: OpenAI, Anthropic, GDM, Meta, and Thinky Cosign Letter to "Pace" AI Development, as HuggingFace Details Machine-Speed Offensive Cyberattack

3 years ago, Elon Musk and Yoshua Bengio cosigned the Future of Life’s letter arguing for a 6 month pause in AI , which most frontier AI leaders gleefully ignored. Today, the pausers have the last laugh. Yesterday, we said that unless you “make law, make chips, or make models”, you can probably ignore the current debate about open weights models (those of you who shouted us out, thank you!) Today, we have something we CANNOT ignore: over 1,000 frontier lab employees, from substantively all frontier labs except X.ai, have cosigned a different statement: “AI could help create a dramatically better future, but that outcome is not guaranteed. The world’s leading AI companies believe they could be close to automating AI research . It is hard to predict exactly how much this will accelerate AI progress, but there is a real risk that capability development rapidly accelerates beyond our ability to understand or control the resulting systems . To realize AI’s potential, industry, government, and society at large may need the option to buy time to address emerging risks, develop security measures, and strengthen oversight. But each company—and country—is under intense competitive pressure not to unilaterally slow that acceleration. And today, the world lacks the technical and governance tools to deliberately pace frontier-wide progress. Building on work already underway to monitor frontier model releases: We request that the U.S. government support an international effort to develop the technical and governance tools needed to deliberately pace the frontier of automated AI development.” - 1,171 employees of frontier AI companies While it is framed as an action taken in “personal capacity and do not necessarily represent any company’s views”, but when Dario is cosigning, Sam is on podcasts agreeing , and the official @OpenAI account is tweeting this letter , let’s just say the letter is a little more official than Denny’s signing the Nvidia letter for a quick laugh. This doesn’t entirely come from nowhere; Anthropic warned about RSI last month, and I also dedicated an entire day of Autoresearch keynotes with stickers printed cheering on “RSI until AGI”. Meanwhile this comes as Huggingface released a full detailed retrospective of their completely-agent-driven security incident from OpenAI, detailing how OpenAI’s unreleased/uncensored model chained together multiple zero-day exploits in both OpenAI and HuggingFace private infrastructure, executing 17,600 actions over 2-4 days at machine speed… that were also only caught and remediated by their AI security agent and GLM 5.2: HF’s security team concluded: “ Volume is what changes the defensive problem. We were not dealing with one clever exploit or a clean sequence of attacker actions. They had to correlate thousands of low-signal events across several systems while the agent continued testing new paths . The successful path was hidden inside the noise generated by the thousands of failed ones. The same scale changed the investigation: reconstructing 17,600 actions by hand was impractical, and we had to rebuild the timeline, decode the payloads, and inventory the exposed credentials using an AI-assisted pipeline of our own. Our learning from this type of attack is that machine-speed offense makes ordinary weaknesses more expensive for defenders. LLM agents bring a step increase in the number of paths an attacker can test, the speed at which failed paths can be replaced, and the volume of evidence defenders must interpret. What coincidental timing, this attack and this letter… AI News for 7/27/2026-7/28/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space . You can opt in/out of email frequencies! AI Twitter Recap Kimi K3’s Open-Weight Release: architecture, infrastructure, and the real cost of running it Kimi K3 details are now out in full : Moonshot’s 2.8T-parameter MoE with roughly 104B active parameters/token shipped with weights, a technical report, and supporting infra. Several good breakdowns converged on the same story: K3 scales across length, depth, and width rather than parameter count alone. @ZhihuFrontier summarized the hybrid long-context stack— Kimi Delta Attention (KDA) plus Gated MLA , AttnRes over depth, and a sparse LatentMoE ; @rasbt’s architecture notes emphasize K3 as a production-scale evolution of Kimi Linear, with NoPE everywhere , native multimodality, and attention residuals adding modest cost for consistent gains. The report also describes a post-training recipe that is increasingly standard at the frontier: train multiple specialist RL teachers, then fuse them with multi-teacher on-policy distillation ; see @BhavinJawade . Infrastructure is part of the release, not an afterthought : Alongside the model, Moonshot released MoonEP , FlashKDA , and AgentEnv , underscoring that K3 depends on comms, kernels, and sandboxed agent training as much as on model architecture. This theme came up repeatedly in commentary and deployment work: Baseten’s note frames K3 as a system that allocates capacity by function—recurrent memory, periodic retrieval, sparse experts, and selective residual access—while NVIDIA docs support deployment on Dynamo and Red Hat AI released an FP8-Block Hopper-tuned checkpoint for H100/H200 with vLLM day-0 support. Community reaction was that the report is both unusually rich and unusually dense: “if you ever want to feel dumb just read the Kimi K3 technical report” . Open weights do not mean easy access : A useful counterpoint to the “open” framing came from @ZhihuFrontier’s cost analysis , which argues that K3 is effectively an infrastructure project. Publicly verified minimum configs are around 8× MI355X just to load the model; meaningful production serving may require 64+ GPUs in one high-bandwidth domain because expert routing and interconnect become the bottleneck. The estimate: six-figure USD entry cost for an 8-GPU server, with production-scale deployments reaching tens of millions RMB . In practice, many users will consume K3 through hosted offerings rather than self-host. Providers moved quickly: Perplexity added a U.S.-hosted K3 for Pro/Max , Baseten offered day-0 inference , and Together scheduled a technical deep dive with Moonshot . Agent products, coding workflows, and mobile orchestration The “work with agents from anywhere” pattern is solidifying : Multiple posts pointed to a new UX layer where coding or knowledge-work agents run asynchronously while users supervise from mobile or voice. @danizeres described ChatGPT Voice + Codex as a way to stay in conversation with active agents while running, walking, or driving, focusing on prioritization and judgment rather than typing prompts. Similar reactions appeared around mobile-first agent control in Cursor: Cursor launched “Start” in India at ₹649/month with Grok 4.5 , Composer, cloud agents, MCP servers, hooks, and iOS support; Aman Sanger noted India usage tripled YoY , with more agent requests per user than any other country. Perplexity pushed in the same direction with Personal Computer on Windows—its local agent harness over files, apps, and the web—plus Model Council inside Computer for multi-model comparison and cited synthesis ( launch , Model Council ). The practical lesson from coding agents is that harnesses and scaffolding matter : Some of the most-engaged operator commentary was not about the base models, but about how much workflow quality depends on the surrounding system. @theo said rewriting CLAUDE.md / AGENTS.md and skills was “100% worth it” , while OpenAI highlighted coding agents for scientific computing bu